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1 STATE OF THE CLIMATE I N 2015 Special Supplement to the Bulletin of the American Meteorological Society Vol. 97, No. 8, August 2016

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3 STATE OF THE CLIMATE I N 2015 Editors essica Blunden D erek S. Arndt J Chapter Editors Jeremy T. Mathis Howard J. Diamond Jacqueline A. Richter-Menge Ademe Mekonnen A. Johannes Dolman Ahira S ánchez-Lugo A. Rost Parsons Robert J. H. Dunn Carl J. Schreck III James A. Renwick Dale F. Hurst Sharon Stammerjohn Gregory C. Johnson Kate M. Willett Technical Editors Kristin Gilbert Tom Maycock Susan Osborne Mara Sprain meric A m eteorologic Al S ociety An

4 Credits : over C F ront : Reproduced by courtesy of Jillian Pelto Art/University of Maine Alumnus, Studio Art and Earth Science — © 2015 by the artist. Landscape of Change B aCk Reproduced by courtesy of Jillian Pelto Art/University of Maine Alumnus, Studio Art and Earth Science — : © 2015 by the artist. Salmon Population Decline - uses data about sea level rise, glacier volume decline, increasing global temperatures, and the increas Landscape of Change ing use of fossil fuels. These data lines compose a landscape shaped by the changing climate, a world in which we are now living. (Data sources available at www. jillpelto.com/landscape-of-change; 2015.) Salmon Population Decline uses population data about the Coho species in the Puget Sound, Washington. Seeing the rivers and reservoirs in western Washington looking so barren was frightening; the snowpack in the mountains and on the glaciers supplies a lot of the water for this region, and the additional lack of precipitation has greatly depleted the state’s hydrosphere. Consequently, the water level in the rivers the salmon spawn in is very low, and not cold enough for them. The salmon are depicted swimming along the length of the graph, following its current. While salmon can swim upstream, it is becoming more of an uphill battle with lower streamflow and higher temperatures. This image depicts the struggle their population is facing as their spawning habitat declines. (Data sources available at www.jillpelto.com/salmon-populagtion-decline; 2015.) How to cite this document: Citing the complete report: Bull. Amer. Meteor. Soc. 97 A rndt, Eds., 2016: State of the Climate in 2015. Blunden, J. and D. S. , (8), S1–S275, DOI:10.1175/2016BAMSStateoftheClimate.1 Citing a chapter (example): Mekonnen, A., J. A. Renwick, and A. Sánchez-Lugo, Eds., 2016: Regional climates [in “State of the (8), S173–S226. 97 , Bull. Amer. Meteor. Soc. Climate in 2015”]. Citing a section (example): Tsidu, M., 2016: Southern Africa between 5° and 30°S [in “State of the Climate in 2015”]. Bull. Amer. , (8), S192– S193. Meteor. Soc. 97

5 Beti alpha name ) EDITOR AND ( AUTHOR AFFILIATIONS By Cal Bedka, Kristopher M., NASA Langley Research Center, Aaron-Morrison, Arlene P., Trinidad & Tobago Hampton, Virginia Meteorological Service, Piarco, Trinidad Oregon State University, Behrenfeld, Michael J., Ackerman, Steven A., CIMSS, University of Wisconsin– Corvallis, Oregon Madison, Madison, Wisconsin Bell, Gerald D., NOAA/NWS Climate Prediction Center, Adams, Nicolaus G., NOAA/NMFS Northwest Fisheries College Park, Maryland Science Center, Seattle, Washington Belmont, M., Seychelles National Meteorological Services, Adler, Robert F., Earth System Sciences Interdisciplinary Pointe Larue, Mahé, Seychelles Center, University of Maryland, College Park, College Park, Maryland European Centre for Medium-Range Benedetti, Angela, Weather Forecasts, Reading, United Kingdom Albanil, Adelina, National Meteorological Service of Mexico, Mexico Bernhard, G., Biospherical Instruments, San Diego, California Alfaro, E.J., Center for Geophysical Research and School of Physics, University of Costa Rica, San José, Costa Rica European Centre for Medium-Range Berrisford, Paul, Weather Forecasts, Reading, United Kingdom Allan, Rob, Met Office Hadley Centre, Exeter, United Kingdom Berry, David I., National Oceanography Centre, Southampton, United Kingdom Centro de Ciencias do Sistema Alves, Lincoln M., Terrestre, Instituto Nacional de Pesquisas Espaciais, Departamento Ciencias de la Bettolli, María L., Cachoeira Paulista, Sao Paulo, Brazil s ó Atm fera y los Océanos, Facultad de Ciencias Exactas y Amador, Jorge A., Center for Geophysical Research and Naturales, Universidad de Buenos Aires, Argentina School of Physics, University of Costa Rica, San José, Bhatt, U. S., Geophysical Institute, University of Alaska Costa Rica Fairbanks, Fairbanks, Alaska Section for Glaciers, Ice and Snow, Andreassen, L. M., Bidegain, Mario, Instituto Uruguayo de Meteorologia, Norwegian Water Resources and Energy Directorate, Montevideo, Uruguay Oslo, Norway NOAA/NMFS Northwest Fisheries Science Bill, Brian D., Applied Physics Laboratory, University of Arendt, A., Center, Seattle, Washington Washington, Seattle, Washington Scripps Institution of Oceanography, Billheimer, Sam, Arévalo, Juan, Instituto Nacional de Meteorología e University of California, San Diego, La Jolla, California Hidrología de Venezuela, Caracas, Venezuela Deutscher Wetterdienst, WMO RA VI Bissolli, Peter, NOAA/NESDIS National Centers for Arndt, Derek S., Regional Climate Centre Network, Offenbach, Germany Environmental Information, Asheville, North Carolina Blake, Eric S., NOAA/NWS National Hurricane Center, Arzhanova, N. M., Russian Institute for Miami, Florida Hydrometeorological Information, Obninsk, Russia NOAA/NESDIS National Centers for Blunden, Jessica, UiT The Arctic University of Norway, Aschan, M. M., Environmental Information, Asheville, North Carolina Tromsø, N or w ay Global Modelling and Assimilation Bosilovich, Michael G., Instituto Pirenaico de Ecología, Azorin-Molina, César, Office, NASA Goddard Space Flight Center, Greenbelt, Consejo Superior de Investigaciones Científicas, Maryland Zaragoza, Spain Laboratoire de Météorologie Boucher, Olivier, NOAA/NESDIS National Centers for Banzon, Viva, Dynamique, Institut Pierre Simon Laplace, CNRS/UPMC, Environmental Information, Asheville, North Carolina Paris, France Islamic Republic of Iranian Meteorological Bardin, M. U., Climate Center, Institute of Meteorology Boudet, Dagne, Organization, Iran of Cuba, Cuba Barichivich, Jonathan, Instituto de Conservación, Box, J. E., Geological Survey of Denmark and Greenland, Biodiversidad y Territorio, Universidad Austral de Chile, Copenhagen, Denmark and Center for Climate and Resilience Research (CR)², NOAA/NESDIS National Centers for Boyer, Tim, Chile Environmental Information, Silver Spring, Maryland NOAA/OAR Atlantic Oceanographic Baringer, Molly O., Braathen, Geir O., WMO Atmospheric Environment and Meteorological Laboratory, Miami, Florida Research Division, Geneva, Switzerland Argentine Naval Hydrographic Service, Barreira, Sandra, Bromwich, David H., Byrd Polar and Climate Research Buenos Aires, Argentina Center, The Ohio State University, Columbus, Ohio NOAA/NWS Climate Prediction Baxter, Stephen, Brown, R., Climate Research Division, Environment and Center, College Park, Maryland Climate Change Canada, Montreal, Quebec, Canada Bazo, Juan, Servicio Nacional de Meteorología e Bulygina, Olga N., Russian Institute for Hidrología de Perú, Lima, Perú Hydrometeorological Information, Obninsk, Russia Global Precipitation Climatology Becker, Andreas, Geological Survey of Canada, Ottawa, Burgess, D., Centre, Deutscher Wetterdienst, Offenbach, Germany Ontario, Canada Si | AUGUST 2016 STATE OF THE CLIMATE IN 2015

6 Crouch, Jake, NOAA/NESDIS National Centers for Calderón, Blanca, Center for Geophysical Research, Environmental Information, Asheville, North Carolina University of Costa Rica, San José, Costa Rica Davis, Sean M., Cooperative Institute for Research in Lamont-Doherty Earth Camargo, Suzana J., Environmental Sciences, University of Colorado Boulder, Observatory, Columbia University, Palisades, New York and NOAA/OAR Earth System Research Laboratory, Department of Physics, The Campbell, Jayaka D., Boulder, Colorado University of the West Indies, Jamaica Marine Institute, Newport, Ireland de Eyto, Elvira, Cappelen, J., Danish Meteorological Institute, de Jeu, Richard A. M., Transmissivity, and VanderSat, Copenhagen, Denmark Noordwijk, Netherlands Carrasco, Gualberto, Servicio Nacional de Meteorología de Laat, Jos, Royal Netherlands Meteorological Institute e Hidrología de Bolivia, La Paz, Bolivia (KNMI), DeBilt, Netherlands Carter, Brendan R., Joint Institute for the Study of the King County Water and Land DeGasperi, Curtis L., Atmosphere and Ocean, University of Washington, and Resources Division, Seattle, Washington NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, Washington University of Saskatchewan, Degenstein, Doug, Saskatoon, Saskatchewan, Canada Chambers, Don P., College of Marine Science, University of South Florida, St. Petersburg, Florida Demircan, M., Turkish State Meteorological Service, A nk ar a , Tur key Chandler, Elise, Bureau of Meteorology, Melbourne, Victoria, Australia Derksen, C., Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada Arctic Geology Department, Christiansen, Hanne H., UNIS-The University Centre in Svalbard, Longyearbyen, Antigua and Barbuda Meteorological Service, Destin, Dale, Norway St. John’s, Antigua Christy, John R., University of Alabama in Huntsville, University of Illinois at Urbana– Di Girolamo, Larry, Huntsville, Alabama Champaign, Urbana, Illinois Chung, Daniel, Department of Geodesy and European Centre for Medium-Range Di Giuseppe, F., Geoinformation, Vienna University of Technology, Weather Forecasts, Reading, United Kingdom Vienna, Austria NOAA/NESDIS National Centers Diamond, Howard J., Rosenstiel School of Marine and Chung, E.-S., for Environmental Information, Silver Spring, Maryland Atmospheric Science, University of Miami, Miami, Florida Dlugokencky, Ed J., NOAA/OAR Earth System Research Cinque, Kathy, Melbourne Water, Melbourne, Australia Laboratory, Boulder, Colorado School of Geography, Environment, and Clem, Kyle R., Earth and Space Research, Seattle, Dohan, Kathleen, Earth Sciences, Victoria University of Wellington, Washington Wellington, New Zealand Research Institute for Limnology, Dokulil, Martin T., Coelho, Caio A.S., CPTEC/INPE Center for Weather University of Innsbruck, Mondsee, Austria Forecasts and Climate Studies, Cachoeira Paulista, Brazil Knipovich Polar Research Institute of Marine Dolgov, A. V., Department of Geography, Trent University, Cogley, J. G., Fisheries and Oceanography, Murmansk, Russia Peterborough, Ontario, Canada Dolman, A. Johannes, Department of Earth Sciences, German Aerospace Center Coldewey-Egbers, Melanie, Earth and Climate Cluster, VU University Amsterdam, (DLR) Oberpfaffenhofen, Wessling, Germany Amsterdam, Netherlands Colwell, Steve, British Antarctic Survey, Cambridge, Domingues, Catia M., Institute for Marine and Antarctic United Kingdom Studies, University of Tasmania, and Antarctic Climate Cooper, Owen. R., Cooperative Institute for Research in and Ecosystems Cooperative Research Centre, and Environmental Sciences, University of Colorado Boulder, Australian Research Council’s Centre of Excellence for and NOAA/OAR Earth System Research Laboratory, Climate System Science, Hobart, Tasmania, Australia Boulder, Colorado Donat, Markus G., Climate Change Research Centre, Department of Geography, University of Copland, L., University of New South Wales, Sydney, New South Ottawa, Ottawa, Ontario, Canada Wales, Australia NOAA/OAR Pacific Marine Cosca, Catherine E., NOAA/OAR Atlantic Oceanographic and Dong, Shenfu, Environmental Laboratory, Seattle, Washington Meteorological Laboratory, and Cooperative Institute for Marine and Atmospheric Science, Miami, Florida NOAA/OAR Pacific Marine Cross, Jessica N., Environmental Laboratory, Seattle, Washington Dorigo, Wouter A., Department of Geodesy and Geoinformation, Vienna University of Technology, Cooperative Institute for Research in Crotwell, Molly J., Vienna, Austria Environmental Sciences, University of Colorado Boulder, and NOAA/OAR Earth System Research Laboratory, NOAA/NOS National Centers for Coastal Dortch, Quay, Boulder, Colorado Ocean Science, Center for Sponsored Coastal Ocean Research, Costal Ocean Program, Silver Spring, Maryland Sii | AUGUST 2016

7 Met Office Hadley Centre, Exeter, United Folland, Chris, Doucette, Greg, NOAA/NOS National Centers Kingdom for Coastal Ocean Science, Center for Coastal Environmental Health and Biomolecular Research, Climate Center, Institute of Meteorology of Fonseca, C., Charleston, South Carolina Cuba, Cuba Drozdov, D. S., Earth Cryosphere Institute, Tyumen, and Institute of Marine Research, Bergen, Norway Fossheim, M., Tyumen State Oil and Gas University, Tyumen, Russia Department of Geology, CIMSS, Foster, Michael J., Lamont–Doherty Earth Observatory, Ducklow, Hugh, University of Wisconsin–Madison, Madison, Wisconsin Columbia University, New York, New York Fountain, Andrew, Portland State University, Portland, Dunn, Robert J. H., Met Office Hadley Centre, Exeter, Oregon United Kingdom Nigerian Meteorological Agency, Abuja, Francis, S. D., Durán-Quesada, Ana M., Center for Geophysical Nigeria Research and School of Physics, University of Costa Rica, NASA Goddard Space Flight Center, Franz, Bryan A., San José, Costa Rica Greenbelt, Maryland Dutton, Geoff S., Cooperative Institute for Research in CIMSS, University of Wisconsin– Frey, Richard A., Environmental Sciences, University of Colorado Boulder, Madison, Madison, Wisconsin and NOAA/OAR Earth System Research Laboratory, NASA Goddard Space Flight Center, Frith, Stacey M., Boulder, Colorado Greenbelt, Maryland Egyptian Meteorological Authority, Cairo, Ebrahim, A., Froidevaux, Lucien, Jet Propulsion Laboratory, California Egypt Institute of Technology, Pasadena, California ElKharrim, M., Direction de la Météorologie Nationale Bureau of Meteorology, Melbourne, Ganter, Catherine, Maroc, Rabat, Morocco Victoria, Australia NOAA/OAR Earth System Research Elkins, James W., NOAA/OAR Atlantic Oceanographic and Garzoli, Silvia, Laboratory, Boulder, Colorado Meteorological Laboratory, and Cooperative Institute Espinoza, Jhan C., Instituto Geofisico del Perú, Lima, Perú for Marine and Atmospheric Science, Miami, Florida Etienne-LeBlanc, Sheryl, Meteorological Department of Norwegian Polar Institute, Fram Centre, Gerland, S., St. Maarten, St. Maarten Tromsø, N or w ay Evans III, Thomas E., NOAA/NWS Central Pacific Land Resources Monitoring Unit, Gobron, Nadine, Hurricane Center, Honolulu, Hawaii Institute for Environment and Sustainability, Joint Famiglietti, James S., Department of Earth System Research Centre, European Commission, Ispra, Italy Science, University of California, Irvine, California Goldenberg, Stanley B., NOAA/OAR Atlantic Earth System Science Interdisciplinary Center, Farrell, S., Oceanographic and Meteorological Laboratory, Miami, University of Maryland, College Park, College Park, Florida Maryland Sorbonne Universités (UPMC-Paris Gomez, R. Sorbonne, Fateh, S., Islamic Republic of Iranian Meteorological 6), LOCEAN-IPSL, CNRS-IRD-MNHN, Paris, France Organization, Iran NOAA/OAR Atlantic Oceanographic and Goni, Gustavo, Fausto, Robert S., Geological Survey of Denmark and Meteorological Laboratory, Miami, Florida Greenland, Copenhagen, Denmark Goto, A., Japan Meteorological Agency, Tokyo, Japan Fedaeff, Nava, National Institute of Water and Forschungszentrum Jülich, Jülich, Germany Grooß, J.-U., Atmospheric Research, Ltd., Auckland, New Zealand Gruber, Alexander, Department of Geodesy and Feely, Richard A., NOAA/OAR Pacific Marine Geoinformation, Vienna University of Technology, Environmental Laboratory, Seattle, Washington Vienna, Austria Pacific Northwest National Laboratory, Richland, Feng, Z., Guard, Charles “Chip”, NOAA/NWS Weather Forecast Washington Office, Guam Fenimore, Chris, NOAA/NESDIS National Centers for Department of Theoretical and Gugliemin, Mauro, Environmental Information, Asheville, North Carolina Applied Sciences, Insubria University, Varese, Italy University of Liège, Liège, Belgium Fettweis, X., Gupta, S. K., Science Systems and Applications, Inc., Environment and Climate Change Fioletov, Vitali E., Hampton, Virginia Canada, Toronto, Ontario, Canada Instituto de Física de Cantabria (CSIC- Gutiérrez, J. M., European Centre for Medium- Flemming, Johannes, UC), Santander, Spain Range Weather Forecasts, Reading, United Kingdom Atmospheric Sciences and Global Change Hagos, S., Canadian Hurricane Centre, Fogarty, Chris T., Division, Pacific Northwest National Laboratory, Environment and Climate Change Canada, Dartmouth, Richland, Washington Nova Scotia, Canada Hahn, Sebastian, Department of Geodesy and Department of Geography, Ohio University, Fogt, Ryan L., Geoinformation, Vienna University of Technology, Athens, Ohio Vienna, Austria Siii | AUGUST 2016 STATE OF THE CLIMATE IN 2015

8 Hurst, Dale F., Cooperative Institute for Research in Department of Meteorology and Haimberger, Leo, Environmental Sciences, University of Colorado Boulder, Geophysics, University of Vienna, Vienna, Austria and NOAA/OAR Earth System Research Laboratory, Finnish Meteorological Institute, Helsinki, Hakkarainen, J., Boulder, Colorado Finland Finnish Meteorological Institute, Helsinki, Ialongo, I., Hall, Brad D., NOAA/OAR Earth System Research Finland Laboratory, Boulder, Colorado Nigerian Meteorological Agency, Abuja, Ijampy, J. A., Halpert, Michael S., NOAA/NWS Climate Prediction Nigeria Center, College Park, Maryland Ingvaldsen, R. B., Institute of Marine Research, Bergen, Hamlington, Benjamin D., Center for Coastal Physical Norway Oceanography, Old Dominion University, Norfolk, European Centre for Medium-Range Inness, Antje, Virginia Weather Forecasts, Reading, United Kingdom Department of Geography, University of Hanna, E., Isaksen, K., Norwegian Meteorological Institute, Blindern, Sheffield, Sheffield, United Kingdom Oslo, Norway Danish Meteorological Institute, Copenhagen, Hansen, K., Ishii, Masayoshi, Japan Meteorological Agency, Tsukuba, Denmark Japan Hanssen-Bauer, I., Norwegian Meteorological Institute, Jevrejeva, Svetlana, National Oceanography Centre, Blindern, Oslo, Norway Liverpool, United Kingdom Harris, Ian, Climatic Research Unit, School of Estellus, and LERMA, Observatoire de Paris, Jiménez, C., Environmental Sciences, University of East Anglia, Paris, France Norwich, United Kingdom Jin, Xiangze, Woods Hole Oceanographic Institution, NOAA/NESDIS Center for Heidinger, Andrew K., Woods Hole, Massachusetts Satellite Applications and Research, University of Wisconsin–Madison, Madison, Wisconsin Institute of Marine Research, Bergen, Johannesen, E., Norway Heikkilä, A., Finnish Meteorological Institute, Helsinki, Finland EUMETSAT, Darmstadt, Germany, and Met John, Viju, Office Hadley Centre, Exeter, United Kingdom Max Planck Institute for Chemistry, Mainz, Heil, A., Germany Norwegian Radiation Protection Authority, Johnsen, B., Østerås, Norway Heim Jr., Richard R., NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina Johnson, Bryan, NOAA/OAR Earth System Research Laboratory, Global Monitoring Division, and University Alfred Wegener Institute, Bremerhaven, Hendricks, S., of Colorado Boulder, Boulder, Colorado Germany Johnson, Gregory C., NOAA/OAR Pacific Marine Hernández, Marieta, Climate Center, Institute of Environmental Laboratory, Seattle, Washington Meteorology of Cuba, Cuba Climatic Research Unit, School of Jones, Philip D., Hidalgo, Hugo G., Center for Geophysical Research and Environmental Sciences, University of East Anglia, School of Physics, University of Costa Rica, San José, Norwich, United Kingdom Costa Rica Joseph, Annie C., Dominica Meteorological Service, Hilburn, Kyle, Remote Sensing Systems, Santa Rosa, Dominica California Météo France, Réunion Jumaux, Guillaume, Ho, Shu-peng (Ben), COSMIC, UCAR, Boulder, Colorado Kabidi, Khadija, Holmes, R. M., Woods Hole Research Center, Falmouth, Direction de la Météorologie Nationale Massachusetts Maroc, Rabat, Morocco Hu, Zeng-Zhen, NOAA/NWS National Centers for Max Planck Institute for Chemistry, Kaiser, Johannes W., Environmental Prediction, Climate Prediction Center, Mainz, Germany, and European Centre for Medium- College Park, Maryland Range Weather Forecasts, Reading, United Kingdom Huang, Boyin, NOAA/NESDIS National Centers for Kato, Seiji, NASA Langley Research Center, Hampton, Environmental Information, Asheville, North Carolina Virginia State University of New York, Huelsing, Hannah K., Kazemi, A., Islamic Republic of Iranian Meteorological Albany, New York Organization, Iran Huffman, George J., NASA Goddard Space Flight Center, Department of Atmospheric and Oceanic Keller, Linda M., Greenbelt, Maryland Sciences, University of Wisconsin–Madison, Madison, Wisconsin University of Liverpool, and National Hughes, C., Oceanography Centre, Liverpool, United Kingdom Kendon, Mike, Met Office Hadley Centre, Exeter, United Kingdom Kennedy, John, Met Office Hadley Centre, Exeter, United Kingdom Siv | AUGUST 2016

9 Lazzara, Matthew A., Space Science and Engineering Trinidad & Tobago Meteorological Service, Kerr, Kenneth, Center, University of Wisconsin–Madison, and Piarco, Trinidad Department of Physical Sciences, Madison Area Kholodov, A. L., Geophysical Institute, University of Technical College, Madison, Wisconsin Alaska Fairbanks, Fairbanks, Alaska Lemons, P., U.S. Fish and Wildlife Service, Anchorage, Islamic Republic of Iranian Khoshkam, Mahbobeh, Alaska Meteorological Organization, Iran Leuliette, Eric, NOAA/NESDIS NCWCP Laboratory for Killick, Rachel, Met Office Hadley Centre, Exeter, United Satellite Altimetry, College Park, Maryland Kingdom NOAA/NWS Climate Prediction L’Heureux, Michelle, Kim, Hyungjun, Institute of Industrial Science, University Center, College Park, Maryland of Tokyo, Japan Lieser, Jan L., Antarctic Climate and Ecosystems Korea Polar Research Institute, Incheon, Kim, S.-J., Cooperative Research Centre, University of Tasmania, Republic of Korea Hobart, Tasmania, Australia NOAA/NWS National Hurricane Kimberlain, Todd B., Lin, I.-I., National Taiwan University, Taipei, Taiwan Center, Miami, Florida Department of Geography, University of Liu, Hongxing, Klotzbach, Philip J., Department of Atmospheric Science, Cincinnati, Cincinnati, Ohio Colorado State University, Fort Collins, Colorado Cooperative Institute for Meteorological Liu, Yinghui, Knaff, John A., NOAA/NESDIS Center for Satellite Satellite Studies, University of Wisconsin–Madison, Applications and Research, Fort Collins, Colorado Madison, Wisconsin Japan Meteorological Agency, Tokyo, Kobayashi, Shinya, Locarnini, Ricardo, NOAA/NESDIS National Centers for Japan Environmental Information, Silver Spring, Maryland Norwegian Polar Institute, Tromsø, Norway Kohler, J., NASA Langley Research Center, Loeb, Norman G., Korhonen, Johanna, Freshwater Centre, Finnish Hampton, Virginia Environment Institute (SYKE), Helsinki, Finland Lo Monaco, Claire, Sorbonne Universités (UPMC-Paris Korshunova, Natalia N., All-Russian Research Institute of 6), LOCEAN-IPSL, CNRS-IRD-MNHN, Paris, France Hydrometeorological Information - World Data Center, NOAA/NWS National Centers for Long, Craig S., Obninsk, Russia Envrionmental Prediction, Camp Springs, Maryland Kovacs, K. M., Norwegian Polar Institute, Tromsø, Norway López Álvarez, Luis Alfonso, Instituto de Hidrología Science Systems and Applications, Kramarova, Natalya, de Meteorología y Estudios Ambientales de Colombia Inc., NASA Goddard Space Flight Center, Greenbelt, (IDEAM), Bogotá, Colombia Maryland National Institute of Water and Lorrey, Andrew M., NASA Langley Research Center, Hampton, Kratz, D. P., Atmospheric Research, Ltd., Auckland, New Zealand Virginia Loyola, Diego, German Aerospace Center (DLR) Kruger, Andries, South African Weather Service, Pretoria, Oberpfaffenhofen, Wessling, Germany South Africa Lumpkin, Rick, NOAA/OAR Atlantic Oceanographic and ERT, Inc., NOAA/NESDIS National Kruk, Michael C., Meteorological Laboratory, Miami, Florida Centers for Environmental Information, Asheville, North Bureau of Meteorology, Melbourne, Victoria, Luo, Jing-Jia, Carolina Australia University of California, Santa Cruz, Kudela, Raphael, Finnish Meteorological Institute, Helsinki, Luojus, K., Santa Cruz, California Finland NOAA/NWS National Centers for Kumar, Arun, Lydersen, C., Norwegian Polar Institute, Tromsø, Norway Environmental Prediction, Climate Prediction Center, College Park, Maryland Lyman, John M., NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, Washington, and Lakatos, M., Hungarian Meteorological Service, Budapest, Joint Institute for Marine and Atmospheric Research, Hungary University of Hawaii, Honolulu, Hawaii Finnish Meteorological Institute, Arctic Lakkala, K., Maberly, Stephen C., Lake Ecosystems Group, Centre for Research Centre, Sodankylä, Finland Ecology and Hydrology, Lancaster, United Kingdom University of Guam, Mangilao, Guam Lander, Mark A., AOS/CIMSS University of Wisconsin– Maddux, Brent C., Landsea, Chris W., NOAA/NWS National Hurricane Madison, Madison, Wisconsin Center, Miami, Florida Malheiros Ramos, Andrea, Instituto Nacional de Lankhorst, Matthias, Scripps Institution of Oceanography, Pesquisas Espaciais, Brasilia, Brazil University of California, San Diego, La Jolla, California Earth Cryosphere Institute, Tyumen, and Malkova, G. V., Lantz, Kathleen, Cooperative Institute for Research in Tyumen State Oil and Gas University, Tyumen, Russia Environmental Sciences, University of Colorado Boulder, and NOAA/OAR Earth System Research Laboratory, Boulder, Colorado Sv | AUGUST 2016 STATE OF THE CLIMATE IN 2015

10 NOAA/OAR Atlantic Meinen, Christopher S., Manney, G., NorthWest Research Associates, and New Oceanographic and Meteorological Laboratory, Miami, Mexico Institute of Mining and Technology, Socorro, Florida New Mexico Department of Energy and Environmental Mekonnen, A., Dominica Meteorological Service, Marcellin, Vernie, Systems, North Carolina A & T State University, Dominica Greensboro, North Carolina Marchenko, S. S., Geophysical Institute, University of Environmental Hydraulic Institute, Menéndez, Melisa, Alaska Fairbanks, Fairbanks, Alaska Universidad de Cantabria, Cantabria, Spain Centro Nacional de Monitoramento e Marengo, José A., Mengistu Tsidu, G., Department of Earth and Alertas aos Desastres Naturais, Cachoeira Paulista, Sao Environmental Sciences, Botswana International Paulo, Brazil University of Science and Technology, Palapye, Botswana, NOAA/NESDIS National Centers for Marra, John J., and Department of Physics, Addis Ababa University, Environmental Information, Honolulu, Hawaii Addis Ababa, Ethiopia Department of Hydrology Marszelewski, Wlodzimierz, Space Science and Engineering Center, Menzel, W. Paul, and Water Management, Nicolaus Copernicus University of Wisconsin–Madison, Madison, Wisconsin University, Toruń, Poland Department of Meteorology, Merchant, Christopher J., Laboratory of Hydrology and Water Martens, B., University of Reading, Reading, United Kingdom Management, Ghent University, Ghent, Belgium Meredith, Michael P., British Antarctic Survey, NERC, CIIFEN Centro Internacional Martínez-Güingla, Rodney, Cambridge, United Kingdom eno de El Niño, n d ó el Fen ó m para la Investigaci Joint Institute for Marine and Merrifield, Mark A., Guayaquil, Ecuador Atmospheric Research, University of Hawaii, Honolulu, Massom, Robert A., Australian Antarctic Division, and Hawaii Antarctic Climate and Ecosystems Cooperative Research Metzl, N., Sorbonne Universités (UPMC-Paris 6), Centre, University of Tasmania, Hobart, Tasmania, LOCEAN-IPSL, CNRS-IRD-MNHN, Paris, France Australia Science Directorate, NASA Langley Minnis, Patrick, Mata, Mauricio M., ó r Laborat io de Estudos dos Oceanos Research Center, Hampton, Virginia e Clima, Instituto de Oceanografia, Universidade Federal do Rio Grande-FURG, Rio Grande, Brazil Department of Earth Sciences, VU Miralles, Diego G., University Amsterdam, Amsterdam, Netherlands NOAA/OAR Arctic Research Mathis, Jeremy T., Program, Climate Observation Division, Silver Spring, Mistelbauer, T., Department of Geodesy and Maryland Geoinformation, Vienna University of Technology, and EODC, Vienna, Austria May, Linda, Centre for Ecology and Hydrology, Edinburgh, United Kingdom College of Marine Science, University Mitchum, Gary T., of South Florida, St. Petersburg, Florida Mayer, Michael, Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria Monselesan, Didier, CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia Mazloff, Matthew, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California CSIR Natural Resources and the Monteiro, Pedro, Environment, Stellenbosch, South Africa McBride, Charlotte, South African Weather Service, Pretoria, South Africa Montzka, Stephen A., NOAA/OAR Earth System Research Laboratory, Boulder, Colorado McCabe, M. F., Biological and Environmental Sciences and Met Office Hadley Centre, Exeter, United Morice, Colin, Engineering Division, King Abdullah University of Science Kingdom and Technology, Thuwal, Saudi Arabia Department of Geography, The University of Mote, T., McCarthy, M., Met Office Hadley Centre, Exeter, United Georgia, Athens, Georgia Kingdom Department of Physics, University of Toronto, Mudryk, L., Marine Science Institute, University of McClelland, J. W., Toronto, Ontario, Canada Texas at Austin, Port Aransas, Texas Mühle, Jens, Scripps Institution of Oceanography, Bureau of Meteorology, Melbourne, McGree, Simon, University of California, San Diego, La Jolla, California Victoria, Australia Mullan, A. Brett, National Institute of Water and CSIRO Land and Water Flagship, McVicar, Tim R., Atmospheric Research, Ltd., Wellington, New Zealand Canberra, Australian Capital Territory, and Australian Nash, Eric R., Science Systems and Applications, Inc., Research Council Centre of Excellence for Climate NASA Goddard Space Flight Center, Greenbelt, Maryland System Science, Sydney, New South Wales, Australia Naveira-Garabato, Alberto C., University of Remote Sensing Systems, Santa Rosa, Mears, Carl A., Southampton, National Oceanography Centre, California Southampton, United Kingdom NASA Goddard Space Flight Center, Greenbelt, Meier, W., Maryland Svi | AUGUST 2016

11 Petropavlovskikh, Irina, NOAA/OAR Earth System Colorado Center for Astrodynamics Nerem, R. Steven, Research Laboratory, Global Monitoring Division, and Research, Cooperative Institute for Research in University of Colorado Boulder, Boulder, Colorado Environmental Sciences, University of Colorado Boulder, Boulder, Colorado Pezza, Alexandre B., Greater Wellington Regional Council, Wellington, New Zealand NASA Goddard Space Flight Center, Newman, Paul A., Greenbelt, Maryland Phillips, David, Environment and Climate Change Canada, Toronto, Ontario, Canada CIIFEN Centro Internacional para la Nieto, Juan José, n d eno de El Niño, Guayaquil, el Fen Investigaci ó ó m Land Resource Management Unit, Pinty, Bernard, Ecuador Institute for Environment and Sustainability, European Commission Joint Research Centre, Ispra, Italy WSL Institute for Snow and Avalanche Noetzli, Jeannette, Research, Davos, Switzerland Pitts, Michael C., NASA Langley Research Center, Hampton, Virginia O’Neel, S., USGS, Alaska Science Center, Anchorage, Alaska Pons, M. R., Agencia Estatal de Meteorología, Santander, Spain Osborn, Tim J., Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Porter, Avalon O., Cayman Islands National Weather Norwich, United Kingdom Service, Grand Cayman, Cayman Islands NOAA/OAR Pacific Marine Environmental Overland, J., UiT The Arctic University of Norway, Primicerio, R., Laboratory, Seattle, Washington Tromsø, N or w ay Oyunjargal, Lamjav, Hydrology and Environmental Proshutinsky, A., Woods Hole Oceanographic Institution, Monitoring, Institute of Meteorology and Hydrology, Woods Hole, Massachusetts National Agency for Meteorology, Ulaanbaatar, Mongolia Quegan, Sean, University of Sheffield, Sheffield, United School of Civil and Environmental Parinussa, Robert M., Kingdom Engineering, Water Research Centre, University of New Quintana, Juan, ica de Chile, Chile Direcci ó g n Meteorol ó South Wales, Sydney, New South Wales, Australia Rahimzadeh, Fatemeh, Atmospheric Science and Korea Meteorological Administration, Park, E-hyung, Meteorological Research Center, Tehran, Iran Republic of Korea Rajeevan, Madhavan, Earth System Science Organization, Parker, David, Met Office Hadley Centre, Exeter, United Ministry of Earth Sciences, New Delhi, India Kingdom Service de la Climatologie et du Randriamarolaza, L., European Centre for Medium-Range Parrington, M., Changement Climatique, Direction Générale de la Weather Forecasts, Reading, United Kingdom Météorologie, Madagascar Parsons, A. Rost, NOAA/NESDIS National Centers for Razuvaev, Vyacheslav N., All-Russian Research Institute Environmental Information, Silver Spring, Maryland of Hydrometeorological Information, Obninsk, Russia NOAA/NWS National Hurricane Pasch, Richard J., NOAA/NESDIS National Centers for Reagan, James, Center, Miami, Florida Environmental Information, Silver Spring, Maryland, Pascual-Ramírez, Reynaldo, National Meteorological and Earth System Science Interdisciplinary Center/ Service of Mexico, Mexico Cooperative Institute for Climate and Satellites– Dorset Environmental Science Paterson, Andrew M., Maryland, University of Maryland, College Park, Centre, Ontario Ministry of the Environment and Maryland Climate Change, Dorset, Ontario, Canada Australian Bureau of Meteorology, and ACE Reid, Phillip, Paulik, Christoph, Department of Geodesy and CRC, Hobart, Tasmania, Australia Geoinformation, Vienna University of Technology, Department of Geodesy and Reimer, Christoph, Vienna, Austria Geoinformation, Vienna University of Technology, and National Institute of Water and Pearce, Petra R., EODC, Vienna, Austria Atmospheric Research, Ltd., Auckland, New Zealand Laboratoire de Météorologie Dynamique, Rémy, Samuel, Nichols College, Dudley, Massachusetts Pelto, Mauri S., Paris, France UCAR COSMIC, Boulder, Colorado Peng, Liang, Victoria University of Wellington, Renwick, James A., Wellington, New Zealand Climate Change Research Perkins-Kirkpatrick, Sarah E., Centre, University of New South Wales, Sydney, New Revadekar, Jayashree V., Indian Institute of Tropical South Wales, Australia Meteorology, Pune, India USACE, ERDC, Cold Regions Research Perovich, D., Richter-Menge, J., USACE Cold Regions Research and and Engineering Laboratory, and Thayer School of Engineering Laboratory, Hanover, New Hampshire Engineering, Dartmouth College, Hanover, Riffler, Michael, GeoVille Information Systems, Innsbruck, New Hampshire Austria, and Institute of Geography, University of Bern, Bern, Switzerland Svii | AUGUST 2016 STATE OF THE CLIMATE IN 2015

12 Sensoy, Serhat, Turkish State Meteorological Service, Kinneret Limnological Laboratory, Israel Rimmer, Alon, Kalaba, Ankara, Turkey Oceanographic and Limnological Research, Migdal, Israel Setzer, Alberto, National Institute for Space Research, Rintoul, Steve, CSIRO-CMAR/CAWCR/ACE-CRC, São Jose dos Compos-SP, Brazil Hobart, Tasmania, Australia Sharp, M., Department of Earth and Atmospheric Sciences, Department of Geography, Rutgers Robinson, David A., University of Alberta, Edmonton, Alberta, Canada University, Piscataway, New Jersey Meteorological Service, Jamaica, Kingston, Shaw, Adrian, Rodell, Matthew, Hydrological Sciences Laboratory, Jamaica NASA Goddard Space Flight Center, Greenbelt, Maryland Shi, Lei, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina National Meteorological Service Rodríguez Solís, José L., of Mexico, Mexico Shiklomanov, A. I., University of New Hampshire, Durham, New Hampshire, and Shirshov Institute of Romanovsky, Vladimir E., Geophysical Institute, Oceanology, Moscow, Russia University of Alaska Fairbanks, Fairbanks, Alaska Shiklomanov, Nikolai I., Department of Geography, Ronchail, Josyane, Université Paris Diderot (UPMC-Paris George Washington University, Washington, D.C. 7), LOCEAN-IPSL, CNRS-IRD-MNHN, Paris, France University of California, Santa Barbara, Siegel, David A., Rosenlof, Karen H., NOAA/OAR Earth System Research Santa Barbara, California Laboratory, Boulder, Colorado Science Application International Signorini, Sergio R., Roth, Chris, University of Saskatchewan, Saskatoon, Corporation, Beltsville, Maryland Saskatchewan, Canada Division of Meteorology, Department of Sima, Fatou, Rusak, James A., Dorset Environmental Science Centre, Water Resources, Banjul, The Gambia Ontario Ministry of the Environment and Climate Change, Dorset, Ontario, Canada Simmons, Adrian J., European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom NOAA/OAR Pacific Marine Sabine, Christopher L., Environmental Laboratory, Seattle, Washington Institute for Marine and Atmospheric Smeets, C. J. P. P., Research Utrecht, Utrecht University, Utrecht, Sallée, Jean-Bapiste, Sorbonne Universités (UPMC-Paris Netherlands 6), LOCEAN-IPSL, CNRS-IRD-MNHN, Paris, France, and British Antarctic Survey, NERC, Cambridge, United Smith, Sharon L., Geological Survey of Canada, Natural Kingdom Resources Canada, Ottawa, Ontario, Canada Sánchez-Lugo, Ahira, NOAA/NESDIS National Centers Meteorological Service, Jamaica, Spence, Jaqueline M., for Environmental Information, Asheville, North Carolina Kingston, Jamaica NASA Jet Propulsion Laboratory, Santee, Michelle L., India Meteorological Department, Srivastava, A. K., Pasadena, California Jaipur, India Science Systems and Applications, Sawaengphokhai, P., NASA Langley Research Center, Stackhouse Jr., Paul W., Inc., Hampton, Virginia Hampton, Virginia Sayouri, Amal, Direction de la Météorologie Nationale Institute of Arctic and Alpine Stammerjohn, Sharon, Maroc, Rabat, Morocco Research, University of Colorado Boulder, Boulder, National Snow and Ice Data Center, Scambos, Ted A., Colorado University of Colorado Boulder, Boulder, Colorado Steinbrecht, Wolfgang, German Weather Service Schemm, Jae, NOAA/NWS Climate Prediction Center, (DWD), Hohenpeissenberg, Germany College Park, Maryland Stella, José L., g ico Nacional, Buenos ó Servicio Meteorol Schladow, S. Geoffrey, Tahoe Environmental Research Aires, Argentina Center, University of California, Davis, Davis, California Deutscher Wetterdienst, Offenbach, Stengel, Martin, NOAA/OAR Atlantic Oceanographic Schmid, Claudia, Germany and Meteorological Laboratory, Miami, Florida Department of Physics, The Stennett-Brown, Roxann, Schmid, Martin, Eawag, Swiss Federal Institute of Aquatic University of the West Indies, Jamaica Science and Technology, Dübendorf, Switzerland Department of Physics, The Stephenson, Tannecia S., Schmidtko, Sunke, GEOMAR Helmholtz Centre for University of the West Indies, Jamaica Ocean Research Kiel, Kiel, Germany Strahan, Susan, Universities Space Research Association, Schreck III, Carl J., Cooperative Institute for Climate and NASA Goddard Space Flight Center, Greenbelt, Satellites, North Carolina State University, Asheville, Maryland North Carolina Department of Geography, George Streletskiy, D. A., Universities Space Research Association, Selkirk, H. B., Washington University, Washington, D.C. NASA Goddard Space Flight Center, Greenbelt, Maryland Sun-Mack, Sunny, Science Systems and Applications, Inc., Scripps Institution of Oceanography, Send, Uwe, Hampton, Virginia University of California, San Diego, La Jolla, California Sviii | AUGUST 2016

13 van der Schrier, Gerard, Royal Netherlands CSIR Southern Ocean Carbon & Swart, Sebastiaan, Meteorological Institute (KNMI), De Bilt, Netherlands Climate Observatory, Stellenbosch, South Africa Faculty of Earth and Life van der Werf, Guido R., NOAA/NOS Center for Operational Sweet, William, Sciences, VU University Amsterdam, Netherlands Oceanographic Products and Services, Silver Spring, Maryland Van Meerbeeck, Cedric J., Caribbean Institute for Meteorology and Hydrology, Bridgetown, Barbados Scripps Institution of Oceanography, Talley, Lynne D., University of California, San Diego, La Jolla, California Velicogna, I., University of California, Irvine, California Grenada Airports Authority, St. George’s, Tamar, Gerard, Verburg, Piet, National Institute of Water and Grenada Atmospheric Research, Ltd., Hamilton, New Zealand Tank, S. E., University of Alberta, Edmonton, Alberta, Vigneswaran, Bala, Water Quality and Spatial Science Canada Section, WaterNSW, Penrith, New South Wales, Australia Taylor, Michael A., Department of Physics, The University of the West Indies, Jamaica Environment and Climate Change Vincent, Lucie A., Canada, Toronto, Ontario, Canada Tedesco, M., Lamont–Doherty Earth Observatory, Columbia University Palisades, New York, and NASA NOAA/OAR Atlantic Oceanographic and Volkov, Denis, Goddard Institute of Space Studies, New York, New Meteorological Laboratory, and Cooperative Institute Yor k for Marine and Atmospheric Science, Miami, Florida Research Institute for Limnology, Teubner, Katrin, NOAA/NESDIS National Centers for Vose, Russell S., University of Innsbruck, Mondsee, Austria Environmental Information, Asheville, North Carolina NOAA/NWS, Alaska Region, Fairbanks, Thoman, R. L., Wagner, Wolfgang, Department of Geodesy and Alaska Geoinformation, Vienna University of Technology, Vienna, Austria Thompson, Philip, Joint Institute for Marine and Atmospheric Research, University of Hawaii, Honolulu, Department of Earth Sciences, University Wåhlin, Anna, Hawaii of Gothenburg, Göteborg, Sweden Thomson, L., Department of Geography, University of Wahr, J., Department of Physics and Cooperative Institute Ottawa, Ottawa, Ontario, Canada for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado Timmermans, M.-L., Yale University, New Haven, Connecticut International Arctic Research Center, University Walsh, J., of Alaska Fairbanks, Fairbanks, Alaska Laboratory of Systems, Tirnanes, Joaquin A., Technological Research Institute, Universidad de NOAA/OAR Atlantic Oceanographic and Wang, Chunzai, Santiago de Compostela, Santiago de Compostela, Spain Meteorological Laboratory, Miami, Florida Tobin, Skie, Bureau of Meteorology, Melbourne, Victoria, State University of New York, Albany, Wang, Junhong, Australia N ew Yor k Trachte, Katja, Laboratory for Climatology and Remote Department of Geography and Anthropology, Wang, Lei, Sensing, Philipps-Universität, Marburg, Germany Louisiana State University, Baton Rouge, Louisiana Trainer, Vera L., NOAA/NMFS Northwest Fisheries Wang, M., Joint Institute for the Study of the Atmosphere Science Center, Seattle, Washington and Ocean, University of Washington, Seattle, Washington Arctic and Antarctic Research Institute, St. Tretiakov, M., Petersburg, Russia Wang, Sheng-Hung, Byrd Polar and Climate Research Bureau of Meteorology, Melbourne, Trewin, Blair C., Center, The Ohio State University, Columbus, Ohio Victoria, Australia NOAA/OAR Atlantic Oceanographic Wanninkhof, Rik, Caribbean Institute for Meteorology Trotman, Adrian R., and Meteorological Laboratory, Miami, Florida and Hydrology, Bridgetown, Barbados Tahoe Environmental Research Watanabe, Shohei, Aerospace Engineering Sciences, University of Tschudi, M., Center, University of California, Davis, Davis, California Colorado Boulder, Boulder, Colorado Weber, Mark, University of Bremen, Bremen, Germany Geological Survey of Denmark and Greenland, van As, D., Weller, Robert A., Woods Hole Oceanographic Copenhagen, Denmark Institution, Woods Hole, Massachusetts van de Wal, R. S. W., Institute for Marine and Weyhenmeyer, Gesa A., Department of Limnology, Atmospheric Research Utrecht, Utrecht University, Department of Ecology and Genetics, Uppsala Utrecht, Netherlands University, Uppsala, Sweden Royal Netherlands Meteorological van der A, Ronald J., Whitewood, Robert, Environment and Climate Change Institute (KNMI), DeBilt, Netherlands Canada, Toronto, Ontario, Canada Transmissivity, and VanderSat, van der Schalie, Robin, CSIRO Oceans and Atmosphere, Wijffels, Susan E., Noordwijk, Netherlands Hobart, Tasmania, Australia Six | AUGUST 2016 STATE OF THE CLIMATE IN 2015

14 Yamada, Ryuji, Japan Meteorological Agency, Tokyo, Japan Wilber, Anne C., Science Systems and Applications, Inc., Hampton, Virginia Korea Meteorological Administration, Yim, So-Young, Republic of Korea Wild, Jeanette D., INNOVIM, NOAA/NWS National Centers for Environmental Prediction, Climate ERT, Inc., NOAA/NESDIS National Yin, Xungang, Prediction Center, College Park, Maryland Centers for Environmental Information, Asheville, North Carolina Met Office Hadley Centre, Exeter, Willett, Kate M., United Kingdom Young, Steven H., Independent Researcher, Long Beach, California National Institute of Water and Williams, Michael J.M., Atmospheric Research, Ltd., Wellington, New Zealand Woods Hole Oceanographic Institution, Woods Yu, Lisan, Hole, Massachusetts St. Lucia Meteorological Service, St. Lucia Willie, Shem, Maldives Meteorological Service, Maldives Zahid, H., Alaska Division of Geological and Geophysical Wolken, G., Surveys, Fairbanks, Alaska Zambrano, Eduardo, Centro Internacional para la Wong, Takmeng, NASA Langley Research Center, ó n d el Fen ó eno El Niño, Guayaquil, Ecuador m Investigaci Hampton, Virginia Zhang, Peiqun, Beijing Climate Center, Beijing, China Department of Civil and Environmental Wood, E. F., Zhao, Guanguo, University of Illinois at Urbana– Engineering, Princeton University, Princeton, New Jersey Champaign, Urbana, Illinois Woolway, R. Iestyn, Department of Meteorology, Cold and Arid Regions Environmental and Zhou, Lin, University of Reading, Reading, United Kingdom Engineering Research Institute, Lanzhou, China Wouters, B., School of Geographical Sciences, University Goddard Earth Sciences Technology Ziemke, Jerry R., of Bristol, Bristol, United Kingdom and Research, Morgan State University, Baltimore, NOAA/NWS National Centers for Xue, Yan, Maryland, and NASA Goddard Space Flight Center, Environmental Prediction, Climate Prediction Center, Greenbelt, Maryland College Park, Maryland EDITORIAL AND PRODUCTION TEAM Ambrose, Barbara J., Graphics Support, Riverside Love-Brotak, S. Elizabeth, Lead Graphics Production, Technology, Inc., NOAA/NESDIS National Centers NOAA/NESDIS National Centers for Environmental for Environmental Information, Stennis Space Center, Information, Asheville, North Carolina Mississippi Gilbert, Kristin, Bulletin of the American Meteorological Graphics Support, Cooperative Institute Griffin, Jessicca, Society, Boston, Massachusetts for Climate and Satellites–NC, North Carolina State Maycock, Tom, Technical Editor, Cooperative Institute University, Asheville, North Carolina for Climate and Satellites–NC, North Carolina State Graphics Support, TeleSolv Consulting, Misch, Deborah J., University, Asheville, North Carolina NOAA/NESDIS National Centers for Environmental Osborne, Susan, Technical Editor, TeleSolv Consulting, Information, Asheville, North Carolina NOAA/NESDIS National Centers for Environmental Riddle, Deborah B., Graphics Support, NOAA/NESDIS Information, Asheville, North Carolina National Centers for Environmental Information, Technical Editor, LAC Group, NOAA/ Sprain, Mara, Asheville, North Carolina NESDIS National Centers for Environmental Graphics Support, STG, Inc., NOAA/ Yo u ng , Te r e s a , Information, Asheville, North Carolina NESDIS National Centers for Environmental Visual Communications Team Lead, Veasey, Sara W., Information, Asheville, North Carolina NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina Sx | AUGUST 2016

15 Sxi | AUGUST 2016 STATE OF THE CLIMATE IN 2015

16 Sxii | AUGUST 2016

17 TABLE OF CONTENTS List of authors and affiliations ... i ... xvi Abstract ... 1 1. INTRODUCTION ide Bar 1.1: t he 2015/16 e l n iño Compared with other re Cent events 5 s ... ... 7 2. GLOBAL CLIMATE ... Overview a 7 . b. T emperature ... 12 1 . Surface temperature ... 12 . L ower and midtropospheric temperatures ... 13 2 ower stratospheric temperature . L ... 15 3 4 . L ake surface temperatures ... 17 . L and surface temperature extremes ... 19 5 c Cryosphere . ... 20 . P ermafrost thermal state ... 20 1 2 . N orthern Hemisphere continental snow cover extent ... 22 . A lpine glaciers and ice sheets ... 23 3 d Hydrological cycle ... 24 . . Surface humidity ... 24 1 2 . T ... 25 otal column water vapor 3 . U pper tropospheric humidity ... 27 4 . Precipitation ... 27 5 . Cloudiness ... 28 . R iver discharge ... 29 6 Gr . oundwater and terrestrial water storage ... 30 7 8 . Soil moisture ... 31 9 . M onitoring global drought using the self-calibrating Palmer drought severity index ... 32 ... ide Bar 2.1: G lo Bal land evaporation s 34 e . Atmospheric circulation ... 36 1 . M ean sea level pressure and related modes of variability ... 36 . Surface winds ... 38 2 3 . U pper air winds ... 40 ... E arth radiation budget ... 41 f . 1 . Earth radiation budget at top-of-atmosphere ... 41 2. Mauna Loa clear-sky “apparent” solar transmission ... 43 g. A tmospheric composition ... 44 1. Long-lived greenhouse gases ... 44 . Ozone-depleting gases ... 47 2 . Aerosols ... 47 3 4 . Stratospheric ozone ... 49 . S tratospheric water vapor ... 51 5 6. Tropospheric ozone ... 53 Carbon monoxide ... 55 7. s ide Bar 2.2: a tmospheri C Composition Chan Ges due to the extreme 2015 i ndonesian Fire season GG ered By e l n iño ... 56 tri and surface properties h L . ... 58 1 . L and surface albedo dynamics ... 58 2 . T errestrial vegetation dynamics ... 59 3 . Biomass burning ... 60 GLOBAL OCEANS ... 63 3. a . Overview ... 63 b . S ea surface temperatures ... 63 . O cean heat content ... 66 c s ide Bar 3.1: a widespread harm Ful al Gal Bloom in the northeast p aCiFiC ... 66 . Salinity ... 70 d 70 1 . Introduction ... ea surface salinity 71 ... 2 . S Sxiii | AUGUST 2016 STATE OF THE CLIMATE IN 2015

18 Subsurface salinity ... 72 3. e O cean surface heat, freshwater, and momentum fluxes ... 74 . 1 S urface heat fluxes . ... ... 75 2 . S urface freshwater fluxes ... 76 . Wind stress ... 77 3 4 . Long-term perspective ... 77 s ide Bar 3.2: e xtraordinarily weak ei Ghteen de Gree water produ Ction Con Curs with stron Gly n orth a tlanti C os Cillation in late winter 2014/15 ... 78 positive f S ea level variability and change ... 80 . S urface currents ... 82 g. h . M eridional overturning circulation observations in the North Atlantic Ocean ... 84 i . G lobal ocean phytoplankton ... 87 j. G lobal ocean carbon cycle ... 89 . A ir–sea carbon dioxide fluxes ... 90 1 2 . C arbon inventories from the GO-SHIP surveys ... 91 4. TROPICS ... 93 THE a Overview ... 93 . E NSO and the tropical Pacific b ... 93 . 1 . Oceanic conditions ... 94 2 . A tmospheric circulation: tropics and subtropics ... 96 3. Rainfall impacts ... 97 c . T ropical intraseasonal activity ... 98 ntertropical convergence zones . I ... 101 d 1 . Pacific ... 101 . Atlantic ... 102 2 e . Tropical cyclones ... 104 Overview . 1 ... 104 2 . Atlantic basin ... 105 . E 3 astern North Pacific and central North Pacific basins ... 108 4 . W estern North Pacific basin ... 110 orth Indian Ocean 5. N ... 114 6 . S outh Indian Ocean ... ... 115 . A ustralian basin ... 116 7 8 . S outhwest Pacific basin ... 118 ... T ropical cyclone heat potential 120 f . A tlantic warm pool ... 123 g. ... h . I ... 124 ndian Ocean dipole s ide Bar 4.1: t he re Cord - shatterin G 2015 n orthern h emisphere tropi Cal CyClone season 127 ... ide Bar 4.2: a southeast p aCiFiC Basin su Btropi Cal CyClone oFF the C hilean Coast ... 129 s 5. THE ARCTIC ... 131 a . Introduction ... 131 b . Air temperature 132 ... . S c ea ice cover ... 134 s ide Bar 5.1: w alruses in a time oF Climate Chan Ge ... 136 d . S ea surface temperature ... 137 s 5.2: C limate Chan Ge is pushin G Boreal Bar Fish northward to the a rCti C : ide the Case oF the B arents s ea ... 139 . G reenland Ice Sheet ... 140 e laciers and ice caps outside Greenland f . G ... 142 g. T errestrial snow cover ... 145 h . R iver discharge ... 147 i errestrial permafrost . T ... 149 j . O zone and UV radiation ... 152 6. ANTARCTICA ... 155 . Overview ... 155 a b . A tmospheric circulation ... 156 . S c urface manned and automatic weather station observations ... 157 159 ... d . Net precipitation (P – E) Sxiv | AUGUST 2016

19 e . S easonal melt extent and duration ... 161 s ide 6.1: e l n iño and a ntar Cti Ca ... 162 Bar 163 S ea ice extent, concentration, and duration ... f . ... outhern Ocean g. 166 S A ntarctic ozone hole h ... 168 . s ide Bar 6.2: p olar eCosystems and their sensitivity to Climate pertur ... 170 Bation REGIONAL CLIMATES ... ... 173 7. 173 Overview ... a . b . North America ... 173 1 Canada ... ... 173 . 2 . United States ... 175 Mexico . 3 ... 176 c . C entral America and the Caribbean ... 178 . Central America ... 178 1 2 . Caribbean ... 181 . South America ... 182 d 1 . N orthern South America and the tropical Andes 183 ... 2 . T ropical South America east of the Andes ... 184 . S 3 outhern South America ... 185 e . Africa ... 187 . Northern Africa ... 187 1 . West Africa ... 188 2 . Eastern Africa 3 ... ... 189 4 . S outhern Africa between 5º and 30ºS ... 192 South Africa . 5 ... 193 6 . W estern and central Indian Ocean island countries ... 195 f . E urope and the Middle East ... 197 . Overview ... 198 1 2 C entral and western Europe . ... 200 3 . T ... 201 he Nordic and the Baltic countries . Iberian Peninsula 4 ... ... 202 s ide Bar 7.1: u nusually stron G and lon G - lastin G heat wave in e urope ... 204 M editerranean and Balkan States ... 205 5. astern Europe 6 . E ... 206 7 . M iddle East ... 207 g. Asia ... 209 1 . Overview ... 209 . Russia 2 ... ... 209 . 3 East Asia ... 212 ide Bar 7.2: e xtremely wet Conditions in J apan in late summer 2015 ... 213 s . South Asia ... 215 4 5 . Southwest Asia ... 216 h . Oc eania ... 217 Overview . 1 ... 217 2 . N orthwest Pacific and Micronesia ... 217 . Southwest Pacific ... 219 3 . Australia ... 221 4 5. New Zealand ... 223 s ide Bar 7.3: a ustralia ’ s warm ride to end 2015 ... 224 ... 227 APPENDIX 1: Relevant Datasets and Sources ... ... 237 ACKNOWLEDGMENTS 239 ... ACRONYMS AND ABBREVIATIONS 241 ... REFERENCES Sxv | AUGUST 2016 STATE OF THE CLIMATE IN 2015

20 ABSTRACT —J. BLUNDEN AND D. S. ARNDT the capacity to contribute ~7 m to sea level rise, experienced In 2015, the dominant greenhouse gases released into melting over more than 50% of its surface for the first time — carbon dioxide, methane, and nitrous Earth’s atmosphere since the record melt of 2012. — all continued to reach new high levels. At Mauna Loa, oxide Other aspects of the cryosphere were remarkable. Alpine Hawaii, the annual CO concentration increased by a record 2 glacier retreat continued, and preliminary data indicate that 3.1 ppm, exceeding 400 ppm for the first time on record. The 2015 is the 36th consecutive year of negative annual mass 2015 global CO average neared this threshold, at 399.4 ppm. 2 balance. Across the Northern Hemisphere, late-spring snow Additionally, one of the strongest El Niño events since at least cover extent continued its trend of decline, with June the sec - 1950 developed in spring 2015 and continued to evolve through ond lowest in the 49-year satellite record. Below the surface, the year. The phenomenon was far reaching, impacting many record high temperatures at 20-m depth were measured at regions across the globe and affecting most aspects of the all permafrost observatories on the North Slope of Alaska, climate system. –1 increasing by up to 0.66°C decade - Owing to the combination of El Niño and a long-term up since 2000. - ward trend, Earth observed record warmth for the second con In the Antarctic, surface pressure and temperatures were secutive year, with the 2015 annual global surface temperature - lower than the 1981–2010 average for most of the year, consis - surpassing the previous record by more than 0.1°C and exceed tent with the primarily positive southern annular mode, which ing the average for the mid- to late 19th century—commonly saw a record high index value of +4.92 in February. Antarctic considered representative of preindustrial conditions—by sea ice extent and area had large intra-annual variability, with more than 1°C for the first time. Above Earth’s surface, lower a shift from record high levels in May to record low levels in troposphere temperatures were near-record high. August. Springtime ozone depletion resulted in one of the Across land surfaces, record to near-record warmth was largest and most persistent Antarctic ozone holes observed reported across every inhabited continent. Twelve countries, since the 1990s. - including Russia and China, reported record high annual tem Closer to the equator, 101 named tropical storms were peratures. In June, one of the most severe heat waves since observed in 2015, well above the 1981–2010 average of 82. The 1980 affected Karachi, Pakistan, claiming over 1000 lives. On eastern/central Pacific had 26 named storms, the most since 27 October, Vredendal, South Africa, reached 48.4°C, a new 1992. The western north Pacific and north and south Indian global high temperature record for this month. Ocean basins also saw high activity. Globally, eight tropical In the Arctic, the 2015 land surface temperature was 1.2°C cyclones reached the Saffir–Simpson Category 5 intensity level. - above the 1981–2010 average, tying 2007 and 2011 for the high Overlaying a general increase in the hydrologic cycle, the est annual temperature and representing a 2.8°C increase since strong El Niño enhanced precipitation variability around the the record began in 1900. Increasing temperatures have led to world. An above-normal rainy season led to major floods in decreasing Arctic sea ice extent and thickness. On 25 February Paraguay, Bolivia, and southern Brazil. In May, the United States - 2015, the lowest maximum sea ice extent in the 37-year satel recorded its all-time wettest month in its 121-year national lite record was observed, 7% below the 1981–2010 average. record. Denmark and Norway reported their second and third Mean sea surface temperatures across the Arctic Ocean dur - wettest year on record, respectively, but globally soil moisture ing August in ice-free regions, representative of Arctic Ocean was below average, terrestrial groundwater storage was the summer anomalies, ranged from ~0°C to 8°C above average. lowest in the 14-year record, and areas in “severe” drought As a consequence of sea ice retreat and warming oceans, vast rose from 8% in 2014 to 14% in 2015. Drought conditions walrus herds in the Pacific Arctic are hauling out on land rather prevailed across many Caribbean island nations, Colombia, than on sea ice, raising concern about the energetics of females Venezuela, and northeast Brazil for most of the year. Several and young animals. Increasing temperatures in the Barents Sea South Pacific countries also experienced drought. Lack of are linked to a community-wide shift in fish populations: boreal rainfall across Ethiopia led to its worst drought in decades communities are now farther north, and long-standing Arctic and affected millions of people, while prolonged drought in species have been almost pushed out of the area. South Africa severely affected agricultural production. Indian Above average sea surface temperatures are not confined summer monsoon rainfall was just 86% of average. Extremely to the Arctic. Sea surface temperature for 2015 was record dry conditions in Indonesia resulted in intense and widespread high at the global scale; however, the North Atlantic southeast fires during August–November that produced abundant car - of Greenland remained colder than average and colder than bonaceous aerosols, carbon monoxide, and ozone. Overall, 2014. Global annual ocean heat content and mean sea level emissions from tropical Asian biomass burning in 2015 were also reached new record highs. The Greenland Ice Sheet, with almost three times the 2001–14 average. Sxvi | AUGUST 2016

21 1. NTRODUCTION D. S. Arndt, J. Blunden, and Our cover images this year ref lect these intimate — I K. M. Willett connections. Many of the shapes in the images are drawn, quite literally, from time series represented This is the 26th edition of the annual assessment State of the Climate . The year 2015 in this volume. The artist, Jill Pelto, is a practicing now known as - Earth scientist whose work ref lects her field experi - saw the toppling of several symbolic mileposts: no ence and her interpretation of the connection be tably, it was 1.0°C warmer than preindustrial times, - tween global change, landscape, and life. Her father, and the Mauna Loa observatory recorded its first Mauri, is both a longtime contributor to the annual mean carbon dioxide concentration greater State of the Climate series and a steward of a prominent than 400 ppm. Beyond these more recognizable global glacier dataset. markers, changes seen in recent decades continued. The year’s exceptional warmth was fueled in part To convey these connections so beautifully and - generously is a gift; we are thankful to artist and sci by a nearly year-round mature El Niño event, which entist alike, for sharing their talents and disciplines is an omnipresent backdrop to the majority of the with the community. sections in this edition. Finally, we wish one of our dearest and most The ENSO phenomenon is perhaps the most vis - valuable connections, our technical editor, Mara ible reminder of connections across regions, scales, Sprain, a speedy recovery from an unexpected health and systems. It underscores the circumstance that challenge. Her consistency and diligence continue the climate system’s components are intricately to be a model for this series. connected, to each other and to the world’s many An overview of findings is presented in the natural and human systems. Abstract, Fig. 1.1, and Plate 1.1. Chapter 2 features To that end, this year’s SoC has an emphasis on global-scale climate variables; Chapter 3 highlights - ecosystems; several chapters have dedicated a side the global oceans; and Chapter 4 includes tropical bar to the complex relationship between a changing climate phenomena including tropical cyclones. The climate and its impact on living systems. This notion between climate, landscape, and Arctic and Antarctic respond differently through of connectedness — time and are reported in separate chapters (5 and 6, life; between our daily work and the expression of respectively). Chapter 7 provides a regional perspec - its meaning; between planetary-scale drivers and humble living things; between the abstraction and tive authored largely by local government climate specialists. Sidebars included in each chapter are rigor of data and the reality and complexity of their importance; and especially between one generation intended to provide background information on a inspires and informs much of the — and the next significant climate event from 2015, a developing work within this volume. technology, or an emerging dataset germane to the chapter’s content. A list of relevant datasets and their sources for all chapters is provided as an Appendix. | S1 AUGUST 2016 STATE OF THE CLIMATE IN 2015

22 ESSENTIAL CLIMATE VARIABLES —K. M. Willett, J. BLUNDEN AND D. S. ARNDT ECVs in this edition that are considered “par - Time series of major climate indicators are again presented in this introductory chapter. Many tially monitored,” meeting some but not all of the of these indicators are essential climate variables above requirements, include: - (ECVs), originally defined in GCOS 2003 and up • dated again by GCOS in 2010. Atmospheric Upper Air: cloud properties. The following ECVs, included in this edition, • Atmospheric Composition: aerosols and their precursors. are considered “fully monitored,” in that they are • Ocean Surface: carbon dioxide, ocean acidity. observed and analyzed across much of the world, Ocean Subsurface: current, carbon. • with a sufficiently long-term dataset that has peer- reviewed documentation: Terrestrial: soil moisture, permafrost, glaciers • and ice caps, river discharge, groundwater, ice sheets, fraction of absorbed photosynthetically • Atmospheric Surface: air temperature, precipita - active radiation, biomass, fire disturbance. tion, air pressure, water vapor, wind speed and direction. Atmospheric Upper Air: earth radiation budget, • Remaining ECVs that are desired for the future temperature, water vapor, wind speed and direc include: - tion. Atmospheric Surface: surface radiation budget. • Atmospheric Composition: carbon dioxide, meth - • Ocean Surface: sea state. ane, other long-lived gases, ozone. • Ocean Surface: temperature, salinity, sea level, sea • • Ocean Subsurface: nutrients, ocean tracers, ocean ice, current, ocean color, phytoplankton. acidity, oxygen. • Terrestrial: water use, land cover, lakes, leaf area Ocean Subsurface: temperature, salinity. • index, soil carbon. Terrestrial: snow cover, albedo. • late 1. 1. Global (or representative) average time series for essential climate variables. Anomalies are shown P relative to the base period in parentheses although original base periods (as shown in other sections of the report) may differ. The numbers in the square brackets that follow in this caption indicate how many reanaly - sis (blue), satellite (red), and in situ (black) datasets are used to create each time series in that order. (a) N. Hemisphere lower stratospheric ozone (March) [0,5,1]; (b) S. Hemisphere lower stratospheric ozone (Octo - ber) [0,5,1]; (c) Apparent transmission (Mauna Loa) [0,0,1]; (d) Lower stratospheric temperature [3,3,4]; (e) Lower tropospheric temperature [3,2,4]; (f) Surface temperature [4,0,4]; (g) Extremes (warm days (solid) and cool nights (dotted)) [0,0,1]; (h) Arctic sea ice extent (max (solid) and min (dashed)) [0,0,2]; (i) Antarctic sea ice extent (max (solid) and min (dashed)) [0,0,2]; (j) Glacier cumulative mean specific balance [0,0,1]; (k) N. Hemisphere snow cover extent [0,1,0]; (l) Lower stratospheric water vapor [0,1,0]; (m) Cloudiness [1,6,1]; (n) Total column water vapor–land [0,1,2]; (o) Total column water vapor–ocean [0,2,0]; (p) Upper Tropospheric Humidity [1,1,0]; (q) Specific humidity–land [3,0,4]; (r) Specific humidity–ocean [3,1,3]; (s) Relative humid - ity–land [2,0,4]; (t) Relative humidity–ocean [2,0,2]; (u) Precipitation–land [0,0,3]; (v) Precipitation–ocean [0,3,0]; (w) Ocean heat content (0–700 m) [0,0,4]; (x) Sea level rise [0,1,0]; (y) Tropospheric ozone [0,1,0]; (z) Tropospheric wind speed at 300 hPa for 20°–40°N [5,0,1]; (aa) Land wind speed [0,0,2]; (ab) Ocean wind speed [4,1,2]; (ac) Biomass burning [0,2,0]; (ad) Soil moisture [0,1,0]; (ae) Terrestrial groundwater storage [0,1,0]; (af) FAPAR [0,1,0]; (ag) Land surface albedo–visible (solid) and infrared (dashed) [0,2,0]. | S2 AUGUST 2016

23 | S3 AUGUST 2016 STATE OF THE CLIMATE IN 2015

24 1.1. Geographical distribution of notable climate anomalies and events occurring around the world in 2015. . ig F | S4 AUGUST 2016

25 THE 2015/16 EL NI ÑO COMPARED WITH OTHER SIDEBAR 1.1: RECENT EVENTS —D. E. PARKER, K. M. WILLETT, R. ALLAN, C. SCHRECK, AND D. S. ARNDT The climate of 2015 was clearly influenced by the strong relation to global surface temperature suggests that the ongo - ño. This sidebar places the event, still ongoing as 2015/16 El Ni ing event will likely have a slightly greater effect on the global ños of May 2016, into context by comparison to recent El Ni surface temperature of 2016 than on that of 2015. of similar magnitude. Primary indicators of ENSO are predominantly based on SST and surface pressure changes from across the Indo-Pacific region. By most measures, the 2015/16 El Niño was one of the strongest on record, on par with those of 1982/83 and 1997/98. Figure SB1.1 shows the time evolution of tropical Pacific SSTs (from HadISST1.1) since 1970. The SST imprint for each event is unique. For example, the strongest SST anomalies in 2009/10 occurred in the central Pacific, while those for 2015/16, 1997/98, and 1982/83 were strongest in the eastern Pacific. The 2015/16 event stands as one of the more protracted warm events, with warm anomalies first appearing in summer 2014 and becoming firmly established in spring 2015. Regionally-averaged SST anomalies (Fig. SB1.2) highlight the 2015/16 event’s position among the most intense El Niño events. Notably, the Niño-4 index reached a record +1.8°C during November 2015. The 2015/16 event was only the third since 1980 (following 1982/83 and 1997/98) to exceed +2.0°C in the Niño-3, Niño-3.4, and Niño-1+2 regions; however, across Niño-1+2, the 2015/16 event, while quite strong, was °C weaker than the two strongest events: 1982/83 almost 2 and 1997/98. The 2015/16 El Niño appeared in the Southern Oscillation index (SOI; sea level pressure difference between Darwin and Tahiti; section 2e1, Fig. 2.30a,b, Fig. 4.1b) early in 2014, maturing in early 2015 and continuing into 2016. By this measure, it is a protracted event (Allan and D’Arrigo 1999). However, many other indicators are in use, reflecting the large variation in duration and character of each event. The oceanic Niño index (ONI; seasonal 3-month average of Niño-3.4 SSTs) and the Equatorial Southern Oscillation index (EQ-SOI; surface pres - sure difference between Indonesia and the eastern equatorial Pacific) showed neutral conditions until early 2015 (section 4b; Fig. 4.1). The Niño-3 and 3.4 regions, although mostly warm during 2014, were neither consistently nor significantly warmer than the designated threshold until early 2015 (sections 3b, 4b; Fig. 4.3). Nevertheless the protracted warmth over the tropical Pacific is clear from early 2014 onwards, as is the very different nature of each preceding El Niño event and its wider F SB1.1. Sea surface temperature (relative to 1961– . ig influence on climate. 90 base period) averaged between 5°S and 5°N over - El Niño events tend to elevate global mean surface tem the Pacific from 120°E to 80°W, based on HadISST1.1 (Rayner et al. 2003). peratures and, indeed, 2015 reached record warmth (section 2b1). The history of these events since the mid-20th century in | S5 AUGUST 2016 STATE OF THE CLIMATE IN 2015

26 CONT. SIDEBAR 1.1: THE 2015/16 EL NI ÑO COMPARED WITH OTHER RECENT EVENTS —D. E. PARKER, K. M. WILLETT, R. ALLAN, C. SCHRECK, AND D. S. ARNDT Subsurface ocean temperature anomalies along the equatorial Pacific show significant El Niño characteristics from March–May onwards (section 4b1; Fig. 4.6). Compared to 1997 (which predated the ARGO float network) the precursor (January) warmth near the thermocline was much weaker, but the anomalies nearer the surface in December were of similar magnitude (Online Fig. S1.1). Characteristic weakening/reversal of easterlies in the equatorial central Pacific was evident in the 2015 annual average surface winds (Plate 2.1s) with a similar signal at 850 hPa (Plate 2.1r). In late 2015 when El Niño was strongest, the negative wind anomaly in the tropical Pacific did not extend as far SB1.2. Time series of various ENSO indicators: (a) Niño- . ig F eastward as in late 1997, and the patterns were much 3: 5°S –5°N, 150°–90°W; (b) Niño-4: 5°S –5°N, 160°E–150°W; less organized in the Indian and Atlantic Oceans (c) Niño-3.4: 5°S –5°N, 170°–120°W; (d) Niño-1+2: 10°S –0°, (Online Fig. S2.22). 90°–80°W; (e) oceanic Niño index (ONI); (f) Southern Oscillation During El Niño events, cooling (warming) of index (SOI); (g) Equatorial Southern Oscillation index (EQ-SOI). The Niño region time series are from HadISST1.1 (Rayner et al. the ocean surface and subsurface in the western 2003). The ONI and EQ-SOI are from the NOAA Climate Predic - (eastern) tropical Pacific, in addition to reduced drag tion Center (www.cpc.ncep.noaa.gov/data/indices/). The SOI is on the ocean surface by weakened easterly winds, from the Australian Bureau of Meteorology. drives sea level fall (rise) in the western (eastern) Although global average total cloudiness did not change in tropical Pacific. The net effect is an increase in global sea level 2015 and shows no clear ENSO signal (Fig. 2.20), there was a (section 4f; Fig. 3.17), evident in both 1997/98 and 2015/16. dramatic shift of ice cloud from the warm pool region of the Similar to other major El Niños, the 2015/16 event affected western Pacific to the central Pacific during 2015, and likewise many parts of the global climate. Tropical cyclone activity, with during 1997 (section 2d4; Fig. 2.21). This shift followed the respect to accumulated cyclone energy (ACE), was suppressed displacement of convection during the events. The eastward in the Atlantic Ocean (section 4e2) but enhanced across the displacement was greater in 1997/98, matching that event’s North Pacific regarding both ACE and number of storms more eastward peak SST anomaly. Related regional features (sections 4e3, 4e4) The central Pacific was particularly active, are apparent in 2015 annual averages of many hydrological setting several records. Global rainfall patterns were also cycle ECVs (Plate 2.1). greatly impacted (Section 4d1). The equatorial Pacific, Gulf of The tendency for increased drought in the tropics during El Mexico, and South America saw enhanced rainfall. Meanwhile, from increased tropical Niño leads to increased release of CO southern Africa, Australia, the Amazon, Caribbean, and Cen - 2 wildfires. In 2015, out-of-control agricultural biomass burning tral America saw decreased rainfall. These patterns led to a was exacerbated in Indonesia (see Sidebar 2.2) by ignition substantial increase in the global land area covered by severe of the subsurface peat. These changes in terrestrial carbon or extreme drought in 2015, similar to 1982/83 but not 1987/88 storage likely contributed to the record 3.1 ppm increase in or 1997/98, possibly owing to countervailing influences such at Mauna Loa Observatory from 1 January atmospheric CO as extratropical atmospheric circulation patterns (section 2d9; 2 2015 to 1 January 2016. The previous highest annual increase Fig. 2.28; Plate 2.1f; Fig. 2.29). of 2.9 ppm occurred in 1998. The warmth in 2015 enabled an increase in total column Biomass burning in Indonesia also led to regional increases water vapor (TCWV) of ~1 mm globally over both land and in atmospheric carbon monoxide, aerosols, and tropospheric - ocean (section 2d2; Figs. 2.16, 2.17). There were broadly simi ozone in 2015 (Sidebar 2.2). Huijnen et al. 2016 suggest that lar increases following 1987/88, 1997/98, and 2009/10. Over the 2015 carbon emissions from the Indonesian fires were the the Pacific, 2015 lacked the dry anomaly north of the equator The dry anomaly over the present in 1997 (Online Fig. S2.13). largest since those during the El Niño year of 1997 (section Maritime Continent extended much farther west in 1997. 2g7; Fig. 2.60), although still only 25% of the 1997 emissions. | S6 AUGUST 2016

27 2. GLOBAL CLIMATE — K. M. Willett, D. F. Hurst, Significant forest fires were noted in many of R. J. H. Dunn, and A. J. Dolman - the terrestrial variables, with emissions from tropi Overview K. M. Willett, D. F. Hurst, R. J. H. Dunn, and cal Asian biomass burning almost three times the — a. A. J. Dolman 2001–14 average. Drier-than-average conditions were also evident over the global landmass. Soil moisture Following the warmest year on record in 2014 was below average for the entire year, and terrestrial according to most estimates, 2015 reached record groundwater storage was lower than at any other time warmth yet again, surpassing the previous record - by more than 0.1°C. The considerable warmth, during the record, which began in 2002. Areas in “se protracted strong El Niño, and new record levels of vere” drought greatly increased, from 8% at the end greenhouse gases provided climatological highlights of 2014 to 14% by the end of 2015. In keeping with the for the year. prevailing theme of warmer/drier, the global average - The progressing El Niño is a common theme wo surface air temperature record was accompanied by record high frequency of warm days and record low ven throughout the essential climate variables (ECVs) frequency of cool days. The lower troposphere was also presented here; its characteristic signature in tempera - ture and water-related ECVs is clear across the maps close to record warmth. Despite drier conditions on the ground, there was in Plate 2.1. Having appeared in some indicators in 2014, and maturing in early 2015, this now-protracted generally more moisture in the air as shown by the event became the strongest since the 1997/98 El Niño. peaks in surface specific humidity and total column water vapor. These peaks were especially high over Indeed, many sections in this chapter compare the two events. Although strength-wise there are similarities, oceans, consistent with the generally warmer air. These warmer, moister conditions tend to lag El Niño their characteristics are quite different (see Sidebar 1.1). by a few months, and the event was ongoing at year Atmospheric burdens of the three dominant end. , N , CH O) all continued to greenhouse gases (CO 2 4 2 In the cryosphere, Northern Hemisphere snow cov - mole increase during 2015. The annual average CO 2 er extent was slightly below average. However, alpine fraction at Mauna Loa, Hawaii (MLO), exceeded glacier retreat continued unabated and, with an update 400 ppm, a milestone never before surpassed in the to the now 41-reference glacier dataset, 2015 became MLO record or in measurements of air trapped in ice cores for up to 800 000 years. The 2015 global the 36th consecutive year of negative mass balance. CO average at Earth’s surface was not far below, at In addition to the strong El Niño, 2015 saw 2 - mostly positive Antarctic Oscillation (AAO) condi 399.4 ± 0.1 ppm. The 3.1 ppm (0.76%) increase in CO 2 at Mauna Loa during 2015 was the largest annual tions throughout the year, contributing to stronger - increase observed in the 56-year record. Global aver wind speed anomalies both at the surface and aloft age surface methane increased 11.5 ± 0.9 ppb (0.6%) (850 hPa). This typically leads to reduced west Ant - from 2014 to 2015, the largest annual increase since arctic Peninsula (WAP) sea ice extent, but it was 1997–98. Many ozone-depleting substances (ODS) opposed in 2015 by the El Niño, which is more often associated with a weaker polar jet stream. The North continued to decline, lowering the stratospheric - loading of halogen and the radiative forcing associ Atlantic Oscillation (NAO) was broadly positive for the fifth year in a row. Land wind speed continued ated with ODS. Recent ozone measurements in the a slight increase, similar to 2014, following a long, extra-polar upper stratosphere (~40 km) show a small steady decline over the entire record from 1973. increase that may be a first sign of long-term ozone The lake temperatures section returns this year layer recovery. Despite this, the 2015 Antarctic ozone hole was near-record in terms of size and persistence. after two years of unavailability. Additionally, two Stratospheric water vapor just above the tropical sidebars are included: Sidebar 2.1 explores our ability tropopause increased 30% from December 2014 to to monitor evaporation over land, a crucial missing December 2015, likely due to the combined changes in link for studying the hydrological cycle; Sidebar 2.2 provides an overview of atmospheric chemical com phase of the quasi-biennial oscillation (QBO) (cold to - warm) and the El Niño–Southern Oscillation (ENSO) position changes in 2015 as a result of El Niño–related during 2015. The strong El Niño in 2015 produced ex forest fires. - Time series and anomaly maps for many EVCs are tremely dry conditions in Indonesia, contributing to intense and widespread fires during August–Novem - shown in Plates 1.1 and 2.1 respectively. Supplementary ber that produced anomalously high abundances of online figures can be found at: http://journals.ametsoc carbonaceous aerosols, carbon monoxide, and ozone .org/doi/suppl/10.1175/2016BAMSStateoftheClimate.1. in the tropical troposphere (Sidebar 2.2). | S7 AUGUST 2016 STATE OF THE CLIMATE IN 2015

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29 | S9 AUGUST 2016 STATE OF THE CLIMATE IN 2015

30 | S10 AUGUST 2016

31 P l at e 2.1. (a) ERA-Interim lower stratospheric temperature; (b) ERA-Interim lower tropospheric temperature; (c) NOAA/NCEI surface tempera - ture (contoured) and lake temperatures (circles); (d) GHCNDEX warm day threshold exceedance - (TX90p); (e) GHCNDEX cool day threshold exceed ance (TX10p); (f) ESA CCI soil moisture; (g) GRACE 2015 difference from 2014 water storage; (h) GPCP precipitation; (i) ELSE system runoff; (j) ELSE system river discharge; (k) HadISDH (land) and NOCSv2.0 (ocean) surface specific humidity; (l) ERA-Interim surface relative humidity; (m) PATMOS-x cloudiness; (n) HIRS upper tropospheric humidity; (o) Microwave radiometer retrievals (ocean), COSMIC GPS-RO data (land), and GNSS (circles, land) total column water vapor; (p) sc-PDSI drought annual average 2015 anomaly; (q) GOME-2 (using GOME, SCIAMACHY, and GOME-2 for the climatology) stratospheric (total column) ozone; (r) ERA-Interim 850-hPa wind speed; (s) ERA-Interim (worldwide grids) and HadISD (points) surface wind speed; (t) HadSLP2r sea level pressure; (u) Tropospheric ozone; (v) CAMS total aerosol optical depth; (w) CAMS aerosol optical depth from dust; (x) CAMS aerosol optical depth from biomass burning; (y) SeaWiFS/MERIS/MODIS fraction of absorbed photosynthetically active radiation (FAPAR); (z) Surface visible-light albedo from MODIS White Sky broad - band; (aa) Surface near-infrared albedo from MODIS White Sky broadband; (ab) GFASv1.3 carbonaceous emissions from biomass burning; (ac) CAMS total column CO anomalies. | S11 AUGUST 2016 STATE OF THE CLIMATE IN 2015

32 b. certainty in observational datasets tend to be associ - Temperature S — A. Sánchez-Lugo, C. Morice, and 1) urface ated with changes in measurement practices and with temperature sparse spatial sampling, both of which can vary with P. Berrisford time. When taking into consideration the estimated The 2015 global land and ocean temperature set uncertainty of the global land and ocean annual new records, exceeding the previous records set in 2014 (and 2010 depending on the in situ dataset) by temperature in the annual ranking, following the method of Arguez et al. (2013), it is virtually certain a wide margin of 0.13°–0.18°C. Much-warmer-than- - average conditions across much of the world’s sur that 2015 was the warmest year since records began, - face and a strong El Niño contributed to the highest with a probability >99%, according to the NOAAGlo temperature since records began in the mid- to late balTemp dataset (see Arguez and Applequist 2015). The near-surface temperature analyses assessed 1800s, according to four independent in situ analy - ses (NASA–GISS, Hansen et al. 2010; HadCRUT4, here are derived from air temperatures observed at Morice et al. 2012; NOAAGlobalTemp, Smith et al. - weather stations over land and sea surface tempera tures (SST) observed from ships and buoys. While - 2008; JMA, Ishihara 2006). The 2015 globally aver aged surface temperature was 0.42°–0.46°C (Table 2.1) each analysis differs in methodology, all four analyses above the 1981–2010 average. Note that ranges of are in close agreement (Fig. 2.1). Plate 2.1c and Online Figs. S2.1, S2.2, and S2.3 show the differences between temperature anomalies provided in this summary are ranges of best estimates for the assessed in situ the datasets, which are mainly due to how each meth - datasets. These ranges do not include additional odology treats areas with little to no data and how each analysis accounts for changes in measurement uncertainty information from each in situ analysis, methods [for more details see Kennedy et al. (2010); which can be found in Table 2.1. Hansen et al. (2010); and Huang et al. (2015)]. The last time a record high temperature surpassed Global average surface air temperatures are also the previous record by such a wide margin was 1998, estimated using reanalyses, which blend information which surpassed the previous 1997 record by 0.12°– 0.16°C. Similar to 2015, a strong El Niño developed from a numerical weather prediction model with during the latter half of 1997, reaching its maturity observations. Reanalysis produces datasets with during the first part of 1998 (see Sidebar 1.1). The uniform spatial and temporal coverage, but suffers from model biases and problems arising from time presence of El Niño typically increases concurrent variations in amount and/or quality of assimilated global temperatures and those in the year following observations. Surface temperatures from reanalyses its onset. The year 2015 also marked the first time the global are consistent with observations in regions of good average surface temperature reached more than 1°C observational coverage at the surface, due in part to above the average of the mid- to late 19th century, a the large volumes of assimilated observations (e.g., more than 40 billion to date in the ERA-Interim period in which temperatures are commonly taken to be representative of pre industrial conditions. The reanalysis). best-estimate global average surface temperatures According to ERA-Interim (Dee et al. 2011), the 2015 globally averaged, analyzed 2-m temperature - were 1.03°–1.09°C above the mid- to late 19th cen tury average in assessed datasets. Fourteen of the was the highest since records began in 1979. The 15 warmest years on able 2.1. Temperature anomalies (°C) and uncertainties (where available) for t record have occurred 2015 (base period: 1981–2010). The uncertainties indicate the scope of the range since the beginning around the central value. For ERA-Interim, the values shown are the analyzed 2-m temperature anomalies (uncorrected). Note that the land values computed of the 21st century, for HadCRUT4 used the CRUTEM.4.4.0.0 dataset (Jones et al. 2012), the ocean with 1998 the only values were computed using the HadSST.3.1.1.0 dataset (Kennedy et al. 2011a, exception (ranking 2011b), and the global land and ocean values used the HadCRUT4.4.0.0 dataset. between third and Uncertainty ranges are represented in terms of a 95% confidence interval, with eighth warmest year, the exception of JMA which has a 90% confidence interval. - depending on the da NOAA- NASA–GISS Global H ad C RU T4 JMA ERA-Int taset). Global Temp Every estimate of +0.72±0.18 +0.65 +0.70 +0.66±0.14 Land +0.64 global average tem - +0.36 +0.28 Ocean +0.33 +0.39±0.07 +0.37±0.16 perature has inher - Land and ent uncertainty. The +0.44±0.05 +0.45 ±0.08 +0.46 ±0.08 +0.42±0.13 +0.38 Ocean main sources of un - | S12 AUGUST 2016

33 temperature was 0.38°C above the 1981–2010 aver tion methods [NOAA (Zhou and Wang 2011) and UW - (Po-Chedley et al. 2015)]. Previously utilized datasets age (Table 2.1) and 0.10°C above its previous record set in 2005. The magnitude of the anomaly would be from UAH and RSS have been updated (UAHv6.0, larger had the temperature analyses been corrected Asia-Pacific J. Atmos. Spencer et al. 2016 submitted to for changes in the source of the prescribed SST, which Sci.; RSSv4.0 for the midtroposphere, Mears and - was uniformly cooler by about 0.1°C relative to Had CRUT4 from 2002 onwards (Simmons and Poli 2014). The only land areas with temperatures below average, according to the in situ datasets, were parts of southern South America, eastern Canada, Green - land, and Antarctica. Overall, the globally averaged annual temperature over land [including the land- only Berkeley Earth analysis (Rohde et al. 2013)] was 0.61°–0.72°C above average—the highest on record. - This exceeds the previous 2007 record (and, depend ing on the in situ dataset, 2010) by 0.12°–0.26°C. The strong El Niño maturing during 2015 resulted in record high SSTs across much of the tropical Pa - cific Ocean. However, areas in the North Atlantic, South Pacific, and the waters south of South America experienced below-average conditions (Plate 2.1c). The globally averaged annual temperature across the oceans was 0.33°–0.39°C above average—the highest on record according to the in situ datasets, surpassing the previous record set in 2014 by 0.10°–0.12°C (see section 3b for more detailed SST information). Similarly, ERA-Interim for 2015 shows warmer- than-average conditions over many, but not all, regions of the world (Online Fig. S2.1). The average analyzed 2-m temperature over land was 0.65°C above average (0.09°C above the previous 2007 record), and, over the oceans, it was 0.28°C above average (0.10°C above the previous 2005 record). Spheric temper ature S — 2) L ower and midtropo J. R. Christy The 2015 globally averaged annual temperature of the lower troposphere (LT, the bulk atmosphere below 10 km altitude or roughly the lower 70% by mass) was approximately +0.3°C above the 1981–2010 mean. This placed 2015 first to fourth warmest of the past 58 years, depending on the dataset, and was on average about 0.2°C cooler than the warmest year, 1998, varying from just above the 1998 value in two radiosonde datasets to 0.1°–0.3°C below in the . 2.1. Global average surface temperature anoma - ig F lies (°C, 1981–2010 base period). In situ estimates are remaining datasets (Fig. 2.2). shown from NOAA/NCEI (Smith et al. 2008), NASA– Direct measurement of the LT bulk temperature GISS (Hansen et al. 2010), HadCRUT4 (Morice et al. utilizes radiosonde datasets since 1958, comple - 2012), CRUTEM4 (Jones et al. 2012), HadSST3 (Ken - mented by satellites since late 1978. The datasets are nedy et al. 2011a, b), JMA (Ishihara 2006), and Berkeley described in Christy (2015) with new additions of Earth (Rohde et al. 2013). Reanalyses estimates are the UNSW radiosonde dataset from Sherwood and shown from ERA-Interim (Dee et al. 2011), MERRA-2 - Nishant (2015) and, for use in the tropical midtropo (Gelaro et al. 2016; Bosilovich et al. 2015), and JRA-55 (Ebita et al. 2011). sphere, two satellite datasets with similar construc - | S13 AUGUST 2016 STATE OF THE CLIMATE IN 2015

34 The long-term global LT trend based on radio - −1 sondes (starting in 1958) is +0.15° ± 0.02°C decade and based on both radiosondes and satellites (start - −1 . The range ing in 1979) is +0.13° ± 0.03°C decade - represents the variation among the different datas ets, which then serves as an estimate of structural uncertainty in Fig. 2.2. When taking into account the magnitude of the year-to-year variations, there −1 , is a statistical confidence range of ± 0.06°C decade meaning that the trends are significantly positive. - Major volcanic events in 1963, 1982, and 1991 con tributed to cooler temperatures during the early part of the LT record, especially in the satellite era, thus increasing the upward trend to some extent. - With this edition we introduce the midtropo spheric temperature (MT, surface to around 70 hPa) product for the tropical atmosphere (Fig. 2.3). The - MT profile extends higher than that of LT, enter ing the stratosphere, but only slightly in the tropics where the tropopause is at approximately 16-km . 2.2. Global average lower tropospheric tempera - F ig altitude. The dominant signal of this product is in ture annual anomalies (°C; 1981–2010 base period) for the mid- to-upper troposphere, thus capturing the the MSU LT equivalent layer. (a) Radiosonde: RATPAC layer in the tropics which represents the maximum (Free et al. 2005; 85 stations), RAOBCORE and RICH response to forcing (e.g., increased greenhouse gases, (Haimberger et al. 2012; 1184 stations), and UNSW warm surface waters from El Niño, volcanic cooling, (Sherwood and Nishant 2015, 460 stations). (b) Satel - Asia- lites: UAHv6.0 (Spencer et al. 2016 submitted to - etc.). MT is constructed from the Microwave Sound ) and RSSv3.3 (Mears and Wentz Pacific J. Atmos Sci ing Unit (MSU) channel 2 and the Advanced MSU 2009). (c) Reanalyses: ERA-Interim, MERRA-2, and JRA-55 are shown as described in Fig. 2.1. Wentz 2016). In addition, three reanalyses products are also shown. There is close agreement in the inter - annual variability between all products; ERA-Interim is used here to provide the spatial depictions (Plate 2.1b and Online Fig. S2.4). The global LT anomaly at any point in time is closely tied to the phase of the El Niño–Southern Oscillation (ENSO). The year 2015 is analogous to 1997 in that a warm ENSO phase began and peaked in the Pacific Ocean. The year 1998 was approximately +0.5°C warmer than 1997, and thus a comparison of 2016 with 1998 will indicate how similar the ENSOs evolved, having been quite similar for 1997 vs. 2015. Regionally, warm anomalies extended from the Arctic equatorward to the eastern Pacific and much of Europe. The midlatitude belt in the Southern - Hemisphere was mostly warmer than average. Cool er-than-average temperatures occupied northeast North America–Greenland, portions of Russia, and ig F . 2.3. Tropical (20°S –20°N) anomalies of midtro - the far Southern Ocean (Plate 2.1b). The latitude–time pospheric temperature relative to the 1981–2010 base depiction of the LT temperatures beginning in 1979 period. Data sources are as described in Figs. 2.1 and indicates tropical warming that is particularly strong 2.2 with the addition of NOAA (Zhou and Wang 2011), during 2015, associated with the ongoing El Niño UW (Po-Chedley et al. 2015) and RSSv4.0 (Mears and Wentz 2016). (Online Fig. S2.4). | S14 AUGUST 2016

35 channel 5 (Christy et al. 2003). MT tropical trends are inf luenced by lower stratospheric cooling by −1 approximately 0.03°–0.04°C decade , which is fully accounted for in comparison with theory. Examining the various datasets of tropical MT trends (1979–2015), there are two clusters of results: −1 +0.08°C decade (most radiosonde datasets and −1 (RICH radio satellite UAH) and +0.12°C decade - sonde dataset and satellite datasets of RSS, NOAA, and UW; Table 2.2). A significant difference between UAH and the other satellite datasets is evident over the oceans. This suggests the disagreement is due to differing assumptions regarding basic calibration issues rather than corrections for the diurnal drift of the spacecraft, and is an active area of research. The MT time series and trends (Fig. 2.3, Table 2.2) through 2015 continue the characteristic noted reports that observed MT in past State of the Climate trends tend to be below estimates anticipated from basic lapse-rate theory—the theory that indicates a magnification of trend with height (Christy 2014). This is especially true in the tropics where theory ig F . 2.4. Global mean annual temperature anomalies of the lower-stratosphere temperatures derived from (a) suggests amplification by a factor of 1.4 ± 0.2 of the radiosonde, (b) satellite, and (c) reanalysis. Anomalies mid tropospheric trend over the surface trend. The are from the 1981–2010 mean. Data sources are as range of trends for 1979–2015 from the different ra - described in Figs. 2.1 and 2.2, additional data sources: −1 and diosonde datasets is +0.07° to +0.11°C decade NOAA (Zhou and Wang 2010). −1 from satellites is +0.07° to +0.14°C decade compared −1 . Thus, the here is between +0.09 and 0.10°C decade with the tropical surface trend (average of NOAAGlo - −1 balTemp and HadCRUT4) of +0.12°C decade . The current tropical MT/surface ratio from observations median trend of all observational datasets examined since 1979 (0.8 ± 0.3) continues to be less than theory. 1 − ower Spheric 3) Strato L 2.2. Linear trends (°C decade ) in lower tropospheric (LT) and able t midtropospheric (MT) temperatures. The tropics region spans 20°S–20°N. t e m p e r at u r e C. S. — Long and J. R. Christy Global LT Tropics MT The globally averaged 1979 1958 Start Year 1958 1979 temperature in the lower Radiosondes stratosphere (TLS) for +0.08 +0.13 RAOBCORE +0.15 +0.13 - 2015, as measured by ra +0.10 +0.15 +0.15 RICH + 0 .11 diosonde and satellite and +0.15 +0.07 +0.09 +0.15 R ATPAC analyzed by reanalyses, ranged from slightly above +0.07 UNSW + 0.17 +0.16 +0.10 to approximately 0.5°C be - Satellites low the 1981–2010 clima - + 0 .11 UAHv6.0 x +0.07 x tology (Fig. 2.4). All TLS RSSv3.3 x +0.09 x +0.12 estimates agree that glob - x +0.14 x RSSv4.0 x ally 2015 was about the x +0.13 NOAAv3.0 x x same as 2014. This year’s persistence of last year’s x +0.12 x x UWv1.0 annual temperatures only Reanalyses slightly impacted the near- x +0.12 x +0.08 ERA-I neutral to very gradual +0.08 JRA-55 x +0.15 +0.16 warming trend observed MERRA +0.19 x x +0.16 from 1995 to present. | S15 AUGUST 2016 STATE OF THE CLIMATE IN 2015

36 Despite similarity in the global average value, spatial patterns are different than those of 2014. The annual averaged temperature analysis (Plate 2.1a) shows negative anomalies in both hemispheres’ polar latitudes. The Arctic negative anomalies extended into Siberia but positive anomalies were centered near Iceland. This strong positive anomaly was mirrored in the lower troposphere and surface by a strong cool anomaly (Plates 2.1b,c). The Antarctic had negative anomalies throughout the entire zone. In lower lati - tudes, positive anomalies generally prevailed over the Atlantic and eastern Asia, with negative anomalies over the central Pacific. These anomalies were related to the El Niño that grew during the latter half of 2015. The northern polar region oscillated between cold ig F 2.5. Daily time series (blue lines) for 2015 lower . and warm anomalies for the first five months of 2015 stratosphere temperatures from the CFSR (Saha et al. 2010a) for the (a) northern high latitudes (60°–90°N) (Online Fig. S2.5). The southern polar region was and (b) southern high latitudes (60°–90°S). The 1979– anomalously cold from August through December. 2015 daily maximum and minimum temperatures for These negative temperature anomalies coincided with each latitude region are shown in black. the large and persistent ozone hole for 2015 (section est cooling of the radiosonde datasets and MERRA-2 2g4). The tropical warm anomalies were a result of the thermal response to the descending quasi-biennial having the greatest cooling of the reanalyses, while oscillation (QBO) westerlies during 2015 and the ERA-Interim and JRA-55 have the least cooling. The post-1995 trends also vary considerably. All three upper troposphere warming from the El Niño in the latter half of 2015. satellite, JRA-55, RATPAC, and NSW trends are near A cooler stratosphere is consistent with a warmer neutral. The ERA-Interim and MERRA-2 reanalyses, RICH, and RAOBCORE have a slightly positive trend. troposphere in the case of rising greenhouse gases, as more outgoing energy is trapped in the troposphere. As shown in Long and Christy (2015), the trends discussed above are not uniformly distributed across The TLS is a weighted layer-mean temperature of the all latitudes, rather there is considerable variability part of the atmosphere observed by specific channels from satelliteborne microwave sounding instruments. with latitude. - Figure 2.5 shows time series of daily TLS anoma It ranges from around 200–20 hPa (12–27 km) and is entirely in the lower stratosphere polewards of 35°. lies for the 60°–90°N and 60°–90°S bands for 2015. The southern high latitudes were exceptionally cold But equatorward of this, it extends into the upper troposphere, which needs to be accounted for when from August through December 2015. Monthly mean assessing latitudinal trends. For further details see height analyses show that the polar circulation in late 2015 was centered over the South Pole (not shown). Long and Christy (2015). All radiosonde datasets (RATPAC, RAOBCORE, Additionally, wave activity was minimal, keeping the RICH, NSW) show a cooling trend in the lower circulation very zonal and cold. The low temperatures stratosphere from 1958 to 1995. However, after 1995 and persistent circulation aided the destruction of there is not much of a trend to the present (Fig. 2.4). ozone, resulting in a larger ozone hole than in recent The pre-1995 cooling trend is only interrupted by years (section 2g4). In the northern high latitudes, a few midwinter warmings affected the upper strato - several volcanoes [Agung (1963), El Chichón (1982), sphere but did not propagate down into the middle and Mt. Pinatubo (1991)], which imparted a warm or lower stratosphere. A final warming in mid-March pulse for about two years following each eruption. The satellite MSU channel 4 datasets (RSS, NOAA, propagated down to the TLS region and increased the temperatures in the polar zone (Fig. 2.5). This and UAH) and four recent reanalysis datasets (CFSR; warming is classified as a “final” warming as the ERA-Interim; JRA-55; MERRA-2) also show general atmospheric temperatures and circulation did not agreement with the radiosonde time series. Table return to a winter pattern but continued to transi 2.3 provides the trends for various time periods for - the radiosondes, satellites, and reanalyses. There is tion to a summer pattern. During the boreal autumn and early winter, Arctic TLS temperatures were well variability among the datasets in the cooling trend below normal. - from 1979 to 1995, with RATPAC having the great | S16 AUGUST 2016

37 mean LSSWTs in three − 1 t 2.3. Computed trends (°C decade ) for radiosonde, satellite, and re - able Austrian lakes (Mondsee, analysis data for the periods: 1958–95, 1979–95, 1995–2015, and 1979–2015. - Neusiedler See, Wörther 1995 is chosen as an inflection point distinguishing the earlier downward trend from the near-neutral trend of recent years. see; Fig. 2.6; Online Fig. Global (82.5ºN–82.5ºS) TLS Temperature Anomaly Trends 2.6) with anomalies up (1981–2010 base period) to +1.6°C. Similarly, satel - 1979 –2015 1958–95 1995 –2015 Dataset 1979 –95 lite-based LSSWT anom- alies of 25 European lakes Radiosonde in and near the Alps were RAOBCORE 0.260 − 0.306 0.208 − 0 .117 − in excess of 1.0°C in 2015 RICH 0.219 − − − 0.278 0.484 0.282 (Fig. 2.7a), the second R ATPAC − − 0.467 − 0.257 0.649 − 0.010 warmest anomaly year UNSW 0.039 − 0. 317 0.330 − 0.474 − since the record summer Satellite of 2003 (Beniston 2004). RSS × High LSSWTs were also − − 0.261 − 0.012 0.336 observed in other regions S TA R × − − 0.364 − 0.002 0.262 of the world (Plate 2.1c; UAH × − 0.399 0.046 − 0.312 − Online Fig. 2.6), with Reanalysis - anomalies for lakes in Se 0.106 CFSR × 0.652 − 0.348 − attle [Washington (state), U.S.], for example, up to ERA-Interim × 0.199 − 0.187 0 .119 − +1°C in 2015. 0.018 JRA-55 × − − 0.235 0. 217 - LSSWTs are inf lu MERRA-2 × 0.171 0.300 − − 0.199 enced by a combination - of broad climatic vari — R. I. Woolway, K. Cinque, 4) L ake Surface temperature S ability and local characteristics, so regional and E. de Eyto, C. L. DeGasperi, M. T. Dokulil, J. Korhonen, subregional differences in LSSWTs are common. S. C. Maberly, W. Marszelewski, L. May, C. J. Merchant, A. M. Paterson, LSSWTs in Britain and Ireland during 2015 were M. Riffler, A. Rimmer, J. A. Rusak, S. G. Schladow, M. Schmid, K. Teubner, ~0.6°C below average, in contrast to central Europe. P. Verburg, B. Vigneswaran, S. Watanabe, and G. A. This likely ref lects cool anomalies in SAT in early Weyhenmeyer and mid-2015 (e.g., www.met.ie/climate/Monthly Lake summer surface water temperatures (LSSWT) Weather/clim-2015-ann.pdf). in 2015 strongly ref lected the decadal patterns of Although the Great Lakes (United States and - warming noted in the scientific literature. North - Canada) have warmed faster than SAT in recent de ern Hemisphere summer refers to July–September cades, the 2015 LSSWTs were relatively cool. This is whereas Southern Hemisphere summer refers to attributable to above-average winter ice cover during January–March. A recent worldwide synthesis of 2014/15, which shortened the warming season. The lake temperatures (O’Reilly et al. 2015) found that annual maxima of percent ice cover (Great Lakes −1 LSSWTs rose by, on average, 0.034°C yr between Environmental Research Laboratory; www.glerl. 1985 and 2009, ~1.4 times that of the global surface noaa.gov/) in 2014 (92.5%) and 2015 (88.8%) were air temperature (SAT) in general. Data from lakes substantially above the 1973–2015 average (53.2%). in various regions collated here show that during These were the first consecutive high-ice-cover years since the 94.7% maximum ice coverage recorded in 2009–15 lake temperatures continued to rise. During 2015, LSSWT of many lakes exceeded their 1979. The strong El Niño conditions of 2015 lessen the 1991–2010 averages by 1°C or more (Online Fig. S2.6; chance that 2016 will imitate 2014 and 2015. Plate 2.1c). Strong warm anomalies in LSSWT Despite these recent cooler LSSWTs, the average were most prominent in central Europe [Austria, warming rate for the Great Lakes is approximately −1 Switzerland, and Poland (data from the Institute 0.05°C yr (1979–2015). This rate contrasts with the Dorset lakes in Ontario, Canada (surface areas of Meteorology and Water Management, Poland)], where anomalies above 1°C were recorded. The hot <100 ha), which do not show a statistically significant trend in LSSWT between 1980 and 2015. In 2015, central European summer (JJA) of 2015 (sections 2b6 7f, and Sidebar 7.1) is ref lected in relatively high LSSWT anomalies in these lakes were ~+0.6°C. These | S17 AUGUST 2016 STATE OF THE CLIMATE IN 2015

38 lakes display large interannual variation in LSSWT, umn mixing and precipitation, leading to a relatively weak relationship between SAT and LSSWT. The mainly ref lecting interannual differences in SAT, LSSWT of New Zealand’s largest lake, Lake Taupo, is with strong agreement in high and low years. The relationship between SAT and LSSWT can thought to be inf luenced by interannual variation in geothermal heating (de Ronde et al. 2002) and shows be complicated by several processes. For Lake Erken, Sweden, LSSWT is strongly inf luenced by water col - no significant trend. Furthermore, an analysis of the 47-year record (1969–2015) of LSSWT from Lake Kinneret, Israel, reveals warming of ~1.65°C over the −1 period (~0.036°C yr ). Two factors explain most of the 2 variability (r = 0.67): SAT and water levels (Rimmer et al. 2011; Ostrovsky et al. 2013). In recent years there has been a strong emphasis on investigating LSSWT warming, with only a few investigations focusing on the winter months (e.g., Dokulil et al. 2014) due to a lack of available data. Winter temperature changes can be quite distinct - from LSSWT trends. For example, the regional av erage warming rate for lakes in Britain and Ireland −1 is substantially higher during winter (0.028°C yr ; −1 Fig. 2.7b) than in summer (0.018°C yr ; Fig. 2.7d). Future assessments that focus on all seasons will provide a more complete picture. F ig . 2.6. Lake summer (Jul–Sep in Northern Hemi - sphere, Jan–Mar in Southern Hemisphere) surface water temperature anomalies relative to 1991–2010 for (a) the United States (Washington, Sammamish, Union, and Tahoe); (b) the Laurentian Great Lakes, [Superior (buoys 45001, 45004, 45006), Michigan (buoys 45002, 45007), Huron (buoys 45003, 45008), and Erie (buoy 45005)]; (c) Dorset, Ontario, Canada [Blue Chalk, Chub, Crosson, Dickie, Harp, Heney Plastic, and Red Chalk (East and Main basin)]; (d) Britain and Ireland [Bassenthwaite Lake, Blelham Tarn, Derwent - F ig . 2.7. Satellite-derived lake surface water tempera Water, Esthwaite Water, Lough Feeagh, Grasmere, ture anomalies for (a) summer (Jul–Sep; 1991–2015) Loch Leven, and Windermere (North and South ba - for European Alpine lakes (all natural water bodies in 2 sins)]; (e) Scandinavia (Erken, Inarijärvi, Kitusjärvi, or near the Alps larger than 14 km ; Riffler et al. 2015) Lappajärvi, Päijänne, Pielinen, and Saimaa); (f) central and (b) winter (Jan–Mar, 1961–2015) for Britain and Europe (Charzykowskie, Jeziorak, Lubie, Mondsee, Ireland (base period: 1991–2010). Gray lines indicate Neusiedler See, Wörthersee, and Zurich); (g) Israel the temperature for each individual lake and the thick - (Kinneret); and (h) Australia and New Zealand (Burra black line indicates the average lake temperature for gorang, Cardinia, Sugarloaf, Taupo, and Upper Yarra). the region. The trend for the regionally averaged tem - Gray lines indicate the temperature for each individual peratures is shown in red, and the equation describing lake and the thick black line indicates the average lake the change is presented. The lakes included are the temperature for the specified region. The trend for same as those shown in Online Fig. 2.6 and Plate 2.1c. the regionally averaged temperatures is shown in red, and the equation describing the change is presented. Note that the warming rates are not comparable among the different regions due to the different time periods shown. | S18 AUGUST 2016

39 5) shows anomalously high numbers of warm days and Surface temperature extreme S — M. G. Donat, L and R. J. H. Dunn, and S. E. Perkins-Kirkpatrick low numbers of cool days in Africa and large parts The year 2015 not only set the highest global an of South America, where GHCNDEX lacks coverage, - nual mean temperature on record, it also brought suggesting that most global land areas saw warm some extreme temperature events, most anomalously anomalies in 2015 (see Online Fig. S2.8). The first half of the year had some strong cold warm. Regionally, the frequencies of warm days and warm nights were the highest on record in western anomalies over the eastern United States, persisting after the cold winter 2014/15 into spring and even North America, parts of central Europe, and central Asia (Plates 2.1d,e). The GHCNDEX dataset (Donat early summer. This resulted in comparatively lower values of warm extremes, though some cold extremes et al. 2013) is used to monitor temperature extremes indices only showed cold anomalies during boreal for 2015. GHCNDEX is a quasi-global gridded dataset of land-based observed temperature and precipitation winter (December–February; Fig. 2.9a). Similar be - extremes. A suite of temperature and precipitation havior was observed during 2013 and 2014. extremes indices (Zhang et al. 2011) is first calculated Notable extreme temperature events included the European summer heat waves (late June–early July for daily station time series from the GHCN-Daily archive (Menne et al. 2012), before interpolating the and early August); a number of Asian heat waves in, for example, India, Pakistan, and Indonesia; and indices on global grids. At the time of writing, and the warm spring and autumn in Australia, Alaska, similar to Dunn et al. (2015), some of the indices fields and western Russia. Winter (December–February) have limited spatial coverage for 2015, especially those showed strong warm anomalies over much of the derived from minimum temperatures across central and eastern Asia, compared to those calculated from - Northern Hemisphere, including large parts of Eu rope, Asia, and western North America. Most of these - maximum temperatures. This limited spatial cover events are evident in higher frequencies of warm days age is related to an excessive number of missing values throughout the year, whereas monthly indices fields and lower frequencies of cool nights (TN10p) and - they mainly occurred during the shoulder seasons. are more complete. For more details on the complete The heat waves of Pakistan, India, and Indonesia ness requirements see Zhang et al. (2011). Here, results for TX90p (frequency of warm days, could not be monitored from GHCNDEX due to lack of coverage. However, results from the ERA-Interim defined as number of days above the seasonal 90th percentile of daily maximum temperatures over the reanalysis (see Online Fig. S2.8) indicate anomalously - 1961–90 base period), TX10p (frequency of cool days, high annual frequencies of warm days and low fre quencies of cold nights over these areas during 2015. defined as number of days with maximum tempera - The European heat wave is clearly evident in tures below the seasonal 10th percentile), TXx (the June–August hottest days (TXx), with anomalies hottest daily maximum temperature) and TNn (the coldest daily minimum temperature) are presented. of 4°–5°C, and to a lesser extent in corresponding Some of the extreme temperature indices showed global average records during 2015. For example, 2015 had the largest number of warm days (TX90p, 1.8 times compared to the 1961–90 baseline) and the smallest number of cool days (TX10p, 0.6 times the baseline; Fig. 2.8) in the GHCNDEX record going back to 1951. Note the limited spatial coverage of GHCNDEX; however, similar results also indicating the highest number of warm days and lowest number of cool days are found in the ERA-Interim reanalysis that provides complete coverage (see Online Fig. S2.7). Several regions, including western North America, Europe, and large parts of Asia and Australia, expe - - rienced strong warm anomalies, i.e., high frequen cies of warm days and low frequencies of cool days, throughout much of the year (Plates 2.1d,e). As F ig . 2.8. Global average time series of the number GHCNDEX has limited spatial coverage, the ERA- of (a) warm days (TX90p) and (b) cool days (TX10p) Interim reanalysis product (Dee et al. 2011) is used to over land. The dashed line shows a 5-year binomial provide a more complete picture. ERA-Interim also smoothed time series. (Source: GHCNDEX.) | S19 AUGUST 2016 STATE OF THE CLIMATE IN 2015

40 coldest nights (TNn), with anomalies of 1°–2°C (see during autumn. Interestingly, the northern central Asia autumn was relatively cold for both warm and Online Fig. S2.9). The frequency of both warm days and nights were also about double the normal for this cold extremes (Fig. 2.9). period (see Fig. 2.9 and Online Fig. S2.7). Cryosphere c. The Australian spring (September–November) St experienced frequencies of cool nights and warm days 1) p J. Noetzli, H. H. Christiansen, — State L therma ermafro M. Gugliemin, V. E. Romanovsky, N. I. Shiklomanov, S. L. Smith, well below and above average, respectively (Fig. 2.9), although anomalies in the hottest day and coldest and L. Zhao night were not as extreme. The Russian and western The Global Terrestrial Network for Permafrost - - North American springs (March–May) were also no (GTN-P) brings together long-term records on per tably warm, similarly manifested in high frequencies mafrost from permafrost regions worldwide (Smith of warm days and nights (Fig. 2.9). and Brown 2009; Biskaborn et al. 2015). The two - current observation elements are permafrost tem The European autumn (September–November) also had anomalously high frequencies of warm peratures and active layer thickness (ALT). The ALT is the layer that thaws and freezes over the seasonal days, whereas over northern North America and Greenland high frequencies of warm nights were cycle; it generally increases in warmer conditions. more notable. Northern Russia and central Europe Permafrost has warmed over the past 2–3 decades, and generally continues to warm across the circum - experienced warm days 3°–5°C warmer than normal polar north . Record-high temperatures were observed in 2015 on the Alaskan North Slope region and a noticeable warming has been recorded at several sites in the Alaskan Interior. Similar results have been obtained for north - western Canada, Russia, and the Nordic regions. ALT for 2015 was generally greater than the long-term average. A detailed discussion of measurement results from Arctic terrestrial perma - frost is provided in section 5i. In this section, results from the European Alps, central Asia, and continental Antarctica are summarized. - Mountain permafrost in the Euro pean Alps is patchy and its character and thermal conditions are spatially heterogeneous. The majority of per - mafrost is found between 2600 and 3000 m a.s.l. (Boeckli et al. 2012) in shady debris slopes and rock glaciers. There, permafrost temperatures have been measured for 1–2 decades and are typically above −3°C (Fig. 2.10). Recent installations on very high eleva - tion shaded bedrock slopes show that the highest peaks can be significantly colder. For example, the Aiguille du Midi north face in the Mont Blanc area at 3840 m a.s.l. (see Figs. 2.10a,b), and the Matterhorn summit north slope at 2.9. Seasonal anomalies of the frequency of (a–d) warm days . F ig 4450 m a.s.l. experience annual mean (TX90p) and (e–h) cool nights (TN10p) for 2015 relative to the temperatures near the surface as low as 1961–90 base period. There must be at least two months of data −10°C (Paolo Pogliotti, Environmental present within each season. (Source: GHCNDEX.) | S20 AUGUST 2016

41 Protection Agency of Valle d’Aosta, 20 February in the Swiss Alps since 2009 has been accompanied by an increase of rock glacier velocities, as observed 2015, personal communication). Records measured within the Swiss Permafrost Monitoring Network at multiple sites within Switzerland. In the warm permafrost of the higher elevations (PERMOS) during the past 10 to 25 years show a of central Asia, ground temperatures have increased general warming trend at depths to 10 and 20 m, −1 especially over the past seven years (Figs. 2.10a,b). since the early 1990s, and a by up to 0.5°C decade The recent warming is accentuated in 2015, when general increase in ALT has been observed (e.g., Zhao the highest permafrost temperatures were recorded et al. 2010). The ground temperature at sites along the Qinghai–Xizang Highway increased between 2004 at most PERMOS sites. This is a cumulative effect of −1 and 2014 by 0.04°–0.5°C decade the continuously warm weather conditions in recent at 10-m depth, −1 at 20-m depth (Fig. years rather than a result of the extremely warm sum and about 0.01°–0.29°C decade - mer 2015. ALT reached new record values in 2015 at 2.10c,d). Based on monitoring results extended by a many PERMOS sites. Absolute ALT changes depend freezing–thawing index model, the average increase −1 strongly on surface processes—mainly snow cover of ALT was about 28 cm decade from 1981 to 2015 Fig. 2.1 along the Qinghai– Xizang Highway ( duration and thickness—and subsurface ice content 1). The average ALT from 2011 to 2015 in Fig. 2.11 was about (PERMOS 2013). The recent warming of permafrost 15 cm more than the 2001–10 average. The mean annual air the Tibetan Pla - temperature in teau region increased at an ave - −1 rage rate of 0.68 °C decade over the past 35 years (Fig. 2.11). Permafrost temperature at 20-m depth along the latitudi - nal transect in Victoria Land, continental Antarctica between Wright Valley and OASI (Terra Nova Bay), has increased by about 0.5°C since 2008 (Balks et al. 2016; Fig. 2.1 0e). This in - crease is independent of the air temperature, which has been stable since 1960. In contrast, there is no apparent trend in permafrost temperatures in maritime Antarctica (Rothera, 0e) despite recorded air Fig. 2.1 warming in the area. ALT is strongly increasing in the coastal areas of continental Antarctica, −1 between 5 cm year at Marble Point (Balks et al. 2016) and −1 cm year at Boulder Clay 0.8 (Guglielmin et al. 2014a). In maritime Antarctica, at Signy Is - . 2.10. Temperatures measured in permafrost boreholes. Boreholes for ig F land, the active layer has ranged central and northern Europe at approximately (a) 10-m and (b) 20-m depth, between 124 and 185 cm since with actual depths shown in parentheses; along Qinghai–Xizang Highway on 2006 (Guglielmin et al. 2012), the Tibetan Plateau at (c) 10-m and (d) 20-m depth; and (e) in Antarctica at while at Livingstone Island be - 20-m depth: WV = Wright Valley; MP= Marble Point; OASI in Continental tween 124 and 145 cm (De Pablo Antarctica; and Rothera in Maritime Antarctica. (Sources: Swiss Permafrost et al. 2014), both without any Monitoring Network PERMOS; Norwegian Meteorological Institute and trends despite air temperatures the Norwegian Permafrost Database, NORPERM; EDYTEM/University of having increased in this area. Savoie; Cryosphere Research Station on Qinghai–Xizang Plateau, CAS.) | S21 AUGUST 2016 STATE OF THE CLIMATE IN 2015

42 2 ruary, with SCE 1.1 million km below average, mostly due to the ninth lowest SCE over EU. Both continents ranked among their 10 smallest for SCE during March. Spring melt proceeded faster over NA than EU, with the overall NH April coverage in the middle tercile. May and June behaved like most years within the past decade, quickly losing continental snow cover. This resulted in the sixth lowest May NH SCE and second lowest in June within the satellite era. Much as in the previous two years, snow arrived early over NH continents during autumn 2015, with SCE 14th highest in September. Coverage continued expanding quickly in October and November, each ig F . 2.11. Annually-averaged ALTs and MAATs along month ranking seventh most extensive. December Qinghai–Xizang Highway on the Tibetan Plateau (modified after Li et al. 2012 based on new data). saw the brakes put on this rapid expansion, with 2 (Sources: Cryosphere Research Station on Qinghai– coverage 0.2 million km below average, or 32nd Xizang Plateau, CAS.) most extensive. SCE over the contiguous United States was at the 2) n continenta Sphere h emi L Snow cover orthern - boundary of the middle and lower tercile in Janu D. A. Robinson — extent ary 2015. It was within the middle tercile but nearer Annual snow cover extent (SCE; Table 2.4; the above-normal side in February. The situation - Fig. 2.12) over Northern Hemisphere (NH) lands aver changed considerably in spring, with March SCE the 2 2 aged 24.6 million km in 2015. This is 0.5 million km fifth lowest on record and April ninth least extensive. Autumn 2015 SCE began building slowly in October, less than the 46-year average and ranks 2015 as having ranking ninth lowest. This changed in November the 36th most extensive (or 10th least extensive) cover and December, which ranked 19th and 22nd most on record. This evaluation considers snow over NH extensive, respectively. continents, including the Greenland ice sheet. SCE in 2015 ranged from 2 47.1 million km in t 2.4. Monthly and annual climatological statistics on Northern Hemisphere able - January to 3.0 mil - and continental snow extent between November 1966 and December 2015. In 2 lion km in August. cluded are: number of years with data used in the calculations, means, standard - Monthly SCE is cal 2 km deviations, 2015 values, and ranks. Areas are in (millions). 1968, 1969, and 1971 culated at the Rutgers have 1, 5, and 3 missing months, respectively, thus are not included in the annual Global Snow Lab from calculations. North America (N. Am.) includes Greenland. Ranks are from most daily SCE maps pro - extensive (1) to least (ranges from 46 to 50, depending on the month). duced by meteorolo - 2015 Std. N. Am. Eurasia gists at the National N. Hem Mean Ye ar s 2015 rank rank Dev. rank Ice Center (a U.S. joint NOAA, Navy, and Jan 32 49 47.1 1.6 47. 3 22 18 Coast Guard facility), 49 20 41 1.8 36 Feb 45.0 46.1 who rely primarily on 41 40 43 Mar 49 40.6 1.8 38.5 - visible satellite imag 30.6 Apr 49 35 1.7 30.1 28 21 ery to construct the 44 17.0 38 47 May 49 19.3 1.9 maps. Jun 5.4 2.4 9.7 48 47 47 47 SCE across the 42 44 39 2.5 Jul 46 4.0 1.2 - NH was close to aver age in January 2015, 3.0 Aug 47 23 0.7 2.6 34 39 a balance between 8 18 14 5.9 Sep 47 5.4 1.0 above-average cover 6 11 Oct 48 18.3 2.6 21.4 7 in Eurasia (EU) and 19 Nov 50 34.0 2.1 36.2 7 7 below-average over 22 50 43.7 1.9 43.5 32 30 Dec North America (NA). Ann 46 25.1 0.8 24.6 36 29 39 - This reversed in Feb | S22 AUGUST 2016

43 The unprecedented ongoing retreat is a result of strongly negative mass balances over the last 32 years (Zemp et al. 2015). An examination of the WGMS record by Zemp et al. (2015) found that the rates of early 21st century mass loss are without precedent on a global scale, at least for the time period observed. F . 2.12. Twelve-month running anomalies of monthly ig The Randolph Glacier Inventory version 3.2 (RGI) snow cover extent over Northern Hemisphere lands was completed in 2014, compiling digital outlines of as a whole and Eurasia and North America (including alpine glaciers using satellite imagery from 1999 to Greenland) separately between Nov 1966 and Dec 2015. Anomalies are calculated from NOAA snow maps (http:// 2010. The inventory identified 198 0 00 glaciers, with a 2 snowcover.org) relative to 1981–2010. Monthly means 00 ± 34 8 total extent estimated at 726 00 km (Pfeffer 0 for the period of record are used for 9 missing months - et al. 2014). This inventory was crucial for glacier run between 1968 and 1971 in order to create a continuous off modelling that indicates 11 of 13 alpine regions are series of running means. Missing months fall between Jun experiencing decreased runoff (Bliss et al. 2014). This and Oct; no winter months are missing. is due to a greater loss of glacier area than increased rate of glacier melt. The volume loss of alpine glaciers - Maps depicting daily, weekly, and monthly con ditions, daily and monthly anomalies, and monthly has led to a current sea level rise equivalent of ap - −1 proximately 0.8–1.0 mm year climatologies for the entire period of record may be (Marzeion et al. 2012). viewed at the Rutgers Global Snow Lab website (http:// The cumulative mass balance loss from 1980 to 2015 is 18.8 m, the equivalent of cutting a 20.5 m thick snowcover.org). Monthly SCE for the NH, EU, NA, contiguous U.S., Alaska, and Canada are also posted, slice off the top of the average glacier (Fig. 2.13). The along with information on how to access weekly areas trend is remarkably consistent from region to region (WGMS 2015a). The decadal mean annual mass and weekly and monthly gridded products. balance was −261 mm in the 1980s, −386 mm in the Lpine gLacier S 727 mm for 2000s, and −818 mm from 2010 to − 1990s, M. S. Pelto and ice Sheet 3) a — S The World Glacier Monitoring Service (WGMS) - 2015. The declining mass balance trend during a pe riod of glacier retreat indicates alpine glaciers are not record of mass balance and terminus behavior provides approaching equilibrium and retreat will continue a global index for alpine glacier behavior. The WGMS 0 00 observa dataset for terminus change contains 42 to be the dominant terminus response (Zemp et al. - 2015). The recent rapid retreat and prolonged negative tions from 2000 glaciers extending from the mid-19th balances have led to many glaciers disappearing and century. There are 5200 geodetic and glaciological others fragmenting (Pelto 2010; Carturan et al. 2015). mass balance observations in this dataset. Annual mass balance is the annual change in volume due to In South America, seven glaciers in Colombia, snow and ice accumulation and snow and ice losses. Argentina, and Chile reported mass balance in 2015. Here, WGMS mass balance is reported in mm of water All seven glaciers had losses greater than 1200 mm, equivalent (Fig. 2.13). In 2014 mean mass balance was −798 mm for the 41 long-term reference glaciers and −586 mm for all 130 observed glaciers. Preliminary data for 2015 from 16 nations with more than one reporting glacier from Argentina, Austria, Canada, Chile, Italy, Kyrgyzstan, Norway, Switzerland, and the - United States indicate that 2015 will be the 36th con secutive year of negative annual balances with a mean loss of −1162 mm for 27 reporting reference glaciers and −1481 mm for all 59 reporting glaciers (WGMS 2016). Reference glaciers are those with records longer than 30 years, hence the increase from 37 in 2014 to 41 this year. The number of reporting reference glaciers is 90% of all reporting glaciers but only 50% of all glaciers that . 2.13. Mean annual (red bars) and cumulative (red F ig have reported to date. The preliminary data indicate line) annual balance reported for the 41 reference 2015 mass balance will be one of the two most negative glaciers to the WGMS (1980–2015). The data for 2015 along with 2003, with 2003 at −1268 mm for reference are preliminary, only including 27 reference glaciers glaciers and −1198 mm for all glaciers. at the time of publication. | S23 AUGUST 2016 STATE OF THE CLIMATE IN 2015

44 with a mean of −2200 mm. These Andean glaciers span 58° of latitude. In the European Alps, mass balance has been reported for 15 glaciers from Austria, France, Italy, Spain, and Switzerland. All 15 had negative balances exceeding −1000 mm, with a mean of −1860 mm. - This is an exceptionally negative mass balance, rival ing 2003 when average losses exceeded −2000 mm. The negative mass balances were largely due to an exceptionally hot summer (see section 7f), as in 2003. In Norway, mass balance was reported for seven glaciers in 2015; all seven were positive with a mean of 860 mm. This is the only region that had a positive balance for the year. In Svalbard six glaciers reported mass balances, with all six having a negative mass balance averaging −675 mm. In North America, Alberta, British Columbia, Washington (state), and Alaska mass balance data from 17 glaciers were reported with a mean loss of −2590 mm, with all 17 negative. This is the largest negative mass balance for the region during the period of record. From Alaska south through British . 2.14. Columbia Glacier, Washington: 1 of 41 ig F Columbia to Washington the accumulation season WGMS reference glaciers, viewed on 4 Aug 2015 from temperature was exceptional with the mean for No - (a) below the terminus and (b) above the head of the vember–April being the highest observed (Fig. 2.14). glacier. Note the lack of retained snowcover with seven In the high mountains of central Asia, seven weeks left in the melt season. Numerous annual firn glaciers from China, Russia, Kazakhstan, and Kyr - and ice layers exposed. (Photo credit: M. Pelto) gyzstan reported data; all were negative with a mean (HadISDH.2.1.0.2015p) are in broadscale agreement of –705 mm. with the ERA-Interim and JRA-55 reanalyses in terms of overall behavior, HadISDH presents 2015 as slightly d. Hy drological cycle humidity - more moist than 2014 whereas both reanalyses pres 1) S urface — K. M. Willett, D. I. Berry, M. G. Bosilovich, and A. Simmons ent 2015 as slightly more arid. However, for HadISDH at least, the 2015−2014 difference is smaller than the - Surface moisture values in 2015 were at their high est level since the last El Niño event in 2010 (Fig. 2.15). annual uncertainty estimate for 2015 (±0.2% rh) All estimates contain uncertainty. Arguably the Over land, levels of water vapor in the air (specific humidity) were well above the 1981–2010 average and largest sources of uncertainty, generally, are the gaps approaching those of 1998 and 2010. Over oceans, in sampling both in space and time. There is also annual average specific humidity values were higher uncertainty stemming from systematic errors in than at any other point in the record that began in the data and the different methods for dealing with the early 1970s. The ability of the atmosphere to these by bias correction or homogeneity detection and adjustment. Over the ocean (Berry and Kent carry water vapor is limited by its temperature. The extra warmth associated with the El Niño, ongoing 2009, 2011), ship heights have increased over time, requiring height adjustment to avoid erroneously in some respects since 2014, together with generally decreasing specific and relative humidity. Systematic above-average global temperatures, is consistent with biases have also been found between psychrometers the high atmospheric humidity seen in 2015. Similar housed within screens versus those that are hand anomalously high humidity levels are seen in the held. Over land (Willett et al. 2013b, 2014b), changes years following previous El Niño events, with the to observing instruments, locations, or processes have atmospheric humidity typically lagging the tempera - ture changes by a few months. been common and poorly documented, requiring statistical methods to account for them. Measurement Relative humidity levels in 2015 remained well uncertainty also plays a role. Reanalyses (Simmons below average, continuing an apparent declining trend since the early 2000s. While the land in situ data et al. 2010; Simmons and Poli 2014) have the benefit | S24 AUGUST 2016

45 of the physical model and assimilation of high density observations with which to reduce the errors. However, they are not fully immune to such issues, and changing data streams over time can introduce inhomogeneities that can be substantial (Kent et al. 2014). - Despite these uncertain ties, there is generally good agreement between the vari - ous estimates presented here [described more fully in Willett et al. (2013a, 2014a)]. F ig . 2.15. Global average surface humidity annual anomalies (base period: The new MERRA-2 reanaly - 1979–2003). For in situ datasets, 2-m surface humidity is used over land and sis (R. Gelaro et al. 2016 ~10-m over the oceans. For the reanalysis, 2-m humidity is used across the unpublished manuscript; globe. For ERA-Interim, ocean-only points over open sea are selected and background forecast values are used as opposed to analysis values because of Bosilovich et al. 2015) shows unreliable use of ship data in producing the analysis. All data have been adjusted better agreement than the to have a mean of zero over the common period 1979–2003 to allow direct previously used MERRA, comparison, with HOAPS given a zero mean over the 1988–2003 period. ERA owing to improved data se - values over land are from ERA-40 prior to 1979 and ERA-Interim thereafter. lection, inclusion of mod - [Sources: HadISDH (Willett et al. 2013a, 2014a); HadCRUH (Willett et al. ern data, and model and 2008); Dai (Dai 2006); HadCRUHext (Simmons et al. 2010); NOCSv2.0 (Berry data assimilation advances. and Kent, 2009, 2011); HOAPS (Fennig et al. 2012) and reanalyses as described in Fig. 2.1. Data provide by authors, A. Dai, M. Bosilovich and S. Kobayashi.] MERRA-2 uses observation- corrected precipitation for forcing the land surface, which helps constrain the Relative humidity was anomalously low over much near-surface temperature and moisture over land of the land (Plate 2.1l; Online Fig. S2.12). Interestingly, some regions, such as southern Africa and Australia, (Reichle and Lui 2015). While the year-to-year vari - ability is similar to the other estimates, there are experienced both below-average water vapor amounts (specific humidity) and levels of saturation (relative some deviations around 2002 and 2007–09 (Fig. 2.15). These are thought to be linked to variability in the humidity), while other regions, such as the United precipitation forcing at those times. All agree on the States and southern India, experienced above-average water vapor but below-average saturation. The regions - most recent period having the highest specific humid of low relative humidity are broadly, but not exactly, ity levels on record while also being the most arid in consistent with below-average precipitation (Plate relative humidity terms (Fig. 2.15). 2.1h). Over the oceans there was a strong dipole along Spatially, specific humidity was anomalously high the equatorial Pacific with much lower-than-average over much of the land, especially over India and values to the south. This was slightly farther north Southeast Asia, which was also common to 1998 and than the specific humidity dipole associated with the 2010 (Plate 2.1k; Online Figs. S2.10, S2.11). In contrast to 2014, the United States experienced almost entirely El Niño warm pool. above-average specific humidity. Southern Africa water - vapor — C. Mears, S. Ho, J. Wang, 2) t ota L was particularly dry. Over oceans, data quality sig co Lumn H. Huelsing, and L. Peng nificantly impacts the spatial coverage of the in situ Total column water vapor (TCWV) rapidly - data, meaning that the key El Niño–Southern Oscil increased during 2015 in response to the 2015/16 lation (ENSO) region of the Pacific Ocean is not well observed. ERA-Interim and MERRA-2 show strong El Niño event (Fig. 2.16), with the annual average anomaly lying well above the long-term average. moist anomalies there, in good agreement with the other hydrological cycle ECVs and the very warm Estimates come from satelliteborne microwave ra - diometers over ocean (Wentz 1997, 2015), COSMIC SSTs (Plate 2.1c, Online Figs. S2.1 to S2.3). | S25 AUGUST 2016 STATE OF THE CLIMATE IN 2015

46 anomaly in the tropical Pacific Ocean, coupled with the lack of large dry anomalies across the rest of the world. The radiometer data show a discernible increasing trend over the period. The different re - analysis products show reasonable agreement from the mid-1990s but deviations prior to that, resulting in a range of long-term trends. Minima are apparent in Northern Hemisphere winters during the La Niña events of 1984/85, 1988/89, 1999/2000, 2007/08, and late-2010 to mid-2012. The ocean-only COSMIC data are in general agreement with the reanalysis and radiometer data, but show a sharp peak in early 2012 and a small dip relative to the other data after 2013. Over land, average anomalies from the ground- based GNSS stations are used in place of the satellite radiometer measurements (Figs. 2.16c,d), providing a record back to 1995, alongside the much shorter COSMIC record. The various reanalysis products, - COSMIC, and GNSS are in good agreement through out the record and all show a subtle increase in F ig . 2.16. Global average total column water vapor TCWV, similar to over ocean. anomalies (mm; 1981–2010 reference period) for (a,b) A land-and-ocean time–latitude plot derived ocean only and (c,d) land only for observations and from JRA-55 (Fig. 2.17) indicates that the long-term - reanalyses (see Fig. 2.1 for reanalyses references) av increase in TCWV is occurring at all latitudes, with eraged over 60°S–60°N. The shorter time series have less variability outside the tropics. The El Niño events been given a zero mean over the period of overlap with are clear, especially the 1997/98 event. The previous ERA-Interim (1988–2015 for RSS Satellite, 1995–2015 strong El Niño events during 1983/84 and 1997/98 for GNSS, 2007–15 for COSMIC). showed pronounced drying in the northern tropics - that accompanied moistening on the equator and the GPS-RO (Global Positioning System–Radio Occulta tion) over land and ocean (Ho et al. 2010; Teng et al. southern subtropics. Although similar in strength in 2013; Huang et al. 2013), and ground-based GNSS terms of sea surface temperature, the TCWV response to the current El Niño does not show this feature (see (Global Navigation Satellite System) stations (Wang Sidebar 1.1; Online Fig. S2.13). et al. 2007) over land. The 2015 anomaly map (Plate 2.1o) combines data from satellites over ocean and COSMIC GPS-RO over land with ground-based GNSS stations (Wang et al. 2007) also shown. Most of the tropical Pacific showed a large wet anomaly, which grew to unprecedented size by the end of 2015. Wet anomalies, albeit less pronounced, covered most of the rest of the globe, except for dry anomalies over the Maritime Continent, north of New Zealand, to the south of Greenland, southern Africa, and the Ama - zon basin. The spatial patterns in TCWV over the ocean (Plate 2.1o) are confirmed by similar features in COSMIC ocean measurements and supported by reanalysis output. Over the ocean, the TCWV anomaly time series (Fig. 2.16a,b) from reanalysis and microwave radi - . 2.17. Hovmöller plot of total column water vapor F ig ometers show maxima in 1983/84, 1987/88, 1997/98, anomalies (mm; base period 1981–2010) including land 2009/10, and late 2015, each associated with El Niño and ocean derived from JRA-55 reanalysis. events. The December 2015 anomaly is the largest recorded for any month, particularly in the satel - lite radiometer data. This is a result of the large wet | S26 AUGUST 2016

47 3) u tropo Spheric humidity — V. O. John, L. Shi, pper R. S. Vose, A. Becker, K. Hilburn, — recipitation 4) p G. Huffman, M. Kruk, and X. Yin and E.-S. Chung Precipitation over the global land surface in 2015 Global scale monitoring of upper tropospheric relative humidity (UTH) was first reported last year, was far below the long-term average (Fig. 2.19). In fact, using one dataset of satellite origin and one reanalysis. 2015 was the driest year on record in two prominent global products: the Global Precipitation Climatology However, the reanalysis data showed drying of the Centre (GPCC) dataset (Schneider et al. 2011; Becker upper troposphere since 2001 that was not present in the satellite data. Therefore, for this year, two et al. 2013), which is based on surface stations, and independent UTH satellite datasets are used. One the Global Precipitation Climatology Project (GPCP) is the infrared-based HIRS dataset (Shi and Bates version 2.3 (Adler et al. 2003), which is based on both 2011) which was used last year, and the other is the satellite data and surface stations. Last year was also - among the five driest years on record in a new (experi microwave-based UTH dataset (Chung et al. 2013). - UTH represents a weighted average of relative humid mental) version of another prominent global product, the Global Historical Climatology Network (GHCN) ity in a broad layer, roughly between 500 and 200 hPa. dataset (Peterson and Vose 1997; Menne et al. 2012), Humidity distribution at these levels of the atmo - sphere is a key climate variable due to its strong con - which contains about five times as many surface sta - tions as its operational counterpart (version 2). trol on the outgoing longwave radiation (OLR) which From a spatial perspective, coherent anomaly makes a strong feedback factor in the climate system. patterns were evident across the global land surface Area-weighted anomaly time series of UTH for the 60°N–60°S latitude belt are shown in Fig. 2.18. in 2015 (Plate 2.1h). El Niño affected precipitation in many areas; in particular, below-average precipita The anomalies are computed relative to 2001–10 - tion fell over much of northeastern South America, because the microwave-based UTH dataset begins southern Africa, the Maritime Continent, and north - only in 1999. A slightly below-average 2015 anomaly is observed. A near-zero trend in the UTH time se ern Australia, while above-average precipitation fell - over the southeastern quadrants of North and South ries indicates an increase in specific humidity in the warming upper troposphere and is consistent with a America. Relative to 2014, northern and eastern Asia positive water vapor feedback (Chung et al. 2016). It became much wetter while western Europe became much drier. is encouraging to see good agreement between the In contrast to global land areas, precipitation over two independent datasets despite their differences in sampling: microwave data have an almost all-sky the global ocean surface in 2015 was much above the sampling whereas HIRS data samples mainly clear- long-term average, continuing the general increase - of the last five years (Fig. 2.19). Above-normal pre sky areas. The annual average of UTH for 2015 (Plate 2.1n; Online Fig. S2.14) shows large moist anomalies cipitation over the ocean served as a counterpoint to over the central and eastern tropical Pacific and dry below-normal precipitation over land, and thus the global value for 2015 was slightly above the long-term anomalies over the Maritime Continent, which re - sults from the strong El Niño of 2015. This signal is stronger in the microwave dataset (Online Fig. S2.14) compared to HIRS (Plate 2.1n), possibly because of the sampling differences. The weak dry anomalies over India are an indication of the weak monsoon season in 2015 (see section 7g4). . 2.19. Globally averaged precipitation anomalies ig F (mm) for (a) four in situ datasets over land (1961–90 ig . 2.18. Anomaly time series of upper tropospheric F base period) and (b), (c) one satellite-based dataset - humidity using HIRS (black) and microwave (blue) da over ocean (1988–2010 base period). Ocean averages tasets. The anomalies are computed based on 2001–10 are for the global ocean equatorward of 60° latitude average, and the time series is smoothed to remove using a common definition of “ocean” and the annual variability on time scales shorter than 3 months. cycle has been removed. | S27 AUGUST 2016 STATE OF THE CLIMATE IN 2015

48 average. The ongoing El Niño, which was particularly and CLARA-A2 (Cloud, ALbedo and RAdiation data - dominant in the tropics in the latter half of the year, set; Karlsson et al. 2013) shows a modest decrease of resulted in several distinct anomaly patterns over the 0.25%. Three of the records—PATMOS-x, CLARA- A2, and SatCORPS—are derived from the AVHRR ocean (Plate 2.1h). In particular, an intense positive anomaly of rainfall stretched across the Pacific Ocean (Advanced Very High Resolution Radiometer) on the along the Inter-Tropical Convergence Zone, just north NOAA Polar Orbiter Environmental Satellite series of the equator. The western Pacific experienced two and more recently the EUMETSAT Polar System distinct anomaly maxima (a larger one slightly to Metop series. SatCORPS is the most recent addition the south of the equator and a secondary one to the and was developed through the NOAA Climate Data north). In addition, there was a strong negative rain - Record program. CLARA-A2 is the successor to fall anomaly over the seas of the Maritime Continent. CLARA-A1 and includes several changes, the most notable to impact global cloudiness being improve - Other large anomalies for 2015 include above-normal ments in cloud detection over semiarid regions. In rainfall over the central Indian Ocean and the eastern Pacific Ocean (east of the Hawaiian Islands) as well as addition to instrument sensitivity and calibration, below-normal precipitation in parts of the northern several factors contribute to differences among the records. For example, the AVHRR-derived records and southern Pacific Oceans. use different methods to account for satellite diurnal drift, while HIRS is primarily focused on detection 5) c Loudine SS — M. J. Foster, S. A. Ackerman, K. Bedka, R. A. Frey, L. Di Girolamo, A. K. Heidinger, S. Sun-Mack, of cirrus cloud. B. C. Maddux, W. P. Menzel, P. Minnis, M. Stengel, and G. Zhao The satellite records are in good agreement post- 2000, but prior to 2000, global cloudiness was more Based on the longest continuous record of cloud variable among the records and was relatively higher - cover, PATMOS-x (Pathfinder Atmospheres Extend for all series with the exception of SatCORPS. Four of ed; Heidinger et al. 2013), 2015 was 1.4% less cloudy than the 35-year average, making it the 10th least the records in Fig. 2.20 are derived from instruments - f lown on the NASA Earth Observing System (EOS) cloudy year on record. Global mean annual cloudi ness anomalies from eight satellite records are shown satellite missions, beginning in 1999 with the launch in Fig. 2.20. Four of the records—MISR (Multiangle Te r ra . MISR is f lown on Te r ra , while CERES and of and Te r ra MODIS are f lown on Imaging Spectroradiometer; Di Girolamo et al. 2010), (launched in Aqua Aqua 2002). Recent calibration issues with IR channels MODIS C6 (Moderate Resolution Imaging Te r ra on /MODIS have resulted in artificial positive Radiospectrometer Collection 6; Ackerman et al. CALIPSO 2008), (Cloud-Aerosol Lidar and Infrared trends in cloudiness, noticeable from around 2010, was Pathfinder Satellite Observation; Winker et al. 2007), CALIPSO /MODIS is included here. so only Aqua launched in 2006. and CERES (Clouds and the Earth’s Radiant Energy One explanation posited for the discrepancy be MODIS (CERES-MODIS; Minnis et al. - System) Aqua tween pre- and post-2000 is the lack of strong El Niño 2008; Trepte et al. 2010) show little change in global cloudiness from 2014 to 2015 (<0.1%). PATMOS-x and events in recent years. The last strong El Niño event was observed in 1997/98. Thus 2015 is significant in HIRS (High Resolution Infrared Sounder; Wylie et al. 2005; Menzel et al. 2014)—show modest increases that it is the first year with a strong El Niño event of 0.30% and 0.29%, respectively, while SatCORPS during the NASA EOS/post-2000 era. The close (Satellite ClOud and Radiative Property retrieval agreement between cloud records and lack of a large System; Minnis et al. 2015) shows an increase of 1.1%, positive cloudiness anomaly in relation to the current El Niño suggest that El Niño events in the 1980s and 1990s are, by themselves, not sufficient to explain the larger variability and higher cloudiness seen in the records of that time. Figure 2.21 shows the shift in the tropical ice clouds during the 2015/16 and 1997/98 El Niño events (see Sidebar 1.1). The 1998 and 2016 images show . 2.20. Annual global cloudiness anomalies for ig F observations during strong El Niño months while the 1981–2015 (base period, 2003–14, a period common to 1997 and 2015 images show the same month of the the satellite records excluding CALIPSO , where the entire previous year (preceding the El Niño). Both El Niño record was used instead). Datasets include PATMOS-x, events see a dramatic shift of ice clouds in January HIRS, MISR, AQUA MODIS C6, CALIPSO , CERES, SatCORPS and CLARA-A2. from the Warm Pool region of the western Pacific to | S28 AUGUST 2016

49 southern United States coincided with its wettest month on record in May (see section 7b2). H. Kim — di Scharge iver 6) r Runoff is one of the key components of the terres - trial water cycle. It serves as an integrated residual of the various hydraulic and hydrological processes after water has fallen on the land as precipitation. River discharge accumulates and transports total runoff generated in upstream watersheds to the ocean, play - ing a significant role in the freshwater balance and the salinity of the ocean. - Because of the lack of an observational methodol ogy for real-time global long-term monitoring (Fekete et al. 2012), off line model simulation has been the primary method rather than in situ networks [e.g., the Global Runoff Data Centre (GRDC); Fekete, 2013]. A 58-year (1958–2015) terrestrial hydrologic simulation is performed by the ensemble land surface estimator (ELSE; Kim et al. 2009). The atmospheric ig F . 2.21. Mean ice cloud fractions for Jan 1997, 1998, boundary condition has been updated to use the sec - 2015 and 2016. Data from 1997 and 1998 are from NOAA-14/AVHRR and data from 2015 and 2016 from ond Japanese global atmospheric reanalysis (JRA-55; SNPP/VIIRS. Kobayashi et al. 2015) and the Monitoring Product - version 5 (Schneider et al. 2015) monthly obser central Pacific. This caused statistically significant vational precipitation by the Global Precipitation (at the 5% level) lower cloud cover over the Maritime Climatology Centre (GPCC). The other parts of the Continent and the equatorial western Pacific and simulation framework remain as described by Kim correspondingly significant higher cloud cover across and Oki (2015). ELSE has been validated against the the central and eastern Pacific (see Plate 2.1m). As GRDC and also terrestrial water storage from GRACE the warm SSTs shift to the east, so do the tropical (Kim et al. 2009). - convection and the associated ice cloud. The 1997/98 The global distributions of runoff and river dis data were taken from the AVHRR PATMOS-x record, charge anomalies in 2015 (relative to the 1958–2015 which spans 1982–present. The 2015/16 data were base period) are illustrated in Plates 2.1i and 2.1j, generated using the SNPP/VIIRS instrument, which respectively. Most river basins in the tropics, such as is the successor to the AVHRR. the Congo, Zambezi, Tocantins, and São Francisco, Smaller, but still statistically significant, anomalies show anomalously dry conditions. Among the major were observed across the globe, with many regional river basins in the subtropics and temperate regions, anomalies being attributable to teleconnections asso the Danube, La Plata, Indus, and Yangtze were wetter - ciated with El Niño. For example, the Amazon basin than the climate normal. The Mississippi, Nile, and Volga were drier than their long-term mean states. experienced significant drought, which corresponded to a reduction in cloud cover. The cloud cover over Many of the basins in northern latitudes (e.g., Ob, the southeast Pacific off the coast of Peru and the Yenisei, and Lena) were wetter than their climato - stratocumulus deck off the coast of southwest North logical mean. The 58-year time series of total terrestrial runoff America were both anomalously low. Interestingly, other atmospheric oscillations such as the record anomalies and of the ENSO intensity are shown in Fig. 2.22. Variations of annual mean runoff and the positive Pacific decadal oscillation (PDO) showed oceanic Niño index (ONI) time series, smoothed by little impact on the annual and monthly cloud cover. 12-month window moving average, are significantly Other anomalies, not statistically significant for the entire year, were observed in the first half of the anticorrelated with each other (R = −0.63). Because year, prior to El Niño (see Online Figs. S2.15, S2.16). a strong El Niño developed through 2015, at the Some of these anomalies corresponded with a record global scale the mean anomaly turned into a dry positive PDO, but were not directly attributable to phase. However, it still remained close to the climate it. Large positive anomalies in cloud cover over the normal because of the lingering effect of La Niña | S29 AUGUST 2016 STATE OF THE CLIMATE IN 2015

50 servations of TWS variations which are a reasonable proxy for unconfined (having a free water table that responds to atmospheric pressure and processes like plant uptake and evaporation) groundwater varia - tions at climatic scales. Changes in mean annual TWS from 2014 to 2015 are plotted in Plate 2.1g as equivalent heights of wa - ter in cm. TWS can be thought of as an integrator ig F . 2.22. (a) Interannual variability of global runoff of other hydroclimatic variables (see Plates 2.1f–p). anomalies relative to the 1958–2015 base period (thick line for 12-month window moving average) and (b) In addition to being very warm, 2015 was a dry year the oceanic Niño index (ONI) [red and blue shades in terms of water in the ground, particularly in the for positive and negative phases, respectively, with southern tropics. TWS decreased in central and east - lighter (darker) shades representing weaker (stronger) ern South America, in southern Africa, and in central phases in (a)]. Australia. In 2014 the former two regions mostly had gained TWS. The year 2015 was also dry for much of the western United States and Canada, as the historic drought in California reached a crescendo in autumn before El Niño brought some relief. Drought dimin - ished water levels across a swath of central Europe, from France across to western Russia. A combination of drought and water consumption most likely con - tinued to diminish groundwater in the Middle East (Voss et al. 2013), northern India (Rodell et al. 2009; ig - . 2.23. Seasonal variations of global and continen F Panda and Wahr 2016), and the North China Plain tal runoff. Gray bars show the 58-year climatology (Feng et al. 2013). On the other hand, Turkey recov - and colored lines (1958–2015) with error bars for 2 σ ered from a major drought, and TWS also increased for the most recent 4 yrs. in a longitudinal band from Pakistan north through Afghanistan, Kazakhstan, and west central Russia. TWS also increased appreciably in Morocco (heavy after the 2009/10 El Niño. As shown in Fig. 2.23, seasonal variations of global runoff remained near rainfall in August), Texas and northern Mexico the long-term mean during the boreal spring and (continued recovery from a deep drought), central Argentina (heavy rains in February and August), and summer and turned into a strong dry phase (~2σ) from the boreal autumn onwards. Regionally, North Peru and western Brazil. Northern Africa and eastern America, Asia, and Europe experienced significantly Asia were a mosaic of increasing and decreasing TWS, as seen in Plate 2.1g. Significant reductions in TWS in dry conditions during the latter half of 2015, and the Greenland, Antarctica, and southern coastal Alaska dry condition was persistent in Africa through the entire year. During the most recent four years, large represent ongoing ice sheet and glacier ablation, not interannual variability affected South America, Eu - groundwater depletion. Figures 2.24 and 2.25 show time series of zonal rope, and Australia during February–July while the mean and global, deseasonalized monthly TWS seasonal f luctuations in Africa were weak. anomalies from GRACE, excluding Greenland and Stria water Storage — 7) g L roundwater and terre Antarctica. Data gaps occur in months when the M. Rodell, D. P. Chambers, and J. S. Famiglietti satellites were powered down during certain parts of Terrestrial water storage (TWS) comprises - the orbital cycle to conserve battery life. Relative dry ness in the southern tropics and northern equatorial groundwater, soil moisture, surface water, snow, zone in 2015 is clear in Fig. 2.24, while the northern and ice. Groundwater varies more slowly than the TWS components that are more proximal to the midlatitudes maintained their low TWS conditions. - atmosphere, but often it is more dynamic on multian All told, Earth’s nonpolar TWS hit a new GRACE- period low in 2015, −2.3 cm equivalent height of water nual timescales (Rodell and Famiglietti 2001). In situ (Fig. 2.25), equivalent to about 9 mm of sea level rise groundwater data are archived and made public by only a few countries. However, since 2002 the Gravity across the global oceans. Recovery and Climate Experiment (GRACE; Tapley - et al. 2004) satellite mission has been providing ob | S30 AUGUST 2016

51 Loew et al. 2013), revealing a good performance across the globe except for densely vegetated areas. - The surface soil moisture content sensed by the mi crowave satellites is closely linked to that of the root - zone (Paulik et al. 2012), except for very dry condi tions where they may become decoupled (Hirschi et al. 2014). Based on the ESA CCI SM dataset, the yearly and monthly anomalies are computed here with respect to a 1991–2014 climatology. For 2015, spatial anomaly patterns (Plate 2.1f) are markedly different from 2014 when, on a global scale, near-normal conditions prevailed (Dorigo et al. 2015a). The anomalous dry conditions in central- eastern Europe and Spain mainly resulted from the F . 2.24. GRACE zonal mean terrestrial water stor - ig excessively warm and dry summer and autumn in age anomalies (cm equivalent height of water; base this region (http://edo.jrc.ec.europa.eu/; ZAMG 2016, period: 2005–10). Gray areas indicate months when see October monthly anomalies; Online Fig. S2.17j). data were unavailable. For eastern Brazil, strong anomalous negative soil moisture conditions were observed for the fourth consecutive year (Dorigo et al. 2014, 2015a; Parinussa et al. 2013), which may further exacerbate shortfalls in water supply in the states of São Paulo, Rio de Janeiro, and Minas Gerais. Below-average soil moisture condi - tions in southern Africa resulted from a dry episode . 2.25. GRACE global average terrestrial water ig F in the southwest in early 2015, in combination with storage anomalies (cm equivalent height of water, base aggravating drought conditions towards the end of period: 2005–10). the year in the southeastern part of the continent L (Online Fig. S2.17), increasing the risk of crop failure S moi Sture — W. A. Dorigo, D. Chung, A. Gruber, 8) oi S. Hahn, T. Mistelbauer, R. M. Parinussa, C. Paulik, C. Reimer, and food shortage in South Africa, Mozambique, Madagascar, Malawi, and Zimbabwe. For parts of R. van der Schalie, R. A. M. de Jeu, and W. Wagner Queensland, Australia, negative anomalies were a Satellite-mounted microwave instruments can - continuation of drought conditions observed in this measure the moisture content of the upper few centi region over the past three years (BoM 2016; Dorigo meters of the unsaturated soil column. Dedicated soil moisture missions, such as the Soil Moisture Active et al. 2014, 2015a). Even though most parts of Indo - Passive (SMAP) mission launched in 2015 by NASA, nesia and Papua New Guinea are masked as missing are able to provide nearly contiguous global spatial because of dense vegetation, which is impenetrable for coverage at daily time scales but, as stand-alone the microwave sensors used in ESA CCI SM, strong missions, are too short for assessing soil moisture negative anomalies were still observed in the agricul - variability and change in the context of a changing tural areas. Dry conditions promoted deforestation climate. To bridge this gap, the ESA Climate Change and biomass burning practices in this area, causing severe air quality problems during several months Initiative (CCI) developed the first multisatellite (sections 2g3, 2h3; Sidebar 2.2). surface soil moisture dataset (ESA CCI SM), which - combines observations from a large number of his Prevailing wet soil moisture anomalies were observed for most of the United States, including torical and present-day passive and active microwave instruments (De Jeu et al. 2012b; Liu et al. 2012; the southwest, which was previously plagued by a Wagner et al. 2012). The current version of the dataset persistent drought for several years (Dorigo et al. 2014, 2015a). Large parts of the United States experi - combines nine different sensors (SMMR, ERS-1/2, enced their wettest May on record (see section 7b2), TMI, SSM/I, AMSR-E, ASCAT, WindSat, AMSR2, and SMOS) between late 1978 and December 2015. It which is ref lected by the strong positive soil moisture has been used for a wide range of applications (e.g., anomalies (Online Figs. S2.17e,f). The shift from dry Dorigo and De Jeu 2016) and has been benchmarked to wet conditions from October through November against a large number of land surface models and in was remarkable, following the passage of Hurri - situ datasets (Albergel et al. 2013; Dorigo et al. 2015b; cane Patricia (Online Fig. S2.17). Anomalous wet | S31 AUGUST 2016 STATE OF THE CLIMATE IN 2015

52 soil moisture conditions were also observed in eastern China with reported severe f loods in May–June. The southern part of South America also experienced wetter-than-usual conditions, - including severe f looding in Ar gentina and heavy precipitation ig . 2.26. Time series of average global soil moisture anomalies for F 1991–2015 (base period: 1991–2014). Data were masked as missing where in the Chilean Atacama Desert retrievals were either not possible or of very low quality (dense forests, in March (see section 7c3). frozen soil, snow, ice, etc.). (Source: ESA CCI.) To a large extent, the spa - tially distinct patterns in 2015 can be related to the strong El Niño conditions during the second half of the year (NOAA/ESRL 2016). ENSO anomalies are known to potentially cause continentwide deviations in terrestrial water storages (Bauer-Marschallinger et al. 2013; Boening et al. 2012; De Jeu et al. 2011, 2012a; Miralles et al. 2014c). ENSO-driven global negative soil moisture anomalies were clear during the 1997/98 El Niño, while positive anomalies were observable for the strong La Niña episode of 2010/11, especially for the Southern Hemisphere (Fig. 2.26). The negative soil moisture anomalies in the Southern Hemisphere are visible in the time–latitude diagram (Fig. 2.27), which shows the strongest anomalies in the southern . 2.27. Time–latitude diagram of soil moisture F ig tropics. However, even though El Niño conditions in anomalies (base period: 1991–2014). Data were masked 2015 were almost as strong as in 1997/98, its impact as missing where retrievals are either not possible or of up to the end of 2015 on global soil moisture was not low quality (dense forests, frozen soil, snow, ice, etc.). as strong. This suggests that other climate oscilla - (Source: ESA CCI.) tions may have partly counterbalanced the effects of El Niño during 2015 at least. mate of drought called the self-calibrating Palmer drought severity index is presented (scPDSI; Palmer No evident large-scale, long-term global soil 1965; Wells et al. 2004; van der Schrier et al. 2013a) moisture trends can be observed (Figs. 2.26, 2.27). However, this does not exclude the existence of long- using precipitation and Penman–Monteith potential term trends at the regional or local scale (Dorigo et al. ET from an early update of the CRU TS 3.24 dataset 2012). Trends in average global soil moisture should (Harris et al. 2014). Moisture categories are calibrated be treated with caution owing to dataset properties over the complete 1901–2015 period to ensure that changing over time and the inability to observe “extreme” droughts and pluvials relate to events that do not occur more frequently than in approximately beneath dense vegetation, for mountain areas, or 2% of the months. This affects direct comparison with frozen soils (cf. gray regions in Plate 2.1f and Online Fig. S2.17). other hydrological cycle variables in Plate 2.1, which use a different baseline period. Other drought indices can give varied results (see van der Schrier et al. 2015). d r o u g h t u S i n g t h e 9) m o n i t o r i n g g L o b a L van der Schrier et al. (2015) noted that 2014 ap - c a L i b r at i n g p - d ro u g h t S e v e r i t y S e L f a L m e r i n d e x T. J. Osborn, J. Barichivich, I. Harris, — peared to have a remarkably small global area affected G. van der Schrier, and P. D. Jones by drought, but the updated analysis (Fig. 2.28, with Hydrological drought results from a period of additional precipitation data that was not available at the time) now suggests that 2014 was affected by more abnormally low precipitation, sometimes exacerbated extensive droughts (8% of land in severe drought at - by additional evapotranspiration (ET), and its occur the end of 2014, compared with only 5% previously rence can be apparent in reduced river discharge, soil estimated). See Online Fig. S2.18 for a comparison moisture, and/or groundwater storage, depending with last year’s analysis. - on season and duration of the event. Here, an esti | S32 AUGUST 2016

53 in the El Niño-sensitive regions of northeastern Bra - zil, Venezuela, and Colombia; these are expected to impact water supplies, hydroelectric power, and crop yields as El Niño continues into 2016. Parts of Chile remained in a severe 6-year drought in 2015 despite wetter El Niño conditions (www.cr2.cl/megasequía). Drought conditions developed in some Central American and Caribbean nations, such as Guatemala and Haiti, contributing to food insecurity in the re - gion. California continued to experience severe or extreme drought conditions, while most of the U.S. Midwest, South, and East were moderately or very F . 2.28. Percentage of global land area (excluding ice ig wet, extending into Ontario, Canada. sheets and deserts) with scPDSI indicating moderate Dry conditions were widespread across Australia, (< –2), severe (< –3) and extreme (< –4) drought for continuing from 2014. Severe or extreme drought each month of 1950–2015. Inset: 2015 monthly values. conditions were apparent along the west coast, the southeast, and parts of Queensland, a region par There was a large expansion in the overall area - of drought across the globe in 2015 (Fig. 2.28, inset), ticularly susceptible to drought during protracted with 14% of global land seeing severe drought condi El Niño events, like the current one (section 2e1). - tions (scPDSI < –3) by the end of the year. The areas Farther north, dry conditions were established across where scPDSI indicates moderate (30%), severe (14%), many parts of the Maritime Continent and parts of Southeast Asia, especially Myanmar and southwest - or extreme (5%) droughts by the end of 2015 are ern China (Plate 2.1p). Drought also affected parts among the highest in the post-1950 record, exceeded of northern China and Mongolia in 2015 according only by some years in the mid-1980s. The 2015 peak should be interpreted cautiously, given that more to the scPDSI metric. In contrast with 2014, drought conditions were not evident in India despite a dry observations for the final months of 2015 will become monsoon season. This was due to heavy out-of-season available in due course (see Online Fig. S2.18). - rainfall both early and late in the year. Dry condi The regional patterns of drought (Plate 2.1p) are partly associated with the strong El Niño event that tions were, however, apparent over many Middle East countries. developed during 2015. The full effect of this event may not be apparent until 2016, and other factors In Europe, there was a strong contrast between dominate in regions where the inf luence of the tropi - the wet conditions of the southeast and Turkey and cal Pacific is weak. Averaged over 2015, almost no the severe drought indicated by scPDSI in eastern regions of Africa experienced wet spells, and indeed Europe and western Russia, affecting important most land areas south of 20°N across all continents crop production regions. Though not apparent in the annual-mean scPDSI (Plate 2.1p), July to December were either near-normal (31% with scPDSI within ±1) or subject to some degree of drought (56% with was very dry in Turkey, consistent with the strong positive North Atlantic Oscillation in late 2015 (sec - scPDSI <–1). tions 2e1, 7f). Extensive severe or extreme drought affected many countries in southern Africa, intensifying as The expansion in drought-affected areas during 2015 is similar to the earlier expansion during 1982 the 2015 El Niño progressed. These areas had been slowly recovering since a dry spell that began with (Figs. 2.28, 2.29a), also a year when a strong El Niño the previous El Niño in 2010. In the Horn of Africa, developed, and is consistent with the reduction in - the atmospheric transport of moisture from oceans severe drought affected Ethiopia and some neighbor to land during El Niño events (Dai 2013). The pat ing regions in 2015, with significant impacts despite - being apparent only over a relatively small region in terns of scPDSI drought (Plate 2.1p) correspond partly to those regions where El Niño events are as - the scPDSI data (Plate 2.1p). Very few areas of Africa sociated with reduced rainfall (southeastern Africa, exhibited wet spells in the 2015 mean scPDSI. northeastern Australia, the Maritime Continent, and The effects of the 2014 drought in southeastern - northeastern Brazil). There is weaker agreement with Brazil continued to be felt in 2015, though high rain the 1997 pattern (Fig. 2.29b), which had less extensive fall farther south over the Paraná basin (consistent droughts than in 2015, contributing to the absence with previous strong El Niño events) replaced drought with wet conditions. New regions of drought emerged of a clear signal in drought-affected area during the | S33 AUGUST 2016 STATE OF THE CLIMATE IN 2015

54 SIDEBAR 2 .1: —D. G. MIRALLES, B. MARTENS, GLOBAL LAND EVAPORATION SIDEBAR 2 .1: —D. G. MIRALLES, B. MARTENS, GLOBAL LAND EVAPORATION A. J. DOLMAN, C. JIMÉNEZ, M. F. MCCABE, AND E. F. WOOD A. J. DOLMAN, C. JIMÉNEZ, M. F. MCCABE, AND E. F. WOOD Evaporation of water from soils, snow-covered surfaces, Records of observation-based global evaporation only span continental water bodies, and vegetation (either via transpiration the satellite era. This has not prevented a handful of studies from or interception loss) accounts for approximately two-thirds of attempting to disentangle the impact of climate change on trends continental precipitation. As such, land evaporation represents in evaporation. Jung et al. (2010) suggested a reversal in the rise - a key mechanism governing the distribution of hydrological re of evaporation since the late 1990s, which was later shown to sources, spanning catchment to planetary scales. The ability to be a temporary anomaly caused by ENSO (Miralles et al. 2014b). - monitor land evaporation dynamics is also critical in climatologi Nonetheless, these studies, together with more recent contribu - cal applications, since evaporation 1) represents the exchange tions (Zhang et al. 2015, 2016), have indicated the existence of of latent energy from land to atmosphere, directly affecting air a slight positive trend over the last few decades, in agreement temperature; 2) influences air humidity and cloud formation, with expectations derived from temperature trends and global playing a strong role in driving atmospheric feedbacks; and 3) greening, and the theory of an accelerating hydrological cycle. is intrinsically connected to photosynthesis, echoing changes Although many of the models used for global flux estimation in vegetation carbon fixation. A number of recent studies have were originally intended for climatological-scale studies, some highlighted the impact of evaporation on climate trends (e.g., have evolved to provide estimates of evaporation in operational - Douville et al. 2013; Sheffield et al. 2012) and hydrometeoro logical extremes (e.g., Teuling et al. 2013; Miralles et al. 2014a). To date, land evaporation cannot be observed directly from space. However, a range of approaches have been proposed to indirectly derive evaporation by applying models that combine the satellite-observed environmental and climatic drivers of the flux (e.g., Price 1982, Nemani and Running 1989; Anderson et al. 1997; Su 2002). Pioneering efforts targeting the global scale (Mu et al. 2007; Fisher et al. 2008) have been advanced by interna - tional activities to further explore and develop global datasets, such as the European Union Water and global Change (WATCH) project, the LandFlux initiative of the Global Energy and Water- cycle Exchanges (GEWEX) project, and the European Space Agency (ESA) Water Cycle Multi-mission Observation Strategy (WACMOS)-ET project. Nonetheless, continental evaporation remains one of the most uncertain components of Earth’s energy and water balance. Both the WACMOS-ET and LandFlux projects have brought to light the large discrepancies among widely used, observation- based evaporation datasets, particularly in semiarid regimes and tropical forests (e.g., Michel et al. 2016; Miralles et al. 2016; McCabe et al. 2016). Figure SB2.1 displays the spatial variability of land evaporation over the 2005–07 period based on data from the Penman–Monteith model that forms the basis of the official MODIS product (PM–MOD; Mu et al. 2007), the Priestley and Taylor Jet Propulsion Laboratory model (PT–JPL; Fisher et al. 2008), the Model Tree Ensemble (MTE; Jung et al. 2010), and the Global Land Evaporation Amsterdam Model (GLEAM; Miralles et al. 2011). The ERA-Interim reanalysis (Dee et al. 2011) is also included for comparison. Global estimates range between the low values of PM–MOD and the high values of ERA-Interim, F - . SB2.1. Mean land evaporation patterns for differ ig with the remaining models showing a higher degree of spatial ent datasets. The right panel illustrates the latitudinal agreement. averages over the 2005–07 period. Adapted after Miralles et al. (2016). | S34 AUGUST 2016

55 SIDEBAR 2 .1: GLOBAL LAND EVAPORATION —D. G. MIRALLES, B. MARTENS, A. J. DOLMAN, C. JIMÉNEZ, M. F. MCCABE, AND E. F. WOOD mode, with ongoing efforts aiming to reduce product latency does not seem particularly anomalous, as climate variability is –1 - and improve spatial resolution. This opens up a range of pos . For most superimposed on a positive trend of ~0.4 mm yr sible applications, from global drought monitoring to irrigation of the Northern Hemisphere, evaporation was above the management. Some examples of evaporation datasets targeting multidecadal mean, with the notable exception of California, near-real-time simulation at continental scales include the Land which experienced extraordinary drought conditions. Surface Analysis Satellite Applications Facility (LSA SAF) product With the development of improved algorithms dedicated (Ghilain et al. 2011) and the Atmosphere–Land Exchange Inverse to estimating evaporation from satellite observations, global (ALEXI) datasets (Anderson et al. 1997, 2011). While GLEAM operational monitoring of land evaporation is becoming a was not deliberately designed with an operational intent, the realistic proposition. While discrepancies amongst current current version 3 dataset has been updated to include 2015, using models are still large (Michel et al. 2016; McCabe et al. 2016), observations from the Soil Moisture and Ocean Salinity (SMOS) several of the existing datasets compare well against each mission (www.gleam.eu). Figure SB2.2 shows the anomalies in other and against in situ measurements. These datasets open evaporation for 1980–2015 based on this new dataset. new pathways to diagnose large-scale drought and irrigation Periods of global decline in evaporation typically coincide needs, and to improve water resources management and the with El Niño conditions, and are associated with drought in the characterization of hydrological cycles. Satellite-based evapora - water-limited ecosystems of the Southern Hemisphere (Miralles tion estimates respond to long-term changes in Earth’s water et al. 2014b). The year 2015 was no exception: despite El Niño and energy budgets and are able to capture fluctuations due to conditions intensifying only in the second half of 2015, Fig. SB2.2 internal climate variability. The mean distribution of evapora - shows anomalously low evaporation in central Australia, eastern tion anomalies in 2015 (Fig. SB2.2) is a clear example of the South America, Amazonia, and southern Africa. Considering the underlying effects of multidecadal climate trends and climate entire multidecadal record, the continental evaporation in 2015 oscillations on the terrestrial water cycle. F ig - . SB2.2. (a) 2015 land evaporation anomalies. (Source: GLEAM). (b) Mean continental evapo ration anomaly time series for the satellite era, based on an ensemble of GLEAM datasets (after Miralles et al. 2014b). The MTE dataset (Jung et al. 2010), the satellite-based multimodel range by Mueller et al. (2013), and the Southern Oscillation index (SOI) are also shown. GLEAM runs for 2012–15 incorporate SMOS data. Anomalies are calculated relative to the 1997–2007 period in which all datasets overlap. | S35 AUGUST 2016 STATE OF THE CLIMATE IN 2015

56 2010, continuation as a protracted La Niña (with cold SST anomalies in the Niño-4 region) until its demise in early 2012, and then near-normal conditions until early 2013. Mainly positive (La Niña–type) values followed until a swing to negative (El Niño–type) conditions since early 2014 (Fig. 2.30; with warm SST anomalies in the Niño-4 region). Apart from April and May 2014, the SOI was negative from February 2014 onwards (Fig. 2.30). Accordingly, the Niño-3 and 4 regional SST anomalies have been positive since April and February 2014 respectively (section 4b). Following Allan and D’Arrigo (1999), by these measures this constitutes a protracted El Niño epi - sode: “...periods of 24 months or more when the SOI and the Niño 3 and 4 SST indices were of persistently negative or positive sign, or of the opposite sign in a maximum of only two consecutive months during the Figure 2.30 shows the presence of these period...” protracted El Niño and La Niña episodes in the SOI record since 1876, demonstrating that they can last up to six years (e.g., the 1990–95 protracted El Niño; see Gergis and Fowler 2009). Major El Niño and La Niña events can be near- F ig . 2.29. Mean scPDSI for (a) 1982 and (b) 1997, years global in their inf luence on world weather patterns, in which a strong El Niño developed. No calculation is owing to ocean–atmosphere interactions across the - made (gray areas) where a drought index is meaning Indo-Pacific region, with teleconnections to higher less (e.g., ice sheets and deserts with approximately zero mean precipitation). latitudes in both hemispheres. Protracted El Niño and La Niña episodes tend to be more regional in strong 1997/98 El Niño (Fig. 2.28). Indeed, the other post-1950 years with scPDSI drought areas as large as in 2015 (31% in moderate drought; e.g., 1985 and 1987) have quite different spatial patterns (Online Fig. S2.19), with severe drought in the Sahel and India, for example; 1985 was not a strong El Niño while 1987 was part of the long 1986/87 event. At mospheric circulation e. Sea Leve 1) m L pre of S mode Lated re and SS ure ean variabi Lity — R. Allan and C. K. Folland Mean sea level pressure (MSLP) provides diag - nostics of the major modes of variability that drive significant weather and climate events (Kaplan 2011). . 2.30. Time series for modes of variability described ig F using sea level pressure for the (left) complete period Arguably, the most globally impactful mode is the of record and (right) 2006–15. (a),(b) Southern Oscilla - El Niño–Southern Oscillation (ENSO), for which the tion index (SOI) provided by the Australian Bureau of sea level pressure-derived Southern Oscillation index Meteorology; (c),(d) Arctic Oscillation (AO) provided [SOI; Allan et al. 1996; normalized MSLP difference by NCEP Climate Prediction Center; (e),(f) Antarctic between Tahiti and Darwin (various other indices are Oscillation (AAO) provided by NCEP Climate Predic - also commonly used); Kaplan 2011; section 4b] is an tion Center; (g),(h) Winter (Dec–Feb) North Atlantic indicator. For 2015, the SOI was negative, indicating Oscillation (NAO) average provided by NCAR (pre - sented for early winter of each year so winter 2015/16 the presence of the strongest El Niño since 1997/98 is not shown); (i),(j) Summer (Jul–Aug) North Atlantic (see Sidebar 1.1). Oscillation (SNAO) average (Folland et al. 2009). The SOI trace since 2009 highlights the shift from El Niño to strong La Niña conditions around mid- | S36 AUGUST 2016

57 their impacts (Allan and D’Arrigo 1999; Allan et al. 2003). The main inf luence appears to be periods of persistent drought (widespread f looding) in Queensland, Australia, which often occur during protracted El Niño (La Niña) episodes (Murphy and Ribbe 2004). The dry 2014/15 across much of Queensland ref lects this latest protracted El Niño episode (section 2d9). More regionally, the Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and the Antarctic Oscillation (AAO) indices can also be derived from mean sea level pressure. In the Northern Hemisphere, the last five boreal winters have displayed broadly - positive NAO conditions but a diverse range of cir culation patterns. In the winter of 2013/14, a strong northeastward-displaced North Pacific anticyclone (Fig. 2.31a) was accompanied by a positive AO and a deep trough over central Canada and the United States. The subtropical jet stream was enhanced and displaced southward, extending across the Atlantic to the United Kingdom and Europe under strong posi - tive NAO conditions (Fig. 2.31d). This led to severe cold winter conditions across much of the United States and a succession of major midlatitude storms being steered across the Atlantic to Ireland and the United Kingdom. By contrast, during the 2014/15 bo - real winter the North Pacific anticyclone was weaker and the Aleutian low was prominent (Fig. 2.31b). The exceptional storm track from the United States to Europe in the 2013/14 boreal winter was not evident in 2014/15. During early winter of 2015/16, a deep trough over the North Atlantic led to an enhanced jet stream that directed a series of extratropical cyclones towards northern Ireland and Scotland–northern England (Figs. 2.31c,f). By the mid-to-latter part of the 2015/16 winter, the pattern had changed, with the Aleutian low enhanced and troughing over the North Atlantic–northern Europe. Midlatitude storm tracks were displaced farther north. In the Southern Hemisphere, the AAO was in its positive phase during 2015/16 (Fig. 2.30), which favors reduced sea ice extent in the West Antarctic . 2.31. Boreal winter sea level pressure anomalies F ig - Peninsula (WAP) region, owing to enhanced west (hPa, base period: 1981–2010) averaged over Dec–Feb erly wind conditions (Stammerjohn et al. 2008). for (a) 2013/14, (b) 2014/15, and (c) 2015/16. NAO daily During the current situation, however, the WAP sea time series (hPa) for winter (d) 2013/14, (e) 2014/15, ice margins were extended (http://nsidc.org/data and (f) 2015/16. The 5-day running mean is shown /seaice_index/), because in the interplay between the by the solid black line. The data are from HadSLP2r protracted El Niño, which should favor a weaker polar (Allan and Ansell 2006). jet stream, and the positive AAO mode, with stronger In 2015, the summer NAO (SNAO), defined over - westerly winds, the former appeared to have domi July and August as in Folland et al. (2009), continued nated. Related positive wind speed anomalies were noted at 850 hPa (section 2e3) over the midlatitude its marked tendency to a more negative state in the last decade. Only 2013 was a prominent exception. The Southern Ocean. | S37 AUGUST 2016 STATE OF THE CLIMATE IN 2015

58 irregular decline in the SNAO index since its peak in boundary between warmer-than-normal conditions over most of Europe and distinctly cool conditions in the 1970s is striking (Fig. 2.30i). A negative state of - the central extratropical North Atlantic Ocean. The the SNAO is consistent with generally strongly posi latter is consistent with the July and August MSLP tive Atlantic multidecadal oscillation conditions over - anomalies, but its strength may also ref lect a persis the last decade (Sutton and Dong 2012). However, tent tendency to cool conditions in this region over evidence is strengthening that reductions in summer Arctic sea ice due to warming of the Arctic may also the last few years. The pattern of rainfall anomalies - favor a negative SNAO (e.g., Knudsen et al. 2015, Pet varied consistently with MSLP patterns between July and August; most of northwestern Europe had above- rie et al. 2015). The July 2015 MSLP anomaly pattern strongly resembled the negative SNAO. Although Au - average rainfall in July, with most of southern Europe drier than normal. In August, most of Scandinavia gust also projected weakly onto the negative SNAO, Scandinavia had a high pressure anomaly with very and eastern Europe were drier than normal, with a warm temperatures over and to its south (Figs. 2.32a, more restricted wet area than in July extending from b). Daily SNAO values ref lect the somewhat different Ireland through France to the Netherlands (section characters of July and August (Fig. 2.32c), with fewer 7f; Online Fig. S2.21). days of negative SNAO in August. Despite this, the R. J. H. Dunn, C. Azorin-Molina, — 2) S urface Central England Temperature (Parker et al. 1992) wind S C. A. Mears, P. Berrisford, and T. R. McVicar was close to its 1961–90 normal in both months. The HadCRUT4 temperature for July and August (Online During 2015, over land, observational datasets have revealed generally higher surface wind speeds Fig. S2.20) shows that central England was on the (Plate 2.1s; Fig. 2.33a) than in the last 20 years. This “recovery” continues the behavior observed since 2013 and concurs with Kim and Paik (2015), who reported a break from the decreasing trend in surface wind speed around the Republic of Korea during the most recent decade. . 2.33. Global (excluding Australia) and regional ig F annual time series of land surface wind speed for 1981–2015 using HadISD and ERA-Interim showing 1 − (a) wind speed anomaly (m s ) relative to 1981–2010, . 2.32. HadSLP2r mean sea level pressure anomalies ig F and occurrence frequencies (in %) for wind speeds (b) for Europe for (a) Jul and (b) Aug 2015. (c) EMULATE − − 1 1 >3 m s and (c) >10 m s . Frequencies for Australia - PMSL daily SNAO time series for Jul–Aug 2015 normal are not shown in (b) and (c). ized over 1850–2015. | S38 AUGUST 2016

59 The observed global (excluding Australia) aver - with positive anomalies. At 15% of the stations, the −1 wind speed was at least 0.5 m s age anomaly from the 1981–2010 climatology was above the 1981–2010 −1 −1 −1 below it +0.025 m s climatology while it was at least 0.5 m s in (Table 2.5) compared to −0.030 m s 2014. As a result of unresolved differences between at 11% of the stations. The wind speed was at least −1 above and below the climatology at 3.4% and 1.0 m s the two observing systems used in Australia (wind run, compared to wind speed in HadISD), and given 2.3% of stations, respectively. Continentally, negative long-term trends of agreement of modeling pan evaporation trends when using wind run (Roderick et al 2007), Australia is observed land surface wind speed dominate over treated separately and the wind run results updated 1979–2015, with a terrestrial global (excluding −1 −1 from McVicar et al. (2008) are used. In Australia, , varying decade Australia) change of −0.087 m s the positive anomaly made 2015 the fourth windiest from −0.070 (East Asia) to −0.151 (Central Asia) −1 −1 year in the 1979–2015 record. There were positive m s decade (Table 2.5, Fig. 2.34), with Australia at −1 −1 . Although the ERA-Interim pat - −0.062 m s decade anomalies in all other regions, with the exception of a tern of reanalyzed trends (Fig. 2.34) is consistent with noticeable negative anomaly in North America. Over this latter region, 2015 was the ninth calmest year in the observational HadISD dataset, the magnitude of changes is underestimated, as previously noted the observed record, with a slightly lower occurrence −1 −1 ) and strong (>10 m s of both moderate (>3 m s ) for other reanalysis products (McVicar et al. 2008; winds (Figs. 2.33b,c), in agreement with Iacono and Azorin-Molina (2014). Overall increases in Europe, - central Asia, and East Asia ref lected a higher occur rence of moderate winds, and particularly of strong winds in Europe (Fig. 2.33b,c). Adapting the approach of Berrisford et al. (2015), two quality-controlled wind speed datasets from instrumental records are used: 1) the global HadISD (1973–2015, Dunn et al. 2012), with the highest sta - tion density in the Northern Hemisphere, and 2) an Australian database (1979–2015, McVicar et al. 2008). The 10-m wind speed fields from ERA-Interim (Dee et al. 2011) are also used to investigate the spatial and - . 2.34. Land surface wind speed trends for the ob ig F temporal variability of winds over regions that have servational HadISD and Australian datasets (points) few observations. Over land surfaces with high-den - and the ERA-Interim reanalysis (worldwide grids) - sity wind observations, the large-scale anomaly pat over 1979–2015. terns from ERA-Interim (Plate 2.1s) are relatively consistent t 2.5. Global and regional statistics for land surface wind speed using able with the instrumental records. observational HadISD and Australian (McVicar et al. 2008) datasets. Reanalysis products provide contiguous global informa - Tre nd 1979 –2015 Mean –1 –1 tion but have shortcomings decade ) and (m s Number of Anomaly 2015 1981–2010 Region –1 stations (m s ) 5th to 95th percentile –1 in their representation of sur - ) (m s confidence range face layer processes and hence Globe near-surface winds speeds (see 0.087 − 2264 3.309 (excluding +0.025 − 0.081) 0.094)–( − ( McVicar et al. 2008; Pryor et al. Australia) 2009; Vautard et al. 2010 for 0.100 − North America 587 3.685 − 0.130 - examples). In addition, reana 0 .111) – ( 0.088) − ( − lyzed winds are representative 0.087 − 589 +0.063 3.747 Europe of larger spatial and temporal 0.071) − 0.100)–( − ( scales than point observations. 0.151 − 263 2.887 +0.212 Central Asia The percentage of stations − ( 0.133) 0.162) – ( − showing positive and negative 0.070 − anomalies in 2015 from Had - 399 +0.092 East Asia 2.623 ( − 0.079)–( − 0.060) ISD is split almost evenly, with Australia 2.066 +0.160 41 − 0.062 a slight dominance of stations | S39 AUGUST 2016 STATE OF THE CLIMATE IN 2015

60 Pryor et al. 2009; Vautard et al. 2010). This worldwide During 2015, ocean winds showed large negative slowdown of land surface wind speed observed since anomalies in the central tropical Pacific associated with the ongoing El Niño event (Plate 2.1s), similar the 1980s has been reported over many regions (see to those found above the surface at 850 hPa (Plate McVicar et al. 2012 for a review). The precise causes of this weakening in wind speed 2.1r). This weakening was most apparent in the lat - ter half of 2015. Compared to the 1997/98 El Niño, remain largely uncertain and do not necessarily re - f lect wind tendency at higher altitudes (McVicar and the region of weakening did not extend as far east, Körner 2013) than the standard 10-m observations and Indian and Atlantic Ocean patterns were much (Vautard et al. 2010; Troccoli et al. 2012). Increase less striking (Online Fig. S2.22). Other regions with of surface roughness due to forest growth, land use negative anomalies include much of the tropical changes, and urbanization (Vautard et al. 2010; Bichet Indian Ocean and the southern Pacific midlatitudes between New Zealand and Chile. Other regions of the et al. 2012; Wever 2012; Wu et al. 2016); changes in Southern Ocean showed positive anomalies, which large-scale atmospheric circulation (Azorin-Molina were also present in the western Pacific surrounding et al. 2014, 2016); instrumentation changes (Wan et al. 2010); and air pollution (Xu et al. 2006) are among the the Maritime Continent and in the eastern tropical major identified hypothetical causes, which differ in Pacific south of the equator. Over land, the anomalies - were less pronounced, with most land areas showing importance regionally. Unlike the long-term declin small positive anomalies. ing trend over land, there is an apparent reversal of the trends since 2013, but still with overall negative L. Haimberger and M. Mayer — S wind 3) u air anomalies. pper Over oceans, estimates of globally-averaged wind Upper air wind is measured routinely with bal - loons and aircraft. Today it is also inferred from satel - obtained from satelliteborne microwave radiometers lite imagery, at least in the lower to midtroposphere. (Wentz 1997; Wentz et al. 2007) were slightly lower - than average in 2015 (Fig. 2.35). Estimates from re Historical upper air wind data are particularly crucial analysis products differ, with JRA-55 and ERA-Interim for detecting signals associated with meridionally showing that 2015 was above average, and MERRA-2 asymmetric aerosol forcing (Allen et al. 2014), for showing the opposite. Reanalysis winds, which are in example, or with ENSO. relatively good agreement with both the satellite data The buildup of a strong El Niño event was one and each other from 1990 to 2009, diverge after 2010 of the major large-scale climate anomaly signals in (Figs. 2.35a–c). A comparison of annual mean anomaly 2015. There are many ways to depict this event, but it is useful to see its impact on upper level divergent global ocean average wind speed between ERA-Interim and satellite radiometers shows moderate agreement f low. Figure 2.36 compares divergent wind anomalies on short time scales and poorer agreement on long at 200 hPa in late 2015 (3-month average centered time scales, with the ERA-Interim results showing a around November 2015 to maximize the signal) larger long-term increasing trend. All products show with those from the strongest ENSO event in recent an increasing trend from 1990 to 2007, followed by a history (also centered around November 1997). The divergent f low of the 2015 event, while having the drop-off in 2008–09, and a recovery in 2010. Since strongest divergence maximum east of the date line, then, the winds have fallen slightly in most products. was much more confined to the tropical Pacific than was the case in 1997, where the f low patterns over the whole tropics were massively perturbed. Regions with divergent f low are associated with deep convection and strong thermodynamic coupling between sea surface and the atmosphere, which also has a strong imprint on regional-scale energy f lows (Mayer et al. 2013, 2014). Regions with strong convergence at this level are associated with large-scale subsidence and suppressed convection. As may be expected (Zhang and Zhu 2012), this pattern fits well with the activity pattern of tropical . 2.35. Global average surface wind anomaly over ig F cyclones in late 2015. There was an all-time record ocean relative to the 1981–2010 base period from (a) of 13 tropical cyclones in the central Pacific and satellite radiometers, (b) ships, and (c) reanalyses (as - enhanced tropical cyclone activity in the east Pa described in Fig. 2. 1). | S40 AUGUST 2016

61 cific (www.nhc.noaa.gov/text/MIATWSEP.shtml; see section 4e3). It is also interesting to note that the anomalous divergence pattern over the Arabian Sea coincided with the occurrence of two strong tropical cyclones in this region (on average there is less than one tropical cyclone per year), affecting Yemen and Oman (see section 4e5). Tropical cyclone activity over the western Pacific (which is the region of strongest divergence in the climatological mean) and Atlantic was normal or below normal. This can be seen in Fig. 2.36a from the locations of peak intensities of tropical cyclones that reached at least Category 1 on the Saffir–Simpson scale. The near-average cyclone activity in the western Pacific can partly be explained by cyclones originating in the central Pacific that reached peak intensity farther west. The upper level convergence over Indonesia and particularly Australia in late 2015 is also consistent with the observed severe drought conditions over parts of these regions (see Fig. 2.29). As can be seen from Fig. 2.36, the upper level divergence pattern is generally less perturbed in 2015 than in 1997. If the F ig . 2.36. Three-month averages of velocity poten - RMS of the divergent wind speed in the tropics is used tial and divergent wind at 200 hPa compared to the −1 in late 1997 compared as a measure, it was 1.4 m s 1979–2014 climatology. Anomalies centered around −1 to 1.3 m s in late 2015. Nevertheless, its spread over (a) Nov 2015 and (b) Nov 1997. On panel (a) crosses indicate location of peak intensities for Category 1 the whole tropics is remarkable. or higher tropical cyclones in second half of 2015. The imprint of the 2015 El Niño event can also Percentage of tropical cyclone frequency compared be seen in the 850-hPa wind speed anomaly map in - to the National Hurricane Center’s 1966–2009 clima - Plate 2.1r. There is a distinct weakening of the tropi tology is also indicated. cal easterlies just west of the upper level divergence reanalysis run called ERA-preSAT (1939–67, H. maximum in Fig. 2.36a or the SST anomaly maxi - mum in Plate 2.1c. Other features of the anomaly map Hersbach et al. 2016,unpublished manuscript) which are a slight poleward shift of Southern Hemisphere helped to extend the quasi-biennial oscillation (QBO) time series from reanalyses backward in time to at midlatitude westerlies that leads to enhanced wind least the late 1940s. speeds over the seas adjacent to Antarctica. These are likely related to the positive phase Antarctic Figure 2.37 shows time series of zonal belt mean wind speeds in the tropics at 50 hPa to cover the Oscillation (AAO) during 2015 (section 2e1), and QBO signal. The experimental ERA-preSAT shows possibly enhanced through the exceptionally warm potential to extend reanalysis time series backward troposphere farther equatorward (Plate 2.1b). The in a more realistic manner than the surface data only positive wind speed anomaly over the eastern North reanalyses (Haimberger 2015). The depiction of the Atlantic is consistent with the positive phase of the QBO signal back to the early 1950s is particularly North Atlantic Oscillation (NAO) prevailing in 2015 (s e c t ion 2 e1). encouraging. There are practically no digitized upper Radiosonde and pilot balloon are the best sources air data prior to the early 1950s reaching high enough altitudes in the tropical belt. for station-based upper air wind climatologies, dating back to the early 1940s in the northern extratropics. - arth radiation budget E f. Maps of upper air winds are best inferred from at top - of - atmo Sphere — 1) e arth radiation budget mospheric reanalyses, which are well constrained by at P. W. Stackhouse, Jr., T. Wong, D. P. Kratz, P. Sawaengphokhai, observations. It is difficult to get wind climatologies A. C. Wilber, S. K. Gupta, and N. G. Loeb from satellite data because the altitude of observations The Earth’s radiation budget (ERB) at the top-of- (mostly cloud-based atmospheric motion vectors) is highly variable. Since last year’s article, early upper atmosphere (TOA) is the balance between the incom - ing total solar irradiance (TSI) and the sum of the air data have been assimilated in an experimental | S41 AUGUST 2016 STATE OF THE CLIMATE IN 2015

62 (MEI; Wolter and Timlin 1993, 1998; www.esrl.noaa .gov/psd/enso/mei/) and intensified into late 2015. Note that the MEI index is more appropriate than the SOI for - comparing to radiative f luxes because it integrates in formation from across the Pacific. In contrast, 2013 was a neutral year while 2014 featured a slight shift from typical east–west Pacific conditions toward El Niño, becoming “marginal” by year end. Global annual −2 mean OLR in 2015 increased ~0.15 W m since 2014, −2 larger than for 2013 (Table 2.6). but was ~0.30 W m Meanwhile, the global-annual mean RSW decreased by −2 −2 from 2014 and was ~0.75 W m smaller ~0.45 W m than for 2013. In 2015, the global annually-averaged −2 TSI was ~0.05 W m larger than that of both 2013 and 2014. The combination of these components amounts −2 to an addition of 0.40 W m in the total net radiation into the Earth climate system relative to 2014 and −2 corresponded to a ~0.50 W m increase relative to 2013. All the global annual mean changes appear to be amplifying relative to the neutral ENSO year of 2013, perhaps indicative of the atmospheric response due F ig . 2.37. Time series of zonal mean U-wind com - to the circulation anomalies over the last two years. ponent in the (a) 20°–40°N belt at 300 hPa and (b) - Relative to the 2001–14 average, the 2015 global an tropical belt 20°S–20°N at 50 hPa, calculated from nual mean f lux anomalies (Table 2.6) are +0.30, +0.10, ERA-Interim, MERRA, JRA-55, and ERA-preSAT re - −2 −0.55, and +0.35 W m for OLR, TSI, RSW, and total analyses and pilot balloon/radiosonde winds (GRASP; net f lux, respectively. These changes, except for the Ramella-Pralungo et al. 2014). Note that positive RSW anomaly, are within the corresponding 2-sigma (negative) changes in the zonal wind sped imply an interannual variability (Table 2.6) for this period and increase in westerlies (easterlies). Data have been thus not viewed as particularly large anomalies. The smoothed using a 12-point boxcar filter. 2015 global annual mean RSW f lux anomaly greatly - ref lected shortwave (RSW) and outgoing longwave ra exceeds typical variability, implying a darkening of Earth’s TOA albedo. Attribution of this to El Niño diation (OLR). This balance defines the energetic state of the Earth–atmosphere system that drives weather and/or other large-scale processes requires further - processes, climate forcing, and climate feedbacks. analysis. However, it appears that reduction of the an The year 2015 is remarkable due to the development nually averaged RSW is resulting in a relative increase to the total net absorbed f lux of the Earth–atmosphere of an intense El Niño that reached official status in April–May according to the multivariate ENSO index system, indicating a net heating over the last two years. 2.6. Global-annual mean TOA radiative flux changes between 2013 and 2015, 2014 and able t 2015, the 2015 global-annual mean radiative flux anomalies relative to their corresponding interannual variabilities of the 2001–14 σ 2001–14 mean climatological values, and the 2- −2 global-annual mean fluxes (all units in W m ) for the outgoing longwave radiation (OLR), total solar irradiance (TSI), reflected shortwave (RSW), and total net fluxes. All flux values − 2 have been rounded to the nearest 0.05 W m . 2015 Anomaly Global-annual Mean Global-annual Mean Interannual variability Difference (relative to Difference (2001 to 2014) climatology) (2015 minus 2014) (2015 minus 2013) –2 (W m ) –2 –2 –2 (W m (W m (W m ) ) ) +0.15 +0.30 ±0.50 +0.30 OLR +0.05 TSI ±0.20 +0.10 +0.05 ±0.40 RSW − 0.55 −0.45 −0.75 +0.35 +0.50 Net ±0.65 +0.40 | S42 AUGUST 2016

63 Monthly mean anomaly TOA f lux time series TSI data are from the Total Irradiance Monitor (Fig. 2.38) show that the OLR anomaly began 2015 (TIM) instrument aboard the Solar Radiation and −2 - , but then mostly oscil with a value of 0.9 W m Climate Experiment (SORCE) spacecraft (Kopp and −2 , which led to the lated between −0.2 and +0.7 W m Lean 2011) and the Royal Meteorological Institute of slightly positive annual OLR anomaly (see Table 2.6) Belgium (RMIB) composite dataset (Dewitte et al. with higher values toward the end of 2015. This ob 2004), both renormalized to the SORCE Version - served OLR variability is generally consistent with 15 data. RSW and OLR data were obtained from the Clouds and the Earth’s Radiant Energy System the NOAA-HIRS OLR (Lee et al. 2011). The absorbed shortwave (TSI − RSW) anomaly started the year with (CERES) mission (Wielicki et al. 1996, 1998), deriving −2 −2 , increased to just over 1.1 W m a value of 0.2 W m Te r ra spacecraft. in Aqua and f lux data from the Time series (Fig. 2.38) were constructed from the September, but then decreased the last few months of CERES EBAF (Energy Balanced And Filled) Ed2.8 the year. The positive values towards the latter half of product (Loeb et al. 2009, 2012) from March 2000 - the year were large enough to dominate the annual av to October 2015 and the CERES Fast Longwave and erage, leading to a large absorbed shortwave anomaly Shortwave Radiative Fluxes (FLASHFlux) products for the year. The total net anomaly, which contains the combined OLR and absorbed shortwave anomalies, - (Kratz et al. 2014; Stackhouse et al. 2006), for Novem −2 ber and December 2015. The FLASHFlux data are , then jumped began 2015 with a value of −0.7 W m −2 normalized to the EBAF data using the following pro - to positive values, peaking in September at 0.9 W m −2 before falling below 0 W m cedure based on overlapping data from January 2009 by the end of the year. The positive absorbed shortwave anomaly dominates through December 2014. First, successive versions of the net, resulting in the positive annual total net globally-averaged FLASHFlux TOA components are normalized to each other relative to the current ver - anomaly. Long-term trend analyses that include the sion 3B. Then, this unified 6-year FLASHFlux dataset last two months of the merged dataset are discouraged is cross-calibrated to the corresponding EBAF Ed2.8 due to the natural f luctuation in ERB components, the uncertainty from the data merging process, and data using TOA f luxes from both datasets, account - ing for multiyear bias, linear change, and seasonal potential for drift in the FLASHFlux product. dependent differences. Finally, these coefficients are used to cross-normalize FLASHFlux to EBAF and provide an estimate of monthly globally aver - aged TOA f lux components. The resulting 2-sigma monthly uncertainty of the normalization procedure for the 6-year overlap period was ±0.22, ±0.07, ±0.19, −2 for the OLR, TSI, RSW, and total and ±0.22 W m net radiation, respectively. a u n a L o a 2) m - S k y “ a p pa r e n t ” S o L a r c L e a r tran Smi SS ion — K. Lantz NOAA’s Global Monitoring Division (GMD) maintains one of the longest continuous records of solar transmission at the Mauna Loa Observatory (MLO) in Hawaii. Because of the observatory’s remote Pacific location and high elevation above local inf lu - ences (3400 m a.s.l.), the solar transmission represents . 2.38. Time series of global-monthly mean i g F the free troposphere and above with limited local − 2 ) of TOA earth deseasonalized anomalies (W m inf luences. This record is often used to show the Radiation Budget for OLR (upper panel), absorbed inf luence of large explosive volcanic eruptions and shortwave (TSI–RSW; middle panel), and total net is useful as an indicator of changes in background (TSI–RSW–OLR; lower panel) from Mar 2000 to Dec stratospheric aerosols. The “apparent” clear-sky 2015. Anomalies are relative to their calendar month solar transmission (AT) is calculated from the ratio climatology (2001–2014). The time series shows the of direct-beam broadband irradiance measurements CERES EBAF Ed2.8 1Deg data (Mar 2000 to Oct from a pyrheliometer using fixed atmospheric paths 2015) in red and the CERES FLASHFlux version 3B (Ellis and Pueschel 1971). This technique is advanta - data (Nov–Dec 2015) in blue (Source: https://eosweb. geous because using the ratio of fixed atmospheric larc.nasa.gov/project/ceres/ceres_table.) | S43 AUGUST 2016 STATE OF THE CLIMATE IN 2015

64 paths removes inf luences due to extraterrestrial ir - radiance and instrument calibrations. Past studies of changes in clear-sky AT at MLO have looked at the inf luence of volcanic aerosol, aerosol transport from Asia, water vapor, ozone, and inf luences of the quasi-biennial oscillation (QBO; Bodhaine et al. 1981; Dutton et al. 1985; Dutton 1992; Dutton and Bodhaine 2001). Effects due to aerosol are the most prominent in the record. The monthly record of clear-sky apparent solar transmission has been updated through December 2015 (Fig. 2.39). The monthly values are calculated using morning values to remove boundary layer in - f luences that occur predominantly in the afternoon - . 2.39. (a) Monthly mean of the clear-sky Appar ig F due to prevailing upslope wind conditions (Ryan ent Transmission at Mauna Loa Observatory. The 1997). Major eruptions from Agung, El Chichón, dashed line is the background level from 1958 to 1972. (b) Enlarged plot to highlight the seasonal (red line, and Mount Pinatubo are clearly visible in the record 6-month running smoothed fit) and long-term (blue in 1964, 1982, and 1991, respectively (Fig. 2.39). The line, 24-month smoothed fit) changes in the clear-sky cleanest period of observations is between 1958 and AT record. 1962, except for a brief period in 1978. As such, this ments of CO began at Mauna Loa, Hawaii (MLO), period is treated as the “clean” background with 2 in 1958, when the atmospheric mole fraction was which to compare all other variations (dashed line ~315 ppm (parts per million in dry air). In 2015 the in Fig. 2.39). Seasonal trends are highlighted by a 6-month running smoothed fit to the monthly values MLO annual average mole fraction of CO exceeded 2 and have been attributed primarily to Asian aerosol 400 ppm (400.8 ± 0.1 ppm) for the first time (www.esrl transport in the spring (Bodhaine et al. 1981). Long- - .noaa.gov/gmd/ccgg/trends/), while the global aver age CO mole fraction at Earth’s surface was 399.4 term changes are highlighted by a 24-month running 2 ± 0.1 ppm (Fig. 2.40a, www.esrl.noaa.gov/gmd/ccgg smoothed fit. The monthly clear-sky AT eventually /trends/global.html). returned to near-background conditions in mid-1998 Atmospheric CO growth since 1958 is largely after the eruption of Mount Pinatubo in 1991. The 2 attributable to a concurrent, fourfold increase in 24-month fit shows a slow decrease in AT over the anthropogenic emissions from fossil fuel combus subsequent decade (Fig. 2.39b). This slow decrease in - clear-sky AT was attributed to increased stratospheric tion and cement production (Boden et al. 2015). remains in aerosol due to small volcanic eruptions (Solomon About half of this anthropogenic CO 2 the atmosphere, while the other half is taken up by et al. 2011; Vernier et al. 2011). These eruptions have been shown to contribute aerosol to the layer between the terrestrial biosphere and oceans, where it acidi - fies seawater (see section 3l). The global growth rate the tropopause and 15 km in mid- to high latitudes −1 of CO in the early (Ridley et al. 2014). The last several years have not has risen from 0.6 ± 0.1 ppm yr 2 −1 shown a continued increase in the clear-sky AT. There 1960s to an average of 2.1 ± 0.1 ppm yr during the past 10 years. However, the increase at MLO during is a negligible change in the mean of the monthly clear-sky AT in 2015 with respect to 2014 (−0.0006). 2015 was 3.05 ± 0.11 ppm (0.76 ± 0.03%), the largest annual increase observed in the 56-year measurement The amplitude of the seasonal changes in clear-sky record. The largest previous increase (2.93 ppm) oc - AT in 2015 is ~0.006, which is comparable to results reported previously of ~0.007 (Bodhaine et al. 1981). curred in 1998, which was also a strong El Niño year. ENSO plays a role in the interannual variability of the mospheric composition g. At CO growth rate through its inf luence on terrestrial 2 Lived - ong L carbon f luxes (Bastos et al. 2013). 1) E. J. Dlugokencky, — SeS ga Se greenhou B. D. Hall, M. J. Crotwell, S. A. Montzka, G. Dutton, J. Mühle, Methane is emitted from both anthropogenic and J. W. Elkins (60%) and natural (40%) sources (Fung et al. 1991). - Anthropogenic sources include agriculture (e.g., Carbon dioxide (CO ), and ni ), methane (CH 2 4 O), in decreasing order, are the most ruminants and rice), fossil fuel extraction and use, trous oxide (N 2 biomass burning, landfills, and waste. Natural sources dominant long-lived greenhouse gases (LLGHG) include wetlands, geological sources, oceans, and - contributing to climate forcing. Systematic measure | S44 AUGUST 2016

65 nearly zero in the early 2000s, then increased to an −1 since 2007 (Fig. 2.40b). Surface average of ~7 ppb yr observations, including its rate of increase and spatial distribution, provide strong top-down constraints on the CH source and sink budgets. Based on NOAA 4 background air sampling sites, the 2015 globally aver - mole fraction at Earth’s surface was 1834.0 ± aged CH 4 0.8 ppb. The 11.5 ± 0.9 ppb increase in annual means from 2014 to 2015 is the largest since 1997/98. Nitrous oxide is a powerful greenhouse gas pro - duced by natural (~60%) and anthropogenic (~40%) sources and is also an ozone-depleting substance (Ciais et al. 2013; Ravishankara et al. 2009). The observed 21% increase in atmospheric N O over pre - 2 industrial levels (270 to 328 ppb) is largely the result of nitrogen-based fertilizer use (Park et al. 2012). The O mole fraction in 2015 mean global atmospheric N 2 was 328.2 ± 0.1 ppb, an increase of 1.1 ppb from the 2014 mean (Fig. 2.40c). The average N O growth rate 2 −1 since 2010 is 0.98 ± 0.02 ppb yr , higher than the −1 0.75 ± 0.02 ppb yr average growth over the previous decade. Halogenated gases, such as chlorof luorocarbons (CFCs), hydrochlorof luorocarbons (HCFCs), hy - drof luorocarbons (HFCs), and CCl also contribute 4 to radiative forcing. Atmospheric mole fractions of some CFCs, such as CFC-12 and CFC-11, have been decreasing for a decade or more in response to pro - duction and consumption restrictions imposed by the Montreal Protocol and its Amendments (Fig. 2.40d; Table 2.7). However, as a result of the CFC phase- F ig . 2.40. Global mean surface mole fractions (in dry out, the atmospheric burdens of CFC replacement air) of (a) CO O (ppb), (ppb), (c) N (ppm), (b) CH 2 4 2 gases—HCFCs and HFCs—have increased (Fig. 2.41; and (d) CFC-12 and CFC-11 (ppt) derived from the Table 2.7; Carpenter et al. 2014; Montzka et al. 2014). NOAA sampling network. Interestingly, of the most abundant ozone-depleting substances that were controlled initially by the Mon termites (Dlugokencky et al. 2011). Fossil fuel exploi - - tation (coal, oil, and natural gas) contributes ~20% treal Protocol, the surface mole fraction of only one of total global CH emissions (Kirschke et al. 2013). chemical, halon-1301, is not yet decreasing (Table 2.7). 4 Having increased 250% since pre industrial time, Trends in the combined direct radiative forcing O, CFC-11, by five major LLGHGs (CO , N the atmospheric CH burden currently contributes , CH 2 4 4 2 −2 ~0.5 W m direct radiative forcing, with an additional and CFC-12) and 15 minor gases are summarized by −2 the NOAA Annual Greenhouse Gas Index (AGGI; ~0.3 W m indirect radiative forcing coming from Hofmann et al. 2006; www.esrl.noaa.gov/gmd/aggi/). and stratospheric the production of tropospheric O 3 H - This index represents their annual cumulative radia O from methane (Myhre et al. 2013). Total global 2 −1 yr CH tive forcing relative to the Kyoto Protocol baseline emissions are estimated at ~540 Tg CH 4 4 12 g), with a relatively small uncertainty (1 Tg = 10 year of 1990. The AGGI does not include indirect - radiative forcings (e.g., inf luences on ozone and water of ~±10%, based on observations of globally aver −2 contributed 1.94 W m direct aged CH , its rate of increase, and an estimate of its vapor). In 2015, CO 2 4 lifetime (~9.1 yr). The complexity of the atmospheric radiative forcing, 65% of the combined forcing of 2.98 −2 CH by the 5 major LLGHGs and 15 minor gases W m budget, with many sources that are difficult to 4 quantify individually, makes bottom-up estimates by (Fig. 2.42). The combined forcing in 2015 represents a 38% increase (2015 AGGI = 1.38) since 1990, and a increase country and source difficult. The rate of CH 4 −1 in the 1980s to 1.4% increase over 2014 (AGGI = 1.36). slowed from more than 10 ppb yr | S45 AUGUST 2016 STATE OF THE CLIMATE IN 2015

66 t able Summary table of long-lived greenhouse gases for 2015 (CO 2 .7. mixing ratios are in ppm, N O 2 2 in ppb, and all others in ppt). and CH 4 Mean Surface Mole Radiative Chemical Lifetime Industrial Designation Fraction, 2015 AGGI Efficiency ODGI (change from prior Formula or Common Name (years) – – 1 2 a ppb ) (W m b year) c –5 Y Carbon Dioxide N CO 399.4 (2.3) 10 × 1.37 2 c – 4 9.1 N CH Methane 1834.0 (11.5) Y 3.63 10 × 4 c,d –3 Nitrous Oxide N 123 O Y N 328.2 (1.1) 10 × 3.00 2 Chlorofluorocarbons c,d C F C -11 Y Y 0.26 CCl F 1.4) − 232.1 ( 52 3 c,d Y F Y 0.32 CCl CFC-12 516.1 ( − 3.4) 102 2 2 c,d Y Y 0.30 FCClF CCl C F C -113 − 71.9 ( 0.5) 93 2 2 Hydrochlorofluorocarbons c Y Y 0.21 HCFC-22 CHClF 11. 9 233.0 (4.1) 2 c CH HCFC-141b F Y Y 0.16 CCl 24.3 (0.5) 9.4 3 2 c HCFC-142b CH 0.19 Y Y 18 CClF − 21.8 ( 0.1) 3 2 Hydrofluorocarbons c 0.16 HFC-134a Y N FCF CH 14 83.5 (5.9) 2 3 c N CHF Y 0.10 HFC-152a CH 1.6 6.6 (0.2) 3 2 c 0.16 CF Y N HFC-143a CH 51 16.1 (1.4) 3 3 c 0.23 N CF Y CHF HFC-125 31 17.0 (1.9) 2 3 c N N 0 .11 F CH HFC-32 5.4 9.9 (1.6) 2 2 c Y N 0.18 CHF HFC-23 228 28.1 (1.0) 3 c N CF CH 0.22 CF N HFC-365mfc CH 8.7 0.8 (0.08) 3 3 2 2 c N 0.26 N CHFCF CF HFC-227ea 36 1.1 (0.09) 3 3 Chlorocarbons c CCl Methyl Chloroform CH Y Y 0.07 5.0 0.6) − 3.1 ( 3 3 c,d Carbon Tetrachloride CCl Y Y 0.17 26 1.3) 82.5 ( − 4 c CH Methyl Chloride N Y 0.01 0.9 Cl 550 (6) 3 Bromocarbons c Br N Y 0.004 CH Methyl Bromide 0.8 − 0.04) 6.6 ( 3 c Y Y 0.29 CBrClF Halon 1211 16 − 3.61 ( 0.08) 2 c CBrF Y Y 0.30 3.27 (0.01) 72 Halon 1301 3 c 28 0.01) − 0.43 ( CBrF Halon 2402 0.31 Y CBrF Y 2 2 Fully fluorinated species c SF N 0.57 8.60 (0.33) Sulfur Hexafluoride >600 Y 6 c CF PFC-14 0.09 N N ~50 000 81.9 (0.7) 4 c C 000 F N N 0.25 4.49 (0.08) P F C -116 ~10 6 2 a Radiative efficiencies were taken from IPCC AR5 (Myhre et al. 2013). Steady-state lifetimes were taken from Myhre et al. (2013) (CH ), 4 ), Ko et al. (2013), and Carpenter et al. (2014). For CO , numerous removal processes complicate the Ravishankara et al. (2009) (SF 6 2 derivation of a global lifetime. b Mole fractions are global, annual surface means for the indicated calendar year determined from the NOAA global cooperative air sampling network (Hofmann et al. 2006), except for PFC-14, PFC-116, and HFC-23, which were measured by AGAGE (Mühle et al. 2010; Miller et al. 2010). Changes indicated in brackets are the differences between the 2015 and 2014 global mean mole fractions. c Preliminary estimate. d Global means derived from multiple NOAA measurement programs (“Combined Dataset”). | S46 AUGUST 2016

67 SeS 2) o Leting ga dep — B. D. Hall, S. A. Montzka, zone - stratospheric halogen load is evaluated by the NOAA G. Dutton, and J. W. Elkins Ozone-Depleting Gas Index (ODGI; Hofmann and In addition to direct radiative forcing, chlorine- Montzka 2009). The ODGI relates EESC in a given and bromine-containing gases contribute indirectly year to the EESC maximum (ODGI = 100) and 1980 to radiative forcing through their destruction of value (ODGI = 0), a benchmark often used to assess stratospheric ozone. The emissions and atmospheric progress towards reducing stratospheric halogen to burdens of many of the most potent ozone-depleting pre-ozone hole levels (Fig. 2.43). - gases have been decreasing in response to produc The EESC and ODGI are calculated for two repre - tion and consumption restrictions imposed by the sentative stratospheric regions—Antarctica and the Montreal Protocol and its Amendments (Figs. 2.40d, midlatitudes—that differ in total available reactive 2.41). For example, the abundance of CH CCl halogen (Fig. 2.43a). At the beginning of 2015, EESC at 3 3 Earth’s surface has declined 98% from its peak in values were ~3820 ppt and ~1620 ppt over Antarctica and the midlatitudes, respectively. EESC is larger in 1992 (Fig. 2.41). CFC-11 and CFC-12, which have the Antarctic stratosphere than in the midlatitudes much longer atmospheric lifetimes (Table 2.7), have because more ozone-reactive halogen is released dur - declined by 7.7% and 2.3%, respectively, from their peak mole fractions in 1994 and 2002. ing transit to the Antarctic. Corresponding ODGI Equivalent effective stratospheric chlorine (EESC) values at the beginning of 2015 were 82.9 and 59.5, compared to 84.3 and 61.5 at the beginning of 2014. is a measure of the ozone-depleting potential of the These represent ~17% and ~40% reductions from the halogen loading in the stratosphere at a given time and place. As EESC declines, stratospheric ozone is peak values in EESC over Antarctica and the mid - expected to show signs of recovery. Some recovery is latitudes, respectively, towards the 1980 benchmarks - indeed evident in the upper stratosphere, and is at (Fig. 2.43b). tributable, in part, to the decrease in EESC (Pawson — S. Rémy, A. Benedetti, and O. Boucher SoLS ero 3) a et al. 2014; see section 2g4). EESC is calculated from - Aerosol particles play an important role in the surface measurements of halogenated, ozone-deplet - ing gases and weighting factors that include surface- atmosphere through various mechanisms. They in to-stratosphere transport times, mixing during f luence the radiation budget, directly by scattering transit, photolytic reactivity, and ozone-destruction and absorbing short- and long-wave radiation, and Schauff ler et al. 2003; efficiency (Daniel et al. 1995; indirectly by affecting the concentrations, sizes, and Newman et al. 2007). Progress towards reducing the chemical composition of cloud condensation nuclei (CCN) that impact the life cycle, optical properties, and precipitation activity of clouds. More information about the radiative forcing by aerosols is provided by Boucher et al. (2013). Aerosols also impact air quality and may cause serious public health issues, F ig . 2.41. Global mean surface mole fractions at 2 − ) due to 5 . 2.42. Direct radiative forcing (W m F ig Earth’s surface (ppt, dry air) for several halogenated major LLGHG and 15 minor gases (left axis) and the See Table 2.7 for the long-lived greenhouse gases. Annual Greenhouse Gas Index (right axis). 2015 global mean mole fractions of these gases. | S47 AUGUST 2016 STATE OF THE CLIMATE IN 2015

68 - matter, and sulfate aerosols produced by anthropo genic and natural sources. Biomass burning aerosol emissions are the sum of the black carbon and organic matter emitted by open fires and biofuel combustion. Open fire emissions for this new reanalysis were provided by the Global Fire Assimilation System (GFAS) inventory (Kaiser et al. 2012) that estimates emissions from MODIS observations of fire radiative power. Preliminary verification of total AOD using independent observations from the ground-based Aerosol Robotic Network (AERONET) shows that the reanalysis has a global average bias of −2.5% but personal com - is consistent over time (J. Flemming, munication , Feb 2016). The 2015 annual average anomalies of AOD due to total aerosols, dust, and biomass burning (Plates 2.1v,w,x, respectively) depict strong regional anoma - lies from biomass-burning events in Alaska, Siberia, Canada, and Indonesia. Overall, the 2015 anomalies of biomass burning aerosols (Plate 2.1x) are consis - tent with those of tropospheric ozone (section 2g6), carbon monoxide (section 2g7), and fires (section 2h3). Besides the large events already mentioned, the anomaly map of biomass burning aerosols reveals that F ig . 2.43. (a) Equivalent Effective Stratospheric Chlo - the 2015 seasonal burning in Africa was more severe rine (EESC) and (b) the NOAA Ozone-Depleting than usual south of the equator and less severe north Gas Index (ODGI). The ODGI represents the rela - of it. Biomass burning in the Amazon basin in 2015 tive mole fractions of reactive halogen in the mid - was similar to the 2003–14 average, interrupting the latitude and Antarctic stratosphere scaled such that decreasing trend observed for several previous years. ODGI=100 at maximum EESC and zero in 1980. Both EESC and ODGI are derived from NOAA surface There is a positive anomaly of dust extending west measurements of ozone-depleting gases (symbols) from western Sahara across the tropical Atlantic to or, for earlier years, WMO scenarios (dashed lines, Central America (Plate 2.1w), pointing to more active Harris and Wuebbles 2014). The EESC and ODGI transatlantic dust transport in 2015 than in previous values from 1992 forward are for Jan of each year. years. On the other hand, dust episodes were less important in 2015 over the northern Sahara and the as documented by the world media during strong particulate pollution outbreaks in 2015 in parts of Mediterranean Sea, and less dust was transported from the Taklamakan and Gobi Deserts into China. western Europe (March), Indonesia and adjacent countries (September–October), and northern China Sea salt aerosol anomalies (not shown) were strongly negative in the equatorial Pacific Ocean and west of (November). Indonesia, probably due to disturbances in the trade For the first time in this section, a new interim winds by the strong El Niño conditions during the reanalysis of global aerosols is utilized that spans second half of the year. Positive anomalies of sea salt 2003–15. This was developed within the framework of the Copernicus Atmosphere Monitoring Service in the North Atlantic Ocean were caused by a string personal communication (CAMS; J. Flemming, of active storms there in November–December. , Feb 2016). Collection 5 retrievals of aerosol optical depth Global maps of the total 550-nm AOD average for 2003–14 and statistically significant (95% confidence) (AOD) at 550 nm from the satellite-based Moderate linear trends from 2003 through 2015 are shown in Resolution Imaging Spectroradiometer (MODIS; - Fig. 2.44. The highly polluted areas of eastern Asia Remer et al. 2005) were used as observational con straints. All relevant physical aerosol processes, such and India are prominent features in the map of as emissions, wet/dry deposition, and sedimentation, long-term average total AOD (Fig. 2.44a), as are the dust-producing regions of the Sahara, the Arabian are included and fully coupled with the meteorology. Peninsula, the Middle East, and the Taklamakan and The aerosol types treated are naturally produced sea Gobi Deserts. Large AOD values over the Amazon salt and desert dust, as well as black carbon, organic | S48 AUGUST 2016

69 ig F . 2.45. Global averages of total AOD at 550 nm - averaged over monthly (red) and annual (blue) peri ods for 2003–15. Aerosol monitoring relies on a multistream global observing system. Routine aerosol observations are mainly provided by two federated, ground-based networks: AERONET and Global Atmospheric Watch (GAW), which in 2015 operated 311 and >220 stations, Aqua respectively. MODIS satellite instruments on and Te r ra have continued to provide retrievals of AOD during 2015, while the Visible Infrared Imaging Radiometer Suite (VIIRS) on has provided Suomi NPP aerosol data products since 2013. Geostationary sat - ellites are also increasingly being used to measure aerosols. For instance, AOD derived from Meteosat Second Generation (MSG) observations over Europe and Africa is available from 2014 (Carrer et al. 2014). AOD observations are now routinely incorporated - into atmospheric models using data assimilation al ig . 2.44. (a) Total 550-nm AOD averages for 2003–14. F gorithms (e.g., Zhang et al. 2008; Benedetti et al. 2009; (b) Linear trends from 2003 through 2015 of total AOD (AOD unit per year). Only trends that are Inness et al. 2015b) to combine them with short-term statistically significant at the 95% level of confidence forecasts. Such observationally constrained models are shown. can be used to build a reanalysis of atmospheric - composition. Reanalyses can, to a large extent, be con sidered a good proxy for observed conditions. They basin, equatorial Africa, and Indonesia are caused provide whole atmosphere coverage and the ability to provide variables not routinely observed, such as by seasonal biomass burning. The linear trends high - light long-term decreases in anthropogenic aerosols the AOD of different aerosol types (e.g., dust, sea salt, and carbonaceous). However, their limitations should over the eastern United States, Europe, and parts of be kept in mind. To accommodate limited computing southern China, while increases occurred over most of the Indian subcontinent. The area of decreasing resources, models usually simplify aerosol processes - trends in the southern Amazon basin is associated and may not take into account all of the aerosol spe with reduced deforestation there (Chen et al. 2013). cies and/or their interaction. This means that the The decreasing trends over the northern Sahara and atmospheric composition reanalysis aerosol products western Mediterranean indicate lower frequencies or usually do not capture all of the observed variability and complexity of aerosol fields. Assessing the relative intensities of dust episodes in these regions. Though many positive trends over the Southern Hemisphere weight of observations and model values in the data assimilation scheme of such systems is not trivial; oceans are not statistically significant, those that are this can also lead to uncertainties (Inness et al. 2013). - could be an artefact of the MODIS Collection 5 obser vations used in the reanalysis. Time series of globally- trato averaged total AOD during 2003–15 (Fig. 2.45) show ozone — M. Weber, W. Steinbrecht, C. Roth, 4) S Spheric M. Coldewey-Egbers, D. Degenstein, Y. E. Fioletov, S. M. Frith, strong seasonality, typically with yearly maxima in L. Froidevaux, J. de Laat, C. S. Long, D. Loyola, and J. D. Wild March–April and August–September driven mainly Total ozone columns in 2015 were close to the by dust episodes and biomass burning in Africa and - South America. 1998–2008 average for most of the globe, except in ex tended regions at high latitudes in both hemispheres, | S49 AUGUST 2016 STATE OF THE CLIMATE IN 2015

70 where ozone columns were largely below average (Plate 2.1q). The strong negative anomalies at high - Southern Hemisphere latitudes ref lect the large Ant arctic ozone hole observed in September–December, whose size reached maximum values that were near the all-time record high (see section 6h). In Fig. 2.46 the total ozone annual means from different data sources are shown for 1970–2015 in various zonal bands: near-global (60°S–60°N), mid - latitudes in both hemispheres (35°–60°), and the inner tropics (20°S–20°N). Also shown are the polar time series in March (Northern Hemisphere, 60°–90°N) and October (Southern Hemisphere, 60°–90°S), the months when polar ozone losses are largest in each hemisphere. Poleward of 60°S, a record low October mean was observed (Fig. 2.46e). Weaker-than-usual dynamical wave activity in the Southern Hemisphere winter diminished transport from the tropics, reduc - ing ozone at Southern Hemisphere midlatitudes and in the collar region of the polar vortex, and permitting a very stable and cold polar vortex. The high vortex stability and low temperatures resulted in larger-than- usual polar ozone losses and a near-record ozone hole in terms of size and persistence. Ozone annual mean columns at mid- to polar latitudes (35°–90°) in each hemisphere are largely determined by winter/spring ozone levels. These vary considerably with changes in stratospheric meteorological conditions (e.g., Steinbrecht et al. 2011; Weber et al. 2011; Kuttippurath et al. 2015). The year-to-year variability seen in all ozone time series also ref lects quasi-biennial oscil - lation (QBO)-related variations extending from the tropics into the extratropics (Randel and Wu 1996; Strahan et al. 2015). It is clear that the Montreal Protocol and its Amendments have been successful in stopping the multidecadal decline in stratospheric ozone by the late 1990s (WMO 2011). However, at most latitudes, it has not yet been possible to determine a statisti - cally significant increase in total column ozone or lower stratosphere ozone because the expected small increases are masked by large interannual variability (e.g., Chehade et al. 2014; Coldewey-Egbers et al. 2014; Frith et al. 2014; Kuttippurath et al. 2015; Nair F ig . 2.46. Time series of annual mean total ozone in (a–d) four zonal bands and (e) polar (60°–90°) total ozone in Mar (Northern Hemisphere) and Oct (Southern Hemisphere). Data are from WOUDC ground- based measurements combining Brewer, Dobson, SAOZ, and filter spectrometer data (red: Fioletov et al . 2002, 2008); the BUV/SBUV/SBUV2 V8.6 merged products from NASA (MOD V8.6, dark blue, Chiou et al. 2014; Frith et al. 2014) and NOAA (light blue, Wild et al. 2012); the GOME/SCIAMACHY/ GOME-2 products GSG from University of Bremen (dark green, Weber et al. 2011) and GTO from ESA/ DLR (light green, Coldewey-Egbers et al. 2015); and the MSR V2 assimilated dataset extended with GOME-2 data (van der A et al. 2015). WOUDC values for 2015 are preliminary because not all ground station data were available in early 2016. | S50 AUGUST 2016

71 et al. 2015; de Laat et al. 2015). The 2015 total ozone columns in Fig. 2.46 are consistent with this overall picture and lie within the expected usual variations. In the tropics, no discernible long-term trends in total column ozone have been observed for the entire 1970–2015 period (see Fig. 2.46). Ozone trends in the tropical lower stratosphere are mainly determined by tropical upwelling (related to changes in sea surface temperature). In a changing climate it is expected that tropical upwelling will increase and thus ozone will continue to decline (Zubov et al. 2013; WMO 2014). However, there is some evidence of a hiatus in tropical upwelling trends and corresponding lower stratospheric ozone trends during the last decade (Aschmann et al. 2014). Because tropospheric ozone contributes to the total ozone columns, trends in total ozone, despite major contributions from the lower stratosphere, may differ from trends in lower stratospheric ozone (Shepherd et al. 2014). The most recent ozone assessment (WMO 2014) and studies (Nair et al. 2015; Harris et al. 2015) indicate that the clearest signs of significant ozone increases should occur in the upper stratosphere −1 at ~2 hPa or 40 km; see Fig. 2.47). (2%–4% decade However, there still are uncertainties associated . 2.47. Annual mean ozone anomalies at 2 hPa F ig (~40 km, upper stratosphere) in three zonal bands. with the various available data records and with the Data are from the merged SAGE II/OSIRIS (Bourassa proper interpretation of statistical approaches used et al. 2014) and GOZCARDS (Froidevaux et al. to derive and attribute trends (e.g., Nair et al. 2015; 2015) records and from the BUV/SBUV/SBUV2 v8.6 Kuttippurath et al. 2015; Harris et al. 2015). This is merged products from NASA (McPeters et al. 2013; ref lected in the updated Stratospheric Aerosol and Frith et al. 2014) and NOAA (Wild et al. 2012) (base Gas Experiment (SAGE)–Optical Spectrograph and period: 1998–2008). The orange curves represent Infrared Imager System (OSIRIS) record, which now EESC (effective equivalent stratospheric chlorine), better accounts for tangent altitude drifts, and in the scaled to reflect the expected ozone variation due updated Solar Backscatter Ultraviolet (SBUV) data to stratospheric halogens. Data points for 2015 are preliminary, because SAGE-OSIRIS data were not from NOAA with improved inter-satellite adjust - yet available after July 2015, and adjusted SBUV2 ments. Overall, the 2015 annual means in Fig. 2.47 v8.0 data are used after July 2015 instead of v8.6 data, - support the claim of recent increases in upper strato which are not available in early 2016. - spheric, extra-polar ozone. These suggest the Mon tropical lowermost SWV increased to near-record treal Protocol has successfully turned the previous levels, especially over the tropical western Pacific and downward trend in ozone into an ozone increase, at Indian Ocean regions. The deep tropical-averaged least in the upper stratosphere. (15°S–15°N) SWV anomaly at 82 hPa, based on data 5) S t r at o S p h e r i c from the Aura Microwave Limb Sounder (MLS), was wat e r va p o r — S. M. Davis, K. H. Rosenlof, D. F. Hurst, and H. B. Selkirk +0.7 ppm (+17%) in November and +0.9 ppm (+24%) in December. These values are in stark contrast to Variations in stratospheric water vapor (SWV) over interannual-to-decadal timescales have the the weak negative (dry) tropical average anomalies of about −0.2 ppm (−6%) in November–December potential to affect stratospheric ozone (Dvortsov 2014 (Figs. 2.48, 2.49). Since the MLS record began in and Solomon 2001) and surface climate (Solomon August 2004, the November–December 2015 anoma - et al. 2010). Throughout the first 10 months of 2015, lies at 82 hPa are surpassed only by +0.9 ppm (+25%) water vapor mixing ratios in the tropical lowermost −1 stratosphere were within 10% (0.4 ppm, μmol mol deep tropical anomalies in February–March 2011. ) of the previous decade’s average. Then, starting The +0.7 ppm (+19%) average deep tropical anomaly in November and continuing through December, - at 100 hPa in November–December 2015 is the high | S51 AUGUST 2016 STATE OF THE CLIMATE IN 2015

72 ig F . 2.49. Global stratospheric water vapor anomalies 1 − ) centered on 82 hPa in (a) Dec 2014 and (μmol mol Microwave Limb Sounder. Aura (b) Dec 2015 from the variations in the TTL. The dramatic increase in tropi - ig F . 2.48. (a) Vertical profiles of MLS tropical (15°S– cal lower SWV at the end of 2015 is consistent with 1 − 15°N) water vapor anomalies (μmol mol ) and (b) the observed ~1°C increase in tropical CPTs over the latitudinal distributions of MLS water vapor anoma - same period (Fig. 2.50c). 1 − lies (μmol mol ) at 82 hPa. Anomalies are differences - Interannual variations in CPTs are potentially re from the 2004–15 mean water vapor mixing ratios lated to the changing phases of the El Niño–Southern for each month. - Oscillation (ENSO) and the stratospheric quasi-bien est ever observed by MLS at that pressure level. The nial oscillation (QBO). In October, the QBO phase change in tropical lower SWV from December 2014 transitioned from easterly (cold) to westerly (warm) - to December 2015 was +1.1 ppm, ~50% of the typi and persisted in the westerly phase through the end of 2015 (see sections 2b3, 2e3). The evolution towards cal seasonal mixing ratio amplitude at 82 hPa in the tropics. Strong water vapor increases in the tropical a warmer TTL and wetter tropical lower stratosphere at the end of 2015 is consistent with this reversal of - lower stratosphere at the end of 2015 were also ob served at Hilo, Hawaii (20°N), and San José, Costa the QBO phase. Regionally, the enhancement of SWV Rica (10°N), by balloonborne frost point hygrometers in the tropical western Pacific and Indian Ocean (Figs. 2.50b,c). regions is consistent with the adiabatic response of the TTL to reduced convection in this region as a The seasonal variability of water vapor in the tropical lower stratosphere is predominantly con - result of the El Niño conditions present during 2015. Other factors such as variations in the strength of the trolled by the annual cycle of cold-point temperatures Brewer–Dobson circulation can also impact SWV (CPTs) in the tropical tropopause layer (TTL). These minimum temperatures determine the amounts of anomalies on an interannual timescale. However, given the potential interrelationships between ENSO, water vapor that remain as moist tropospheric air masses are freeze-dried during their slow ascent into QBO, and the Brewer–Dobson circulation, a rigorous attribution of the positive SWV anomalies present at the stratosphere. Seasonal-to-interannual variations in tropical lower SWV are highly correlated with CPT the end of 2015 is not possible. | S52 AUGUST 2016

73 the 2014 Antarctic vortex being anomalously weak, warm, and less dehydrated (Davis et al. 2015; see sec - tions 2b3 and 6h). In general, Southern Hemisphere midlatitude SWV can vary interannually with the degree of seasonal dehydration within the Antarctic vortex and the strength of the poleward transport of dehydrated air masses (Fig. 2.48b). Indeed, the 2015 Antarctic vortex was particularly strong (see section 6h), as evidenced by the appearance of a −0.5 ppm anomaly in the high southern latitudes near the end of 2015 (Fig. 2.48b). — Spheric ropo 6) t J. R. Ziemke and O. R. Cooper ozone - Two of the most important reasons to monitor tro pospheric ozone are that it is a surface pollutant with harmful biological effects and is a greenhouse gas that affects long-term climate change. Tropospheric ozone is also the primary source of the hydroxyl radical (OH), the main oxidizing agent for pollutants in the troposphere. Sources of tropospheric ozone include transport from the stratosphere, photochemical - production from lightning NO , and photochemi x cal production from precursor gases emitted by the combustion of fossil fuels, biofuels, and biomass (e.g., Sauvage et al. 2007; Martin et al. 2007; Leung et al. 2007; Murray et al. 2013; Hess and Zbinden 2013; Young et al. 2013). The variability of tropospheric ozone, from urban to hemispheric scales, is driven by a combination of . 2.50. Lower stratospheric water vapor anomalies F ig photochemical ozone production and atmospheric 1 − ) at 82 hPa over four balloonborne frost (μmol mol transport. Tropospheric ozone production varies (a) – (d) show the point (FP) hygrometer stations. because its precursor gases and sunlight are vari - anomalies of individual FP soundings (black) and of able. Transport phenomena that drive large-scale monthly zonal averages of MLS retrievals in the 5° latitude band containing the FP station (red). High- variability include ENSO (e.g., Chandra et al. 1998, - resolution FP vertical profile data were averaged be 2009; Sudo and Takahashi 2001; Doherty et al. 2006; tween 70 and 100 hPa to emulate the MLS averaging Koumoutsaris et al. 2008; Voulgarakis et al. 2011) kernel for 82 hPa. Each MLS monthly zonal mean was and the Madden–Julian oscillation (MJO: Sun et al. determined from 2000 to 3000 profiles. Tropical cold- - 2014). Small- to large-scale tropospheric ozone vari point temperature anomalies based on the MERRA re - ability also occurs over shorter periods, including analysis [(c), blue curve] are generally well correlated weekly baroclinic timescales (e.g., Ziemke et al. 2015, with the tropical lower SWV anomalies. and references therein), and finer scale airstream transport on the order of hours to days. Changes in Anomalies in tropical lower SWV propagate from the tropics to the midlatitudes of both hemispheres, tropospheric ozone at hemispheric and global scales include decadal trends (e.g., Hess and Zbinden 2013; as is visually demonstrated by the many “C”-shaped contours in Fig. 2.48b. The late 2015 wet anomaly Cooper et al. 2014; Lin et al. 2014; Parrish et al. 2014). in tropical lower SWV (Figs. 2.48b, 2.50c) was just Global maps of annual means and anomalies of tropospheric column ozone from the satellite-based starting to reach the midlatitudes of each hemisphere Ozone Monitoring Instrument (OMI) and MLS for at the end of 2015. 2015 are shown in Fig. 2.51 and Plate 2.1u, respective - During 2015, SWV anomalies over Lauder, New ly. As in previous reports, OMI/MLS ozone trends are Zealand, were close to zero or slightly positive calculated only for latitudes 60°S–60°N where there is (Fig. 2.50d). These are consistent with the poleward - full annual coverage by OMI. In 2015, as for the last transport of weak dry tropical SWV anomalies pres ent at the end of 2014 and early 2015 (Fig. 2.49a), and decade, annual average tropospheric column ozone | S53 AUGUST 2016 STATE OF THE CLIMATE IN 2015

74 F . 2.51. Average OMI/MLS tropospheric ozone col - ig - F ig . 2.52. Monthly averages of OMI/MLS tropo umn ozone for 2015. Data poleward of ±60° are not spheric ozone burdens (Tg) from Oct 2004 through shown due to the inability of OMI to measure ozone Dec 2015. The top curve (black) shows 60°S–60°N during polar night. monthly averages with 12-month running means. amounts in the Northern Hemisphere exceeded those The bottom two curves show monthly averages and running means for the Northern Hemisphere (red) in the Southern Hemisphere. Some basic features and Southern Hemisphere (blue). Slopes of linear fits - of tropospheric column ozone include strong topo -1 ( Tg y r ) of all three curves are also listed along with graphical effects, such as greatly reduced amounts statistical uncertainties. σ their ±2 over the Tibetan Plateau and the western U.S. Rocky −1 yr Mountain region, with much larger amounts east and . The combined OMI/MLS record now exceeds 11 years and the measured increases are becoming west of these regions over both land and ocean. The greatest annual mean tropospheric column values more indicative of true long-term trends, building on similar findings from previous reports. were observed over the Mediterranean–South Asian Cooper and Ziemke (2013) reported surface ozone region and from eastern China eastward toward North America. In the tropics, the west-to-east zonal increasing since 1990 over eastern Asia and the west - ern United States, but decreasing over the eastern wave-1 pattern (Fishman et al. 1990) is evident, with United States, using measurements by ground- and high values over the Atlantic and low values over the Pacific. An extended band of high ozone was present satellite-based instruments. Cooper and Ziemke (2014) presented a time series of near-global (60°S– at 30°S, with the greatest amounts between southern Africa and Australia. Zonally-averaged tropospheric 60°N) tropospheric burdens determined from satellite column averages and their 95% confidence intervals measurements that indicated a statistically significant for 2015 were 30.7 ± 2.2 DU for 60°S–60°N, 32.1 ± increase over 2005–13 and Cooper and Ziemke (2015) 2.6 DU for 0°–60°N, and 29.4 ± 1.9 DU for 0°–60°S. showed that the increase in global tropospheric ozone continued through 2014. - These column averages convert to tropospheric bur dens of 291.2 ± 20.9, 152.1 ± 12.3, and 139.1 ± 9.0 Tg, State of the Climate For the past two years, the 12 tropospheric ozone summary was based upon only g), respectively. For comparison, the tro - (Tg = 10 pospheric column averages for 2005–15 for the three the OMI/MLS satellite measurements (Ziemke et al. 2006) due to insufficient updated analyses of regions were 29.5 ± 2.1, 30.7 ± 2.5, and 28.2 ± 2.2 DU (279.0 ± 19.9, 145.4 ± 11.8, and 133.6 ± 10.4 Tg). the ground-based measurement network data since The 2015 average tropospheric ozone burdens for 2012. Updates of the surface ozone data and trends have continued to be infrequent during 2015, so once each hemisphere and the globe were greater than those in 2014, and 12-month running averages of each again only the OMI/MLS satellite data are used. One significant change from previous reports is the use of show steady increases since October 2004 (Fig. 2.52). −1 Linear trends (in Tg yr new MLS version 4.2 ozone retrievals. A new activity ) with their ± 2σ statistical uncertainties are also given. The increasing trends in of the International Global Atmospheric Chemistry (IGAC) project began in earnest in 2015 to produce OMI/MLS tropospheric column ozone are statisti - cally significant for both hemispheric means and the a Tropospheric Ozone Assessment Report (TOAR). near-global mean. Relative to the average burdens for The TOAR is expected to be completed by the end of 2016 and will summarize the global distribution 2005–15 the three trends all depict increases of 0.8% | S54 AUGUST 2016

75 and trends of tropospheric ozone through 2014/15 the 2015 Indonesian fire period. This El Niño–related - (depending on the product) using a variety of satel increase in Indonesian fires and CO emissions was lite, surface, ozonesonde, lidar, and aircraft ozone already reported for 2014 (Flemming and Inness 2015) measurements (www.igacproject.org/TOAR). and high fire activity is anticipated for the March– April fire season in 2016. An analysis by Huijnen — J. Flemming and A. Inness et al. (2016) suggests that the 2015 carbon emissions 7) c monoxide arbon Carbon monoxide (CO) is not a greenhouse from the Indonesia fires were the second largest since the extreme El Niño year of 1997, although the 2015 gas, but plays a significant role in determining the emissions were only 25% of those in 1997. abundance of climate forcing gases like methane (CH ), through hydroxyl radical (OH) chemistry, Plate 2.1ac shows the relative 2015 anomalies of the 4 and tropospheric ozone (O ), as a chemical precursor - total column CO (TCCO) from the CAMS interim re 3 (Hartmann et al. 2013). Thus, CO is regarded as an analysis with respect to 2003–15. The strong positive indirect climate forcing agent. Sources of CO include TCCO anomalies were located predominately over the Indonesian region and the eastern Indian Ocean, incomplete fossil fuel and biomass combustion and and other in situ production via the oxidation of CH but the fire emissions increased CO over much of the 4 organic trace gases. Combustion and chemical in tropics. Tropospheric CO mixing ratios between 50° situ sources typically produce similar amounts of and 100°E in the tropics in September and October CO each year. were 50%–100% higher than the CO climatology. New in 2015 is a CAMS-based retrospective Larger-than-usual wildfire activity in North America - during 2015 produced >10% anomalies in June–Au analysis of CO for the period 2003–15 based on total column CO retrievals from the Measurements of gust and led to a positive anomaly in total column CO for the year. The CO anomaly of −10% over the Pollution in the Troposphere (MOPITT) instrument Amazon basin ref lects a decadal decrease in fires in (Deeter et al. 2013, Version 5). This dataset is part of - that region, but the 2015 anomaly was not as strongly the CAMS interim reanalysis of atmospheric compo negative as in the two previous years. sition, an extended and temporally more consistent The high global CO burden in 2015 occurred dataset than the previous Monitoring Atmospheric against a 12-year backdrop of a decreasing global Composition and Climate (MACC) reanalysis (Inness CO burden. Figure 2.53 shows the time series of et al. 2013). The MACC has been used in previous State of the Climate assessments of CO and aerosols. monthly mean global CO burdens since 2003. A −1 MOPITT retrievals between 65°N and 65°S were decreasing linear trend of −0.86 ± 0.23% yr is evident, yet the monthly averaged global burdens for assimilated into the European Centre for Medium- October–December 2015 are the highest values in the Range Weather Forecasts (ECMWF) Integrated entire record. Worden et al. (2013) estimate trends of Forecasting System (IFS) that has been extended to −1 simulate atmospheric chemistry (Flemming et al. for both the globe and Northern Hemisphere −1% y r 2015). The assimilation technique is documented in over the last decade by studying observations from Inness et al. (2015b). The anthropogenic emissions for the assimilating model were taken from the MACCity inventory (Granier et al. 2011) that accounts for projected emission trends. Biomass burning emis - sions were taken from the Global Fire Assimilation System (v1.2, Kaiser et al. 2012). The global three- dimensional CO distribution from the CAMS interim reanalysis is used here to assess the anomalies in CO total columns for 2015. The global CO burden in 2015 was significantly in - creased by the intensive El Niño-induced wildfires in - Indonesia from mid-August to mid-November (Side bar 2.2). Annual wildfire emissions from this region - contributed 31% (140 Tg) of the global wildfire emis sions in 2015, whereas for 2003–14 the contributions ranged from 5% to 20%. The highest total (biomass burning and anthropogenic) monthly CO emissions F ig . 2.53. Time series of monthly global CO burdens (Tg) from the CAMS interim reanalysis. since 2003 were injected into the atmosphere during | S55 AUGUST 2016 STATE OF THE CLIMATE IN 2015

76 SIDEBAR 2.2: ATMOSPHERIC COMPOSITION CHANGES DUE TO THE EXTREME 2015 INDONESIAN FIRE SEASON TRIGGERED BY EL NIÑO —A. BENEDETTI, F. DI GIUSEPPE, J. FLEMMING, A. INNESS, M. PARRINGTON, S. RÉMY, AND J. R. ZIEMKE One of the most extreme events of 2015 was the ex - To this end, we use the data assimilation system of tensive burning of peat throughout large parts of Indonesia. the Copernicus Atmosphere Monitoring Service (CAMS) As a common practice in Indonesia, fires are set during developed at the ECMWF since 2005. The interim CAMS the dry season (July–October) to clear land and remove reanalysis is an improved version of the previous MACC agricultural residues. During intense dry seasons these reanalysis (Inness et al. 2013) and is updated in quasi near– fires can penetrate into degraded subsurface peat soil with real time. Observational datasets used, among others, are - enhanced flammability. They are extremely difficult to ex the NASA MODIS Aerosol Optical Depth Collection 5 tinguish and can burn continuously until the return of the product (Remer et al. 2005) and the MOPITT V5 total monsoon rains, usually in late October or early November. column carbon monoxide (CO) retrievals. A reanalysis In 2015, the annual fires were more widespread and intense dataset provides a dynamically consistent 3D estimate of than those that have typically occurred in Kalimantan since the climate state at each time step and can be considered a the 1980s and in Sumatra since at least the 1960s (Field good proxy for atmospheric conditions, including variables et al. 2009). The strength and prevalence of these fires that are not directly observed. Here, 2015 anomalies of are strongly influenced by large-scale climate patterns like CO and carbonaceous aerosols are determined from the El Niño (Field et al. 2004; van der Werf 2008). Research 2003–15 CAMS reanalysis, while the ozone anomalies are started after the strong 1997/98 El Niño, which induced a 2005–14 ozone records from NASA’s Ozone based on the - severe fire/haze disaster in Indonesia, has provided a reli Monitoring Instrument (OMI) and Microwave Limb Sounder able understanding of how much fire and haze may occur (MLS) (Ziemke et al. 2006). for a given drought strength (Usup et al. 2004; Field et al. Realistic biomass burning emissions estimates, provided 2009). Despite this predictive capability, the 2015 fires in by the Global Fire Assimilation System (GFAS; Kaiser et Indonesia still escalated to an environmental and public al. 2012; Di Giuseppe et al. 2016, manuscript submitted health catastrophe (Thielen et al. 2015; Inness et al. 2015; to J. Geophys. Res. Atmos. ), are an important input to the Field et al. 2015, manuscript submitted to Proc. Natl. Acad. CAMS system. In the GFAS, the fire radiative power (FRP) Sci. USA ). measured by the MODIS sensors on the and Terra Aqua The 2015 Indonesia fire season began in August, and satellites is converted into emissions of 44 constituents by September much of Sumatra, Kalimantan, Singapore, using the regression coefficients of Wooster et al. (2003). and parts of Malaysia and Thailand were covered in thick - The FRP observations accumulated over the period Au smoke, affecting the respiratory health of millions of people. gust–October 2015 (Fig. SB2.3) provide an overview of Visibility was also reduced to less than 10% of normal over the extent and severity of the 2015 Indonesian fire season. large parts of the region could not be seen Borneo, and Fire emissions in Indonesia during August–October from space, as was documented for previous fire events were consistently and extraordinarily strong, as clearly - in that region (Marlier et al. 2013; Wang et al. 2004). Pre shown by the number of days in 2015 when daily emissions liminary estimates suggest that greenhouse gas emissions of CO and biomass burning aerosols [black carbon (BC), equivalent) exceeded Japan’s 2013 from the burning (in CO 2 emissions from fossil fuel combustion (Van der Werf 2015). Even after the worst of the 2015 Indonesian fires were no longer burning, the remaining pollution stretched halfway around the globe. Ongoing research into the socioeconomic drivers of the fires is beginning to identify the responsibilities of the landholders and the need for political action in regulating the agricultural practices in the region (Tacconi 2003). While finding the socioeconomic causes of this event is beyond the scope of this work, we can utilize analytical results from ob - servations and reanalyses of atmospheric composition over − 2 . SB2.3. Fire radiative power (W m - ) accumu ig F Indonesia to provide an assessment of the current monitoring lated over Indonesia during the 2015 fire season capabilities of observational and modeling systems. (Aug– Oct). | S56 AUGUST 2016

77 − − 1 1 ) and OM+BC aerosols (kt day F ig . SB2.4. (a) Daily Indonesian fire emissions in 2015 of CO (Mt day ). Red bars show the days in 2015 with emissions greater than the previous (2003–14) maximum emission estimate − 1 for that day. (b) Annual fire emissions of CO and OM+BC aerosols (Mt yr ) from Indonesia indicating their scale relative to the 2015 total anthropogenic CO emissions from the United States (red line) and Europe (blue line) from the MACCity emissions inventory. GFASv1.2 emissions of CO and OM+BC from biomass burning are directly proportional. and organic matter (OM)] exceeded the maximum daily The CAMS reanalysis is a valid tool for monitoring the emissions during the same days in 2003–14 (Fig. SB2.4a). evolution of large-scale pollution events in quasi near–real Total annual fire emissions over Indonesia (10°S–5°N, time and providing useful information at the onset of a 60°–180°E) computed by the GFASv1.2 system for CO and - pollution-related crisis. Because El Niño is highly predict BC+OM are substantially greater for El Niño years 2006, able on a seasonal timescale and Indonesian fires are known 2009, and 2015 (Fig. SB2.4b). For perspective, CO emissions - to assume catastrophic proportions during exceptionally in from the Indonesian fires for 2015 were approximately tense El Niño years, further development of CAMS towards three times the 2015 total anthropogenic emissions from integrating a seasonal prediction system with fire risk and the continental United States (25°–50°N, 70°–130°W) and air quality forecasts would provide comprehensive informa - Europe (30°–70°N, 10°W–45°E). tion for early warnings and planning of mitigation actions. Inness et al. (2015a) utilized reanalysis data to investigate connections between El Niño/La Niña and atmospheric composition fields such as ozone, CO, and aerosols. They concluded that anomalies of CO and biomass burning aerosols depend mainly on local emissions. Hence, their strong positive anomalies over Indonesia during August– October 2015 (Figs. SB2.5a,b) were a direct consequence of the widespread fires in that region. Anomalies in ozone ; Fig. SB2.5c), also produced by these fires, were further (O 3 affected by El Niño–induced dynamical changes that altered from the stratosphere and the downward transport of O 3 photolysis rates. Total column CO anomalies modified O 3 that reached 500% in the core of the fire region were remarkable (Fig. SB2.5a), but even more striking were the extremely large anomalies (~2000%) in total AOD at 550 ig - F . SB2.5. Anomalies (%) averaged over the 2015 Indone nm for biomass burning (OM+BC) aerosols that covered sian fire season (Aug–Oct) from the CAMS reanalysis of (a) large areas of the Indian and western Pacific Oceans (Fig. total column CO and (b) biomass burning AOD at 550 nm. SB2.5b). For tropospheric ozone (Fig. SB2.5c), the positive (c) Mean OMI/MLS tropospheric column ozone anomalies anomalies over Indonesia were a more modest 30%–40%. for Aug–Oct 2015, with contours drawn every 5%. | S57 AUGUST 2016 STATE OF THE CLIMATE IN 2015

78 different satellite-based instruments. The spatial - distribution of CO trends from the CAMS reanaly sis (Fig. 2.54) shows significant decreasing trends −1 of −1.0% to −1.5% year in most regions north of −1 40°N, up to −3.0% year over the Amazon basin and −1 its outf low regions, −0.5% to −1.0% year for most of the rest of the globe, and almost no trends over India, eastern China, and a large region surrounding Indonesia. Diminished anthropogenic emissions in - North America and Europe as well as strong reduc tions in fire emissions over South America are the main causes for the decreasing global CO burden during 2003–15. 1 − ) in total column CO F ig . 2.54. Linear trends (% yr from the CAMS interim reanalysis for the period h. and Surface Properties L All trends are statistically significant at 2003 –15. B. Pinty and L and Surface aLbedo dynamic S — 1) the 95% level of confidence except for those inside red contours. N. Gobron The land surface albedo is the fraction of solar tral Africa and Queensland, Australia. These are radiation scattered backward by land surfaces. In generally associated with less favorable vegetation the presence of vegetation, surface albedo results growing conditions compared with previous years from complex nonlinear radiation transfer processes determining the amount of radiation that is scattered (section 2h2), although contamination of the albedo by the vegetation and its background, transmitted retrievals by clouds and aerosol load, especially in - Indonesia (Sidebar 2.2), may also have induced some through the vegetation layer, or absorbed by the veg artifacts. The majority of snow-free regions exhibit etation layer and its background (Pinty 2012). noticeable negative anomalies, particularly in the The geographical distribution of normalized visible domain, across Mexico and the southern anomalies in visible and near-infrared surface albedo United States and over the southern regions of South for 2015 calculated with respect to a 2003–15 base - America, Australia, India, and China. The unusu period [for which two MODIS sensors are available ally warm conditions over northern regions such as (Schaaf et al. 2002)] are shown in Plates 2.1z and 2.1aa, respectively. Mid- and high-latitude regions of the western Europe, Turkey, and northwestern Iran may Northern Hemisphere are characterized by both posi have contributed to the observed limited negative - tive and negative anomalies, mainly as a consequence anomalies. A significant fraction of these variations are attributable to vegetation dynamics (Pinty et al. of interannual variations in cover, amount, and dura - 2011a, 2011b) over these regions, which are sensitive tion of snow in winter and spring seasons. The large to stress from ambient conditions and, in particular, negative anomalies over eastern Europe, southern water availability. Although weaker in the near- Sweden, western Russia, Caucasus, southwestern infrared, these negative anomalies sometimes occur Siberia, and northern China are probably associated with a below-average snow cover in winter and early simultaneously in the visible and the near infrared. Generally, the amplitude of both positive and negative spring seasons, due to the occurrence of relatively anomalies changes seasonally. high temperatures in some of these regions. Similarly, negative anomalies over Canada can be related to an Analysis of the zonally-averaged albedo anomalies in the visible and near-infrared (Fig. 2.55) spectral unusually small snow cover extent (section 2c2). The domains indicates considerable interannual varia - - amplitude of these negative changes can reach (or lo cally exceed) ±30% in relative units and is larger in tions related to the occurrence of snow events in winter and spring at mid- and high latitudes but also the visible than the near-infrared domain, although - in vegetation conditions during the spring and sum with the same sign. By contrast, the average February snow cover extent across the eastern United States mer periods. Strong negative anomalies are noticeable between 20° and 45°S, featuring a deviation from resulted in a positive annual anomaly. A few snow-free regions show positive anomalies, average conditions mainly over the southern regions especially in the visible domain. In the equatorial in Latin America, Africa, and Australia. Limited but regions, these are well marked over Indonesia and, consistent positive anomalies are discernible across equatorial regions in 2015. - with more limited amplitude, over Amazonia, cen | S58 AUGUST 2016

79 F Globally-averaged MODIS White Sky . 2.56. ig - broadband surface albedo (NASA) normalized anom alies with a 12-month running mean in the (a) visible and (b) near-infrared domain relative to a 2003–15 base period at the global scale (black), Northern Hemisphere (blue), and Southern Hemisphere (red). sorbed Photosynthetically Active Radiation (FAPAR) from three different sensors: SeaWiFS (NASA), MERIS (ESA), and MODIS (NASA) (Gobron et al. 2010; Pinty et al. 2011b; Gobron and Robustelli 2013). A large number of regions experienced seasonal precipitation deficits in 2015 (sections 2d4 and 2d9), especially in the Southern Hemisphere, along with much higher-than-average annual temperatures across most of the globe (section 2b1). This translates into a large variation in vegetated surface conditions. - The largest annual negative anomalies (not favor able for vegetation) occurred over the high northern latitudes from Alaska to Sweden and Norway, and - also over the equatorial belt from central and north F ig Zonal means of the MODIS White Sky . 2.55. eastern Brazil, central Africa, and Indonesia. To a broadband surface albedo (NASA) normalized anom - lesser extent, regions near the Black and Caspian Seas alies in the (a) visible and (b) near-infrared domain were also affected. relative to a 2003–15 base period. The largest positive annual anomalies appeared over Mexico, the southern United States, Minas The 12-month running mean globally averaged Gerais (Brazil), Turkey, and China. Limited positive normalized anomalies (Fig. 2.56) vary within ~±5% anomalies occurred over eastern parts of Europe, (~±3%) in the visible (near-infrared) domain. Antarc - tica is excluded owing to missing data. The year began India, and the Ural region of Russia. with globally averaged negative albedo anomalies and Below-normal precipitation occurred during the second half of the year in Brazil and Indonesia, im - ended with slightly positive anomalies. The trend towards positive anomalies was driven by contribu - pacting the annual anomalies. The anomalies over tions from the Southern Hemisphere. Figure 2.56 southwestern and central Africa were mainly due to also shows analogous interannual and multiannual a warmer-than-normal spring together with below- variations in the visible and near infrared during the normal precipitation. Higher precipitation in spring over Mexico and the 2003–15 base period, with mostly positive anomalies southern United States and in autumn over western at the beginning of this base period. - China contributed to favorable conditions for vegeta vegetation tion health and growth, as was the case in 2014. Over L Stria erre 2) t dynamic S — N. Gobron Turkey, the positive anomalies were mainly correlated Analysis of the 18-year record shows that large with a slight excess of rainfall and higher tempera - - spatiotemporal deviations in vegetation dynamics oc tures compared to previous years. curred at regional and continental scales during 2015 Zonally averaged monthly mean anomalies (Plate 2.1y). The state of vegetation is examined by - merging 1998–2015 estimates of the Fraction of Ab (Fig. 2.57) illustrate the differences between the two | S59 AUGUST 2016 STATE OF THE CLIMATE IN 2015

80 . 2.58. Average monthly FAPAR anomalies with a ig F 12-month running mean (base period: 1998–2015) at the global scale (black), Northern Hemisphere (blue), and Southern Hemisphere (red). savannas and the use of fire to clear forest and make way for agricultural land. In temperate and boreal regions, fires tend to occur less frequently and can be either human or lightning ignited. Since the late 1990s, fire occurrence and the as - F ig . 2.57. Time series of monthly zonal anomalies (base sociated burned area has been routinely detected - period: 1998–2015) of the Fraction of Absorbed Photo by satellites. The Global Fire Assimilation System synthetic Radiation (FAPAR) from SeaWiFS, MERRIS, (GFAS) builds on active fire detections and their as - and MODIS sensors. Gray areas indicate missing data. sociated fire radiative power to estimate emissions in near–real time (Kaiser et al. 2012). GFAS is calibrated hemispheres, with persistent negative anomalies to partly match the Global Fire Emissions Database (GFED), which estimates emissions based on burned over the Southern Hemisphere during all seasons from around 2002 to 2009. A succession of positive area and fuel consumption which have a much longer latency (van der Werf et al. 2010). The combined use and negative anomalies, suggesting a seasonal cycle, of GFAS (2001–15) and GFED (1997–2014) indicates are depicted between 10°S and 30°S since 2010. In −1 contrast, strong positive anomalies are observed that fire emissions were on average 2.1 Pg C year 15 over regions between 20° and 60°N since 2012; these g), with substantial interannual variability, (Pg = 10 the latter mostly stemming from tropical deforestation persisted during 2015. Larger seasonal negative anomalies are seen over mid- and high latitudes in the zones and the boreal region where fire activity varies Northern Hemisphere since mid-2012. A strong nega - more from year to year than in most savanna areas. In 2015, total global fire emissions were somewhat tive anomaly is depicted in 2015 around the equatorial above average (+4%, see Table 2.8). By far, the largest regions, likely inf luenced by low precipitation and - anomaly was found in tropical Asia, where emis severe fires over Indonesia (Sidebar 2.2); it appeared to extend into the entire Southern Hemisphere during sions were almost three times as high as the 2001–14 the last quarter of 2015. average (Plate 2.1ab, Fig. 2.59). As in 2014, North The monthly mean averaged anomalies smoothed America also saw higher-than-average emissions (see sections 7b1 and 7b2). These positive anomalies using a 12-month running average (Fig. 2.58) indicate were partially compensated for on a global scale by that 2015 shows a relatively unhealthy state of the veg - below-average emissions from South America and etation over the Southern Hemisphere compared with Northern Hemisphere Africa. The former is related a more healthy state over the Northern Hemisphere. to a downward trend in deforestation during the last 3) b decade (Chen et al. 2013), although fire emissions burning SS — J. W. Kaiser, G. R. van der Werf, and ioma A. Heil in 2015 were somewhat higher than in the previous Climate and weather provide boundary conditions two years. The latter is in line with an ongoing trend, for biomass burning or landscape fires to occur; in possibly due to expansion of cropland (Andela and van der Werf 2014) return these fires inf luence climate and weather The exceptional fire season in tropical Asia is by emitting greenhouse gases and aerosols and by apparent in the pronounced aerosol and carbon modifying surface properties such as albedo and monoxide (CO) anomalies (sections 2g3, 2g7; Sidebar roughness. Generally, most fires occur in the tropics 2.2). The fires were most active during September and where they are often started by humans to manage the landscape. This includes frequent burning in many October (see Fig. 2.60) and located predominantly in | S60 AUGUST 2016

81 2.8. Annual continental-scale biomass burning budgets in terms of carbon emission (Tg C able t –1 yr ). 2001–02 from GFASv1.0 (Remy and Kaiser 2014), 2003–15 from GFASv1.3. 2015 Time Period 14 2001 − Quantity Anomaly Mean Value Value –1 Tg C yr (%) (Range) 86 (4%) 2201 Global 2116 (18 0 3 – 2 371) 30°–57°N North America 117 (50 –171) 172 +55 (+47%) 170°W–30°W 0°–30°N Central America 71 (54 –102) 72 +1 (+1%) 170°W–30°W 0°–60°S 314 (170 – 477 ) 246 S. Hem. America 22%) − 68 ( − 170°W–30°W 30°–75°N Europe and Mediterranean 39 (26–60) 36 9%) 3 ( − − 30°W–60°E 0°–30°N 405 (337–506) 369 N. Hem. Africa 36 ( − 9%) − 30°W–60°E 0°–35°S 519 (473 – 585) 509 S. Hem. Africa 10 ( − 2%) − 30°W–60°E 30°–75°N 227 (122–449) 202 Northern Asia − − 25 ( 11% ) 60°E–170°W 10°–30°N 129 (83 –173) 116 Southeast Asia − 10%) 13 ( − 60°E–170°W 10°N–10°S 123 (40–240) +217 (+176%) 340 Tropical Asia 60°E–170°W 10°–50°S 172 (58 –296) 140 Australia 18%) − 32 ( − 60°E–170°W Sumatra 65 (17–147 ) 183 +118 (+182%) 41 (8 –93) +58 (+142%) Borneo 99 can burn easily under El Niño-induced drought con Sumatra and Kalimantan (see Table 2.8, Plate 2.1ab, - - Fig. 2.59 and Fig. SB2.3). These regions are most vul ditions. Such peat fires are difficult to extinguish and nerable to ENSO because drainage and deforestation usually last until the onset of the wet season in late have created large areas with degraded peatlands that October or early November. Accordingly, increased emissions were observed during the previous El Niño years of 2004, 2006, and 2009 (Fig. 2.60a). Smoke from open fires in Indonesia has a strong impact on residents and economy (Marlier et al. 2013; Sidebar 2.2). In addition, peat burning represents a net source of CO to the atmosphere because drainage 2 prevents regrowth of peat. During the 2015 fire season of tropical Asia, about 80% of the pyrogenic carbon f lux occurred in peatlands. Both the carbon f lux and its relative peatland contribution were the highest since the MODIS record started in 2001 (Fig. 2.60a). Pinpointing the exact magnitude of emissions - remains challenging. This is largely due to difficul - ties in estimating the burn depth of peat fires, lead ig F . 2.59. Global map of fire activity in 2015 in terms ing to larger-than-average uncertainties in any kind (Source: GFASv1.3.) of actual carbon consumption. | S61 AUGUST 2016 STATE OF THE CLIMATE IN 2015

82 of emission assessment. Instead of fire observations, Huijnen et al. (2016) used satellite-based CO observations of the smoke plume and in situ measurements of the CO emission factors to estimate a carbon f lux of 227 ± 66 Tg C for the most affected subregion of tropical Asia during September and October. The corresponding values for GFASv1.2 and GFASv1.3 are 320 and 250 Tg C, respectively, while preliminary GFED4 estimates are about 400 Tg C (www .globalfiredata.org/updates.html), but this estimate includes the full fire season. Compared to GFASv1.2, GFASv1.3 in - cludes an improved representation of the diurnal variability of cloud cover, which prevents satellite observations of fires, and a higher-resolution peat map based . 2.60. (a) Temporal evolution of fire emissions in tropical ig F on Wetland International (Wahyunto Asia during the Sep–Oct 2015 fire season, compared to the four most active fire seasons since 2003 (5-day smoothed GFASv1. 3 et al. 2003, 2004). While 2015 was the data). The inset shows annual total emissions since 2003 and the highest fire year in the GFAS record in - relative contribution of fire emissions from peat fires, highlight - tropical Asia, scaling the 2015 GFAS re ing the increased relative importance of these fires in high fire cord to GFED based on a common base years. (b) Monthly fire activity in tropical Asia for 1997–2015. period in 2006 indicates that 2015 was The y -axis ranges are adjusted so that GFED4s and GFASv1.3 only about half as strong as the extreme coincide graphically in Oct 2006. year 1997 (Fig. 2.60b). | S62 AUGUST 2016

83 3. G LOBAL OCEANS —G. C. Johnson and A. R. Parsons, in the Atlantic meridional overturning circulation, Eds. as observed over the past decade. Overview— G. C. Johnson In the Indian Ocean, anomalously northwesterly a. - The significant 2015 El Niño included a reduc winds east of Madagascar in 2015 resulted in anoma - lous eastward f low (a diminished westward South tion in Pacific trade winds with anomalous cross- Equatorial Current), consistent with slightly low sea equatorial southerly surface winds in the eastern level and OHC anomalies east of Madagascar, coupled Pacific and a shift in tropical precipitation eastward from the Maritime Continent to a region extending with much higher sea level and OHC anomalies to the north. Surface currents on the equator were from the date line to South America, mostly slightly anomalously westward. Overall sea level and OHC north of the equator, associated with an eastward shift in fresh surface salinities. Sea surface temperatures remained elevated in the Indian Ocean, with a record (SSTs) were anomalously warm in 2015 from the date - high for SST. In the Southern Ocean (see also section 6g), line all the way to South America along the equator, anomalously easterly winds (diminished westerlies) with anomalously low chlorophyll- a owing to sup - at about 40°S in the Indian sector and 50°S in the Pa - pression of nutrient-rich upwelling. Redistribution of warm ocean waters to the surface during El Niño cific sector in 2015 were associated with anomalously high sea level and OHC at the northern edge of the contributed, along with a long-term upward trend, to record high global average SSTs in 2015. Anomalously Antarctic Circumpolar Current, consistent with a southward expansion of the subtropical gyres. eastward currents along the equator and in the North Globally, ocean heat content and sea level both Equatorial Countercurrent continued a pattern from continued to rise, reaching record high values in 2014. These anomalous currents contributed to sea 2015. The ocean rate of uptake of carbon from the level and upper ocean heat content (OHC) falling in atmosphere has risen along with atmospheric CO the western tropical Pacific and rising in the east, 2 concentrations. again building on a 2014 pattern. To summarize in haiku form: Y. Xue, Z.-Z. Hu, A. Kumar, b. ea surface temperatures— S El Niño waxes, V. Banzon, B. Huang, and J. Kennedy - Sea surface temperatures play a key role in regulat warm waters shoal, flow eastward, Earth’s fever rises. ing climate and its variability by modulating air–sea - f luxes and tropical precipitation anomalies. In par - In the North Pacific, anomalously warm SSTs, ticular, slow variations in SST, such as those associ a ated with the El Niño–Southern Oscillation (ENSO), high OHC, high sea level, and low chlorophyll- persisted (as did the warm offshore “Blob”) along the Atlantic multidecadal oscillation (AMO), Pacific west coast of North America in 2015, the second year decadal oscillation (PDO), Indian Ocean dipole of the warm phase of the Pacific decadal oscillation. (IOD), and Atlantic Niño, are sources of predictability In these warm conditions, widespread harmful algal for climate f luctuations on time scales of a season and blooms developed along much of the West Coast. longer (Deser et al. 2010). This summary of global SST variations in 2015 emphasizes the evolutions of North Atlantic SSTs southeast of Greenland were El Niño, the record warming in the tropical Indian even colder in 2015 than the already cold previous - - Ocean, and the persistent warming in the North Pa year, with anomalously low OHC, fresh sea surface sa cific. The 2015 SST anomalies are also placed in the linity (SSS) and subsurface salinity, low chlorophyll- , a and anomalous heat f lux into the ocean. In contrast, context of the historical record since 1950. western North Atlantic subtropical SSTs were anoma To quantify uncertainties in SST estimates, four - lously warm in 2015, with high OHC and sea level SST products are examined: 1) the weekly Optimal Interpolation SST version 2 (OISST; Reynolds et al. along the east coast of North America. Subtropical 2002); 2) the Extended Reconstructed SST version mode water formation rates in the region were weak in 2014 and weaker in 2015, consistent with weaker- 3b (ERSST.v3b; Smith et al. 2008); 3) the Met Office than-normal winds and anomalous heat f lux into the Hadley Centre’s sea ice and SST dataset (HadISST; ocean in their formation region. These signatures Rayner et al. 2003); and 4) the recent update of ERSST, are consistent with a strong positive North Atlantic version 4 (ERSST.v4; Huang et al. 2015). OISST is a satellite-based analysis that uses in situ data for bias ad Oscillation index in 2014 and 2015. The SST pattern - is also associated in climate models with a reduction justments of Advanced Very High Resolution Radiom - eter (AVHRR) data with 1° resolution, available since | S63 AUGUST 2016 STATE OF THE CLIMATE IN 2015

84 November 1981. ERSST.v3b, ERSST.v4, and HadISST values that commenced in 2014. In the Atlantic Ocean, SST was above normal in the Gulf of Mexico are historical analyses beginning in the 19th century, and all apply statistical methods to data from the and along the east coast of North America, and below recent period to extend the SST analysis back in time normal in the subpolar region. In the tropical Indian Ocean, positive SSTA exceeding +0.6°C was observed when in situ observations were sparse. ERSST.v3b and across much of the basin. ERSST.v4 include in situ data only and are produced SSTA tendencies from 2014 to 2015 (Fig. 3.1b) at 2° resolution; both are presented here because v4 is new to this report. HadISST includes both in situ show a substantial warming in the central eastern Pacific and cooling in the western tropical Pacific, measurements and AVHRR SST retrievals from 1982 onward, available at 1° resolution. Here, SST variations ref lecting the transition from a weak central Pacific are quantified as SST anomalies (SSTA), defined as - warming in 2014 to a strong eastern Pacific warm ing in 2015. Compared to the mean SSTA in 2014, departures from the 1981–2010 climatology (www.cpc the positive SSTA extending from Hawaii to Baja .ncep.noaa.gov/products/people/yxue/sstclim). Yearly mean 2015 SSTA (Fig. 3.1a) were charac California was enhanced, while the negative SSTA - in the southeastern subtropical Pacific diminished. terized by a basinwide warming with a maximum There was a warming tendency across the tropical amplitude exceeding +2°C in the equatorial eastern Indian Ocean and a cooling tendency in the subpolar Pacific, ref lecting the dominant inf luences of the 2015 El Niño. Warming was asymmetrical around North Atlantic. the equator, with a second warming center around Boreal winter 2014/15 (December–February; 15°N that extended from west of Hawaii to Baja Fig. 3.2a) was characterized by positive SSTA exceed - California. In the high latitude North Pacific, strong ing +1 standard deviation (STD; Fig. 3.2a, black solid contour) in the western equatorial Pacific, positive positive SSTA in the northeast Pacific around 45°N dubbed “The Blob” emerged around the end of 2013 SSTA exceeding +2.5 STD (Fig. 3.2a, white solid (Bond et al. 2015) and largely persisted in 2014/15. contour) along the west coast of North America, and In 2015, the normalized monthly PDO index had negative SSTA exceeding −1 STD in the southeastern an average value of +1.1 (www.cpc.ncep.noaa.gov subtropical Pacific. By spring 2015 (Fig. 3.2b), the positive SSTA in the western equatorial Pacific built /products/GODAS/), continuing a shift to positive and shifted eastward to near the date line. Positive SSTA emerged in the far eastern equatorial Pacific, while the negative SSTA in the southeastern Pacific diminished. NOAA declared El Niño conditions by March 2015 (see section 4b1). The positive SSTA in the central eastern equatorial Pacific grew rapidly in summer/autumn 2015, and exceeded +2.5°C in September–November (Fig. 3.2c). With the rapid growth of the 2015 El Niño, the positive SSTA near Baja California extended southwestward to Hawaii and strengthened to exceed +2.5 STD over a large area in the northeastern subtropical Pacific in sum - mer/autumn 2015. These conditions were favorable for eastern Pacific hurricane activity (section 4e3). Coinciding with the rapid growth of El Niño, positive SSTA in the tropical Indian Ocean grew to exceed - +2.5 STD in autumn 2015 (Fig. 3.2d). In the high lati tude North Pacific, positive SSTA exceeding +2.5 STD along the west coast of North America persisted most of the year. In the North Atlantic, positive SSTA along the east coast of North America and negative SSTA in the subpolar region also persisted. To provide a historical perspective for regional and global yearly mean SSTA in 2015, three historical . 3.1. (a) Yearly mean OISST anomaly in 2015 (°C, ig F analyses (ERSST.v4, ERSST.v3b, and HadISST) are relative to the 1981–2010 average) and (b) 2015–2014 OISST difference. - compared from 1950 to 2015 and one modern analy | S64 AUGUST 2016

85 the 2000–14 trend. Because of this in - crease, the warming trend in 2000–15 (rising 0.13°C, 0.07°C, 0.08°C, and −1 0.08°C decade i n ER SST.v4 , ER SST.v3b, HadISST, and OISST, respectively) be - came comparable to the warming trend in 1950–99 (rising 0.09°C, 0.07°C, and −1 0.06°C decade i n ER S ST.v4 , ER S ST. v3b, and HadISST, respectively). Com - pared to ERSST.v3b and HadISST, the warming trend in ERSST.v4 was 0.05°– −1 0.06°C decade higher in 2000–15. Three factors contribute to the stronger warming trend in ERSST.v4 relative to other products in the more recent period (Karl et al. 2015; Huang et al. 2015): 1) the correction of buoy data to ship data and an increase in buoy data (which 3.2. Seasonal mean SSTA from OISST (shading, °C, relative . F ig were not included in ERSST.v3b and to the 1981–2010 average) for (a) Dec 2014 to Feb 2015, (b) Mar to OISST); 2) more weight given to more May 2015, (c) Jun to Aug 2015, and (d) Sep to Nov 2015. Black solid accurate buoy data in the reconstruction contours are +1, black dashed – 1, white solid +2.5, and white dashed – 2.5 normalized seasonal mean SSTA, based on 1981–2010 seasonal of SST; and 3) a continuous correction mean standard deviations. of ship data based on night marine air sis (OISST) from 1982 to 2015 (Fig. 3.3). The SSTA temperature. Huang et al. (2015) and Kennedy (2014) time series of OISST is largely consistent with those discuss bias correction uncertainties of ship and buoy of ERSST.v3b in the common period, 1982–2015. data and reconstruction of historical SST analyses. HadISST also agrees well with OISST and ERSST. The tropical Indian Ocean SSTA is dominated v3b except it is generally cooler in the tropical Indian by an upward trend superimposed with interannual Ocean and the differences can reach 0.2°C. However, ERSST.v4 is noticeably warmer than other SST products (Karl et al. 2015), as discussed below. The global mean SSTA is dominated by a warming trend superimposed with interannual variations largely associated with El Niño and La Niña events (Fig. 3.3a), where the peaks and valleys in the global ocean SSTA often correspond with those in the tropical Pacific SSTA (Fig. 3.3b). The mean SSTA in the tropical Pacific increased by 0.23°–0.29°C from 2014 to 2015, and 2015 surpassed 1997 as the warm - est year since 1950. Partially owing to the strong warming in the tropical Pacific, the mean SSTA in the global ocean increased by 0.08°–0.11°C from 2014 to 2015, depending on the dataset examined, and 2015 surpassed 2014 as 3.3. Yearly mean SSTA (°C, relative to 1981–2010 averages) . ig F the warmest year since 1950. for ERSST.v4 (black), ERSST.v3b (blue), and HadISST (purple) for For the global ocean, the surface 1950–2015 and OISST (yellow) for 1982–2015, averaged over the (a) warming trend for 2000–15 increased global, (b) tropical Pacific, (c) tropical Indian, (d) tropical Atlantic, −1 (e) North Pacific, (f) North Atlantic, and (g) Southern Ocean. compared to by 0.03°–0.04°C decade | S65 AUGUST 2016 STATE OF THE CLIMATE IN 2015

86 variations (Fig. 3.3c). The interannual variations in North Atlantic SSTA reached a historical high in 2012, and cooled since that time (Fig. 3.3f). In the the tropical Indian Ocean SSTA correspond well with Southern Ocean, ERSST.v4 was warmer by about those in the tropical Pacific SSTA due to the remote inf luences of ENSO (Kumar et al. 2014). The tropi 0.08°C than ERSST.v3b, HadISST, and OISST, which - show consistent values after 2009 (Fig. 3.3g). cal Indian Ocean SSTA increased by 0.13°–0.20°C from 2014 to 2015, making 2015 the warmest year c. since 1950. cean heat content— G. C. Johnson, J. M. Lyman, T. Boyer, O C. M. Domingues, M. Ishii, R. Killick, D. Monselesan, and S. E. Wijffels Tropical Atlantic SST reached a historical high Storage and transport of heat in the ocean are cen - in 2010, cooled down substantially in 2011/12, and tral to aspects of climate such as ENSO (Roemmich rebounded gradually in 2013–15 (Fig. 3.3d). North and Gilson 2011), tropical cyclones (Goni et al. 2009), Pacific SSTA increased by 0.10°–0.17°C from 2013 to 2014, and changed little from 2014 to 2015 (Fig. 3.3e). sea level rise (e.g., Domingues et al. 2008), variations SIDEBAR 3.1: A WIDESPREAD HARMFUL ALGAL BLOOM IN THE —V. L. TRAINER, Q. DORTCH, N. G. ADAMS, B. D. BILL, G. DOUCETTE, NORTHEAST PACIFIC AND R. KUDELA - In the late spring and summer 2015, a widespread harm typical springtime bloom experienced in 2005 illustrates the Pseudo-nitzschia, ful algal bloom (HAB) of the marine diatom magnitude of the 2015 domoic acid event (Fig. SB3.2). stretching off the west coast of North America from central Scientists quickly recognized that the bloom extended California to British Columbia, Canada, resulted in significant from California’s Channel Islands to as far north as Vancouver - impacts to marine life, coastal resources, and the human com Island. The bloom is the largest and its effects have been the Pseudo- munities that depend on these resources. Blooms of nitzschia produce a potent neurotoxin, domoic acid, which can accumulate in shellfish, other invertebrates, and sometimes fish, leading to illness and death in a variety of seabirds and marine mammals. Human consumption of toxin-contaminated shellfish can result in Amnesic Shellfish Poisoning (ASP), which can be life threatening. Detectable concentrations of toxin, although well below levels of concern for human consumption, have been measured in finfish like salmon, tuna, and pollock. The greatest human health risk is from recreationally harvested shellfish; commercial supplies are closely monitored and have not resulted in human illnesses. States maintain websites indi - cating where shellfish can be safely harvested. Although these blooms can occur annually at “hot spots” along the U.S. West Coast, the largest impacts and most wide - spread closures typically occur in autumn. Samples collected on two research cruises in June and July 2015 demonstrated that domoic acid was measurable at most sites in Washington and Oregon (Fig. SB3.1). - The 2015 bloom was detected in early May, and in re sponse, Washington State closed its scheduled razor clam Pseudo-nitzschia digs on coastal beaches. The abundance of and concentrations of domoic acid in razor clams on Washington F ig . SB3.1. Cellular domoic acid (DA) in phytoplankton State beaches in 2015 greatly exceeded values observed dur - net tows (several liters seawater filtered; Research Ves - ing springtime blooms that have only rarely occurred on the sel (R/V) Frosti , sampled north to south) or quantified Washington coast since 1991, when domoic acid events were Ocean on 0.45 mm filters (1 liter seawater filtered; R/V first recognized on the U.S. West Coast. Comparison with a , sampled south to north). Starr | S66 AUGUST 2016

87 (Riser et al. 2016) data downloaded in January 2016. in the global average surface warming rate (Meehl et al. 2013), and melting of ice sheet outlet glaciers Near-global average seasonal temperature anomalies around Greenland (Straneo and Heimbach 2013) (Fig. 3.5) vs. pressure from Argo data (Roemmich and Gilson 2009, updated) since 2004 and in situ global and Antarctica (Rignot et al. 2013). Ocean warming - - accounts for about 93% of the total increase in en estimates of OHCA (Fig. 3.6) for various pressure lay ergy storage in the climate system from 1971 to 2010 ers from multiple research groups are also discussed. (Rhein et al. 2013). Here, increases in OHCA are sometimes referred to as warming and OHCA decreases as cooling. Maps of annual (Fig. 3.4) upper (0–700 m) ocean For the second consecutive year (see Johnson heat content anomaly (OHCA) relative to a 1993–2015 baseline mean are generated from a combination of et al. 2015a) dramatic upper OHCA cooling east - of the Philippines fed warming in the equato in situ ocean temperature data and satellite altimetry data following Johnson et al. (2015a), but using Argo rial Pacific between 2014 and 2015 (Fig. 3.4b) via longest-lasting of all U.S. West Coast Pseudo-nitzschia events in at least the past 15 years; concentrations of domoic acid in seawater, some forage fish, and crab samples were among the highest ever reported for this region. By mid-May, domoic acid concentrations in Monterey Bay, California, were 10 to 30 times the level that would be considered high for a normal - Pseudo-nitzschia bloom. Other HAB toxins also have been de tected on the West Coast in 2015. For example, an increase in saxitoxin-producing algae has been reported in areas of Alaska. Impacts include shellfish and Dungeness crab harvesting clo - sures in multiple states, targeted finfish closures, public health advisories for certain fish species in some areas of California, and sea lion strandings in California and Washington. Other marine mammal and bird mortalities have been reported in multiple states; domoic acid has not been confirmed as the primary cause of death, although the toxin has been detected in recovered birds. On 20 August 2015, NOAA declared an Unusual Mortality Event for large whales in the western Gulf of Alaska. Scientists have recorded the mortality of 30 large whales between May 2015 and February 2016. HABs are sus - pected of playing a role in the deaths of these whales given the . SB3.2. Concentrations of Pseudo-nitzschia (cells ig F noted warmer-than-average ocean temperatures in the Gulf of –1 liter from 1 Mar–1 Sep) and domoic acid in razor Alaska and the algal bloom documented in neighboring areas. clams (ppm) in (a) 2005 and (b) 2015 on Long Beach, However, there is as of this writing no conclusive evidence Wash. (location shown in Fig. SB3.1). Inset: Chains of linking the whale deaths to HAB toxins. overlapping - cells, the diatom that pro Pseudo-nitzschia While exact causes of the severity and early onset of the image Pseudo-nitzschia duces the toxin domoic acid. [ bloom are not yet known, unusually warm surface water in courtesy of Zachary Forster, Washington Department of Fish and Wildlife.] the Pacific is considered a factor (R. M. McCabe et al. 2016, manuscript submitted to Nat. Commun. ). First reported along the West Coast in the 1990s, blooms have Pseudo-nitzschia also been observed off the U.S. East Coast and in the Gulf of Mexico. | S67 AUGUST 2016 STATE OF THE CLIMATE IN 2015

88 stronger-than-normal eastward f low in the North Equatorial Countercurrent and along the equator (see Fig. 3.19). Hence, most of the equatorial Pacific was anomalously warm in 2015 (Fig. 3.4a), consistent with El Niño conditions (see section 4b). The cooling east of the Philippines brought upper OHCA (Fig. 3.4a) and sea level (see Fig. 3.15) in 2015 well below mean values there. Conversely, eastern North Pacific upper OHCA warmed from 2014 to 2015 all along the west coast of North America (Fig. 3.4b), whereas the central North Pacific cooled. This pattern of change, together with the equatorial warming, ref lects a transition of the Pacific decadal oscillation (PDO; Mantua et al. 1997) from negative in 2013 to positive in 2014 (http:// research.jisao.washington.edu/pdo/). In 2015, North Pacific SST anomalies (see Fig. 3.1), upper OHCA anomalies (Fig. 3.4a), and sea level anomalies (see Fig. 3.15) ref lect this positive PDO. This shift may result - in an increased rate of global average surface warm ing (e.g., Meehl et al. 2013) and also affects regional rates of sea level rise (e.g., Zhang and Church 2012). In the South Pacific, there was a large patch of cooling in the subtropics between 2014 and 2015 (Fig. 3.4b), but much of the South Pacific remained warm relative to 1993–2015 (Fig. 3.4a). In the Indian Ocean there was generally warming, with weak cool - ing in the far east and a zonal band of stronger cool - ing extending east of Madagascar, consistent with a reduction in the strength of the South Equatorial Current in 2015 relative to 2014 (an increase in east - ward f low, see Fig. 3.19). The Brazil Current in the South Atlantic and Agulhas Current in the South 3.4. (a) Combined satellite altimeter and in situ F ig . Indian Ocean remained warm in 2015, despite some ocean temperature data estimate of upper (0–700 m) cooling of the latter from 2014 to 2015. Upper OHCA –2 9 OHCA (×10 J m ) for 2015 analyzed following Willis in the Indian Ocean remained mostly warm in 2015 et al. (2004), but using an Argo monthly climatology (Fig. 3.4a), with cool patches in the far east and also and displayed relative to the 1993–2015 baseline. (b) - east of Madagascar. In both locations there was cool 2015 minus 2014 combined estimates of OHCA ex - –2 ing from 2014 to 2015 (Fig. 3.4b). pressed as a local surface heat flux equivalent (W m ). –2 For panel (a) and (b) comparisons, note that 95 W m Much of the subpolar North Atlantic cooled from 9 − 2 applied over one year results in a 3 × 10 J m change 2014 to 2015 while much of the Nordic Seas warmed. of OHCA. (c) Linear trend from 1993–2015 of the With these changes, in 2015 the subpolar region was combined estimates of upper (0–700 m) annual OHCA anomalously cool (Fig. 3.4a), although warm upper –2 (W m ). Areas with statistically insignificant trends OHCA persisted offshore of much of the east coast are stippled. of North America, north of the Gulf Stream Exten - significant over much of the area north of 35°S, with sion. These changes may be related to a reduction in the strength of the Atlantic meridional overturning almost no statistically significant cooling trends in that region. circulation (AMOC; see section 3h) in recent years In the Atlantic Ocean, the eastern seaboard of the (e.g., Saba et al. 2016). North Atlantic, the Labrador Sea, and the Nordic Distinct and statistically significant (Fig. 3.4c) Seas all trend warmer over 1993–2015 (Fig. 3.4c), all regional patterns stand out in the 1993–2015 local linear trends of upper OHCA. In the Indian Ocean, statistically robust over that interval. Eastern portions of the subtropical Atlantic and most of the tropics also the warming trend is widespread and statistically | S68 AUGUST 2016

89 strengthening of eastward currents across this dipole, in the region of the Antarctic Circumpolar Current. Elsewhere in the region there is a cooling around South America. The apparent warming trends adjacent to Antarctica are located in both in situ and altimeter data-sparse regions and are not as robust as suggested by the statistics. Near-global average seasonal temperature anoma - lies (Fig. 3.5a) largely ref lect ENSO redistributing heat (e.g., Roemmich and Gilson 2011) in the upper 400 dbar (1 dbar ~ 1 m). During La Niña (most notably around 2008 in the Argo era), temperatures in the upper 100 dbar tend to be colder than average and those from around 100–300 dbar warmer because cold water is brought to the surface in the eastern equatorial Pacific as the thermocline shoals, and warm water is - sequestered below the surface in the western equato rial Pacific as the thermocline deepens there. During El Niño years (most notably around the end of 2015), the sign of this pattern f lips, resulting in very warm ig F - 3.5. (a) Near-global (60°S–60°N, excluding mar . SSTs (section 3b) that, along with global warming, ginal seas and continental shelves) integrals of monthly contributed to record high global average surface tem - temperature anomalies [°C; updated from Roemmich peratures in 2015. In addition to the ENSO signature, and Gilson (2009)] relative to record-length average there is an overall warming trend (Fig. 3.5b) from 2004 monthly values, smoothed with a 5-month Hanning −1 near the sur - to 2015 that approaches 0.15°C decade filter and contoured at odd 0.02°C intervals (see −1 colorbar) vs. pressure and time. (b) Linear trend of by 400 dbar, face, declines to around 0.02°C decade temperature anomalies over time for the length of and remains near that rate down to 2000 dbar. This –1 the record in (a) plotted vs. pressure in °C decade . warming trend is found mostly south of the equator since 2006 (Roemmich et al. 2015; Wijffels et al. 2016). trend warmer across both hemispheres. There is also a warming trend in the western South Atlantic around A decade is short for defining long-term trends with statistical confidence, especially in the upper ocean the Brazil Current. Statistically significant cooling trends in the Atlantic are found east of Argentina where ENSO causes large interannual perturbations, so the analysis is extended further back in time and and in the region of the Gulf Stream Extension and North Atlantic Current. deeper using historical data collected mostly from ships. Five different estimates of globally integrated Statistically significant 1993–2015 regional trends (Fig. 3.4a) in the Pacific Ocean include warming in in situ upper (0–700 m) OHCA (Fig. 3.6a) all reveal a large increase since 1993 and indicate a record high the western tropical Pacific and extra-equatorial cool - OHCA value in 2015. Causes of the differences among ing in the east, consistent with strengthening of the estimates are discussed in previous reports (e.g., John - interior subtropical–tropical circulation attributed to son et al. 2015a). OHCA variability and net increases trade-wind intensification (Merrifield et al. 2012). This are also found from 700 to 2000 m (Fig. 3.6b) and even pattern, linked to the surface warming hiatus (England et al. 2014), weakened in 2014 (Johnson et al. 2015a) deeper in the ocean from 2000 to 6000 m (Fig. 3.6b), though for the latter, trends can only be estimated and reversed in 2015 (Fig. 3.4a), reducing the strength from differences between decadal surveys (Purkey of the long-term trend through 2015 compared with that through 2013 (Johnson et al. 2014). and Johnson 2013). The rate of heat gain from linear trends fit to each In the Southern Ocean, a distinct trend of upper OHCA over 1993–2015 (Fig. 3.4c) emerges: a primar - of the global integral estimates of 0–700 m OHCA from 1993 through 2015 (Fig. 3.6a) are 0.26 (±0.05), ily zonal narrow band of warming immediately north 0.31 (±0.12), 0.43 (±0.11), 0.35 (±0.07), and 0.41 (±0.22) of a band of cooling is visible from the western South −2 - applied over the surface area of the Earth (5.1 × Atlantic where the Brazil and Falkland/Malvinas Cur W m 2 14 rents meet, extending eastward across much of the m 10 ) for the MRI/JMA, CSIRO/ACE CRC/IMAS- UTAS, PMEL/JPL/JIMAR, NCEI, and Met Office South Atlantic and Indian Oceans all the way to south Hadley Centre estimates, respectively. Linear trends of New Zealand. The geostrophic relation implies a | S69 AUGUST 2016 STATE OF THE CLIMATE IN 2015

90 −2 for 1993–2015 are 0.19 (±0.09) W m from 700 to Summing the three layers, the full-depth ocean heat −2 −2 gain rate ranges from 0.52 to 0.74 W m from 700 to 1800 m, . 2000 m, 0.24 (±0.04) W m −2 and 0.19 (±0.08) W m from 700 to 2000 m for the Sa G. C. Johnson, J. Reagan, J. M. Lyman, T. Boyer, d. MRI/JMA, PMEL/JPL/JIMAR, and NCEI estimates, linity— respectively. Here, 5%–95% uncertainty estimates for C. Schmid, and R. Locarnini ntroduct I G. C. Johnson and J. Reagan — Ion the trends are based on the residuals, taking their tem 1) - Salinity patterns, both long-term means and their poral correlation into account when estimating degrees of freedom (Von Storch and Zwiers 1999). For 2000– variations, ref lect ocean storage and transport of −2 6000 m, the linear trends are about 0.07 (±0.04) W m freshwater, a key aspect of global climate (e.g., Rhein (again at 5%–95% uncertainty) from 1992 to 2009 et al. 2013). Ocean salinity distributions are largely determined by patterns of evaporation, precipitation, (update of Purkey and Johnson 2010; D. Desbruyères and S. G. Purkey, 2016, personal communication). and river runoff (e.g., Schanze et al. 2010), and in some high-latitude regions, sea ice formation, advec - tion, and melt (e.g., Petty et al. 2014). The result is relatively salty sea surface salinity (SSS) values in the subtropics, where evaporation dominates, and fresher SSS values under the intertropical convergence zones (ITCZs) and in the subpolar regions, where precipi - tation dominates. These fields are further modified by ocean advection (e.g., Yu 2011). In the subsurface, fresher subpolar waters slide along isopycnals to intermediate depths, underneath saltier subtropical waters, which are in turn capped at low latitudes by fresher tropical waters (e.g., Skliris et al. 2014). Salinity changes in these layers quantify the increase of the hydrological cycle with global warming over the recent decades, likely more accurately and directly than evaporation and precipitation estimates (Skliris et al. 2014). Below that, the salty North Atlantic Deep Waters formed mostly by open ocean convection are found, with salinities that vary over decades (e.g., - 3.6. (a) Time series of annual average global inte . ig F van Aken et al. 2011). Fresher and colder Antarctic grals of in situ estimates of upper (0–700 m) OHCA Bottom Waters, formed mostly in proximity to ice 21 (1 ZJ = 10 J) for 1993–2015 with standard errors of the shelves, fill the abyss of much of the ocean (Johnson mean. The MRI/JMA estimate is an update of Ishii and 2008), freshening in recent decades (e.g., Purkey and Kimoto (2009). The CSIRO/ACE CRC/IMAS-UTAS Johnson 2013). Salinity changes also have an effect on estimate is an update of Domingues et al. (2008). The sea level (e.g., Durack et al. 2014) and the thermoha - PMEL/JPL/JIMAR estimate is an update of Lyman and line circulation (e.g., Kuhlbrodt et al. 2007). Johnson (2014). The NCEI estimate follows Levitus To investigate interannual changes of subsurface et al. (2012). The Met Office Hadley Centre estimate is - computed from gridded monthly temperature anoma salinity, all available subsurface salinity profile data lies (relative to 1950–2015) following Palmer et al. are quality controlled following Boyer et al. (2013) and (2007). See Johnson et al. (2014) for more details on then used to derive 1° monthly mean gridded salinity uncertainties, methods, and datasets. For comparison, anomalies relative to a long-term monthly mean for all estimates have been individually offset (vertically years 1955–2006 (World Ocean Atlas 2009; Antonov on the plot), first to their individual 2005–15 means et al. 2010) at standard depths from the surface to - (the best sampled time period), and then to their col 2000 m (Boyer et al. 2012). In recent years, the single lective 1993 mean. (b) Time series of annual average global integrals of in situ estimates of intermediate largest source of salinity profiles for the world’s ocean (700–2000 m for MRI/JMA and NCEI, 700–1800 m for is the Argo program with its f leet of profiling f loats PMEL/JPL/JIMAR) OHCA for 1993–2015 with standard (Riser et al. 2016). These data are a mix of real-time errors of the mean, and a long-term trend with one (preliminary) and delayed-mode (scientific quality standard error uncertainty shown from 1992–2009 controlled). Hence, the estimates presented here could for deep and abyssal (z > 2000 m) OHCA updated change after all data have been subjected to scientific (D. Desbruyères and S. G. Purkey, 2016, personal quality control. The SSS analysis relies on Argo in situ communication) following Purkey and Johnson (2010). | S70 AUGUST 2016

91 data downloaded in January 2016, with annual maps generated following Johnson and Lyman (2012) as well as monthly maps from BASS (Xie et al. 2014), a bulk (as opposed to skin) SSS data product that blends in situ SSS data with data from the Aquarius (Le Vine et al. 2014) and SMOS (Soil Moisture and Ocean Salinity; Font et al. 2013) satellite missions. The Aquarius mission ended in June 2015, leaving SMOS as the sole source for satellite SSS for the rest of 2015. BASS maps can be biased fresh around land (includ - ing islands) and should be compared carefully with in situ data-based maps at high latitudes before trusting features there. Salinity is measured as a dimensionless quantity and reported on the 1978 Practical Salinity Scale, or PSS-78 (Fofonoff and Lewis 1979). Surface salinity values in the open ocean range from about 32 to 37.5, with seasonal variations exceeding 1 in a few locations (Johnson et al. 2012). S ea Surface Sal InIt y (SSS)— 2) G. C. Johnson and J. M. Lyman The 2015 SSS anomalies (Fig. 3.7a, colors) reveal some large-scale patterns that largely held from 2004 State to 2014 (e.g., Johnson et al. 2015b, and previous of the Climate reports.). Regions around the subtropi - cal salinity maxima are generally salty with respect to ( WOA ) World Ocean Atlas (Antonov et al. 2010). 2009 Most of the high latitude, low-salinity regions appear fresher overall than WOA 2009 , both in the vicinity of much of the Antarctic Circumpolar Current near 50°S and in portions of the subpolar gyres of the North Pa - cific and North Atlantic. These multiyear patterns are consistent with an increase in the hydrological cycle F ig 3.7. (a) Map of the 2015 annual surface salinity (that is, more evaporation in drier locations and more anomaly (colors in PSS-78) with respect to monthly precipitation in rainy areas) over the ocean expected climatological salinity fields from WOA 2009 (yearly in a warming climate (Rhein et al. 2013). The large, average—gray contours at 0.5 PSS-78 intervals). (b) relatively fresh patch in 2015 west of Australia and Difference of 2015 and 2014 surface salinity maps –1 the Indonesian Throughf low was more prominent [colors in PSS-78 yr to allow direct comparison with in previous years back to 2011 (Johnson and Lyman (a)]. White ocean areas are too data-poor (retaining < 80% of a large-scale signal) to map. (c) Map of local 2012). Its origin is associated with the strong 2010–12 linear trends estimated from annual surface salinity La Niña and other climate indices (Fasullo et al. 2013; –1 anomalies for 2005–15 (colors in PSS-78 yr ). Areas Johnson et al. 2015b). with statistically insignificant trends are stippled. All Sea surface salinity changes from 2014 to 2015 maps are made using Argo data. (Fig. 3.7b, colors) strongly ref lect 2014 anomalies (see Fig. 3.19) of relatively fresh water in the tropical in evaporation minus precipitation (see Fig. 3.12). Advection by anomalous ocean currents (see Fig. 3.19) Pacific. The salinification over the tropical warm also plays a role in SSS changes. The most prominent pool is associated with reduction in freshwater f lux large-scale SSS changes from 2014 to 2015 were fresh - anomalies there. These changes are related to the - ening under the Pacific ITCZ and salinification in the strong El Niño event of 2015 (section 4b). In the subpo lar North Atlantic, there was widespread freshening, tropical warm pool around the Maritime Continent (Fig. 3.7b). The freshening is associated with stronger- strongest south of Iceland, but north of Iceland SSS than-usual freshwater f luxes into the ocean under the becomes saltier. In the Indian Ocean, SSS decreased ITCZ (see Fig. 3.12) and anomalous eastward f low south of India from 2014 to 2015, consistent with the | S71 AUGUST 2016 STATE OF THE CLIMATE IN 2015

92 westward spreading and weakening - of the prominent fresh anomaly gen erated west of Australia circa 2011. - Seasonal variations of SSS anom alies in 2015 (Fig. 3.8) from BASS (Xie et al. 2014) show the buildup of anomalously fresh water associated with the tropical Pacific and west - ern tropical Atlantic ITCZs (includ - ing just offshore of the Orinoco and Amazon Rivers), the increase in SSS in the tropical warm pool, and the decrease in fresh anomalies under the South Pacific convergence zone (SPCZ). Despite the lower accura - cies of the satellite data relative to . 3.8. Seasonal maps of SSS anomalies (colors) from monthly blended ig F that of the Argo data, their higher maps of satellite and in situ salinity data (BASS; Xie et al. 2014) relative spatial and temporal sampling al - to monthly climatological salinity fields from WOA 2009 for (a) Dec–Feb lows higher spatial and temporal 2014/15, (b) Mar–May 2015, (c) Jun–Aug 2015, and (d) Sep–Nov 2015. Ar - resolution maps than are possible eas with maximum monthly errors exceeding 10 PSS-78 are left white. using in situ data alone. during 2015 similar to the previous 10 years, with Sea surface salinity trends for 2005–15 exhibit salty anomalies above 700 m and fresh anomalies striking patterns in all three oceans (Fig. 3.7c). These trends are estimated by local linear fits to annual av - below (Fig. 3.9a). From 2014 to 2015 salinity increased erage SSS maps from Argo data with a starting year in the upper 300 m of the Atlantic, reaching a maxi - mum increase of ~0.01 near the surface (Fig. 3.9b). of 2005, because that is when Argo coverage became The Pacific Ocean has exhibited fresh anomalies near-global. Near the salinity maxima in each basin of about −0.02 from 200 to 500 m over the last five - (mostly in the subtropics but closer to 30°S in the In years (Fig. 3.9c). However, the upper 75 m was about dian Ocean), there are regions of increasing salinity, especially in the North Pacific to the west of Hawaii. −0.04 fresher in 2015, in contrast to salty condi - tions there from mid-2008 to mid-2014. This change In contrast, there are regions in the Southern Ocean where the trend is toward freshening. Again, these ref lects the enhanced precipitation along the ITCZ (see Fig. 3.12d) and anomalous eastward equatorial patterns are reminiscent of the multidecadal changes discussed above and suggest an intensification of the currents (see Fig. 3.19) during the 2015 El Niño (see hydrological cycle over the ocean, even over the last section 4b). Salty anomalies from 100 to 200 m have been present since 2011. From 2014 to 2015 the Pacific 11 years. There is a strong freshening trend in much of the subpolar North Atlantic, roughly coincident (Fig. 3.9d) freshened in the upper 75 m, approaching with anomalously low upper ocean heat content there about −0.03 at 30 m, and became saltier from 100 (see Fig. 3.4) suggesting an eastward expansion of the to 200 m, approaching ~0.01 at 125 m. The Indian subpolar gyre that may be linked to reductions in the Ocean continued to show similar salinity anomaly AMOC over the past decade (section 3h). In addition structure to that of the previous two years, with a fresh surface anomaly from 0 to 75 m, salty subsurface to these patterns there is a freshening trend in the anomaly from 100 to 300 m, a slightly fresh anomaly eastern Indian Ocean, probably owing to a lingering signature of the strong 2010–12 La Niña, as discussed (maximum of about −0.01) from 400 to 600 m, and a slightly salty anomaly (maximum of ~0.01) from 600 above. Freshening trends are also apparent in the to 800 m (Fig. 3.9e). From 2014 to 2015 there was weak eastern tropical Pacific and the South China Sea. The freshening (maximum of about −0.01 at 50 m) near region to the northwest of the Gulf Stream is trending the surface and salinification from 100 to 200 m, with saltier, as well as warmer (section 3c). a maximum of ~0.014 at 150 m (Fig. 3.9f). ub Surface Sal North Atlantic 2015 volume-weighted salinity InIty — J. Reagan, T. Boyer, C. Schmid, 3) S anomalies from 0 to 1500 m (Fig. 3.10a) were mostly and R. Locarnini positive, with values >0.10 along the Gulf Stream. Atlantic Ocean basin-average monthly salinity The eastern portion of the subpolar gyre in the North anomalies for 0–1500 m depth displayed a pattern | S72 AUGUST 2016

93 Atlantic exhibited a large (about −0.10) fresh anomaly. Pacific there was a salty anomaly (Fig. 3.10a) in close proximity to a region of anomalously warm SSTs (see This fresh feature coincided with anomalously cool up - Fig. 3.1). The warm SSTs were at least partly due to per ocean heat content (see Fig. 3.4). The South Atlantic a persistent atmospheric ridge in the region (Bond was dominated by positive salinity anomalies in 2015, et al. 2015). With ridging, less precipitation and with fresh anomalies south of 40°S, perhaps ref lecting an anomalously northward position of the low salinity more evaporation are expected. This expectation was subantarctic front. From 2014 to 2015, positive salinity partially met (see Fig. 3.12) and likely to have been partially responsible for the observed salty anomaly anomalies in the subtropics persisted with little change in strength, while the freshening north of the Azores strengthening from 2014 to 2015 (Fig. 3.10b). The subtropical North Pacific was anomalously salty in Islands continued to strengthen (Fig. 3.10b). 2015, contrasting with fresh anomalies along the The Indian Ocean displayed a dipole of salinity – P ITCZ, consistent with the 2015 anomalies north of the equator during 2015, with salty anomalies (see E anomalies in the Arabian Sea and fresh anomalies in - Fig. 3.12). Salty anomalies were present in the sub the Bay of Bengal (Fig. 3.10a). Salty anomalies along tropical South Pacific in 2015, with fresh anomalies - the equator transitioned to fresh anomalies across along the SPCZ. These tropical and subtropical sa the entire basin south of 15°S to 30°S. These fresh linity anomaly features were mostly enhanced when anomalies strengthened east of Madagascar from 2014 compared to 2014, with the exception of a weakening to 2015 but weakened west of Australia (Fig. 3.10b) as discussed in section 3d2. From 35°S to 50°S there was a transition from salty to fresh salinity anomalies, likely due to the position of the subantarctic front in 2015 (Fig. 3.10a). The North Pacific, north of 20°N, was dominated by fresh anomalies in 2015; however, in the northeast ig . 3.9. Average monthly salinity anomalies from F . 3.10. Near-global 0–1500 m volume-weighted ig F 0–1500 m for the (a) Atlantic from 2005–15 and (b) salinity anomalies (a) for 2015, (b) change from 2014 the change from 2014 to 2015; (c) Pacific from 2005–15 –1 to 2015, and (c) linear trend from 2005 to 2015 (yr ). and (d) the change from 2014 to 2015; and (e) Indian Anomalies are relative to the long-term WOA 2009 from 2005–15 and (f) the change from 2014 to 2015. monthly salinity climatology (Antonov et al. 2010). Data were smoothed using a 3-month running mean. Annual figures were computed by averaging the 12 Anomalies are relative to the long-term WOA 2009 monthly salinity anomalies over calendar years. monthly salinity climatology (Antonov et al. 2010). | S73 AUGUST 2016 STATE OF THE CLIMATE IN 2015

94 f luxes are the primary mecha - nisms for keeping the global climate system in balance with the incoming insolation at Earth’s surface. Most of the shortwave radiation (SW) ab - sorbed by the ocean’s surface is vented into the atmosphere by three processes: longwave radiation (LW), turbulent heat loss by evaporation (latent heat f lux, or LH), and turbu - lent heat loss by conduction (sensible heat f lux, or SH). The residual heat is stored in the ocean and transported away by the ocean’s surface F . 3.11. (a) Surface heat flux (Q ) anomalies for 2015 relative to a 5-year ig net circulation, forced primarily (2010–14) mean. Positive values denote ocean heat gain. Panels (b), (c), and (d) by the momentum transferred are the 2015–2014 anomaly tendencies for Q , surface radiation (SW+LW), net to the ocean by wind stress. and turbulent heat fluxes (LH+SH), respectively. Positive anomalies denote that the ocean gained more heat in 2015 than in 2014. LH+SH are produced Evaporation connects heat by the OAFlux high-resolution satellite-based analysis, and SW+LW by the and moisture transfers, and NASA FLASHFlux project. - the latter, together with pre positive salinity anomaly over the central subtropical cipitation, determines the local surface freshwater f lux. Identifying changes in the air–sea f luxes is essential North Pacific in 2015 (Fig. 3.10b). The South Pacific enhancement from 2014 to 2015 is inconsistent with in deciphering observed changes in ocean circulation – 2015 anomalies (see Fig. 3.12). and its transport of heat and salt from the tropics to P E The 2005–15 linear trends of the 0–1500 m salin the poles. In particular, 2015 witnessed the interplay - of three different warmings: the warm “Blob” in the ity anomalies (Fig. 3.10c) reveal strong similarities to SSS trends over the same time period (see Fig. 3.7c and discussion above). This match is not surprising as most of the salinity variability from 0 to 1500 m over the global ocean occurs in the upper 300 m (Fig. 3.9). The large −1 (> −0.01 yr ) freshening trend in the North Atlantic subpolar - gyre could be partially respon sible for the observed decline in the strength of the AMOC (Smeed et al. 2014). e. O cean surface heat, freshwa - ter, and momentum fluxes— L. Yu, R. F. Adler, G. J. Huffman, X. Jin, – E ) flux anomalies for 2015 relative to a 27- F ig . 3.12. (a) Surface freshwater ( P S. Kato, N. G. Loeb, P. W. Stackhouse, year (1988–2014) climatology. 2015–2014 anomaly tendencies for (b) P – E , (c) R. A. Weller, and A. C. Wilber E ), respectively. Green colors denote ), and (d) precipitation ( evaporation ( P The ocean and atmosphere anomalous ocean moisture gain, and browns denote loss, consistent with the communicate via interfacial reversal of the color scheme in (c). P is computed from the GPCP version exchanges of heat, freshwater, 2.2 product, and from OAFlux high-resolution satellite-based analysis. E and momentum. These air–sea | S74 AUGUST 2016

95 North Pacific (Bond et al. 2015), a strong El Niño in the intense and widespread compared to the long-term tropical Pacific, and a warm PDO phase. Large-scale mean background. The Q anomaly pattern was net determined primarily by the LH+SH anomaly pattern anomalies appear in observations of SST (see Fig. 3.1), (Fig. 3.11d), with the SW+LW anomalies contributing heat content (see Fig. 3.4), sea surface salinity (see Fig. mostly in the tropical ocean. 3.7), sea level (see Fig. 3.15), ocean surface currents The 2015 Q anomalies in the tropical Pacific (see Fig. 3.19), and chlorophyll- a (see Fig. 3.26a). All net are associated with El Niño, with mean 2015 SST in of these anomalies are related to changes in ocean surface forcing functions in 2015 and how the ocean the eastern equatorial Pacific more than 2°C above and atmosphere generated anomalous conditions with normal (see Fig. 3.1). The eastward displacement of convection typically found over the west tropical unusual magnitudes and spatial dimensions. Pacific is identified in the SW+LW anomaly field, Air–sea heat f lux, freshwater f lux, and wind stress - featuring a striking band of negative SW+LW anoma in 2015 and their relationships with the ocean are –2 in the central assessed. The net surface heat f lux, Q lies of magnitude exceeding 20 W m , is the sum net and eastern equatorial Pacific (Fig. 3.11c). The band’s of four terms: SW + LW + LH + SH. The net surface freshwater f lux into the ocean (neglecting riverine and core was centered near the international date line and glacial f luxes from land) is simply Precipitation ( zonally elongated, extending eastward along 2°–3°N. ) P E f lux. Wind stress is The precipitation anomaly field (Fig. 3.12d) reveals an minus Evaporation ( E ), or the P – almost identical band structure of enhanced tropical computed from satellite wind retrievals using the bulk - parameterization of Edson et al. (2013). The produc rainfall. During an El Niño, the eastward movement - tion of the global maps of Q of the ITCZ leads to the suppression of deep convec , P – E , and wind stress net in 2015 (Figs. 3.11–3.13) and the long-term perspective tive clouds over the Indo-Pacific warm pool and the Maritime Continent, and consequently, an increase of the change of the forcing functions (Fig. 3.14) are made possible by integrating the efforts of four world- in the net downward radiation. These typical ENSO class f lux analysis groups. The Objectively Analyzed composite features (Rasmusson and Wallace 1983), that is, negative SW+LW anomalies in the central and air–sea Fluxes (OAFlux; http://oaf lux.whoi.edu/) eastern equatorial basin and positive SW+LW anoma project at the Woods Hole Oceanographic Institu - - lies in the equatorial Indo-Pacific, were clear in 2015. tion (Yu and Weller 2007) provides the satellite-based The ENSO LH+SH anomalies (Fig. 3.11d) were high-resolution version of the turbulent ocean f lux components, including LH, SH, E, and wind stress (Yu dominated by the LH anomalies and produced largely by SST and wind anomalies. As the warmer sea sur and Jin 2012; 2014a,b; Jin and Yu 2013; Jin et al. 2015). - face tends to evaporate more quickly, latent heat loss The Clouds and the Earth’s Radiant Energy Systems (CERES) Fast Longwave and Shortwave Radiative increased along the equatorial warm tongue in the Fluxes (FLASHFlux; https://eosweb.larc.nasa.gov eastern Pacific. In the central and western equatorial basin, however, the LH anomalies were not a response /project/ceres/ceres_table) project at the NASA Langley Research Center (Stackhouse et al. 2006) provides the to the SST condition; instead they were the source of heating contributing to the ocean warming there: surface SW and LW products. The Global Precipitation Climatology Project (GPCP; http://precip.gsfc.nasa. trade winds weakened considerably within the deep convection center near the date line (Fig. 3.13a). This gov) at the NASA Goddard Space Flight Center (Adler et al. 2003) provides the precipitation products. The weakening, in turn, subdued the latent heat loss by –2 CERES Energy Balanced and Filled (EBAF) surface , creating a warming effect at more than 20 W m the ocean surface. The warming effect of the LH+SH SW and LW products (http://ceres.larc.nasa.gov; Kato et al. 2013) are used in the time series analysis. anomalies exceeded the cooling effect of the SW+LW anomalies, leading to a marginally positive net heat urface heat fluxe S input to the ocean area that hosted the center of deep 1) S - convection. The global Q anomaly pattern in 2015 over net relationship from the all showed a remarkable hemispheric asymmetry The changing SST–Q net (Fig. 3.11a), with net ocean heat loss (negative) eastern to the central equatorial Pacific demonstrates that Q anomalies dominating the Northern Hemisphere has a dual role in the dynamics of large- net and net heat gain (positive) anomalies command - contributes scale SST anomalies. On one hand, Q net ing the Southern Hemisphere. The 2015 minus 2014 to the generation of SST anomalies. On the other Q hand, Q tendency map (Fig. 3.11b) had a similar pattern, acts as a damping mechanism to suppress net net except for the northeast Pacific, where the net heat the SST anomalies once they are generated, thereby loss associated with the warm “Blob” was more providing a feedback to control the persistence | S75 AUGUST 2016 STATE OF THE CLIMATE IN 2015

96 and the amplitude of the SST anomalies (Frankignoul and Hasselmann 1977). Outside the equatorial ocean, the heat f lux feedback offers an explanation for the large heat loss over the exceptionally warm waters off the North American coast. The warm “Blob” has persisted since 2013, and the enormous latent heat loss that it produced in 2015 exceeds the climatological background by a large amount (Fig. 3.11a). The Q forcing is evident net in basinwide warming in the subtropical Indian and Atlantic - sectors, as well as in the south − 2 F ig . 3.13. (a) Wind stress magnitude (N m ; colored background) and vector east Pacific. The southeast trade anomalies for 2015 relative to a 27-yr (1988–2014) climatology, (b) 2015–2014 winds are usually weaker dur - anomaly tendencies in wind stress, (c) Ekman vertical velocity ( W ) anoma - EK ing an El Niño (Rasmusson and − 1 lies (cm day ) for 2015 relative to a 27-year (1988–2014) climatology, and (d) Carpenter 1982). Similar to the W 2015–2014 anomaly tendencies in . In (c) and (d), positive values denote EK wind–evaporation–SST mecha - upwelling anomalies and negative downwelling. Winds are computed from nism that operated in the central the OAFlux high-resolution satellite-based vector wind analysis. equatorial Pacific, the subdued LH+SH loss due to the weakened tendencies in the subtropical sectors, with the northern winds appears to be a source of surface heating for the region. basin evaporating more and the southern basin less. anomalies in the North Atlantic In the North Pacific, one interesting anomaly The 2015 Q net - exhibited a tripole-like pattern, with strong net heat feature in 2015 is the concurrence of increased evapo −2 ) centered in ration with reduced precipitation along a band that loss (negative) anomalies (< −20 W m extended northeastward across the central Pacific, the Labrador Sea and extending across the subpolar - from the western tropical Pacific toward the Gulf gyre north of 50°N, net heat gain (positive) anoma −2 ) in the eastern region between lies (10–15 W m of Alaska. Both effects resulted in an enhanced net moisture release to the atmosphere. This band of 30° and 50°N, and strong net heat loss anomalies −2 (< −20 W m ) in the northwest Atlantic, including – P anomalies is not a standalone feature. Along E the location of the anomalies in this anomaly band, downward E the Gulf Stream region. The Q – P net - tripole-like pattern were generally negative in regions SW + LW increased (Fig. 3.11a), and wind-induced Ek of positive SST anomalies and positive in regions of man upwelling anomalies were observed (Fig. 3.13c). negative SST anomalies (Fig. 3.1), indicating that the More importantly, ocean surface warming occurred. atmospheric thermal forcing in the North Atlantic The net downward heating, net moisture loss from was a response to the SST variability. the ocean, and the surface warming imply a close coupling between the atmosphere and the ocean in the extratropical central Pacific. 2) urface fre Shwater S S fluxe Above-normal freshwater input was observed in anomaly tendency E The 2014 to 2015 P – (Fig. 3.12a,b) is dominated by precipitation (Fig. 3.12d) the tropical Pacific, associated with the development over the globe except for the extratropical North Pa of the strong El Niño. Consistent with the eastward - - displacement of the ITCZ, the core of the precipita cific, where evaporation (Fig. 3.12c) is stronger in 2015 tion band was centered near the date line and elon - - than 2014. The hemispheric asymmetry, which is fea gated eastward along the El Niño region. Along the tured in the 2015 Q anomaly map, is not as obvious net E anomalies (Fig. 3.12a) but is visible in the 2015 P – band, the maximum increase of rainfall exceeded tendencies (Fig. 3.12c). In the latter, the asym - 1.5 m during 2015, and its impact on the tropical sea E in the surface salinity (see Fig. 3.7) is evident. The entire E metry is more about the contrast in the signs of the | S76 AUGUST 2016

97 equatorial Pacific experienced a surface freshening positive upwelling anomalies poleward of 60°N, nega - tive downwelling anomalies between 40° and 60°N, by as much as 0.1 PSS-78. As a consequence of the and positive downwelling anomalies in the northwest ITCZ migration, the rainfall deficit over the Indo- subtropical Atlantic. The region of the warm “Blob” Pacific warm pool and the Maritime Continent was as large as 80 cm. in the northeast Pacific experienced an enhanced Interestingly, the changing E forcing in the downwelling motion. – P Indian and Atlantic basins is only loosely linked to observed SSS anomalies (see Fig. 3.7). For instance, per Spect Ive 4) l ong - term the tropical Indian Ocean was mostly in a wetter con – E , - The time series of yearly variations of Q , P net and wind stress averaged over the global oceans dition, whereas the regional sea surface became saltier (Fig. 3.14) provide decadal perspective on the ocean- over a wide area. The southern Atlantic was mostly in a drier condition, whereas the regional sea surface surface forcing functions in 2015. The Q time series net (Fig. 3.14a) indicates that, despite interannual vari was fresher. The low correlation between salinity - and ability, net heat gain by the ocean shifted from nearly P – , in sharp contrast to the close correlation E steady to higher variability around 2007, after which discussed above, ref lects that between SST and Q net – Q shows a slight upward tendency. Whether the - variations can create or modify existing salin E P net ity anomalies but are not a damping mechanism for change is associated with the phase transition of the existing anomalies. The lack of a negative feedback of PDO is yet to be determined. The global average does not represent the change on the regional scale: for SSS to the P f lux suggests that SSS anomalies have E – instance, decadal decrease of net downward heat f lux a longer persistence than SST, and are more strongly inf luenced by horizontal processes anomalies such as is observed at a buoy off northern Chile (Weller 2015). wind-driven Ekman advection (Yu 2015). – P The E time series (Fig. 3.14b) is up slightly in 2015, perhaps ref lecting the 2015 El Niño’s inf luence SS - 3) w Ind Stre on tropical oceanic precipitation. The GPCP pre cipitation dataset shows that changes over land and The 2015 wind stress anomalies were largely aligned zonally, ref lecting the f luctuations of the ocean during El Niño or La Niña years balance to first major wind belts (Fig. 3.13a,b). The vector wind anomaly directions indicate that there was a coher - ent weakening of the midlatitude westerlies in the Northern and Southern Hemispheres (30°–60°N and 30°–60°S). In response to the strong El Niño in the Pacific, the tropical trade winds were also weaker (Rasmusson and Carpenter 1982). The reduction in the magnitude of the trades is most evident in the center of the southeast trades (e.g., ~15°S in the Indian and Pacific basins). In the equatorial region, the shift of the deep convection to the date-line moved the Walker Circulation eastward, resulting in the equa - torial westerly anomalies in the west and equatorial easterly anomalies in the east. The spatial variations of wind stress (τ) cause di - vergence and convergence of the Ekman transport, leading to a vertical velocity, denoted by Ekman pumping (downward) or suction (upward) velocity, W , at the base of the Ekman layer. Computation of EK ), where follows the equation: W f = 1/ W ∇ × (τ/ ρ EK EK 3.14. Annual-mean time series of global averages . ig F f the Coriolis force. The 2015 W ρ is density, and EK of (a) net surface heat flux (Q ) from the combination net - pattern (Fig. 3.13c,d) shows strong upwelling (posi of CERES EBAF SW+LW and OAFlux LH+SH, (b) net tive) anomalies in the western and central equatorial freshwater flux ( E P ) from the combination of GPCP – Pacific. The pattern corresponds well with the cooling P and OAFlux E , and (c) wind stress magnitude from of the upper ocean in the observed region in OHC OAFlux high-resolution vector wind analysis. Shaded (see Fig. 3.4) and SSH (see Fig. 3.15). In the North areas indicate one standard deviation of annual-mean variability. tripole anomaly pattern is present: W Atlantic, a EK | S77 AUGUST 2016 STATE OF THE CLIMATE IN 2015

98 SIDEBAR 3.2: EXTRAORDINARILY WEAK EIGHTEEN DEGREE WATER PRODUCTION CONCURS WITH STRONGLY POSITIVE NORTH ATLANTIC OSCILLATION IN LATE WINTER 2014/15 —S. BILLHEIMER AND L. D. TALLEY - The Gulf Stream and Kuroshio Extension are the larg est oceanic heat loss regions in the Northern Hemisphere. Associated with that enormous winter cooling are deep winter mixed layers, which become what is referred to as subtropical mode water. Mode waters constitute a large portion of the upper - ocean volume and are composed of nearly uniform tem perature and salinity. These water masses outcrop at the surface during winter, at which point they are stamped with the current atmospheric conditions. Vigorous con - vection drives the creation of deep mixed layers that into the entrain heat, freshwater, and anthropogenic CO 2 upper ocean. When air–sea heat flux changes sign, gen - erally in the spring, the upper ocean begins to restratify, and the thick subtropical mode water layer (Fig. SB3.3a) is isolated from the atmosphere by the development of a seasonal pycnocline (a strong vertical density gradient). In subsequent winters, when the seasonal pycnocline breaks - down, it exposes a thick layer of nearly uniform tempera ture set by previous winters’ heat loss to the atmosphere, which renews the mode water. During normal seasonal cycles, mode water acts both to integrate several years of likely variable atmospheric conditions and to modify wintertime air–sea exchange. With several years of abnor - mally limited mode water renewal, the anomalous heat, associated with the freshwater, and anthropogenic CO 2 mode water reservoir would diffuse into the permanent thermocline below. F ig SB3.3. Eighteen Degree Water (EDW) potential . Eighteen Degree Water (EDW) is the subtropical vorticity (log scale color) and EDW thickness (black mode water associated with the Gulf Stream extension contours). “EDW PV” is taken as potential vorticity on in the western North Atlantic (Worthington 1959). EDW − 3 –3 = 2 6.5 kg m the potential density surface. “EDW = kg σ m 26.2–26.7 Θ volume and properties are affected both regionally by the 3 − –3 26.2–26.7 kg m = thickness” is the thickness of the kg 26.2–26.7 = σ m Θ Gulf Stream and by large-scale atmospheric conditions. potential density layer. Contours are at 50 m intervals. The strength of EDW formation during winter is High PV (orange/brown) corresponds with very weak strongly associated with the North Atlantic Oscillation (thin) EDW. (a) Climatology of Mar/Apr EDW PV and thickness during the Argo era. (b) 2015 Mar/Apr EDW (NAO; Talley 1996; Joyce et al. 2000; Fig. SB3.4). During PV anomaly and Mar/Apr 2015 EDW thickness. strongly negative NAO index winters, including 2014/15, the ocean-to-atmosphere heat flux that produces deep by Billheimer and Talley (2013). In late winter 2013/14, mixed layers occurs primarily in the subtropical regions extraordinary ocean cooling in the subpolar North Atlan - - (Dickson et al. 1996), resulting in vigorous EDW forma tic that created an extreme type of Labrador Sea Water tion. During strongly positive NAO winters, vigorous (Josey et al. 2015) coincided with a strongly positive NAO buoyancy forcing occurs in the subpolar regions and the index and weak EDW formation, illustrating the spatially subtropical EDW region is deprived of strong winter bimodal nature of NAO-related surface buoyancy forcing. atmospheric forcing, resulting in weak to near cessation The Gulf Stream also plays a role in EDW formation. of EDW formation, as demonstrated for winter 2011/12 Strong lateral and vertical shears within the Gulf Stream | S78 AUGUST 2016

99 jet modify convective processes, driving cross-frontal mixing (Joyce et al. 2009, 2013; Thomas et al. 2013). The entrainment of fresh slope water originating north of the Gulf Stream, which occurs approximately between 65°W and 55°W, produces a colder, fresher variety of EDW. This mechanism of EDW formation is apparently much less affected by the intensity of winter subtropical surface heat flux (Billheimer and Talley 2013). One measure of EDW formation strength is its winter Potential Vorticity (PV), defined by the planetary compo - nent only, neglecting relative vorticity: is the Coriolis parameter and ρ is density. High PV where f is associated with strong stratification. Hence, high EDW PV during the EDW formation season indicates strong stratification and abnormally weak EDW formation. . SB3.4. (a) Jan–Mar 2015 NAO index (red) and F ig – Here, EDW PV is calculated using the Roemmich annual minimum EDW PV (blue), which tends to be Gilson climatology of Argo profiling floats (Roemmich in Mar or Apr. EDW PV is the regional average of the and Gilson 2009). EDW PV is taken as the PV along the domain mapped in Fig. SB3.3. (b) EDW volume, taken –3 , considered = 26.5 kg m σ potential density contour –3 Θ as the volume of the σ = 26.2–26.7 kg m potential Θ the “EDW core” of low PV EDW (Talley and Raymer density layer integrated over the domain mapped in 1982; Talley 1996). Fig. SB3.3. The Gulf Stream north wall is the EDW The map of climatological EDW PV for March/April, northern boundary. when EDW PV is lowest, shows the thickest, low PV EDW tion exists between EDW PV and the NAO, where high concentrated in two pools (Fig. SB3.3a). We hypothesize NAO winters, particularly the late winter of 2014/15, are that the pool centered at 52°W near the Gulf Stream associated with abnormally weak EDW formation (Fig. extension in the northeastern Sargasso Sea is largely SB3.4a). EDW volume in 2014/15 was consistent with the produced within the Gulf Stream via cross-frontal mixing lack of EDW formation indicated by high winter EDW PV (Joyce et al. 2009, 2013; Joyce 2012), whereas the pool (Fig. SB3.4b). Peak EDW volume in late winter 2014/15 centered at roughly 66°W within the tight recirculation was at a minimum over the past 10 years, indicating an gyre of the Sargasso Sea is largely formed and renewed extraordinary lack of EDW production for a second by simple one-dimensional convection—driven by surface consecutive year. buoyancy flux (Worthington 1959). Lack of EDW renewal prior to 2011/12 was very In March/April 2015, EDW PV was substantially higher, unusual. Since 1954, when the time series at Bermuda hence EDW was abnormally weak throughout the entire station “S” was established, only one instance of near EDW region, particularly in the northern Sargasso Sea EDW depletion occurred, coinciding with the strongly (Fig. SB3.3b). A thick (~450–500 m) EDW density layer positive NAO during the mid-1970s (Talley and Raymer persisted in the northeastern Sargasso Sea near the Gulf 1982; Talley 1996; Joyce et al. 2000). Stream, where one would expect to find EDW formed via In summary, winter 2014/15 was the weakest EDW cross-frontal mixing, though in 2015 its PV was anoma - formation year on record during the Argo era and was lously high. associated with an extreme, strongly positive winter - Atmospheric conditions of late winter 2014/15 ap NAO. Three of the past four winters have had below peared to be a continuation of conditions of the previous average EDW renewal, with the most recent being the - winter (Josey et al. 2015; section 3e). A strong correla most extreme. | S79 AUGUST 2016 STATE OF THE CLIMATE IN 2015

100 order, giving a global time series that is much more nearly constant than the land-only or ocean-only P E time time series. Over the 27-year period, the – series shows a slight decrease during the 1990s, then no obvious subsequent trend. A strengthening of the global winds in the 1990s, also indicated in the global wind time series (Fig. 3.14c), leveled off since the late 1990s. Global average winds were slightly reduced after the dip in 2009. S ea level variability and change— M. A. Merrifield, E. Leuliette, f. P. Thompson, D. Chambers, B. D. Hamlington, S. Jevrejeva, J. J. Marra, M. Menéndez, G. T. Mitchum, R. S. Nerem, and W. Sweet Sea level variations and trends during 2015 feature the largest El Niño event since 1997/98, the high - est annual average global mean sea level (GMSL) recorded since the altimeter era began in 1993, and reversals in short-term regional trends ref lecting the changing state of the PDO and other climate modes. Regional and global sea level patterns are described here using satellite altimeter data, and extreme coastal sea levels are described using tide gauge data. The signature of El Niño is clear in the 2015 annual mean sea level anomaly (Fig. 3.15a), the change in sea - level from 2014 to 2015 (Fig. 3.15b), and the 2015 devi ation of sea level (minus GMSL) (Fig. 3.15c). All show the characteristic El Niño sea level pattern resulting from weakened trades and westerly wind anomalies in the tropical Pacific (see Fig. 3.13a), i.e., low sea lev - els in the western equatorial Pacific and high sea levels in the cold tongue region of the eastern equatorial Pacific. During the growth phase of El Niño in 2015, low sea levels in the western equatorial Pacific were more prominent north of the equator than south, with weak negative anomalies in the southern convergence zone region (Fig. 3.15c). Peak low sea levels south of the equator are expected during the decline phase of El Niño in 2016 (Widlansky et al. 2014). The poleward . 3.15. (a) Mean 2015 sea level anomaly (cm) relative F ig extension of high sea levels to mid- and high latitudes to a 1993–2014 baseline; (b) 2015 minus 2014 annual along the Pacific coasts of North and South America means; and (c) 2015 annual mean sea level anomaly signifies coastal-trapped wave propagation of the with the GMSL trend removed and normalized by the high anomaly from the tropics, as well as local wind standard deviation of annual mean anomalies from 1993–2014. Data from the multimission gridded sea forcing at midlatitudes that drives positive anomalies surface height altimeter product produced by Ssalto/ along the eastern boundaries, particularly off North Duacs, distributed by AVISO, with support from CNES America (Enfield and Allen 1980; Chelton and Davis (www.aviso.altimetry.fr). 1982). The El Niño–related negative sea level anomaly in the western equatorial Pacific is associated with Ocean are likely associated with anomalous easterlies linked to El Niño. reduced transport and a shallower thermocline in Other prominent patterns in the annual mean the Indonesian Throughf low (Sprintall et al. 2014). anomalies, particularly in the standard deviations The thermocline depth anomalies propagate into the (Fig. 3.15c), include: 1) anomalously low sea levels Indian Ocean along the coastal waveguide, reducing sea level along the west coast of Australia (Fig. 3.15c). around Antarctica and an associated meridional sea High sea levels in the western equatorial Indian level gradient, indicative of an intensified Antarctic | S80 AUGUST 2016

101 Circumpolar Current (ACC); 2) generally high sea The rise in sea level over the altimeter period levels between 30° and 60°S related to the subtropical has not been uniform, with regions of high rates located in the western Pacific and Indian Oceans gyre circulations (see Fig. 3.4a; Roemmich et al. 2007); (Fig. 3.17c). Sea level rates have been near zero or 3) high sea levels in the North Atlantic subtropical gyre region suggesting an intensified anticyclonic falling over areas of the eastern Pacific, Southern, gyre circulation; and 4) low sea levels in the North and North Atlantic Oceans. These regional patterns Atlantic subpolar region corresponding to a strength are largely linked to trending wind patterns over the - ened cyclonic gyre circulation. past 20 years associated with climate modes, such as the PDO (e.g., Merrifield et al. 2012). These are not The seasonal progression of the 2015 El Niño (Fig. 3.16) was marked by a steady autumn of west - long-term trends and have reversed in many regions ern Pacific sea levels throughout the year and the over the past five years (Fig. 3.17c compared to 3.17d). eastward propagating equatorial Kelvin waves that In particular, sea level has been falling in the western tropical Pacific and rising in the eastern Pacific due strengthened in boreal spring (March–May) and to a change from negative to positive PDO phase, and fully impacted the South American coast by autumn (September–November). High sea levels along North with that a shift in basin-scale wind patterns, as well as the 2014 weak Modoki-like near-El Niño and the - and South America preceded the arrival of the propa gating high anomaly from the equatorial Pacific, strong 2015 El Niño. Extreme sea level events, measured as the average indicating that these high anomalies were partly of the 2% highest daily values above the annual mean remnants from the previous year, which featured a weak Modoki-like (Ashok et al. 2007) near-El Niño, from tide gauges, showed a characteristic dependence as well as locally generated wind-forced anomalies. on latitude, ref lecting stronger atmospheric forcings outside the tropics (Fig. 3.18a). The 2015 extreme Other features in the annual mean sea level (Fig. 3.15) that exhibited a marked variation over the levels were above climatology at the eastern equatorial year (Fig. 3.16) include the Indian Ocean high sea Pacific and along the Pacific coast of North America (Fig. 3.18b), ref lecting the contribution of positive levels that developed over the second half of 2015. El Niño anomalies and possibly some shifts in storm In addition, the low sea levels along Antarctica and the heightened ACC sea level gradient were more - forcing. The high pattern did not extend to mid latitudes along the South American coast. Extremes prominent in the first half of 2015 than the second were below climatology along the east coast of North half. Sea level changes over the course of the year America, due in part to slightly below average Atlan - were also evident in the marginal seas of the North Atlantic, with high sea levels in the North Sea peaking tic hurricane activity (section 4e2). in summer and dropping in autumn, low sea levels in the Mediterranean over most of the year switching to high levels during autumn, and high sea levels in Hudson Bay peaking in spring that fell below climatology levels over the remainder of the year. GMSL has risen over the satel - lite altimeter record (1993–pres - ent) at an average rate of 3.3 ± −1 0.4 mm yr (Fig. 3.17a). GMSL in 2015 was the highest yearly aver - age over the record and ~70 mm greater than the 1993 average, due in part to the linear trend but also to the 2015 El Niño, which - caused sea level to rise an addi tional ~10 mm above the long-term trend (Fig. 3.17b). The high GMSL F 3.16. Seasonal sea level anomalies (cm, relative to the 1993-2014 . ig anomaly during 2015 exceeded the average) for (a) Dec–Feb 2014/15, (b) Mar–May 2015, (c) Jun–Aug 2015, anomaly during the previous large and (d) Sep–Nov 2015. El Niño in 1997/98 (Fig. 3.17b). | S81 AUGUST 2016 STATE OF THE CLIMATE IN 2015

102 tendencies along the NECC than the 2015 anomaly map (Fig. 3.20). In contrast to the annual mean picture, 2015 began with westward anomalies between 5°S and 5°N across the eastern half of the basin - (Fig. 3.20a), with peak west −1 ward anomalies of ~25 cm s at 1°N. These anomalies were an enhancement of the northern branch of the westward South Equato - rial Current (SEC) as seen in December 2014 (Dohan et al. 2015). Immediately north of 5°N, the eastward NECC was only marginally faster than its climatological January 3.17. (a) Global mean sea level (mm) obtained from consecutive satellite . ig F strength. In February, intense altimeter missions, with 60-day smoothing and seasonal signals removed (black El Niño–related eastward –1 line indicates a rise rate of 3.3 mm yr ); (b) Detrended GMSL compared with anomalies first appeared in the multivariate ENSO index (MEI; obtained from http://sealevel.colorado. –1 the western tropical Pacific as edu); (c) Sea level trends (cm decade ) 1993–2015; and (d) Sea level trends –1 −1 (cm decade ) 2011–15. Scales differ in (c) and (d). at anomalies of 20–40 cm s 145°–175°E, 2.5°S–4°N. urface currents— R. Lumpkin, G. Goni, and K. Dohan g. S Throughout boreal spring, the El Niño–related Ocean surface current changes, transports derived anomaly pattern propagated eastward (Fig. 3.20b), - reaching 160°W by March and 90°W by April. Dur from ocean surface currents, and features such as ing these months, warm water advected by these rings inferred from surface currents, all play a role current anomalies caused the NINO3 and NINO3.4 in ocean climate variations. Surface currents de - SST indices to increase rapidly (see section 4b). In scribed here are obtained from in situ (global array of −1 were - drogued drifters and moorings) and satellite (altim April, eastward anomalies of 40–50 cm s etry, wind stress, and SST) observations. Transports present at 95°–130°W, 2.5°S–2.5°N. Throughout are derived from a combination of sea level anomalies March and April, equatorial zonal currents in the band 120°W–180° were close to their climatologi - (from altimetry) and climatological hydrography. For cal average, straddled by the eastward anomalies details of these calculations, see Lumpkin et al. (2011). Anomalies are calculated with respect to 1992–2007. centered at 5°–6°N (the latitude of the NECC) and 1°–2°S (Fig. 3.20b). In May, the anomalies south of Annually averaged zonal current anomalies for 2015, −1 - the equator diminished to <20 cm s changes in anomalies from 2014 to 2015 (Fig. 3.19), , while anoma −1 persisted in the NECC band. The lies of 35–40 cm s and seasonal average 2015 anomalies (Fig. 3.20) are discussed below by individual ocean basin. eastward advection of relatively fresh water, combined The dramatic El Niño of 2015/16 dominated with an El Niño–driven shift in the ITCZ (section - annual mean current anomalies in the Pacific ba 3e), likely accounts for the development of fresh SSS sin (Fig. 3.19a), with annually averaged eastward anomalies (section 3d). −1 between 1.5° and 6°N and anomalies >20 cm s Throughout boreal summer (June–August; weaker eastward anomalies in the rest of the latitude Fig. 3.20c), eastward anomalies persisted across the basin, with equatorial eastward anomalies returning band between 10°S and 10°N. Because 2014 was characterized by westward anomalies on the equa - across the western half of the basin in July and across the entire basin in August. Averaged over these three tor and eastward anomalies in a strengthened North Equatorial Countercurrent (NECC) at 5°–6°N, the months (Fig. 3.20c), eastward anomalies exceeded −1 2015 minus 2014 map (Fig. 3.19b) has larger east - from 6°S to 9°N, with peak anomalies of 30– 5 cm s −1 35 cm s ward anomaly tendencies on the equator and weaker at 4°–6°N. This pattern persisted in boreal | S82 AUGUST 2016

103 Surface current anomalies in the equatorial Pa - cific typically lead SST anomalies by several months, with a magnitude that scales with the SST anomaly magnitude. A return to normal current conditions is also typically seen before SST returns to normal. Thus, current anomalies in this region are a valu - able predictor of the evolution of SST anomalies and their related climate impacts. This leading nature can be seen in the first principal empirical orthogonal function (EOF) of surface current (SC) anomaly and separately of SST anomaly in the tropical Pacific basin (Fig. 3.21). For 1993–2015, the maximum correlation between SC and SST is 0.70 with SC leading SST by 71 days. Both SC and SST EOF amplitudes were positive throughout 2015, with the cumulative effect of positive SC EOF amplitude resulting in a steadily increasing SST EOF amplitude to almost 3 standard deviations in November 2015, nearing the November 1997 value of 3.2. Throughout most of 2015, Indian Ocean mon - - soon-driven currents were much closer to climatol ogy than the dramatic anomalies seen in the Pacific (Fig. 3.19a). This normality is in contrast to the strong eastward anomalies seen across the basin in 2013–14 (Lumpkin et al. 2014; Dohan et al. 2015). Those eastward anomalies dominate the 2015 minus 2014 F ig . 3.18. (a) Annual maximum sea level (cm) during 2015 computed from the mean of the 2% highest daily values relative to the 2015 annual mean, measured at tide gauges (http://uhslc.soest.hawaii.edu); (b) The 2015 annual maximum from (a) divided by the time- averaged annual maximum measured at each station with at least 20 years of data. autumn (Fig. 3.20d), with another pulse of extremely −1 strong (>50 cm s ) eastward anomalies appearing at 170°E–150°W, 3°–5°N in August and peaking at >60 −1 cm s in October; these were the strongest monthly averaged broad-scale current anomalies seen in 2015. This pattern propagated eastward in November and weakened significantly through December. The year −1 concluded with eastward anomalies of ~20 cm s across the basin from 3°N to 6°N, consistent with a stronger and wider NECC than in the December cli - matology. The northern edge of this NECC was close to its climatological northern extent but extended south to 2°N, compared to 3.5°N in climatology. The Kuroshio was shifted anomalously northward in 2010–14, although this shift diminished during 2014 (Dohan et al. 2015). During 2015, the Kuroshio ig - 3.19. Annually averaged geostrophic zonal cur F . - was close to its climatological latitude, with a maxi –1 rent anomalies (cm s ) for (a) 2015 and (b) 2015 minus −1 at 35°N mum annually averaged speed of 35 cm s 2014 derived from a synthesis of drifters, altimetry, between 140° and 170°E (Fig. 3.19). and winds. Positive (red) values denote anomalously eastward velocity. | S83 AUGUST 2016 STATE OF THE CLIMATE IN 2015

104 The Gulf Stream in 2015 - remained close to its cli matological position with little change from 2014 (Fig. 3.19). The North Brazil Cur - rent, which sheds rings that carry waters from the Southern Hemisphere into the North Atlantic and has important ecosystem im - pacts downstream (Kelly et al. 2000), exhibited an annual transport smaller than its long-term (1993– 2015) value. As in 2014, it shed eight rings in 2015, a larger-than-average value. –1 ) with re F 3.20. Seasonally averaged zonal geostrophic anomalies (cm s - ig . Sea level anomalies in the spect to seasonal climatology, for (a) Dec 2014–Feb 2015, (b) Mar–May 2015, - region, which have gener (c) Jun–Aug 2015, and (d) Sep–Nov 2015. ally increased since 2001 zonal current tendencies in the Indian Ocean basin (apart from lows in 2003 and 2008), remained higher than average in 2015. (Fig. 3.19b). In 2015, the strongest anomalies with In the southwest Atlantic Ocean, the Brazil Cur respect to monthly climatology were seen in October - rent carries waters from subtropical to subpolar re - and November, with an unusually early development of the North Monsoon Current (e.g., Beal et al. 2013) gions, mainly in the form of large anticyclonic rings −1 at associated with westward anomalies of ~30 cm s - (Lentini et al. 2006). The separation of the Brazil Cur rent front from the continental shelf break continued 3°S–2°N, 60°–80°E during these months (Fig. 3.20d). Large-scale current anomalies returned to near- to exhibit a seasonal cycle, which is mainly driven by wind stress curl variations and the transport of this climatological December values by the end of 2015. current. During 1993–98, the annual mean separa - The Agulhas Current transport is a key indicator tion of the front shifted southward in response to a of Indian–Atlantic Ocean interbasin water exchanges. long-term warming in South Atlantic temperatures The annual mean transport of the Agulhas Current has been decreasing from a high set in 2013, with (cf. Lumpkin and Garzoli 2010; Goni et al. 2011). In −1 3 6 s m ), 53 Sv in values of 56 Sv in 2013 (1 Sv 10 2015, the Brazil Current front and its separation from the continental shelf break persisted south of its mean 2014, and 50 Sv in 2015. The 2015 transport of 50 Sv is equal to the Agulhas’ long-term (1993–2015) mean. position, unchanged from 2014. Annual mean anomalies in the Atlantic Ocean −1 (Fig. 3.19a) indicate a 5–7 cm s eridional overturning circulation observations in h. M strengthening of the the North Atlantic Ocean— eastward NECC at 4.5°–6.5°N, 30°–50°W, and condi M. O. Baringer, M. Lankhorst, - D. Volkov, S. Garzoli, S. Dong, U. Send, and C. S. Meinen tions close to climatology along the equator. However, the annual average hides a pattern of reversing equa - This section describes the Atlantic meridional torial anomalies between boreal winter and spring overturning circulation (AMOC) and the Atlantic (Fig. 3.20). The year began with eastward anomalies meridional heat transport (AMHT), determined by −1 from 3°S to 2°N across much of the basin, of 20 cm s the large-scale ocean circulation wherein northward which weakened through February and were present moving upper layer waters are transformed into deep only at 25°–35°W in March/April. In May, westward waters that return southward, redistributing heat, −1 anomalies of 10–15 cm s State of freshwater, carbon, and nutrients. Previous developed across the basin the Climate reports (e.g., Baringer et al. 2013) and - from 2°S to 2°N. These anomalies weakened consider ably through June and were no longer present in July. reviews (e.g., Srokosz and Bryden 2015; Perez et al. 2015; Carton et al. 2014; Srokosz et al. 2012) discuss No significant basinwide equatorial anomalies were seen in the remainder of 2015. the AMOC’s impact on climate variability and ecosys - tems. The AMOC is computed as the maximum of the | S84 AUGUST 2016

105 vertical accumulation of the horizontally integrated velocity across a section (i.e., the maximum transport that occurs in either the upper or lower layer before the circulation starts to change direction again). The AMHT involves the co-variability of temperature and velocity and is only meaningful as a f lux (and hence, independent of the absolute temperature scale used) when the total mass transport can be accounted for (i.e., sums to zero). Observing systems can measure both temperature and velocity, usually with tradeoffs in system design that favor the computation of one quantity over the other. Here we describe the AMOC from observing systems at 41°N, 26°N, and 16°N, and AMHT at 41°N, 26°N, and 35°S. In the future, AMOC observing systems in the South Atlantic and subpolar North Atlantic should provide additional time series (e.g., Srokosz et al. 2012). The longest time series of ocean transport to serve - as an index of the AMOC’s strength in the North At lantic (e.g., Frajka-Williams 2015; Duchez et al. 2014) is from the Florida Current (FC, as the Gulf Stream is called at 26°N), measured since 1982 (Fig. 3.22). FC F ig . - 3.22. (a) Daily estimates of Florida Current trans –1 3 6 and AMOC transport variations at all time scales also port (10 m s ) during 2015 (orange solid line), 2014 are inversely linked to sea level variations along the (dashed purple line), and 1982–2012 (light gray lines) with 95% confidence interval of daily transport values east coast (Goddard et al. 2015; McCarthy et al. 2015). computed from all years (black solid line) and the The median 1982–2015 transport of the FC is 31.9 ± long-term mean (dashed black). (b) Daily estimates of 0.25 Sv (one standard error of the mean assuming a –1 3 6 Florida Current transport (10 s m ) for the full time 20-day integral time scale) with a small downward series record (light gray), smoothed using a 12-month −1 (errors estimating trend of −0.31 ± 0.26 Sv decade second-order Butterworth filter (heavy black line), 95% significance as above). The 2015 median FC mean transport for the full record (dashed black line), transport was 31.7 ± 1.7 Sv, only slightly below the and linear trend from 1982 through 2015 (dashed blue line). Two-year low-passed Atlantic Multidecadal long-term average. Daily FC transports compared (AMO, yellow line) and North Atlantic Oscillation to those of all previous years (Fig. 3.22) indicate that (NAO, red line) indices are also shown. 2015, like 2014, had several unusually low transport anomalies (extremes defined as outside the 95% confidence limits for daily values). These occurred during 8–9 May, 24–29 September, and 5–9 October 2015. The lowest daily 2015 FC transport was 22.2 Sv on 8 October, with transports < 23 Sv for five days around this date. During 2015 there was only one high transport event, with an average transport of > 39 Sv from 8 to 13 July. At 41°N, a combination of profiling Argo f loats (that measure ocean temperature and salinity for the - upper 2000 m on broad spatial scales, as well as veloc ity at 1000 m) and altimetry-derived surface velocity (Willis and Fu 2008) are used to estimate the AMOC ig F . 3.21. EOF of surface current (SC) and SST anomaly (Fig. 3.23) and AMHT (Fig. 3.24). This time series has variations in the tropical Pacific from the OSCAR not been updated since last year’s report (Baringer et model (Bonjean and Lagerloef 2002; www.esr.org - al. 2015a,b), extending from January 2002 to Decem /enso_index.html). (a) EOF Amplitude time series ber 2014. At 26°N, the AMOC/AMHT (Figs. 3.23 and normalized by their respective standard deviations. (b) EOF Spatial structures. 3.24) is measured with full-water column moorings | S85 AUGUST 2016 STATE OF THE CLIMATE IN 2015

106 missed large signals and high correlations in the surface waters. These data have been updated since last year’s report and now extend from February 2000 to February 2016. At 35°S in the South Atlantic, the AMHT (Fig. 3.24) is estimated using a combination of high-density (closely spaced) expendable bathy - thermograph (XBT) and broader-scale Argo profiling f loat data (Garzoli et al. 2012). While the AMOC has also been estimated at 35°S (e.g., Dong et al. 2009), those estimates (not shown) are rough because the XBTs only extend to 750 m. These data are collected and analyzed in near–real time, with values spanning July 2002 to October 2015. Some guidance on 2015 AMOC and AMHT variability can be gained from state estimation - F ig . 3.23. Estimates of Atlantic meridional overturn model output, constrained by observations. February –1 3 6 ing circulation (1 Sv 10 ≡ s m ; AMOC) from the 1992–November 2015 monthly estimates of AMOC Argo/Altimetry estimate at 41°N (black; Willis 2010), and AMHT from the global MITgcm in ECCO2 the RAPID-MOC/MOCHA/WBTS 26°N array (red; (cube-sphere) configuration (e.g., Menemenlis et McCarthy et al. 2015), and the German/NOAA MOVE array at 16°N (blue; Send et al. 2011) shown versus year. al. 2008), forced with the new JRA-55 atmospheric All time series have a three-month second-order But - fields (Kobayashi et al. 2016) and GPCP precipitation terworth low-pass filter applied. Horizontal lines are mean transports during similar time periods as listed in the corresponding text. Dashed lines are trends for each series over the same time period. For MOVE data, the net zonal and vertical integral of the deep circulation represents the lower limb of the AMOC (with a negative sign indicating southward flow), and hence a stronger negative (southward) flow represents an increase in the AMOC amplitude. Light gray lines show ECCO2-derived transports: (top) thin gray is the 41°N transport, thick gray is the 26°N transport, (bottom) shows the negative meridional overturning circulation in the model for ease of comparison with the 16°N data. that span the full basin and include direct transport measurements in the boundary currents as part of the large RAPID-MOC/MOCHA/WBTS 26°N mooring array (Smeed et al. 2015). The data from these moor - ings are collected every 18 months, most recently in December 2015; too late to be calibrated and finalized for this report. The 26°N data shown here extend from April 2004 to March 2014 (see last year’s report for full details). At 16°N, a mooring array of inverted . ig F 3.24. Observed time series of Atlantic meridional echo sounders, current meters, and dynamic height - heat transport (PW; AMHT) at (a) 41°N (from profil moorings (Send et al. 2011) measures the f low below ing floats following Hobbs and Willis 2012; blue lines), with uncertainties (light blue lines) and the trend 1000 m (the southward f lowing part of the AMOC - (dashed blue line), at (b) 26°N (from mooring/hydrog “conveyor belt”) that sends North Atlantic Deep raphy data) 12-hourly values (gray line), filtered with Water toward the equator; hence, the AMOC esti - a 3-month low-pass filter (black line), and the trend mate at this latitude (Fig. 3.23) is a negative number (black dashed line), and at (c) 30°–35°S (from XBTs) (southward deep f low) to distinguish these observa - quarterly values (light green), filtered with yearly tions from the full water column systems. Since this boxcar (dark green line), and the trend (dashed green array only measures the deep circulation, an estimate line). Heat transports simulated by ECCO2 (orange lines) are shown at all latitudes. of the AMHT is impossible at 16°N because of the | S86 AUGUST 2016

107 (Huffman et al. 2012), are analyzed here. The ECCO2 provide observations of sufficient frequency and model output is well correlated with the instrument- geographic coverage to globally monitor changes in based measurement of the AMOC (Fig. 3.23) and the near-surface concentrations of the phytoplankton −3 pigment chlorophyll- ; mg m a (Ch l a AMHT (Fig. 3.24) at 26°N and 41°N, with correlations provides a ). Chl a first-order index of phytoplankton abundance and of 0.58/0.59 and 0.57/0.38, respectively, all signifi - is proportional to the maximum sunlight energy cantly above the 95% confidence level. ECCO2 model absorbed for photosynthesis (Behrenfeld et al. 2006). output is not statistically significantly correlated with a Here, global Chl distributions for 2015 are evaluated the 16°N AMOC or 35°S AMHT transports (correla - within the context of the 18-year continuous record tion values of 0.12 and 0.13, respectively). At 26°N and provided through the combined observations of 41°N the AMOC and AMHT in the ECCO2 output Aqua (MODISA, SeaWiFS (1997–2010), MODIS on show a slight increase from 17.6 Sv and 1.02 PW 15 - (2011–pres Suomi-NPP 2002–present), and VIIRS on (1 PW = 10 W) in 2014 to 18.3 Sv and 1.09 PW in data used in this analysis correspond ent). All Chl 2015. Preliminary analysis of the new data from 26°N a - (not shown) indicates that the transport has contin to version R2014.0 (http://oceancolor.gsfc.nasa.gov ued fairly unchanged since 2011 (through December /cms/reprocessing/), which uses common algorithms 2015), with values lower than the earlier part of the and calibration methods to maximize consistency in the multimission satellite record. record (D. A. Smeed, 2016, personal communica - tion). Additionally, there is no unusual “event” in The spatial distribution of VIIRS annual mean for 2015 (Fig. 3.25) is generally consistent with a Chl the assimilation time series, as has been clearly seen the well-established, physically driven distribution of in other time periods (e.g., Fig. 3.24). This finding is nutrients (e.g., Siegel et al. 2013). To assess changes in supported by the FC time series and the ECCO2 state a Chl estimation (Fig. 3.22). a in each for 2015, mean values for VIIRS Chl month of 2015 were subtracted from monthly cli - At 16°N, the time series of the AMOC estimate de - matological means for MODISA (2003–2011) within creased from 29.0 Sv in 2013, to 28.4 Sv in 2014, and to - 27.2 Sv in 2015 (as stated earlier the decrease in south globally distributed geographic bins, and then those ward f low implies an increase in the AMOC at this monthly anomaly fields were averaged (Fig. 3.26a). latitude; Fig. 3.23). This reduction has led to a reduced Identical calculations were performed on MODISA estimate of the long-term trend of the AMOC from SST (°C) data to produce a companion SST annual February 2000 to February 2016 at 16°N to be +3.6 Sv ± mean anomaly (Fig. 3.26b). −1 a 2.5 Sv decade In 2015, the phytoplankton Chl concentrations . This trend is of the opposite sign from −1 the trends at 26°N and 41°N (−4.1 ± 3.2 Sv decade across much of the equatorial Pacific were strongly −1 ). A similar situation exists depressed, with concentrations 20%–50% below the and −1.3 ± 4.9 Sv decade - climatological norm. This response is generally corre with the 35°S AMHT transport estimate. In the south, the AMHT has remained essentially constant for the last three years (mean value 0.6 PW northward). This implies a virtually steady AMOC as well (the AMOC and AMHT being highly correlated). This recently constant AMHT has reduced the long-term trend of −1 an increasing AMHT to +0.11 ± 0.10 PW decade . From these data it is clear that the variability at all latitudes in the Atlantic is not well correlated and, therefore, data from more than one latitude are needed to describe the state of the ocean. i. G lobal ocean phytoplankton— B. A. Franz, M. J. Behrenfeld, D. A. Siegel, and S. R. Signorini –3 ) derived distribution (mg m a 3.25. Mean 2015 Chl . ig F Marine phytoplankton represent roughly half the from VIIRS with the location of the mean 15°C SST iso - net primary production (NPP) on Earth, fixing atmo - therm (black lines) delineating boundaries of the per - spheric CO into food that fuels global ocean ecosys - 2 a data are from manently stratified ocean (PSO). Chl tems and drives biogeochemical cycles (e.g., Field et NASA Reprocessing version 2014.0. Data are averaged al. 1998; Falkowski et al. 1998). Satellite ocean color into geo-referenced equal area bins of approximately sensors, such as SeaWiFS (McClain 2009), MODIS 4.6 km × 4.6 km and mapped to an equi-rectangular projection centered at 150°W. (Esaias et al. 1998), and VIIRS (Oudrari et al. 2014), | S87 AUGUST 2016 STATE OF THE CLIMATE IN 2015

108 Typically, chlorophyll anoma - lies in the PSO exhibit an inverse relationship with SST anomalies (Behrenfeld et al. 2006), as an - nual mean SST anomalies largely coincide with surface mixed layer depth (MLD) changes in the PSO. A shallower MLD means that phytoplankton spend more time near the ocean’s surface and thus - have higher daily sunlight expo - sures than deeper mixing popula tions. Phytoplankton respond to this increased light by decreasing their cellular chlorophyll levels in - a response called photoacclima tion (thus, increased SST leads to decreased MLD, which leads to a - decreased Chl ). A potential sec ond consequence of a decrease in MLD is a decrease in the vertical - transport of nutrients to the sur face layer, but coupling between the MLD and nutricline depths throughout much of the PSO is known to be weak (Lozier et al. 2011). In the equatorial Pacific, a however, the anomalously low Chl - and high SST in 2015 were primar ily driven by nutrient availability changes due to the El Niño event, a ig F . 3.26. Spatial distribution of summed monthly 2015 (a) VIIRS Chl wherein the westerly winds weaken anomalies expressed as % difference from climatology and (b) MODISA along the equator allowing warm SST anomalies shown as absolute differences. (c) Relationships between water, normally confined to the anomalies from (a) and (b), with colors differ - the signs of SST and Chl a a entiating sign pairs and absolute changes of less than 3% in Chl or 0.1°C western Pacific, to migrate east - in SST masked in black. Monthly differences are derived relative to a ward. Wind-driven upwelling, a MODISA 9-year climatological record (2003–11). Location of the mean process that brings cold, nutrient- 15°C SST isotherm (black lines) delineates the PSO. rich water to the surface along the equator, was also greatly reduced, - lated with elevated surface temperatures (Fig. 3.26c), causing SST to rise and significantly lowering biologi consistent with a well-developed El Niño. Depressed cal productivity. At higher latitudes, outside the PSO, was also observed in climatologically warmer a Chl the relationship between SST changes and light and nutrient conditions is more complex, resulting in a waters of the northern Indian Ocean, northeastern a was Pacific, and Sargasso Sea, while elevated Chl wide diversity of responses between anomalies in SST , (Fig. 3.26c). a observed in the cooler waters of the western North and Chl a Pacific, much of the South Pacific, and throughout Spatially integrated monthly mean Chl - con centrations in the PSO (Fig. 3.27a) vary by ~20% the tropical Atlantic. These regions fall within the −3 - (±0.03 mg m ) around a long-term mean of approxi permanently stratified ocean (PSO; Figs. 3.25 and −3 3.26, black lines at approximately 40°N and 40°S), over the 18-year time series. This mately 0.15 mg m - defined here as the region where annual average sur - variability includes seasonal cycles and larger depar face temperatures are >15°C (Behrenfeld et al. 2006). tures from the climatological mean associated with The PSO is characterized by nutrient-depleted surface climatic events. The long-term mean is approximately −3 mixed layers shallower than the nutricline. higher than previous reports (Franz et al. 0.01 mg m | S88 AUGUST 2016

109 sistent with expectations based on multivariate ENSO index variations (MEI; Wolter and Timlin 1998). a Distinguishing the different drivers of Chl variability is important for interpreting the satellite record. Light-driven decreases in chlorophyll are associated with constant or even increased rates of photosynthesis, while nutrient-driven decreases are associated with decreased photosynthesis. An analy - sis of photoacclimation and nutrient-driven changes in growth rate and biomass from the MODIS record shows that the inverse relationship between SST and anomalies is overwhelmingly due to light- and Chl a division rate-driven changes in cellular pigmentation, rather than changes in biomass (Behrenfeld et al. 2016). This study also shows that photoacclimation contributed 10%–80% of the variability in cellular pigmentation, suggesting the 2015 anomaly patterns for the PSO (Fig. 3.26c) were largely driven a in Chl by photoacclimation. An additional contributor to - is the misrepresenta a the anomaly patterns in Chl . F a 3.27. Eighteen-year, multimission record of Chl ig changes due to colored dissolved organic tion of Chl a averaged over the PSO (see Fig. 3.25) for (black) Sea - matter (cDOM) signals (Siegel et al. 2005). Sunlight WiFS, (blue) MODISA, and (red) VIIRS. (a) Indepen - dent records from each mission, with the multimission degrades cDOM, and this degradation is more ex - a concentration for the region (horizontal mean Chl tensive for shallow MLDs, yielding in the PSO an black line). (b) Monthly anomalies for SeaWiFS, inverse relationship between cDOM and SST (Nelson MODISA, and VIIRS after subtraction of the 9-year and Siegel 2013) that may be mistakenly attributed to MODISA monthly climatological mean (2003–11), changes (Siegel et al. 2013). a Chl with the averaged difference between SeaWiFS and MODISA over the common mission lifetime (gray j. R. A. Feely, R. Wanninkhof, lobal ocean carbon cycle— G region). The MEI (green diamonds, see text) inverted anomalies. and scaled to match the range of the Chl a B. R. Carter, J. N. Cross, J. T. Mathis, C. L. Sabine, C. E. Cosca, and J. A. Tirnanes - The global ocean is a major sink for anthropo 2015). This difference is not due to a change in global phytoplankton abundances but rather is a conse - genic carbon dioxide (CO ) that is released into the 2 - quence of the R2014.0 reprocessing that includes cali atmosphere from fossil fuel combustion, cement bration updates and a revised chlorophyll algorithm production, and land-use changes. Over the last (Hu et al. 2012). The time series demonstrates the high decade, the global ocean has continued to take up a ) substantial fraction of anthropogenic carbon (C level of consistency between the overlapping periods anth of the SeaWiFS and MODISA missions. Beyond 2012, emissions and is therefore a major mediator of global the MODISA time series becomes increasingly erratic climate change. Air–sea f lux studies, general ocean (not shown), ref lecting a growing uncertainty in the circulation models including biogeochemistry, and data-constrained inverse models suggest the ocean calibration of that instrument (Franz et al. 2015). 15 grams Consistency between MODISA and VIIRS in 2012, ≡ 10 absorbed approximately 46 Pg C (1 Pg C between 1994 and 2014 (Le Quéré of carbon) of C however, provides confidence for extension of the anth et al. 2015; DeVries 2014), with an increase in the rate multimission trends into 2015. −1 a during the uptake from 2.2 ± 0.5 Pg C yr of C Chl monthly anomalies within the PSO anth −1 during the 1990s to approximately 2.6 ± 0.5 Pg C yr (Fig. 3.27b) exhibit variations of ~15% over the most recent decade from 2005 to 2014 (Table 3.1). A multimission time series, with climatic events such as El Niño and La Niña clearly delineated. In 2015, - summary of the air–sea exchange and ocean inven consistent with a strong El Niño, Chl trends in the tory of C a based on both observations and model anth PSO approached the lowest levels measured since the results through 2014 is presented. Data for 2015 are - 1997/98 El Niño. Furthermore, mean Chl a concentra not available owing to the need for careful scientific quality control of ocean carbon data prior to analysis. tions in the PSO declined by approximately 20% from - the peak observed during the 2010/11 La Niña, con | S89 AUGUST 2016 STATE OF THE CLIMATE IN 2015

110 1) a Ir carbon dIox Ide fluxe S – Sea uptake can be estimated from air– Ocean CO 2 p CO sea differences in CO ) and partial pressure ( 2 2 gas transfer velocity, which is mainly a function of wind speed. Significant improvement in global and regional CO f lux estimates have been made in the 2 CO past year as part of Surface Ocean Mapping p 2 - Intercomparison (SOCOM), comparing 13 indepen dent data-based methods of global interpolation of p CO (Rödenbeck et al. 2015). Recent research has 2 also decreased uncertainty on the equations used to exchange from air–sea estimate CO CO p differences 2 2 (Wanninkhof 2014; Ho and Wanninkhof 2016). Large increases in autonomous p CO measurements over 2 time have been achieved with ships of opportunity (SOOP-CO ) and moorings. The third update of 2 Atlas (SOCAT) with over the Surface Ocean CO 2 14 million data points was released to the public in 2015 (Bakker et al. 2016). Subsequent data releases will occur annually such that the data can inform sources and the annual assessment of global CO 2 sinks provided by the Global Carbon Project (www . 3.28. (a) Average annual air–sea CO flux for 2005– F ig 2 .globalcarbonproject.org). The increased data cover - 14 based on the AOML–EMP approach (Park et al. age and new mapping techniques make it possible 2010). Positive values are effluxes and negative values are influxes. The SST anomaly interpolation method f luxes at monthly time scales, to obtain air–sea CO 2 used for this analysis is less robust than more recent allowing investigation of variability on subannual to and sophisticated approaches (Rödenbeck et al. 2015), decadal time scales and the causes thereof. An impor - but faithfully reproduces the major anomaly features, - tant recent result illuminated by these improved ap especially in the highly data-constrained equatorial proaches is the reinvigoration of the Southern Ocean Pacific. (b) Air–sea CO flux anomaly in 2014 compared 2 carbon sink since 2002 (Landschützer et al. 2015), to ten-year average (2005–14). Positive values are in - which had previously been found to be decreasing creased effluxes (or decreased influxes) and negative (Le Quéré et al. 2007). values are increased influxes (or decreased effluxes). The newly released datasets have been used to verify the magnitude of the anthropogenic air–sea CO f luxes over the last decade and in 2014. The ocean 2 sink in 2014 was 10% above the 2005–14 average of −1 2.6 ± 0.5 Pg C yr (Table 3.1). In 2014, the ocean and land carbon sinks removed 27% and 37% of total CO 2 emissions, respectively, leaving 36% of emissions in - the atmosphere, compared to 44% as a decadal aver age (Le Quéré et al. 2015). - Ocean uptake anomalies (Fig. 3.28b) in 2014 rela tive to the 2005–14 average (Fig. 3.28a) are attributed to several climate reorganizations. The lower CO 2 eff luxes in the equatorial Pacific are attributed to anomalously high regional SST and reduced upwell - F ig measurement from a ship of opportunity 3.29. CO . ing of CO -rich subsurface waters due to a weak 2 2 (SOOP) from New Zealand to Long Beach, CA, show - Modoki-like near-El Niño in 2014. Stronger eff luxes ing anomalously high surface water partial pressure of are evident in the northeast Pacific due to the warm CO p ) values in 2014 and 2015 in the anomalously CO ( 2 2 “Blob” (Bond et al. 2015) as well as warm conditions warm surface water offshore of the California coast. offshore of the California coast (Fig. 3.29). A cold Equatorial CO p values are depressed in the boreal 2 anomaly in the southern Labrador Sea and adjacent spring of 2014 and 2015 compared to climatological - regions (Josey et al. 2015) associated with deep mix values. | S90 AUGUST 2016

111 Table 3.1. Global ocean C σ . uptake rates. All uncertainties are reported as ±1 anth –1 Mean C Yea r s Uptake (Pg C yr ) Reference anth 1960–69 1.1 ± 0.5 Le Quéré et al. 2015 1970–79 1.5 ± 0.5 Le Quéré et al. 2015 1980 – 89 2.0 ± 0.5 Le Quéré et al. 2015 1990–99 2.2 ± 0.5 Le Quéré et al. 2015 1994–2006 2.6 ± 0.5 Sabine and Tanhua 2010 2000–09 2.3 ± 0.5 Le Quéré et al. 2015 Khatiwala et al. 2013 1994–2010 2.3 ± 0.5 2.9 ± 0.4 Kouketsu and Murata 2014 2000–10 Le Quéré et al. 2015 2005–14 2.6 ± 0.5 Le Quéré et al. 2015 2014 2.9 ± 0.5 method (eMLR) distinguishes these changes from ing led to larger eff luxes in the northwest Atlantic. A large negative anomaly in the northwest Pacific, large natural decadal changes in dissolved inorganic perhaps related to a shift in the PDO, contributed carbon (DIC) concentrations between cruises (e.g., uptake. to the higher-than-average 2014 ocean CO Friis et al. 2005; Sabine et al. 2008). The method 2 data in the western A recent synthesis of - p CO has recently been modified to permit basinwide es 2 C Arctic showed that the Arctic biogeochemical sea trends by utilizing data from repeat timates of - anth scape is in rapid transition. An analysis of nearly hydrography cruises and climatological data from CO World Ocean Atlas 2013 (Sabine and Tanhua 2010; measurements from p 600 0 00 surface seawater 2 −1 Locarnini et al. 2013; Zweng et al. 2013; Williams 2003 to 2014 found 0.0109 ± 0.0057 Pg C yr entered - et al. 2015). Global-scale results from this modi the ocean in the western Arctic coastal ocean (north fied eMLR approach indicate a C of the Bering Strait) during this period, and that this uptake rate of anth −1 ~2.6 Pg C yr (1994–2006). This estimate is consistent uptake would be expected to increase by 30% under (within uncertainties) with model-based (Khatiwala decreased sea ice cover conditions expected with Arctic warming (Evans et al. 2015). Reductions in et al. 2013; Talley et al. 2016) and data-based estimates (Table 3.1) for this period. ice cover may have a more moderate impact on other areas of the western Arctic, such as south of Bering C storage rates vary widely regionally (Fig. 3.30), anth −1 −2 yr Strait (Cross et al. 2014). ranging from 0.1 ± 0.02 to 2.2 ± 0.7 mol C m (Williams et al. 2015). For comparison, the 2.3– −1 2.9 Pg C yr arbon Inventor global mean uptake rate estimates above IeS S 2) c from the go -S hIp Survey Repeat Hydrography Global C correspond to a global mean storage rate between T h e C L I VA R / C O 2 anth −1 −2 0.53 and 0.67 mol C m Ocean Ship-Based Hydrographic Investigations - yr . Updating regional stor Program (GO-SHIP; www.go-ship.org/) collects age estimates with measurements from the most recent GO-SHIP hydrographic surveys is an ongoing high-quality surface-to-bottom water property effort. Recent estimates (Fig. 3.30b) suggest greater measurements along transoceanic sections at decadal storage in the Atlantic in the recent decade than in the intervals. These data are essential for estimating storage changes within the ocean preceding decade (Woosley et al. 2016), but consistent C decadal anth interior. The extended multiple linear regression storage between the two decades in the Pacific. | S91 AUGUST 2016 STATE OF THE CLIMATE IN 2015

112 F i g 3.30. Regional C . (anthropogenic carbon) anth storage rate estimates in literature as colored dots with positions corresponding to the approximate centers of the broad regions considered. Estimates are from: A. Williams et al. (2015), B. Sabine et al. (2008), C. Sabine et al. (2008), D. Peng et al. (2003), E. Peng et al. (2003), F. Murata et al. (2009), G. Wakita et al. (2010), H. Sabine et al. (2008), I. Waters et al. (2011), J. Waters et al. (2011), K. Waters et al. (2011), L. Sabine et al. (2008), M. Matear and McNeil (2003), N. Murata et al. (2007), O. Murata et al. (2010), P. Peng et al. (1998), Q. Peng et al. (1998), R. Murata et al. (2008), S. Peng and Wanninkhof (2010), T. Friis et al. (2005), U. Tanhua et al. (2007), V. Olsen et al. (2006), W. Wanninkhof et al. (2010), and X. Quay et al. (2007). Storage rate estimates that use data from cruises in the year 2011 or afterward are mapped in (b), and all other estimates are mapped in (a). Atlantic estimates in (b) are from Woosley et al. (2016). Colored lines are provided representing preliminary storage rate estimates along the labeled P16 and P02 sections in the decades spanning the (a) 1990s to 2000s and (b) 2000s to 2010s occupations. The similar line in (b) for S4P is from Williams et al. (2015). | S92 AUGUST 2016

113 4. TH — H. J. Diamond and C. J. Schreck, Eds. E TROPICS three consecutive seasons in the North Atlantic were H. J. Diamond and C. J. Schreck a. Overview— not above normal in terms of ACE (Bell et al. 2015). The editors of this chapter would like to insert a From the standpoint of the El Niño–Southern personal note recognizing Dr. William M. (Bill) Gray, Oscillation (ENSO), 2015 featured one of the three emeritus professor of atmospheric science at Colorado strongest El Niño episodes (1982/83, 1997/98, and State University. Dr. Gray, who pioneered the develop - 2015) since 1950. The end of 2014 was characterized by borderline El Niño conditions, and 2015 began ment of seasonal tropical cyclone outlooks and was with above-average SSTs across the central and east- one of the most inf luential meteorologists of the past 50 years, passed away on 16 April 2016 in Fort Collins, central equatorial Pacific, with the largest anomalies Colorado, at the age of 86. Speaking on behalf of the - °C) confined to the region around the internation (>1 al date line. However, this warmth was accompanied entire community, we will always be indebted to, and by little-to-no atmospheric response, indicating that benefit from, the accomplishments made during his incredibly long and outstanding career. El Niño had not fully developed. SST anomalies then increased across the central and eastern equatorial E b. NSO and the tropical Pacific— G. D. Bell, M. Halpert, Pacific during March–May. This evolution, combined - and M. L’Heureux with a coupling of the SST anomalies to the atmo The El Niño–Southern Oscillation is a coupled spheric wind and convection patterns, resulted in the development of El Niño conditions during March ocean–atmosphere phenomenon over the tropical - Pacific Ocean. Two indices used to monitor and as 2015. El Niño’s strengthening accelerated during - June–August, and again during September–Novem ber, when SST anomalies increased sharply across the eastern half of the equatorial Pacific. Globally, 101 named tropical storms were observed during 2015. This overall tropical cyclone (TC) activ - ity is well above the 1981–2010 global average of 82 storms and 10% higher than the 91 TCs recorded in 2014 (Diamond 2015). The eastern/central Pacific experienced significantly above-normal activity in 2015, and the western north Pacific and north and south Indian Ocean basins were also above normal; all other basins featured either near or below-normal TC activity. These levels of activity are consistent with the El Niño conditions in place. The 26 named storms in the eastern/central Pacific basin was the highest count in that basin since 1992 and was four more than the previous record of 22 named storms recorded in 2014, as documented in the International Best Tracks . ig F 4.1. The evolution of three strong El Niño events Archive for Climate Stewardship (IBTrACS; Knapp (1982, 1997, and 2015) is compared using time series of (a) the oceanic Niño index (ONI; ºC), and (b) the Globally, eight TCs reached the Saffir– et a l. 2010). standardized 3-month running equatorial Southern Simpson hurricane wind scale Category 5 intensity Oscillation index (EQ–SOI, std. dev.). Each time series level—five in the western North Pacific basin, one starts with the JAS season in the year prior to the event in the southern Indian Ocean, one in the eastern (year–1) and ends with the OND season in the year that North Pacific, and one in the southwest Pacific. This the event formed (year). For the 1982, 1997, and 2015 was three more than were recorded in 2013 and one 1” corresponds to 1981, 1996, and 2014, − El Niños, “year more than recorded in 2014 (Diamond 2014, 2015). respectively. ONI values are derived from the ERSST.v4 dataset (Huang et al. 2014). EQ–SOI values are derived In terms of accumulated cyclone energy (ACE), the from the monthly EQ–SOI index based on the Climate North Atlantic basin season was below normal, also Forecast System Reanalysis (CFSR) (Saha et al. 2010b). consistent with the El Niño conditions in place. The The EQ–SOI is calculated as the standardized anomaly actual number of storms, on the other hand, was close of the difference between the area-average monthly sea to normal due to a large number of weak and short- level pressure over the eastern equatorial Pacific (5°N– lived storms. Following a near-normal hurricane 5°S, 80°–130°W) and Indonesia (5°N–5°S, 90°–140°E). season in 2014 and a below-normal season in 2013, [Data available at: www.cpc.ncep.noaa.gov/data/indices /reqsoi.for and discussed by Barnston (2015).] this marked the first time since 1992–94 in which | S93 AUGUST 2016 STATE OF THE CLIMATE IN 2015

114 sess the strength of ENSO are the oceanic Niño index fected by one of the three strongest El Niño episodes (ONI) and the equatorial Southern Oscillation index (1982/83, 1997/98, and 2015/16) dating back to 1950. (EQ–SOI). The ONI (Fig. 4.1a) is the seasonal running ceanic average of sea surface temperature (SST) anomalies 1) cOnditi Ons O °–120 °W) using The SST evolution across the Pacific basin during in the Niño-3.4 region (5 °N–5 °S, 170 2015 (Figs. 4.2, 4.3) is shown based on OISST data ERSST.v4 data (Huang et al. 2015). NOAA’s Climate (Smith and Reynolds 1998). In 2015, the year began Prediction Center classifies ENSO events histori - with above-average SSTs across the central and east- cally using the ONI. At the end of 2015 ONI values central equatorial Pacific, with the largest anomalies °C, comparable to the strongest El Niño were +2.25 (>1 °C) confined to the region around the date line (1997/98) in the 1950–2015 record. (Fig. 4.2b). The corresponding weekly SST indices The EQ–SOI measures the difference in surface air - for the Niño-4 (Fig. 4.3a) and Niño-3.4 (Fig. 4.3b) pressure anomalies between Indonesia and the east °C and 0.5 ern equatorial Pacific Ocean, two large areas along the °C, respectively regions were above 0.8 (regions shown in Fig. 4.3e). The ONI for December equator (Barnston 2015). Therefore, the EQ–SOI is a –February 2014/15 (DJF) was +0.52 °C, which is near more robust measure of ENSO than the traditional the NOAA threshold for El Niño conditions (ONI ≥ SOI, which is based on measurements at only two sta - 0.5 °S, °C). However, this warmth was accompanied by tions, both of which are off-equatorial (Tahiti at 18 Darwin at 12 °S; Troup 1965; Trenberth 1984). Large little-to-no atmospheric response (Figs. 4.4a, 4.5a), indicating that El Niño had not fully developed. negative values as seen during 2015 typify El Niño (Fig. 4.1b), and ref lect the combination of decreased SST anomalies then increased across the central and eastern equatorial Pacific during March–May surface air pressure over the eastern equatorial Pacific and increased air pressure over Indonesia. Overall, (MAM; Figs. 4.2d, 4.3). This evolution, combined with a coupling of the SST anomalies to the atmo - the combined time series of the EQ–SOI and ONI - suggest that the global climate during 2015 was af spheric wind and convection patterns (Figs. 4.4b, . 4.2. Seasonal SST (left) and anomaly (right) for (a, b) DJF 2014/15, (c, d) MAM 2015, (e, f) JJA 2015, and (g, ig F h) SON 2015. Contour interval for total SST is 1°C. For anomalous SST, contour interval is 0.5°C for anomalies between ±1ºC, and interval is 1ºC for anomalies > 1ºC or < –1ºC. Anomalies are departures from the 1981–2010 seasonal adjusted OI climatology (Smith and Reynolds 1998). | S94 AUGUST 2016

115 . 4.4. Anomalous 850-hPa wind vectors and speed ig F F - ig . 4.3. Time series during 2015 of weekly area-aver 1 − ) and anomalous OLR (shad - (contour interval is 2 m s aged SST anomalies (°C) in the four Niño regions: (a) 2 − ed, W m ) during (a) DJF 2014/15, (b) MAM 2015, (c) Niño-4 region [(5ºN–5ºS, 160ºE–160ºW, yellow box in JJA 2015, and (d) SON 2015. Anomalies are departures (e)], (b) Niño-3.4 region [(5ºN–5ºS, 170º–120ºW, thick from the 1981–2010 period monthly means. black box in (e)], (c) Niño-3 region [5ºN–5ºS, 150º– 90ºW, red box in (e)], and (d) Niño-1+2 region [0º–10ºS, - Fig. 4.1a). These values are comparable to the stron 90º–80ºW, blue box in (e)]. Values are departures from gest El Niño episodes in the 1950–2015 record. the 1981–2010 weekly adjusted OI climatology (Smith This evolution is ref lected by large SST index et al. 1998). values for all four Niño regions, with the weekly Niño-4 index reaching +1.8 °C in November and 4.5b), resulted in the development of fully-coupled °C in December (Fig. 4.3a). The average Niño-4 +1.7 El Niño conditions during March 2015. The presence of El Niño during MAM was also indicated by an index values for November and December 2015 were eastward shift of the 30 °C isotherm to the date line, 1.75 °C and 1.64 °C, surpassing the previous record highs of 1.28 °C and 1.2 - along with a weaker and reduced westward extent of °C set in November and De the equatorial cold tongue (Fig. 4.2c). In fact, the SSTs cember 2009, respectively. Also, the weekly Niño-3.4 (Fig. 4.3b) and Niño-3 (Fig. 4.3c) indices reached were nearly uniformly warm (above 27 °C) throughout the eastern half of the cold tongue, indicating that +3.0 °C by the end of 2015, while the weekly Niño-1+2 the normal east–west SST gradient in that region had index remained near +2.5 °C (Fig. 4.3d). nearly disappeared. During the last half of the year, the anomalous El Niño’s strengthening accelerated during June– warming largely ref lected a weakening of the annual - cycle in SSTs across the Pacific basin, with actual tem August (JJA; Figs. 4.2e,f) and September–November - (SON; Figs. 4.2g,h), as SST anomalies increased peratures remaining nearly constant instead of cool sharply across the eastern half of the equatorial ing off as they would in a typical year, in association Pacific. The ONI for JJA was 1.23 with a strengthening and expanding equatorial cold °C, increased to - 2.04 °C during SON, and reached 2.25 tongue. This cold tongue, which normally intensi °C for the fies during JJA and SON, was nearly absent in 2015 last three months of the year (October–December; | S95 AUGUST 2016 STATE OF THE CLIMATE IN 2015

116 . ig 4.6. Equatorial depth–longitude section of ocean F . 4.5. Anomalous 200-hPa wind vectors and speed ig F temperature anomalies (°C) averaged between 5°N and − 1 (contour interval is 4 m s ) and anomalous OLR (shad - 5°S during (a) DJF 2014/15, (b) MAM 2015, (c) JJA 2015, 2 − ed, W m ) during (a) DJF 2014/15, (b) MAM 2015, (c) and (d) SON 2015. The 20°C isotherm (thick solid line) JJA 2015, and (d) SON 2015. Anomalies are departures approximates the center of the oceanic thermocline. from the 1981–2010 period monthly means. - The data are derived from an analysis system that as similates oceanic observations into an oceanic general (Figs. 4.2e,g), as was the typical westward advection circulation model (Behringer et al. 1998). Anomalies are of cooler waters toward the date line. Consistent with departures from the 1981–2010 period monthly means. these conditions, the normal westward migration of t he +30 °C isotherm to New Guinea did not occur dur - ing JJA and SON. Instead, these exceptionally warm subsurface temperature anomalies decreased during - temperatures actually migrated eastward, further the year. These conditions ref lected a progressive f lat strengthening El Niño and its associated atmospheric tening of the oceanic thermocline (indicated by the response. °C isotherm, thick solid line), which is typical of 20 Consistent with the evolution of the equatorial a strong El Niño pattern of anomalous downwelling - SSTs, positive subsurface temperature anomalies in (upwelling) in the eastern (western) equatorial Pacific creased east of the date line throughout the year (Wang et al. 1999; Wang and Weisberg 2000). (Fig. 4.6). A significant temperature increase occurred 2) a t m O s p h e r i c c i r c u l at i O n : t r O p i c s during MAM (Fig. 4.6b) in response to the combina a n d - subtr tion of the evolving El Niño and the downwelling Opics phase of a strong equatorial oceanic Kelvin wave (sec During DJF 2014/15, the atmospheric circulation - tion 4c) that was initiated by a westerly wind burst. across the tropical Pacific ref lected ENSO-neutral Subsequent westerly wind bursts in late June/early conditions, with near-average low-level (850-hPa) winds (Fig. 4.4a) and no consistent El Niño signal in July, early August, and early October also initiated downwelling equatorial oceanic Kelvin waves, which the upper-level winds (Fig. 4.5a). Also, convection - was slightly suppressed over the east-central equato helped to maintain well-above-normal subsurface ocean temperatures through the end of the year rial Pacific in the area of anomalously warm SSTs, (Figs. 4.6c,d). In contrast, in the western Pacific, indicating a lack of oceanic–atmospheric coupling. | S96 AUGUST 2016

117 - In March, the atmospheric pressure, wind, and hemisphere, anomalous westerly winds along the pole ward f lank of the anomalous anticyclonic circulation convection patterns became coupled to the increas - ref lect major dynamical and kinematic changes in the - ingly warm SST anomalies, signifying the develop jet stream over the Pacific basin. As seen during JJA ment of El Niño. The atmospheric response to El Niño and SON in the Southern Hemisphere (Figs. 4.5c,d), was evident through the remainder of the year, inten - the westerly wind anomalies between 20 °S ° and 30 sifying as El Niño strengthened. The tropical atmospheric response to El Niño dur ref lected a strengthening and eastward extension of - the wintertime jet steam to well east of the date line, ing MAM through SON featured an east–west dipole pattern of anomalous convection, with convection along with an eastward shift of that jet’s exit region to expanding and strengthening over the central and the eastern South Pacific. This wintertime jet stream pattern represents a fundamental manner in which east-central equatorial Pacific while becoming more El Niño’s circulation impacts are communicated suppressed over Indonesia and the eastern Indian downstream and poleward into the extratropics. Ocean (Figs. 4.4b–d, 4.5b–d). This pattern ref lected 1) a pronounced eastward extension of the primary area impacts 3) r ainfall of tropical convection to well east of the date line and, - at times, an actual shift of the main region of tropical Because of the rapid strengthening and expan convection to the eastern half of the tropical Pacific - sion of the El Niño–related convection and circula tion anomalies during MAM and JJA, many typical (not shown), and 2) a strengthening and equatorward shift of the intertropical convergence zone (ITCZ) in El Niño rainfall impacts (Ropelewski and Halpert the Northern Hemisphere. 1987) were evident during the year. The accumulated A key El Niño–related feature of the low-level precipitation deficits and surpluses during June– (850-hPa) winds during JJA through SON was an December, along with time series of area-averaged - extensive area of anomalous westerlies that strength monthly precipitation totals and percentiles during the year, highlight these impacts (Fig. 4.7). ened and expanded along the equator as the year pro - Two main regions with above-average precipita gressed (Figs. 4.4b–d). This anomaly pattern ref lected - −1 tion during June–December 2015 were the central a marked weakening (3–6 m s below normal) of the equatorial Pacific and within the Pacific ITCZ. The easterly trade winds, with departures exceeding 6 m −1 s near the date line in SON (Fig. 4.4d). enhanced rainfall for both regions began in March An El Niño–related upper-level wind pattern also and subsequently intensified with area-averaged monthly totals during May–December (red line, became established during MAM and strengthened as the year progressed. This pattern featured an Fig. 4.7b) all being in the upper 10th percentile of extensive area of easterly wind anomalies across the occurrences (black bars). For the June–December central and east-central tropical Pacific (Figs. 4.5b–d), period, rainfall surpluses in both areas exceeded 800 mm, with the largest surpluses exceeding 1200 mm. along with near-average winds over both the eastern - equatorial Pacific and Indonesia. During June–October, these conditions were associat ed with strong hurricane seasons for both the central - The overall circulation also featured a combi and eastern Pacific hurricane basins (see section 4e3). nation of anomalous upper-level convergence and Two other regions that typically record above- low-level divergence over Indonesia and the western average precipitation during El Niño include south - tropical Pacific, and a combination of anomalous upper-level divergence and low-level convergence eastern South America and the Gulf Coast region over the central and east-central equatorial Pacific. of the United States. The extended South Pacific The resulting vertical motion pattern was consistent jet stream contributed to precipitation surpluses of with the observed east–west dipole pattern of tropi - 100–200 mm in southeastern South America during cal convection, as was also noted by Bell and Halpert June–December, with above-average precipitation (1998) for the 1997/98 El Niño. Collectively, these recorded in nearly every month from July to De - cember (Fig. 4.7c; section 7d). Along the U.S. Gulf wind, convection, and vertical motion patterns ref lect Coast, above-average precipitation was recorded a markedly reduced strength of the equatorial Walker circulation typical of El Niño (Bjerknes 1969). from October to December, with area-averaged to - In the subtropics, the upper-level winds during tals above the 90th percentile of occurrences during November–December (Fig. 4.7d). JJA–SON 2015 featured anticyclonic anomalies in both hemispheres straddling the area of enhanced Many other areas typically record below-average equatorial convection. This anticyclonic couplet is a precipitation during El Niño. One such region is typical feature of El Niño (Arkin 1982). In the winter Indonesia, where cumulative deficits during June– | S97 AUGUST 2016 STATE OF THE CLIMATE IN 2015

118 December 2015 exceeded 1000 mm. The most significant deficits oc - curred during July–October, when monthly totals of less than 100 mm were generally half of normal and - in the lowest 10th percentile of oc currences (Fig. 4.7e). Other regions with below-average precipitation during the period from June to December included: • The South African monsoon season (October–April) is typically suppressed during El Niño, and from October– December precipitation totals were well below average, with monthly totals in the lowest 10th percentile of occurrences in all three months (Fig. 4.7f). The Amazon basin recorded • significantly below-average rainfall throughout the year, with monthly totals generally in the lowest 10th percentile of occurrences (Fig. 4.7g). During June–December 2015, much of the region recorded deficits of 400–600 mm. . 4.7. Precipitation during 2015: (a) Accumulated precipitation de F - ig The Central America/Caribbe • - partures during (b–i) Jun–Dec (mm), Time series of area-averaged an Sea region (Fig. 4.7h) and the monthly precipitation for regions indicated with red boxes in (a). Bars tropical Atlantic (Fig. 4.7i) had -axis), and red and blue lines show monthly percentile percentiles (left y rainfall that was below average show monthly observed and climatological mean precipitation (right y - axis), respectively. Rainfall amounts are obtained by merging rain gauge during almost every month observations and satellite-derived precipitation estimates (Janowiak and from April to December, with Xie 1999). Precipitation percentiles are based on a gamma distribution monthly totals in the lowest fit to the 1981–2010 base period. Anomalies are departures from the 20th percentile of occurrences 1981–2010 means. in most months. Below-average Roundy 2012a,b). There were three distinct periods totals across the tropical Atlantic were also consis - of MJO activity during 2015 affecting a total of six tent with the overall below-average strength of the 2015 Atlantic hurricane season (see section 4e2). months (Figs. 4.8–4.10), which were interspersed with the convectively coupled waves. Between these three ropical intraseasonal activity— periods, the intraseasonal variability was dominated S. Baxter, C. J. Schreck, c. T by atmospheric Kelvin waves and tropical cyclone and G. D. Bell Tropical intraseasonal variability was prominent activity. Within the Pacific Ocean, strong intrasea - sonal variability throughout the year was ref lected during 2015 in both the atmosphere and ocean, in a series of upwelling and downwelling equatorial even in the presence of strong lower-frequency vari - oceanic Kelvin waves (Fig. 4.11). ability associated with El Niño. In the atmosphere, The MJO is a leading intraseasonal climate mode two aspects of this intraseasonal variability were - of tropical convective variability. Its convective anom the Madden–Julian oscillation (MJO; Madden and - alies often have the same spatial scale as ENSO, but Julian 1971, 1972, 1994; Zhang 2005) and convec differ in that they exhibit a distinct eastward propaga tively coupled equatorial waves, which include - equatorial Rossby waves and atmospheric Kelvin tion and generally traverse the globe in 30–60 days. waves (Wheeler and Kiladis 1999; Kiladis et al. 2009; The MJO impacts weather patterns around the globe | S98 AUGUST 2016

119 F ig . 4.9. Time–longitude section for 2015 of anomalous - F . 4.8. Time–longitude section for 2015 of 5-day run ig − 2 outgoing longwave radiation (OLR; W m ) averaged − 2 1 6 ning anomalous 200-hPa velocity potential (× 10 ) m s for 10°N–10°S. Negative anomalies indicate enhanced averaged for 5°N–5°S. For each day, the period mean convection and positive anomalies indicate suppressed is removed prior to plotting. Green (brown) shading convection. Contours identify anomalies filtered for highlights likely areas of anomalous divergence and the MJO (black) and atmospheric Kelvin waves (red) rising motion (convergence and sinking motion). Red as in Kiladis et al. (2006) and Straub and Kiladis (2002), lines and labels highlight the main MJO episodes. respectively. Purple shaded ovals indicate hurricanes Anomalies are departures from the 1981–2010 base named on figure. Red labels highlight the main MJO period daily means. − 2 episodes. Contours are drawn at ±10 W m , with the enhanced (suppressed) convective phase of these (Zhang 2013), including monsoons (Krishnamurti phenomena indicated by solid (dashed) contours. and Subrahmanyam 1982; Lau and Waliser 2012), Anomalies are departures from the 1981–2010 base period daily means. tropical cyclones (Mo 2000; Frank and Roundy 2006; Camargo et al. 2009; Schreck et al. 2012), and Real-time Multivariate MJO (RMM) index (Fig. 4.10). extratropical circulations (Knutson and Weickmann 1987; Kiladis and Weickmann 1992; Mo and Kousky In the time–longitude plots, the MJO exhibits east - 1993; Kousky and Kayano 1994; Kayano and Kousky ward propagation. In the RMM, the MJO propaga - tion and intensity are seen as large, counterclockwise 1999; Cassou 2008; Lin et al. 2009; Riddle et al. 2012; circles around the origin. These diagnostics point to Schreck et al. 2013; Baxter et al. 2014). The MJO is often quite variable in a given year, with periods of three main MJO episodes during 2015. MJO #1 was a strong episode from March into early April. MJO moderate-to-strong activity sometimes followed by #2 was a strong event that began in late May and little or no activity. The MJO tends to be most active lasted through mid-July during ENSO neutral and weak El Niño periods, and . MJO #3 was a moderately is often absent during strong El Ni strong event that lasted from October through the ño events (Hendon end of the year. et al. 1999; Zhang and Gottschalck 2002; Zhang 2005). Given a background El Niño rivaling one of MJO #1 featured a zonal wave-1 pattern of strong - the strongest on record during 2015, the MJO events convective anomalies, with a periodicity of approxi observed during the year are remarkable. mately 40 days (Figs. 4.8, 4.9, 4.10a,b). The plot of anomalous velocity potential shows that this event Common metrics for identifying the MJO include circumnavigated the globe once (Fig. 4.8). The RMM time–longitude plots of anomalous 200-hPa velocity potential (Fig. 4.8) and outgoing longwave radiation index achieved record amplitude of 4.03 standard (OLR, Fig. 4.9), as well as the Wheeler–Hendon (2004) deviations on 16 March (Fig. 4.10a). Historically, the | S99 AUGUST 2016 STATE OF THE CLIMATE IN 2015

120 around the globe. Its convective anomalies masked only prior MJO event to eclipse 4.0 occurred 30 years the strengthening El Niño in early and mid-June, ago in February 1985 (4.02). The 2015 event ended then accentuated the El Niño signal during late June in April when the convective anomalies became dominated by a series of fast-propagating atmospheric - and early July (Fig. 4.9). The RMM index showed re markable amplitude in early July, again approaching Kelvin waves (Fig. 4.9). One of the largest impacts from MJO #1 was the four standard deviations (Fig. 4.10c). As is common with many MJO episodes (Straub et al. 2006; Sobel interaction with a high-amplitude downwelling and Kim 2012), the convective signal of MJO #2 was equatorial oceanic Kelvin wave (Fig. 4.11b). This partially masked by atmospheric Kelvin wave activity oceanic Kelvin wave was triggered during March (Fig. 4.9). This MJO provided especially conducive by a westerly wind burst associated with enhanced conditions for producing tropical cyclones. Twelve convection over the western Pacific (Fig. 4.11a). This wave reached the eastern Pacific in May and produced storms, spanning from the Arabian Sea to the North Atlantic, developed in association with this event. a significant increase in the upper ocean heat content while El Niño was developing. MJO #1 also impacted These storms included “twin” tropical cyclones the extratropical circulation, mainly during mid- to Raquel and Chan-hom that straddled the equator in late March, when suppressed convection and anoma - the western Pacific and contributed to a particularly strong westerly wind burst (Fig. 4.11a). lous upper-level convergence were present over the eastern Indian Ocean, and enhanced convection and Following MJO #2, enhanced tropical cyclone activity across the central and eastern North Pacific anomalous upper-level divergence were present over from August through October contributed to the the western and central Pacific Ocean (Fig. 4.8). These atmospheric intraseasonal variability. Some of these conditions contributed to an eastward extension of storms (named and purple shaded ovals, Fig. 4.9) the East Asian jet stream and a subsequent cold air outbreak over the continental United States. can be identified as westward-moving patterns of MJO #2 began in late May and lasted through anomalous upper-level divergence and enhanced mid-July, with its wave-1 signal also making a full trip OLR (storm names). MJO #3 lasted from mid-October through the end of the year. The periodicity of this event is difficult to assess, though it likely exceeded 60 days and is at the slower end of the MJO spectrum (Fig. 4.10d). After being initiated over the western Pacific, the area of enhanced convection associated with MJO #3 propa - gated over the Indian Ocean, where it then became quasi-stationary for most of November. It could be argued that this event did not begin in earnest until - its eastward propagation resumed in early Decem ber. Similar to MJO #2, this event at times masked the El Niño convection pattern and at other times - accentuated it. Across the Pacific Ocean, intrasea sonal variability associated with equatorial oceanic Kelvin wave activity was seen throughout the year (Fig. 4.11b). All three MJO events featured westerly wind bursts (Fig. 4.11a) that triggered downwelling Kelvin waves. Overall, downwelling Kelvin waves tended to be strong, helping to strengthen and main - tain the anomalous warmth associated with El Niño. - . 4.10. Wheeler–Hendon (2004) Real-time Multivar ig F iate MJO (RMM) index for (a) Jan–Mar, (b) Apr–Jun, (c) In contrast, the upwelling Kelvin waves tended to Jul–Sep, and (d) Oct–Dec 2015. Each point represents be weak throughout the year and had little net impact the MJO amplitude and location on a given day, and the on the surface and subsurface warmth associated with connecting lines illustrate its propagation. Amplitude El Niño. This suppression of the upwelling waves is is indicated by distance from the origin, with points linked to sustained anomalous westerly winds over inside the circle representing weak or no MJO. The - the central and western equatorial Pacific in associa 8 phases around the origin identify the region expe - tion with El Niño (see Figs. 4.4b–d). riencing enhanced convection, and counterclockwise movement is consistent with eastward propagation. | S100 AUGUST 2016

121 Figure 4.12 summarizes - the convergence zone be havior for 2015 and allows comparison of the 2015 seasonal variation against the longer term (1998–2014) climatology. Rainfall tran - sects over 20°N to 30°S are - presented for each quar ter of the year, averaged across successive 30-degree longitude bands, starting in the western Pacific at °E– 150 180 °. With the demise of the TRMM satellite in mid- 2015, the rainfall data for this year’s chapter are taken from NOAA’s “CMORPH” F ig . 4.11. (a) Time–longitude section for 2015 of anomalous 850-hPa zonal wind − 1 - global precipitation analy ) averaged for 10°N–10°S. Black contours identify anomalies filtered for (m s MJO. Red labels highlight the main MJO episodes. Significant westerly wind sis (Joyce et al. 2004). This bursts (WWB) are labeled. (b) Time–longitude section for 2015 of the anoma - dataset, derived from low lous equatorial Pacific Ocean heat content, calculated as the mean tempera - orbiter satellite microwave ture anomaly between 0 and 300 m depth. Yellow/red (blue) shading indicates observations (as is TRMM above- (below-) average heat content. The relative warming (solid lines) and 3B43), is available at the cooling (dashed lines) due to downwelling and upwelling equatorial oceanic same 0.25 ° resolution as the Kelvin waves are indicated. Anomalies are departures from the 1981–2010 base - TRMM 3B43 used previ period pentad means. ously (e.g., Mullan 2014). ntertropical convergence zones d. Although not identical, CMORPH and TRMM 3B43 I rainfall are similar in pattern and magnitude at the 1) acific — A. B. Mullan p broad scale discussed here. The broad-scale patterns of tropical Pacific rainfall are dominated by two convergence zones, the inter - In the western North Pacific, rainfall generally tropical convergence zone (ITCZ) and the South Pa - exceeded climatology from early in the year. The - second quarter bulletin of the Pacific ENSO Applica cific convergence zone (SPCZ). The ITCZ lies between 5° and 10°N and is most active during the August to tions Climate Center (www.weather.gov/media/peac - December period, when it lies at its northernmost po /PEU/PEU_v21_n2.pdf) commented that: “In eastern sition. The SPCZ extends diagonally from around the Micronesia [5°–10°N, 140°–160°E,] ... extraordinary amounts of rainfall occur[ed] in March and April.” As Solomon Islands (10°S, 160°E) to near 30°S, 140°W, a result of the El Niño event, from March to December and is most active in the November–April half-year. Both convergence zones are strongly inf luenced by convection was greatly enhanced over climatology the state of ENSO. - from the date line eastward, especially in the North ern Hemisphere for the ITCZ (Figs. 4.12b–d). Not During 2015, an El Niño event that had established only was the ITCZ closer to the equator, but the region itself in March continued to intensify through the end of the year. The monsoon of the western North Pacific of convection also had a broader latitude extent with a larger rainfall maximum. Convection at the equator extended far to the east to bring unusually strong and persistent westerly winds to the date line and beyond. itself was typically about double the climatological value for sectors 150 °E– 180° and 180 °–150°W. Figure Sea surface and subsurface temperatures were much 4.13 gives the 2015 annual average precipitation in the warmer than normal, and the convergence zones were Pacific and clearly shows the broader ITCZ: rainfall is more active. For most months from May to December, the NASA ENSO Precipitation index (ESPI; Curtis twice the climatology along a line a few degrees north of the equator and again near 15°N, while rainfall is and Adler 2000) was close to +2 or more, well above close to climatology along 10°N. the +1 threshold associated with El Niño conditions. | S101 AUGUST 2016 STATE OF THE CLIMATE IN 2015

122 Enhanced convection near the equator, around and east of the date line, is typical of El Niño conditions. However, the degree of enhancement was quite extreme in 2015, - as was the extent of warm ing in equatorial sea surface temperatures. Figure 4.14 shows precipitation transects for the last quarter of each year 1998–2015, averaged over the 180 °–150°W sector. Rainfall within 5 degrees of the equator during 2015 was well above that for any other year in the relative - ly short CMORPH record (starting January 1998). It is likely, however, that October– − 1 ig ) from CMORPH analysis for (a) Jan–Mar, (b) . 4.12. Rainfall rate (mm day F December 1997 was simi - Apr–Jun, (c) Jul–Sep, and (d) Oct–Dec 2015. Each panel shows the 2015 rainfall lar, given the high rainfall cross section between 20°N and 30°S (solid line) and the 1998–2014 climatology along the equator in Janu - (dotted line), separately for four 30° sectors from 150°E–180° to 120°–90°W. ary–March 1998 under the very intense 1997/98 El Niño. The CMORPH analysis matches reasonably well with observed rainfall in the Pacific Islands, although there is much more variability at the island scale. For Hawaii, at the northern edge of the 180 °–150°W sector, the third quarter rainfall varied from about twice the average at Hilo, to ten times the average in Honolulu (www.weather.gov/media/peac/PEU /PEU_v21_n4.pdf ). Christmas Island (or Kiritimati) in eastern Kiribati ig F . 4.13. Annual-average CMORPH precipitation for 2015, as a percentage of the 1998–2014 average. The - lies on the equator in the same sector as Hawaii; rain white areas have precipitation anomalies within 25% fall was above normal for each of the last nine months of normal. of 2015 (www.niwa.co.nz/climate/icu), and Kiritimati received about ten times its normal December rainfall (667 mm). In contrast, islands along the southern edge of the SPCZ experienced well-below-normal rainfall from about April 2015 onward (www.niwa .co.nz/climate/icu). For example, the islands of New Caledonia, Fiji, Niue, and Tahiti were generally drier than normal for 8 or 9 of the last nine months of 2015. tlantic 2) a A. B. Pezza and C. A. S. Coelho — The Atlantic ITCZ is a well-organized convective band that oscillates approximately between 5° and 12°N during July–November and 5°N and 5°S during − 1 ) for Oct– F ig . 4.14. CMORPH rainfall rate (mm day January–May (Waliser and Gautier 1993; Nobre and Dec period for each year 1998 to 2015, averaged over Shukla 1996). Equatorial Kelvin waves can modulate the longitude sector 180°–150°W. The cross sections the ITCZ intraseasonal variability (Guo et al. 2014). are color-coded according to NOAA’s ONI, except for 2015 (an El Niño year) shown in black. ENSO is also known to inf luence the ITCZ on the | S102 AUGUST 2016

123 interannual time scale (Münnich and Neelin 2005). In 2015, weak positive SST anomalies prevailed in the equatorial Pacific until March, followed by the development of a strong El Niño event from March - onward, with a marked signature in the annual aver age (Fig. 4.15). Consistent with Münnich and Neelin (2005), these conditions were associated with relatively warmer waters in the North Atlantic sector after the establish - ment of the El Niño, leading to a sharp negative peak in the Atlantic index (Fig 4.16a) in the second half of 2015, as measured by the north–south SST gradient (Fig. 4.16a). As a consequence, the ITCZ oscillated F ig . 4.15. Spatial distribution of average global SST well north of its climatological position for most of anomalies (°C, Reynolds et al. 2002) during 2015. the year, with an overall suppression of any significant activity in the Southern Hemisphere. An exception occurred in March and April (Fig. 4.16b), when the F ig . 4.16. (a) Atlantic ITCZ position inferred from outgoing longwave radiation during May 2014. The colored thin lines indicate the approximate position for the six pentads of the month. The black thick line in - dicates the Atlantic ITCZ climatological position. The SST anomalies for May 2014 based on the 1982–2013 climatology are shaded (°C). The two boxes indicate the areas used for the calculation of the Atlantic index in (b). (b) Monthly SST anomaly time series averaged over the South American sector (SA region, 10°–50°W, 1 − 5°S –5°N) minus the SST anomaly time series averaged ) during . 4.17. (a) Observed precipitation (mm day ig F over the North Atlantic sector (NA region, 20°–50°W, 2015, (b) 1998–2014 precipitation climatology (mm − 1 1 − 5°–25°N) for the period 2010–14, forming the Atlantic day ) in 2015 ), and (c) observed anomaly (mm day - index. The positive phase of the index indicates favor derived from CPC Morphing technique (CMORPH; able conditions for enhanced Atlantic ITCZ activity. Joyce et al. 2004). | S103 AUGUST 2016 STATE OF THE CLIMATE IN 2015

124 ITCZ moved south of the equator during a short gap before the air–sea teleconnection effects of the strong ENSO event became fully established. This southern burst was accompanied by a brief but sharp increase of the Atlantic index. Despite that, the effects of the southern passage of the ITCZ on potentially enhancing the convective - activity over the drought-prone areas of northeast ern Brazil were only minor, with an overall annual balance of well-below-average precipitation in most of the region (Fig. 4.17a-c). This “lack of convective coupling” was associated with a widespread drought . 4.18. Global summary of TC tracks with respect ig F within most of the Amazon as well as in central to SST anomalies for the 2015 TC season. Brazil. Persistent low vegetation health indices and reduced soil moisture likely contributed to lowering et al. 2014). Of these, 36 storms reached major HTC −1 status (wind speeds ≥ 96 kts or 49 m s ; WMO 2015), the rate of evapotranspiration and relative humidity, facilitating higher temperatures as observed during which is well above the average of 21. To assist in tallying the basin totals, this year we normalized heat waves in Europe (Whan et al. 2015). This large- scale drought pattern has also extended into south the counts by basing them on WMO-defined basin - boundaries and by using the Saffir–Simpson scale eastern Brazil in recent years (Coelho et al. 2015a,b) and was already established before the onset of the to represent intensities for all basins, realizing that the Saffir–Simpson scale is not operationally used in latest El Niño. Otto et al. (2015) explore whether all basins. Therefore, Fig. 4.18 depicts as close to an droughts in different parts of Brazil could either be overall picture of global TCs as possible, and each of - part of a longer-term natural oscillation or attribut the basin sections (4e2–4e8) has a graphic ref lecting able to anthropogenic forcing. those normalized basin totals. ropical cyclones There were eight Saffir–Simpson level Category 5 e. T — H. J. Diamond and C. J. Schreck 1) O verview systems during the year (one more than in 2014, and three more than in 2013): Patricia in the eastern North The IBTrACS dataset comprises historical tropical Pacific; Super Typhoons Maysak, Noul, Dolphin, cyclone (TC) best-track data from numerous sources Soudelor, and Atsani in the western North Pacific; around the globe, including all of the WMO Regional - Cyclone Eunice in the south Indian Ocean; and Tropi Specialized Meteorological Centers (RSMC; Knapp cal Cyclone Pam in the southwest Pacific. Patricia, et al. 2010). To date, IBTrACS represents the most with maximum sustained surface winds of 174 kt complete compilation of global TC data and offers a −1 unique opportunity to revisit the global climatology (88 m s ) and a minimum central pressure of 879 hPa, of TCs. Using IBTrACS data (Schreck et al. 2014) a set records for these parameters for any tropical cy - 30-year average value for storms (from WMO-based clone anywhere in the Western Hemisphere. Patricia RSMC numbers) is noted for each basin. was also characterized by an extraordinarily fast The global tallying of total TC numbers is chal - intensification, with a 100-hPa drop in its minimum lenging and involves more than simply adding up central pressure within a 24-hour period. There were also several Saffir–Simpson Category basin totals because some storms cross basin bound - 3 and 4 intensity-level systems during 2015 that had aries, some basins overlap, and multiple agencies are involved in the tracking and categorization of TCs. major impacts: 1) Joaquin in the North Atlantic; 2) Hilda, Ignacio, and Kilo in the eastern North Pacific; Compiling the activity using the IBTrACS dataset 3) Koppu, Chan-hom, and Melor in the western North over all seven TC basins (Fig. 4.18), the 2015 season Pacific; 4) Chapala and Megh in the north Indian (2014/15 in the Southern Hemisphere) had 101 named −1 storms [wind speeds ≥ 34 knots (kt; 1 kt = 0.51 m s ) Ocean; 5) Chedza, Fundi, and Haliba in the south −1 or 18 m s ], which is well above the 1981–2010 aver - Indian Ocean; and 6) Marcia in the southwest Pacific. It should be noted that although TCs in the south age of 82 (Schreck et al. 2014) and 10 more than the 2014 total of 91 (Diamond 2015). The 2015 season also Indian Ocean impacted life and property, the great - featured 62 Hurricanes/Typhoons/Cyclones (HTC; est impacts were caused by those storms that did not −1 ), which is also well even become cyclones. This observation speaks to the wind speeds ≥ 64 kts or 33 m s above the 1981–2010 average of 46 HTCs (Schreck damage that tropical cyclones can sometimes inf lict | S104 AUGUST 2016

125 while not at the named storm level of intensity. The only 36.1% of the median. This result highlights the large number of weak and short-lived storms during North Atlantic hurricane season was below normal the season. Combined with a near-normal hurricane (section 4e2), and both the central and eastern Pacific hurricane seasons were well above normal (section season in 2014 and a below-normal season in 2013 4e3), consistent with the El Niño conditions in place (Bell et al. 2015), 2013–15 marks the first time since (section 4b). Sidebar 4.1 also provides analysis and a 1992–94 in which three consecutive seasons were not summary of the overall Northern Hemisphere TC above normal. seasons and highlights the special role that El Niño Since the current high-activity era for Atlantic hurricanes began in 1995, 13 of 21 seasons (62%) plays with respect to TCs. Sidebar 4.2 describes a rare have been above normal, and four seasons (19%) have and interesting subtropical cyclone that developed - been near normal. The 2015 season marks only the over the southeast Pacific, a region usually not con ducive to such development. fourth below-normal season since 1995. The 2015 activity was well below the averages during the recent G. D. Bell, C. W. Landsea, E. S. Blake, active period (1995–2014) of 15 named storms, 7.6 2) a tlantic basin — hurricanes, 3.5 major hurricanes, and 141.6% of the J. Schemm, S. B. Goldenberg, T. B. Kimberlain, and R. J. Pasch (i) 2015 seasonal activity - 1981–2010 median ACE. A yearly archive of condi The 2015 Atlantic hurricane season produced 11 tions during these seasons can be found in previous named storms, of which four became hurricanes and State of the Climate reports. two became major hurricanes. These values are not A main delineator between more- and less-active - - Atlantic hurricane seasons is the number of hurri far below the HURDAT2 30-year (1981–2010) sea sonal averages of 11.8 tropical storms, 6.4 hurricanes, canes and major hurricanes that originate as named storms within the Main Development Region (MDR; and 2.7 major hurricanes (Landsea and Franklin 2013). Many of the storms during 2015 were weak and green boxed region in Fig. 4.20a) which spans the short-lived, and the seasonal accumulated cyclone tropical Atlantic Ocean and Caribbean Sea between 9.5° and 21.5°N (Goldenberg and Shapiro 1996; energy (ACE) value (Bell et al. 2000) was 67.8% of 2 4 kt Goldenberg et al. 2001; Bell and Chelliah 2006). Only ; Fig. 4.19). This the 1981–2010 median (92.4 × 10 value is below NOAA’s upper threshold (71.4% of the five named storms formed in the MDR during 2015, - with two becoming hurricanes and one of those be median) for a below-normal season (see www.cpc ing a short-lived major hurricane. The resulting ACE .ncep.noaa.gov/products/outlooks/background _information.shtml), and consequently the season is value from these five storms was only about 27% of classified as below-normal. the median, which is comparable to the 1981–2010 - A single storm, Major Hurricane Joaquin, pro below-normal season average for the MDR of 18.1%. These values are well below the above-normal and duced nearly one-half of the season’s total ACE value; the remaining ten storms produced an ACE value of near-normal season ACE averages for the MDR of 151.1% and 57.9% of the median, respectively. Storm tracks (ii) Two tropical storms made landfall in the United States during 2015: Tropical Storm Ana which made landfall in South Carolina in May, and Tropical Storm Bill which made landfall in Texas in June. No hur - ricanes made landfall in the United States this season. No hurricanes tracked through the Caribbean Sea during 2015. This region has seen only one hur - . 4.19. NOAA’s Accumulated Cyclone Energy (ACE) ig F ricane in the last three seasons: Gonzalo in 2014. As index expressed as percent of the 1981–2010 median discussed below, and also by Bell et al. (2014, 2015), value. ACE is calculated by summing the squares of this dearth of hurricane activity over the Caribbean the 6-hourly maximum sustained surface wind speed - Sea has ref lected a lack of storms forming in the re (knots) for all periods while the storm is at least gion due to strong vertical wind shear and anomalous tropical storm strength. Red, yellow, and blue shadings sinking motion, and also a lack of storms propagating correspond to NOAA’s classifications for above-, near- westward into the region. and below-normal seasons, respectively. The 165% threshold for a hyperactive season is indicated. Verti - cal brown lines separate high- and low-activity eras. | S105 AUGUST 2016 STATE OF THE CLIMATE IN 2015

126 (iii) Atlantic sea surface temperatures SST anomalies warmed across the MDR as the summer progressed, with below-average SSTs during June–July and above-average SSTs during August–November. For the MDR as a whole, the area-averaged SST anomaly for October (+0.64°C) was the warmest in the 1950–2015 record, and the area-averaged anomaly for November (+0.48°C) tied for the warmest on record. For the peak months (August–October, ASO) of the Atlantic hurricane season the mean SST depar - ture in the MDR was +0.43°C (Fig. 4.20b), which ties for fifth warmest in the record (Fig. 4.20b). Consistent with the ongoing warmth in the MDR since 1995, o - bjective measures of the Atlantic multidecadal oscil , such lation (AMO; Enfield and Mestas-Nuñez 1999) as NOAA’s operational Kaplan AMO index, indicate a continuance of the AMO warm phase during ASO 2015 (Fig. 4.21). In contrast, the AMO index for Janu - ary–March has been near zero for the past two years. The warm AMO phase and the associated posi - tive phase of the Atlantic Meridional Mode (Vimont and Kossin 2007; Kossin and Vimont 2007) are the primary climate factors associated with high-activity eras for Atlantic hurricanes (Goldenberg et al. 2001; Bell and Chelliah 2006; Bell et al. 2011, 2012). This warm phase features anomalously warm SSTs in the MDR compared to the remainder of the global tropics . 4.20. (a) ASO 2015 SST anomalies (°C), with the F ig (Fig. 4.20c). However, the mean SST anomaly within MDR indicated by the green box. (b) Time series for the MDR during ASO 2015 was less than the mean 1950–2015 of ASO area-averaged SST anomalies in the anomaly for the entire global tropics, due partly to MDR. (c) Time series showing the difference between the intensifying El Niño (see section 4b). ASO area-averaged SST anomalies in the MDR and those for the entire global tropics (20°N–20°S). Red lines show a 5-pt. running mean of each time series. (iv) Atmospheric conditions Anomalies are departures from the ERSST.v3b (Smith a. Atlantic basin et al. 2008) 1981–2010 period monthly means. The below-normal 2015 Atlantic hurricane season resulted mainly from a set of atmospheric conditions during ASO that made the central and western MDR extremely unfavorable for TC activity. These condi - tions included: 1) anomalously strong vertical wind shear extending from the Caribbean Sea northeast - ward to the central Atlantic (Fig. 4.22), 2) anomalous upper-level (200-hPa) convergence and lower-level (850-hPa) divergence (Fig. 4.23a), 3) anomalous sink - ing motion throughout the troposphere (Fig. 4.23b) and, 4) midlevel drier air (Fig. 4.23c). The vertical wind shear averaged across the Caribbean Sea during ASO was the third strongest −1 ) in the ASO 1970–2015 record (Fig. 4.22b). (12.4 m s . 4.21. Unfiltered index of the Atlantic multidecadal F ig The two ASO seasons with larger shear values in this oscillation (AMO) during 1950–2015 averaged over region were the El Niño years of 1972 and 1986. For ASO (red line) and JFM (blue line). Based on the Kaplan the June–November hurricane season as a whole, SST dataset (Enfield et al. 2001; www.esrl.noaa.gov the vertical wind shear over the Caribbean Sea was /psd/data/timeseries/AMO). | S106 AUGUST 2016

127 becoming the only long-lived major hurricane of the season (Joaquin). Together, these five storms produced about 60% of the total seasonal ACE value. ño impacts El Ni b. The 200-hPa circulation patterns during ASO - 2015 (Fig. 4.24) show that El Niño impacted atmo spheric conditions across the tropical Pacific and Atlantic Oceans in both hemispheres, so as to weaken the Atlantic hurricane season and simultaneously - strengthen both the central and eastern Pacific hur ricane seasons (see section 4e3). The velocity potential, which is related to the divergent component of the wind, showed an anom - aly pattern during ASO that is typical of El Niño (Fig. 4.24a). This pattern featured a core of negative anomalies across the eastern half of the Pacific ba - sin, along with cores of positive anomalies over the western Pacific/Australasia and also over the Amazon . 4.22. 200–850 hPa vertical wind shear during ig F − 1 basin and MDR. The associated pattern of divergent ) and (b) anomalous ASO 2015: (a) magnitude (m s magnitude and vector. In (a), orange-red shading indi - wind vectors shows a suppressive pattern for Atlantic cates areas where the vertical wind shear magnitude hurricanes of anomalous upper-level convergence − 1 is ≤10 m s . In (b), vector scale is below right of plot. over the Caribbean Sea and central MDR. - Green box denotes the MDR. Anomalies are depar The 200-hPa streamfunction pattern also showed tures from the 1981–2010 means. a typical El Niño signal, with anticyclonic anomalies −1 across the subtropical Pacific Ocean in both hemi ), exceeding the the strongest in the record (17.3 m s - −1 recorded in 1972. previous largest value of 15.4 m s spheres f lanking the region of enhanced El Niño– related convection (see Fig. 4.5c), along with cyclonic On monthly time scales, shear values greater than −1 8–10 m s are generally considered nonconducive to anomalies extending downstream from the Americas hurricane formation. (Fig. 4.24b). Regionally, the streamfunction pattern included The main activity during the 2015 hurricane an anomalous upper-level subtropical trough that season ref lected more conducive conditions over the eastern MDR and also over the western subtropical - extended across the entire MDR. This feature re North Atlantic north of the MDR. In portions of f lected an amplification of the mean tropical upper the eastern MDR the combination of weak vertical tropospheric trough (TUTT; white dashed line) in wind shear (Fig. 4.22a), - anomalous rising mo tion (Fig. 4.23b), and increased midlevel moisture (Fig. 4.23c) contributed to the development of five - named storms, includ ing two hurricanes. - Over the western sub tropical North Atlantic, a similar combination F ig . 4.23. ASO 2015: Atmospheric height–longitude sections averaged for 9.5°– of conditions contrib - − 6 1 − 21.5°N, of (a) anomalous divergence (× 10 s ), (b) anomalous vertical velocity uted to the development − − 2 1 (× 10 - Pa s ), and (c) percent of normal specific humidity. Green shading indi of five named storms cates anomalous divergence, anomalous rising motion, and increased moisture, north of the MDR. Two respectively. Brown shading indicates anomalous convergence, anomalous sinking of these storms became motion, and decreased moisture. Zero lines are drawn on each panel. Anomalies hurricanes, with one are departures from the 1981–2010 means. | S107 AUGUST 2016 STATE OF THE CLIMATE IN 2015

128 bined statistics, along with information specifically addressing the observed activity and impacts in the CNP region. The ENP/CNP hurricane season officially spans from 15 May to 30 November. Hurricane and tropical storm activity in the eastern area of the basin typically peaks in September, while in the central Pacific, TC activity normally reaches its seasonal peak in August (Blake et al. 2009). During the 2015 season, a total of 26 named storms formed in the combined ENP/CNP basin. This total included 16 hurricanes, 11 of which were major hurricanes. The 1981–2010 IBTrACS sea - sonal averages for the basin are 16.5 named storms, 8.5 hurricanes, and 4.0 major hurricanes (Schreck et al. - 2014). The 2015 season’s 26 named storms is the high est storm count since the 1992 season. In late August, Hurricanes Kilo, Ignacio, and Jimena reached Category . 4.24. 200-hPa circulation during ASO 2015: F ig 4 status at the same time (Fig. SB4.1a). This was the first 1 2 − 6 s ) and (a) anomalous velocity potential (× 10 m time on record that three Category 4 or stronger TCs − 1 anomalous divergent wind vector (m s ), and (b) total were present at the same time in any global TC basin. (contours) and anomalous (shaded) streamfunction Given that 68% of the ENP/CP hurricanes in 2015 1 6 − 2 (× 10 m s ). Divergent wind vector scale in (a) is reached major hurricane status, it is no surprise that below right of plot. In (b), white dashed line indicates amplified tropical upper tropospheric trough (TUTT). Anticyclonic anomalies are indicated by positive values (orange/red) in the NH and negative values (blue) in the SH. Cyclonic anomalies are indicated by negative values in the NH and positive values in the SH. Green boxes indicate the Atlantic hurricane MDR. Anomalies are based on the 1981–2010 climatology. the western MDR and a disappearance of the mean upper-level subtropical ridge normally located over the central and eastern MDR. These conditions contributed anomalous upper-level westerly winds, increased vertical wind shear, and anomalous sink - ing motion across the MDR (Figs. 4.22, 4.23), the combination of which suppressed the 2015 Atlantic hurricane season. and 3) e astern n Orth p acific centr al n Orth p basins — M. C. Kruk, C. J. Schreck, and T. Evans acific (i) Seasonal activity The eastern North Pacific (ENP) basin is offi - cially split into two separate regions for the issuance of warnings and advisories by NOAA’s National Weather Service. NOAA’s National Hurricane Center in Miami, Florida, is responsible for issuing warnings in the eastern part of the basin (ENP) that extends . 4.25. Seasonal TC statistics for the full ENP/CNP ig F from the Pacific Coast of North America to 140°W, basin over the period 1970–2015: (a) number of named while NOAA’s Central Pacific Hurricane Center in storms, hurricanes, and major hurricanes, and (b) Honolulu, Hawaii, is responsible for issuing warnings 4 2 the ACE index (× 10 kt ) with the 2015 seasonal total in the central North Pacific (CNP) region between highlighted in red. The time series shown includes the 140°W and the date line. This section summarizes corresponding 1981–2010 base period means for each the TC activity in both warning areas using com - parameter. | S108 AUGUST 2016

129 the ACE index for 2015 was high as well, with a sea (Fig. 4.26c). The vertical wind shear anomalies were - 2 4 sonal value of 251.6 × 10 (Fig. 4.25), which is nearly generally easterly from 120°E to the date line, which kt 2 4 double the 1981–2010 mean of 132.0 × 10 (Bell et al. kt likely contributed to the record season in the CNP. - 2000; Bell and Chelliah 2006; Schreck et al. 2014). A The broad area of warm SSTs, enhanced convec record-shattering 16 tropical cyclones developed in, or tion, and moderate shear in 2015 all contributed to - entered into, the CNP basin during 2015, with a dis favorable conditions that resulted in above-normal tribution of eight hurricanes (five major), six tropical hurricane activity. - Figure 4.26d shows a broad area of 850-hPa west storms, and two depressions (Fig. 4.25); the previous record season was 1992 with a total of 12 TCs. The erly anomalies near the equator. Similar patterns were seen in 2012–14 (Diamond 2013, 2014, 2015), long-term 1981–2010 IBTrACS mean is 4.7 storms although these years also featured stronger easterly passing through the CNP per season. anomalies to the north. Even on their own, the west - season influences erly anomalies produced the region of enhanced cy on - the 2015 (ii) Environmental Figure 4.26 illustrates the background conditions clonic vorticity within which most of the ENP storms for TC activity in the ENP and CNP during 2015. developed. Many of these storms developed where the Consistent with the strong El Niño conditions, the enhanced vorticity intersected the westerly anoma - lies. The westerlies could have strengthened easterly equatorial Pacific was dominated by anomalously wave activity in this region through barotropic energy warm SST anomalies (Fig. 4.26a). As in 2014, these conversion and wave accumulation (Maloney and - warm SSTs extended throughout most of the sub Hartmann 2001; Aiyyer and Molinari 2008; Rydbeck tropical ENP, which would be exceptionally favorable and Maloney 2014). for TC activity. The ITCZ was also strongly enhanced ENP TC activity is strongly inf luenced by the MJO in association with the warm SSTs, but the strongest enhancement of convection was southward of where (Maloney and Hartmann 2001; Aiyyer and Molinari TCs form (Fig. 4.26b). Vertical wind shear magni 2008; Slade and Maloney 2013), and recent studies - tudes were slightly below their climatological values have found a greater role for convectively coupled F ig . 4.26. May–Nov 2015 anomaly maps of (a) SST − 2 , Lee (ºC, Banzon and Reynolds 2013), (b) OLR (W m − 1 2014), (c) 200–850-hPa vertical wind shear (m s ) vec - tor (arrows) and scalar (shading) anomalies, and (d) 1 − 2 − 850-hPa winds (m s , arrows) and zonal wind (shading) , . 4.27. Longitude–time Hovmoller of OLR (W m ig F anomalies. Anomalies are relative to the annual cycle Lee 2014) averaged 5°–15ºN. Unfiltered anomalies from 1981–2010, except for SST which is relative to - from a daily climatology are shaded. Negative anoma 1982–2010 due to data availability. Hurricane symbols lies (green) indicate enhanced convection. Anomalies with letters denote where each ENP TC attained filtered for Kelvin waves are contoured in blue at − 2 tropical storm intensity. Wind data obtained from –10 W m . Hurricane symbols and letters indicate NCEP–NCAR reanalysis I (Kalnay et al. 1996). genesis of ENP TCs. | S109 AUGUST 2016 STATE OF THE CLIMATE IN 2015

130 Kelvin waves in modulating tropical cyclogenesis completely defoliated, power outages were common, and torrential rains f looded roads and resulted in (Schreck and Molinari 2011; Ventrice et al. 2012a,b; Schreck 2015). Figure 4.27 uses OLR to examine the landslides. In the town of Tamaulipas, 193 mm of rain evolution of convection during the 2015 ENP hur - was recorded from the storm. Roughly 9000 homes ricane season. Following Kiladis et al. (2009), the were damaged or destroyed and many agricultural croplands, in particular banana crops, were wiped blue contours identify the Kelvin-filtered anomalies. Easterly waves are also apparent in the unfiltered out by the wind and rain from Patricia. Despite no direct landfalls, high surf, coastal anomalies (shading) as westward moving features, - f looding, f looding rains, and oppressive heat im such as the ones leading up to Hurricanes Norbert pacted the Hawaiian Islands throughout the 2015 and Simon. season. The largest surf came from Hurricane Ignacio During the 2015 ENP hurricane season, intrasea - as it passed to the east and northeast of the main onal variability was dominated by eastward moving signals that straddled the boundaries between Kelvin Hawaiian Islands. Ignacio produced large waves and a small storm surge resulting in water and debris waves and the MJO (Roundy 2012a,b). Three events are particularly noteworthy: early July, late August, on roadways along the Big Island’s (also known as - and October. These events were all prolific TC pro Hawaii Island) and Oahu’s eastern coastline, causing ducers, spawning strings of TC genesis from the north road and beach park closures. Heavy rain associated with Ignacio fell across the main Hawaiian Islands Indian Ocean to the North Atlantic. In the ENP/CNP causing widespread f looding, including in portions alone, 5–7 TCs developed in association with each of of Honolulu. Hurricanes Hilda and Kilo forced deep these events, accounting for 18 of the 26 ENP/CNP tropical moisture over the main Hawaiian Islands, TCs in 2015. which led to significant f looding rains. Impacts of this f looding included a massive sewage spill when (iii) TC impacts the Honolulu drainage system was overwhelmed, During the 2015 season, only 2 of the season’s 26 f looded homes and businesses, and one f lash f lood combined ENP/CNP tropical storms made landfall - fatality. Hurricane Guillermo had the closest ap along the western coast of Mexico or Baja California, proach to the main Hawaiian Islands and produced while remarkably no storms in the CNP region made landfall in Hawaii. The long-term annual average coastal f looding as large waves closed roads and beach parks. Portions of the northwest Hawaiian Islands, number of landfalling storms on the western coast which are not populated but host research teams, of Mexico is 1.8 (Raga et al. 2013). were evacuated due to large waves associated with The first storm to make landfall along the Mexican coastline was Hurricane Blanca (31 May to 9 June), Hurricane Kilo and Tropical Storm Malia. which had maximum sustained winds of 120 kt −1 (61 m s n estern ) and a minimum central pressure of 936 hPa. acific p Orth 4) w S. J. Camargo — basin (i) Blanca weakened to a tropical storm before making Introduction The WNP is unique in that TCs are tracked simul - landfall in Baja California and made the earliest landfall in that region on record. Even as the storm taneously by several agencies in that region. Among these are the United States military’s Joint Typhoon was weakening, strong rip currents associated with Warning Center (JTWC) and the WMO-sanctioned the storm claimed four lives off the coast of Mexico. RSMC-Tokyo, Japan Meteorological Agency (JMA). The second landfalling storm of 2015 was Major Hurricane Patricia from 20–24 October, with Data from JTWC are used here; best-track dataset for −1 maximum sustained winds of 174 kt (88 m s the period 1945–2014 and from the JTWC’s prelimi - ) and nary operational data for 2015. The best-track data - a minimum central pressure of 879 hPa. The baro from the RSMC-Tokyo, Japan Meteorological Agency metric pressure and maximum sustained winds, both as measured by hurricane reconnaissance aircraft, (JMA), was used in Fig. 4.28b. All other figures were - produced using JTWC TC data. Climatology is de - are now the lowest on record for pressure and high est on record for winds anywhere in the Western fined using the period 1981–2010, with the exception of landfall statistics, where 1951–2010 is used. - Hemisphere. The hurricane also intensified extraor dinarily quickly, dropping 100 hPa in just 24 hours. Seasonal activity (ii) Fortunately for the major cities and towns in coastal Mexico, Patricia made landfall as a Category 5 storm The TC season in the western North Pacific (WNP) near Jalisco, Mexico, a relatively rural area, though in 2015 was above normal by most measures of TC it still caused a range of impacts. Many trees were activity considered. According to the JTWC, the 2015 | S110 AUGUST 2016

131 of 8), 21 typhoons (above the 75th percentile of 20), 8 of which became super typhoons (winds −1 ≥ 137 kt; 71 m s ; i n the top 5th percentile, the 75th percentile is 5). - In Fig. 4.28a, the num ber of tropical storms, typhoons, and super typhoons per year is shown for the period 1945–2015. The number of super typhoons is one of the measures for the intensity of the 2015 sea - son that was well above - normal. A high num ber of super typhoons is a typical feature of El Niño events (Camar - go and Sobel 2005). The percentage of typhoons that reached super ty - phoon status in 2015 (38%) was in the top . 4.28. (a) Number of tropical storms, typhoons, and super typhoons per year F ig 10%. Climatologically, in the western North Pacific for the period 1945–2015 based on the JWTC best- only 23% of typhoons track dataset. (b) Number of TCs (all storms which reach tropical storm intensity reach super typhoon or higher) for 1951–76; number of tropical storms, severe tropical storms, and typhoons for 1977–2015 based on the JMA best-track dataset. (c), (d) The number intensity each season. of TCs with tropical storm intensity or higher [named storms (c) and typhoons The JMA total for (d)] per month in 2015 (black line) and the climatological means (blue line). The 2015 was 27 TCs (above blue plus signs denote the maximum and minimum monthly historical records, JMA’s climatological and the red error bars show the climatological interquartile range for each month median of 26), includ - (in the case of no error bars, the upper and/or lower percentiles coincide with the ing Hurricanes/Ty - median). (e), (f) The cumulative number of named storms (e) and super typhoons phoons Halola and Kilo. (f) per month in the WNP in 2015 (black line) and climatology (1971–2010) as box plots [interquartile range: box; median: red line; mean: blue asterisk; values in the Tropical Storms 12W top or bottom quartile: blue crosses; high (low) records in the 1945–2015 period: and Vamco were only red diamonds (circles)]. [Sources: 1945–2014 JTWC best-track dataset, 2015 JTWC considered to be tropical preliminary operational track data for (a), (c), (d), (e), and (f); 1951–2015 RSMC- depressions by JMA, and Tokyo, JMA best-track dataset for panel (b).] TDs are not included in the JMA database. Of those 27, nine were greater than - season had 29 TCs form in the basin, with two addi tropical storm strength (equal to the 25th percentile tional TCs (Halola and Kilo) that formed in the central for JMA), and 18 were typhoons (top quartile for JMA). North Pacific (CNP) then crossed into the WNP. This The number of TCs (1951–76), or tropical storms, total of 31 storms active in the basin is above the medi - an of the climatological distribution (the climatologi severe tropical storms, and typhoons (1977–2015) ac - - 1 cal median is 28.5, the 75th percentile is 33). Of these, cording to the JMA are shown in Fig. 4.28b. 28 TCs reached tropical storm intensity or higher (the climatological median is 26, the 75th percentile is 1 I t is well known that there are systematic differences between 29.5) and 27 of them were named (only one, 12W, was the JMA and the JTWC and the datasets, which have been - not formally named). There were 3 tropical depres extensively documented in the literature (e.g., Wu et al. 2006; sions (TDs; slightly below the climatological median Nakazawa and Hoshino 2009; Song et al. 2010; Ying et al. of 3.5), 7 tropical storms (below the 25th percentile 2011; Yu et al. 2012; Knapp et al. 2013; Schreck et al. 2014). | S111 AUGUST 2016 STATE OF THE CLIMATE IN 2015

132 The number of named storms and typhoons per month in 2015, compared with the climatological distribution, is shown in Figs. 4.28c,d. Super Typhoon Maysak was one of the strongest March storms in the historical record, reaching the same record intensity of Super Typhoon Mitag (February–March 2002) for that month. In May, two super typhoons formed in the WNP, Noul and Dolphin, while only Tropical 2 July was an active Storm Kujira was active in June. month with six storms present in the WNP, includ - ing Super Typhoons Nangka and Soudelor. On 9 July, three storms (Chan-hom, Nangka, and Linfa) were active simultaneously on the WNP, a rare event for July. September and October had five active storms each, including Super Typhoons Champi and Lando in October. ig . 4.29. (a) ACE index per year in the western North F Considering the number of TCs and named Pacific for 1945–2015. The solid green line indicates the median for the climatology years 1971–2010, and storms, the 2015 typhoon season had an active, early the dashed lines show the climatological 25th and season (January–June), with 8 TCs (top quartile), an 75th percentiles. (b) ACE index per month in 2015 average peak season (July–October) with 20 TCs (me - (red line) and the median during 1971–2010 (blue line), dian is 19), and a quiet late season with 3 TCs (bottom where the green error bars indicate the 25th and 75th quartile), as can be seen in the cumulative number of percentiles. In case of no error bars, the upper and/or - named of storms per month in 2015 and the clima lower percentiles coincide with the median. The blue tological distribution (Fig. 4.28e). The occurrence of “+” signs denote the maximum and minimum values during the period 1945–2014. (Source: 1945–2014 a high number of super typhoons, a typical feature JTWC best-track dataset, 2015 JTWC preliminary - of El Niño years, was clear in 2015, with 8 super ty operational track data.) phoons, 3 of which formed in the early season and 5 during the peak season. The occurrence of three super third highest value in the historical record, just be - - typhoons in the early season is quite unusual, hav low the values in 2004 and 1997, both El Niño years ing only occurred twice previously in the historical record, 2002 and 2004; these were also El Niño years. (Fig. 4.29a). The bulk of the seasonal ACE occurred during July and August (Fig. 4.29b), contributing to The cumulative number of super typhoons in 2015 compared with the climatological baseline is shown 21% and 24% of the total ACE, respectively. The ACE for May was the largest in the historical record for that in Fig. 4.28f. Previously, only one super typhoon had month. Other high monthly values of ACE reached formed in March, in 1961 (while STY Mitag reached the third (February and August), fourth (July), and is lifetime maximum intensity in March, it formed fifth (March) highest values in the historical record in February). The 2015 season is the first time in the historical record that two super typhoons formed in for those months. In contrast, the June ACE was in the bottom quartile. Eight TCs in 2015 were in the May; the previous historical maximum for that month was one. An active July, with two tropical storms, two top 10% of the ACE per storm, together contributing a total of 58.5% of the total ACE for the season. With typhoons, and two super typhoons was followed by a relatively quiet August. The two typhoons (one of the exception of Typhoon Goni, the other seven TCs - with highest ACE in 2015 reached super typhoon sta them a super typhoon) in August is in the bottom tus. The top ACE values in 2015 are from TCs Noul, quartile for that month. Two more super typhoons occurred in October, in the top 10% for that month. Champ, Dolphin, Maysak, Soudelor, Atsani, Goni, and Typical of El Niño years, the total ACE in 2015 Nangka, in that order. Additionally, JTWC tracked the −1 was high (Camargo and Sobel 2005), reaching the ), but it is peak wind speed for Goni at 115 kt (59 m s noteworthy that 5.5 of its 11 days had winds ≥ 100 kts. The ACEs of each of the top three named storms (Noul, 2 H ere, if a storm forms in the two last days of a month, it is Champ, Dolphin) reached the top 5% and contributed counted for the following month if it lasts more than two 25.7% of the total ACE in the season. Other storms in - days in the next month. This was the case in 2015 of ty the top quartile of ACE per storm in 2015 were Koppu, phoons Chan-hom (formed 29 June) and Mujigae (formed Dujuan, Halola, In-fa, Kilo, and Chan-hom. 30 September). | S112 AUGUST 2016

133 the central and western North Pacific basins. The longest-living storm that formed in the WNP was Super Typhoon Nangka, which lasted a total of 15.75 days from 3–19 July. The mean genesis location for storms with genesis in the WNP in 2015 (13.4°N, 147.3°E) was slightly eastward from the clima - tological mean of WNP storms (13.2°N, 141.6°E, with standard deviations of 1.9° and 5.6°). The mean track position (16.7°N, 144.5°E) was also southeastward relative to the WNP climatologi - cal mean (17.3°N, 136.6°E, with standard deviations of 1.4° and 4.7°). Although a southeastward shift is typical of El Niño years (e.g., Chia and Ropelewski 2002; Camargo et al. 2007), this 2015 shift was mostly eastward, with almost no change (mean first position) or a small southward shift (mean track) in the meridi - onal direction. - Figure 4.30 shows the envi - ronmental conditions associ ated with the typhoon activity in 2015. The warm SST anomalies . 4.30. (a) SST anomalies, (b) potential intensity anomalies, (c) relative F ig during July–October (JASO; humidity 600-hPa anomalies, (d) genesis potential index anomalies in JASO Fig. 4.30a) were large in the 2015, and (e) zonal winds in Jul–Oct 2015 (positive contours are shown in eastern and central Pacific, but solid lines, negative contours in dash dotted lines and the zero contour in a dotted line). [Source: atmospheric variables: NCEP–NCAR reanalysis data small in the WNP. These large (Kalnay et al. 1996); sea surface temperature (Smith et al. 2008).] SST anomalies led to high values of potential intensity (Emanuel 1988 and 1995; Fig. 4.30b) and 600-hPa relative There were 174.75 days with TCs in 2015, near humidity (Fig. 4.30c) anomalies on the eastern and the 75th percentile (176.75 days), and 148.75 days central Pacific in two bands, the first in the equatorial with storms that reached tropical storm or higher, - region, the second near Hawaii. The genesis poten - in the top 5% (median 111.75 days). From those ac tive days, 90.75 days had typhoons, the third highest tial index (GPI; Emanuel and Nolan 2004; Camargo value in the historical record, less than only 1997 and et al. 2007) had positive anomalies on the eastern 2004. There were 36.75 days with intense typhoons part of the basin and negative on the western side (Categories 3–5), in the top 10% (median 20 days). (Fig. 4.30d), typical of El Niño years. The maximum extent of the monsoon reached the date line, as docu In 2015, the percentage of days with typhoons and - mented via the zonal winds depicted in Fig. 4.30e; this intense typhoons were 51.9% and 21.0%, in the top monsoonal extent helps explain the eastward shift of 1% and 10%, respectively (median 37.9% and 12.2%, the location of cyclogenesis in the basin for the season. respectively). The median lifetime of named storms in 2015 was 8.75 days, slightly above the median of 8 days. The two longest-living storms were Kilo and Halola, which lasted 22 days (20 August to 11 Septem - ber) and 17.75 days (10–26 July), while crossing both | S113 AUGUST 2016 STATE OF THE CLIMATE IN 2015

134 (iii) TC impacts seasonal averages for the basin are 3.9 tropical storms, 3 There were 18 storms that made landfall in 2015, - 1.4 cyclones, and 0.6 major cyclones. The season pro slightly above the 1951–2010 climatological median duced its highest ACE index since 1972 with a value 4 2 (17). Of these, three systems made landfall as a TD of 30.4 × 10 kt , well above the 1981–2010 mean of 4 2 (Fig. 4.31b). Typically, there is enhanced 12.5 × 10 kt (median is three), seven storms made landfall as tropical storms (median is six), two struck as Category TC activity, especially in the Bay of Bengal, during the 1–2 typhoons (median is five). Five storms made cool phase of ENSO (Singh et al. 2000); however, most of this season was characterized by a strong develop landfall as intense typhoons, among the top 10% of - the 1951–2010 climatological distribution (the median ing El Niño. Four of the five storms developed in the is two): Dujuan, Goni, Koppu, Melor, and Mujigae. Arabian Sea, and only tropical storm Two (29–30 July) Many storms led to social and economic impacts in developed in the Bay of Bengal. There were two noteworthy storms during the 2015. Typhoon Maysak made landfall in both Chuuk season: Cyclones Chapala and Megh. Chapala (28 and Yap States of the Federated States of Micronesia in March and was responsible for four deaths and October–4 November) formed in the Arabian Sea and became a “severe cyclonic storm” (wind ≥ 96 kts) led to significant damage to homes and crops in both states. Typhoon Koppu (known as Lando in the on 29 October with maximum sustained winds near −1 ) and a minimum central pressure of 114 kt (58 m s Philippines) caused at least 58 deaths and f looding 940 hPa. What made these storms unique was how in the northern Philippines, as well as heavy agri - they tracked westward over the island of Socotra and cultural and economical damage across the country. into the Gulf of Aden—a very unusual track com - The double hit of Typhoon Melor and a tropical de - pared to historical records. This resulted in extreme pression in December in the Philippines led to f loods and at least 45 deaths. The storms with the largest damage across Socotra and the country of Yemen, economic impacts in 2015 were Typhoons Soudelor which rarely experiences tropical cyclone landfalls, much less the tremendous rains associated with them. (3.2 billion U.S. dollars) and Chan-hom (1.5 billion In fact, these were not only the first tropical cyclones U.S. dollars). Soudelor caused severe impacts in the Commonwealth of the Northern Mariana Islands, to strike Socotra since 1922, but most interesting was Taiwan, and eastern China (at least 38 deaths), as that they did so during the same week. Rainfall data are spotty for the region, but satellite estimates sug well as some lesser impacts in Japan, the Republic of - gest 610 mm of rainfall along the Yemeni coastline, Korea, and the Philippines. Chan-hom also affected many countries in the WNP basin, particularly Japan which is 700% of the annual average for the region. (Okinawa), Taiwan, China, the Republic of Korea Eight people died in Yemen, most by drowning, and (Jeju Island), and North Korea. - several hundred homes and businesses were dam aged by f looding. A storm surge of nearly 10 m was Orth 5) n - M. C. Kruk — observed in the coastal town of Al Mukalla, destroy cean O ndian i The north Indian Ocean (NIO) TC season typi ing the city’s seafront and inundating many coastal - structures with saltwater. cally extends from April to December, with two peaks The second major storm of the season was Very in activity: during May–June and again in November, Severe Tropical Cyclone Megh, which occurred from when the monsoon trough is positioned over tropical 5 to 10 November in the Arabian Sea, about a week waters in the basin. TCs in the NIO basin normally - develop over the Arabian Sea and Bay of Bengal be after Cyclone Chapala. The track of Megh was similar to that of Chapala, moving over the island of Socotra tween 8° and 15°N. These systems are usually short- and into the Gulf of Aden. Maximum sustained wind lived and relatively weak and often quickly move into −1 speeds reached 95 kt (48 m s ) with a minimum the Indian subcontinent. central pressure of 964 hPa. Megh made landfall in According to the JTWC, the 2015 TC season Socotra as a Category 3 equivalent storm, causing produced five tropical storms, two of which were extensive devastation, resulting in nearly 20 deaths. major cyclones (Fig. 4.31a). The 1981–2010 IBTrACS Additionally, upwards of 3000 homes were either completely destroyed or damaged by the cyclone, 3 Landfall is defined when the storm track is over land and the which also caused havoc with local fishing operations. - previous location was over ocean. In order not to miss land fall over small islands, first the tracks were interpolated from 6-hourly to 15-minute intervals, before determining if the storm track was over land or ocean using a high-resolution land mask. | S114 AUGUST 2016

135 4 2 ACE index of 114.7 × 10 kt , which was above the 4 2 (Fig. 4.32b). This kt 1981–2010 average of 91.5 × 10 is the second consecutive year with above-average ACE values for the SIO. As a result of warmer-than- normal SSTs, coupled with generally below-average wind shear (Fig. 4.32), the overall season was above average. Figure 4.33a indicates that the seasonally av - eraged SST anomalies were above normal, stretching between 10° and 30°S across the width of the southern Indian Ocean. Moreover, Fig. 4.33c demonstrates that deep-layer vertical wind shear was also anomalously low across the same latitude belt, on the order of 1– −1 , below normal for the season. It appears likely 3 m s that the combination of warm waters and a favorable low-shear environment helped to sustain not only the number of storms this season but also their above- average intensities, as ref lected by the ACE index. During the 2014/15 season, the strongest storm was Cyclone Eunice (27 January–2 February), which reached Category 5 equivalency with peak maximum F ig . 4.31. Annual TC statistics for the NIO for 1970– 2015: (a) number of tropical storms, cyclones, and major cyclones and (b) the estimated annual ACE 2 4 index (in kt × 10 ) for all TCs during which they were at least tropical storm strength or greater intensity (Bell et al. 2000). The 1981–2000 means (green lines) are included in both (a) and (b). O cean — Outh M. C. Kruk and C. Schreck 6) s i ndian The south Indian Ocean (SIO) basin extends south of the equator from the African coastline to 90°E, with most cyclones developing south of 10°S. The SIO TC season extends from July to June encompassing equal portions of two calendar years (the 2015 season is comprised of storms from July to December 2014 and from January to June 2015). Peak activity typi - cally occurs during December–April when the ITCZ is located in the Southern Hemisphere and migrating toward the equator. Historically, the vast majority of landfalling cyclones in the SIO affect Madagascar, Mozambique, and the Mascarene Islands, includ - ing Mauritius and Réunion Island. The RSMC on 4.32. Annual TC statistics for the SIO for 1980– . ig F La Réunion serves as the official monitoring agency 2015: (a) number of tropical storms, cyclones, and for TC activity within the basin. major cyclones and (b) the estimated annual ACE The 2014/15 SIO storm season was much above 4 2 index (in kt ) for all TCs during which they were × 10 average, with 14 tropical storms, of which 6 were at least tropical storm strength or greater intensity cyclones and 4 were major cyclones (Fig. 4.32a). The (Bell et al. 2000). The 1981–2000 means (green lines) 1981–2010 IBTrACS seasonal median averages are are included in both (a) and (b). Note that ACE index eight tropical storms, four cyclones, and one major cy - is estimated due to lack of consistent 6-h sustained winds for each storm. clone. The active season is also ref lected in the 2014/15 | S115 AUGUST 2016 STATE OF THE CLIMATE IN 2015

136 resulting in extensive f looding following weeks of extreme wet weather across the island. This resulted in widespread mudslides across the region, damaging roads and homes. Nearly 4400 homes were destroyed by the f loods and unfortunately the storm resulted in 80 fatalities, most of which were from landslides. The 0 f looding rains inundated over 9000 ha (24 00 acres) of rice fields and displaced 1200 cattle. - In early February, Severe Tropical Storm Fundi de veloped over the southwestern shores of Madagascar, and by 6 February, the storm had reached maximum −1 sustained winds of 55 kt (28 m s ) and a minimum central pressure of 985 hPa. Fundi brought 109 mm of rainfall to the southwestern Madagascar town of Tulear and as far inland as Toliara where five people - died due to f loods. While the storm never made land fall, the damage to water and sewer infrastructure caused by weeks of antecedent heavy rains, including those from Chedza, hindered ongoing relief efforts and increased the number of personal health and hygiene risks. - The final notable storm of the season was Moder ate Tropical Storm Haliba (7–10 March), which was a tropical disturbance that formed east of Madagascar and tracked southeast near Réunion Island. During its development stages, Haliba produced heavy rains 00 peo - across eastern Madagascar, affecting over 95 0 ple and killing 26. The storm intensified on 8 March −1 with maximum sustained winds of 43 kt (22 m s ) and a minimum central pressure of 993 hPa. As it moved southeast, exceptional rain was recorded at F ig . 4.33. Jul–Jun 2014/15 anomaly maps of (a) SST Ganga Talao, with 135.6 mm falling in just 24 hours. 2 − , (°C, Banzon and Reynolds 2013), (b) OLR (W m The storm went on to produce 796 mm of rainfall Lee 2014), (c) 200–850-hPa vertical wind shear over northern Réunion Island, and while that is a − 1 (m s ) vector (arrows) and scalar (shading) anomalies, large amount of precipitation, it is not a particularly − 1 and (d) 850-hPa winds (m s arrows) and zonal wind unusual amount for a tropical system at this latitude. (shading) anomalies. Anomalies are relative to the annual cycle from 1981–2010, except for SST which is basin — B. C. Trewin 7) a ustralian relative to 1982–2010 due to data availability. Letter symbols denote where each SIO TC attained tropical (i) Seasonal activity storm intensity. Wind data obtained from NCEP–DOE The 2014/15 TC season was near normal in the Reanalysis 2 (Kanamitsu et al. 2002). broader Australian basin (areas south of the equa - −1 4 ) and an estimated tor and between 90° and 160°E, sustained winds of 139 kt (70 m s which includes Australian, Papua New Guinea, and Indonesian minimum central pressure of 900 hPa. The storm formed in the middle of the south Indian Ocean and areas of responsibility), with a slightly below-normal - number of cyclones but an above-normal number of remained there throughout its lifecycle, generally mov severe cyclones. The season produced 9 TCs, near the ing southeast before weakening over cooler waters. 5 Severe Tropical Storm Chedza (14–22 January 1983/84–2010/11 average of 10.8 and consistent with neutral to warm ENSO conditions. The 1981–2010 2015) was the deadliest storm of the season. Chedza formed off the southeast coast of Africa and intensi - fied over the Mozambique Channel where it attained 4 The Australian Bureau of Meteorology’s warning area over - −1 maximum sustained winds of 57 kt (29 m s ) and a laps both the southern Indian Ocean and southwest Pacific. 5 - minimum central pressure of 975 hPa. On 16 Janu Averages are taken from 1983/84 onwards as that is the start ary, Chedza made landfall in western Madagascar, of consistent satellite coverage of the region. | S116 AUGUST 2016

137 (ii) Landfalling and other significant TCs The most intense cyclone of the season was Mar - cia. TC Marcia formed in the monsoon trough to the northeast of Cairns on 15 February and moved slowly east, reaching tropical cyclone intensity on - 18 February. It then intensified rapidly on 19 Febru ary, intensifying from Category 1 to Category 5 on the Australian scale (see www.bom.gov.au/cyclone/about /intensity.shtml for details) in the space of 15 hours on 19 February, with maximum 10-minute sustained −1 ), as it moved southwest winds of 110 kt (57 m s towards the central Queensland coast. Marcia made landfall at near peak intensity in Shoalwater Bay at 2200 hours UTC on 19 February (0800 20 February local time), weakening rapidly as it tracked southward over land and falling below tropical cyclone intensity by 1500 hours UTC on 20 February near Monto. The remnant tropical low moved back out over water off southeast Queensland on 21 February and drifted in the Coral Sea for several days, but did not regain cyclone intensity. Marcia caused significant wind damage near the landfall point, especially in and around the towns of Yeppoon and Byfield, and less intense but more widespread damage in the major regional centre of F . ig 4.34. Annual TC statistics for the Australian basin - Rockhampton, where it was the most significant cy for 1980–2015: (a) number of tropical storms, cyclones, and major cyclones and (b) the estimated annual ACE clone impact since at least 1949. Some f looding also 4 2 index (in kt × 10 ) for all TCs during which they were occurred in regions south of Rockhampton. Marcia at least tropical storm strength or greater intensity is the southernmost known Category 5 landfall on (Bell et al. 2000). The 1981–2000 means (green lines) the east coast of Australia. are included in both (a) and (b). Note that ACE index Cyclone Lam formed in the monsoon trough is estimated due to lack of consistent 6-h sustained south of Papua New Guinea on 12 February. It moved winds for each storm. - westward as a tropical low, crossing Cape York Pen insula, and then intensified over the northern Gulf of IBTrACS seasonal averages for the basin are 9.9 NSs, 7.5 TCs, and 4.0 major TCs, which compares with the Carpentaria, where it reached cyclone intensity on 16 2014/15 counts of 9, 7, and 5 respectively. February. The system intensified steadily as it passed 6 of the near the Wessel Islands, then turned southwest and There were four TCs in the eastern sector Australian region during 2014/15, two in the northern reached Category 4 intensity west of Elcho Island 7 Four systems sector, and five in the western sector. early on 19 February, with maximum 10-minute −1 made landfall in Australia as tropical cyclones, one in sustained winds of 100 kt (51 m s ). Lam crossed the coast between Milingimbi and Elcho Island at Western Australia, two in the Northern Territory (one after an initial landfall in Queensland), and a fourth peak intensity later that day (early on 20 February in Queensland (Fig. 4.34). Fig. 4.34 (as noted in sec local time). Lam caused significant wind damage to - a number of Aboriginal communities along eastern tion 4e1) is standardized on the Saffir–Simpson scale. parts of the northern Arnhem Land coast and nearby islands, with Ramingining on the mainland coast and Galiwin’ku on Elcho Island the most severely 6 he western sector covers areas between 90° and 125°E. T impacted. This was the first known instance of two - The eastern sector covers areas east of the eastern Austra tropical cyclones of Category 4 or greater intensity lian coast to 160°E, as well as the eastern half of the Gulf of making landfall in Australia on the same day. Carpentaria. The northern sector covers areas from 125°E - Cyclone Olwyn formed as a tropical low approxi east to the western half of the Gulf of Carpentaria. 7 - mately 900 km north of Exmouth on 8 March, mov Lam and Nathan each passed through both the eastern and ing southward and slowly strengthening. It reached northern sectors. | S117 AUGUST 2016 STATE OF THE CLIMATE IN 2015

138 8 tropical cyclone intensity at 0600 hours UTC on 11 from 1800 hours UTC on 30 June in the western March, and continued to intensify as it approached South Pacific northeast of the Solomon Islands. It is - the coast at North West Cape. It reached its peak in the first instance in the satellite era of a July tropical cyclone in the Australian sector of the South Pacific, tensity of Category 3, with 10-minute sustained winds −1 and the first instance since 1972 in the Southern ), while it was located near North of 75 kt (39 m s Hemisphere winter months (June, July, or August). West Cape, just west of Exmouth, at 1800 hours UTC on 12 March. Olwyn then moved southward along — the west coast with only minimal weakening, pass acific P. R. Pearce, A. M. Lorrey, - basin 8) s p Outhwest and H. J. Diamond ing just to the west of Carnarvon at 0600 hours UTC (i) Seasonal activity on 13 March and crossing the coast in the Shark Bay The 2014/15 TC season in the southwest Pacific area a few hours later. Reported wind gusts included −1 −1 began in late November. The first storm developed as ) at Learmonth and 79 kt (41 m s 97 kt (50 m s ) a tropical depression near Wallis and Futuna, and the at Carnarvon. It was the most significant cyclone impact in the Carnarvon area for many years, with season concluded very late with TC Raquel affecting major crop losses (including the near-total loss of the the Solomon Islands in late June-early July. Storm- banana crop), substantial wind damage to buildings track data for November 2014–July 2015 was gathered in the town, and power and water outages that lasted by the Fiji Meteorological Service, Australian Bureau of Meteorology, and New Zealand MetService, Ltd. for several days. Damage in Exmouth, where cyclones are a more common occurrence, was much less severe. The southwest Pacific basin as defined by Diamond The fourth landfalling cyclone of the season was et al. (2012) (135°E–120°W) had nine tropical cy - Nathan. Nathan formed on 10 March in the Coral Sea, clones, including five severe tropical cyclones (based on the Australian TC intensity scale). As noted in south of the eastern tip of Papua New Guinea. It made section 4e1, Fig. 4.35 shows the standardized TC dis - an initial approach towards the east coast of Cape tribution based on the basin spanning the area from York Peninsula as a Category 1 system on 13 March 160°E–120°W to avoid overlaps with the Australian before turning east again but then turned toward the basin that could result in double counting of storms. coast again on 18 March and intensified, reaching its peak intensity of Category 4 on 19 March with maxi - However, it is important to use the above definition −1 mum 10-minute sustained winds of 90 kt (46 m s ). of the southwest Pacific basin as that is how annual Nathan made landfall at near peak intensity near Cape TC outlooks are produced and disseminated. The 1981–2010 South Pacific Enhanced Archive Melville around 1800 hours UTC on 19 March and weakened to a tropical low as it crossed Cape York of Tropical Cyclones (SPEArTC) indicates a seasonal Peninsula. It reintensified as it reached the Gulf of average of 10.4 named tropical cyclones and 4.3 major tropical cyclones. The ratio of severe TCs relative to Carpentaria, making a second landfall as a Category the total number of named TCs in 2014/15 was 56%, 2 system near Nhulunbuy on 22 March. Nathan con - up from 36% during the previous season. Severe tinued to track along the north coast before turning Tropical Cyclones Pam, Lam, and Marcia caused southwest and weakening. Minor to moderate damage - considerable damage and loss of life across the ba was reported, principally to communities in northeast sin. Severe TC Pam, which devastated Vanuatu in Arnhem Land and Elcho Island, and on Lizard Island March, was the most intense TC in the basin since off the Cape York Peninsula coast. Three other cyclones reached Category 4 intensity Zoe in 2002. during the season, all in the Indian Ocean: Kate in TCs significant other and (ii) Landfalling December, Ikola in April, and Quang in late April and early May. Neither Kate nor Ikola approached the - The first named TC of the 2014/15 season was re mainland coast, although Kate passed near the Cocos ported as a tropical disturbance on January 19 to the (Keeling) Islands on 25 December with some minor northeast of the island of Tahiti in French Polynesia. f looding reported. Quang weakened as it neared the On 20 January, the disturbance was upgraded to a coast, causing a brief period of storm-force winds and Category 1 storm and named TC Niko. Over the next associated minor damage in the Exmouth area before two days the system gradually intensified further and weakening to a tropical low at landfall on 1 May. A noteworthy out-of-season cyclone was Raquel 8 By definition, the formal TC year in the Southern Hemi - (a twin of Typhoon Chan-hom in the western North sphere goes from July to June, and any storm forming in Pacific), which reached Category 1 intensity brief ly June would be considered to be in the previous TC season (in this case the 2014/15 season). | S118 AUGUST 2016

139 - The first Category 5 TC of the season was Se vere Tropical Cyclone Marcia, which developed in the Coral Sea on 16 February. See section 4e7 for a detailed timeline of Marcia’s development, landfall, decay, and impacts. Due to explosive intensification, Marcia became a Category 5 TC early on 20 February, with a peak 10-minute sustained wind speed of 110 kt −1 (57 m s ) and a minimum central pressure of 930 hPa. The storm wrought extensive damage in Queensland, with losses amounting to 590 million U.S. dollars. The most significant TC of the 2014/15 season was Severe Tropical Cyclone Pam, which developed on 6 March east of the Solomon Islands. On 9 March, Pam was named as a Category 1 storm. Located in an area of favorable conditions, Pam gradually intensified into a powerful Category 5 severe TC by 12 March. Pam’s 10-minute maximum sustained −1 wind speed peaked at 135 kt (69 m s ), along with a minimum central pressure of 896 hPa, making Pam the most intense TC of the southwest Pacific basin since Zoe in 2002, and the third-most intense storm in the Southern Hemisphere, after Zoe in 2002 and Gafilo in 2004. In addition, Pam had the highest −1 ) 10-minute sustained wind speed (135 kt; 69 m s recorded of any South Pacific TC, and it is tied with 4.35. Annual TC statistics for the southwest Pacific F ig . Orson in 1989 and Monica in 2006 for having the for 1980–2015: (a) number of tropical storms, cyclones, and major cyclones and (b) the estimated annual ACE strongest winds of any cyclone in the Southern 2 4 index (in kt × 10 ) for all TCs during which they were Hemisphere. at least tropical storm strength or greater intensity Early in Pam’s history, a damaging storm surge (Bell et al. 2000). The 1981–2000 means (green lines) was felt in Tuvalu, forcing a state of emergency are included in both (a) and (b). Note that ACE index declaration after 45% of the nation’s residents were is estimated due to lack of consistent 6-h sustained - displaced. Torrential rainfall occurred in the south winds for each storm. east Solomon Islands, with trees and crops f lattened. In the Santa Cruz Islands, a 24-hour rainfall total became a Category 2 TC early on 22 January. On 25 January, Niko completed its extratropical transition. of 495 mm was recorded. The storm also struck the - Severe Tropical Cyclone Ola was named on 30 Janu remote islands of Anuta and Tikopia on 12 March, causing extensive damage. Approximately 1500 ary as a Category 1 storm. Over the next two days, the system intensified and became a Category 3 TC homes were damaged or destroyed, and Tikopia lost 90% of its food crop and fruit trees. Several hours early on 1 February. Ola’s peak 10-minute sustained −1 wind speed was 81 kt (42 m s after being named a Category 5 TC on 12 March, the ) and central pressure - was 955 hPa at its lowest. TC began to curve towards the south-southeast, pass The third TC of the season was Severe Tropical ing by some islands in Vanuatu but making a direct Cyclone Lam, which began as a tropical disturbance hit on others. Pam caused catastrophic damage to Efate, the main island of Vanuatu where the capital, over the Gulf of Carpentaria on 13 February. See sec - Port Vila, is located. The islands of Erromango and tion 4e7 for a detailed timeline of Lam’s development, Tanna were also devastated. landfall, decay, and impacts. Lam was the strongest storm to strike Australia’s Northern Territory since Pam became the single worst natural disaster in the history of Vanuatu, crippling its infrastructure. TC Monica in 2006. In its formative stages, Lam An estimated 90% of the nation’s buildings were produced heavy rainfall and f looding in Far North impacted by the storm’s effects, telecommunica - Queensland, and later set daily precipitation records tions were paralyzed, and water shortages occurred. in the Northern Territory. Total damage in the North - Communications with many islands were completely ern Territory reached at least 64 million U.S. dollars. | S119 AUGUST 2016 STATE OF THE CLIMATE IN 2015

140 severed during the storm, and four days after the Islands, on 28 June. Over the next couple of days, the storm nearly 60% of the nation’s inhabited islands system moved westward into the Australian region, - where it was named a TC. Raquel then moved east remained cut off from the outside world. According ward into the South Pacific basin, where it weakened to UNESCO, 268 million U.S. dollars was required for total recovery and rehabilitation of Vanuatu. into a tropical depression. On 4 July, the system The storm’s winds gradually slowed afterwards as moved south-westward and impacted the Solomon Islands with high wind gusts and heavy rain. Pam tracked west of the Tafea Islands. However, the Fiji Meteorological Service indicated that the TC’s T f. pressure dropped farther to 896 hPa on 14 March. G. J. Goni, J. A. Knaff, ropical cyclone heat potential— and I.-I. Lin As Pam travelled farther south, the storm’s eye faded - This section summarizes the previously described away and Pam’s low-level circulation became dis tropical cyclone (TC) basins from the standpoint of placed from its associated thunderstorms, indicating a rapid weakening phase. Later on 15 March, Pam en - tropical cyclone heat potential (TCHP) by focusing on vertically integrated upper ocean temperature condi tered a phase of extratropical transition and affected - northeast New Zealand and the Chatham Islands tions during the season for each basin with respect to their average values. The TCHP (Goni and Trinanes with high winds, heavy rain, and rough seas. A state 2003), defined as the excess heat content contained of emergency was declared in the Chatham Islands. in the water column between the sea surface and the - At least 15 people lost their lives either directly or in depth of the 26°C isotherm, has been linked to TC directly as a result of Pam, with many others injured. intensity changes (Shay et al. 2000; Goni and Trinanes Shortly after Pam was classified, its outer rain - bands led to the formation of a tropical low east of 2003; Lin et al. 2014). The magnitude of the in situ - TCHP was also identified as impacting the maxi Cape York Peninsula, Australia, on 9 March. The mum potential intensity (MPI) through modulating Category 1 TC Nathan was named later that day. near-eyewall SSTs (and heat f luxes) occurring when It slowly executed a cyclonic loop over the next few - TC winds mechanically mix the underlying ocean days, moving across Arnhem Land, Northern Ter (Mainelli et al. 2008; Lin et al. 2013). In general, fields ritory, and into Western Australia. See section 4e7 of TCHP show high spatial and temporal variability for a detailed timeline of Nathan’s development, associated mainly with oceanic mesoscale features, landfall, decay, and impacts. On 19 March, a tropical disturbance developed about 375 km to the southwest interannual variability (e.g., ENSO), or long-term decadal variability. This variability can be assessed of Apia, Samoa. From 20 to 22 March, the resulting using satellite altimetry observations (Goni et al. tropical depression produced heavy rain and strong winds over Fiji’s Lau Islands. The system moved 1996; Lin et al. 2008; Goni and Knaff 2009; Pun et al. 2013) or using a combination of altimetry and southward as it was classified as a tropical depression. hydrographic data (Domingues et al. 2015), and has Early on 22 March, Tropical Cyclone Reuben was been used to assess meridional heat transport and named as a Category 1 storm, located about 220 km the overturning circulation in the Atlantic Ocean to the south of Nuku’alofa, Tonga. On 23 March, TC (Dong et al. 2015). Reuben began extratropical transition. - Tropical Cyclone Solo developed within the mon Globally, the number of tropical cyclones was 10% higher than the previous season; however, in the soon trough on 9 April, about 465 km to the south of eastern North Pacific (ENP), the number increased Honiara, Solomon Islands. Due to ideal conditions, significantly from an already high number in 2014. the system rapidly developed as it moved southward and was named a Category 1 storm. Solo peaked The 2014 and 2015 ENP hurricane seasons were the −1 with winds of 54 kt (28 m s ), making it a Category most active in recorded history. In the western North 2 storm. As Solo turned to the south-southeast from Pacific (WNP) basin, the 2015 number was similar to - 11 to 12 April, it moved between mainland New Cale the long-term climatological average. Nevertheless, donia and the Loyalty Islands. Rainfall totals up to it is a ~40% increase as compared to the very low 222 mm were recorded in New Caledonia. Significant occurrence in 2014. The two following factors best illustrate the over - damage was reported there, with roads impassable in all global TCHP interannual variability within and places and contaminated drinking water in the mu - nicipality of Pouébo. Finally, and as noted in section among the basins: 1) the TCHP anomalies (departures 4e7, Tropical Cyclone Raquel, the last storm of the from the 1993–2014 mean values) during the TC sea - 2014/15 season, developed as a tropical disturbance sons in each hemisphere; and 2) differences in TCHP about 718 km to the northeast of Honiara, Solomon between the 2015 and 2014 seasons. | S120 AUGUST 2016

141 Most basins exhibited positive TCHP anomalies (Fig. 4.36), except for the WNP and the western portion of the South Pacific basin. The WNP basin experienced a significant reduction in TCHP of ~20%, which is typical of El Niño years (Zheng et al. 2015). The TCHP in the Gulf of Mexico exhibited large positive anomalies due to the intrusion of the Loop Current and a long residence time of Loop Current rings. Despite these positive anomalies, there were no hurricanes in the Gulf of Mexico (just one tropical storm—Bill). . 4.37. Differences between the TCHP fields in 2015 F ig In the ENP basin, the positive TCHP anomalies − 2 ). and 2014 (kJ cm were consistent with strong El Niño conditions and a continued positive phase of the Pacific decadal oscil (Pun et al. 2013; England et al. 2014). However, with - lation. The combination of these two phenomena is the developing El Niño, the warming had stopped. With 2015 being the strongest El Niño event since manifest in positive SST anomalies in that region and 1997, the TCHP over the WNP MDR (4°–19°N, extending to the date line. Consequently, the TCHP values in this region during the season were even 122°E–180°) fell considerably, as characterized by evident negative anomalies (Figs. 4.36, 4.37; Zheng et higher than in previous years (Fig. 4.37). As in 2014, al. 2015). With the relaxation of the trade winds dur - positive TCHP and SST anomalies contributed to ing El Niño, warm water returning from the western elevated tropical cyclone activity, with 16 hurricanes to the eastern Pacific produced a positive anomaly in in the ENP during 2015 (Fig. 4.36). the ENP while the WNP exhibited a negative anomaly The WNP basin also usually exhibits anomalies related to ENSO variability (Lin et al. 2014; Zheng (Figs. 4.36, 4.37; Zheng et al. 2015). For each basin, the differences in the TCHP values et al. 2015). From the 1990s to 2013, it experienced a long-term decadal surface and subsurface warming - between the most recent cyclone season and the pre associated with prevalent La Niña–like conditions vious season (Fig. 4.37) indicate that the southwest Indian Ocean, the northwest Indian Ocean, and the western portion of the ENP continued to exhibit an increase in TCHP values. TC activity in terms of Category 4 and 5 storms was correspondingly - elevated in these basins. The largest changes with re spect to the previous seasons occurred in the ENP and WNP basins, with differences greater in magnitude −2 than 20 kJ cm . Compared to 2014, the percentage of Category 5 TCs in the WNP was quite low, with only two of 15 TCs (13%) attaining Category 5. In contrast, in 2014, though there were only eight TCs during the TC season, there were three Category 5 TCs or 38%. The evident reduction in TCHP over the ig F . 4.36. Global anomalies of TCHP corresponding WNP may have acted as a damper by increasing the to 2015 computed as described in the text. The boxes ocean cooling effect on restraining TC intensification indicate the seven regions where TCs occur, from left (Zheng et al. 2015). to right: Southwest Indian, North Indian, West Pacific, The 2015 season was noteworthy for several Southeast Indian, South Pacific, East Pacific, and North reasons with respect to intensification of TCs, in - Atlantic (shown as Gulf of Mexico and tropical Atlantic cluding Hurricane Patricia, the strongest Western separately). The green lines indicate the trajectories of all tropical cyclones reaching at least Category 1 Hemisphere hurricane ever recorded and Hurricane − 1 status (1-min average wind ≥64 kts, 33 m s ) and above Joaquin, the most intense TC on record to strike the during Nov–Apr 2014/15 in the Southern Hemisphere Bahamas. A summary of the ocean conditions for and Jun–Nov 2015 in the Northern Hemisphere. The these and some other selected TCs are as follows. numbers above each box correspond to the number of Category 1 and above cyclones that travel within each • - Typhoon Koppu (Lando; Fig. 4.38a) was a Cat box. The Gulf of Mexico conditions during Jun–Nov egory 4 TC that formed east of the Commonwealth 2015 are shown in the inset in the lower right corner. | S121 AUGUST 2016 STATE OF THE CLIMATE IN 2015

142 of the Northern Mariana Islands (CNMI) on 10 • Category 5 typhoon Soudelor (Hanna; Fig. 4.38c) was the second-strongest tropical cyclone to October. This storm reached its peak intensity on 17 October, with sustained winds of over 100 kt develop in the Northern Hemisphere in 2015. −1 (51 m s - Though not as intense as Haiyan in 2013 (Lin et al. ), and 1-minute sustained winds of ap −1 2014), it was as intense as Vongfong in 2014 (Goni ). Though it eventually proximately 130 kt (67 m s reached Category 4, Koppu did not intensify as et al. 2015). This is in spite of the reduced TCHP in the WNP, associated with the 2015 El Niño year. rapidly as most intense TCs over the WNP (e.g., This drop from the preexisting extremely high Haiyan in 2013; Lin et al. 2014). The negative TCHP may have slowed down its intensification TCHP condition (Pun et al. 2013; Lin et al. 2014) rate (Zheng et al. 2015). However, since the TCHP was still able to provide favorable conditions for intensification. Soudelor intensified over a very over the WNP is among the highest globally in a −2 , which climatological sense, even with reduced TCHP, favorable TCHP field of over 120 kJ cm it is possible for intense TCs to develop (Zheng may have contributed to its ability to attain wind et al. 2015). During El Niño years, TCs tend to form towards the southeast and closer to the date line. As a result, a TC can travel a longer distance across the ocean during intensifica - tion, through over reduced TCHP conditions (Zheng et al. 2015). Koppu made landfall in the north of the Philippines and quickly weakened due to its inter - action with land. The cool - ing of SSTs caused by this typhoon was more evident west of 130°E, in both the surface and upper layer. • Typhoon Chan-hom (Fal - con; Fig. 4.38b) was char - acterized by its large size and long duration over the ocean. Chan-hom devel - oped on 29 June from an at - mospheric system that also developed TC Raquel in the southwest Pacific Ocean. Chan-hom’s sustained winds reached values up −1 - to 89 kt. (46 m s ). This ty phoon continuously inten - sified while traveling over warm waters with moderate −2 ) TCHP values. (> 80 kJ cm A cooling of the surface (−2°C) and the upper layer . 4.38. (left) Oceanic TCHP and surface cooling given by the difference F ig −2 (40 kJ cm ) under the track between post- and pre-storm values of (center) tropical cyclone heat potential of this typhoon occurred and (right) sea surface temperature, for 2015 Tropical Cyclones (a) Koppu (b) when its intensity reached Chan-hom, (c) Soudelor, (d) Patricia, and (e) Joaquin. The TCHP values cor - Category 1. respond to two days before each storm reached its maximum intensity value. | S122 AUGUST 2016

143 −1 speeds of 116 kt (60 m s ) on 3 August. Its high −1 ) during intensifica - translation speed (~5–8 m s tion helped to reduce the ocean cooling during the TC life cycle, thus supplying more air–sea f lux for intensification (Lin et al. 2009). This was the most intense storm to strike Saipan, CNMI, in the last 25 years. Cooling of the surface waters of over 5°C was observed under the full track of this typhoon, while cooling of the upper ocean layers (TCHP) was restricted to between 135° and 150°E. • Hurricane Patricia (Fig. 4.38d) was the most intense tropical cyclone ever recorded in the Western Hemisphere in terms of barometric pressure, and the strongest ever recorded globally in terms of maximum sustained winds of 185 kt −1 (95 m s ; Kimberlain et al. 2016). Patricia started as a tropical depression off the coast of Mexico on 20 October, and developed into a Category 5 storm within 66 hours. During its rapid intensification −2 the TCHP values were higher than 80 kJ cm . . 4.39. The AWP index for 1900–2015. The AWP ig F • Hurricane Joaquin (Fig. 4.38e) was an intense TC area index (%) is calculated as the anomalies of the that evolved near the Bahamas on 26 September - area of SST warmer than 28.5°C divided by the cli matological Jun–Nov AWP area. Shown are the (a) and was one of the strongest storms to affect these total, (b) detrended (removing the linear trend), (c) islands. Joaquin underwent rapid intensification multidecadal, and (d) interannual area anomalies. The and became a Category 3 hurricane on 1 October, multidecadal variability is obtained by performing a exhibiting maximum sustained winds of approxi - 7-year running mean to the detrended AWP index. −1 mately 135 kt (69 m s ) on 3 October (Berg 2016). The interannual variability is calculated by subtracting The upper ocean conditions were supportive of At - the multidecadal variability from the detrended AWP lantic tropical cyclone intensification (Maineli et al. index. The black straight line in (a) is the linear trend 2008). This rapid intensification occurred during that is fitted to the total area anomaly. The extended reconstructed SST dataset is used. a short travel time over very high TCHP values −2 ). The cooling of the ocean waters was (> 100 kJ cm evident both in the upper layer and at the surface. tropical North Atlantic (Figs. 4.40c,d). By September, the AWP had further expanded southeastward and g. lantic warm pool— C. Wang the 28.5°C isotherm covered nearly the entire tropical At The description and characteristics of the Atlantic North Atlantic (Fig. 4.40e). The AWP started to decay warm pool (AWP), including its multidecadal vari - after October when the waters in the Gulf of Mexico began cooling (Fig. 4.40f). In November, the 28.5°C ability, have been previously described (e.g., Wang isotherm still covered the Caribbean Sea and part of 2015). Figure 4.39 shows the extension of the AWP time series through 2015 varying on different time the western North Atlantic Ocean (Fig. 4.40g). scales. The effect of the AWP on TC steering f lows and While the AWP in 2015 showed similarities to tracks has been previously documented (Wang 2015). 2014, there were some key differences. As in 2014, The TC steering f low anomalies were consistent with those of other observed large AWP years (Wang et the AWP in 2015 was larger than its climatological al. 2011). The TC steering f low anomalies during the mean each month, with the largest AWP occurring North Atlantic TC season are depicted in Fig. 4.41. in September (Fig. 4.40a). However, the AWP in 2015 With the exception of June and November, the TC started in February and lasted through December, steering f low anomalies were unfavorable for TCs longer than its normal period of May to October, and making landfall in the United States. From July to had an anomalously larger value in November. After October, the TC steering f low anomalies were mostly starting in February, the AWP appeared in the Gulf of Mexico in June (Fig. 4.40b). By July and August, the southward or eastward in the western tropical North AWP was well developed in the Gulf of Mexico and Atlantic, and northward and northeastward in the Caribbean Sea and reached eastward into the western open ocean of the North Atlantic. This distribution | S123 AUGUST 2016 STATE OF THE CLIMATE IN 2015

144 3 . 4.41. The TC steering flow anomalies (10 ig F hPa m − 1 s ) in the 2015 Atlantic TC season of (a) Jun, (b) Jul, (c) Aug, (d) Sep, (e) Oct, and (f) Nov. The TC steering flow anomalies are calculated by the vertically averaged wind anomalies from 850 hPa to 200 hPa relative to the 1971–2000 climalogy. The NCEP–NCAR reanalysis field (Kalnay et al. 1996) is used. first time since the last positive IOD event in 2012 (Luo 2013). The positive IOD in 2015 is the 10th such 2 12 . 4.40. (a) The monthly AWP area in 2015 (10 ; ig m F event since 1981. blue) and the climatological AWP area (red) and the SSTs and upper ocean (0–300 m) mean tempera - spatial distributions of the 2015 AWP in (b) Jun, (c) Jul, (d) Aug, (e) Sep, (f) Oct, and (g) Nov. The AWP ture in most of the tropical IO were warmer than is defined by SST larger than 28.5°C. The black thick normal throughout the year (Figs. 4.42, 4.43), in contours in (b)–(g) are the climatological AWP based association with the inf luence of a strong El Niño on the data from 1971 to 2000 and the white thick con - in the Pacific and a pronounced long-term warming tours are the 2015 28.5°C SST values. The extended trend of the IO SST in recent decades (e.g., Luo et al. reconstructed SST dataset is used. 2012). During December–February 2014/15, surface of these anomalies was consistent with the fact that westerly anomalies occurred across the equatorial for all TCs that formed in the Atlantic MDR, none IO, corresponding to the dry–wet contrast between - the IO and the Maritime Continent–western Pacific made landfall in the United States. For the two land (Figs. 4.42a, 4.43a). This is consistent with a central falling North Atlantic TCs (Ana and Bill), neither one formed in the Atlantic MDR (see section 4e2). Pacific–El Niño condition. The westerly anomalies across the equatorial IO shallow (deepen) the oceanic ndian Ocean dipole— thermocline in the western (eastern) IO, which helps I J.-J. Luo h. induce cold (warm) SST anomalies in the equatorial Year-to-year climate variability in the tropical Indian Ocean (IO) is largely driven by local ocean– western (eastern) IO (Figs. 4.42a, 4.43a). From March atmosphere interactions and ENSO (e.g., Luo et al. to November, in accordance with a rapid development of a strong El Niño in the Pacific (see Fig. 4.3), rainfall 2010). Among the former, the Indian Ocean dipole (IOD) represents one major internal climate mode in over the Indonesia–western Pacific decreased due to a - weakened Walker Cell. Meanwhile, SSTs in the west the IO, which may exert significant climate impacts ern IO increased quickly and reached ~0.8°C greater on countries surrounding the IO. The IOD often than the climatology (1982–2014) during Septem - starts to grow in boreal summer, peaks in Septem - ber–November, and deteriorates rapidly in December ber–November (Figs. 4.42, 4.44). Correspondingly, in association with the reversal of monsoonal winds easterly anomalies developed in the IO beginning along the west coast of Sumatra. During late boreal in boreal spring (Figs. 4.43, 4.44). Weak anomalous summer to fall 2015, a positive IOD occurred for the southeasterlies initially appeared along the west coast | S124 AUGUST 2016

145 of Sumatra in May and then grew gradually with a ous events (Fig. 4.44). Compared to the 1997 IOD that westward expansion. This might have been largely occurred with a similarly strong El Niño, the 2015 IOD was much weaker. Although the western IO SST driven by the surface divergence over the Indone - sia–western Pacific due to the weakened Walker Cell. in 2015 is warmer than that in 1997, the eastern IO SST anomalies in 2015 are positive, in stark contrast During June–August, considerable dry anomalies to the strong cold anomalies in 1997. Indeed, both appeared west of Sumatra, consistent with a positive the western and eastern IO SSTs in 2015 are warmer IOD index and easterly anomalies in the eastern IO than those in previous nine positive IOD events, in (Figs. 4.42c, 4.43c, 4.44c). The positive IOD kept growing and reached a peak in September–November association with warmer general conditions across the tropical IO basin (Fig. 4.44f). While the 10 posi (Figs. 4.42d, 4.43d). In December, the eastern IO SST - tive IOD occurred with either El Niño or La Niña, anomaly increased sharply, which reduced the IOD (Figs. 4.44a–d). the probability of the occurrence of positive IOD - There is no clear evidence that supports local pro during El Niño is about twice that during La Niña cesses generating the positive IOD in 2015. Rather, it (Figs. 4.44c, e). appears that the development of a strong El Niño in In summary, the positive IOD event in 2015 was the Pacific played an important, if remote, role. The likely driven by the development of a strong El Niño - in the Pacific. However, the intensity of this IOD is 2015 IOD shows distinct features compared to previ much weaker than that in 1997, mainly because of the absence of cold SST anomalies in the eastern IO F ig . 4.42. SST ( ° C, colored scale) and precipitation (contour interval: 0, ±0.5, ±1, ±2, ±3, ±4, and ±5 mm − 1 day ; solid/dashed lines denote positive/negative values, and thick solid lines indicate zero contour anomalies during (a) Dec–Feb 2014/15, (b) Mar–May C, ° . 4.43. Upper 300-m mean ocean temperature ( ig F − 1 2015, (c) Jun–Aug 2015, and (d) Sep–Nov 2015. The colored scale) and surface wind (m s ) anomalies dur - anomalies were calculated relative to the climatology ing (a) Dec–Feb 2014/15, (b) Mar–May 2015, (c) Jun–Aug over the period 1982–2014. These are based on the 2015, and (d) Sep–Nov 2015. These are based on the NCEP optimum interpolation SST (Reynolds et al. NCEP ocean reanalysis (available at www.cpc.ncep - 2002) and monthly GPCP precipitation analysis (avail .noaa.gov/products/GODAS/) and JRA-55 atmospheric able at http://precip.gsfc.nasa.gov/). reanalysis (Ebita et al. 2011). | S125 AUGUST 2016 STATE OF THE CLIMATE IN 2015

146 in 2015. It appears that the multidecadal basinwide warming trend of the tropical IO SST (partly due to increasing radiative forcing) might have affected and will continue to affect the evolution of IOD. ig . 4.44. Monthly SST anomalies in the (a) western IO (IODW, 50 ° –70 F E, 10 ° S –10 ° N) ° and (b) eastern IO (IODE, 90 ° – 110 ° E, 10 ° S–0 ° ) and (c) the IOD index (measured by the SST anomaly difference between the IODW and the IODE) during 10 positive IOD events since 1981. (d) As in (c) but for the surface zonal wind anomaly in the central equatorial IO (70 ° –90 ° E, 5 ° S–5 ° N). (e)–(f) As in (a)–(b), but for the monthly SST anomalies in the Niño-3.4 region (190°–240°E, 5°S–5°N) and the tropical IO basin (40°–120°E, 20°S–20°N). | S126 AUGUST 2016

147 SIDEBAR 4.1: THE RECORD-SHATTERING 2015 NORTHERN HEMI - —P. J. KLOTZBACH AND C. T. FOGARTY SPHERE TROPICAL CYCLONE SEASON The 2015 Northern Hemisphere tropical cyclone (TC) season was one for the record books. The Atlantic basin hurricane season recorded below-average activity with an 4 2 . The 1981–2010 median ACE for the kt ACE of 60 × 10 Atlantic is 92, and NOAA defines any season with less than 66 ACE units as a below-average season. The remainder of the Northern Hemisphere basins (the eastern North Pacific, the western North Pacific, and the north Indian) were conversely quite active. Some of the most notable records set during this record-breaking year for these three basins individually, then collectively, for the North - ern Hemisphere are documented. Table SB4.1 summarizes the statistics by basin and denotes records achieved in 2015. All statistics described are based on operational advisories from the National Hurricane Center, Central Pacific Hurricane Center, and Joint Typhoon Warning Center, and are then compared with archived best-track data compiled by those agencies. The data in these basins date back to 1851 in the North Atlantic, 1949 in the northeast Pacific, 1945 in the northwest Pacific, and 1972 in the north Indian; however, it should be noted that the data quality among these datasets is not uniform prior to about 1985 (Chu et al. 2002). Pacific Eastern North . SB4.1. Satellite imagery showing (a) from left to F ig The eastern North Pacific (to 180°) season in 2015 right: Kilo, Ignacio, and Jimena at Category 4 intensity tied its record for number of hurricanes and set a record on 30 Aug 2015 and (b) Hurricane Patricia near time for major hurricanes. ACE for the eastern North Pacific of peak intensity on 23 Oct 2015. in 2015 was also quite high, trailing only 1992. Two of the most notable storm events of 2015 occurred in this basin. In late August, Hurricanes Kilo, Ignacio, and Jimena able T SB4.1. Northern Hemisphere TC summary statistics by basin. Cat. 4–5 Named Major ACE Hurricanes Basin Hurricanes Hurricanes Storms 11 (12 ) 4 (6.5) 2 (2) 1 (1) 60 (92) North Atlantic Eastern North (4) 288 (119 ) (9) 16* (17) 26 10 11 (2) Pacific Western (7) 14 479 (305) (9) 16 20 (17) 26 (26.5) North Pacific 5 (5) 2 (1) 2* (1) 1* (0) North Indian 36 (16) Northern 865 (545) 68 (59) 42 (33.5) 31 (16. 5) 26 (11) Hemisphere The 1981–2010 median values are in parentheses. Record high values are highlighted in bold-faced font, while second highest values are italicized. An asterisk by a record means that several years tied for that record. A TC is counted based in the basin - where the storm first achieved a specific intensity. Northern Hemisphere ACE does not exactly add as sum of four individ ual basins due to rounding. In the case of Halola, it was counted as a named storm in the northeast Pacific and a hurricane in the northwest Pacific. Hurricanes are used colloquially to refer to all hurricane-strength TCs in the Northern Hemisphere. | S127 AUGUST 2016 STATE OF THE CLIMATE IN 2015

148 CONT. THE RECORD-SHATTERING 2015 NORTHERN HEMI SIDEBAR 4.1: - —P. J. KLOTZBACH AND C. T. FOGARTY SPHERE TROPICAL CYCLONE SEASON . SB4.1a). f ig reached Category 4 status at the same time ( North Indian This was the first time on record that three Category 4 or The north Indian Ocean also experienced well above- 4 2 kt generated, which stronger TCs were present at the same time in any global × 10 average ACE in 2015, with 30 is over twice the median value for the basin. Cyclones TC basin. On October 23, Hurricane Patricia became Chapala and Megh were significant storms that resulted - the strongest hurricane on record in the Western Hemi in serious impacts on the island of Socotra. This was the sphere when an aircraft reconnaissance plane estimated first time in recorded history that two cyclone-strength 1-minute maximum sustained winds of 175 knots (Fig. - TCs made landfall on Socotra in the same year (see sec SB4.1b). The central North Pacific (180°–140°W) portion tion 4e5). Chapala also became the first cyclone-strength of the eastern North Pacific was extraordinarily active storm to make landfall in Yemen in recorded history, (Collins et al. 2016, manuscript submitted to Geophys. and just a week later Cyclone Megh also made landfall Res. Lett .). Eight named storms formed in this portion in Yemen. of the basin, shattering the old record of four named storms set in 1982, and an additional eight storms spent Northern Hemisphere some portion of their life in the basin. The central Pacific 2 4 kt , The Northern Hemisphere shattered several records × 10 alone also accounted for an ACE level of 127 2 4 kt set in 1994. The breaking the record of 107 × 10 for intense TCs. A total of 31 major (Category 3–5) TCs 4 2 ACE level is especially impressive given that kt 127 × 10 - occurred in 2015, breaking the old record of 23 major hur the 1981–2010 median for the full northeast Pacific basin ricanes set in 2004. The previous record of 18 Category 4 2 kt . was 119 × 10 4–5 TCs, set in 1997 and tied in 2004, was also eclipsed in 2015, with 26 Category 4–5 TCs occurring. In addition, 62% Western Pacific North of all hurricane-strength TCs that formed in 2015 reached The western North Pacific was quite active from an Category 4–5 intensity, breaking the old record of 50% that ACE perspective, generating the third highest ACE value of happened four different times (1994, 1997, 2002, and 2011). all time for the basin. In addition, 16 major (Category 3–5) - As noted in Klotzbach and Landsea (2015), significant under 4 2 kt set typhoons occurred, breaking the record of 15 × 10 estimates in Category 4–5 TCs are likely prior to ~1990. In in 1958 and tied in 1965, both well before the era of reliable terms of integrated metrics, Northern Hemisphere ACE best track data (Chu 2002). As is typically the case in strong was at its second highest level on record. The 2015 season 2 4 2 4 kt value kt , trailing only the 876 × 10 El Niño seasons, while ACE increases significantly, the num - generated 821 × 10 generated in 1992. In summary, the Northern Hemisphere ber of storm formations changes little (Camargo and Sobel TC season was extraordinarily active, due in large part to the 2005). The western North Pacific was extraordinarily active strong El Niño conditions that prevailed throughout the year. during the month of May. Two typhoons, Noul and Dolphin, reached Category 5 status (> 137 knots) in May. This was the first time on record that two typhoons reached Category 5 status in May. | S128 AUGUST 2016

149 SIDEBAR 4.2: A SOUTHEAST PACIFIC BASIN SUBTROPICAL —S. H. YOUNG CYCLONE OFF THE CHILEAN COAST - TCs are formally defined by NOAA’s National Hur ricane Center as “a warm-core nonfrontal synoptic-scale cyclone, originating over tropical or subtropical waters, with organized deep convection and a closed surface wind circulation about a well-defined center.” However, closely related to TCs are subtropical cyclones, which derive a significant proportion of their energy from baroclinic sources and are generally cold core in the upper tropo - sphere and are often associated with an upper-level low or trough. Additionally, maximum winds and convection are often at a distance generally more than 110 km from the center (see www.nhc.noaa.gov/aboutgloss.shtml#s). Until recently, TCs were believed not to form in the Mediterranean Sea, the Atlantic basin south of the equator, and the far eastern Pacific basin south of the equator (Gray 1968). Here we describe a subtropical storm identified in the southeastern Pacific basin off the Chilean coast farther east than any in the historic record as documented in either the IBTrACS (Knapp et al. 2010) or SPEArTC (Diamond et al. 2012) datasets and outside of the responsibility of any global RSMC. The formation of Hurricane Catarina off the coast of Brazil in 2004 (McTaggart et al. 2006; Gozzo et al. 2014) demonstrated that TCs can occasionally form in previously unsuspected areas such as the South Atlantic. The existence of possible TCs in the Mediterranean Sea, which are overwhelmingly subtropical in nature, has also satellite image of sub-TC Katie F . SB4.2. Aqua ig on 1 May 2015 at 2055 UTC. generated interest in recent years (Moscatello et al. 2008; Pantillon et al. 2013; and Cavicchia et al. 2014). In late April, Earth Observing (EOSDIS) satellite im - The location of the system was in a “no man’s land” of sorts agery showed a cyclonic circulation in the southeastern as it is not within the forecast or warning area of responsibility Pacific basin that appeared to meet the definition of a of any RSMC. It was too far to the east of the Nadi RSMC’s subtropical cyclone. Originating from a stalled frontal - area of responsibility, and while there is no formal RSMC cov zone near 25°S, 102°W the storm developed into a clearly ering the area east of 120°W, the system will be incorporated nonfrontal system with the majority of convection initially into the SPEArTC dataset under the informal name of Katie. to the southeast of low-level circulation. This cyclonic Therefore, while the system will not necessarily be formally storm was approximately 30° east of any previously re - picked up by the IBTrACS dataset, which reflects RSMC tracks corded TC. ASCAT satellite derived winds were as much on behalf of the World Meteorological Organization’s TC as 50 kt. The system was visible on imagery during the Programme, the more research-oriented SPEArTC dataset, period from 30 April to 5 May (Fig. SB4.2). which focuses on the southwest Pacific, will include this storm The NCEP–NCAR reanalysis data (Kalnay et al. 1996) in its listing of 2014/15 storms with appropriate notation of its for 29 April at 1200 hours UTC showed a broad low unique subtropical nature. An upper air analysis using NCAR pressure area located near 25°S, 102°W. As the system data shows a trough over the system at the 300-hPa surface and developed, it drifted toward the southeast before stalling a possible warm core at the 850-hPa surface. This is consistent near 28°S, 100°W for approximately 36 hours. From 2 to with the NHC definition of a subtropical system. 3 May, the system moved west then northwest, dissipating From 29 April to 4 May, the Chilean Navy Weather Service on 6 May near 18°S, 110°W. included the system in their high seas warnings, reporting an | S129 AUGUST 2016 STATE OF THE CLIMATE IN 2015

150 CONT. SIDEBAR 4.2: A SOUTHEAST PACIFIC BASIN SUBTROPICAL CYCLONE OFF THE CHILEAN COAST —S. H. YOUNG estimated minimum central pressure of 993 hPa on 1 May at 0600 hours UTC. Using the method described by Knaff and Zehr (2007), this corresponds to a maximum –1 ). sustained wind speed of approximately 40 kt (21 m s Several RapidScat passes (W. L. Poulsen 2015, personal communication) from 1 to 2 May showed winds in excess –1 –1 ). ), with some returns of 50 kt (26 m s of 40 kt (21 m s These peak winds were at some distance from the center of circulation, which is also consistent with a subtropical nature of the system (Fig. SB4.3). Phase diagrams (Hart 2003) using relative 900–600-hPa thickness symmetry and thermal winds for the system indicated that this system was warm core and symmetrical in early May (Fig. SB4.4), and the conditions described also support the identification of the system as either a tropical or subtropical cyclone. Satellite imagery, phase diagrams, and surface analysis show that “Katie” was a tropical system located far from any previously identified TC listed in IBTrACS for the South Pacific basin. Although it may briefly have exhibited TC characteristics, and while the imagery is consistent . SB4.3. RapidScat Wind retrieval for 2 May 2015 F ig with a subtropical cyclone, the system should be further 1 − ). starting at 1038 UTC (m s examined for inclusion as a Southern Hemisphere tropical cyclone in the formal global archives. . SB4.4. System phase diagram for 3 May 2015 at 0600 UTC. ig F | S130 AUGUST 2016

151 5. —J. Richter-Menge and J. Mathis, Eds. T HE ARCTIC the 1982–2010 average in eastern Baffin Bay and the —J. Richter-Menge Kara Sea north of central Eurasia. and J. Mathis a. Introduction The impact of sea ice retreat and warming ocean The Arctic chapter describes a range of observa - temperatures on the ecosystem is well demonstrated tions of essential climate variables (ECV; Bojinski - by changes in the behavior of walrus and fish com et al. 2014) and other physical environmental pa - munities. rameters, encompassing the atmosphere, ocean, and In the Pacific Arctic, vast walrus herds are now hauling out on land rather than on sea ice as land in the Arctic and subarctic. As in previous years, the ice retreats far to the north over the deep Arctic the 2015 report illustrates that although there are regional and seasonal variations in the state of the - Ocean, raising concern about the energetics of fe Warming trends in water males and young animals. Arctic environmental system, it continues to respond temperatures in the Barents Sea, which started in the to long-term upward trends in air temperature. Over late 1990s, are linked to a community-wide shift in Arctic landmasses, the rate of warming is more than fish populations: boreal communities are twice that of low and midlatitude regions. now found farther north and the local Arctic (cold-water affinity) In 2015, the average annual surface air tempera - community has been almost pushed out of the area. ture anomaly over land north of 60°N was +1.2°C, Ice on land, including glaciers and ice caps outside relative to the 1981–2010 base period. This ties the Greenland (Arctic Canada, Alaska, Northern Scan recent years of 2007 and 2011 for the highest value - dinavia, Svalbard, and Iceland) and the Greenland in the temperature record starting in 1900 and repre - Ice Sheet itself, continues to lose mass. In 2015, the sents a 2.8°C increase since the beginning of the 20th Greenland Ice Sheet, with the capacity to contribute century. Evidence of strong connections between the ~7 m to sea level rise, experienced melting over more Arctic and midlatitude regions occurred from 1) No - than 50% of the ice sheet for the first time since vember 2014 through June 2015, when anomalously the exceptional melting of 2012 and exceeded the warm conditions in the Pacific Arctic region were associated with southerly air f low into and across 1981–2010 average on 50 of 92 days (54%). Ref lecting the pattern of ice melt, which is driven by the pattern Alaska, and 2) February through April 2015, when of surface air temperature anomalies, the average anomalously cold conditions from northeastern albedo in 2015 was below the 2000–09 average in North America to southwest Greenland were associ - ated with northerly air f low. northwest Greenland and above average in southwest There is clear evidence of linkages among the Greenland. various components of the Arctic system. Under Despite above-average snow cover extent (SCE) in April, Arctic SCE anomalies in May and June 2015 the inf luence of persistent warming temperatures, were below the 1981–2010 average, a continuation the Arctic sea ice cover is diminishing in extent and of consistent early spring snowmelt during the past thickness. The lowest maximum sea ice extent in the decade. June SCE in both the North American and 37-year satellite record occurred on 25 February 2015, at 7% below the average for 1981–2010. This date of Eurasian sectors of the Arctic was the second lowest in the satellite record (1967–present). The rate of June occurrence was the second earliest in the record and SCE reductions since 1979 (the start of the passive 15 days earlier than the average date of 12 March. microwave satellite era) is 18% per decade. Minimum sea ice extent in September 2015 was 29% less than the 1981–2010 average and the fourth lowest In 2014, the most recent year with complete data, value in the satellite record. In February and March, the combined discharge of the eight largest Arctic 3 from Eurasia (Pechora, S. Dvina, the oldest ice (>4 years) and first-year ice made up 3% rivers [2487 km Ob’, Yenisey, Lena, and Kolyma) and North America and 70%, respectively, of the pack ice compared to (Yukon and Mackenzie)] was 10% greater than the values of 20% and 35%, respectively, in 1985. As the extent of sea ice retreat in the summer average discharge during 1980–89. Since 1976, dis - continues to increase, allowing previously ice-covered charge of the Eurasian and North American rivers water to be exposed to more solar radiation, sea sur - has increased 3.1% and 2.6% per decade, respectively. - face temperature (SST) and upper ocean temperatures Regional variability in permafrost temperature re are increasing throughout much of the Arctic Ocean cords indicates more substantial permafrost warming since 2000 in higher latitudes than in the subarctic, in and adjacent seas. The Chukchi Sea northwest of Alaska and eastern Baffin Bay off west Greenland agreement with the pattern of average air temperature have the largest warming trends: ~0.5°C per decade anomalies. In 2015, record high temperatures at 20-m since 1982. In 2015, SST was up to 4°C higher than depth were measured at all permafrost observatories on the North Slope of Alaska, increasing between | S131 AUGUST 2016 STATE OF THE CLIMATE IN 2015

152 −1 0.21°C and 0.66°C decade since 2000. Permafrost warming in northernmost Alaska exemplifies what is happening to permafrost on a pan-Arctic scale. Arctic cloud cover variability significantly inf lu - ences ultraviolet index (UVI) anomaly patterns. Ref lecting this inf luence, monthly average noontime UVIs for March 2015 were below the 2005–14 means in a belt stretching from the Greenland Sea and Iceland in the east to Hudson Bay and the Canadian Arctic Archipelago in the west. This region roughly . 5.1. Arctic (land stations north of 60°N) and F ig agrees with the regions where the atmospheric total global mean annual land surface air temperature (SAT) ozone columns (TOC) were abnormally high in anomalies (in °C) for the period 1900–2015 relative to March 2015. At the pan-Arctic scale, the minimum the 1981–2010 mean value. Note that there were few TOC in March was 389 Dobson Units (DU), 17 DU stations in the Arctic, particularly in northern Canada, (5%) above the average of 372 DU for the period before 1940. (Source: CRUTEM4.) 1979–2014 and 23 DU (6%) above the average for the past decade (2000–14). This overview alone refers to a number of differ include reduced summer albedo due to sea ice and - snow cover loss, the decrease of total cloudiness in ent periods of observation for which average values and departures from average (anomalies) have been summer and an increase in winter, and the additional calculated. For the World Meteorological Organiza heat generated by increased sea ice free ocean areas - that are maintained later into the autumn (Serreze tion, and national agencies such as NOAA, 1981–2010 and Barry 2011; Makshtas et al. 2011). Arctic am - is the current standard reference period for calculat - plification is also enhanced because radiational loss ing climate normals (averages) and anomalies. In of heat from the top of the atmosphere is less in the this report, the current standard reference period Arctic than in the subtropics (Pithan and Mauritsen is used when possible, but it cannot be used for all 2014). the variables described; some organizations choose not to use 1981–2010 and many observational re Although there is an Arctic-wide long-term pat - - cords postdate 1981. The use of different periods to tern of temperature increases, regional differences describe the state of different elements of the Arctic can be manifest in any given season based on natural variability of the atmospheric circulation (Overland environmental system is unavoidable, but it does not change the fact that change is occurring throughout et al. 2011; Kug et al. 2015). the Arctic environmental system. Seasonal air temperature anomalies are described in Fig. 5.2 for winter [January–March (JFM)], spring [April–June (AMJ)], summer [July–September (JAS)], A ir temperature b. —J. Overland, E. Hanna, I. Hanssen-Bauer, S.-J. Kim, J. Walsh, M. Wang, U. S. Bhatt, and R. L. Thoman and autumn [October–December (OND)] of 2015. All - Arctic air temperatures are both an indicator and seasons show extensive positive temperature anoma lies across the central Arctic with many regional a driver of regional and global changes. Although seasonal temperature anomalies greater than +3°C, there are year-to-year and regional differences in - air temperatures due to natural variability, the mag relative to a 1981–2010 base period. Warm temperature anomalies in winter 2015 ex - nitude and Arctic-wide character of the long-term tended across the Arctic, from the Pacific sector to the temperature increase are major indicators of global warming (Overland 2009). Atlantic sector (Fig. 5.2a). The warmest temperature anomalies were centered on Alaska and far eastern The mean annual surface air temperature anomaly Siberia, including the Chukchi and East Siberian for 2015 for land stations north of 60°N was +1.2°C, Seas. In Svalbard, in the Atlantic sector northeast of relative to the 1981–2010 mean value (Fig. 5.1). This ties the recent years of 2007 and 2011 for the highest Greenland, winter temperatures were typically 2°C value in the record starting in 1900. Currently, the - above the 1981–2010 average. In contrast, cold (nega tive) temperature anomalies of −2° to −3°C extended Arctic is warming at more than twice the rate of lower from southwest Greenland to central Canada and into latitudes (Fig. 5.1). the eastern United States. The greater rate of Arctic temperature increase A broad swath of warm temperature anomalies compared to the global increase is referred to as Arctic amplification. Mechanisms for Arctic amplification continued to stretch across the Arctic in spring | S132 AUGUST 2016

153 In autumn, particularly warm air temperature anomalies were seen in the subarctic regions of the Barents and Bering Seas (Fig. 5.2d). While the central Arctic remained relatively warm, cold anomalies were seen in northeastern North America similar to winter 2015. A difference, however, is that central Asia was also relatively cold in autumn compared to the warmer previous winter. Both winter and autumn 2015 illustrate extensive interaction of large-scale weather systems between the Arctic and midlatitudes. The anomalously warm temperatures across Alaska in winter and spring 2015 (Fig. 5.2a,b) extend a pattern that began during autumn 2014. The persistent positive (warm) near- surface air temperature anomalies in Alaska and extending into the Chukchi and Beaufort Seas were associated with warm sea surface temperatures in the Gulf of Alaska and a pattern of geopotential height anomalies characterized by higher values along the Pacific Northwest coast of North America and lower values farther offshore (Fig. 5.3a). Consequently, - warm air over the northeast Pacific Ocean was ad ig F . 5.2. 2015 Seasonal anomaly patterns for near- vected by southerly winds into and across Alaska, surface air temperatures (°C) relative to the baseline period 1981–2010 in (a) winter, (b) spring, (c) summer, contributing to high mass loss on glaciers (see section and (d) autumn. Temperatures are from somewhat 5f). Associated with the southerly winds, a downslope - above the surface layer (at 925 mb level) to empha component of the wind on the north side of the Alaska size large spatial patterns rather than local features. Range and into Interior Alaska caused dry conditions (Source: NOAA/ESRL.) and reinforced high temperatures. The warm and dry 2015, with a continuing warm anomaly over Alaska conditions in Interior Alaska during May and June contributed to the second worst fire season on record (Fig. 5.2b). However, unlike the winter pattern for those months, eclipsed only by 2004. (Fig. 5.2a), spring saw a shift to a very warm anomaly - (+4°C) over central Eurasia. A significant cold anom aly (−3°C) was centered over Greenland. In contrast to Greenland, spring temperatures at the weather station in Svalbard were typically 2°C above the 1981–2010 average, as Svalbard was located on the margin of the broad swath of positive temperature anomalies that extended from Alaska to Eurasia. A warm temperature anomaly over much of the Arctic Ocean, with the exception of a moderately cold - anomaly over the Beaufort Sea north of Alaska, char acterized summer 2015 (Fig. 5.2c). Particularly cold anomalies occurred over western Eurasia. As noted in section 5f, a new record August low temperature of −39.6°C occurred on 28 August at Summit (elevation 3216 m in the center of the ice sheet), while summer F . 5.3. (a) Large geopotential height anomalies over ig temperatures measured at most coastal weather sta - western and eastern North America and continuing tions were above average (Tedesco et al. 2015). Similar into the North Atlantic sector in winter 2015. (b) - to coastal Greenland locations, at the Svalbard weath Negative geopotential height anomalies over the er station the average temperature was 1°–2°C above North Atlantic and Bering Sea sectors in autumn 2015. the 1981–2010 average, the highest JAS average ever The arrows indicate anomalous warm (red) and cold recorded in the composite Longyearbyen–Svalbard (blue) air flow generated as a result of these anomaly patterns. Airport record that dates to 1898 (Nordli et al. 2014). | S133 AUGUST 2016 STATE OF THE CLIMATE IN 2015

154 2 In contrast to the warm temperature anomalies in mum of 3.39 million km set in 2012. However, the winter in Alaska (Fig. 5.2a) due to warm, southerly air 2015 summer minimum extent was still 1.81 million 2 km f low (Fig. 5.3a), the cold anomalies extending from (29%) less than the 1981–2010 average minimum 2 ice extent and 0.62 million km eastern Canada to southwest Greenland (Fig. 5.2a) (12%) less than the were associated with strong northwesterly air f low. 2014 minimum. These cold anomalies extended into early spring. The Sea ice extent has decreasing trends in all months and nearly all regions (the exception being the Bering potential source of these relatively cold temperatures is illustrated by the extensive winter (JFM) negative Sea during winter). In 2015, the largest losses were in geopotential height anomaly pattern (Fig. 5.3a) that the eastern Arctic in regions of warm air temperature shows high values over northwestern North America anomalies in spring and summer (section 5b, Fig. 5.2). - and low values over eastern North America, Green The September monthly average decline for the entire −1 land, and across the central Arctic Ocean to central relative to the Arctic Ocean is now −13.4% decade - 1981–2010 average (Fig. 5.5). The trend is smaller dur Eurasia. Northwesterly winds on the west side of the trough between the two height centers channeled cold air southward from the source region in the central Arctic into northeastern North America. This geopotential height anomaly pattern may also explain the above-average winter air temperatures in Svalbard, which were associated with warm air advection across western Eurasia and into the central Arctic Ocean (Figs. 5.2a,b). Autumn 2015 was noted for large active low pres - sure systems in the North Atlantic and Bering Sea (Fig 5.3b). These low height anomaly patterns with southerly wind components to their east kept the Chukchi and Barents Seas relatively warm and sea ice free well into the autumn season. F . 5.4. Average sea ice extent in (a) Mar and (b) Sep ig ea ice cover c. —D. Perovich, W. Meier, M. Tschudi, S. Farrell, S 2015 illustrate the respective winter maximum and S. Gerland, and S. Hendricks summer minimum extents. The magenta line indicates Three key variables are used to describe the state the median ice extents in Mar and Sep, respectively, during the period 1981–2010. (Source: NSIDC.) of the ice cover: the ice extent, the age of the ice, and the ice thickness. Sea ice extent is used as the basic description of the state of Arctic sea ice cover. Satel - lite-based passive microwave instruments have been used to determine sea ice extent since 1979. There are two months each year that are of particular interest: September, at the end of summer, when the ice reaches its annual minimum extent, and March, at the end of winter, when the ice typically reaches its maximum extent. Maps of monthly average ice extents in March 2015 and September 2015 are shown in Fig. 5.4. Based on estimates produced by the National Snow and Ice Data Center (NSIDC), the 2015 sea ice cover reached its maximum extent on 25 February, at a 2 . This was 7% below the value of 14.54 million km 1981–2010 average and the lowest maximum value F . 5.5. Time series of ice extent anomalies in Mar (the ig in the satellite record. Also notable, the maximum month of maximum ice extent) and Sep (the month extent occurred 15 days earlier than the 1981–2010 of minimum ice extent). The anomaly value for each average (12 March) and was the second earliest of year is the difference (in %) in ice extent relative to the satellite record. The annual minimum extent of the mean values for the period 1981–2010. The black 2 4.41 million km was reached on 11 September. This and red lines are least squares linear regression lines. - was substantially higher (30%) than the record mini Both trends are significant at the 99% confidence level. | S134 AUGUST 2016

155 −1 ing March (−2.6% decade Sea. The lack of ice older than one year in the east - ) but is still a statistically significant rate of decrease in sea ice extent. ern Arctic (on the Eurasian side of the Arctic basin) - foreshadows its susceptibility to melt out in summer. Prior to 2007, there had not been a March to Sep 2 of ice in the The ice in the southern Beaufort and Chukchi Seas tember loss of more than 10 million km has also melted completely in the past few summers, record, but now such large losses are not unusual. 2 More typical of recent years, 10.13 million km of ice with even the oldest ice not surviving the season. Observations of sea ice thickness and volume - was lost between the March maximum and Septem from multiple sources have revealed the continued ber minimum extent in 2015. The age of sea ice serves as an indicator for ice decline of the Arctic sea ice pack over the last decade (Kwok and Rothrock 2009; Laxon et al. 2013; Kwok physical properties, including surface roughness, melt - and Cunningham 2015). Figure 5.6c shows ice thick pond coverage, and thickness. Older ice tends to be nesses derived from CryoSat-2 satellite results and thicker and thus more resilient to changes in atmo - IceBridge aircraft observations in March–April 2015. spheric and oceanic forcing than younger ice. The age of the ice is estimated using satellite observations and The oldest ice north of Greenland and the Canadian drifting buoy records to track ice parcels over several Arctic Archipelago remains thicker than 3 m. There years (Tschudi et al. 2010; Maslanik et al. 2011). This is a strong gradient to thinner, seasonal ice in the method has been used to provide a record of the age Canada basin and the eastern Arctic Ocean, where of the ice since the early 1980s (Tschudi et al. 2015). ice is 1–2 m thick. The oldest ice (>4 years old) continues to make up a small fraction of the Arctic ice pack in March, when the sea ice extent has been at its maximum in most years of the satellite record (Figs. 5.6a,b). In 1985, 20% of the ice pack was >4 years old, but in March 2015, this ice category only constituted 3% of the ice pack. Furthermore, we note that first-year ice now dominates the ice cover, comprising ~70% of the March 2015 ice pack, compared to about 50% in the 1980s. Given that older ice tends to be thicker, the sea ice cover has transformed from a strong, thick pack in the 1980s to a more fragile, thin, and younger pack in recent years. The thinner, younger ice is more vul - nerable to melting out in the sum - mer, resulting in lower minimum ice extents. The distribution of ice age in March 2015 was similar to that in March 2014 (Fig. 5.6a). - Most of the oldest ice accu mulates along the coast of North - Greenland and the Queen Eliza - beth Islands of the Canadian Arc tic Archipelago, and much of this ice has resided in this area for sev - eral years (Fig. 5.6b). In 2015, as in F ig . 5.6. (a) Time series of sea ice age in Mar for 1985–present, (b) most years, ice transport patterns sea ice age in Mar 2015, and (c) sea ice thickness derived from ESA resulted in the movement of old CryoSat-2 (background map) and NASA Operation IceBridge mea - ice from this area into the Beaufort surements (color coded lines) for Mar/Apr 2015. | S135 AUGUST 2016 STATE OF THE CLIMATE IN 2015

156 SIDEBAR 5.1: WALRUSES IN A TIME OF CLIMATE CHANGE — K. M. KOVACS, P. LEMONS, AND C. LYDERSEN - Climate change-induced alterations in Arc tic ecosystems are having impacts at all trophic levels, which are already being described as “transformative” (Johannessen and Miles 2011). However, it remains a challenge to pre - dict impacts in terms of population trends of even highly visible, top trophic animals on mul - tidecadal scales, based on changes occurring in - primary physical features that determine habi tat suitability. For example, sea ice declines are clearly a major threat to ice-associated marine mammals (e.g., Kovacs et al. 2012; Laidre et al. 2015), but documented regional patterns in sea ice losses are not necessarily reflected in the trajectories of ice-dependent marine mammal populations on a regional basis. In this regard, walruses ( Odobenus rosmarus ) make an interesting case study. Walruses of both subspecies, O. r. divergens O. r. rosmarus in the North Pacific Arctic and in the North Atlantic Arctic, mate along ice edges in the drifting pack ice during winter and give birth on sea ice in the late spring. . SB5.1. Regional comparison of trends in sea ice (length of ig F –1 the summer season – number of days less coverage decade ) Both subspecies use sea ice extensively as a and walrus stocks according to Laidre et al. (2015) and expert haul-out platform throughout much of the opinion for Pacific (purple) and Atlantic walrus (red) by region. year if it is available close enough to foraging Stocks are identified by black boundary lines. areas. This habitat also provides shelter from storms and protection from some predators. Despite land-based haulouts where trampling increases mortality these shared critical links to sea ice, the population of young animals (Fischbach et al. 2009; Udevitz et al. 2012) trajectories for the two subspecies do not consistently and (2) the decline in sea ice reducing walruses’ access to reflect the relative patterns of sea ice losses in the two prey, which could impact the adult female body condition, broad regions occupied by the two subspecies. ultimately reducing calf survival and recruitment (Jay et al. The latest research indicates that the Pacific walrus 2011; Taylor and Udevitz 2015). The use of land-based population in the Bering and Chukchi Seas likely declined haulout areas is not novel for Pacific walruses, but females from about 1980 to 2000 (Taylor and Udevitz 2015). Prior with dependent young typically utilize sea ice for hauling out (Fay 982), which allows them to avoid particularly 1 to this time, subsistence harvest restrictions had allowed large land-based groups where crowding and trampling this population to recover from earlier overexploitation events can result in high calf mortality. A lack of sea ice (Fay et al. 1989) to a level that likely approached the car - over the shelf in summer in the Bering and Chukchi Seas rying capacity of the environment (e.g., Hills and Gilbert is already resulting in increased use of coastlines and is - 1994). But, population models suggest that a subsequent lands by females with calves, which has in turn resulted in decline of approximately 50% took place in the Pacific significant calf mortalities in recent years (Fishbach et al. population (Taylor and Udevitz 2015), which was likely 2009). Additionally, there is ongoing concern about the initially stimulated by changes in vital rates (e.g., birth impacts of declining sea ice on the future energetics of - rates, calf survivorship) within the population. This de females and young animals. These conditions require the cline has almost certainly been exacerbated by declines animals to take significantly longer feeding trips between in sea ice in the region (Fig. SB5.1), associated with global the coastal haul outs and offshore areas with high prey climate change (Taylor and Udevitz 2015). Hypothesized abundance (180 km one-way), rather than utilizing nearby mechanisms include: (1) the retreat of sea ice to a position ice edges for resting as they did in the past. over the deep Arctic Ocean basin, forcing walruses to use | S136 AUGUST 2016

157 Sea ice losses in the North Atlantic Arctic, in particular the archipelago are increasing exponentially (Kovacs et the Barents Sea region, have been much more extreme - al. 2014). Walruses in this area were hunted without re than in the North Pacific (Fig. SB5.1). But, Atlantic wal - striction over several hundred years, up until the 1950s. rus abundance is increasing or stable for all stocks for When they finally became protected in 1952, there were which the trend is known (see Laidre et al. 2015) despite at best a few hundred animals left. Now, after 60 years reductions in carrying capacity that are almost certainly of complete protection from hunting, with some special taking place due to the sea ice declines. Concern does no-go reserve areas, recovery is taking place, despite remain regarding possible overharvesting of several stocks major reductions in sea ice. More females with calves are with currently unknown trends in Canada/Greenland. documented during surveys and historically used sites are However, the positive turnarounds that have occurred being reoccupied as walruses continue to expand through are responses to protective management regimes that the archipelago. These changes are occurring despite have been instituted in the early- and mid-1900s (1928 in the fact that overall carrying capacity of the region for Canada, 1952 in Norway, and 1956 in Russia), and, in the walruses is likely declining. case of Greenland, much more recently, with quotas being The population trajectories of many walrus stocks established there in 2006 (see Wiig et al. 2014 for more are currently a result of distant past, or more recent, details). Perhaps the most extreme example of walrus hunting regimes. However, there is little question that abundance increasing where environmental conditions sea ice declines are going to be a challenge for walruses are deteriorating due to climate change occurs in the - in the future along with other climate change related fac Svalbard Archipelago. Svalbard is an Arctic hot spot that tors such as increased shipping and development in the is experiencing dramatic sea ice declines and warming north, increased disease and contaminant risks, and ocean ocean and air temperatures, and yet walrus numbers in acidification impacts on the prey of walruses. spatial distribution as the August mean for the period S ea surface temperature —M.-L. Timmermans and d. 1982–2010 (Timmermans and Proshutinsky 2015; A. Proshutinsky Fig. 5.24b). The August 2015 SST pattern is also simi - Summer sea surface temperatures in the Arctic Ocean are set by absorption of solar radiation into the lar to that of recent years, for example 2012 (Fig. 5.7b), surface layer. In the Barents and Chukchi Seas, there which was the summer of lowest minimum sea ice is an additional contribution from advection of warm extent in the satellite record (1979–present). water from the North Atlantic and Pacific Oceans, Most boundary regions and marginal seas of the respectively. Solar warming of the ocean surface layer Arctic had anomalously warm SSTs in August 2015 compared to the 1982–2010 August mean (Fig. 5.7c). is inf luenced by the distribution of sea ice (with more SSTs in these seas, which are mostly ice free in solar warming in ice-free regions), cloud cover, water August, are linked to the timing of local sea ice re - color, and upper-ocean stratification. In turn, warmer treat; anomalously warm SSTs (up to +3°C relative SSTs can drive intensified cyclonic activity; cyclones to 1982–2010) in August 2015 in the Beaufort and propagating in marginal ice zones are associated with large ocean-to-atmosphere heat f luxes in ice-free re Chukchi Seas were associated with low sea ice extents - and exposure of surface waters to direct solar heat gions (e.g., Inoue and Hori 2011). Here, August SSTs - are reported, which are an appropriate representation ing (Fig. 5.7c; see also section 5c). The relationship of Arctic Ocean summer SSTs and are not affected by between warm SSTs and reduced sea ice is further ap - parent in a comparison between August 2015 and Au the cooling and subsequent sea ice growth that takes - place in the latter half of September. SST data are from gust 2014 SSTs: anomalously warm regions (including to the east of Svalbard, where SSTs were up to +3°C the NOAA Optimum Interpolation (OI) SST Version warmer in 2015) are associated with relatively lower 2 product, which is a blend of in situ and satellite sea ice extents in 2015 compared to 2014 (Fig. 5.7d). measurements (Reynolds et al. 2002, 2007; www.esrl Although SSTs were warmer in general, August 2015 .noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html). SSTs were cooler relative to average in some regions, Mean SSTs in August 2015 in ice-free regions for example, along the southern boundaries of the ranged from ~0°C in some places to around +7°C Beaufort and East Siberian Seas (Fig. 5.7c), where to +8°C in the Chukchi, Barents, and Kara Seas and summer air temperatures were also below average eastern Baffin Bay off the west coast of Greenland (see section 5b). (Fig. 5.7a). August 2015 SSTs show the same general | S137 AUGUST 2016 STATE OF THE CLIMATE IN 2015

158 past decades (Timmermans and Proshutinsky 2015, their Fig. 5.26a). The seasonal evolution of SST in the marginal seas exhibited the same general trends and regional differences in 2015 (Fig. 5.8b) as for the preceding decade. Seasonal warming in the marginal seas begins as early as May, and the seasonal cooling period begins as early as mid-August, with cooling observed through December. The asymmetry in rates of seasonal warming and cooling, most notable in the Chukchi Sea and East Baffin Bay, suggests a source of heat in addition to solar radiation. Advection of warm water from the Bering Sea and North Atlantic likely inhibits SST cooling (e.g., Carton et al. 2011; Chepurin and Carton 2012). ig . 5.7. (a) Mean SST (°C) in Aug 2015. White shad - F ing is the Aug 2015 mean sea ice extent. (b) Mean SST in Aug 2012. White shading is the Aug 2012 sea ice extent. Gray contours in (a) and (b) indicate the 10°C SST isotherm. (c) SST anomalies (°C) in Aug 2015 relative to the Aug mean for the period 1982–2010. White shading is the Aug 2015 mean ice extent and the black line indicates the median ice edge in Aug for the period 1982–2010. (d) SST anomalies (°C) in Aug 2015 relative to Aug 2014; white shading is the Aug 2015 mean ice extent and the black line indicates the median ice edge for Aug 2014. Sea ice extent and ice edge data are from NSIDC. Anomalously warm August 2015 SSTs in eastern Baffin Bay were notable, with values as much as 4°C higher than the 1982–2010 August mean; SSTs over the region indicate a general warming trend F - . 5.8. (a) Time series of area-averaged SST anoma ig −1 since 1982 (Fig. 5.8a). Over of about 0.5°C decade lies (°C) for Aug of each year relative to the Aug mean for the period 1982–2010 for the Chukchi and the past two decades, the linear warming trend in Kara Seas and eastern Baffin Bay (see Fig. 5.7b). The - the surface waters of eastern Baffin Bay has acceler dash-dotted black line shows the linear SST trend for −1 −1 ). Along the ated to about 1°C decade (+0.10°C yr the Chukchi Sea (the same warming trend as eastern boundaries of the Arctic basin, the only marginal seas - Baffin Bay). Numbers in the legend correspond to lin to exhibit statistically significant warming trends are –1 ear trends (with 95% confidence intervals) in °C yr . the Chukchi and the Kara Seas. Chukchi Sea August (b) SST (°C) in 2014–15 for each of the marginal seas, −1 SSTs are warming at a rate of about +0.5°C decade , where the OISST V2 weekly product has been used in the calculation. For sea ice concentrations greater commensurate with declining trends in summer sea than 50%, the SST product uses a linear relationship ice extent in the region. In the Kara Sea, August 2015 with sea ice concentration to infer SST; variations SSTs were also up to 4°C higher than the 1982–2010 in freezing temperature as a consequence of salinity August mean; SSTs in this sea have warmed by about variations imply that SSTs inferred from sea ice can be −1 +0.3°C decade since 1982. In other marginal seas, erroneously cool by as much as 0.2°C, with the highest warm August SST anomalies observed in 2015 are of errors in the Canadian sector (see Timmermans and similar magnitude to warm anomalies observed in Proshutinsky 2015). | S138 AUGUST 2016

159 SIDEBAR 5.2: CLIMATE CHANGE IS PUSHING BOREAL FISH NORTHWARD TO THE ARCTIC: THE CASE OF THE BARENTS M. FOSSHEIM, R. PRIMICERIO, E. JOHANNESEN, R. B. INGVALDSEN, M. M. ASCHAN, AND A. V. DOLGOV — SEA - Under climate warm ing, species tend to shift their distributions pole - ward (IPCC 2014). Some of the most rapid shifts are taking place in the Arctic, where warm - ing is currently twice the global average (see section 5.b, Fig. 5.1; Hoegh-Guldberg and Bruno 2010; Doney et al. 2012). Poleward shift - ing marine species have been entering the Arctic Ocean from both the F - . SB5.2. Comparison of the fish communities between the beginning of the Ecosys ig tem Survey taken in the Barents Sea in (a) 2004 and (b) 2012, indicates a significant Atlantic and the Pacific change in distribution. The Atlantic (red) and central (yellow) communities (boreal (Grebmeier et al. 2010; fish species) have shifted north and east, taking over areas previously occupied by Wassmann et al. 2011). the Arctic (blue) community (arctic fish species). Data are available only for the Boreal (warm-water af - shaded areas. (After Fig. 1 in Fossheim et al. 2015.) finity) species of fish have shifted extensively northward into the Arctic (Mueter and The fish species increasing in the north are large boreal Litzow 2008; Grebmeier et al. 2006; Rand and Logerwell Gadus morhua fish predators, such as cod ( ), beaked redfish 2011; Christiansen et al. 2013; Fossheim et al. 2015). Sebastes mentella ( - Hippoglossoides pla ), and long rough dab ( As an example, we present the recent climate-induced tessoides ). These fish species are considered “generalists” changes in the fish communities of the Barents Sea, the in that they can use a wide range of habitats and feed on a entrance point to the Arctic Ocean from the Atlantic. The diverse set of prey. As such, they are better able to thrive results are based on a large-scale annual Ecosystem Survey in a changing environment. Their northward expansion is that monitors the whole ice-free shelf of the Barents Sea likely related to warmer water temperatures and greater in August–September, the season with the least sea ice. food availability for these fish species (Fossheim et al. This cooperative survey between Russia (Knipovich Polar 2015). For instance, increased primary productivity in the Research Institute of Marine Fisheries and Oceanography) previously ice-covered area (Dalpadado et al. 2014) and and Norway (Institute of Marine Research) was initiated increasing abundance and biomass of Atlantic zooplankton in 2004. Our focus is on observations for the period in the northern Barents Sea (Dalpadado et al. 2012) likely 2004–12, as they have been most thoroughly assessed. favor boreal over Arctic fish species. In the Barents Sea, the present warming trend in Cod, the most important commercial species, has water temperatures started in the late 1990s (Boitsov et reached a record high population size due to a favorable al. 2012). The late summer temperature at the seafloor climate and lower fishing pressure (Kjesbu et al. 2014). has increased by almost 1°C during the last decade alone. The cod stock in the Barents Sea has not been this high In this region, sub-zero water masses in late summer since the 1950s. High abundances have also been recorded have almost disappeared and the sea ice is retreating. In ), the other main for haddock ( Melanogrammus aeglefinus association with this warming, boreal fish species have commercial species, and for long rough dab, a common entered the northern parts of the Barents Sea in large and widespread species in the Barents Sea. A poleward numbers. The expansions of these fish species have led - expansion of cod and haddock and a northeastward dis to a community-wide shift: boreal communities are now Sebastes mentella ) have been placement of beaked redfish ( found farther north and the local Arctic (cold-water documented (Renaud et al. 2012; Hollowed et al. 2013; affinity) community has been almost pushed out of the Fossheim et al. 2015). area (Fig. SB5.2). - The Arctic fish community, including various snail | S139 AUGUST 2016 STATE OF THE CLIMATE IN 2015

160 CLIMATE CHANGE IS PUSHING BOREAL FISH SIDEBAR 5.2: CONT. NORTHWARD TO THE ARCTIC: THE CASE OF THE BARENTS M. FOSSHEIM, R. PRIMICERIO, E. JOHANNESEN, R. B. INGVALDSEN, M. M. ASCHAN, AND A. V. DOLGOV — SEA fishes, sculpins, and eel pouts, does not seem to cope is changing due to rising water temperatures, and new well with warming water temperatures (Fossheim et al. competitors and predators are arriving. It is anticipated 2015). Most of these Arctic fish species are relatively that this could result in the local extinction of some Arctic small, stationary, and feed on organisms living on the sea Liparis fabricii ) fish species, such as the gelatinous snailfish ( bottom. These species have a more specialized diet than and even the most abundant Arctic species, the Polar cod the boreal fish species and are thus more vulnerable to Boreogadus saida ( ). climate change (Kortsch et al. 2015). In addition, they One consequence of the general nature of large boreal are adapted to life on the shallow shelf of the Barents fish moving into the Arctic is the development of novel Sea. Because the central Arctic Ocean is much deeper, - feeding links between incoming and resident species, ul it is unlikely that these species will move farther north. timately changing the configuration of the Arctic marine However, they can be found farther to the east on the food web (Kortsch et al. 2015). Arctic food webs contain neighboring shelf (e.g., Kara Sea; Fig. SB5.2). fewer feeding links than boreal food webs. As cod and Large fish and marine mammals can move quickly other large fish species feeding on many prey move into over large distances, while other species, such as small arctic waters, they establish many new links in the Arctic Arctic fish species and organisms that live on or near the food web, which becomes more tightly connected. The seafloor, are more stationary. As a result, two previously ecological effects of perturbations will spread faster separate communities are now mixing together (Fossheim and more widely in a more interconnected arctic food et al. 2015). The larger fish species from the south will web, making it more susceptible to environmental stress compete with the smaller Arctic species for food, and (Kortsch et al. 2015). even prey on them directly. Thus, the Arctic community is being pressured from two sides: the marine environment G in 2014 reached a maximum of 39% of the ice sheet reenland Ice Sheet —M. Tedesco, J. E. Box, J. Cappelen, e. X. Fettweis, K. Hansen, T. Mote, C. J. P. P. Smeets, D. van As, area and ~90% in 2012. A second period of melting, R. S. W. van de Wal, I. Velicogna, and J. Wahr which began in late August, covered between 15% and The Greenland Ice Sheet covers an area of 20% of the ice sheet (a mean of ~5% over the same pe - 2 . With a volume of 2.85 million 1.71 million km riod) and lasted until early September. In the summer 3 of 2015 (June–August), the number of melting days , it is the second largest glacial ice mass on Earth, km along the southwestern and southeastern margins of smaller only than the Antarctic ice sheet. The amount of freshwater stored in the Greenland Ice Sheet has the ice sheet was close to or below the long-term aver - a sea level equivalent of ~7 m. The discharge of the age, with maximum negative anomalies (i.e., below the 1981–2010 average) of 5–10 days (Fig 5.9a). In ice to the ocean through runoff and iceberg calving contrast, the number of melt days in the northeastern, not only increases sea level, but can also alter the ocean thermohaline circulation and global climate western, and northwestern regions was up to 30–40 (Rahmstorf et al. 2015). Moreover, the high albedo days above the 1981–2010 average, setting new records in terms of meltwater production and runoff over the (ref lectivity) of the ice sheet surface (together with that of sea ice and snow on land) plays a crucial role northwestern regions. in the regional surface energy balance (Tedesco et al. The surface mass balance measured along the 2011) and the regulation of global air temperatures. southwestern portion of the ice sheet at the K-transect Estimates of the spatial extent of Greenland Ice for September 2014 through September 2015 (van de Sheet surface melting (e.g., Mote 2007; Tedesco 2007; Wal et al. 2005, 2012) was the third least negative since the beginning of the record in 1990 (Tedesco et Tedesco et al. 2013) show that in 2015 (Fig. 5.9a) melt - ing occurred over more than half of the ice sheet for al. 2015). This is consistent with the negative melting the first time since the exceptional melt events of July anomalies along the southwestern portion of the ice sheet (Fig. 5.9a). At all PROMICE network stations - 2012 (Nghiem et al. 2012). The 2015 melt extent ex ceeded two standard deviations above the 1981–2010 (www.promice.dk; Ahlstrøm et al. 2008; van As et average, reaching a maximum of 52% of the ice sheet al. 2011) summer 2015 ablation was low with respect to the 2011–15 period of record (Fig. 5.9b), except at area on 4 July (Fig. 5.9d). By comparison, melt extent | S140 AUGUST 2016

161 the most northerly latitudes (Kronprins Christian Land, KPC, 80°N, 25°W; Thule, THU, 76°N, 68°W), where melt totals were slightly above average. The highest recorded melt in 2015, 5.1 m on the Qa - ssimiut lobe (QAS_L station, 61°N, 47°W), was just over half the record-setting 9.3 m at that site in 2010 (Fausto et al. 2012). Consistent with the dis - tribution of melt anomalies, measurements at weather stations of the Danish Me - teorological Institute (DMI; Cappelen 2015) during spring 2015 indicate that summer average temperature anoma - lies (relative to the 1981–2010 - average) were positive at sev eral northerly stations around the Greenland coastline, with - values exceeding one stan dard deviation at Pituffik (+1.2 °C), Upernavik (+1.2°C) and Danmarkshavn (+0.9°C). In contrast, temperatures in south and southwest Green - - land (e.g.,Paamiut, Narsar suaq, Qaqortoq, and Prins Christian Sund) were 1.5 standard deviations below . 5.9. (a) Map of the anomaly (with respect to the 1981–2010 average) of the ig F the 1981–2010 average, with number of days when melting was detected in summer 2015 using spaceborne temperature anomalies as passive microwave data. The locations of the stations used for the in situ much as −2.6°C at Narsarsuaq analysis of surface mass balance and temperature are reported on the map (Tedesco et al. 2015). These as black disks (PROMICE) and cyan triangles (K-transect). (b) Summer 2015 widespread low temperatures ablation at PROMICE stations with respect to the 2011–15 period of record. are consistent with a strong (c) Greenland Ice Sheet surface albedo anomaly for JJA 2015 relative to the negative spring temperature average for those months between 2000 and 2009 derived from MODIS data. anomaly centered over Green - (d) Daily spatial extent of melting from Special Sensor Microwave Imager/ Sounder (SSMIS) as a percentage of the total ice sheet area for all of 2015. land (see section 5b, Fig. 5.2b). The 1981–2010 average spatial extent of melting (dashed line) and ±2 std. Danmarkshavn also experi - dev. of the mean (shaded) are also plotted for reference. enced its warmest January on record, with a +7.7°C anoma - average in the southwest (Fig. 5.9c), consistent with ly. A new record August low temperature of −39.6°C the negative surface mass balance and melting day at Summit (3216 m a.s.l.). occurred on 28 August anomalies measured over the same region (Fig. 5.9a). The average albedo for the Greenland Ice Sheet in summer 2015, derived from data collected by the The trend of mean summer albedo over the entire ice sheet for the period 2000–15 remained negative Moderate-resolution Imaging Spectroradiometer and was estimated to be −5.5% ± 0.4%. In July 2015, (MODIS, after Box et al. 2012), was below the 2000–09 when extensive melting occurred (Fig. 5.9d), albedo average over the northwestern region and above the | S141 AUGUST 2016 STATE OF THE CLIMATE IN 2015

162 averaged over the entire ice sheet was 68%. Albedo in July 2015 was as much as 15%–20% below average along the northwestern ice sheet and along the west coast, where a large increase in melting days was observed in 2015. Over the entire summer, however, the albedo anomaly along the southwestern ice sheet margin coast was positive, consistent with a relatively shorter melt season and with the presence of summer snow accumulation. GRACE satellite data (Velicogna et al. 2014) are used to estimate monthly changes in the total mass of the Greenland Ice Sheet, including mass gain due to accumulation and summer losses due to runoff and calving (Fig. 5.10). Between the be - ginning of September 2014 and the beginning of 2 F , left y -axis and ig . 5.11. Cumulative net area change (km September 2015 GRACE recorded a 174 ± 45 Gt -axis) of 45 of the widest and fastest- y square miles, right 9 10 tons) mass loss, versus an average Sep ≡ (Gt - flowing marine-terminating glaciers of the Greenland Ice tember-to-September loss of 278 ± 35 Gt for the Sheet (Box and Hansen 2015; Jensen et al. 2016). The linear 2002–15 period. As a comparison, the 2013–14 regression is dashed. September-to-September loss was 236 ± 45 Gt (7% of the total loss of ~ 3500 Gt since the beginning annual net area loss in the 16-year period of obser - 2 of the GRACE record in 2002) and that for 2011–12 vations (1999–2015), being −16.5 km or 7.7 times was 638 ± 45 Gt (18% of the total loss). The relatively lower than the annual average area change trend of 2 −1 - −127 k m modest loss for the 2014–15 period is consistent with (Fig. 5.11). Specifically, Petermann Gla yr reduced melting over the southwest portion of the ice cier advanced by 0.68 km across a width of 17.35 km, sheet and increased summer snowfall. and Kangerdlugssuaq Glacier advanced by 1.68 km across a width of 6.01 km. - Glacier front classification in LANDSAT and AS TER imagery (after Jensen et al. 2016) reveals that 45 of the widest and fastest f lowing marine-terminating f. G laciers and ice caps outside Greenland —G. Wolken, M. Sharp, L. M Andreassen, A. Arendt, D. Burgess, J. G. Cogley, L. Copland, glaciers retreated at a slower rate in 2013–15 than in J. Kohler, S. O’Neel, M. Pelto, L. Thomson, and B. Wouters the 1999–2012 period (Fig. 5.11). Between the end of Mountain glaciers and ice caps cover an area of the 2014 melt season and the end of the 2015 melt 2 in the Arctic and are a leading over 400 0 00 km season, 22 of the 45 glaciers retreated, but the advance of 9 relatively wide glaciers resulted in the lowest contributor to global sea level change (Gardner et al. 2011, 2013; Jacob et al. 2012). They gain mass by snow accumulation and lose mass by surface melt runoff, and by iceberg calv - ing where they terminate in water (ocean or lake). The total mass balance (ΔM) is defined as the difference between annual snow accu - mulation and annual mass losses (by iceberg calving plus surface melt runoff). Of the 27 glaciers currently monitored, however, only three (Kongsvegen, Hansbreen, and Devon - Ice Cap NW) lose any mass by iceberg calv ing into the ocean. For all glaciers discussed here, the climatic mass balance is reported , the difference between annual snow (B clim . 5.10. Cumulative change in the total mass (Gt) of the ig F accumulation and annual runoff). B is a Greenland Ice Sheet between Apr 2002 and Sep 2015 estimated clim widely used index of how glaciers respond to from GRACE measurements. The square symbols denote Apr climate variability and change. values for reference. | S142 AUGUST 2016

163 B measurements for mass balance year 2014/15 clim are available for only 9 of the 26 glaciers that are monitored across the Arctic (three each in Alaska and Svalbard, and one in Norway), and some of these are still provisional. Therefore, we focus on the 2013/14 - measurements, which are available for 21 gla B clim ciers (WGMS 2015b). These glaciers are located in Alaska (three), Arctic Canada (four), Iceland (seven), Svalbard (three), Norway (three), and Sweden (one; Fig. 5.12; Table 5.1). For these glaciers as a group, in 2013/14 was negative. However, the mean B clim five glaciers [one each in Arctic Canada (Meighen Ice Cap) and Iceland (Dyngjujökull) and three in Svalbard (Midre Lovenbreen, Austre Broggerbreen, and Kongsvegen)] had positive balances. For the Arctic as a whole, 2013/14 was the 17th most negative mass balance year on record (the first record dates from 1946) and the 12th most negative . 5.12. Locations (green circles) of 27 Arctic glaciers F ig with long-term records of annual climatic mass bal - - year since 1989 (i.e., the median for the 25-year peri ance (B ). See Table 5.1 for glacier names. Regions od), when annual measurements of at least 20 glaciers clim outlined in yellow are the Randolph Glacier Inventory began. This balance year continues the increasingly (RGI) regions of the Arctic (Pfeffer et al. 2014). In re - negative trend of cumulative regional climatic mass gions where individual glaciers are located too close balances, calculated by summing the annual mean together to be identifiable on the map, their numbers - mass balances for all glaciers in each reporting re are shown at the edge of the RGI region in which they gion of the Arctic (Fig. 5.13). For Svalbard, 2013/14 occur. Red shading indicates glaciers and ice caps, including ice caps in Greenland outside the ice sheet. was among the least negative mass balance years on Yellow shading shows the solution domains for regional record, and the climatic balances of each of its three mass balance estimates for Alaska, Arctic Canada, glaciers were among the 3–9 most positive since 1987. Russian Arctic, and Svalbard derived using gravity data Local meteorological observations suggest that the from the GRACE satellites (see Fig. 5.3). positive balances in Svalbard were attributable to high winter (October–May) precipitation, especially at low elevations, that was followed by a relatively cool sum - mer (June–August). Melt suppression over Svalbard, as well as the Russian Arctic Archipelagos and the northernmost islands of Arctic Canada, was likely linked to negative 850-hPa air temperature anoma - lies in June–September. In contrast, in 2013/14 the mean measured climatic balance of glaciers in Alaska was the fifth most negative since 1966, with Lemon Creek and Wolverine glaciers registering their third and fourth most negative years on record, respec - tively. The negative balances of Alaska, Iceland, and northern Scandinavia glaciers in 2013/14 were most likely linked to melt increases caused by positive air temperature anomalies at the 850-hPa level in July– . 5.13. Cumulative climatic mass balances (B in ig F September that exceeded +2.5°C in northern Norway clim –2 kg m ) for glaciers in five regions of the Arctic and and Sweden (data from NCEP–NCAR reanalysis). for the Arctic as a whole (Pan–Arctic). Mean balances - Indeed, in 2014, many locations in northern Scandi are calculated for glaciers monitored in each region navia reported their highest summer air temperatures in each year and these means are summed over the since records began (Overland et al. 2015). period of record. Note that the period of monitoring Among the nine glaciers for which 2014/15 B varies between regions and that the number and iden - clim tity of glaciers monitored in a given region may vary measurements have been reported, the balances of between years. glaciers in Alaska, Svalbard, and northern Norway | S143 AUGUST 2016 STATE OF THE CLIMATE IN 2015

164 ) of glaciers in Alaska, the Canadian Arctic, 5.1. Measured annual climatic mass balance (B T able clim Iceland, Svalbard, and northern Scandinavia for 2013/14 and 2014/15, along with the 1980–2010 mean and standard deviation for each glacier (column 3). Mass balance data are from the World Glacier Monitoring Service (2015; 2016), with corrections to Svalbard data provided by J. Kohler and to Alaska data provided by S. O’Neel, and with updates from the Norwegian Water Resources and Energy Directorate (NVE) database. Numbers in column 1 identify glacier locations in Fig. 5.1. Note that 2014/15 results may be based upon data collected before the end of the 2015 melt season and may be subject to revision. Standard Mean Climatic Deviation of Climatic Climatic Balance Climatic Mass Balance Glacier Balance Region 2014/15 Balance 2013/14 (Record length, years) 1980 –2010 –1 –2 –1 –2 (kg m 1980 –2010 (kg m yr ) ) yr –1 –2 (kg m ) yr –2 –1 (kg m yr ) Alaska 1 Wolverine (50) −285 1205 −1950 −113 0 3 −584 709 −1825 −2270 Lemon Creek (63) 2 Gulkana (50) −505 738 −220 −1440 Arctic Canada 7 Devon Ice Cap (54) −153 176 −246 +57 −173 5 Meighen Ice Cap (53) 284 −295 Melville South Ice Cap (52) 4 369 −159 6 −239 260 − 417 White (52) Iceland 817 Langjökull S. Dome (18) 8 −1448 −1950 −990 Hofsjökull E (24) −602 1009 9 −950 9 Hofsjökull N (25) −606 787 947 −978 9 Hofsjökull SW (24) −990 14 Köldukvislarjökull (22) −529 738 −887 10 Tungnaarjökull (23) −117 0 873 −1535 +170 912 13 Dyngjujökull (17) −133 −367 Brúarjökull (22) 12 −34 660 813 −867 −353 Eyjabakkajökull (23) 11 Svalbard −450 17 Midre Lovenbreen (48) −356 305 +30 −610 16 Austre Broggerbreen (49) −469 342 +10 Kongsvegen (29) −160 +14 0 378 15 −70 Hansbreen (26) −431 512 −227 18 Nortern Scandinavia +668 Engabreen (45) +463 1091 −892 20 781 −927 −78 0 −800 21 Langfjordjøkelen (25) 525 −430 Marmaglaciaren (23) 22 Rabots Glaciar (29) 560 −394 23 −592 Riukojietna (26) 24 805 698 25 −890 −113 Storglaciaren (68) 1101 −212 Tarfalaglaciaren (18) 26 −790 −777 Rundvassbreen (8) 27 −20 gravimetry available since 2003. Annual storage (Langfjordjøkelen) were negative, while those of glaciers in central Norway were near balance (Rund changes are proxy for changes in the regional annual - glacier mass balance (ΔM) for the heavily glacierized vassbreen) or positive (Engabreen). The pattern of - regions of the Arctic (Luthcke et al. 2013). Measure negative balances in Alaska and Svalbard is also ments of ΔM in 2014/15 for all the glaciers and ice captured in time series of regional total stored water estimates (Fig. 5.14), derived using GRACE satellite caps in Arctic Canada and the Russian Arctic also | S144 AUGUST 2016

165 show a negative mass balance year. The GRACE- derived time series clearly show a continuation of negative trends in ΔM for all measured regions in the Arctic. These measurements of B and ΔM are clim consistent with anomalously warm (up to +1.5°C) June–August air temperatures over Alaska, Arctic Canada, the Russian Arctic, and Svalbard in 2015 (section 5b), and anomalously cool temperatures in northern Scandinavia, particularly in June and July (up to −2°C). errestrial snow cover —C. Derksen, R. Brown, L. Mudryk, g. T . 5.14. Cumulative changes in regional total stored F ig and K. Luojus - water for 2003–15 (Gt), derived using GRACE satel The Arctic (land areas north of 60°N) is always lite gravimetry. Annual storage changes are proxy for completely snow-covered in winter and almost snow changes in the regional annual glacier mass balance free in summer, so the transition seasons of autumn (ΔM). The estimated uncertainty in regional mass − − 1 1 changes is 10 Gt yr for the Gulf of Alaska, 8 Gt yr for and spring are significant when characterizing vari - 1 − the Canadian Arctic, 8 Gt yr for the Russian Arctic, ability and change. The timing of spring snowmelt − 1 and 4 Gt yr for Svalbard. These errors include the is particularly significant because the transition formal error of the least squares fit and the uncertain - from highly ref lective snow cover to the low albedo ties in the corrections for glacial isostatic adjustment, of snow-free ground is coupled with increasing so - Little Ice Age, and terrestrial hydrology. lar radiation during the lengthening days of the high-latitude spring. The 2015 spring melt season provided continued evidence of - earlier snowmelt across the ter restrial Arctic. There is increased awareness of the impact of these - changes on the Arctic climate sys tem, the freshwater budget, other components of the cryosphere (such as permafrost and associated geochemical cycles), and Arctic ecosystems (Callaghan et al. 2011). - Snow cover extent (SCE) anom - alies (relative to the 1981–2010 ref erence period) for the 2015 Arctic spring (April, May, June) were computed separately for the North American and Eurasian sectors of the Arctic from the NOAA snow chart Climate Data Record, maintained at Rutgers University (Estilow et al. 2015; http://climate - .rutgers.edu/snowcover/). Consis tent with nearly all spring seasons of the past decade, both May and June SCE anomalies were strongly negative in 2015 (Fig. 5.15); June F . 5.15. Monthly Arctic snow cover extent standardized (and thus unit - ig SCE in both the North American less) anomaly time series (with respect to 1981–2010) from the NOAA and Eurasian sectors of the Arctic snow chart Climate Data Record for (a) Apr, (b) May, and (c) Jun 1967–2015 was the second lowest in the snow − 1 (solid lines denote 5-yr moving average); (d) % change decade in spring chart record, which extends back snow cover extent for running time series starting in 1979 (1979–98, t o 19 6 7. 1979 –99, 1979 –2000, etc.). | S145 AUGUST 2016 STATE OF THE CLIMATE IN 2015

166 For the fifth time in the past six years (2010–15), 2 despite Arctic SCE in June was below 3 million km never falling below this threshold in the previous 43 years of the snow chart data record (1967–2008). Figure 5.15d shows the changing rate of SCE loss across the Arctic since 1998 via calculations over running time periods since 1979, the first year of the satellite passive microwave record used to track sea ice extent. The April and May SCE reductions have remained relatively consistent year over year, ranging −1 between −1% and −2% decade (April; insignificant −1 - (May; sig at 95%) and −3% and −5% per decade nificant at 99%). A significant rate of June SCE loss was identified over the first 20 years (nearly −16% for 1979–98) due to rapid reductions in the 1980s, which then plateaued due to a period of stable spring snow cover during the 1990s. Since 2005, the rate of June SCE loss has increased again, reaching almost −1 18% decade for the period 1979–2015 (compared to the 1981–2010 mean June SCE). Since 2011, the rate of June snow cover loss has exceeded the much publicized rate of September sea ice loss (section 5c). There are complex interactions between regional variability in the onset of snow cover in the autumn, - subsequent winter season snow accumulation pat - terns (which themselves are driven by the complex in terplay of temperature and precipitation anomalies), and continental-scale spring SCE anomalies (shown in Fig. 5.15). Snow cover duration (SCD) departures (relative to the 1998–2010 period) derived from the NOAA daily Interactive Multi-sensor Snow and Ice Mapping System (IMS) snow cover product (Helfrich et al. 2007) suggest earlier snow cover onset in the autumn over much of the Arctic for the 2014/15 snow year (Fig. 5.16a). This is consistent with premelt April snow depth anomalies (relative to the 1999–2010 average), derived from the Canadian Meteorological . 5.16. Snow cover duration departures (with ig F - Centre (CMC) daily gridded global snow depth analy respect to 1998–2010) from the NOAA IMS data sis (Brasnett 1999), which were largely positive over record for the (a) 2014 autumn season and (b) 2015 much of the Arctic land surface (25.1% and 33.7%, spring season. respectively, for the North American and Eurasian a bias toward higher winter snow depths since 2006 sectors of the Arctic). There was a notable east–west snow depth gradient across Eurasia in April 2015 due to changes in the resolution of the precipitation with above-average snow depth in eastern Siberia forcing used as part of the CMC analysis. Strong positive surface temperature anomalies over central and below-average snow depth across western Siberia and northern Europe. The North American Arctic Siberia, Alaska, and the western Canadian Arctic was characterized by a more latitudinal gradient of in May (which persisted into June; section 5b) were associated with rapid reductions in regional snow deeper-than-normal snow depth north of the bo - depth ref lected in the May and June depth anomalies real tree line and shallower-than-normal snow depth (Figs. 5.17b,c) and earlier than normal snowmelt in across the boreal forest. Note that the CMC results these regions (Fig. 5.16b), which drove the negative shown in Figs. 5.17a–c mask out anomalies over high continental-scale SCE anomalies in May and June elevation areas (in the Canadian Arctic Archipelago, Baffin Island, coastal Alaska) known to be affected by (Figs. 5.16b,c). | S146 AUGUST 2016

167 . 5.17. Snow depth anomaly (% of 1999–2010 average) from the CMC snow depth analysis for (a) Apr, (b) ig F May, and (c) Jun 2015. h. iver discharge —R. M. Holmes, A. I. Shiklomanov, S. E. Tank, R Syvitski 2010; Rawlins et al. 2010). The long-term J. W. McClelland, and M. Tretiakov discharge trend is greatest for rivers of the Eurasian - River discharge integrates hydrologic processes Arctic and constitutes the strongest evidence of in occurring throughout the surrounding landscape. tensification of the Arctic freshwater cycle (Rawlins et al. 2010). Consequently, changes in the discharge of large rivers 3 for can be a sensitive indicator of widespread changes in In 2015, the combined discharge of 2051 km watersheds (Rawlins et al. 2010; Holmes et al. 2013). the six largest Eurasian Arctic rivers was 15% greater Changes in river discharge also impact coastal and than the 1980–89 average (Fig. 5.19; Table 5.2), and - the peak discharge occurred earlier than the average ocean chemistry, biology, and circulation. This inter over the same period (Fig. 5.20). This is the fourth action is particularly strong in the Arctic, given the - highest combined discharge value since measure relative volume of river discharge to ocean volume. Rivers in this region transport >10% of the global river ments began in 1936. The four highest values have discharge into the Arctic Ocean, which represents only ~1% of the global ocean volume (Aagaard and Carmack 1989; McClelland et al. 2012). In this section, annual river discharge values since 2011 are presented for the eight largest Arctic rivers, and recent observations are compared to a 1980–89 reference period (the first decade with data from all eight rivers). Six of the rivers lie in Eurasia and two are in North America. Together, the watersheds of 6 2 these rivers cover 70% of the 16.8 × 10 km pan- Arctic drainage area and, as such, account for the majority of riverine freshwater inputs to the Arctic Ocean (Fig. 5.18). Discharge data for the six Eurasian rivers are analyzed through 2015, whereas data from the Yukon and Mackenzie Rivers in North America are only available through 2014. Most of these data are now available through the Arctic Great Rivers Observatory (www.arcticgreatrivers.org). A long-term increase in Arctic river discharge has been well documented and may be linked to . 5.18. Map showing the watersheds of the eight riv - ig F - increasing precipitation associated with global warm ers featured in this section. The blue dots show the lo - ing (Peterson et al. 2002; McClelland et al. 2006; cation of the discharge monitoring stations and the red Shiklomanov and Lammers 2009; Overeem and line shows the boundary of the pan-Arctic watershed. | S147 AUGUST 2016 STATE OF THE CLIMATE IN 2015

168 T able 5.2. Annual discharge for 2012, 2013, and 2014 for the eight largest Arctic rivers, compared to long-term and decadal averages back to the start of observations. Values for 2015 are provided for the six Eurasian rivers. Red values indicate provisional data, which are subject to modification before official data are released. 3 − 1 Discharge (km yr ) Lena Sum Yukon Mackenzie Pechora S. Dvina Ob’ Yenisey Kolyma 82 2015 123 80 527 654 585 640 2487 2014 227 272 116 91 448 86 607 213 2282 80 600 527 372 97 82 311 2013 2012 306 103 117 232 458 665 59 2240 300 Average 409 293 108 93 212 594 583 75 2366 2010 –15 Average 415 2475 78 603 103 640 305 207 124 2000–09 Average 2338 217 275 117 111 405 613 532 68 1990–99 Average 2262 206 273 108 100 376 582 549 68 1980 – 89 Average 292 108 94 441 591 529 65 2304 184 1970 –79 Average 73 273 112 98 376 546 535 1960 – 69 Average 74 110 108 380 566 511 1950 – 59 Average 102 100 424 578 498 72 1940 – 49 Average for 100 Period of 540 589 401 71 111 286 206 2305 Record all occurred in the past 14 years. Overall, the most and Eurasian rivers. (Increases per decade follow a K recent data indicate a continuing long-term increase Mann – endall trend analysis; error bounds are 95% in Eurasian Arctic river discharge, at a rate of 3.5% confidence intervals for the trend.) −1 ± 2.1% decade since 1976. Looking more closely at recent years, Eurasian Arctic river discharge generally declined between 2007 and 2012 and then began to 3 increase again in 2013. Values for 2012 (1702 km ), 3 3 ) were 5% less , 2013 (1759 km ), and 2014 (1989 km 1% less, and 2% greater than the 1980–89 period, respectively. The short-term variability in Eurasian Arctic river discharge is consistent with previous increases and decreases over 4–6 year intervals in the past (Fig. 5.19). - For the North American Arctic rivers consid ered here (Yukon and Mackenzie), the combined 3 discharge declined each year from 2012 (538 km ) . 5.19. Long-term trends in annual discharge for ig F 3 Eurasian and North American Arctic rivers. The ), yet in each of those years the to 2014 (499 km Eurasian rivers are Severnaya Dvina, Pechora, Ob’, combined discharge was greater than the long-term - Yenisey, Lena, and Kolyma. The North American riv 3 −1 average (493 km year ; Fig. 5.19; Table 5.2). Thus, as ers are Yukon and Mackenzie. Note the different scales discussed for Eurasian rivers, these most recent data for the Eurasian and North American river discharge; indicate a longer-term pattern of increasing river discharge from the former is 3–4 times greater than −1 discharge (Fig. 5.19). At a rate of 2.6% ± 1.7% decade the latter. Reference lines show long-term means for − 3 1 since 1976, the overall trends of increasing discharge the Eurasian (1812 km yr , 1936–2015) and North 1 3 − American (493 km yr , 1976–2014) rivers. are remarkably similar for the North American | S148 AUGUST 2016

169 on the North Slope. Since 2000, temperature at 20-m depth in this region has increased between −1 0.21°C and 0.66°C decade (Fig. 5.22a; Table 5.3). Permafrost temperatures in Interior Alaska were higher in 2015 than 2014 at all sites (Old Man, College Peat, Birch Lake, Gulkana, and Healy in Fig. 5.22b), except for Coldfoot. Notably, this warming followed slight cooling of 2007–13 (Fig. . 5.20. Combined daily discharge for the six Eurasian F ig 5.22b). However, the recent warming in the interior Arctic rivers in 2015 compared to the 1980–89 average. (see section 5b; Fig. 5.2) was not strong enough to bring permafrost temperatures back to the record - highs observed between the mid-1990s and the mid- Considering the eight Eurasian and North Ameri 2000s except at Gulkana (Fig. 5.22b; Table 5.3). can Arctic rivers together, their combined discharge 3 in 2014 (2487 km In northwestern Canada, temperatures in warm ) was 10% greater than the average - permafrost of the central Mackenzie Valley (Nor discharge for 1980–89. Comparing 2014 to 2012, the combined discharge of these eight rivers was almost man Wells and Wrigley in Fig. 5.22b) were similar 3 3 is in 2014/15 to those observed the previous year. greater in 2014. For perspective, 250 km 250 km approximately 14 times the annual discharge of the Hudson River, the largest river on the east coast of the United States. T errestrial permafrost —V. E. Romanovsky, S. L. Smith, i. K. Isaksen, N. I. Shiklomanov, D. A. Streletskiy, A. L. Kholodov, H. H. Christiansen, D. S. Drozdov, G. V. Malkova, and S. S. Marchenko Permafrost is defined as soil, rock, and any other subsurface earth material that exists at or below 0°C continuously for two or more consecutive years. On top of permafrost is the active layer, which thaws during the summer and freezes again the following winter. The mean annual temperature of permafrost and the active layer thickness (ALT) are good indica - tors of changing climate and therefore designated as essential climate variables (Smith and Brown 2009; Biskaborn et al. 2015) by the Global Climate Observ - ing System Program of the World Meteorological Organization. Changes in permafrost temperatures and ALT at undisturbed locations in Alaska, Canada, Russia, and the Nordic region (Fig. 5.21) are reported . 5.21. Location of the permafrost monitoring ig F sites shown in Fig. 5.22 superimposed on average air here. Regional variability in permafrost temperature temperature anomalies during 2000–14 (with respect records, described below, indicates more substantial to the 1971–2000 mean) from the NCEP–NCAR re - permafrost warming since 2000 in higher latitudes analysis (Kalnay et al. 1996) (Source: NOAA/ESRL.) than in the subarctic. This is in general agreement Sites shown in Fig. 5.22 are (a) Barrow (Ba), West with the pattern of average air temperature anomalies. Dock (WD), KC-07 (KC), Deadhorse (De), Franklin In 2015, record high temperatures at 20-m depth Bluffs (FB), Galbraith Lake (GL), Happy Valley (HV), were measured at all permafrost observatories on the Norris Ck (No); (b) College Peat (CP), Old Man (OM), North Slope of Alaska (Barrow, West Dock, Franklin Chandalar Shelf (CS), Birch Lake (BL), Coldfoot (Co), Norman Wells (NW), Wrigley 2 (Wr), Healy (He), Bluffs, Happy Valley, and Galbraith Lake in Fig. 5.22a; Gulakana (Gu), Wrigley 1 (Wr); (c) Eureka EUK4 (Eu), Romanovsky et al. 2015). The permafrost temperature Alert BH2 (Al), Alert BH5 (Al), Resolute (Re), Alert increase in 2015 was substantial and comparable to BH1 (Al), Arctic Bay (AB), Pond Inlet (PI), Pangnirtung the highest rate of warming observed in this region (Pa); (d) Janssonhaugen (Ja), Urengoy #15-10 (Ur), Juv - so far, which occurred during the period 1995–2000; vasshøe (Ju), Tarfalaryggen (Ta), Bolvansky #59 (Bo), 20-m depth temperatures in 2015 were from 0.10°C Bolvansky #65 (Bo), Urengoy #15-06 (Ur), Bolvansky to 0.17°C higher than those in 2014 (Fig. 5.22a) #56 (Bo), Iskoras Is-B-2 (Is). | S149 AUGUST 2016 STATE OF THE CLIMATE IN 2015

170 F ig . 5.22. Time series of mean annual ground temperature at depths of 9–26 m below the surface at selected measurement sites that fall roughly into the Adaptation Actions for a Changing Arctic Project (AMAP 2015) priority regions: (a) cold continuous permafrost of NW North America (Beaufort–Chukchi region); (b) dis - continuous permafrost in Alaska and northwestern Canada; (c) cold continuous permafrost of eastern and high Arctic Canada (Baffin Davis Strait); (d) continuous to discontinuous permafrost in Scandinavia, Svalbard, and Russia/Siberia (Barents region). Temperatures are measured at or near the depth of penetration of the seasonal ground temperature variations. Data series are updated from Christiansen et al. 2010; Romanovksy et al. 2015; Smith et al. 2015; Ednie and Smith 2015. 1 − 5.3. Change in mean annual ground temperature (MAGT; °C decade able T ) for sites shown in Fig. 5.22, for which † data are available for 2015 ( indicates discontinuous permafrost regions). For sites with records initiated prior to 2000, the rate for the entire available record is provided along with the rate for the period after 2000. (Note records for some sites only began after 2007 as shown in Fig. 5.22). Sites Entire Record Since 2000 Region † +0.1 to +0.2 <+0.1 to +0.2 Norman Wells (NW), Wrigley (Wr) Central Mackenzie Valley NA Norris Ck (No), KC-07(KC) +0.4 to +0.7 Northern Mackenzie Valley NA Pond Inlet (PI) +0.7 Baffin Island NA High Canadian Arctic Resolute (Re), Eureka (Eu) +0.4 to +0.7 +0.53, +0.3 to +0.4 High Canadian Arctic Alert (Al), BH5, BH1, BH2 +1.2, +0.7 to +0.9 West Dock (WD), Deadhorse (De), +0.33 to +0.81 +0.36 to +0.66 Alaskan Arctic plain Franklin Bluffs (FB), Barrow (Ba) Happy Valley (HV), Northern foothills of the +0.25 to +0.37 +0.21 to +0.35 Brooks Range, Alaska Galbraith Lake (GL) Southern foothills of the Coldfoot (Co), Chandalar Shelf (CS), +0.07 to +0.31 +0.13 to +0.18 † Brooks Range, Alaska Old Man (OM) College Peat (CP), Birch Lake (BL), † +0.03 to +0.15 –0.05 to +0.02 Interior Alaska Gulkana (Gu), Healy (He) +0.31 to +0.47 Urengoy 15-06 and 15-10 (Ur) North of West Siberia +0.1 to +0.19 +0.18 to +0.46 Russian European North +0.1 to +0.83 Bolvansky 56, 59, and 65 (Bo) +0.7 Svalbard Janssonhaugen (Ja) +0.7 † NA Tarfalarggen (Ta), Iskoras Is-B-2 (Is) +0.1 to +0.4 Northern Scandinavia † +0.2 Juvvasshøe (Ju) +0.2 Southern Norway | S150 AUGUST 2016

171 Permafrost in this region has gener - ally warmed since the mid-1980s, with less warming observed since 2000 (Table 5.3), corresponding to a period of steady air temperatures. In contrast, greater recent warming has been observed in the colder permafrost of the northern Mackenzie (Norris Ck, KC-07 in Fig. 5.22a and Table 5.3) with 2014/15 temperatures higher than those recorded over the previous 5–7 years, ref lecting an increase in F ig . 5.23. Long-term active-layer change from selected sites in six air temperatures over the last decade different Arctic regions as observed by the Circumpolar Active (Fig. 5.21). Layer Monitoring project (Shiklomanov et al. 2012). The data are Mean temperatures for 2014/15 presented as annual percentage deviations from the mean value for in the upper 25 m of the ground at the period of observations. Thaw depth observations from the end Alert, northernmost Ellesmere Island of the thawing season were used. Only sites with at least 10 years of continuous thaw depth observations are shown in the figure. Solid in the high Canadian Arctic, were red lines show mean values. Dashed black lines represent maximum among the highest recorded since 1978 and minimum values. In the Nordic countries (not shown here) active (Fig. 5.22c). Since 2010, temperatures layer records (1996–2015) indicate a general increase in ALT since have changed little or even declined, 1999. Maximum ALT was observed in 2011 followed by a period of consistent with lower air temperatures thinner active layers. since 2010 (Smith et al. 2015). However, −1 higher permafrost temperature at 15-m depth in (Fig. 5.22d; Table 5.3) with 0.1°C and 0.7°C decade - - lower rates of increase occurring at sites in the dis 2014/15 compared to 2013/14 appears to ref lect an in crease in air temperature since 2013. Since 2000, Alert continuous permafrost zone that are affected by latent permafrost temperatures have increased at a higher heat exchange at temperatures close to 0°C. Higher rate (Table 5.3) than that for the entire record (Smith temperature increases occurred at colder permafrost sites on Svalbard and in northern Scandinavia. In et al. 2015), consistent with air temperature anomaly patterns (Fig. 5.21). Short records, from other high southern Norway permafrost was warmer in 2015 Arctic sites in the Queen Elizabeth Islands (Resolute compared to 2014, a warming that followed a period and Eureka) and on Baffin Island (Pond Inlet) in the of cooling between 2011 and 2014. eastern Arctic, indicate some cooling of permafrost - Active layer thickness [determined by probing ac cording to Brown et al. (2000) and Shiklomanov et al. since 2012/13 at 10–15-m depth (Fig. 5.22c). However, (2012)] at North Slope and Alaska Interior locations a general warming trend is observed (Table 5.3) with was on average greater in 2015 than in 2014 (Fig. 5.23). higher temperatures in 2014/15 than in 2008/09 when An increase in the thickness of the ALT indicates measurements began. Similar to northern Alaska and the Canadian warming surface temperature. Of 26 North Slope sites observed in 2015, only nine had ALT values within high Arctic, permafrost temperature has increased 1 cm of those observed in 2014, while the majority of by 1–2°C in northern Russia during the last 30 to sites had greater ALT values than in 2014. The aver - 35 years. In the Russian European North and in the western Siberian Arctic, for example, temperatures age ALT in 2015 for the 20 North Slope sites with at 10-m depth have increased by ~0.4°C to 0.6°C de - records of at least 10 years was 0.51 m, which is 3 cm −1 higher than the 1995–2013 average. In the interior cade since the late 1980s at colder permafrost sites (in Fig. 5.22d, Bolvansky #59, Urengoy #15-5, and of Alaska, three of the four active sites reported an ALT increase in 2015. The most pronounced change #15-10). Less warming has been observed at warm - permafrost sites (Table 5.3; in Fig. 5.22d, sites Bolvan occurred at a site where surface cover was burned in 2010. Here ALT was 1.78 m in 2015, which is 0.10 m sky #56 and Urengoy #15-6; Drozdov et al. 2015). greater than the 2014 value and 1.23 m greater than In the Nordic countries (including Svalbard), the prefire 1990–2010 average. regional warming and thawing of permafrost have been observed recently (Christiansen et al. 2010; Records from 25 sites with thaw tubes in the Mack - enzie Valley, northwestern Canada, indicate that ALT Isaksen et al. 2011; Farbrot et al. 2013). Since 2000, in 2014 (the most recent year data are available) was temperature at 20-m depth has increased between | S151 AUGUST 2016 STATE OF THE CLIMATE IN 2015

172 on average about 4% greater than the 2003–12 mean (Fig. 5.23). Although ALT in this region has generally increased since 2008 (Duchesne et al. 2015), there has been a decrease since 2012. - In Russia, active layer observations were con ducted at 44 sites in 2015. Since 2009, a progressive increase in ALT is evident for western Siberian loca - . 5.24. Time series of area-averaged minimum total F ig tions (Fig. 5.23), with a substantial increase in 2015 ozone (DU) for Mar in the Arctic, calculated as the of 0.05–0.20 m. Locations in the Russian European minimum of daily average column ozone poleward of North have been characterized by almost monotonic 63° equivalent latitude (Butchart and Remsberg 1986). - thickening of the active layer over the 1999–2012 peri Open circles represent years in which the polar vortex od. However, after reaching its maximum in 2012, the broke up before Mar. Ozone in those years was rela - ALT decreased for three consecutive years (Fig. 5.23). tively high due to mixing with air from lower latitudes In central Siberia (Low Yenisey region) ALT increased and higher altitudes and a lack of significant chemical - ozone depletion. Red and green lines indicate the av by 0.07–0.10 m, while ALT in the East Siberian region erage TOC for 1979–2014 and 2005–14, respectively. (Yakutsk) was largely unchanged from 2014 values. In [Sources: Data are adapted from Müller et al. (2008) northeastern Siberia, ALT in 2015 was 4% lower than - and WMO (2014), updated using ERA-Interim reanaly the 2014 peak values. Similarly, in Chukotka (Russian sis data (Dee et al. 2011). Ozone data from 1979 to 2012 Far East) 2015 ALT values were on average 2% lower are based on the combined total column ozone data - than in 2014 (Fig. 5.23). base version 2.8 produced by Bodeker Scientific (www However, ALT was still greater in 2012–15 than .bodekerscientific.com/data/total-column-ozone). the long-term average value. The summer of 2014 was Data for 2013–15 are from OMI.] particularly warm in the Nordic countries and con - tributed to the thickest active layer measured to date Aura satellite were the highest in the MLS record, which started in August 2004 (Manney et al. 2015). at some places. On Svalbard (Janssonhaugen) ALT The altitude of 20 km is representative of the lower increased by 10% in 2015 compared to the 2000–14 mean and was the highest in the entire 1998–2015 stratosphere (altitude range of 15 km to 25 km) where observational record. chemical destruction of ozone is typically observed in spring when temperatures drop below −78°C (equal j. O to about −108°F or 195 K). Chemically induced loss of zone and UV radiation —G. Bernhard, I. Ialongo, ozone was minimal in the spring of 2015 because of a J.-U. Grooß, J. Hakkarainen, B. Johnsen, G.L. Manney, V. Fioletov, minor sudden stratospheric warming (SSW) event in A. Heikkilä, K. Lakkala early January. This event caused lower stratospheric The minimum Arctic daily total ozone column temperatures to rise above the critical temperature (TOC) measured by satellites (Levelt et al. 2006) in - March 2015 was 389 Dobson Units (DU). Measure for the formation of polar stratospheric clouds, which is a prerequisite for heterogeneous chemical ments from March are used for assessing the temporal reactions that destroy ozone. A second reason for the evolution of Arctic ozone because chemically induced loss of ozone typically peaks in the month of March abnormally high ozone concentrations observed in 2015 was larger-than-usual transport of ozone-rich (WMO 2014). The March 2015 value was 17 DU (5%) air into the lower stratosphere from higher altitudes, above the average of 372 DU for the period of available - measurements (1979–2014) and 23 DU (6%) above the as observed by MLS (Manney et al. 2015). As a con sequence, TOCs in the spring of 2015 were relatively average for the past decade, 2005–14 (Fig. 5.24). The record low was 308 DU in 2011. Figure 5.24 also indi - high (Figs. 5.24, 5.25b). Spatial deviations of monthly average TOCs from cates that the Arctic ozone interannual variability is historical (2005–14) means were estimated with –2014 large: the standard deviation for the period 1979 measurements by the Ozone Monitoring Instrument is 35 DU. This large variability is caused by dynamical effects that affect vortex size and longevity, transport Aura (OMI), which is collocated from MLS on the satellite (Figs. 5.25a, 5.25b). Monthly average TOCs of ozone into the lower stratosphere, and stratospheric chemistry via its sensitivity to temperature (e.g., for March 2015 exceeded historical means by more Tegtmeier et al. 2008; WMO 2014). than 10% over Iceland, southern Greenland, the Davis Strait between Greenland and Canada, and eastern Between December 2014 and April 2015, ozone Canada (Fig. 5.25a). In contrast, TOCs over most of concentrations measured at an altitude of 20 km –7.5% below the 2005–14 average Siberia were 2.5% by the Microwave Limb Sounder (MLS) aboard the | S152 AUGUST 2016

173 sites closest to the North Pole having the smallest peak radiation and UVI values <4 all year. UVI values <5 indicate low to moderate risk of erythema (WHO 2002). Maps shown in Figs. 5.25c,d quantify differences of monthly average noontime UVIs from historical (2005–14) means for March and April and are based on observations derived from OMI. The OMI UV algorithm uses a surface albedo climatology (Tanskanen et al. 2003) that does not change from year to year. At places where the actual surface albedo deviates greatly from the OMI albedo climatology (e.g., when snowmelt oc - curred earlier than usual), OMI UVI data may be biased by more than 50%, although differences in absolute values rarely exceed 2 UVI units (Bernhard et al. 2015). Figures 5.25c,d therefore also compare UVI anomalies measured by OMI and ground-based instruments deployed throughout the Arctic and Scandinavia. Anomalies derived from the two datasets agree to within ±12% at all locations, with the exception of F . 5.25. Anomalies of total ozone column and the noontime ig Andøya for April (OMI overestimates UV index in 2015 relative to 2005–14 means. TOC anomaly the actual anomaly by 16%) and Jokio - for (a) Mar and (b) Apr. UVI anomaly for (c) Mar and (d) Apr inen for March (overestimate by 27% or (first value in parenthesis). Data are based on measurements 0.3 UVI units). The large discrepancy from the OMI. Monthly means calculated from OMTO3 Level for Jokioinen can be explained by early 3 total ozone products (Bhartia and Wellemeyer 2002) that snowmelt on 9 March while the OMI are provided in 1° × 1° spatial gridding. (c) and (d) also indicate UVI anomalies measured by ground-based instruments at 12 climatology assumes snow cover through locations (second value presented). Gray shading indicates the month of March. Persistent cloud areas where no OMI data are available. cover in the second half of March also contributed to this discrepancy. with somewhat larger negative departures east of Monthly average noontime UVIs for March 2015 Moscow. Monthly average TOCs for April 2015 were were below the 2005–14 means in a belt stretching from the Greenland Sea and Iceland in the east to above 2005–2014 means over almost the entire Arctic (Fig. 5.25b). Positive TOC anomalies between 10% Hudson Bay and the Canadian Arctic Archipelago in the west (Fig. 5.25c). This region roughly agrees and 20% were observed at the North Pole, northern with the region where TOCs were abnormally high Greenland, and the Canadian Arctic Archipelago. in March 2015 (Fig. 5.25a), but UVI anomalies show UV radiation is quantified with the UV index a larger spatial variability than TOCs because of their (UVI), a measure of the ability of UV radiation to added dependence on cloud cover. Monthly average cause erythema (sunburn) in human skin (WHO 2002). In addition to its inverse dependence on TOC, noontime UVIs for April 2015 were 5%–15% below the 2005–14 means over almost the entire Arctic (Fig. the UVI depends greatly on the sun angle, cloud 5.25d), consistent with the positive ozone anomalies cover, and surface albedo (Weatherhead et al. 2005). In the Arctic, the UVI ranges from 0 to about 7, with observed in this month (Fig. 5.25b). | S153 AUGUST 2016 STATE OF THE CLIMATE IN 2015

174 | S154 AUGUST 2016

175 6. S. Stammerjohn, Ed. ANTARCTICA — in the West Antarctic sector, its impact across the S. Stammerjohn a. Overview— rest of Antarctica was weaker due to an atypical In strong contrast to 2014, 2015 was marked by teleconnection pattern. There was a continuation of near-record high low regional variability in both atmospheric and • oceanic anomalies, at least for the first half of the Antarctic sea ice extent and area for the first half of 2015, with 65 sea ice extent and 46 sea ice area year. The Antarctic-wide distribution of anomalies daily records attained by July. However, at mid - coincided with a strong positive southern annular year, there was a reversal of the sea ice anomalies, mode (SAM) index. However, by October; the high- shifting from record high levels in May to record latitude response to El Niño became evident, but the low levels in August. This was then followed by a associated anomalies were rather atypical compared period of near-average circumpolar sea ice (relative to the mean response from six previous El Niño - events. Simultaneously, a somewhat tardy but unusu to the 36-year satellite record). ally large and persistent Antarctic ozone hole devel • - Together with unusually high sea ice extent, particularly in the West Antarctic sector, SSTs oped. These springtime conditions imparted strong regional contrasts late in the year, particularly in the were also cooler than average, in contrast to West Antarctic sector. Other noteworthy Antarctic warmer-than-normal SSTs equatorward of the climate events from 2015 are below: polar front. South of the polar front, sea surface height anomalies were negative, consistent with the mostly positive SAM index. Compared to For most of the year surface pressure was lower • 2014, there was a small decrease in sea level de - and temperatures were cooler than the 1981–2010 climatology, along with stronger-than-normal tected around the continental margin as well, leading to a slight increase in the estimated volume circumpolar westerly winds, slightly higher-than- transport of the Antarctic Circumpolar Current. normal precipitation over the ocean areas, and These changes are, however, superimposed on mostly shorter-than-normal melt seasons on the continent. These anomalies were consistent with longer-term increases in sea level and a potential the positive SAM index registered in all months small decrease in volume transport. The 2015 deep ocean observations at 140°E indicate a continued except October. February had a record high SAM freshening of Antarctic Bottom Water, relative to index value of +4.92 (13% higher than the previous observations in the late 1960s and more frequent high value recorded over 1981–2010). • There was an abrupt but short-lived switch in the observations since the 1980s. - mean surface temperature anomaly for the con Details on the state of Antarctica’s climate in tinent (from cold to warm) and a weakening of 2015 and other climate-related aspects of the Ant the negative surface pressure anomaly in October - arctic region are provided below, starting with the - 2015. These atmospheric circulation changes co atmospheric circulation, surface observations on incided with the emerging high-latitude response the continent (including precipitation and seasonal to El Niño, the ozone hole, and a shift in the SAM index from positive to negative. • The 2015 Antarctic ozone hole was amongst the largest in areal coverage and most persistent, based on the record of ground and satellite ob - servations starting in the 1970s. This very large ozone hole was caused by unusually weak strato - spheric wave dynamics, resulting in a colder- and stronger-than-normal stratospheric polar vortex. The persistently below-normal temperatures en - abled larger ozone depletion by human-produced chlorine and bromine compounds, which are still at fairly high levels despite their continuing decline resulting from the Montreal Protocol and its Amendments. Although the 2015 El Niño produced strong • atmospheric circulation anomalies in the South F ig . 6.1. Map of stations and other regions used throughout the chapter. Pacific, thus affecting temperatures and sea ice | S155 AUGUST 2016 STATE OF THE CLIMATE IN 2015

176 melt), ocean observations (including sea ice and ocean circulation), and finally the Antarctic ozone hole. Newly included this year is the southern high latitude response to El Niño (Sidebar 6.1) and the state of Antarctic ecosystems in the face of climate perturbations (Sidebar 6.2). Place names used in this chapter are provided in Fig. 6.1. K. R. Clem, S. Barreira, and b. At mospheric circulation— R. L. Fogt The 2015 atmospheric anomalies across Antarctica - were dominated by below-average surface tempera tures over much of coastal and interior Antarctica from January to September, particularly across the Antarctic Peninsula and the surrounding Weddell and Bellingshausen Seas. Negative pressure anomalies in the Antarctic troposphere during the first half of the year weakened in August, while the stratosphere poleward of 60°S became very active beginning in June with strong negative pressure and temperature anomalies and an amplification of the stratospheric vortex. Using a station-based SAM index (normalized difference in zonal mean sea level pressure between ; Marshall 2003), the generally low 40 °S and 65°S pressure conditions gave rise to positive SAM index F ig . 6.2. Area-weighted averaged climate parameter values, which were observed in every month except anomalies for the southern polar region in 2015 rela - October during 2015. Figure 6.2 depicts a vertical tive to 1981–2010: (a) polar cap (60°–90°S) averaged cross section of the geopotential height anomalies geopotential height anomalies (contour interval is (Fig. 6.2a) and temperature anomalies (Fig. 6.2b) 50 m up to ±200 m with additional contour at ±25 m, averaged over the polar cap (60°–90°S), as well as the and 100 m contour interval after ±200 m); (b) polar cap averaged temperature anomalies (contour interval - circumpolar zonal wind anomalies (Fig. 6.2c) aver is 1°C up to ±4°C with additional contour at ±0.5°C, aged over 50°–70°S and the Marshall (2003) SAM and 2°C contour interval after ±4°C); (c) circumpolar index average for each month. (50°–70°S) averaged zonal wind anomalies (contour Climatologically, the year was split into four time − 1 − 1 interval is 2 m s with additional contour at ±1 m s ). periods (denoted by vertical red lines in Fig. 6.2) that Shading represents standard deviation of anomalies were selected based on periods of similar temperature from the 1981–2010 climatological average. (Source: and pressure anomalies (Fig. 6.3). The composite ERA-Interim reanalysis.) Red vertical bars indicate the four separate climate periods used for compositing in anomalies (contours) and standard deviations (from Fig. 6.2; the dashed lines near Dec 2014 and Dec 2015 the 1981–2010 climatological average; shading) for indicate circulation anomalies wrapping around the each of the time periods are shown in Fig. 6.3; surface calendar year. Values from the Marshall (2003) SAM pressure anomalies are displayed in the left column index are shown below panel (c) in black (positive val - and 2-m temperature anomalies in the right column. ues) and red (negative values). During January–March, the large-scale circula - tion was marked with negative geopotential height troposphere and lower stratosphere (Fig. 6.2c). Much of the coastal Antarctic 2-m temperatures were below (Fig. 6.2a) and surface pressure (Fig. 6.3a) anomalies over Antarctica and positive surface pressure anoma average (Fig. 6.3b), with the exception of areas of the - Ross Ice Shelf and Wilkes Land (~90°E–180°). Positive lies over much of the middle latitudes. The Marshall temperature anomalies were observed throughout SAM index was strongly positive, and reached a much of the stratosphere over the polar cap (Fig. 6.2b). record monthly mean high value during February Positive SAM index values continued during April [+4.92; Fig. 6.2; Marshall (2003); SAM index values - but weakened in May. This was due to a strong posi start in 1957]. At this time, the circumpolar zonal −1 winds exceeded 2 m s tive surface pressure anomaly southwest of Australia, (>1.5 standard deviations) above the climatological average throughout the while the remainder of the middle latitudes experi - | S156 AUGUST 2016

177 throughout the troposphere and stratosphere. Strong positive surface pressure anomalies occurred over the South Pacific, southwest of Australia, and over the South Atlantic, while strong negative surface pressure anomalies occurred over the Weddell Sea (Fig. 6.3e); these conditions led to positive SAM index values through September. Antarctic 2-m temperatures were primarily below average (Fig. 6.3f), with anomalies over the Antarctic Peninsula, Bellingshausen Sea, and eastern Amundsen Sea more than 2.5 standard deviations below the climatological average. By October–December, positive surface pressure and 2-m temperature anomalies developed over interior East Antarctica, with the strongest warm - ing noted over Queen Maud Land, while the Drake Passage and coastal Wilkes Land remained colder than average (Figs. 6.3g,h). A strong negative surface pressure anomaly was observed south of New Zealand and a strong positive surface pressure anomaly was observed in the southeastern South Pacific, likely tied to the strengthening of the El Niño conditions in the tropical Pacific. These circulation anomalies over the South Pacific brought cold, southerly f low to the coastal and offshore regions of Wilkes Land and the offshore region of the northwestern Antarctic Peninsula, respectively. Meanwhile, the stratosphere - over the polar cap became very active after Septem ber. Negative temperature and geopotential height anomalies of 1–2 standard deviations below the climatological average propagated down through the stratosphere from October to December. A marked strengthening of the stratospheric circumpolar vortex F ig . 6.3. (left) Surface pressure anomalies and (right) occurred in response to the stratospheric cooling, 2-m temperature anomalies relative to 1981–2010 for with positive zonal wind anomalies exceeding 1–2 (a) and (b) Jan–Mar 2015; (c) and (d) Apr–May 2015; standard deviations above the climatological aver - (e) and (f) Jun–Sep 2015; and (g) and (h) Oct–Dec age throughout the stratosphere to finish the year. 2015. Contour interval for (a), (c), (e), and (g) is 2 hPa; Over this time period (October–December) the SAM - contour interval for (b) and (h) is 1°C and contour in terval for (d) and (f) contour interval is 2°C. Shading index values also weakened, and a negative value represents standard deviations of anomalies relative was observed in October 2015, coincident with the to the selected season from the 1981–2010 average. weaker and more regional nature of the near-surface (Source: ERA-Interim reanalysis.) conditions (Fig. 6.3). S urface manned and automatic weather station enced negative surface pressure anomalies with a c. weakening of the circumpolar zonal winds in May observations— S. Colwell, L. M. Keller, M. A. Lazzara, A. Setzer, (Fig. 6.2c). Much of East Antarctica was colder than R. L. Fogt, and T. Scambos The circulation anomalies described in section 6b average, particularly offshore along coastal Queen Maud Land (30°W–0°) and portions of the Ross Sea are discussed here in terms of observations at staffed and automatic weather stations (AWS). Climate data westward towards Mirny station (~90°E), while the that depict regional conditions are displayed for four Amundsen Sea and the Ronne-Filchner Ice Shelf were staffed stations (Bellingshausen on the Antarctic slightly warmer than average (Fig. 6.3d). During June–September, negative polar-cap av Peninsula, Halley in the Weddell Sea, Mawson in - the Indian Ocean sector, and Amundsen-Scott at the eraged geopotential height anomalies and positive circumpolar zonal wind anomalies were observed South Pole; Figs. 6.4a–d) and two AWSs (Byrd in West | S157 AUGUST 2016 STATE OF THE CLIMATE IN 2015

178 1981–2010 mean, wit h the exception of June and July at Halley. In June, the mean monthly value nearly matched the lowest recorded mean monthly value and included a new re - cord for the extreme daily minimum value, which was −56.2°C. The anomalously cold conditions in June were followed by a respite to anomalously warm conditions in July that were then followed by below- to near-average temperatures for the rest of the year. Around the coast of East Antarctica, all of the Australian stations had near-average tem - peratures at the start and end of the year and colder-than-average temperatures from April to August, except for Casey (not shown) . 6.4. 2015 Antarctic climate anomalies at six representative stations [four staffed ig F - in June when the tem (a)–(d) and two automatic (e)–(f)]. Monthly mean anomalies for temperature (°C) perature was slightly and surface pressure (hPa) are shown, with + denoting record anomalies for a given higher than average. month at each station in 2015. All anomalies are based on differences from 1981–2010 Davis (not shown) and averages, except for Gill, which is based on averages during 1985–2013. Observa - Mawson (Fig. 6.4c) both tional data start in 1968 for Bellingshausen, 1957 for Halley and Amundsen-Scott, had very low monthly 1954 for Mawson, 1985 for Gill AWS, and 1981 for Byrd AWS. mean temperatures in May (a record low at Mawson). Temperatures at Antarctica and Gill on the Ross Ice Shelf; Figs. 6.4e,f). To better understand the statistical significance of Mawson were also anomalously low again in July. At Amundsen-Scott station (Fig. 6.4d), the monthly - records and anomalies discussed in this section, ref mean temperatures were close to the long-term means erences can be made to the spatial anomaly maps in Fig. 6.3 (the shading indicates the number of standard with the exception of October and November when they were warmer than average. deviations the anomalies are from the mean). Monthly mean temperatures at Bellingshausen In the Antarctic Peninsula, an all-time record station (Fig 6.4a) on the western side of the Antarctic warm air temperature for the continent may have Peninsula were similar to the 1981–2010 mean at the been set at Esperança on 24 March, reaching +17.5°C during an intense foehn wind event that spanned start and end of the year, but from May to September, the values were consistently lower than the mean. much of the northeastern Peninsula. Temperatures rose as much as 30°C within 48 hours as an intense Midway down on the west side of the Antarctic Peninsula, the temperatures at Rothera (not shown) high pressure region over the Drake Passage and followed a similar pattern. In the Weddell Sea region, strong low pressure over the northwestern Weddell Sea drove strong downslope winds all along eastern the monthly mean temperatures at Halley (Fig. 6.4b) Graham Land. An automated sensor at Foyn Point in and Neumayer (not shown) were within ±2°C of the | S158 AUGUST 2016

179 the Larsen B embayment recorded still higher values with the record high SAM index value (Fig. 6.2c). brief ly, at +18.7°C on the 24th, and several other Byrd AWS (Fig. 6.4e) in West Antarctica reported weather stations in the region surpassed +17°C on record low pressures in March and November (803.7 23 and 24 March. and 799.8 hPa, respectively), with only four other Temperatures at the AWS locations provide a months reporting pressure anomalies less than 6 hPa broader view of weather records and trends for the below normal. There were also a few reported wind speed records (not shown), but most stations generally continent. For the Ross sector, Possession Island reported only slightly above or below normal wind (not shown) reported a record low temperature of −21.9°C (greater than 2 standard deviations from speeds over the course of the year. Marble Point had −1 the 1981–2010 mean) in September and then tied its a record low monthly mean wind speed of 2.4 m s record high mean temperature of 1.7°C in Decem - in March, and Gill reported a record low wind speed −1 ber. Otherwise, temperatures at Possession Island in April (both more than 2 standard de of 1.5 m s - viations below normal). Relay Station had a record were above normal for February, August, October, −1 in April November, and December and below normal for the high monthly mean wind speed of 9.1 m s (greater than 2 standard deviations above normal). rest of the months (no report for July). The Ross Ice Shelf region (e.g., Gill AWS, Fig. 6.4f) had generally above-normal temperatures from January through D. H. Bromwich and S.-H. Wang E)— – et precipitation (P N d. March and again in August, but these warm periods Precipitation minus evaporation/sublimation were interspersed by colder-than-normal tempera E − ) closely approximates the surface mass balance (P - over Antarctica, except for the steep coastal slopes tures, especially in April, July, and September. In (e.g., Bromwich et al. 2011; Lenaerts and van den West Antarctica, Byrd AWS (Fig. 6.4e) was colder Broeke 2012). Precipitation variability is the dominant than normal for March–April, June–August, and term for P hanges at regional and larger scales E c − November–December and was warmer than normal otherwise. At Relay Station (not shown) on the Polar - over the Antarctic continent. There are few precipita tion gauge measurements for Antarctica, and those Plateau, temperatures were above normal through are compromised by blowing snow. In addition, over May, below normal for June–September, and then 5°C above normal in October. On the other side of the the interior Antarctic plateau, snowfall amounts are Polar Plateau, Dome C II (not shown) did not operate often less than the minimum gauge resolution. As a from May through part of September, but October result, precipitation and evaporation fields from the Japanese 55-year Reanalysis (JRA-55; Kobayashi et al. and November had above-normal temperatures. While stations over Antarctica generally did not - 2015) were examined to assess Antarctic net precipi ) behavior for 2015. JRA-55, the second tation (P − E report record temperature anomalies, many staffed and unstaffed stations reported record low pressure generation of JRA, is produced with a low-resolution anomalies in at least one month. The pressure data version of the Japan Meteorological Agency’s (JMA) - operational data assimilation system as of Decem from all staffed stations showed lower-than-average pressures for all months except October (Figs. 6.4a–d) ber 2009, which incorporated many improvements achieved since JRA-25 (Onogi et al. 2007), including a and January at the Bellingshausen station (Fig. 6.4a). On the Ross Ice Shelf, almost every month had below- revised longwave radiation scheme, four-dimensional normal pressure with a record low anomaly reported - data assimilation, bias correction for satellite radianc es, and assimilation of newly available homogenized for February for Possession Island (−6.7 hPa), Marble Point (−9.2 hPa, greater than 2 standard deviations observations. These improvements have resulted in better fits to observations, reduced analysis incre below normal), Ferrell (−9.9 hPa, about 2 standard - ments and improved forecast results (Kobayashi et deviations below normal), and Gill AWS (−10.5 hPa, al. 2015). The model is run at a spatial resolution of greater than 2 standard deviations below normal; the latter shown in Fig. 6.4f). The record low pressure TL319 (~0.5625° or 55 km) and at 60 vertical levels anomalies ranged from −6.7 to −10.5 hPa. Possession from the surface up to 0.1 hPa. In comparison to other long-term global reanalyses (e.g., NCEP1 and Island was only above normal for May, and Marble NCEP2), JRA has higher horizontal and vertical Point had slightly above-normal pressure for October. Relay Station also had a record low pressure anomaly - model resolution, uses a greater number of observa in February (−5.1 hPa), and pressures were below nor - tions, and has a more advanced model configuration mal through the whole year until October. The record (e.g., Bromwich et al. 2007; Kang and Ahn 2015). low pressure anomalies observed in February on both Figure 6.5 shows the JRA-55 2015 and 2014 an - P nual anomalies of the Ross Ice Shelf and at Relay Station also coincided − E and mean sea level pressure | S159 AUGUST 2016 STATE OF THE CLIMATE IN 2015

180 (MSLP) departures from the - 1981–2010 average. In gen − eral, annual anomalies P E (Figs. 6.5a,b) over the high interior of the continent are −1 ), small (within ±50 mm yr but larger anomalies can be observed along the coast, consistent with the low and ccumulation high snow a in these regions. At higher latitudes (> 60°S) JRA-55 is quantitatively similar to - JRA-25 and ERA-I (Euro pean Centre for Medium- Range Weather Forecasts re P E - − Interim reanalysis) sults (Bromwich and Wang 2014, 2015). The excessively high positive anomalies of JRA-25 over the Southern Ocean north of 60°S (that were noted in last year’s report) are not present in JRA-55. JRA-55 also shows better overall agreement with ERA-I than JRA-25 during 2013 and 2014. Based on JRA-55, the 2014 negative anomalies located at eastern Queen Maud Land (between 15° and 80°E) are weak - er in 2015, and positive F E ig – P and MSLP anomalies: (a) 2015 E – anomalies P 6.5. JRA-55 (a–d) annual . anomalies are observed − 1 − 1 (mm month ); (b) 2014 ); (c) 2015 MSLP anoma - anomalies (mm month P–E over Enderby Land and lies (hPa); and (d) 2014 MSLP anomalies (hPa). All anomalies are departures the Amery Ice Shelf. The from the 1981–2010 mean. (e) Monthly total P – E (mm; dashed green) for the - strong negative features be West Antarctic sector bounded by 75°–90°S, 120°W–180°, along with the SOI tween American Highland (dashed dark blue, from NOAA Climate Prediction Center) and SAM [dashed light blue, from Marshall (2003)] indices since 2010. In (a) and (b), Antarctic and Wilkes Land (between regions with greater than ±30% change are hatched; sloping denotes negative 80° and 150°E) observed values and horizontal denotes positive. Centered annual running means are in 2014 were replaced by plotted as solid lines. weak positive anomalies in the West Antarctic coastline in 2015. Both sides of the 2015, except near the Budd Coast region (near 115°E) Antarctic Peninsula have similar anomaly patterns where negative anomalies were observed again. The P−E George V Coast and Ross Sea had positive anomalies anomaly to 2014, but were weaker. The negative in 2015, in contrast to 2014 conditions. The small center over the Weddell Sea in 2014 was replaced by a positive one in 2015. positive anomalies over the western Ross Sea seen − P These annual - anomaly features were gener E in 2014 were replaced by negative anomalies in 2015. ally consistent with the mean atmospheric circulation Strong positive anomalies over the Amundsen and implied by the MSLP anomalies (Figs. 6.5c,d). In 2015 Bellingshausen Seas (between 150° and 75°W) in the MSLP anomalies surrounding Antarctica were 2014 were weaker but remained positive in 2015. less localized than in 2014 (Figs. 6.5c,d). The MSLP Small negative anomaly centers were present along | S160 AUGUST 2016

181 pattern in 2015 consisted of large negative pressure positively associated with each other, but negatively , in most months from 2010 to E – anomalies over Antarctica (or high latitudes) and a P associated with mid-2011. From then on, the SOI and SAM index ring of positive pressure anomalies at midlatitudes, which resulted in positive SAM index values recorded were negatively associated through 2015. From 2014 into 2015, the SOI became more negative (indicating for most of 2015 (Figs. 6.2c, 6.5e). This MSLP pat - El Niño conditions in the tropical Pacific), while the tern tended to induce higher precipitation from the Southern Ocean into Antarctica. The positive MSLP SAM index became more positive. The atmospheric circulation pattern associated with a positive SAM anomaly over the Ronne Ice Shelf and the Weddell Sea in 2014 was replaced by a strong negative anomaly index modulated the high latitude response to center at the tip of the Antarctic Peninsula in 2015. El Niño, and the associated MSLP anomalies were Enhanced cyclonic f lows induced more inf low from located farther north than normal (Sidebar 6.1). The the ocean and resulted in higher precipitation anoma end result was near-normal precipitation over Marie - lies into the Weddell Sea and Queen Maud Land. A Byrd Land–Ross Ice Shelf (Fig. 6.5e), in contrast to higher-than-normal precipitation during previous strong negative anomaly center at the southern Indian El Niño events (e.g., Bromwich et al. 2004). Ocean (near 105°E) in 2014 was replaced by large posi - tive anomalies, with weak negative anomalies along S e. the coast of East Antarctica. Combined with cyclonic L. Wang and H. Liu easonal melt extent and duration— Seasonal surface melt on the Antarctic continent f low produced by negative anomalies over Weddell during 2014/15 was estimated from daily measure Sea, it produced higher precipitation along Queen - Mary Coast (between 60° and 125°E) in 2015. The ments of passive microwave brightness temperature - large positive anomaly center in 2014 over the South using data acquired by the Special Sensor Micro wave–Imager Sounder (SSMIS) onboard the Defense Pacific Ocean (near 120°W) was enhanced in 2015. satel In combination with the expanded and strengthened F17 Meteorological Satellite Program (DMSP) - lite. The data were preprocessed and provided by the negative anomalies over the western Ross Sea region, U.S. National Snow and Ice Data Center (NSIDC) in above-normal precipitation was observed in the Ross Sea and Amundsen Sea regions (Fig. 6.5a). level-3 EASE-Grid format (Armstrong et al. 1994) and were analyzed using a wavelet transform-based edge - Earlier studies show that almost half of the mois ture transport into Antarctica occurs in the West Antarctic sector. Here, there is also large interannual variability in moisture transport in response to atmospheric circulation patterns asso - ciated with extreme ENSO events (e.g., Bromwich et al. 2004) and high SAM index values (e.g., Fogt et al. 2011). As the seasons progressed from 2014 to 2015, the negative MSLP anomalies over the Ross Sea weakened (Figs. 6.3a,c, 6.5d), while a positive MSLP anomaly deepened offshore of 60°S (Figs. 6.5c,d). A positive anomaly then appeared in the Bellingshausen Sea and strengthened in later months of 2015 (Figs. 6.3e,g). These anomaly features are consistent with a simultaneously strong El Niño event and a positive SAM index. Figure 6.5e shows the time series of average P − E over Marie Byrd monthly total Land–Ross Ice Shelf (75°–90°S, 120°W– - 180°) and the monthly Southern Oscil ig 6.6. Estimated surface melt for the 2014/15 austral summer (a) F . lation index (SOI) and SAM indices melt start day, (b) melt end day, (c) melt duration (days), and (d) melt (with 12-month running means). It is duration anomalies (days) relative to 1981–2010. (Data source: DMSP SSMIS daily brightness temperature observations.) clear that the SOI and SAM index were | S161 AUGUST 2016 STATE OF THE CLIMATE IN 2015

182 SIDEBAR 6.1: R. L. FOGT — EL NIÑO AND ANTARCTICA During 2015, a strong El Niño developed and intensified in the tropical Pacific. Like much of the globe, Antarctica is influenced during ENSO events by a series of atmospheric Rossby waves emanating from the tropical Pacific, extending to high latitudes over the South Pacific Ocean - near West Antarctica (Turner 2004). This pat tern has been widely referred to as the Pacific South American pattern, and during an El Niño event, positive pressure anomalies are typical off the coast of West Antarctica (Mo and Ghil 1987; Karoly 1989). Despite the 2015/16 El Niño’s emergence as a strong event in the Pacific by midyear, its impact near Antarctica was not at all typical. However, ig . SB6.1. (a) SOND MSLP (contoured) and 10-m wind F true to form, in September–December (SOND) anomalies (vectors) from the 1981–2010 climatological 2015, the high-latitude South Pacific was marked - mean. Shading represents the number of standard de by a strong positive pressure anomaly and as - viations the 2015 SOND MSLP anomalies were from the sociated counterclockwise near-surface flow climatological mean; wind vectors are only shown if at (Figs. SB6.1a, 6.3g). The southerly flow in the least one component was a standard deviation outside vicinity of the Antarctic Peninsula partially the climatological mean. (b) MSLP (contoured) and 10-m wind (vectors) anomaly composite for the six strongest explains the persistence of below-average tem - El Niño events in SOND since 1979 (in rank order: 1997, peratures across the Antarctic Peninsula in the 1982, 1987, 2002, 2009, 1991), with shading (from lightest latter half of 2015 (compare Figs. 6.3f,h with to darkest shades) indicating composite mean anomalies Fig. 6.4a). Elsewhere, the pattern of response (of MSLP and winds) significantly different from zero at was quite different from recent strong El Niño p < 0.10, p < 0.01, respectively, based on a two-tailed p < 0.05, - events (Fig. SB6.1b). The southern Pacific posi t test. The shading therefore indicates where the Student’s tive pressure anomaly, although much stronger El Niño composite mean is significantly different from the 1981–2010 climatology. (Source: ERA-Interim reanalysis.) than the El Niño average, was displaced north - ward. While this had consistent temperature (Fogt et al. 2011) in contrast to the 2015 El Niño event. and wind impacts across the Antarctic Peninsula and the Nonetheless, because of its influence on meridional flow South Pacific, much of the rest of West Antarctica was over the ice edge at the time of maximum sea ice extent not strongly impacted in 2015 as is typical during other (Figs. SB6.1a, 6.8c), the end of 2015 was marked by strong strong El Niño events (compare Fig. SB6.1b with Fig. 6.3e,g regional sea ice extent anomalies in the West Antarctic - and Byrd AWS data in Fig. 6.4e). The northward displace sector (Figs. 6.8c,d, 6.9c–e), which were opposite in sign ment of the high pressure anomaly in 2015 is most likely to the long-term trends in sea ice extent in that region due to the fact that much of 2015, with the exception of (Fig. 6.8e). October, was marked by a positive SAM index (compare In summary, the 2015 El Niño indeed produced strong Fig. 6.2c). Because the SAM index monitors the strength atmospheric circulation impacts in the South Pacific, and/or position of the circumpolar jet, which is known which are consistent with the below-average tempera - to influence extratropical Rossby wave propagation and tures across the Antarctic Peninsula and sea ice extent breaking (L’Heureux and Thompson 2006; Fogt et al. 2011; anomalies in the Bellingshausen, Amundsen, and Ross Gong et al. 2010, 2013), the strengthened jet in 2015 was Seas. However, because the teleconnection was displaced not so favorable for Rossby wave propagation into the farther north than normal, its impact across the rest of ) southern latitudes. Thus, the South Pacific higher (>60 ° Antarctica was much weaker than was the case for previ - teleconnection was displaced farther north than normal ous strong El Niño events. (based on Fig. SB6.1b). Historically, many of the strongest El Niño events occurred during negative SAM index values | S162 AUGUST 2016

183 detection method (Liu et al. 2005). The algorithm The melt index for the entire Antarctic continent - has continued to drop since the 2012/13 season delineates each melt event in the time series by track ing its onset and end dates, with the onset day of the (Fig. 6.7a; Wang et al. 2014). The estimated melt index 2 first melt event being the start day of the melt season 52 2 of the 2014/15 season is 29 - in compar 5 00 day·km 2 93 0 35 3 in 2013/14 and 51 00 25 day·km 1 0 ison to 39 (Fig. 6.6a) and the end day of the last melt event being 2 in the 2012/13 season. The melt extent of day·km the end day of the melt season (Fig. 6.6b). The melt the 2014/15 season (Fig. 6.7b), however, is 1 0 58 50 7 duration is then the total number of melting days per 2 2 43 pixel during the defined melt season (excluding any , slightly greater than last year at 1 . 0 50 km 7 km refreezing events that may have occurred during this The melt anomaly map in Fig. 6.6d shows the melt - period; Fig. 6.6c). The melt extent and melt index are season was generally shorter than the historical aver - metrics useful for quantifying the interannual vari age. Therefore, austral summer 2014/15 is classified as a low melt year for Antarctica. The 2014/15 melt ability in surface melt (Zwally and Fiegles 1994; Liu 2 et al. 2006). Melt extent (km ) is the total area that extent and index numbers were almost equivalent experienced surface melt for at least one day, while to those observed during austral summer 2011/12 2 2 2 375 km and 29 0 06 2 50 day·km (944 , respectively). the melt index (day·km ) is the product of duration - = 0.05) nega p Figure 6.7 shows a nearly significant ( and melt extent and describes the spatiotemporal −1 2 yr ) in melt index and a 00 day·km 9 variability of surface melting. The anomaly map tive trend (311 2 −1 00 km (Fig. 6.6d) was created by referencing the mean melt significant (p < 0.01) negative trend (14 yr ) 2 duration computed over 1981–2010 (see also Fig. 3 in in melt extent over 1978/79 to 2014/15, highlighted by the record low melt season observed during austral Liu et al. 2006). The spatial pattern of the melt duration map in summer 2008/09. The negative trends in melt index - austral summer 2014/15 (Fig. 6.6c) was similar to pre and melt extent are consistent with previous reports vious years (Wang et al. 2014). Areas with extended (Liu et al. 2006; Tedesco 2009a,b). melt duration (>45 day duration in orange-red) were ea ice extent, concentration, and duration— S f. the Antarctic Peninsula area, including the Larsen P. Reid, - and Wilkins ice shelves, and parts of coastal East Ant R. A. Massom, S. Stammerjohn, S. Barreira, J. L. Lieser, and T. Scambos Net sea ice areal extent was well above average dur arctica, including the Shackleton ice shelf and other - smaller ice shelves east of there. Areas with moderate ing the first few months of 2015 (Fig. 6.8a). Monthly 6 record extents were observed in January (7.46 × 10 melt duration (16–45 day duration in green-yellow) 2 6 2 6 2 km included much of coastal Queen Maud Land and the ), April (9.06 × 10 ). km km ), and May (12.1 × 10 The January extent marked the highest departure Amery, West, and Abbot ice shelves; short-term melt (<16 day duration in blues) occurred on the coast of from average for any month since records began in 6 2 1979, at 2.39 × 10 - km Marie Byrd Land, including Ross ice shelf and por above the 1981–2010 mean 6 2 tions of Queen Maud Land near the Filchner Ice Shelf. km of 5.07 × 10 , or nearly 50% greater. These early season records follow on from the record high extent and late retreat of sea ice in 2014 (Reid et al. 2015). During the first half of 2015, there were 65 individual days of record daily sea ice extent, the last occurring on 11 July, and 46 record-breaking days of sea ice area within the first half of the year. However, the expan - sion of sea ice slowed so dramatically midyear that although sea ice area was at a record high level in May, it was at a record low level in August, just 83 days later (Fig. 6.8a). Close-to-average net sea ice extent levels were then observed in the latter half of 2015. The record high net sea ice extent in January was 2 6 km · ) from 1978/79 to day 6.7. (a) Melt index (10 . F ig dominated by strong positive regional anomalies - not signifi p 2014/15, showing a slight negative trend ( in sea ice concentration and extent in the Ross and 6 2 cant at 95%). (b) Melt extent (10 km ) from 1978/79 to Weddell Seas (Figs. 6.8b, 6.9c,e) and across East p significant at 2014/15, also showing a negative trend ( Antarctica (~75°–140°E). This was counterbalanced 99%). A record low melt was observed during 2008/09. by strong negative ice concentration and extent The year on the x -axis corresponds to the start of the anomalies that were present in the Bellingshausen– austral summer melt season, e.g., 2008 corresponds Amundsen Seas (Figs. 6.8b, 6.9d). All three regions to summer (DFJ) 2008/09. | S163 AUGUST 2016 STATE OF THE CLIMATE IN 2015

184 of more extensive sea ice coin - cided with anomalously cool SSTs - adjacent to the sea ice. Low atmo spheric pressure anomalies were also present in the Weddell and Ross–Amundsen Seas (Fig. 6.3a). Interestingly, at this time colder- than-normal SSTs were present just to the north of the Belling - shausen–Amundsen Seas, possibly entrained within the ACC but not adjacent to the ice edge itself (and - thus removed from the area expe riencing below-normal ice extent). As shown in Fig. 6.8a, there - was a substantial and rapid de crease in the net ice extent (and area) anomaly from late January to early February, in large part due to changes in the eastern Ross (ref lected in Fig. 6.9c) and western Amundsen (not shown) Seas. This rapid regional “collapse” followed lower-than-normal sea ice concentrations in the central pack ice during the latter part of 2014 (see Reid et al. 2015). In spite of this, net sea ice extent and area continued to track well above average or at record high levels between February and May. The Indian Ocean sector between ~60° and 110°E, the western Ross Sea, and the Weddell Sea showed particularly high or increasing . 6.8. (a) Plot of daily anomalies from the 1981–2010 mean of daily F ig Southern Hemisphere sea ice extent (red line) and area (blue line) for 2015. sea ice extents during the Febru - Blue banding represents the range of daily values of extent for 1981–2010, ary to May period as ref lected while the thin black lines represent ±2 standard deviations of extent. in the regionwide daily anomaly 6 2 Numbers at the top are monthly mean extent anomalies (× 10 ). km series (Figs. 6.9a,c,e, respectively), Sea ice concentration anomaly (%) maps for (b) Jan and (c) Sep 2015 with early-season areal expansion relative to the monthly means for 1981–2010, along with monthly mean spurred on by colder-than-normal SST anomalies (Reynolds et al. 2002; Smith et al. 2008). These maps are SSTs (not shown) and surface air also superimposed with monthly mean contours of 500-hPa geopotential height anomaly (Kalnay et al. 1996; NCEP). Bell is Bellingshausen Sea, temperatures (Figs. 6.3b,d). AIS is Amery Ice Shelf. (d) Sea ice duration anomaly for 2015/16 and (e) June saw the beginning of a duration trend (Stammerjohn et al. 2008). Both the climatology (for major change in the large-scale computing the anomaly) and trend are based on 1981/82 to 2010/11 data atmospheric pattern at higher (Cavalieri et al. 1996, updated yearly), while the 2015/16 duration-year data southern latitudes, with lower- are from the NASA Team NRTSI dataset (Maslanik and Stroeve 1999). than-normal atmospheric pressure the distribution of atmospheric jets (Yuan 2004) and over the Antarctic continent and a strong atmospheric hence cyclonicity at higher southern latitudes. The wave-3 pattern evolving (Fig. 6.3e). This coincided with warmer-than-normal SSTs in lower latitudes of abrupt change in hemispheric atmospheric circula - the Indian and Pacific Oceans (the latter associated tion began a regional redistribution of patterns of sea ice areal expansion (Fig. 6.9). On one hand, there was with the developing El Niño) and their inf luence on | S164 AUGUST 2016

185 anomalies was that net circumpolar sea ice extent and area dropped dramatically at the beginning of July (Fig. 6.8a). This general regional ice anomaly pattern then persisted to the end of September (Fig. 6.8c). Another switch in large-scale regional sea ice extent anomalies occurred in October in response to the dissipation of the atmospheric wave-3 pattern - and subsequent increase in negative pressure anoma lies centered on ~0° and ~170°W and a broad ridge of positive pressure anomalies centered on ~55°S, 90°W (Fig. 6.3g). Positive sea ice extent anomalies were associated with a combination of cold SSTs in - the Bellingshausen–Amundsen Seas and cool atmo spheric temperatures in the western Ross and Weddell - Seas and far eastern East Antarctic. Negative anoma lies were associated with relatively warm atmospheric temperatures to the east of the low pressure systems (Fig. 6.3h). At the same time, sea ice extent in the far eastern Weddell Sea and Indian Ocean sector (~0° to ~60°E) was well below average (Fig. 6.9a) and re - mained so for the rest of the year. This is attributable to the very low sea ice extent in the western Weddell Sea in the previous months (July–September as men - tioned above), leading to lower-than-normal eastward advection of sea ice in the eastern limb of the Weddell Gyre (see Kimura and Wakatsuchi 2011). Similarly, a lack of eastward zonal advection of sea ice from the western Ross Sea resulted in lower-than-normal sea ice extent in the eastern Ross Sea (~150° to ~120°W). 2 6 F ) from the km . 6.9. Plots of daily anomalies (× 10 ig On a smaller scale, in late October through mid- 1981–2010 mean of daily Southern Hemisphere sea ice November several intense low pressure systems caused extent (red line) and area (blue line) for 2015 for the a temporary expansion of the sea ice edge (~50% above sectors: (a) Indian Ocean; (b) western Pacific Ocean; (c) Ross Sea; (d) Bellingshausen–Amundsen Seas; and the long-term average) between ~60° and 90°E. (e) Weddell Sea. The blue banding represents the The net result of the seasonal sea ice anomalies range of daily values for 1981–2010 and the thin red described is summarized by the anomaly pattern in line represents ±2 std dev. Based on satellite passive- the annual ice season duration (Fig. 6.8d). The longer- microwave ice concentration data (Cavalieri et al. than-normal annual ice season in the outer pack 1996, updated yearly). ice of the eastern Amundsen, Bellingshausen, and a reduction in the rate of expansion in the western western Weddell Seas (120°W–0°) was due both to an Weddell and Ross Seas and much of East Antarctica anomalously early autumn ice-edge advance and later - spring ice-edge retreat. In contrast, the longer annual (~30°E–180°). In other regions (i.e., the eastern Wed ice season in the inner pack ice zones of the western dell and Ross Seas and Bellingshausen and Amundsen Weddell Sea and East Antarctic sector (~80°–120°E) Seas), however, a likely combination of wind-driven was the result of anomalously high summer sea ice ice advection and enhanced thermodynamics (colder- - concentrations (Fig. 6.8b) that initiated an anoma than-normal atmospheric temperatures, and in the lously early autumn ice edge advance in those two Bellingshausen and Amundsen Seas region colder- regions. The shorter-than-normal annual ice season than-normal SSTs) led to strongly positive sea ice in the eastern Ross and western Amundsen Seas extent anomalies. The anomalous ice extent patterns in the Ross Sea and Bellingshausen–Amundsen Seas (160°–120°W) was mostly due to an anomalously were opposite to the trends observed over the last early ice edge retreat in spring associated with the increased negative pressure anomalies centered on few decades of greater/lesser sea ice extent in those two regions respectively (Holland 2014). The net 170°W and lack of zonal ice advection from the west. result of this redistribution in regional ice extent Though of lesser magnitude, similar spring factors | S165 AUGUST 2016 STATE OF THE CLIMATE IN 2015

186 (the low pressure at 0° and lack of zonal ice advection rise in sea level in this region. A slight sea level fall in from the west) were also implicated in the shorter- 2015 compared to 2014 remains consistent with this than-normal ice season in the far eastern Weddell trend given the increase from 2014 to 2015 in eastward winds as represented by the SAM index (Fig. 6.10e), Sea and western Indian Ocean sector between 10° which is known to be associated with a fall in sea level and 40°E. The contrast in spring–summer anomaly (Aoki 2002; Hughes et al. 2003). A conversion from patterns between the Bellingshausen–Amundsen Seas sea level to zonally averaged circumpolar transport, and eastern Ross Sea (Figs. 6.8c, 6.9c,d) is a somewhat which is well established for periods of up to five typical response to El Niño and as such is opposite - years, is shown in Fig. 6.10e. This confirms the associ to the sea ice response to the atmospheric circulation pattern associated with a strong positive SAM index ation with the atmospheric structures related to SAM (and is also opposite to the long-term trend in an - but is suggestive of an additional source of variability associated with major El Niño (e.g., 2009/10, 2015/16) nual ice season duration; Fig. 6.8e). However, and as and La Niña (e.g., 1998/99, 1999/2000) events, when described in Sidebar 6.1, the high-latitude response to zonally averaged circumpolar transport anomalies this year’s El Niño was spatially muted relative to past became more negative (decreased transport) and El Niños due to the damping effect of the circulation anomalies associated with a mostly positive SAM positive (increased transport), respectively. index during this time. The horizontal circulation and vertical water-mass circulation are dynamically linked through a series uthern Ocean— J.-B. Sallée, M. Mazloff, M. P. Meredith, - So of processes including surface water-mass trans g. C. W. Hughes, S. Rintoul, R. Gomez, N. Metzl, C. Lo Monaco, formation associated with air–sea–ice interactions. The characteristics of the lightest and densest of the S. Schmidtko, M. M. Mata, A. Wåhlin, S. Swart, M. J. M. Williams, Southern Ocean water masses are now described to A. C. Naveria-Garabata, and P. Monteiro provide an assessment of the vertical circulation and The horizontal circulation of the Southern Ocean, which allows climate signals to propagate across the its contribution to ventilating the world’s oceans. The ocean surface mixed layer is the gateway for air–sea major ocean basins, is marked by eddies and the exchanges and provides a conduit for the sequestra - meandering fronts of the Antarctic Circumpolar tion of heat or carbon dioxide from the atmosphere Current (ACC). In 2015, large observed anomalies into the ocean’s interior, which is ultimately mediated of sea surface height (SSH; Fig. 6.10a) contributed to variations in the horizontal ocean circulation. While by the physical characteristics of the mixed layer. - The 2015 mixed layer temperature anomaly pat many of these anomalies are typical of interannual variability, there were several regions where the 2015 tern revealed a distinct north–south dipole delimited anomaly was noteworthy due either to its extreme by the ACC (Figs. 6.10b,c). Mixed layer conditions in magnitude or its spatial coherence: north of the ACC Antarctic waters were very cold, whereas the mixed layers north of the ACC were warmer than average. in the Southwest Indian Ocean (~20°–90°E); in the entire South Pacific (~150°E–60°W), specifically the This pattern persisted throughout both summer mid-Pacific basin around 120°W; and the anomalous and winter, though with a reduced magnitude in winter. While the warm signal in the mid-Pacific negative SSH anomalies stretching around much of was consistent with the inf luence of the 2015 El Niño the Antarctic south of the ACC, especially over the Weddell Sea (0°–60°W). A large part of the 2015 SSH event (Vivier et al. 2010), the cold signal south of the anomalies in the mid-Pacific, around Australia, and ACC was not. It was consistent, however, with the around South America was likely attributable to the atmospheric circulation pattern associated with a - positive SAM index that included increased north strong El Niño event in 2015, though the low around ward Ekman transport of relatively cool and fresh Antarctica appears unrelated to ENSO variations Antarctic surface waters. In agreement, the southeast (Sallée et al. 2008). It is not straightforward to convert these large- Pacific sector was fresher than the climatological scale SSH anomalies into anomalies of circumpolar average conditions, though other regions showed - volume transport. The best indicator of such varia little homogeneity in salinity anomaly (not shown). tions is bottom pressure averaged on the Antarctic - Mixed layer temperatures have a strong inf lu ence on air–sea CO - continental slope (Hughes et al. 2014), but such ob f luxes and ocean pH. Overall, 2 - servations on the narrow slope regions are not avail the Southern Ocean is a net carbon sink. This sink able. Instead, the focus is on sea level averaged over decreased during the 1990s, but since 2002 has in - −1 this strip (Hogg et al. 2015). Figure 6.10d reveals that creased, reaching a maximum of about 1.3 Pg C yr 15 recent years have shown a resumption of the steady in 2011 (Pg = 10 g; Landschutzer et al. 2015) and was | S166 AUGUST 2016

187 −1 likely stronger than 1 Pg C yr in 2015 (Fig. 6.10f). South of the - ACC, the increase of the sink is ex plained by the cooling of the sur - face layer in summer (Fig. 6.10b) con- and the stability of the CO 2 centrations in winter (Munro et al. 2015). The ocean carbon uptake leads to a decrease in pH, the so-called ocean acidification. A global assessment of surface water pH in 2015 is not possible due to scarcity of observations, so we present the evolution of pH in the South Indian sector, which has been monitored since 1985 (Fig. 6.10g). A rapid pH change was identified in 1985–2001 −1 (−0.03 decade ) but has stabilized since 2002 (Fig. 6.10g), a signal probably associated with a shift in climate forcing (e.g., neutral state of SAM in 2000s; Fig. 6.10e). - The bottom layers of the South ern Ocean are also undergoing substantial changes. Linear trends of deep ocean change constructed from repeat sections between 1992 F ig . 6.10. (a) 2015 anomaly of sea surface height (cm) with respect to the - and 2005 reveal abyssal warm 1993–2014 mean (produced from the Aviso SSH merged and gridded ing, with the strongest warming product). The trend at each location has been removed. (b) Time series close to Antarctica (Purkey and (gray) of sea level anomaly (cm; produced from the Aviso SSH merged and gridded product) representative of a narrow region along the Antarctic Johnson 2010; Talley et al. 2016). coast (see Hogg et al. 2015) smoothed at different time scales. (c) Estimate Antarctic Bottom Water (AABW) of annual mean ACC transport anomaly (Sv, black line) derived from sea is also contracting in volume and level (Hogg et al. 2015) with SAM index (Marshall et al. 2003) superimposed freshening (Purkey and Johnson (dashed orange line). (d) 2015 anomaly of mixed layer temperature (°C) in 2012, 2013; Shimada et al. 2012; summer (Jan–Apr) with respect to the climatological mean (2000–2014; Jullion et al. 2013; van Wijk and computed from all available Argo observations). (e) Same as (d) but for Rintoul 2014; Katsumata et al. the winter anomaly (Jul–Sep). In (a, d, e), the two black lines represent the mean location of the two main fronts of the ACC (Orsi et al. 1995). 2014; Meredith et al. 2014). These 1 − (f) Evolution of the Southern Ocean carbon sink (Pg C yr ) south of 35°S, changes ref lect the response of showing the flux derived from an interpolation method (Landschutzer AABW source regions to changes et al. 2015) based on surface ocean CO p data from SOCAT-V3 (black 2 in surface climate and ocean–ice solid line) and from SOCAT-V2 (black dotted line; Bakker et al. 2014). shelf interaction and to down - Positive values refer to a flux from air to ocean (i.e., ocean acts as a sink). stream propagation of the signal (g) Evolution of pH in the Antarctic surface water (around 56°S, solid by wave and advective processes square) and subantarctic surface water (around 40°S, hollow square) in the South Indian Ocean; only repeat summer stations are used. (h) Potential (Jacobs and Giulivi 2010; van Wijk temperature (°C, black line) and salinity (dashed orange line) of Antarctic and Rintoul 2014; Johnson et al. Bottom Water at 140°E for 1969–2015; only repeat summer stations are 2014). used. Potential temperature and salinity are averaged over the deepest As with pH, observations of 100 m of the water column for stations between 63.2° and 64.4°S, in the the deep ocean remain scarce, core of the AABW over the lower continental slope (average pressure preventing a global assessment of 3690 dbar). The vertical dashed line indicates the date of the calving of the state of the abyssal ocean of the Mertz Glacier Tongue. Note that time axis in (h) is different from in 2015. However, repeat occupa - (b), (c), (f), and (g). | S167 AUGUST 2016 STATE OF THE CLIMATE IN 2015

188 tions of hydrographic sections at 140°E provide a proximately two weeks later than typical. The ozone record of variations in AABW properties immediately hole usually reaches its largest size by mid-September, downstream of a primary source of bottom water but in 2015 the maximum size occurred on 2 October 2 (Fig. 6.10h). Potential temperature shows significant at 28.2 million km . The ozone hole then persisted at 2 ) until 15 November, variability but no long-term trend between 1969 and this large size (>20 million km setting daily records during much of October and 2015. In contrast, the long-term trend in salinity −1 ; Fig. 6.10h) exceeds interannual (~ −0.01 decade November. The development of ozone depletion over variability. Calving of the Mertz Glacier Tongue in time (daily minimum values; Fig. 6.11b) indicates that the ozone minimum was reached near 2 October; 2010 reduced the area of the Mertz polynya and ozone then remained near record low values until thereby reduced the amount of sea ice and dense water formed in the polynya (Tamura et al. 2012; Shadwick early December. The late start, persistent large area, and low ozone minima were caused by unusually et al. 2013), which likely contributed to the AABW weak stratospheric wave dynamics. variations observed after 2010. NOAA ozonesondes are launched regularly h. A ntarctic ozone hole— over South Pole station. In early October 2015, the E. R. Nash, S. E. Strahan, 12–20-km column ozone was close to the long-term N. Kramarova , C. S. Long, M. C. Pitts, P. A. Newman, B. Johnson, M. L. Santee, I. Petropavlovskikh, and G. O. Braathen mean (Fig. 6.12a), while ozone increases thereafter The 2015 Antarctic ozone hole was among the larg - were delayed compared to the long-term record. The minimum 12–20-km column ozone in 2015 was the est and most persistent ever observed, based upon the fourth lowest at 7.2 DU, measured on 21 October record of ground and satellite measurements starting (ozone hole image Fig. 6.11a). The ozonesonde total in the 1970s. Figure 6.11a displays the daily areal coverage of the Antarctic ozone hole during 2015 column minimum was 112 DU on 15 October. The (blue line) compared to the 1986–2014 climatology ozonesonde of 8 December 2015 (ozone hole image (white line). The ozone hole area is defined as the Fig. 6.11b) showed record low total column ozone area covered by total column ozone values less than for early December, highlighting the abnormally late 220 Dobson Units (DU). For 2015, area values greater breakup of the hole. 2 than 5 million km - One of the key factors controlling the severity of first appeared in late August, ap the Antarctic ozone hole is stratospheric temperature. Lower temperatures allow more polar stratospheric cloud (PSC) formation, exacerbating ozone deple - tion. Southern Hemisphere stratospheric dynami - cal conditions were anomalous in spring 2015. The lower stratospheric polar cap temperatures from the NCEP–DOE Reanalysis 2 for 2015 (Fig. 6.12b, blue line) were near the climatological average through August, but were below climatology during September–November. The 100-hPa eddy heat f lux is a measure of wave propagation into the stratosphere. A smaller (larger) magnitude leads to colder- (warmer-) than-average temperatures. The heat f lux was generally below average for July–October (Fig. 6.12c), especially in October. As a result, temperatures warmed at a slower rate in September–October (Fig. 6.12b), and 6.11. (a) Area coverage of the Antarctic ozone . ig F the vortex eroded more slowly than in previous years. hole as defined by total column ozone values less than Consequently, the ozone hole was persistent, and - 220 DU and (b) daily total column ozone minimum val stratospheric ozone levels at South Pole remained ues in the Antarctic region from TOMS/OMI for 2014 below average during October–November (Fig. 6.12a). (red line) and 2015 (blue line). The average of the daily The 2015 ozone hole broke up on 21 December, values (thick white line), the record maximum and about two weeks later than average. The breakup is minimum sizes (thin black lines), and the percentiles (gray regions and legend in a) are based on a climatol - identified as the date when total ozone values below ogy from 1986–2014. The black arrows indicate the 220 DU disappear (see Fig. 6.11). Ozone hole breakup dates of the ozone maps on the right side. is tightly correlated with the stratospheric polar vor - | S168 AUGUST 2016

189 years, EESC shows a 2000–02 peak of 3.8 ppb, with a projected decrease in 2015 of 9% to 3.45 ppb as a result of the Montreal Protocol. This is a 20% drop towards the 1980 (“pre-ozone hole”) level of 2.03 ppb. satellite Microwave Limb Sounder (MLS) Aura NASA O measurements can be used to estimate Antarctic N 2 levels (Strahan et al. 2014). Antarc stratospheric Cl - y −1 ), but tic EESC has a small annual decrease (<1% yr interannual variations in transport to the Antarctic vortex cause Cl to vary by up to ±8% with respect to y expected levels. Similar to 2014, the 2015 Antarctic stratospheric Cl was higher than recent years and y similar to levels found in 2008 and 2010. MLS lower stratospheric chlorine and ozone observations in the vortex were consistent with the F ig . 6.12. (a) Column ozone from NOAA South Pole ozonesondes measured over the 12–20-km (~160– 40-hPa) range. (b) NCEP–DOE Reanalysis 2 of lower °S, 50-hPa). (c) °–90 stratospheric temperature (60 NCEP–DOE Reanalysis 2 of zonal mean eddy heat flux (45 °–75°S, 100 hPa). The blue lines show the 2015 values and the red lines show 2014. The average of the daily values (thick white line), the record maximum - and minimum sizes (thin black lines), and the percen tiles [(gray regions and legend in (b)] are based on a climatology from (a) 1986–2014 and (b), (c) 1979–2014. tex breakup, which is driven by wave events propa - gating upward into the stratosphere, thus enabling transport of ozone-rich air from midlatitudes. The ig F 6.13. Time series of 2014 (red line) and 2015 (blue . 2015 ozone hole broke up late because of weak wave line) Antarctic vortex-averaged: (a) HCl, (b) ClO, and MLS (updated from Manney et al. Aura (c) ozone from driving in October–November (Fig. 6.12c). 2011). These MLS averages are made inside the polar Levels of chlorine and bromine continue to decline vortex on the 440-K isentropic surface (~18 km or in the stratosphere, and improvement of ozone con - 65 hPa). The gray shading shows the range of Antarctic ditions over Antarctica is expected. Ozone depletion values for 2004–14. (d) Time series of 2014 (red line) is estimated using equivalent effective stratospheric and 2015 (blue line) CALIPSO PSC volume (updated chlorine (EESC)—a combination of inorganic chlo - from Pitts et al. 2009). The gray shading shows the ) and bromine. Using a mean age of air of 5.2 rine (Cl range for 2006–14, and the black line is the average. y | S169 AUGUST 2016 STATE OF THE CLIMATE IN 2015

190 SIDEBAR 6.2: POLAR ECOSYSTEMS AND THEIR SENSITIVITY TO C L I M ATE PE RT U R BATI O N —H. DUCKLOW AND A. FOUNTAIN Ice exerts a dominant control on the function and - structure of polar ecosystems. Depending on the organ ism, it provides habitat and foraging platforms, or serves as a barrier to food and the flow of nutrients (Fountain et al. 2012). Polar ecosystems, both terrestrial and marine, have evolved and adapted to pervasive ice conditions, so when air temperatures rise above the melting threshold, the normal balance of water and ice shifts dramatically, resulting in a series of cascading effects that propagate through the entire ecosystem. The effects may persist for years to decades (J. Priscu 2016, manuscript submitted to BioScience ). In Antarctica, the differences between marine and terrestrial ecosystems could not be more extreme. These two biomes are the focus of two NSF-funded Long Term Ecological Research (LTER) programs: the Palmer LTER (or PAL), which is studying the rapidly changing marine ecosystem west of the Antarctic Peninsula (Ducklow et al. 2013), and the McMurdo Dry Valleys LTER (or MCM), which is studying the terrestrial ecosystem in the Dry Valley polar desert (Freckman and Virginia 1997). Estab - lished in the early 1990s, these two Antarctic sites collect - baseline measurements to develop process-level under standing, thus providing necessary context for evaluating ecological responses to climate events. The marine ecosystem surrounding Antarctica includes the coastal and continental shelf region that is influenced by seasonal sea ice cover, as well as the permanently open . SB6.2. (a) The number of breeding pairs of Adélie F ig - ocean zone poleward of the Antarctic Circumpolar Cur and Gentoo penguins near Palmer Station, 1976–2013. rent (Treguer and Jacques 1992). Primary production in The Gentoo is a subpolar, ice-tolerant invasive species these regions is dominantly by phytoplankton. Although that has colonized the polar region as sea ice cover considerable regional and seasonal variability exists, Ant - has declined and water temperatures have increased. arctic food webs are typically supported by diatoms with The first Gentoo pairs were observed at this location in 1994. (b) Monthly mean composite anomaly map of variable contributions by other types of phytoplankton. 500-hPa geopotential height centered over Antarctica Diatom-based food webs are typically characterized by for Sep 2001 to Feb 2002 relative to the mean calcu - highly variable but sometimes vast swarms of Antarctic lated over Sep to Feb 1980–2001. BH and LP denote krill. Krill in turn are the principal food for the conspicuous blocking high pressure and low-pressure anomalies, large predators of Antarctic seas, including penguins and respectively. The yellow X is close to Palmer Station other seabirds, seals, and whales (Hardy 1967). and the yellow circle is close to McMurdo Station. This general picture has served as the paradigm for the (From Massom et al. 2006.) Antarctic marine ecosystem for decades, but it appears to be changing, at least in the rapidly warming (Smith et al. et al. 2012). Diatom blooms, krill recruitment, and penguin 1996) western Antarctic Peninsula region (WAP) of the breeding success are all dependent on the extent of sea Bellinsghausen Sea. Ecological change along the WAP was ice and the timing of its retreat (Saba et al. 2014; Montes- first marked by catastrophic declines in Ad élie penguins - Hugo et al. 2009). Other changes in the freshwater sys (Fig. SB6.2a; Fraser and Hofmann 2003; Bestelmeyer et al. tem are also known to influence the marine ecosystem. 2011). The principal cause of ecological change is decreas - Glacial discharge and melt, for example, have the capacity ing sea ice cover in the WAP and greater Bellingshausen to increase ocean stratification and add bio-available Sea—both its extent and duration (Fig. 6.8e; Stammerjohn micronutrients, such as iron, to the productive upper | S170 AUGUST 2016

191 layers (Boyd and Ellwood 2010; Hawkings et al. 2014). unusually high temperatures across the entire continent Changes in any of these environment variables can lead (Fig. SB6.2b; Massom et al. 2006), which had long-lasting to functionally extinct species and a reorganization of the impacts. marine ecosystem (e.g., Sailley et al. 2013). At MCM, the rapid melting of glacial ice caused streams Antarctic terrestrial ecosystems, at least those that - to flow at record levels, eroding stream banks and rap - inhabit the largest ice-free areas of the Antarctic conti idly raising lake levels (Foreman et al. 2004). The stream nent, the Dry Valleys (78°S, 162°E), exist in a landscape - waters transported unusually high concentrations of sedi that includes glaciers, perennially ice-covered lakes, - ments and nutrients to the ice-covered lakes. Phytoplank seasonal meltwater streams, and arid soils (Ugolini and ton chlorophyll- concentrations reached record high a Bockhiem 2008). No vascular plants or vertebrates inhabit levels that austral summer but also remained at elevated the region, and food webs are dominated by bacteria, levels for almost a decade. Elevated soil moisture caused cyanobacteria, fungi, yeasts, protozoa, and a few taxa of a reorganization of species composition in the soils that metazoan invertebrates (Freckman and Virginia 1997). was still evident seven years later (Barrett et al. 2008). Glacial meltwater is the primary source of water, which At PAL, warm, moist northwesterly winds caused flows in ephemeral streams and conveys water, solutes, a rapid and early ice edge retreat in early spring sediment, and organic matter to the lakes (Fountain (September–October 2001) that subsequently compacted et al. 1998; McKnight et al. 1999). Streams flow for up and piled the ice against the Peninsula. Snowfall was also to 12 weeks in the austral summer providing a habitat anomalously high during this time (Massom et al. 2006). for microbial mats abundant in streambeds stabilized by Abundances of krill species were higher than normal, likely stone pavement (McKnight et al. 1998). Perennial water - due to the high productivity associated with the com environments include ice-covered lakes in the Dry Valleys pacted sea ice inshore (Steinberg et al. 2015). The posi - of Antarctica; they maintain biological activity year-round a anomaly in 2001/02 corresponded to a tive chlorophyll- with food webs dominated by phytoplankton and bacteria statistically significant krill recruitment event (evidenced (Laybourn-Parry 1997). in Adélie penguin diet samples) the following year (Saba The two LTER sites are separated by about 3800 km et al. 2014). However, it was the catastrophic late-season (Fig. 6.1). On annual time scales, air temperatures at snowfalls and subsequent flooding that caused the largest these two sites are inversely related (A. Fountain et al. single-season decline in Adélie penguin breeding success BioScience ; M. Obryk et al. 2016, manuscript submitted to in 30 years (Fraser et al. 2013). There was a devastating BioScience ) due mostly 2016, manuscript submitted to loss of an entire breeding cohort, an effect that is still to the circulation anomalies associated with the SAM evident 10 years later. index (Trenberth et al. 2007). On decadal time scales, The climate event of 2001/02 illustrates the extreme the lower-latitude PAL site is also experiencing rising sensitivity of polar ecosystems and also illustrates how an air temperatures (+3 ° C increase in annual temperatures anomalous event can induce connectivity across different over 1958–2014), while the higher-latitude MCM site is regional climates. As exemplified here, a relatively small ° C over the same experiencing a more modest change [+1 - but critical change in the temporal and spatial distribu - time period; A. Fountain et al. (2016), manuscript submit tions of ice and water exhibited dramatic and persistent BioScience ted to ]. ecological responses, the implications of which are still However, in the austral spring/summer of 2001/02, a being studied. hemisphere-wide atmospheric circulation anomaly caused late start of the 2015 ozone hole (Fig. 6.11a). The ref - forms (e.g., ClO) for catalytic ozone loss. The PSC ormation of hydrogen chloride (HCl; Fig. 6.13a) and Cloud-Aerosol volume (Fig. 6.13d), as measured by the decrease of chlorine monoxide (ClO; Fig. 6.13b) oc - Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) curred late in 2015. The 440-K potential temperature , generally followed the average (black line) for the entire season. However, the October 2015 ozone levels (Fig. 6.13c) were higher than average in 3 July–September, but declined to very low values by volume of 5.6 million km ranked highest of all 10 mid-October, consistent with Fig. 6.12a. years, consistent with the persistent and large October Heterogeneous chemical reactions on PSC surfaces ozone hole (Fig. 6.11a). convert reservoir chlorine (e.g., HCl) into reactive Satellite column observations over Antarctica (not shown) show some indications that ozone loss | S171 AUGUST 2016 STATE OF THE CLIMATE IN 2015

192 be manifested in smaller and shallower Antarctic has diminished since the late-1990s. Averaged daily ozone holes. However, unambiguous attribution of minima over 21 September–16 October (ozone hole the ozone hole improvement to the Montreal Pro - maximum period) have increased since 1998 at a −1 tocol cannot yet be made because of relatively large rate of 1.2 DU yr (90% confidence level). The 2015 year-to-year transport, wave activity, temperature - ozone hole area, averaged over 7 September–13 Oc 2 variability, and observational uncertainty. Further , the fourth tober, was estimated at 25.6 million km information on the ozone hole, with data from satel - largest over the 1979–2015 record. Since 1998, this 2 −1 lites, ground instruments, and balloon instruments, area is decreasing at a rate of –0.09 million km yr , can be found at www.wmo.int/pages/prog/arep/gaw but this trend is not statistically significant. The /ozone/index.ht m l. decline of chlorine concentrations should eventually | S172 AUGUST 2016

193 7. R — A. Mekonnen, J. A. Renwick, EGIONAL CLIMATES anada — R. Whitewood, L. A. Vincent, and D. Phillips 1) C and A. Sanchez-Lugo, Eds. In Canada, 2015 was characterized by higher-than- Overview a. average temperatures stretching from the central regions to the Pacific Coast and lower- and drier- This chapter provides summaries of the 2015 tem - perature and precipitation conditions across seven than-average temperatures in the northeastern region of the country. Anomalies in this section are reported broad regions: North America, Central America and with respect to the 1961–90 base period. the Caribbean, South America, Africa, Europe, Asia, and Oceania. In most cases, summaries of notable (i) Temperature weather events are also included. Local scientists The annual average temperature in 2015 for Canada provided the annual summary for their respective - was 1.3°C above the 1961–90 average, based on pre regions and, unless otherwise noted, the source of the data used is typically the agency affiliated with the au - liminary data. This marks the 11th warmest year since thors. Please note that different nations, even within nationwide records began in 1948. The warmest year on record for Canada was 2010, at 3.0°C above average, the same section, may use unique periods to define their normals. Section introductions will typically and 4 of the 10 warmest years have occurred during the last decade. The national annual average temperature define the prevailing practices for that section, and has increased 1.6°C over the past 68 years (Fig. 7.1). exceptions will be noted within the text. In a similar In 2015, annual departures >+2.5°C were recorded in way, many contributing authors use languages other than English as their primary professional language. the Yukon and western Northwest Territories, while To minimize additional loss of fidelity through re- annual departures <−0.5°C were observed in northern interpretation after translation, editors have been Quebec, Labrador, and Baffin Island (Fig. 7.2a). conservative and careful to preserve the voice of the Seasonally, winter (December–February) 2014/15 was 1.0 °C above average and the 27th warmest author. In some cases, this may result in abrupt transi - since 1948. Warmer-than-average conditions were tions in style from section to section. b. N orth America This section is divided into three subsections: Canada, the United States, and Mexico. Information for each country has been provided by local scientists, and the source of the data is from the agency affili - ated with the authors. Where available, anomalies are reported using a 1981–2010 base period; however, due to the different data sources, some anomalies are reported using other base periods. These are noted in the text. ig ig . 7.1. Annual average temperature anomalies (°C) 7.2. Annual (a) average temperature anomalies . F F for Canada for 1948–2015 (base period: 1961–90). The (°C) and (b) total precipitation anomalies in Canada - red line is the 11-yr running mean. (Source: Environ (% departure; base period: 1961–90). (Source: Environ - ment and Climate Change Canada.) ment and Climate Change Canada.) | S173 AUGUST 2016 STATE OF THE CLIMATE IN 2015

194 observed in Yukon, Northwest Territories, British Summer 2015 was the 17th wettest since 1948, Columbia, Alberta, and Saskatchewan. Most of and national average precipitation was 105% of aver - age. Wetter-than-average conditions were mainly Ontario, Quebec, and the Atlantic provinces ex - observed in the northwestern regions of the country perienced cooler-than-average conditions. During spring (March–May), the same pattern of warmer- whereas drier-than-average conditions occurred in British Columbia and Alberta. Autumn 2015 was than-average conditions in the western and central regions and cooler-than-average conditions in the the 26th wettest since 1948, with nationally averaged precipitation 103% of average. Drier-than-average - eastern regions of the country continued. The nation conditions for the season were experienced in the Yu - ally averaged temperature for spring 2015 was 1.3°C kon, northern British Columbia, most of Quebec, and above the 1961–90 average and the 14th warmest in over Baffin Island in the north. Wetter-than-average the 68-year period of record. Summer (June–August) was 1.0°C above average conditions were observed in the Prairie Provinces and the sixth warmest since 1948. British Columbia (Alberta, Saskatchewan, and Manitoba) and in the and northern Nunavut (a territory in the northeast of rest of Nunavut for the autumn months. the country) experienced warmer-than-average con - (iii) Notable events ditions. Southern Ontario was the only region with slightly cooler-than-average temperature conditions Winter got off to a slow start for the Maritimes, but conditions changed in January. Snow fell from several during summer. Summer temperatures across the storms, often just a few days apart. Atlantic Canada remainder of the country were near-average. was continually battered through February and During autumn (September–November), the pat - tern changed with the central regions of the country, March with storm after storm, leaving behind snow amounts not seen in decades. Numerous records were from Saskatchewan through the Maritimes, and the northern territories all experiencing warmer-than- set over the 2014/15 winter in the Maritimes. Halifax International Airport in Nova Scotia recorded total average conditions, while British Columbia, Alberta, northern Quebec, and Newfoundland and Labrador snow accumulation from January to May of 371 cm (normal is 59 cm). The previous snowiest such pe experienced near-average conditions. The nationally - averaged temperature was 1.7°C above the 1961–90 riod at any Halifax station was 330 cm in 1972. Saint average; the sixth warmest autumn since 1948. John, New Brunswick, received more than double its normal snowfall—495 cm (normal is 240 cm)—its (ii) Precipitation snowiest winter on record. Moncton, New Brunswick, Canada as a whole experienced slightly drier-than- broke the 5-meter level at 507 cm (normal is 325 cm). In Charlottetown, Prince Edward Island, the snowiest average precipitation in 2015. Based on preliminary city in Canada this winter, an April snowstorm helped data, it was the 20th driest year since nationwide set a new record for the most snow in one winter—551 records began in 1948, with nationally averaged cm—12 cm more than the previous record in 1971/72. precipitation 97% of the 1961–90 average. Drier- The wildfire season in Canada began early, ended than-average conditions were observed for eastern late, and was extremely active, especially in the West. Nunavut, northern Quebec and Labrador, in central British Columbia, and Alberta, whereas only the area The national wildland fire season was above average over the Canadian Arctic Archipelago experienced for both number of fires and hectares burned, about four times the 15-year average (2001–15) and three wetter-than-average conditions (Fig. 7.2b). Seasonally, winter 2014/15 was the 13th driest times the 25-year average (1991–2015), respectively. since 1948, and nationally averaged precipitation was Wildfires began in northern Saskatchewan in March. Residents from several communities near La Ronge 90% of the 1961–90 average, with most of the country experiencing drier-than-average conditions. How - and La Loche began evacuating to centers in the ever, wetter-than-average conditions were observed south. Hot temperatures and dry thunderstorms in over much of Nunavut and the Atlantic provinces. May and June contributed to even more volatile fire conditions, with more than 13 Spring 2015 was the 10th driest in the 68-year period 00 people evacuated 0 in what was the largest evacuation in Saskatchewan’s of record with nationally averaged precipitation 89% of average. Drier-than-average conditions continued history. In total, 1.8 million hectares burned in Saskatchewan, six times the provincial average. In across much of the country, with some wetter-than- average conditions in the western Canadian Arctic Alberta, wildfires burned hot and fast in June when Archipelago. half the province came under a fire advisory. British | S174 AUGUST 2016

195 Columbia reported more than 1800 wildfires that burned an estimated 300 00 hectares and cost more 0 than 287 million U.S. dollars to fight. The 20-year (1996–2015) average number of fires is about 1050 with an average 43 2 80 hectares burned. Conditions in British Columbia included extreme heat near 40°C, widespread and persistent dryness, large amounts of dry lightning, and gusty winds, which all contributed to the extreme fire season. S 2) U S — J. Crouch, R. R. Heim Jr., and C. Fenimore nited tate The annual average temperature in 2015 for the contiguous United States (CONUS) was 12.4°C, or 0.9°C above the 1981–2010 average—the second warmest year since records began in 1895, behind 2012 (Fig. 7.3). The annual CONUS temperature over the 121-year period of record is increasing at an average rate of 0.1°C per decade. The nationally averaged precipitation total during 2015 was 111% of average, the third wettest year in the 121-year historical record. The annual CONUS precipitation is increasing at an average rate of 4.1 mm per decade. Outside of the CONUS, Alaska had its 2nd warmest and 15th wettest year since records began in 1925. The statewide temperature was 1.6°C above average, while the precipitation total was 108% of average. Complete U.S. temperature and precipitation maps are available . 7.4. (a) Annual average temperature anomalies F ig at www.ncdc.noaa.gov/cag/. (°C) and (b) % of average annual total precipitation in the contiguous United States (base period: 1981–2010). (Source: NOAA/NCEI.) (i) Temperature the East, with near-average temperatures across During early 2015, record warmth spanned the the West. This pattern resulted in all 48 states in western United States with record and near-record the CONUS observing an above-average annual cold temperatures in the Midwest and Northeast. The last few months of 2015, particularly Decem - temperature (Fig. 7.4a). Florida, Montana, Oregon, and Washington (state) each had their warmest year ber, brought much-above-average temperatures to on record. Twenty-three additional states across the West, Great Plains, Gulf Coast, and East Coast each had annual temperatures that ranked in the highest 10th percentile of their historical records. The winter (December–February) 2014/15 CONUS temperature was 0.4°C above average, ranking in the warmest third of the historical record. Record and near-record warmth were observed in the West, with - six states observing record high seasonal tempera tures. Below-average temperatures occurred in the East; February was particularly cold, with 24 states observing one of their 10 coldest months on record and numerous cities, including Chicago, Illinois, and Buffalo, New York, being record cold. The CONUS spring (March–May) temperature was 0.7°C above F ig . 7.3. Annual mean temperature anomalies (°C) average, the 11th warmest on record. Much-above- for the contiguous United States for 1895–2015 based average temperatures were observed across the West on the 1981–2010 average. The red line is the 10-year and Southeast—Florida observed its warmest spring running mean. (Source: NOAA/NCEI.) | S175 AUGUST 2016 STATE OF THE CLIMATE IN 2015

196 on record. The summer (June–August) CONUS tem - summer precipitation for the CONUS was 108% of perature was 0.5°C above average, the 12th warmest average, the 16th wettest on record. Above-average on record. Above-average temperatures continued in precipitation was observed across the Ohio Valley, the Southeast and West, where California, Oregon, - where record rain fell during June and July. For au tumn, the CONUS precipitation total was 111% of av - and Washington were record warm, while parts of erage and the 15th wettest on record. Above-average the Midwest were cooler than average. The autumn precipitation was observed across the South and along (September–November) temperature was 1.5°C above average, the warmest such period on record the East Coast. South Carolina had its wettest autumn on record with 603.5 mm of rainfall, 321% of average. for the CONUS. Every state had an above-average autumn temperature: 40 states observed one of their December was record wet for the CONUS, at 160% 10 warmest on record, with Florida record warm. of average, becoming the only month in the 121-year December ended the year with a record high monthly period of record that was simultaneously wettest and temperature for the CONUS that was 3.0°C above warmest for its respective month. average. Twenty-nine states across the East were (iii) Notable events record warm, while near-average temperatures were Tornado activity during 2015 was below average observed across the West. for the fourth consecutive year, with a total of 1177 confirmed tornadoes, compared to the 1991–2010 (ii) Precipitation annual average of 1253. Despite the below-average During 2015, much of the central and eastern CO - number of tornadoes, there were 36 tornado-related NUS were wetter than average, while parts of the West - and Northeast were drier than average (Fig. 7.4b). fatalities, with most occurring during a deadly out break in December across the Southern Plains and Fourteen states had an annual precipitation total that was within their wettest 10th percentiles. Okla Lower Mississippi Valley. - Wildfires burned nearly 4.1 million hectares homa and Texas were both record wet with 145% and across the United States during 2015, surpassing 2006 143% of average annual precipitation, respectively. for the most acreage burned since record keeping Drought conditions that began in 2010 in both states were eradicated during 2015. California, which was began in 1960. The most costly wildfires occurred in California, where over 2500 structures were destroyed plagued by drought during all of 2015, had its 13th in the Valley and Butte wildfires in September. driest year on record; end-of-year precipitation par - Numerous major precipitation events impacted dif - tially erased early year deficits. At the beginning of 2015, the CONUS moderate to exceptional drought ferent regions of the CONUS in 2015. Heavy snowfall footprint was 28.7%; it peaked at 37.8% in May and during late winter and early spring set a new seasonal record for Boston, Massachusetts, with 281 cm of snow. ended the year at 18.7%. This end-of-year drought footprint was the smallest for the CONUS since In early October, an upper-level low interacted with December 2010. moisture from Hurricane Joaquin offshore in the Atlantic to produce rainfall totals exceeding 500 mm The CONUS winter precipitation was 90% of average, ranking in the driest third of the historical in parts of North and South Carolina. In the Southern Plains, late-spring rainfall and summer and autumn record (29th driest). Despite near-average precipita - - rains associated with the remnants of east Pacific tropi tion in the West, record warmth caused much of the cal cyclones (see section 4e3) caused several significant high-elevation precipitation to fall as rain and not - snow. The below-average mountain snowpack and f looding events. On 30 October, the remnants of Hur ricane Patricia dumped 389.7 mm of rain on Austin, - subsequent below-average spring and summer run off contributed to near-record low reservoir levels, Texas, 146.3 mm of which fell in a single hour. worsening drought, and a record-breaking wildfire 3) — Co M season. Spring was the 10th wettest on record for the R. Pascual Ramírez, A. Albanil Encarnación, and exi J. L. Rodríguez Solís CONUS, with 117% of average precipitation. Record and near-record precipitation totals were observed in In Mexico, the annual temperature for 2015 tied with 2014 as the highest since national temperature re - the southern Great Plains and Central Rockies, with - below-average precipitation along both coasts. May cords began in 1971. The nationally averaged precipita was an extraordinarily wet month for the CONUS tion total was ninth highest since precipitation records with 112.8 mm of precipitation, 147% of average, began in 1941, with the most notable accumulations the wettest among all months on record. Much of during February and March. the precipitation fell across the Southern Plains. The | S176 AUGUST 2016

197 F . 7.5. Annual mean temperature anomalies (°C, ig 7.6. Nationwide daily temperatures (°C) for . i g F blue) for Mexico (base period: 1981–2010). A linear Mexico. Shaded areas represent the ±2 std. dev. (base trend is depicted by the red line. (Source: National period: 1981–2010). Solid lines represent daily values Meteorological Service of Mexico.) for the three temperature parameters and dotted lines are the climatology. (Source: National Meteorological (i) Temperature Service of Mexico.) The 2015 mean temperature for Mexico was 22.1°C, which was 1.1°C above the 1981–2010 average, tying with 2014 as the warmest year since national records began in 1971 and surpassing the previous record of 21.9°C set in 2006 and 2013 (Fig. 7.5). This was also the 12th consecutive year with an above- average annual temperature. The first three months of the year were near- average; however, the rest of the year was character - ized by above-average temperatures and, in some instances, the daily mean, maximum, and minimum temperatures were close to two standard deviations above average (Fig. 7.6). The mean temperature for - July–September was 2.3°C above average—the warm est such period on record, surpassing the previous record set in 2013 and 2014 and making the last three years the three warmest for the July–September period on record. Regionally, the mean temperature in 2015 was be - - low average in northern Baja California, areas of Chi huahua and its borders with Coahuila and Durango, between Colima and Jalisco, the central region (which includes the states of Mexico, Puebla, and Veracruz), and Oaxaca, while the rest of the country observed near-average to above-average temperatures. Eight F ig 7.7. Annual (a) mean temperature anomalies (°C) . states had their warmest year since records began in and (b) precipitation anomalies (% of normal) observed 1971: Campeche, Quintana Roo, and Yucatan in the in 2015 over Mexico (base period: 1981–2010). (Source: Yucatan Peninsula; Nayarit, Jalisco, Michoacán, and National Meteorological Service of Mexico.) - Guerrero in the west; and Morelos in the central por experienced frost conditions (compared to the tion of the country. Conversely, the state of Veracruz January–March average of 43.3%). Similarly, during observed one of its 20 coldest years on record (Fig. 7.7a). - Frost days, defined as daily minimum tempera October–December 2015, only 28.1% of the country, mainly in the northern areas, observed frost condi tures ≤0°C, is typical in Mexico during October– - tions, compared to the October–December average March, while hot days—daily maximum tempera - of 38.2%. During April–June, 20.2% of the country, tures ≥40°C—are typical during April–September. mainly across northwestern and southern Mexico, During January–March 2015, only 26.0% of the observed hot days (compared to the average of 41.8%), country, mostly confined to the central region, | S177 AUGUST 2016 STATE OF THE CLIMATE IN 2015

198 while 16.7% of the country, mainly in the northern cific shores as a Category 5 storm since records began regions, recorded hot days during July–September in the Pacific basin in 1949. The previous Category 5 (much below the average of 29.6%). landfall was in October 1959, when Hurricane No. 12 made landfall in the Tenacatita Bay, Jalisco, similar to (ii) Precipitation Patricia’s trajectory. Above-average rainfall was observed across the c. north-central region in 2015, while below-average con - entral America and the Caribbean C Meri — ditions were present across northern Baja California, 1) C entr al a Ca J. A. Amador, H. G. Hidalgo, E. J. Alfaro, A. M. Durán-Quesada, and B. Calderón the South Pacific (coastal areas of Guerrero, Oaxaca, and Chiapas), Veracruz, and the northern Yucatan For this region, nine stations from five countries were analyzed (Fig. 7.8). Stations on the Caribbean Peninsula (Fig. 7.7b). The 2015 national rainfall total slope are: Philip Goldson International Airport, Belize; - of 872.0 mm (110.8% of normal) was the ninth high est annual total since national records began in 1941. - Puerto Barrios, Guatemala; Puerto Lempira, Hondu ras; and Puerto Lim Ón, Costa Rica. Stations located on March was exceptionally wet. Two winter storms the Pacific slope are: Tocumen International Airport and four frontal passages led to the rainiest March and David, Panama; Liberia, Costa Rica; Choluteca, since records began in 1941, with 69.6 mm of rain, providing 8.0% of the annual rainfall for the year Honduras; and Puerto San Jose, Guatemala. For 2015, the NOAA/NCEI GHCN daily precipitation dataset compared to a normal contribution (14.7 mm) close to 2.0%. September, which climatologically provides the showed a considerable amount of missing data. For - some stations, the daily rainfall amount was incom greatest amount to the annual rainfall total (18.5%), - plete, whereas in other cases the value was f lagged be added 132.7 mm in 2015, which represents 15.2% of cause it did not pass a quality control test. Precipitation 2015 annual rainfall. historical records for the above-mentioned stations Nine hurricanes, which all formed in the eastern were recovered from Central American national North Pacific basin (see section 4e3), impacted the weather services (NWS). The station climatology nation’s western coastal region, leaving, in most cases, (1981–2010) and anomalies for 2015 were recalculated significant rainfall. The most activity occurred in using NWS data by filling the gaps in the daily data September when Tropical Storm Kevin, Hurricane records of the NOAA/NCEI database (especially those Linda, Hurricane Marty, and Tropical Depression 16-E brought heavy rain to northwestern and south considered initially as zero based on the f lags listed in - western parts of the nation. the metadata of this database). In some stations (e.g., Overall, Aguascalientes (central Mexico) and David and Choluteca), differences in precipitation Colima (western Mexico) had their wettest year on totals between NWS data and the NOAA/NCEI data - set were as high as 420 and 560 mm, respectively, for record, while Baja California Sur and Chihuahua had their second wettest. Meanwhile, the rainfall deficits 2015. In the station climatology, the largest differences were found in David and Liberia (490 and 820 mm, were remarkable along the South Pacific coast, with Oaxaca having its second driest year since national respectively). Previous years’ station climatology from the NOAA/NCEI database and procedures used for all records began in 1941. variables can be found in Amador et al. (2011). (iii) Notable events An EF3 tornado struck Ciudad Acuña, Coahuila, (i) Temperature Mean temperature (Tm) frequency distributions on the morning of 25 May, causing at least 14 deaths - for the nine stations are shown in Fig. 7.8. Most sta and 290 injuries and destroying 750 homes. This was - tions, with the exception of Limon and Liberia, ex only the second tornado to reach EF3 intensity over perienced a higher frequency of above-average daily the past 15 years, following the tornado in Piedras mean temperatures in 2015. There was a near-normal Negras on 24 April 2007, also in the state of Coahuila. Hurricane Patricia was the strongest hurricane on negative skewness in Tm at Philip Goldson (Tm1) and Puerto Barrios (Tm2) on the Caribbean slope record in the eastern North Pacific basin and one of and a near-average number of cold surges during the the most intense to strike Mexico. It developed on 20 October and reached Category 5 hurricane strength winter months. Stations in Panama (Tm5 and Tm6) and Honduras (Tm8) show a shift to the right of the on the Saffir–Simpson scale, with maximum sustained -1 ) and a minimum pressure of winds of 174 kt (88 m s Tm distribution with a higher frequency of warm Tm 879 mb (see section 3e4). Patricia was only the second values during 2015. tropical cyclone to make landfall in Mexico on the Pa - | S178 AUGUST 2016

199 F ig . 7.8. Mean surface temperature (Tm) frequency (F; days) and accumulated pentad precipitation (P; mm) time series are shown for nine stations (blue dots) in Central America: (1) Philip Goldson International Airport, - Belize; (2) Puerto Barrios, Guatemala; (3) Puerto Lempira, Honduras; (4) Puerto Limón, Costa Rica; (5) Tocu men International Airport, Panamá; (6) David, Panamá; (7) Liberia, Costa Rica; (8) Choluteca, Honduras; and (9) Puerto San José, Guatemala. The blue solid line represents the 1981–2010 average values and the red solid line shows 2015 values. Vertical dashed lines depict the mean temperature for 2015 (red) and the 1981–2010 period (blue). Vectors indicate July wind anomalies at 925 hPa (1981–2010 base period). Shading depicts regional elevation (m). (Source: NOAA/NCEI and Central American NWS.) (ii) Precipitation of this “late-rains” effect. Stations on the Caribbean Annual precipitation totals were below normal slope observed relatively normal accumulations at the at all stations on the Pacific slope (Fig. 7.8). At Li end of the year. Puerto Limon was extremely wet most - beria and Choluteca, the values were extremely low of the time from the beginning of the year to pentad (in the tail of the distribution at the 40 (third week of July). A subsequent reduction of p = 0.05 level), rainfall at this station resulted in moderately wetter- and these areas experienced a long dry spell that extended past pentad 50 (beginning of September). than-normal conditions for the year as a whole. Low-level moisture appeared sensitive to ENSO Subsequent rains helped increase the accumulations later in the year, but they were not sufficient to move conditions. Regional rainfall resembled conditions out of the “extremely dry” classification. A similar associated with the development of the El Niño event in 2015. Near-surface moisture f lux convergence type of variation also occurred in Tocumen, where anomalies were computed based on ERA Interim - lack of precipitation caused an extremely dry condi - reanalysis data. Results (not shown) reveal that wet tion until around pentad 47 (third week of August), but subsequent rains led to a close-to-normal annual ter-than-normal conditions in late 2014 evolved into total. The other stations in the Pacific slope (David drier-than-normal after spring 2015. and Puerto San Jose) showed no or little indication | S179 AUGUST 2016 STATE OF THE CLIMATE IN 2015

200 T able 7.1. Summary of events and impacts, including number of fatalities (f), missing people (m), and affected people (a) by country and specific region. [(Sources for the Guatemala landslide in October 2015: www .redhum.org/documento_detail/17300 and the Pacific slope of Cenral America: OCHA-ROLAC (in Spanish: Oficina de Coordinación de Asuntos Humanitarios-Oficina Regional para América Latina y el Caribe, reliefweb.int/sites/reliefweb.int/files/resources/Crisis%20por%20sequia%20en%20America%20Central%20 en%202015.pdf)] Fatalities (f) Dates Hydrometeorogical Country(ies) Missing People (m) Specific Region Conditions (2015) Affected People (a) Unknown number Extreme below- Azuero 22 Sep Panamá of affected farmers, Peninsula average rains 2500 cattle died 27–28 Floods 4f Central Valley Oct Costa Rica Alajuela and 1f 19 Nov Floods Corredores Heavy rainfall and floods associated Nicaragua 2–14 Jun 000a Managua 6f, 35 with low pressure systems San Cayetano, 15 –20 Floods and Zaragoza, San 4f, more than 210a El Salvador Oct landslides Miguel, Luis de Moscoso Heavy rainfall, 07–15 Jun landslides and 2f, 2m, 300a Tegucigalpa floods 16 –18 Central Floods 8f Oct Honduras Honduras Floods and Northern 3f 7–8 Dec Honduras landslides Landslides 1f Cuculmeca 15 Dec Departments of Guatemala, Floods and Sacatepéquez, 8000a 7 Jun landslides Santa Inés, and San Miguel Petapa Guatemala Floods associated 5f Caribbean slope 8 Aug with a tropical wave El Cambray II Community, and Landslides 13 Oct 274f, 353m Santa Catalina Pinula Azuero Peninsula, An estimated 3.5 Panama; Gua - million people nacaste, Costa Pacific Slope affected, with more Up to 6 Extreme below- Rica; Pacific of Central than 2 million in need average rains Oct - slopes of Nicara America of food, medical, and gua, El Salvador, sanitary assistance Honduras, and Guatemala | S180 AUGUST 2016

201 (iii) Notable events and second highest since 1946 for Piarco, Trinidad Tropical storm activity during 2015 was below - (27.4°C). Other temperature extremes for Piarco in average for the Caribbean basin (6°–24°N, 60 °–92 °W). clude the highest mean maximum temperature since : Danny, Erika, and There were three named storms 1946 for October (33.6°C) and November (32.7°C) Joaquin. Joaquin became a hurricane and reached and the second highest for August (33.6°C). V. C. - major hurricane status in early October. No signifi Bird International Airport, Antigua, recorded its - cant impacts were reported for Central America as second-highest maximum temperature of 34.6°C (on sociated with any of these tropical systems. Stronger- - 30 September) since records began in 1971 and ob than-average Caribbean low-level jet (CLLJ; Amador served a high mean minimum temperature of 24.5°C 1998), 925-hPa winds during July (vectors in Fig. 7.8) for the year, tying the record set in 2001 and 2002. ño (Amador et al. 2006). were consistent with El Ni Sangster International Airport, Jamaica, recorded Central America experienced contrasting hydro - its highest mean maximum temperature for May meteorological conditions between the Pacific and °C) since 1973, and Crown Point, Tobago, set (33.0 Caribbean slopes from January to May. The impacts records for August (33.2 °C), September (33.9 °C), and were severe, but different, across the region (Table 7.1). November (33.0 °C) since records began in 1969. Dur - ing October–December, record high mean maximum 2) aribbean C — T. S. Stephenson, M. A. Taylor, A. R. Trotman, temperatures were observed in Freeport, Bahamas (25.3 °C) since 1990 and °C), and Grand Cayman (31.3 S. Etienne–LeBlanc, A. O. Porter, M. Hernández, D. Boudet, 1971, respectively, and the highest absolute maximum C. Fonseca, J. M. Spence, A. Shaw, A. P. Aaron-Morrison, °C) in temperature was observed for Dominica (35.5 K. Kerr, G. Tamar, D. Destin, C. Van Meerbeeck, V. Marcellin, the 45-year record. A. C. Joseph, S. Willie, R. Stennett-Brown, and J. D. Campbell Prevailing El Niño conditions were associated with below-normal annual rainfall and above-normal (ii) Precipitation annual mean temperatures over much of the region - While annual rainfall for 2015 was below nor mal for most of the Caribbean, contrasting rainfall (Fig. 7.9). Abundant dry and dusty air from the Sahara anomalies were observed in some territories during Desert in Africa also contributed to the dry weather the first quarter of the year. The January–March for the year, particularly during the first six months. The base period for comparisons is 1981–2010. rainfall was above normal for Dominican Republic, Grenada, Aruba, Barbados, and eastern Jamaica, and (i) Temperature below normal for Anguilla, Antigua and Barbuda, and St. Maarten. St. Thomas, U.S. Virgin Islands, Some Caribbean countries, including Anguilla, Barbados, Cayman Islands, Cuba, Dominican Re - recorded its wettest February (339.1 mm) since 1953. public, St. Kitts and Nevis, St. Maarten, and St. Lucia, The transition to drier conditions commenced in the experienced above-normal to record temperatures second quarter for Aruba, Dominican Republic, and during 2015. The average annual temperatures were Jamaica, with Dominica, Guadeloupe, St. Kitts, and the highest on record since 1951 for Cuba (26.6° C) St. Lucia also recording very dry conditions. F ig . 7.9. Annual (a) temperature anomalies (°C) and (b) percent of normal (%) rainfall for 2015 across the Caribbean basin with respect to the 1981–2010 annual mean. (Source: Caribbean Institute for Meteorology and Hydrology and the Instituto de Meteorología de la República de Cuba.) | S181 AUGUST 2016 STATE OF THE CLIMATE IN 2015

202 Dry weather persisted from July to September T able 7.2. List of Caribbean territories and across much of the Caribbean, including Aruba, stations that had their driest year in 2015. Barbados, central Cuba, Grand Cayman, Dominica, Annual southern and eastern Dominican Republic, Grenada, rainfall Year records Station/Country western Jamaica, the Leewards, and St. Lucia, though recorded began (mm) - in August, wet conditions were recorded for Domi 1928 Antigua 574.5 nica and below-normal to near-normal rainfall for Aruba 134.2 1971 Puerto Rico. This is consistent with a below-normal 465.6 1971 St. Barths - Atlantic hurricane season (see section 4e2) in rela 495.4 1953 St. Maarten tion to El Niño that produced strong vertical wind To b a go 1064.6 1969 shear, increased atmospheric stability, and subsidence Grantley Adams, over the Atlantic. July was the driest on record for 1979 789.5 Barbados St. Maarten (8.4 mm) since 1953 and second driest Santo Domingo, for St. Thomas (5.6 mm). Tobago had its fifth driest 1971 813.8 Dominican August (83.3 mm) since 1969. Republic For the last quarter, very dry conditions were Potsdam, 1971 762.0 recorded in Antigua, Aruba, Dominica, and north - Jamaica western Dominican Republic, with very wet condi - George F. L. tions in northern Dominican Republic and western 114 8 . 6 Charles Airport, 1967 St. Lucia Puerto Rico. Antigua’s all island-averaged rainfall for December was 49.0 mm, its 10th driest on record, and rainfall for the three-month period of October– works Dam, with a capacity of 1 billion gallons, being completely dry. There were more bushfires December was the ninth lowest on record (246.1 mm) than usual, and 65% of farmers were forced out of since 1928. Record-low October–December rainfall business. The drought continued throughout 2015 was also observed at a number of stations, including and was deemed the worst on record. The dura - Bowmanston, Barbados, (245.1 mm) since records began in 1981, and Rio San Juan and Villa Vasquez in tion of the drought conditions in Antigua was the second longest of any drought on record and, by Dominican Republic (230.7 and 31 mm, respectively) far, the greatest deficit of rainfall (records date to since 1971. 1928). The longest drought occurred in 1964–67, A number of territories and stations recorded their lasting 32 months. The return period for 2015 driest year (Table 7.2). The second driest year was observed at Hewanorra, St. Lucia, (1336.6 mm) since rainfall is 1 in 500 years. • On 27 A ugust, f lash f loods from Tropical Storm 1973 and the third driest for Jamaica (1308.0 mm) since 1881 and St. Croix (586.0 mm) since 1951. Con Erika caused catastrophic damage across Domi - - versely, St. Thomas (1276.4 mm) observed its sixth nica, dumping over 320.5 mm of rain in 12 hours, wettest year on record since 1953. with 225.0 mm in less than six hours. (iii) Notable events d. S outh America Positive SST anomalies were present along the Several significant events impacted the Caribbean in 2015: tropical equatorial Pacific since the beginning of 2015. With the onset of El Niño, SST anomalies in - creased and expanded along the southeastern Pacific • Prevailing droughts were observed in Anguilla, Ocean during the second half of the year. As is typical, Antigua, Barbados, Cuba, Dominica, Dominican El Niño inf luenced regional weather conditions in Republic, Jamaica, Puerto Rico, St. Kitts and Nevis, South America during most of 2015 (Fig. 7.10). and St. Lucia, with widespread agricultural losses and/ The annual temperature and precipitation anoma or very low water production and rationed distribu - - tion. St. Lucia declared a water emergency for the lies were computed using data from 1190 stations provided by national meteorological services from period May to August amid continuing drought. • South America and processed by El Centro Interna - Water shortage was experienced in the eastern half cional para la Investigación del Fenómeno de El Niño of Puerto Rico, with San Juan (capital of Puerto (CIIFEN). Air temperature was above normal across Rico) having strict water rationing for much of 2015. most of the continent, with anomalies 0.5°–2.0°C • - Low rainfall totals in 2015 in Antigua led to Pot (Fig. 7.11a) above average. El Niño impacts across | S182 AUGUST 2016

203 South America generally include, but are not limited to, drier-than-average conditions across northern South America, with wetter-than-average conditions across the southeast. Dry conditions, observed since 2014, persisted and, in some instances, deteriorated during 2015, especially in northern South America. Above-normal precipitation with severe impacts was observed in southeastern South America (Fig. 7.11b). Along the west coast of South America, the El Niño effects during the last quarter of the year were modu - lated by regional factors such as the persistent posi - tive sea level pressure anomalies in the southeastern Pacific Ocean and strong winds, which reduced convection near Ecuador and northern Peru. All anomalies in this section are with respect to the 1981–2010 average unless otherwise noted. o rt h e r n S o U t h a M e r i C a a n d t h e tro p i C a l 1) n nde — R. Mart ínez, A. Malheiros, J. Arévalo, G. Carrasco, S a L. López Álvarez, J. Bazo, J. Nieto, and E. Zambrano - This subsection covers Bolivia, Colombia, Ecua dor, Peru, and Venezuela. (i)Temperature Above-normal temperatures were predomi - nant across Venezuela throughout the year. In the 7.10. Seasonal mean sea surface temperature . ig F highlands (Tolima) and Caribbean coast (Cesar) anomalies (°C) for 30°N–60°S, 120°–60°W (base of Colombia, record maximum temperatures were - period: 1971–2000). Data source: NOAA–NCEP (Pro observed in September and December, respectively, cessed by CIIFEN, 2016). with anomalies as high as +5°C. In Ecuador, above- average temperatures were present most of the year, with anomalies of +1.5°C to +2.0°C. Temperatures across Peru were above normal during March–May and June–August. During July and August, above- average temperatures (between +1°C and +4°C) were observed along the coastal zone, in some instances surpassing record high temperatures set in 1998. In Bolivia, temperatures were near- to above normal most of the year. From August to November, at least 12 maximum temperature records were reported at stations in central and eastern Bolivia. (ii) Precipitation Venezuela and Colombia experienced drier-than- normal conditions during 2015. During the first ig F . 7.11. 2015 South American annual (a) temperature half of the year, anomalous subsidence was the main anomalies (°C) and (b) precipitation anomalies (%; - base period: 1981–2010). (Sources: Data from 1190 sta driver for the lack of precipitation in northern and tions provided by National Meteorological Services of southeastern Venezuela, which was just 40%–60% Argentina, Brazil, Bolivia, Chile, Colombia, Guyanas, of normal. On the Caribbean coast of Colombia a Ecuador, Paraguay, Peru, Suriname, Uruguay, and slight precipitation deficit was also observed in this Venezuela. The data were compiled and processed by period. During the second half of the year, as a conse - CIIFEN 2016). quence of the El Niño onset, precipitation anomalies were 50%–70% of normal across most of Venezuela | S183 AUGUST 2016 STATE OF THE CLIMATE IN 2015

204 and as little as 20% of normal in the Andean region °–3°C higher than average the region were about 1 most of the year. In São Paulo, Brazil, the January (Departments of Tolima, Huila, Cauca, Valle) and Ca - mean temperature was 3.5°C above normal—the ribbean (central and northern) regions in Colombia. In Ecuador, precipitation was above normal during second warmest January since 1943. In October, –5 the first half of the year, with anomalies up to 200% ° temperatures were about 4 ° C above normal in southeastern and west central Brazil, with the most of normal on the central coast. During the second notable warmth in Rio de Janeiro, which recorded half of the year, precipitation over the Amazon region a maximum temperature of 40°C, compared to the was 50%–80% of normal; meanwhile, precipitation C - . average October maximum temperature of 25 ° was 120%–150% of normal in the northern and cen Maximum temperatures were slightly above average tral coastal regions during September–November. In Peru, extreme below-normal precipitation was for autumn (March–May) and winter (June–August), Notable with a mean temperature anomaly of +1.0°C. observed in the northwest of the country and in the southern Andes. Above-normal precipitation pre - temperatures of 2.0°–3.0 - C above average were ob ° served across Paraguay in June. vailed during the second half of 2015 in the southern Various cold fronts during May–September and central Amazon region. brought well-below-freezing temperatures, hail, and In northern Bolivia, precipitation was above normal from January to August, with anomalies up the highest snowfall in 10 years in the Andean region, located more than 3500 meters a.s.l. to 159% of normal during March–May. During Sep - tember–November, 88% of normal precipitation was observed. Over the Altiplano region (western Bolivia), (ii) Precipitation precipitation was predominately above normal with Below-average rainfall (20%–75% of normal) was observed over southeastern Brazil, eastern Bolivia, anomalies ranging from 117% to 149% of normal throughout the year. In central Bolivia, precipitation - and Paraguay during January–March. An atmo spheric blocking pattern and a high pressure system was near normal. Above-normal precipitation (up to 150% of normal) was recorded in southeastern Bolivia over large parts of tropical Brazil and the South At - lantic, together with the absence of the South Atlantic during June–August. Below-normal precipitation convergence zone during January, were responsible (63% of normal) was observed during September– for the lack of precipitation over most of subtropi - November. cal South America east of the Andes, which lasted through mid-February. Between April and December, (iii) Notable events rainfall totals of 20%–50% of normal were recorded On 24 March, unusually heavy rainfall caused in northeastern Brazil, north-central Amazonia, landslides in the District of Lurigancho-Chosica eastern Peru, and the Amazon lowland sectors of (Lima region), Peru, leading to eight fatalities and destroying over 150 houses. Colombia and Venezuela. A weak and/or anomalously - northward displaced intertropical convergence zone During April, northwestern Venezuela experi - contributed to the below-average precipitation. enced a week-long heat wave, with some stations reg istering daily maximum temperatures as high as 40°C (April average maximum temperature is 34.9°C). (iii) Notable events Northern Ecuador was affected by f looding during Drought conditions in southeastern Brazil that December that caused crop and cattle losses. began in January 2014 (Nobre et al. 2016) continued Colombia and Venezuela were impacted by a through April 2015, particularly over the Cantareira severe drought during most of the year, causing re - reservoir system, which supplies water to nearly half strictions in water supply for human consumption, of São Paulo’s population (about 18 million residents). Summer (December–February 2014/15) rainfall agriculture, and hydropower generation. was marginally less than average. However, during Cal S oUth a Meri Ca ea St of the a nde S November and December 2015, above-average rain 2) t ropi — −1 above normal) fell over the J. A. Marengo, J. C. Espinoza, J. Ronchail, and L. M. Alves (100–150 mm month This region includes Brazil, Paraguay, southern region, allowing the Cantareira Reservoir system to Venezuela, and the Amazon lowland sectors of Peru, recover its volume. Colombia and Bolivia. The drought conditions that started in 2012 in northeast Brazil continued to persist in 2015, however, (i) Temperature with less severity ( F i g . 7.1 2 a) . Figure 7.12b shows that Monthly mean temperatures across most of very dry conditions were present across the northern | S184 AUGUST 2016

205 ing and landslides affected more than 20 0 00 people in Colombia. On 29 June, heavy rainfall in southern and southwestern Venezuela caused f looding, with more than 40 00 people affected. On 4 April, a severe storm 0 hit several towns in the department of Concepción in northern Paraguay, affecting houses, crops, and farm animals. Authorities estimate that 5000 people were affected. Precipitation patterns shifted in October, as - is typical during the presence of El Niño in the tropi cal Pacific Ocean (see section 4b), resulting in above- average rainfall across the same region. Abundant rainfall over southern Brazil and most of the La Plata basin caused significant f loods. During 8–10 July, minimum temperatures between −18°C and −22°C were measured in high areas of the Arequipa, Moquegua, Tacna, and Puno regions of the Peruvian southern Andes. According to the Empresa de Pesquisa Agropecuaria e de Extensao Rural of the state of Santa Catarina (EPAGRI) in southern Brazil, the same cold spell affected the southern region of Brazil, with minimum temperatures ranging between −3.0°C and 2.0°C in the highland city of São Joaquim on 5 July, compared with the average July minimum temperature of 6.1°C. The above-normal rainy season in southeastern South America, which typically starts in October and ends in May, was 100–300 mm above normal in December 2015, leading to f loods in Paraguay, Bolivia, –1 ig F ) . 7.12. (a) Average rainfall anomalies (mm month and southern Brazil due to the overf low of the main during the peak rainy season (Feb–May) in northeast rivers. The highest levels in 110 years were recorded Brazil for 1951–2015. (Source: Global Precipitation along the Paraguay River, which produced slow-onset Climatology Centre; updates from Marengo et al. 5 45 families f looding that forced the evacuation of 18 2013.) (b) Categories of observed precipitation based - on percentiles for northeast Brazil during the hydro 0 in the city of Asunción. Four people died and 130 00 logical year Oct–September (b) 2011/12, (c) 2012/13, (d) were evacuated by the end of the year. 2013/14, and (e) 2014/15. (Source: CEMADEN.) S 3) part of the state of Bahia, and particula the M. Bidegain, J. L. Stella, — Ca Meri a oUth S oUthern rly in semiarid region of northern northeast Brazil M. L. Bettolli, and J. Quintana and the region between southern Bahia and the northern Argentina, Chile, Uruguay, and adjacent areas of parts of the state of Minas Gerais. The extreme dry southern Brazil are considered here. conditions observed in this region contributed to an damages to crops, with local increase in wildfires and (i) Temperature Above-normal temperatures were observed over residents depending on water to be trucked in. 0 Between January and April, 32 most of southern South America (SSA) during 2015, 00 families were °C and with mean temperature anomalies between +0.5 affected by heavy rains in the lowlands of Bolivia, +1.5°C (Fig. 7.11a). According to preliminary analysis with the worst impacts occurring on 20 February when the Acre River f looded the city of Cobija, capital of the official data for 2015, the mean temperature of Pando in western Amazonia. anomaly for Argentina and Uruguay was estimated to As a result of heavy rains in the northwesternmost be +0.71°C and +0.51°C, respectively. Argentina had its second warmest year in the country’s 55-year period of Amazonian regions (north of the Peruvian Amazon and western state of Amazonas in Brazil), the Peru record, behind 2012, with the past four years (2012–15) - the four warmest on record. The cities of Buenos Aires, vian government declared a state of emergency on 9 Iguazú, Santa Fé, Rosario, and Pehuajó were each April. During March and April, more than 115 0 00 record warm in 2015. Chile observed warmer-than- - people were affected by f loods. Also, in April, f lood | S185 AUGUST 2016 STATE OF THE CLIMATE IN 2015

206 northern Uruguay, southern Brazil, and central average monthly temperatures most of the year. The Chile, as is typical during El Niño. The 2015 annual largest positive annual anomalies were observed in the how - rainfall for Argentina and Uruguay was 109% and northern (+1.1°C) and central (+1.0°C) regions; marked the second 103% of normal, respectively, and ever, September and October were cooler than average consecutive year since 2013 in which precipitation . Above-normal in the central and southern regions - was above average in Argentina. However, some maximum temperatures were observed in Chile, par regions south of 34°S in Uruguay and Buenos Aires ticularly in the central region, with anomalies between province ° and +1.5°C. +1. 0 recorded below-normal precipitation in 2015. As a result of severe water deficit, the Minister Summer (December–February) 2014/15 had of Agriculture in Uruguay declared an “agricultural near-average temperatures, with no significant heat emergency” in May to assist farmers. Santiago, the waves observed across Argentina and Uruguay. In Chile, anomalies of −0.8°C were observed across the capital of Chile, had its driest June on record, with no north coast. precipitation recorded for the first time since records Autumn (March–May) was extremely warm. The - began in 1866. During the second half of 2015, espe most notable warmth was observed during April cially during October–December, some locations in and May, with mean temperature anomalies as high northeastern Argentina and northern Uruguay were as +2.0°C and +2.5°C in central Argentina and Uru - severely affected by f loods, especially cities located guay, respectively. Argentina observed its warmest near the Paraná and Uruguay Rivers. autumn since national records began in 1961, with a (iii) Notable events mean temperature 1.51°C above average. Chile had Some areas of southern Chile experienced their above-average temperatures during March–May, with driest January in at least 65 years. In northern Chile, much of the central to northwest regions 1.5°–3.0°C unusually heavy rainfall during 24–26 March im - above average. Above-average temperatures were observed across pacted the extremely dry regions of Atacama and - Antofagasta. Some areas received well over their an much of SSA during winter (June–August), with the nual rainfall during this event. Antofagasta received most notable warmth across northeastern Argentina, Uruguay, southern Brazil, and Chile, where mean 24.4 mm of rainfall in a 24-h period during 25–26 temperatures anomalies were as high as +3.0°C. - March (normal annual average rainfall for this lo cation is 1.7 mm). Three people were killed by the Argentina also had its warmest winter on record. Much warmer-than-average conditions dominated impacts of the f loods in Antofagasta and 23 people the country during August, with many locations perished in Atacama. experiencing record high temperatures. - Heavy precipitation fell across parts of northeast Below-average temperatures were present across ern Argentina in August. The downpours overf lowed rivers and produced f loods. The highest rainfall totals Argentina, Uruguay, and Chile during spring (Sep - tember–November). In Chile and central and south during August were in eastern Argentina, mainly in - ern Argentina, an increase in frequency of frontal íos, the south of the province of Corrientes, Entre R é, where values reached 300 systems and abundant cloudiness resulted in the and northeast of Santa F . In Argentina, region’s coldest October on record mm. There was also significant precipitation in the anomalies were 6°–7°C below average in some areas province of Buenos Aires, with 200–250 mm recorded and more than 35% of stations set new - in August. Many other locations set new August pre daily low temperature records. Extremely cold conditions, cipitation total records (Table 7.3) . During December, abundant precipitation fell over - including rare snowfalls and late frosts, affected Bue nos Aires province during September and the Cuyo northeastern Argentina and Uruguay, with several region during October. locations setting new records for the month (Table 7.4). Heavy rainfall mainly affected Corrientes and Misiones provinces in Argentina, with thousands of (ii) Precipitation people forced to evacuate. Drier-than-average conditions were observed A bove-normal temperatures and below-normal during January–July, especially from March to July, - rainfall at the beginning of 2015 in Patagonia (south in eastern Argentina (Corrientes, Entre Ríos, and Buenos Aires provinces), northeastern Argentina ern Argentina) favored the development of one of the (Misiones province), Uruguay, and central Chile. largest wildfires in the history of Argentina. The fire lasted nearly two months and burned 41 - During August–December, above-average precipita 000 hectares tion fell across central and northeastern Argentina, of native forests. | S186 AUGUST 2016

207 (i)Temperature 7.3. Locations in Argentina that set new T able The annual mean maximum temperature over August precipitation totals (mm) in 2015. northwestern Morocco was about 1°C higher than 2015 Previous normal. However, temperatures during January Locations Record Record and February were markedly below average in as - (mm) (mm) Reconquista 138.8 (1956) 330.2 sociation with a cold air surge from the Black Sea to Mercedes Aero 134.3 (1975) 170.0 the Maghreb (northwestern African countries). In January, temperatures were 2.4°C below normal in Paso de los Libres 188.0 182 .9 (1971) Aero northeastern Morocco. In February they were 2.7°C Monte Caseros below normal in southern Morocco. Generally, the 218.0 (1972) 262.6 Aero winter (December–February 2014/15) mean surface 198.0 (2012) 358.8 Concordia Aero seasonal temperatures over Algeria and Morocco Junin Aero 201.0 151.4 (1976) were about 1°C below normal (Fig. 7.13), while winter San Fernando 237.1 (2012) 252.1 surface temperatures over Egypt were mainly above normal. However, minimum temperatures as low as able T 7.4. Locations in Argentina that set new 1°C were observed in January in northeastern Egypt. December precipitation totals (mm) in 2015. Temperatures during spring, summer, and autumn Previous 2015 were all above normal in Morocco and Algeria. The Locations Record Record average mean monthly temperature in Morocco and (mm) (mm) Formosa 357.5 set in 1979 425.3 Algeria was 3°C above normal in May (Fig. 7.14). 466.9 416.1 set in 2012 Posadas Aero Overall, summer temperatures in Egypt were above 47 7.0 447.5 set in 2012 normal, while isolated locations recorded below- Oberá 337.0 set in 1968 458.1 Mercedes Aero average temperatures. 42.2 El Calafate 30.5 set in 2012 (ii) Precipitation Africa Annual precipitation was marked by deficits over e. southern Egypt and surpluses over the northern In 2015, most of Africa experienced above-average - temperatures and below-average precipitation. regions. Winter precipitation was about 50% of nor Extreme weather caused loss of life and property mal over western Egypt, while heavy rainfall events damage in many parts of the continent. This extreme - weather included torrential rains across western Af rica and heavy rainfall related to a tropical storm over western Indian Ocean island countries. In contrast, eastern African countries, including Ethiopia, Soma - lia, and parts of Kenya, were impacted by drought. The drought in Ethiopia, the worst in several decades, was associated with the El Niño that developed early in the year. Extreme high temperatures were observed over northern, southern, and southwestern parts of Africa. F . 7.13. Dec–Feb 2014/15 mean temperature anomaly ig The 2015 climate assessment for Africa is based on (°C; base period 1981–2010). (Source: NCEP–NCAR reanalysis.) the 1981–2010 reference period. Both observed and reanalysis datasets are presented for analysis. Ca fri a orthern 1) n K. Kabidi, A. Sayouri, M. ElKharrim, — A. Ebrahim, and A. Mekonnen Countries considered in this region are Morocco, - Algeria, and Egypt. Overall, below-normal precipita - tion and above-normal temperature conditions domi nated during 2015. The annual temperature was the warmest since 1960 over Morocco, and successive heat waves were observed both during winter and F ig . 7.14. May 2015 mean temperature anomaly (°C; summer in Egypt. Heavy downpours were reported base period 1981–2010). (Source: NCEP–NCAR re - in May and August 2015 in Morocco. analysis.) | S187 AUGUST 2016 STATE OF THE CLIMATE IN 2015

208 were observed in January. Winter precipitation over resulting in high maximum temperatures. These Morocco was also highly variable. The average deficit heat waves were associated with continental dry air in Morocco was about 89% of normal in January and intrusions from the intense heat source in the Sahara. 71% of normal in February (Fig. 7.15). Lack of rainfall The heat waves, associated with an east wind, caused - was associated with dominant atmospheric high pres several forest fires, which devastated hundreds of sure conditions on the Moroccan Atlantic coast and hectares of forest, especially in northern Morocco. in western Europe. In Luxor, Egypt, a record temperature of 48°C was Monthly precipitation in spring was generally observed on 28 May. below normal in Morocco. However, above-normal eSt W 2) — S. Hagos, I. A. Ijampy, F. Sima, S. D. Francis, fri a Ca rainfall ranging between 145% and 230% of normal and Z. Feng was observed in March across central Morocco. New West Africa refers to the region between 17.5°W 24-h rainfall records ranging between 20 and 55 mm (eastern Atlantic coast) and approximately 15°E were observed during 23–25 May at various places in Morocco. (along the western border of Chad) and north of the Convective precipitation brought extreme weather equator (near Guinea coast) to about 20°N. Countries included are Senegal, the Gambia, Guinea-Bissau, conditions in summer, especially during July and August, leading to excess rainfall, with an average Guinea, Sierra Leonne, Liberia, southern regions of Mali and Niger, Burkina-Faso, Côte d’Ivoire, Ghana, amount of around 158% of normal over Morocco. Togo, Benin, Cameroon, and Nigeria. It is often di - Total precipitation received during August was well above normal (e.g., 45 mm at Marrakech compared to vided into two climatically distinct sub regions: the semiarid Sahel region (north of about 12°N) and the the normal of 2.7 mm; 23.2 mm at Sidi Ifni compared relatively wet coast of Guinea region to the south. to the normal of 2.1 mm). Unlike the recent autumns of 2013 and 2014, which Temperature (i) were marked by a series of above-normal rainy con - ditions, autumn precipitation in 2015 was generally The annual mean temperature over West Africa below normal over most of Morocco. Monthly rainfall was slightly above the 1981–2010 average with much ranged from 7% of normal at Casablanca to about 86% of the northwestern Sahel region about 0.5°C above of normal at Midelt. average. In May, much warmer-than-average con - ditions were reported over the region, with record warmth observed in Togo, Benin, and Burkina Faso. (iii) Notable events During January and February, a series of cold The majority of northern cities in Nigeria experienced spells affected the region, resulting in heavy snow. above-average mean temperatures. Minna, Yelwa, Zaria, Katsina, and Kano experienced the highest Three meters of snow fell over northeastern Morocco annual mean temperature departures for 2015, as did during February, the highest total for February in the past 30 years. In Egypt, Alexandria received much- Benin, Ikom, Ondo, and Warri in the South. Similarly, - above-normal rainfall in October (238% of normal). A record high temperatures were observed across east record rainfall of 127 mm was observed on 6 October ern Senegal in June, while Sierra Leone, central Mau - 2015 at Alexandria. ritania, and eastern Nigeria recorded temperatures up Conversely, May, July, and August were marked to 3°C above normal in July (Fig. 7.16). The maximum by several heat waves (defined as daily maximum temperature over the western part of The Gambia was higher than normal (by 3%–6%), while the minimum temperatures much higher than the daily mean), temperature increased by 5%–8% compared with normal in the central and eastern part of the country. Warmer-than-average conditions persisted over most of West Africa during August and September. (ii) Precipitation Wetter-than-average conditions persisted over most of the Sahel region. Rainfall totals for June– September, during which time the West African monsoon provides much of the annual precipitation, 7.15. Dec–Feb 2014/15 mean precipitation anomaly . F ig are shown in Fig. 7.17a. Relatively dry conditions –1 (mm day ; base period 1981–2010). (Source: NCEP– prevailed over most of the coast of the Gulf of Guinea NCAR reanalysis.) | S188 AUGUST 2016

209 wetter-than-normal conditions were experienced over parts of central and northern Nigeria and drier conditions over pockets in the southwestern and eastern parts. The Gambia experienced a late onset of rains with late withdrawal and, overall, received above-normal rainfall for the season. Significant rainfall amounts prevailed during July–September, with the highest amounts, between 275 and 475 mm, recorded in August. The greater part of The Gambia experienced significant annual rainfall, ranging from 750 mm to more than 1000 mm. The country aver - age seasonal rainfall during 2015 stood at 960.5 mm, 136% of the 1981–2010 mean (705.1 mm). The above- average rainfall over much of West Africa resulted in an above-average harvest according to the Famine . F ig 7.16. Temperature anomalies (°C) for West Africa in Jul 2015 (base period: 1981–2010). (Source: NOAA– Early Warning Systems Network. The dipole-like NCEP reanalysis.) precipitation with a dry Guinean coast and wet Sahel region (Fig. 7.17b) often occurs as the intertropical convergence zone (ITCZ) precipitation is shifted farther north due to warmer SSTs over northeastern subtropical Atlantic and cooler-than-average SST conditions over the southeastern subtropical Atlantic (e.g., Hagos and Cook 2008). This condition persisted throughout the summer, especially notable during August and then into September. El Niño, typically associated with dry conditions over the Sahel (Janicot et al. 1998), had relatively little impact this year. Notable events (iii) In northern Nigeria, torrential rain led to the failure of a dam in August. According to the UN Of - fice for the Coordination of Humanitarian Affairs, 00 people were affected by the associated f loods 300 0 associated. Flash f loods were also reported in some states. The f loods led to 53 fatalities and destruction of property in about 11 states, and displaced about 0 00 people from their homes. 100 In early June, Togo, Benin, and Ghana experienced significant f looding; on 3 June, 84 mm of rain fell in Cotonou, Benin, in a 24-h period. Local media reported that f looding damaged several homes and blocked streets in the largest city and economic center of Benin. ig 7.17. (a) Jun–Sep 2015 precipitation (mm) for . F The 2015 wet season (July–September) for The West Africa as total accumulated precipitation. The Gambia was characterized by several extreme events, red dashed and solid lines mark 100 mm and 600 mm including f loods, lightning, and windstorms, re - isohyets. (b) Jun–Sep 2015 precipitation anomaly, departure from 1981–2010 normal. (Source: NOAA– sulting in loss of life and significant disruption in NCEP Reanalysis.) livelihood. region from Liberia to Cameroon. Specifically, much fri aStern drier-than-normal conditions over the Niger Delta G. Mengistu Tsidu — Ca 3) e a Eastern Africa refers to countries located within and wetter-than-normal conditions in the Lake Chad 20°–50°E and 15°S–20°N. The region is comprised region were observed during summer. Rainfall over most parts of Nigeria was near normal. However, - of the Sudan, South Sudan, Ethiopia, Eritrea, Dji | S189 AUGUST 2016 STATE OF THE CLIMATE IN 2015

210 bouti, and north and central Somalia, which are reported normal to below-normal temperatures (Fig. 7.18c). The mean temperature remained above located north of 5°N, with the main rainfall season in June–September; southern Somalia, Kenya, northern normal during September–November over most parts Tanzania, Uganda, Rwanda, and Burundi, located of the region, with the exception of below-normal between 5°N and 5°S, with the main rainfall season temperatures at places in northern Tanzania and in March–May; and central and southern Tanzania, along the Ethiopia–Somalia border (Fig. 7.18d). located south of 5°S, with the main rainfall season in (ii) Precipitation December–February. Note also that Somalia, Kenya, During December–February 2014/15, southern northern Tanzania, Uganda, Rwanda, Burundi, and Uganda, Rwanda, Burundi, northern Tanzania, and - southern and southeastern Ethiopia receive a sig southern Kenya received substantially below-normal nificant portion of their annual rainfall in autumn, with a peak rainfall shifting from October over rainfall. However, some places in Tanzania and southern Kenya along the coast received 110%–150% Ethiopia and Somalia to November over the rest of the countries following the annual migration of the of their normal precipitation (Fig. 7.19a). Rainfall during March–May was normal to above normal ITCZ. Therefore, rainfall analysis is also included for the extended September–December rainfall season. The assessment for this region is based on rainfall from the latest version-2 Climate Hazards Group Infrared Precipitation with Sta - tions (CHIRPS) data and European Centre for Medium-Range Weather Forecasts (ECMWF) Interim re - analysis (ERA-Interim) daily mean - temperatures at a horizontal resolu tion of 0.25°. (i) Temperature The December–February 2014/15 mean temperature was above normal over Sudan, Eritrea, western Ethiopia, Djibouti, Uganda, Rwanda, Burundi, most parts of Kenya, and northwestern Tanzania (Fig. 7.18a). Near-normal tempera - tures over the eastern half of Ethio - pia and cold anomalies of up to −2°C were observed over part of northern Tanzania. The warm anomalies observed in December–February expanded eastward to cover most - parts of Ethiopia while cold anoma lies over Tanzania during the same season expanded northeastward to cover Kenya, southeastern Ethiopia, and Somalia during March–May (Fig. 7.18b). During June–August, the whole region experienced warm anomalies exceeding +2°C, with F ig . 7.18. Eastern Africa seasonally averaged mean temperature anoma - the exception of some pockets over lies (°C) for (a) DJF 2014/15 and (b) MAM, (c) JJA, and (d) SON 2015, northern Tanzania, western Kenya, with respect to the 1981–2010 base period. and southwestern Ethiopia, which | S190 AUGUST 2016

211 30% of normal rainfall (Fig. 7.19c). - The dry conditions persisted dur ing the usual September–December rainfall season over central and southeastern highlands of Ethiopia (Fig. 7.19d). (iii) Notable events The failure of rainfall in Ethiopia in the summer of 2015, attributed to El Niño, led to the worst drought in decades, as reported by media outlets and later confirmed by the government of Ethiopia. According - to the UN Office for the Coordina tion of Humanitarian Affairs, about 8.2 million people were in need of emergency food aid in Ethiopia. The 2015 drought event can be illustrated using the standardized precipitation index (SPI) which provides a better representation of abnormal wetness and dryness than many other indices (Guttman 1998; McKee et al. 1993, 1995; Hayes et al. 1999). To account for the accumula - tion of drought effects over time, the SPI on 3-, 6-, 9-, and 12-month time scales during October 2014–Sep - tember 2015 are considered based on the climatology of 1981–2015 for the region. Figure 7.20a shows 3-month SPIs from July to September 2015, which reveal moderate (SPI values between −1.0 and −1.49) to extreme . 7.19. Eastern Africa seasonal total rainfall (% of average) for (a) DJF F ig (SPI values less than −2.0) drought 2014/15 and (b) MAM, (c) JJAS, and (d) SOND 2015, with respect to the 1981–2010 base period. - over central, northern, and south over southwestern and southeastern lowlands of eastern Ethiopian highlands as well as central Rift Ethiopia, adjoining areas over South Sudan, most Valley of Ethiopia. Southern South Sudan and ad - parts of Somalia, Kenya, and Tanzania except for joining northern Uganda experienced moderate to small pockets over the southern tip of Tanzania, the severe (SPI values between −1.5 and −1.99) drought. southeastern highlands of Ethiopia and southeastern However, the moderate to severe drought disappeared in the 6-month-SPI (April–September 2015) over Ethiopia, and Somalia border areas, which received 50%–90% of normal rainfall (Fig. 7.19b). Most parts these areas while the moderate to extreme drought - - of Ethiopia, with the exception of southeastern low over Ethiopia persisted (Fig. 7.20b). The moder ate to extreme drought over Ethiopia continued to lands, South Sudan, and southern parts of the Sudan, prevail in the 9-month (January–September 2015) receive their main rainfall during June–September. and 12-month (October 2014–September 2015) SPIs However, below-average rainfall, associated with the (Figs. 7.20c,d) consistent with the prolonged observed strong El Niño event (see section 4b), dominated the rainfall anomalies in 2015 over Ethiopia. Thus, both region in 2015. As a result, northern, central, and the observed rainfall anomalies during the different southeastern Ethiopian highlands received 50%–90% - of their normal rainfall. The most affected north seasons and the SPI confirm the failure of rains over a longer period of time. eastern highlands of Ethiopia received as little as | S191 AUGUST 2016 STATE OF THE CLIMATE IN 2015

212 son roughly from May to October. The east coast is inf luenced by the southward-f lowing Mozambique Current, which brings warm water and humid air from the equator and creates a humid, warm climate while the west coast is inf luenced by the - cold Benguela Current from the At lantic Ocean, which produces a drier climate. Total seasonal rainfall ex - hibits a strong spatial gradient along an axis oriented southwestward from above 700 mm over Zambia, Malawi, and Mozambique to below 25 mm over southern and eastern Namibia, southeastern Botswana, and eastern Angola during the peak rainy period of December–February (not shown). Analyses are based on the same data sources as for section 7e3. (i) Temperature During December–February 2014/15, temperatures were well above normal over southern Angola, much of Namibia, and Botswana, and moderately above normal along the border between Malawi and Zambia (Fig. 7.21a). In contrast, the rest of the region had normal to below-normal temperatures. Warm anomalies exceeding +2°C were observed over the region bordering F ig 7.20. SPI indices for eastern Africa for Oct 2014–September 2015 at . Namibia, Botswana, and Angola. (a) 3-month, (b) 6-month, (c) 9-month, and (d) 12-month times scales, - The warm anomalies in the south based on 1981–2015 rainfall climatology. western part of the region expanded eastward in March–May (Fig. 7.21b) fr i C a S b e t W e e n 5° an d 30°S — 4) o Uth e rn a and covered nearly the whole region in June–August G. Mengistu Tsidu (Fig. 7.21c) and September–November (Fig. 7.21d). The only exceptions were near-normal temperatures This region comprises countries bordering the Ka - over areas that extended from the Mozambique–Zim - lahari Desert within 5°–30°S and 10°–40°E, including babwe border to close to the Mozambique–Malawi Angola, Zambia, Botswana, Zimbabwe, and Namibia . border during June–August and northern Angola The climate ranges from semiarid and subhumid in and Zambia during September–November. Extreme the east to arid in the west. Also included are Malawi warm anomalies exceeding +2°C during this period and Mozambique, located in the east, with climate covered wider areas including the western half of conditions ranging from dry to moist subtropical Botswana, eastern half of Namibia, and southern part to midlatitude types. This region is located between of Angola and Zambia (Fig. 7.21d). two semipermanent high pressure systems over the South Atlantic and south Indian Oceans. The region (ii) Precipitation is prone to frequent droughts and uneven rainfall In December–February, southern Africa received distribution with two distinct seasons: a wet season substantially lower-than-normal rainfall with the from roughly November to April and a dry sea - | S192 AUGUST 2016

213 ig . 7.21. Southern Africa seasonally-averaged mean F F ig . 7.22. Southern Africa seasonal total rainfall (% of temperature anomalies (°C) for (a) DJF 2014/15 and normal) for (a) DJF 2014/15 and (b) MAM, (c) JJA, and (b) MAM, (c) JJA, and (d) SON 2015, with respect to (d) SON 2015, with respect to the 1981–2010 average. the 1981–2010 average. exception of an isolated zonal band of normal to normal using running 5-day windows) is used (de Lima et al. 2013; Zhou and Ren 2011). In 2015, the wet anomalies over northern Zimbabwe bordering Zambia and Mozambique, extending across Malawi longest period of consecutive days warmer than the to eastern Mozambique (Fig. 7.22a). Scattered normal- 90th percentile of the normal maximum was, on average, more than 20 days over northern Namibia to-wet anomalies were observed in March–May during September–November (Fig. 7.23d). Large parts (Fig. 7.22b) and June–August (Fig. 7.22c). The whole of Botswana, Namibia, and southern Angola expe region received below-normal rainfall again during - rienced 9- to 15-day periods warmer than the 90th September–November (Fig. 7.22d). The deficit during this period is significant, as October and November percentile of normal maximum. There were warm anomalies of longer duration during other seasons constitute part of the extended climatological rainy period. Thus, overall rainfall over southern Africa over approximately the same areas (Figs. 7.23a–c). was below normal in 2015. Ca A. C. Kruger and C. McBride — fri a oUth 5) S The year 2015 was dominated by dry and abnor - (iii) Notable events The below-normal rainfall was also investigated mally hot conditions over most of the country. using the standardized precipitation index (SPI) on the 3-, 6-, 9-, and 12-month time scales from May (i) Temperature 2014 to April 2015 which encompasses the peak rainy In some parts of interior South Africa, mean maxi - months over the region based on the climatology of mum temperature deviations for January were more than 3°C above normal. Many areas in Western Cape, 1981–2015 (not shown). The analyses revealed the Free State, Limpopo Province, and Northern Cape presence of moderate to severe drought over the had maximum temperature deviations in excess of northern half of the region. On 10 November, the BBC +2° to +3°C during the first three months of the year. reported that, as a result of the drought, significant The annual mean temperature anomaly for 2015 portions of the population in Malawi and Zimbabwe (based on data from 26 climate stations) was 0.86°C needed food aid, citing a UNICEF assessment. Southern Hemisphere heat waves were observed above the reference period (1981–2010), making it the during SON over much of the region. The 90th per warmest year for South Africa since records began in - −1 1951 (Fig. 7.24). A warming trend of 0.14°C decade centile of heat wave duration (TXHW90, the maxi - is indicated by the data of these particular climate mum number of consecutive days with maximum temperatures higher than the 90th percentile calcu - stations, statistically significant at the 5% level. lated for each calendar day based on the 1981–2010 | S193 AUGUST 2016 STATE OF THE CLIMATE IN 2015

214 F ig . 7.24. Annual mean temperature anomalies (°C; base period 1981–2010) of 26 climate stations in South Africa, as indicated in the map, for the period 1951–2015. The linear trend is indicated. (Source: South African Weather Service.) ig F . 7.23. The 90th percentile TXHW90 anomalies (in days) for Southern Africa during (a) DJF 2014/15 and (b) MAM, (c) JJA, and (d) SON 2015, with respect the 1981–2010 climatology. (ii) Precipitation Figure 7.25 presents the annual rainfall anomalies for 2015 compared to the 1981–2010 reference period. - The most significant feature was below-normal rain fall across most of the country, with particularly dry conditions in northern KwaZulu-Natal province, the far northeast and western North West, and northeast - ig F 7.25. Rainfall anomalies (% of normal; base period . ern Northern Cape provinces. 1981–2010) for South Africa during 2015 (Source: South The beginning of the year was characterized by African Weather Service.) - dry conditions in the western and northwestern in terior and, due to below-normal rainfall conditions ruary some agricultural organizations requested that during the 2014/15 austral summer rainfall season, provinces, such as North West, be declared drought- stricken. In KwaZulu-Natal, a substantial loss in the the northern and northeastern parts were already sugarcane yield was expected, while water restrictions classified as very dry. In June and July, the western half of the country, were in place over much of the province. By March, as well as some parts in the east, got temporary relief other provinces also considered applying to be de - from the dry conditions, with most places receiving clared drought-stricken areas, including the Western more than double their average rainfall for the month. - Cape, Free State, and Limpopo Province. The prov In September the rainy season in the summer-rainfall inces of Northern Cape, North West, KwaZulu-Natal, areas commenced well, with comparatively high rain - Mpumalanga, and Limpopo, and the Free State were fall totals reported in the northern interior. However, all declared drought disaster areas in November. By the end of (austral) summer, the prolonged drought (austral) spring and beginning of summer of 2015 had conditions severely affected maize, sugar cane, and dry conditions accompanied by recurring heat waves sorghum harvests. in many places. The July–June 2014/15 period was on average the In the spring, record high temperatures were driest season for South Africa since 1991/92 and the broken on a regular basis, with Vredendal recording third driest since 1932/33. a temperature of 48.4°C on 27 October 2015, setting a new global record for the highest temperature ever observed for this month. The previous highest (iii) Notable events - With drought conditions firmly in place, by Feb maximum temperature for this station was 42.5°C, | S194 AUGUST 2016

215 recorded on 30 October 1999. Extremely high maxi - 24.2°C, corresponding to an anomaly of about +0.5°C. mum temperatures also occurred in Gauteng from 4 All stations had positive anomalies, with the highest departure observed at Ambohitsilaozana (northeastern - October, and resulted in prolonged heat wave condi Madagascar; 1.8°C above average), except Antsiranana tions for 9 consecutive days in Pretoria and 8 con - secutive days in Johannesburg. Lephalale in Limpopo (northern Madagascar) station (0.1°C below; Fig. 7.27). During austral summer (January–March), the seasonal Province also experienced heat wave conditions for 6 mean temperature was below the reference period. The consecutive days. Heat wave conditions also occurred mean temperature for July–August was above normal. in November, beginning on the 7th and prevailing - For Réunion Island, 2015 was the third warmest over four provinces: Gauteng Mpumalanga, the Lim year since records began there in 1969, with an annual popo Province, and North West. An extensive dust storm occurred about 60 km mean temperature anomaly (based on six stations) north of Bloemfontein between Winburg and Ver - of +0.7°C. Only February and March were below or near-normal. Minimum and maximum annual tem keerdevlei on 11 November. According to reports, the - wall of dust was estimated between 20 and 25 km wide peratures were 0.5°C and 0.9°C above the 1981–2010 and at least 3 km high. The dust storm was accom - mean, respectively. −1 For Mayotte Island (Pamandzi Airport), 2015 was panied by strong winds blowing at 60–70 km hour . the warmest year since records began in 1961, with an Centr al 6) W eStern and i annual mean temperature anomaly of +0.7°C (+0.6°C ndian o Cean iSland for maximum temperature and +0.8°C for minimum CoUntrie S — G. Jumaux, L. Randriamarolaza, M. Belmont, and H. Zahid This region consists of several island countries, namely Madagascar, La Réunion (France), Mayotte (France), Seychelles, and Maldives. Overall, the 2015 mean temperature for the region was well above normal. Precipitation was also gener - ally above normal, especially during the second half - of the year in the Maldives and Réunion, but was be low normal in Mayotte for the same period (Fig. 7.26). (i) Temperature In Madagascar, 2015 was the fourth warmest year since records began in 1971 (the warmest year was 2011). The overall annual mean temperature was . 7.27. Annual mean temperature anomalies (°C) ig F 7.26. Mean annual temperature anomalies (°C), ig F . based on 1981–2010 average. The circle dimension is annual rainfall anomalies (%), and their respective related to the anomaly absolute values. (Source: Cli - deciles for the Indian Ocean islands (Sources: Météo mate Change and Climatology Service, Meteorology France; and Meteorological Services of Madagascar, of Madagascar.) Seychelles, and Maldives.) | S195 AUGUST 2016 STATE OF THE CLIMATE IN 2015

216 temperature, both highest on record). December was the warmest month of the year, with an average daily maximum temperature of 32°C. For Seychelles, all months had above-normal mean maximum temperatures (at Seychelles International - Airport) except January and February. The warm est month was April with a maximum temperature average of 32.3°C and a minimum temperature av - erage of 26.2°C (respective anomalies of +0.7°C and +0.8°C). The annual mean temperature in 2015 was 0.5°C above average, marking the second warmest year since 2009. For the Maldives, the annual mean temperature (based on two stations: Gan and Hulhule) in 2015 - was 28.8°C, +0.5°C compared to normal. Mean tem peratures were above average for all months, with the highest anomaly of +1.2°C observed in December (Fig. 7.28). These elevated temperatures are associ - ated with the 2015 El Niño event. Overall, 2015 was the third warmest year since records began in 1981. (ii) Precipitation In Madagascar, annual accumulated precipitation was slightly above the 1981–2010 average. However, 10 of 22 stations indicated below-average annual total precipitation. The highest positive anomaly was recorded in Morombe (200% of normal) in southwest - ern Madagascar, while the lowest negative anomaly was observed in Sainte Marie (47% of normal) in northeastern Madagascar. In addition, more stations . F 7.29. Annual total precipitation (% of normal) with ig were drier than average in northern Madagascar respect to the 1981–2010 period. The circle dimension than in the south (Fig. 7.29). During austral summer is related to the anomaly absolute values. (Source: (January–March), rainfall was above average, but was Climate Change and Climatology Service, Meteorol - below average from April to December. In addition, ogy of Madagascar.) the number of dry days (rainfall < 0.1mm) were 12 on average during summer, compared with 22 days since records began in 1969. March was the wettest on average for April–December. month of the year due to heavy rainfall in the wake For Réunion Island, the annual rainfall was about of tropical storm Haliba’s passage near Réunion on 9 120% of average, marking the ninth rainiest year March. The number of substantial rainy days (56 days compared with an average of 37) was the highest on record (followed by 1982, 1972, and 2008). For Mayotte Island, the annual rainfall amount (based on two stations) was slightly below average. January was the wettest month of the year, especially on the eastern part of the island. Pamandzi airport recorded 510 mm, which is the rainiest January for this station since records began in 1961 (followed by 1971, 1986, and 2008). In Seychelles, annual rainfall total in 2015 was 105% of normal. Below-normal rainfall and fewer- 7.28. Monthly mean temperature anomalies (°C) F ig . than-normal rain days were reported from January in 2015 in Maldives (average of two stations) with re - to April and in July. May–October is the dry season spect to the 1981–2010 base period. (Source: Maldives Meteorological Service.) in Seychelles but, with the presence of an active | S196 AUGUST 2016

217 El Niño, several months received abnormally high minimum temperature was recorded at Antsirabe (central Madagascar) on 21 July (−1.2°C). rainfall (Fig. 7.30). August received 298.3 mm (normal The highest 24-h accumulated precipitation was is 122.5 mm); October recorded 337.6 mm (normal 318 mm recorded in Maintirano (western Madagas - is 177.7 mm); and November recorded 353.9 mm - car) on 2 February, which is a 12-year return period (normal is 192.5 mm). Many days with daily rain event. Grand-Ilet station (Salazie, in the highlands) fall above 50 mm were recorded during the last five months of 2015. The highest daily value (122.6 mm) recorded 1277 mm in 4 days (5-year return period). Associated with cyclones and other systems in the was recorded on 9 November at Seychelles Airport. - region, the Maldives experienced rough sea condi For the Maldives, the annual rainfall amount in −1 2015 was 2408 mm, 114% of average, making 2015 was tions and f looding. Average winds of 24 km hour prevailed in the central atolls from 10 January until the fourth wettest year since records began in 1981. the end of the month. Due to strong, sustained winds, - August was the wettest month of the year, with an av erage rainfall of 370 mm over the Maldives (Fig. 7.31). moderate to rough seas prevailed in the area, which As is typical, February was the driest month of the caused a passenger boat to run aground on a reef near Kaafu Maniyafushi. All 24 passengers were rescued, year, with average rainfall of 24 mm. On average, the Maldives experienced about 140 rainy days in 2015, but the boat sank in the reef as the Coast Guard was 5 more than average. In 2015, the highest number of unable to continue rescue efforts in the area due to the strong winds and rough seas. No cyclones directly rainy days was recorded in August, September, and impacted the Maldives in 2015. October (19 days each). On the other hand, the lowest number of rainy days (3) was experienced in January. On the other hand, Madagascar was affected by three tropical systems that formed in the Mozambi - (iii) Notable events can Channel on 13 January (Tropical Storm Chaedza), The absolute maximum temperature was recorded 5 February (Tropical Storm Fundi), and 3 March (a - at Antsohihy (northwestern Madagascar) on 13 Oc tropical depression). The persistence of the ITCZ amplified the conditions, leading to an event that tober and 11 November (+38.7°C) and the absolute had never occurred in February since records began in 1961. On 26 February, in Antananarivo, significant rainfall of 129.2 mm caused the destruction of a dam, which led to a major f looding event. Madagascar’s disaster management agency, the Bureau National de Gestion des Risques et des Catastrophes (BNGRC), 9 56 residents dis - reported that 19 lives were lost, 36 0 00 people affected by the placed, and more than 60 disaster. An estimated 517 houses were destroyed and 1698 were damaged in the f loods. BNGRC also reported that the f loods damaged 6339 hectares of F . 7.30. 2015 monthly rainfall anomalies (mm) at ig rice fields. Seychelles International Airport. (Source: Seychelles Associated with a cloud cluster that formed south Meteorological Services.) of the Maldives on 24 November, 228 mm of rain fell in the southernmost region in Addu City, the highest recorded 24-h rainfall for the Maldives, breaking the previous record of 188 mm. Three hours of torrential rain and more than 12 hours of incessant rainfall left most parts of Addu City under water, and f lood water damaged household appliances and furniture in hundreds of households. It is estimated that more than 200 houses experienced f looding, and damage 0 00 U.S. dollars. was estimated to be in excess of 200 f. E urope and the Middle East - This section covers western Europe, from Scandi navia to the Mediterranean, and extends from Ireland 7.31. 2015 monthly rainfall anomalies (mm) in . ig F and the United Kingdom to eastern Europe, European Maldives (Source: Maldives Meteorological Services.) | S197 AUGUST 2016 STATE OF THE CLIMATE IN 2015

218 Russia, and parts of the Middle East. While the entire region is covered in the Overview, not all countries provided input to this report, so some individual national details are not included. Throughout this section, normal is defined as the 1961–90 average for both temperature and precipita - tion, unless otherwise specified. European countries conform to different standard base periods applied by their national weather services. All seasons men - - tioned in this section refer to the Northern Hemi sphere. Significance implies an exceedance of 5th or F ig 7.32. Annual average land surface air temperature . 95th percentiles. anomaly (°C) for the European region (35°–75°N, 10°W–30°E) relative to the 1961–90 base period. The More detailed information, including monthly blue bars show the annual average values and the statistics, can be found in the Monthly and Annual black error bars indicate the 95% confidence range of Bulletin on the Climate in RA VI – European and the uncertainties. The green bar is the annual value the Middle East, provided by WMO RA VI Regional for 2015. Data are from the CRUTEM4 dataset (Jones Climate Centre Node on Climate Monitoring (RCC et al. 2012.) Node-CM; www.dwd.de/rcc-cm). All statistics re - ported here are for three-month seasons. 1) o W vervie Europe was, on average, much warmer than nor - mal in 2015. The mean land surface air temperature for the European region (35°–75°N, 10°W–30°E) from the CRUTEM4 dataset (Jones et al. 2012) was +1.51°C above the 1961–90 normal, only 0.2°C short of the previous record set in 2014 (Fig. 7.32). According to the E-OBS dataset (van der Schrier et al. 2013b; - Chrysanthou et al. 2014), which uses different meteo 7.33. Annual land surface air temperature anom F ig - . rological stations over an area extending farther west aly (°C) for Europe, similar to 7.32, but based on . ig F and east (25°W–45°E), the European annual mean the E-OBS dataset (van der Schrier et al. 2013b and land surface temperature was the highest on record Chrysanthou et al. 2014) from 1950 to 2015. [Source: (+0.93°C above the 1981–2010 average; Fig. 7.33). KNMI (Royal National Meteorological Institute) Neth - However, differences between both datasets are erlands.] - within the level of uncertainty (allowing for the dif ferent base periods). Across Europe and the Middle East, temperature anomalies ranged between +1°C in northwestern areas and +3°C in northeastern and Alpine regions (Fig. 7.34). Precipitation totals in 2015 (Fig. 7.35) were below average across most of continental Europe and Ice - land (60%–80% of normal). Parts of the British Isles, northern Europe, and the central and eastern Medi - terranean recorded significantly above-average totals of 125% of normal and locally up to 170% of normal. - Winter 2014/15 (December–February) was excep - tionally mild over Scandinavia and the eastern Euro pean region, with surface and 850-hPa temperature anomalies up to +4°C (Fig. 7.36a). The Icelandic low (negative anomalies of −12 hPa) and the Azores high (positive anomalies of +12 hPa) were well established . 7.34. Annual mean air temperature anomalies F ig as ref lected by the North Atlantic Oscillation index (°C; 1961–90 base period) in 2015. (Source: DWD.) | S198 AUGUST 2016

219 F ig . 7.35. European precipitation totals (% of 1951–2000 average) for 2015. Hatched areas indicate regions where precipitation is higher than the 95th percentile of the 1961–90 distribution. Only grid points with mean –1 annual precipitation >15 mm month are represented. [Source: Global Precipitation Climatology Centre (Schneider et al. 2015).] [NAO +1.65, normalized pressure difference between the Azores High (Ponta Delgada, Azores) and the Icelandic Low (Reykjavík, Iceland)]. This synoptic pattern allowed for a frequent westerly f low of mild Atlantic air masses that brought precipitation totals of up to 170% of normal, particularly in northern parts of Europe and in the southeast (Fig. 7.37a, hatched). - In contrast, the Iberian Peninsula and southwest ern France had below-average surface temperature anomalies of up to −1°C due to the inf luence of high pressure and precipitation less than 40% of normal in places. During spring (March–May) significant above- . 7.36. Seasonal anomalies of (left) 500-hPa geopo - F ig average 500-hPa heights centered over Iberia led to tential height (contour, gpm) and 850-hPa temperature well-above-normal temperatures in southwestern (shading, °C) and (right) near-surface air temperature, Europe (Fig. 7.36c, dotted). March in particular con - using data from the NCEP–NCAR reanalysis for (a), (b) tributed to the anomalous warmth. It was the third - DJF (winter), (c), (d) MAM (spring), (e), (f) JJA (sum mer), and (g), (h) SON (autumn). In left column, dotted straight month of extensive westerlies and southwest - areas indicate regions where 500-hPa geopotential is erlies advancing over northeastern Europe where above (below) the 95th percentile (5th percentile) of temperature anomalies exceeded +4°C. the 1961–90 distribution, while hatched areas represent Northern Europe was affected by frequent Atlantic the corresponding thresholds but for 850-hPa tempera - cyclones throughout the season that caused a signifi - ture. Base period used for both analyses is 1961–90. cant precipitation surplus of locally more than 180% (Source: Deutscher Wetterdienst.) of normal (Fig. 7.37b, hatched), whereas the western contrast, the British Isles, Scandinavia, and northern half of Europe, including most of the British Isles, had below-average totals. European Russia were inf luenced by frequent low - pressure systems. These regions recorded surface The summer season (June–August) was character temperature anomalies of 0° to −1°C accompanied by ized by a hot spell across western, central, and eastern Europe (see Sidebar 7.1) as a result of significant above-average rain amounts of up to 170% of normal (Fig. 7.37c). above-average 500-hPa heights (Fig. 7.36e, dotted). In | S199 AUGUST 2016 STATE OF THE CLIMATE IN 2015

220 WeStern C entral Urope e and 2) This region includes Ireland, the United Kingdom, - the Netherlands, Belgium, Luxembourg, France, Ger many, Switzerland, Austria, Poland, Czech Republic, Slovakia, and Hungary. (i) Temperature Annual temperatures in central Europe were warmer than normal and nearly all areas of the region - were around 2°C above their long-term means. Swit zerland had its warmest year since national records began in 1864 (+2.1°C). Austria, Germany, Slovakia, and Hungary each experienced their second warm - est year since 1767, 1881, 1961, and 1901, respectively, with anomalies ranging between +1.7°C and +2.2°C. The winter season 2014/15 was exceptionally mild, particularly for the eastern part of the region, which was more often than normal under the inf luence of subtropical air masses. Spring was characterized by 7.37. Seasonal anomalies for 2015 (1961–90 base F . ig above-average temperatures except for most of Ire - period) of sea level pressure (hPa) from NCAR–NCEP land, where deviations of −1°C were recorded. May reanalysis (contours) for (a) DJF (winter); (b) MAM in particular contributed to the cooler-than-normal (spring); (c) JJA (summer); and (d) SON (autumn). conditions in Ireland, where deviations down to - Colored shading represents the percentage of sea −1.7°C were recorded. sonal mean precipitation for 2015 compared with the During summer, the atmospheric circulation 1961–90 mean from the monthly Global Precipitation Climatology Centre (Schneider et al. 2015) dataset featured significantly widespread anomalous high (only grid points with climatological mean seasonal temperatures across continental Europe. Near the –1 precipitation above 15 mm month are represented). Alps, the blocking ridge led to positive temperature Dotted areas indicate regions where SLP is higher anomalies up to +4°C. In contrast, the British Isles (lower) than the 95th percentile (5th percentile) of the were affected by frequent westerly f low of Atlantic air 1961–90 distribution, while hatched areas represent masses that led to a summer with mostly near-normal the corresponding thresholds but for precipitation. temperatures, although a brief heat wave occurred in In autumn the atmospheric circulation featured early July, particularly affecting southern parts of the above-average 500-hPa heights (Fig. 7.36g), and United Kingdom. Temperature anomalies in autumn ranged be - temperatures were warmer than normal in nearly all tween −1°C in parts of France to +2°C in the southern regions. Scandinavia and the eastern Mediterranean, United Kingdom and eastern areas of the region. - including the Black Sea region, were especially af - fected by high pressure conditions and recorded sig November was especially warm (2°–4°C above aver - nificantly positive surface and 850-hPa temperature age), when many daily high temperature records were anomalies of more than +3°C in places. According to broken. Germany reported its warmest November on record (3.5°C above average). The United Kingdom the E-OBS dataset, it was the third warmest autumn and Switzerland reported deviations of +2.6°C and since 1950 for the European region. Eastern Turkey +2.7°C, respectively, with each having their third and the Balkan States received localized precipitation warmest November since 1910 and 1864, respectively. totals of more than 200% of normal (Fig. 7.37d). The year ended exceptionally warm, with a strong The year ended with exceptionally warm Decem - positive NAO (+2.24) phase in December. The syn ber temperatures that were more than +4°C from the - - reference period across nearly the entire region. An optic pattern was associated with exceptionally wide spread positive temperature anomalies that exceeded exceptionally strong southwesterly f low associated +4°C. Large parts of Europe recorded their warmest with a strong positive phase of the NAO contributed - December since 1950. Only the eastern Mediterra to these spring-like temperatures. nean experienced below-average temperatures, with anomalies reaching −2°C. | S200 AUGUST 2016

221 (ii) Precipitation Ireland reported its sixth wettest November since Annual precipitation totals were mostly below the records began in 1866. Newport, on the west coast of long-term mean. Only Ireland, Scotland, Benelux, Ireland, observed a record daily rainfall of 66.2 mm and northern Germany had slightly above-normal (190% of normal) on 14 November. precipitation. - Several storms traversed central Europe in Novem Winter 2014/15 was characterized by below- ber. During the 17th and 18th, a core pressure below −1 average precipitation in central Europe, with locally , 985 hPa brought wind gusts of more than 48 m s less than 60% of normal. The United Kingdom was causing traffic disturbance and damage to trees and affected by frequent cyclonic conditions that gave rise buildings. to a surplus of up to 140% of normal precipitation. Spring was drier than normal across the entire ordi CoUntrie C alti b the and C n he 3) t S region, except in Scotland. France, central Germany, This region includes Iceland, Norway, Denmark, and western Poland each received 60%–80% of nor - Sweden, Finland, Estonia, Latvia, and Lithuania. mal precipitation and locally even less, associated with areas of significantly above-average sea level (i) Temperature pressure (SLP, dotted areas in Fig. 7.37b). Over the Annual temperatures in 2015 were well above nor - British Isles, below-average totals of 70% of normal in mal in the Nordic and Baltic countries. Finland and the southern England contrasted with values in western Baltic States experienced anomalous temperatures of Scotland of more than 150% of normal. +2° to +3°C. Lithuania and Finland experienced their During summer, below-average precipitation warmest year on record, with anomalies of +2.1°C and totals continued, especially in eastern and central +2.6°C, respectively. Norway observed its third warmest Europe and the Alpine region where totals as low as year since records began in 1900, with an anomaly of 40% of normal were registered. Hungary reported its +1.8°C. Iceland, however, recorded only slightly-above- sixth driest June since 1901. average temperatures (0° to +1°C) and had its coldest Autumn precipitation was above normal in the year since 2000. eastern part of the region, while western areas, includ - Winter 2014/15 was exceptionally mild in Scandina - ing the British Isles, experienced a rain deficit. High via and the Baltic States, with the largest deviations of pressure over northern Europe during September and more than +4°C in Estonia, Finland, and central Sweden. - October brought dry conditions to the United King The anomalous temperatures were caused by persistent dom, with 54% and 65% of normal rain, respectively. southwesterly f low, which brought subtropical air far into The season ended with very wet conditions when the Scandinavia. February in particular contributed to the - Icelandic low was well established. This synoptic pat anomalous warmth. Finland, on average, had tempera - tern was associated with 250% of normal totals in most tures 7°C above normal, marking its third warmest Feb - regions, except France and the Alpine region. Repeated ruary, behind 1990 and 2014. Norway’s average anomaly low pressure systems continued to the end of the year, was +4.2°C, with anomalies at stations in southern and and precipitation remained well above normal. central regions up to +6°C and +9°C, respectively. In spring, temperatures remained above the long- (iii) Notable events term mean, with anomalies between +3° and +4°C in Two storms crossed the North Sea during 9–11 Janu - northern Scandinavia (hatched areas in Fig. 7.36b). ary. At the central German mountain station Brocken, March was especially warm when mild subtropical air −1 wind gusts of more than 43 m s advanced far into the north. Lithuania reported positive were measured. During July and August, Hungary reported a record anomalies of +4.9°C. Norway also experienced a mild 27 days of heat wave conditions, and Budapest expe - March, at 3.8°C above average. Locally, in Finnmark and Troms (far northern Norway), deviations of +5° to rienced a record-breaking 34 tropical nights, the most since records began in 1901. +7°C were reported. During summer, temperatures were near-normal on Two intense rainstorms crossed southeastern France during 12–13 September, bringing 200–242 mm rain balance. Above-average temperatures of +1°C over the Baltic States contrasted with below-average conditions within 6 hours. The latter amount is a new record at (−1°C) over Iceland and most of Scandinavia. June and station Grospierres. July were cooler than normal, with anomalies as much In November, many record high temperatures were measured. On 7 November, a station in Freiburg, as −2°C over the Scandinavian countries where below- southwest Germany, recorded 23.2°C, its highest daily average 500-hPa heights prevailed. maximum temperature for November. | S201 AUGUST 2016 STATE OF THE CLIMATE IN 2015

222 Temperatures in autumn were significantly (iii) Notable events In January, Norway and Sweden experienced ex - warmer than normal throughout all regions due to treme precipitation totals. Some stations in Norway dominant high pressure (dotted areas in Fig. 7.36g). The strongest deviations occurred in northern received up to 400% of normal; at Eikemo (coastal Scandinavia, at +2° to +3°C. In November, a strong - western Norway) 782.3 mm was measured, corre positive NAO phase (+1.7) led to many locations in sponding to 280% of normal. Station Piteå in northeast Scandinavia observing temperatures above their 90th Sweden reported a monthly rain accumulation of 1346 mm, which is the most since the record began in 1890. percentile. In December, a combination of prolonged high During 9–11 January, the Danish coast was hit by pressure over the central Mediterranean and warm two successive storms. On the morning of 11 January, water in Lemvig (northwest Denmark) rose to 1.95 m air advection caused exceptionally mild conditions in above normal, breaking the previous record of 1.81 m. the Nordic region. Widespread positive anomalies of During a period of strong westerlies in February, more than +4°C were recorded across most regions, except for Iceland, where below-average temperatures Norway reported record-breaking wind gusts of more −1 0 00 in southern mountainous areas; 70 in western areas contrasted with warmer conditions than 46 m s people lost power. Givær, an island in Bodø (northern in the east. Norway), was evacuated during a spring high tide. In September, Norway was hit by thunderstorms (ii) Precipitation and accompanying extreme precipitation. In the With the exception of Iceland and the Baltic States, south, station Gjerstad received monthly totals of annual precipitation totals were above normal. Den - mark reported its second wettest year since 1874, and 478 mm (330% of normal), and station Postmyr i Drangedal received 449.5 mm (350% of normal). On Norway observed 125% of normal precipitation on average, which is third wettest in its 116-year record. 2 September, the latter recorded its highest daily total of 117.8 mm. Winter 2014/15 was wetter than normal across On 2 October, a storm caused forest damage in nearly all of the Nordic countries due to a strong central Finland and left over 200 00 households 0 positive NAO phase (+1.65). Below-average 500-hPa heights were associated with frequent cyclonic condi without power. - - In November, two storms hit Denmark with re tions that brought up to 170% of normal precipitation cord-breaking wind gusts. During 7–8 November, the to the region (hatched areas in Fig. 7.37a). - first storm produced Hanstholm’s (on the northwest In spring, wetter-than-normal conditions re −1 mained, especially across Scandinavia where coast) highest wind gust of 34.6 m s and a record- −1 breaking 10-minute mean wind of 27.3 m s 125%−170% of normal totals were widely observed. . On 29 Norway experienced its second wettest May on record November, the second storm passed with wind gusts −1 up to 45.9 m s . (after 1949), with 175% of normal rainfall. Precipitation in summer was close to normal except for the Baltic States. A persistent blocking p berian 4) i enin la SU This region includes Spain and Portugal. In this ridge centered over continental Europe resulted in dry conditions, with only 60% of normal rainfall subsection, anomalies refer to a reference period of recorded (dotted in Fig. 7.37c). August was especially 1981–2010, with the exception of precipitation for dry, with nearly all regions recording below-average Portugal, which the country reports with respect to a 1971–2000 reference period. totals. Exceptionally low rainfall of less than 20% of - normal was recorded across the Baltic States. Lithu (i) Temperature ania reported just 16% of its normal rainfall. The Iberian Peninsula experienced a warmer- During autumn, precipitation totals were mostly below the long-term mean, except for parts of north- than-normal year in 2015. Spain recorded an annual central Finland and Denmark (>125% of normal anomaly of +0.9°C and tied with 2011 for its warmest year on record, which dates to 1961. Portugal reported totals). The Baltic States recorded a deficit between 40% and 60% of normal totals. Exceptionally strong - positive anomalies compared to the 1981–2010 refer ence period between +0.6°C in southern regions and southwesterlies in December brought well-above- normal precipitation totals to the Nordic countries. +1.7°C in east-central parts of the country. - Denmark received up to 250% of normal precipita Winter 2014/15 was colder than normal throughout tion. Only parts of central and northern Scandinavia Iberia due to cold air advection from the north. Spain registered a rain deficit, 60%–80% of normal. and Portugal were 0.6°C and 1°C below average, re - | S202 AUGUST 2016

223 spectively. A colder-than-normal winter was followed Precipitation in autumn was below average by a very warm spring, and the entire Iberian Peninsula throughout the Iberian Peninsula, with 60%–80% of normal rainfall over central to northeastern Spain. registered positive temperature anomalies and signifi - Only southeastern areas recorded a surplus, up to cantly above-average 500-hPa heights (dotted in Fig. 125% of normal. 7.36a,c). Spain reported a mean anomaly of +1.5°C, The year ended with very dry conditions, caused with an extremely warm May (+2.4°C), which was the by a strengthening of the positive NAO phase (+2.2 second warmest in its 55-year record. in December). Spain reported December rainfall just - Significantly anomalous above-normal tempera tures remained in summer due to a blocking high 20% of normal, the driest December at many eastern pressure ridge over Europe, and anomalies exceeded stations (several reported no rain at all), and Portugal saw less than 50% of its normal precipitation in some +2.5°C in most areas. During July, Spain experienced regions. its highest monthly average temperature on record. This month also featured unusually persistent heat (iii) Notable events wave conditions. In central and southeastern parts of During the first 10 days of February, Spain re - the country, positive anomalies of +3°C were recorded; corded a significant cold spell due to an intrusion of it was the second warmest summer season on record, continental cold air masses from central Europe. A behind 2003. minimum temperature of −11.9°C was measured at Autumn, overall, was also warmer than normal but with only slightly-above-average values. Very warm the station Molina de Aragon in central Spain. In northern Spain along the coast of the Bay of conditions in November remained in December, with monthly anomalies of +2°C as a result of an eastward Biscay, heavy rainfall in February set new record extending Azores high (positive SLP anomalies of up high totals, with precipitation 300% of the wintertime normal. to +10 hPa over the Iberian Peninsula). Although spring was overall drier than normal in Spain, heavy precipitation events occurred in March. (ii) Precipitation Annual precipitation totals over Iberia were mostly Starting on 5 March, a week of heavy rain, combined below average (60%–80% of normal). For Portugal the with meltwater, led to f looding in the northeast. On 22 March, Castellón de la Plana-Almazora on the year was extremely dry and only 68% of the normal eastern coast recorded 133.8 mm within 24 hours. rain was measured (25% of normal totals based on the In May, Spain and Portugal were affected by a heat 1971–2000 reference period used for precipitation in wave with record-breaking high temperatures. Valen - Portugal). Spain received 77% of its normal precipita - tion, mainly due to extremely dry conditions in April, cia Airport registered 42.6°C on 13 May, 6.6°C higher than the previous record. By 14 May, the southern May, November, and December. Winter 2014/15 was characterized by a strong station of Beja had already reported 19 days in 2015 positive NAO, which was ref lected in the precipitation with maximum temperatures above 30°C, which was distribution over the Iberian Peninsula. While the 14 days more than normal. northernmost part was inf luenced by northerly f low In summer, Spain suffered from an extraordinarily bringing 125% of normal precipitation, the remaining long, intense heat wave (nearly continuous from 27 region experienced a very dry season. Widespread June to 22 July), particularly affecting the central and below-average totals of less than 60% of normal were southern regions, where temperatures above 45°C recorded. were reported on 6 and 7 July. During spring, the Azores high extended far into On 4 September, Palma de Mallorca (island south the European continent and led to well-below-normal - of Barcelona) received 124.3 mm rain from thunder storm activity within 24 hours, the highest for any precipitation totals. May brought an extreme rain defi - time of year since the record began in 1973. cit. Spain reported mean monthly precipitation totals On 15–16 September, a low pressure system with a just 25% of normal, its driest May on record. Portugal also observed extreme rain deficits, but mostly in the core pressure of 990 hPa delivered more than 100 mm southern half of the country. precipitation to several stations in Portugal. Rainfall totals were 150%–200% of normal for September in In summer, wetter-than-normal conditions in northeastern Spain contrasted with below-average northern Portugal. The highest accumulated rain totals in the remainder of the country. Southern Por - was recorded at northern station Cabril (160.4 mm). Intense rainfall occurred on 1 November at the tugal received only 20%–40% of normal totals and locally even less. Algarve in Portugal. Daily accumulated precipitation | S203 AUGUST 2016 STATE OF THE CLIMATE IN 2015

224 S I D E B A R 7.1: UNUSUALLY STRONG AND LONG-LASTING HEAT WAVE IN EUROPE F ig . SB7.1. Monthly air temperature anomalies (°C, 1961–90 reference period) for Europe in (a) Jun, (b) Jul, and (c) Aug 2015. (Source: Deutscher Wetterdienst.) From late June to early September 2015, much of Europe was under the influence of an unusually strong and long-lasting - heat wave. Spain and Portugal also had well-above-normal tem peratures in May. The heat was associated with an exceptional rain deficit that led to drought conditions in several regions from southwestern Iberia to eastern Europe, while at the same time heavy thunderstorms were recorded in the central and eastern Mediterranean. The heat wave affected much of Europe during June, July, - and August (Fig. SB7.1). At the end of June, a blocking high pres sure system developed over southwest-to-central Europe, with a meandering upper level jet stream, allowing hot air to flow from Africa to Europe, where it became trapped. In mid-July, the Azores high extended farther into central Europe, and by the end of the month, it shifted eastward. The anticyclone caused large-scale subsidence, and western Europe recorded maximum temperatures up to around 40°C. By the end of August, two anticyclones developed over eastern Europe. The resulting southerly flow of hot air masses brought high temperatures to eastern and central Europe. On an areal average, the European region experienced its third warmest summer season since 1910, behind 2003 and 2010, with temperatures +1.7°C above the 1961–90 mean. August contributed most to the anomalous warmth, with a record high anomaly of +2.3°C, while July was sixth warmest th warmest, with slightly-above-normal (+1.5°C); June was 15 temperatures (+0.9°C). ig F . SB7.2. Percentages of (a) warm days and (b) warm nights for 2015. A warm day or night is defined as a For much of June, Iberia, France, and the western Alpine day where the maximum or minimum temperature region observed high temperatures, with anomalies of +3° to exceeds the 90th percentile of the values from the +4°C. Portugal registered a monthly mean temperature of 1981–2010 average. (Source: E-OBS dataset, EU - 21.8°C, its fifth highest on record, at +2.4°C above the 1961–90 RO4M.) mean. The absolute maximum of 43.2°C was measured on 29 June at Beja, in the south of the country. In France, many | S204 AUGUST 2016

225 CONT. UNUSUALLY STRONG AND LONG-LASTING HEAT S I D E B A R 7.1: WAVE IN EUROPE maximum temperature records were broken at the end of June. fered from extreme drought conditions (Fig. SB7. 3) in August. On the 30th, temperatures were as much as 12°C above the On 31 August, 74% of Portugal was categorized as severely or seasonal mean in western areas. extremely dry. As a result, wildfires occurred in the Mediter - In July, the core region of the heat wave moved to central ranean from Iberia to Turkey and in the Balkan States. The Europe, the Mediterranean, and the Balkan region. However, rain deficit also caused low water of the rivers Elbe, Rhine, and Spain still experienced its warmest July in its 55-year record, Danube, which affected shipping. The river Dnjepr in Belarus with anomalies of +2.5°C above the 1981–2010 mean. Germany had record low levels. observed a record-breaking maximum temperature of 40.3°C at In contrast, several regions experienced well-above-normal Kitzingen (central region) on 5 July, and France had a new record precipitation during summer, especially in Greece, western maximum temperature, 41.1°C, at Brive-la-Gaillarde (central Turkey, and Sicily. The rain surplus was generated by heavy southern France). Austria recorded temperatures 3.1°C above thunderstorms induced by anomalous warm sea surface normal, its warmest July since records began in 1767. In Vienna, temperatures (anomalies up to +4°C) in the Tyrrhenian Sea. - a new record daily minimum temperature of 26.9°C was mea sured. August brought extremely high temperatures to eastern and central Europe, with anomalies exceeding +4°C. In Belarus - (Brest) and Lithuania (Kaunas), record daily maximum tempera tures of 36.7°C and 35.3°C, respectively, were observed. The unusual and long-lasting high temperatures were reflected in the fact that warm days and nights (see section 2b5) were more than 40% more frequent than in a normal summer (Fig. SB7.2). High nighttime temperatures in particular can affect human health, and in Belgium and the Netherlands, strongly increased mortality was registered during this period. During July and August, Hungary reported a record 27 days of extremely warm conditions, and Budapest experienced a record-breaking 34 tropical nights, the most since records began in 1901. The heat wave was also associated with sub-regional severe rain deficits. Southern Spain and Portugal each received only 10 mm rain per month during June, July, and August, which corresponds to less than 40% of their normal totals. After F . SB7.3. DWD standardized precipitation index ig several weeks of persistent heat and continuous rain deficit, (1961–90 average) for Augv 2015. (Source: Deutscher Wetterdienst.) - southern Portugal and northeastern continental Europe suf +3°C) especially in northern parts of the region. Only exceeded 100 mm. The highest amount of 144.8 mm - was observed in Algoz, near the southern coast. some parts of southern Greece and southern Italy/Sic ily had below-average temperatures, with anomalies and tate S alkan b M 5) up to −1°C. Croatia saw a mild season and registered editerranean S positive anomalies up to +2.7°C in northeastern areas. This region includes Italy, Malta, Slovenia, Croa - Above-average temperatures dominated almost tia, Serbia, Montenegro, Bosnia and Herzegovina, the entire region in spring when the Azores high Albania, Macedonia, Greece, Bulgaria, and Turkey. extended far into the European continent. Serbia (i) Temperature recorded temperature anomalies of +2°C in northern areas. Croatia had positive anomalies of +1.8°C in its Averaged over the year, temperature anomalies in northern areas. During April, colder-than-normal 2015 were between +1°C in the central and western Mediterranean and +2°C over the Balkans. Tempera - conditions occurred over southeastern areas. In central Turkey, temperature anomalies ranged from tures up to 3°C above normal occurred near the Alps. −2° to −3°C. Much of Montenegro experienced its warmest year on record. Slovenia observed its third warmest year. Most of the region experienced a very warm sum - mer, induced by prolonged anticyclonic conditions Winter 2014/15 was warmer than normal (+2° to | S205 AUGUST 2016 STATE OF THE CLIMATE IN 2015

226 centered over continental Europe. The northern Balkans received below-average precipitation. Eastern Balkan States recorded large anomalies that ranged Serbia and Croatia reported 25%–30% of normal rain - fall. In June, heavy rains fell over northern and central between +3°C and +4°C; Serbia and Croatia reported Turkey, bringing totals up to 250% of normal, while anomalies of +2.1°C to +3.8°C. In contrast, Greece’s July was exceptionally dry across the entire region. - Peloponnese had only slightly-above-normal condi tions. Most areas observed less than 40% of normal rainfall, During autumn, temperatures remained above except parts of Italy that received 125% of normal. the long-term mean. With the exception of northern Autumn remained wet over the Balkans, whereas the Alpine region recorded below-normal precipita - Italy, which had only slightly-above-average tem - tion. Serbia reported very wet conditions, with up peratures, anomalies ranged between +1°C and +2°C. to 230% of normal precipitation, and above-normal Southern Croatia reported temperature departures precipitation prevailed in Bulgaria and northern up to +2.8°C. The highest anomalies (+3°C to +4°C) occurred at the Bosporus, due to prevailing high Greece. October contributed to the overall surplus of rain, when the dipole pattern associated with a split pressure. The year ended with contrasting conditions. While f low brought storms and above-average precipitation across southern Europe. Nearly all areas of the region the northernmost areas of the region were under the inf luence of extremely strong westerlies and associ - received more than 170% of their normal precipita - ated mild temperatures (3°–4°C above normal) during tion. Croatia rainfall was 140%–410% of normal. In contrast, November was very dry in the Alpine region, December, the southern Balkans experienced cool with less than 20% of normal rain in northern Italy. anomalies of −1°C. Dry conditions were also evident in December 2015, associated with an exceptionally strong southwesterly (ii) Precipitation - f low. Nearly all of Turkey, Italy, and the northern Bal With the exception of northern Italy, annual kans received less than 40% of their normal rainfall, precipitation totals were above normal. The largest with some areas observing less than 20% of normal. rainfall departures occurred in Sicily, eastern Greece, Bulgaria, and western Turkey, where 125%–170% of normal totals were observed. In the southern Alpine (iii) Notable events region, drier-than-normal conditions of 60%–80% of Southern Italy was hit by heavy thunderstorms on normal were recorded. Croatia reported just 63% of 5 September. The area surrounding Naples observed its normal precipitation in the northwest. hail, the largest with a diameter of 11.5 cm and a weight of 350 g. The hail injured several people and Winter 2014/15 was very wet for most regions animals, and caused damage to vehicles, houses, (hatched in Fig. 7.37a). Over the Balkans, precipita - tion totals of 125%–170% of normal were measured. trees, and crops. Southern Serbia had 175% of normal rainfall, and - During 13–14 September, extremely intense pre cipitation over the Emilia Romania in central-north localized areas in Croatia observed 225% of normal. Precipitation totals in spring mainly ranged be - Italy caused a f lood that destroyed roads and bridges. tween 60% and 125% of normal. While drier-than- Record-breaking rainfall of 123.6 mm within 1 hour normal conditions occurred near the Southern Alps (189.0 mm within 3 hours) at Cabanne and 107.6 mm and in Albania, central and southern Italy, as well within 1 hour (201.8 mm within 3 hours) in Salsomi - as easternmost parts of the region, experienced a nore caused f loods in the basin of the Aveto, Trebbia, and Nure Rivers. At Nure River, the water levels surplus of precipitation. In Serbia, totals ranged from reached 7 m; the water entered the ground f loors of 67% of normal in eastern areas to 180% of normal in localized spots. Croatia experienced dry conditions nearby houses. - Bosnia and Herzegovina reported a nationwide in northwestern parts of the country, with 45% of nor heat wave that lasted six days, starting on 15 Septem mal precipitation. In April, above-average 500-hPa - - - ber. Many new September maximum temperature re heights over Europe led to well-below-average pre cipitation over southern regions. Sicily and southern cords were observed, for example, 38.0°C in Sarajevo Peloponnese had very dry conditions with rainfall and 40.9°C in Zenica, both on 18 September. less than 20% of normal. Urope During summer, below-average rain in the north 6) e aStern e of the region contrasted with wet conditions in the This region includes the European part of Russia, south. Greece and parts of Turkey recorded totals Belarus, Ukraine, Moldova, and Romania. greater than 170% of normal, whereas most of the | S206 AUGUST 2016

227 (i) Temperature Winter 2014/15 was characterized by a strong - Averaged over the year, temperatures across east Icelandic low associated with stronger-than-normal westerly winds that brought a precipitation surplus of ern Europe were well above normal, with departures more than 125% to most of European Russia (hatched - mostly in the +2° to +3°C range. Belarus had its warm in Fig. 7.37a). Along Romania’s Black Sea coast, 170% est year on record, 2.6°C above normal, surpassing of normal precipitation fell. Only southwestern Russia the previous record years of 1989 and 2008. Moldova and Ukraine received below-average totals, less than had its second warmest year, after 2007, and recorded 80% of normal. departures from +2.1° to +2.7°C across the country. In spring, precipitation was near normal for the Temperatures in winter 2014/15 were extremely westernmost areas but above average in the Black mild, especially in northwestern and eastern European Russia, where anomalies exceeded +4°C (hatched in Sea region. The eastern half of Ukraine recorded totals more than 170% of normal. In contrast, parts Fig. 7.36a). Belarus reported a national temperature of northern European Russia experienced drier-than- 3.8°C above average, its fifth warmest such period since normal conditions. records began in 1945. In February, above-average During summer, prevailing high pressure condi - 500-hPa heights over central Siberia caused widespread tions featured a strong rain deficit in western and anomalous mild conditions across eastern Europe (more than +4°C). At the end of February, Moldova southern areas of eastern Europe, where less than 60% of normal totals were observed (dotted in Fig. 7.37c). observed daily temperatures 5°–6.5°C above the long- term mean, which, on average, occurs once every 10 - Northern and eastern European Russia were inf lu enced by frequent low pressure systems that brought years. 125%–170% of normal totals to the region. August was Spring remained warmer than normal, with a me - ridional gradient in the temperature anomalies due to dominated by exceptionally dry conditions in western prolonged high pressure over central Siberia. While and southern areas, with less than 20% of normal northern European Russia experienced anomalies rainfall. Belarus reported just 16% of normal totals, experiencing its driest August on record since 1945. that exceeded +4°C, the Caucasus region had near- Precipitation totals in autumn were unevenly dis - normal conditions. tributed. While the majority of areas had near-normal Summer was characterized by high pressure over precipitation, the western Black Sea region received continental Europe, whereas northern areas were af - more than 170% of normal, and Romania observed fected by frequent cyclones. In northeastern areas of up to 250% of normal precipitation in places. In the region, below-normal temperature anomalies as - low as −1°C were registered, while positive anoma contrast, western European Russia recorded below- average totals, with some localized observations just lies up to +4°C were recorded in westernmost and southernmost places. July was very cool in northern 60% of normal. European Russia (down to −4°C) as a result of SLP In December, exceptionally strong westerlies anomalies of −12 hPa over western Siberia. brought well-above-average precipitation to most of During autumn, temperature departures of −1°C the region, with more than 250% of normal totals measured in southern places. in eastern European Russia contrasted with positive anomalies between +1° and +2°C in the remaining regions. Southeastern Ukraine and southern European (iii) Notable events Belarus reported a thunderstorm on 14 June with Russia observed temperatures up to +3°C due to advec - hailstones measuring 3 cm in diameter. On 27 July, tion of subtropical air masses. 34.5 mm of rain fell within 30 minutes at station The year ended with significant mild conditions. Moldova reported positive deviations of +2.7°C to Zhitkovichi in the south. - - +4.5°C in December. On the 27th, areas across Mol Moldova experienced high temperatures dur ing 1–2 September. Record-breaking maximum air dova set new records in maximum temperature that ranged from 14° to 18°C. temperatures of 35.3°C and 38.6°C were measured. (ii) Precipitation iddle e aSt M 7) Annual precipitation totals in 2015 were above aver - This region includes Israel, Cyprus, Jordan, Leba - age (>125%) over northeastern areas of the region, while non, Syria, West Kazakhstan, Armenia, Georgia, and southwestern areas had near-normal conditions. Only Azerbaijan. the Caucasus region, western Ukraine, and northern Moldova recorded rainfall less than 80% of normal. | S207 AUGUST 2016 STATE OF THE CLIMATE IN 2015

228 (i) Temperature In December, colder-than-normal temperatures in - Annual temperatures were higher than normal, at the southern part of the region contrasted with excep tional positive anomalies in northern areas. Areawide +1° to +2°C above the long-term mean throughout the anomalies exceeded +4°C in western Kazakhstan. Middle East, except for Cyprus, where near-normal conditions prevailed. Armenia observed its third warmest year since records began in 1961 (+1.8°C) (ii) Precipitation Averaged over the year, much of the region saw and Israel also had its third warmest year in its 65- near-normal precipitation totals. Only western year record. Kazakhstan and the Caucasus region experienced Winter 2014/15 was characterized by anomalous drier-than-normal conditions (< 80%), while much of temperatures between +2° and +3°C, associated Jordan received totals up to 125% of normal. with above-average 500-hPa heights and advection Winter 2014/15 was mostly drier than normal, - of subtropical air (Fig. 7.37a). Armenia reported sig nificantly warmer conditions with positive anomalies with as little as 60% of normal precipitation. Only of +2.6°C; locally, in January and February, tempera - areas in the southern Caucasus region and parts of western Kazakhstan received above-average totals tures were 4°–5°C above average. In spring, temperatures were near to slightly below (more than 125% of normal). During February, westernmost Kazakhstan experienced very dry condi - normal in the Caucasus region and western Kazakh - tions, less than 20% of normal precipitation, whereas stan, while the eastern Mediterranean countries northern Israel saw 120%–170% of normal rainfall. experienced warmer-than-normal conditions (+1°C In spring, above-average precipitation in northern to +2°C). March contributed to the positive seasonal areas of the region contrasted with a rain deficit in the anomalies, due to a combination of high pressure eastern Mediterranean countries due to significant over western Russia and warm air advection from subtropical regions. Armenia observed a national above-average SLP. Locally, less than 40% of normal temperature 1.4°C above normal. total precipitation was received in some places. During summer, prevailing anticyclonic conditions During summer, conditions changed when the induced positive temperature anomalies across the region was under the inf luence of significantly above- average SLP (dotted in Fig. 7.37c). Azerbaijan reported entire region (hatched and dotted areas in Fig. 7.36e). a very dry summer, with less than 40% of normal Western Kazakhstan and the Caucasus region had precipitation. In contrast, the eastern Mediterranean anomalies up to +3°C, and the eastern Mediterranean countries mostly received an extreme surplus of rain, countries also experienced higher-than-normal tem - locally exceeding 500% of normal in some areas, de - peratures of 1°–2°C above average. Armenia observed its second warmest summer, behind 2006, in its 55-year spite its being the dry season. record, with anomalies of +2.4°C. The highest values In autumn, western Kazakhstan and much of the were recorded in June, where most stations measured eastern Mediterranean recorded 60%–80% of normal temperatures more than 3°C above the long-term precipitation, whereas the Caucasus region received 125% of normal. September was dry, whereas October mean. In Israel, colder-than-normal anomalies in June - (−1°C) contrasted with well-above-average tempera was wet. Israel reported 130% of normal rainfall in tures in August (+2°C). the central and southern coastal plain. Temperatures in autumn remained anomalously In December, low pressure over central European Russia brought precipitation to western Kazakhstan high in the Middle East. The eastern Mediterranean - that totaled more than 167% of normal. The Mediter region experienced areawide anomalies of +3°C, ranean region was affected by a high pressure ridge whereas the Caucasus and western Kazakhstan were 1°–2°C above normal. Some places in northwestern that caused a rain deficit, as little as 20% of normal, in Kazakhstan saw temperature anomalies down to the eastern half and parts of the Caucasus. −1°C. September was very warm, as a high pressure system associated with large subsidence developed (iii) Notable events over western Kazakhstan. Israel observed tempera Cyprus experienced heavy rainfall accompanied - - by f loods during 5–6 January. Station Kelokedara re tures 2.5°–3°C above normal, marking its warmest September on record, while Cyprus reported its ceived 276.6 mm of rain within 24 hours, the highest 1-day precipitation total during January since 1916. second warmest, with anomalies of +2°C. Armenia On 7 January, Azerbaijan reported a daily maxi also had its second warmest September (2010 was - warmer), exceeding the long-term mean by 3°C. mum temperature of 15°C, the highest for January since records began in 1900. | S208 AUGUST 2016

229 During 6–8 January and 18–19 February, Cyprus received heavy snowfall, with 15 cm accumulation in the first event. In both events, schools in mountainous areas were closed. On 13–14 June, Tbilisi, the capital of Georgia, was hit by heavy rain and thunderstorms. Flooding and - an associated landslide led to 12 fatalities and dam aged the local zoo, where many animals also perished. From 25 to 30 October, Israel was hit by a ma - −1 and jor storm with strong winds of 13–20 m s −1 maximum wind gusts of 36.6 m s . Hailstones with diameters of 4–5 cm damaged agriculture crops. On . 7.38. Annual mean temperature anomalies (°C; F ig 28 October, 80–85 mm rain fell within 2–3 hours 1981–2010 base period) over Asia in 2015. (Source: and caused f loods in central and eastern parts of the Japan Meteorological Agency.) country. A station near Tel Aviv received a monthly accumulation of 246 mm, which is a national record. g. Asia This section covers Russia, East Asia, South Asia, and Southwest Asia. There is no information for Southeast Asia as no corresponding author was identified for the region. Throughout this section the normal periods used vary by region. The current standard is the 1981–2010 average for both tempera - ture and precipitation, but earlier normal periods are still in use in several countries in the region. All seasons mentioned in this section refer to the North - F ig . 7.39. Annual precipitation (% of normal; 1981–2010 ern Hemisphere. - base period) over Asia in 2015. (Source: Japan Meteo rological Agency.) vervie 1) o W Seasonal precipitation amounts were above normal Based on data from WMO CLIMAT reports, annual mean surface air temperatures during 2015 in large areas from western to central Siberia in all were above normal across most of Asia and Siberia seasons, especially in winter and summer. In contrast, (Fig. 7.38). Annual precipitation amounts were above they were below normal in Southeast Asia, especially normal in eastern China, from southern Mongolia in summer and autumn. They showed greater spatial to northwestern China, and from western Siberia variability across East, central, and South Asia. to northern India, and they were below normal in Surface climate anomalies were associated with Southeast Asia (Fig. 7.39). several distinct circulation features. Convective - activity was suppressed over Southeast Asia except Figure 7.40 shows seasonal temperature and pre cipitation departures from the 1981–2010 average in winter (see Fig. 7.41), in association with El Niño conditions. In summer, the monsoon circulation over during the year. Seasonal mean temperatures were the Indian Ocean was weaker than normal (see Fig. above normal across Siberia in all seasons, except - for the east in spring and the south in autumn. Tem 7.41c), and overall activity of the Asian summer mon - soon was below normal. The northwestward seasonal peratures were also above normal in northern China in winter, in parts of central and Southeast Asia in extension of the northwest Pacific subtropical high spring, in Southeast Asia in summer, and across was weaker than normal (see Fig. 7.42c), contributing Southeast Asia and India in autumn. Temperatures to cool wet summer conditions from southeastern were below normal from central China to India in China to western Japan. winter, from the western part of central Asia to India in spring, from eastern China to central Pakistan and 2) r ia — O. N. Bulygina, N. N. Korshunova, M. U. Bardin, USS in European Russia in summer, and across central and N. M. Arzhanova Analyses are based on hydrometeorological Asia in autumn. - observations conducted at Roshydromet Observa | S209 AUGUST 2016 STATE OF THE CLIMATE IN 2015

230 temperature anomalies of +4°–6°C, and +6°–8°C anomalies were observed across the Far East. Daily temperature records were exceeded many times across European Russia. Daily and - monthly record-breaking air tem peratures were repeatedly registered in many cities, including Moscow, St. Petersburg, Tambov, Voronezh, Tomsk, and Kemerovo. Spring 2015 was also very warm, with a Russia-averaged mean seasonal air temperature anomaly of +2.3°С (Fig. 7.43), the fourth highest in the 77-year period of record. In northern - European Russia and western Sibe ria, the spring mean air temperature anomaly reached a record-breaking value of +5.2°С. Summer 2015 continued to be warmer-than-average across Russia, with a national seasonal air tempera - ture anomaly of +1.2°С, the seventh warmest on record (Fig. 7.43). Autumn was mild over most of - Russia with a seasonal mean tem perature anomaly of +0.9°C (Fig. 7.43). Positive anomalies were recorded in all regions, except southern West Siberia. From 11 to 30 September, all regions of European Russia experienced ab - 7.40. Seasonal temperature anomalies (°C, left column) and pre - . F ig normally warm weather, and many cipitation ratios (%, right column) over Asia in 2015 for (a), (b) winter meteorological stations, from Novaya (Dec–Feb 2014/2015); (c), (d) spring (Mar–May); (e), (f) summer (Jun– - Zemlya to northern Caucasia, regis Aug); and (g), (h) autumn (Sep–Nov), with respect to the 1981–2010 tered several daily record-breaking base period. (Source: Japan Meteorological Agency.) maximum temperatures. In December (Fig. 7.44), positive anomalies of tion Network. Datasets are officially registered and available at meteo.ru/english/climate/cl_data.php. mean monthly air temperature were recorded over a The national average temperature and precipitation vast area, from the western boundaries to the Sea of records began in 1935, while seasonal averages are Okhotsk coast. For the whole of Russia, the anomaly was +4.1°С, the second highest on record. The largest considered reliable only since 1939. anomalies occurred in northwestern European Russia and in the central Krasnoyarsk Territory and south - (i) Temperature ern West Siberia. In St. Petersburg, with nearly 200 The mean annual Russia-averaged air tempera - years of meteorological observations, the December ture was 2.2°С above the 1961–90 normal (Fig. 7.43), 2015 mean monthly air temperature of +2.1°С was making 2015 the warmest year since records began in - the second highest for December on record (see inset 1935. Positive mean annual air temperature anoma in Fig. 7.44). lies were observed across all regions of Russia, with the largest anomalies in northern European Russia (ii) Precipitation and western Siberia (Fig. 7.38). In 2015, Russia as a whole received slightly above- For Russia as a whole, winter was record warm, with the mean temperature 3.6°С above normal (Fig. normal precipitation, 106% of the 1961–90 normal (Fig. 7.45). 7.43). Central European Russia experienced mean | S210 AUGUST 2016

231 Winter precipitation was 119% of normal, tying (with 2007/08) as the second wettest since 1935 (the wettest winter was 1965/66, 136% of normal). In spring, Russia on average received 115% of normal precipitation. Over - European Russia, a significant precipi tation deficit was recorded in March. The summer precipitation total averaged over Russia was normal (99%). Near-normal precipitation was also recorded in autumn, 101% of normal. In December, Atlantic cyclones brought heavy precipitation to northwestern European Russia, the Urals, southern western Siberia, and the central Krasnoyarsk Territory. (iii) Notable events 7.41. Seasonal mean anomalies of 850-hPa stream function . ig F On 14 January, Kazan reached –1 6 2 s m ) using data from the JRA-55 reanalysis and (contour, 1 × 10 +2.3°С, the warmest for this date since –2 outgoing longwave radiation (OLR, shading, W m - ) using data origi records began in 1880. nally provided by NOAA for (a) winter (Dec–Feb 2014/15), (b) spring - During the last five days of Janu (Mar–May), (c) summer (Jun–Aug), and (d) autumn (Sep–Nov), with ary, the city of Magadan received respect to the 1981–2010 base period. (Source: Japan Meteorological nearly five times its normal monthly Agency.) precipitation. On 12 April, strong winds (25– −1 31 m s ) in Khakassia caused a rapid propagation of natural fires that killed five people and injured 121. The fire destroyed 1205 homes. On 24–25 June, heavy rain fell - in Sochi, with 122 mm of precipita tion observed in less than 11 hours. As a result, roads, 2000 houses, and the railroad station were inundated. Damage was estimated to be 760 mil - lion rubles (~10 million U.S. dollars). - In the city of Adler, 211 mm of pre cipitation fell in 18 hours; 200 houses, the local airport, and the railroad station were inundated. Damage was estimated to be 10–13 billion rubles (150–195 million U.S. dollars), mostly associated with the temporary closure of the airport. On 11 July, heavy rain and hail fell 7.42. Seasonal mean anomalies of 500-hPa geopotential height . ig F in the Ulyanovsk Region, with 31 mm (contour, gpm) and 850-hPa temperature (shading, °C) for (a) winter of precipitation falling in 48 minutes. (Dec–Feb 2014/15), (b) spring (Mar–May), (c) summer (Jun–Aug), and (d) autumn (Sep–Nov), with respect to the 1981–2010 base period. Hail with diameters reaching 5.6 cm Data from the JRA-55 reanalysis. (Source: Japan Meteorological damaged roofs, glass panes, and 150 Agency.) cars. | S211 AUGUST 2016 STATE OF THE CLIMATE IN 2015

232 the second warmest since national records began in 1973. In 2015, temperatures for most months except summer were higher than normal. May was the warmest on record, at 1.4°C above normal. The annual mean temperature over Mongolia for 2015 was 1.8°C, 1.3°C above normal, the second warmest since national records began in 1961 and 0.8°C warmer than 2014. Most monthly mean temperature anomalies for Mongolia were above normal, rang - ing from +0.2° to +4.4°C. January was the warmest month in 2015 with respect to departures from average, 4.4°C above normal and marking the warmest January for Mongolia in the 55-year record. Posi - tive anomalies were as high as 5°–7°C in some areas. (ii) Precipitation The mean annual total precipitation in China was 648.8 mm, 103% of normal and ig 7.43. Mean annual (1935–2015) and seasonal (1939–2015) air . F 2% higher than 2014. The total seasonal temperature anomalies (°C) averaged over the Russian terri - precipitation was below normal in winter tory for 1939–2015 (base period: 1961–90). Seasons are Dec–Feb (winter) 2014/15 and Mar–May (spring), Jun–Aug (summer), and - (94% of normal) and summer (91% of nor Sep–Nov (autumn) 2015. The smoothed annual mean time series mal), and near-normal in spring but above (11-point binomial filter) is shown in red in the top panel. normal in autumn (126% of normal). In 2015, the major rain belt of China lay south of its normal position, over areas from the middle and On 7–8 September, as a result of heavy rain (20 mm lower reaches of the Yangtze River to South China, in 4 hours), large hail (2.0 cm in diameter), and strong −1 ) in Tatarstan, 19 people were especially during summer and autumn, associated winds (up to 24 m s injured, 31 cars were damaged, trees were toppled, with a weak East Asian monsoon. Regionally, total and roofs were damaged. annual precipitation was significantly above normal in the Yangtze River basin (112% of normal, the wettest in 17 years) and in the Zhujiang River basin P. Zhang, A. Goto, S.-Y. Yim, and L. Oyunjargal aSt a 3) e — Sia Countries considered in this section include: (111% of normal), and below normal in Northeast China, Japan, Korea, and Mongolia. Unless other - China (94% of normal), in the Liaohe River basin (86% of normal), and in the Yellow River basin (73% wise noted, anomalies refer to a normal period of of normal, the driest in 13 years). The rainy season in 1981–2010. the Meiyu region started approximately 16 days early on 26 May and ended around 17 days late on 27 July (i) Temperature The annual mean temperature over China was with about 169% of normal precipitation. The rainy 10.5°C, 0.9°C above normal, the highest since re - season in North China started on 23 July (5 days later cords began in 1961. The seasonal mean surface than normal) and ended on 17 August (slightly earlier temperature anomalies were +1.1°C, +1.0°C, +0.3°C, than normal), and was the second driest season in the past 13 years. and +0.8°C for winter, spring, summer, and autumn, In western Japan, annual precipitation amounts respectively. Annual mean temperatures were above were above normal, especially on the Pacific side, normal across Japan, especially in northern Japan and since the seasonal northward expansion of the North Okinawa/Amami. In western Japan, temperatures Pacific subtropical high was weak and convection was were below normal in summer and autumn but above often active in summer. On the Pacific side of eastern normal for the year as a whole. Japan, annual precipitation amounts were also above The annual mean surface air temperature over the Republic of Korea was 13.4°C, 0.9°C above normal, normal, including record-breaking rain in September. | S212 AUGUST 2016

233 S I D E B A R 7. 2 : EXTREMELY WET CONDITIONS IN JAPAN IN LATE SUMMER 2015 From mid-August to early September 2015, most of days during a normal summer, shifted far southward of its normal position and became a factor in enhancing western to northern Japan experienced unseasonably wet conditions. Regional average precipitation totals in the 32 southwesterly moist air inflow toward Japan in the lower troposphere. These anomalous atmospheric circulation days starting on 11 August were 245% and 209% of normal for the Pacific side of western Japan and eastern Japan, patterns were sustained in connection with suppressed convective activity across the Asian summer monsoon respectively. Sunshine duration averaged over the Sea of area (Fig. SB7.5), which is consistent with that observed Japan side of eastern Japan was nearly half the normal in past El Niño events. Upper tropospheric wave trains amount. Toward the end of the period, record-breaking torrential rainfalls led to large river overflows and flooding propagating from the west across the Eurasian continent may also have played a part in sustaining the cyclonic in parts of eastern Japan. anomalies to the west of Japan. The lasting, extremely wet weather conditions were - associated with low pressure systems repeatedly forming A further contribution to the above-normal precipita tion amount came from two tropical cyclones during the and migrating eastward along a frontal zone that persisted second week of September. Typhoon Etau made landfall over the Japanese Archipelago. The persistence of the on mainland Japan and Typhoon Kilo passed northward frontal zone in turn appears related to warm air and vorticity advection in the middle troposphere induced over the Pacific off the coast of Japan, both of which by nearly stationary cyclonic circulation anomalies to the induced moist air inflow and set the environment con - west of Japan (Fif. SB7.4). Meanwhile the northwestern ducive to torrential rainfalls observed in parts of eastern Pacific subtropical high, which would bring hot and sunny to northern Japan. F ig . SB7.5. Velocity potential anomalies at 200 hPa 6 (thick and thin contours at intervals of 2.0 × 10 and –1 6 2 0.5 × 10 s , respectively) and outgoing longwave m radiation (OLR; shading) anomalies averaged over the . same period as Fig SB7.4 (base period: 1981–2010). Arrows indicate associated divergent flow, where it is significantly different from climatology. [Source: . SB7.4. Geopotential height anomalies (m) at ig F Japanese 55-year reanalysis (velocity potential) and 500 hPa averaged over 11 Aug to 11 Sep, 2015 (base pe - NOAA/CPC (OLR).] riod: 1981–2010). (Source: Japanese 55-year reanalysis.) - agriculture sector. November was the wettest month In the Republic of Korea, the annual total precipi tation was 948.2 mm, 72% of normal, the third lowest of the year and wettest November on record (181% of since national records began in 1973. In Mongolia, normal) while July was the driest month of the year (80% of normal). The high November precipitation the annual average precipitation in 2015 was 202 total included a lot of snowfall, with snow covering - mm, near normal. However, the temporal and spa at least 80% of Mongolia during the month, making tial distribution of precipitation was unfavorable for livestock husbandry difficult. Warm conditions in agriculture. At the beginning of the growing season, December helped alleviate this somewhat. - late June was warmer and drier than normal in Mon golia, resulting in drought and economic losses in the | S213 AUGUST 2016 STATE OF THE CLIMATE IN 2015

234 7.44. Air temperature anomalies (°C) in Dec 2015. Insets show the time series of mean monthly and mean . ig F daily air temperatures (°C) for the month at meteorological stations St. Petersburg, Eniseisk, and Vanavara. (iii) Notable events over China in 2015 occurred in Huanghuai and North Liaoning province in North China had its driest China from late November to early December. It had a 2 - maximum extent of 41.7 km summer since records began in 1961, which contrib , with particulate matter uted to severe drought in the area. Xinjiang had 25 smaller than 2.5 μm in diameter (PM2.5) exceeding −3 days of daily maximum temperature exceeding 35°C 150 μg m and visibility below 3 km. (normal is 10 days). In early September, the Kanto and Tohoku regions of Japan experienced record-breaking rainfall, due to warm, moist airf low associated with approaching typhoons Kilo and Etau. Total precipitation during 7–11 September was 647.5 mm at Imaichi in Tochigi Prefecture and 556.0 mm at Hippo in Miyagi Pre - fecture. Heavy rain caused large river overf lows and serious damage. Typhoon Mujigae in October was the strongest typhoon to make landfall in Guangdong province, China, since records began in 1949. The storm caused . 7.45. Annual precipitation anomaly (% of normal) F ig a major disaster, with 24 deaths and direct economic averaged over the Russian territory for the period losses estimated at over 4.5 billion U.S. dollars (see - 1935–2015. The smoothed time series (11-point bino section 4e4 for more details). mial filter) is shown as a continuous line (base period: The worst large-scale and persistent haze event 1961–90). | S214 AUGUST 2016

235 4) oUth Sia — A. K. Srivastava, J. V. Revadekar, and S over the country was below normal on most days a M. Rajeevan during the season (Fig. 7.48). During winter (January–February), rainfall over Countries in this section include: Bangaladesh, India, Pakistan, and Sri Lanka. Climate anomalies are the country was 92% of its LTA, while it was above - relative to the 1961–90 normal. Monsoon precipita - normal (138% of the LTA) during the premonsoon sea tion is defined relative to a 50-year base period (1951– son (March–May). During the post-monsoon season (October–December), it was 77% of the LTA. 2000) because there is strong interdecadal variability in Indian monsoon precipitation (Guhathakurta et al. The northeast monsoon (NEM) typically sets in over southern peninsular India during October and 2015). In the text below, this is referred to as the long- over Sri Lanka in late November. The NEM gener - term average (LTA). ally contributes 30%–50% of the annual rainfall over southern peninsular India and Sri Lanka as a whole. (i) Temperature South Asia generally experienced well-above- The 2015 NEM seasonal rainfall over southern pen - normal temperatures in 2015. The annual mean land surface air temperature averaged over India was 0.7°C above the 1961–90 average, making 2015 the third warmest year since records commenced in 1901 (Fig. 7.46; 2009 and 2010 are warmest and second warmest, respectively). Record warmth was observed during July–September (+0.9°C) and October– December (+1.1°C). (ii) Precipitation The summer monsoon set in over Kerala (south - ern peninsular India) on 5 June, 4 days later than normal, but covered the entire country on 26 June, 7.46. Annual mean temperature anomalies (base ig . F 20 days ahead of its normal date of 15 July. The pace period: 1961–90) averaged over India for the period 1901–2015. The smoothed time series (9-point binomial of advance of the monsoon over different parts of the filter) is shown as a continuous line. country was the third fastest in the 1950–2015 period. Indian summer monsoon rainfall (ISMR) during 2015 was significantly below normal, 86% of its LTA of 890 mm. ISMR during 2015 was characterized by marked spatial and temporal variability. The eastern/northeastern region of the country received normal rainfall overall, with regional variability, while the central, peninsular, and northwestern regions of - the country received below-normal rain F . ig 7.47. Spatial distribution of monsoon seasonal (Jun–Sep) fall (Fig. 7.47). Rainfall over many parts of rainfall (mm) over India in 2015 for (a) observed rainfall, (b) northern, western, and central India was normal rainfall, and (c) the difference between (a) and (b). less than 70% of the LTA. Rainfall activity was also variable in time. During the first half of the season (1 June–31 July), the coun - try received 95% of the LTA, falling to 77% of the LTA in the second half of the season (1 August–30 September). During the monsoon season, only 1 me - teorological subdivision (West Rajasthan) of 36 received excess rainfall. Eighteen subdivisions received normal rainfall, and F ig . 7.48. Daily standardized rainfall time series averaged over the remaining 17 received below-normal the monsoon core zone over India (1 Jun–30 Sep). rainfall. Except for June, rainfall averaged | S215 AUGUST 2016 STATE OF THE CLIMATE IN 2015

236 most India) and more than 50 deaths in the adjoining insular India and Sri Lanka was above normal (132% state of Andhra Pradesh. Heavy rainfall and f looding of the LTA). Sri Lanka received below-normal rainfall affected around 1.8 million people in Tamil Nadu. - during its summer monsoon season (May–Septem Tambaram (near Chennai) reported an all-time 24-h ber). However, northeast monsoon rainfall activity record rainfall of 490 mm on 2 December, while over the island nation during October–December was Chennai reported 345 mm of rain on the same day. enhanced. Economic loss due to these events was estimated to Pakistan, at the western edge of the pluvial region of be around 2 billion rupees (~29 million U.S. dollars). the South Asian monsoon, generally receives 60%–70% of its annual rainfall during its summer monsoon Northeast monsoon activity during the first week of December also led to f loods in Sri Lanka, which season (July–September). In 2015, summer monsoon caused 40 deaths and displaced more than 1.2 mil rainfall over Pakistan was 117% of the LTA and was - lion residents. marked by spatial and temporal variability. Southwest - ern/southern Pakistan received below-normal rainfall, Sia a WeSt oUth S 5) while other regions received normal or above-normal F. Rahimzadeh, M. Khoshkam, S. Fateh, — rainfall during the season. Bangladesh also received and A. Kazemi above-normal rainfall overall during its summer This subsection currently covers only Iran. Turkey monsoon season. is incorporated in the Europe subsection. Climate anomalies are relative to the 1981–2010 normal. (iii) Notable events A severe Nor’wester (a line of strong thunder - (i) Temperature Winter 2014/15 and spring 2015 were considerably storms) affected 12 districts of Bihar (eastern India) during the nighttime/early morning hours of 22–23 warmer than normal, with anomalies up to +6.4°C during winter. Most of the country was also warmer April. Over 50 lives were lost. - than normal in summer and near-normal overall in Heat wave conditions prevailed over central, pen autumn (Fig. 7.49). insular, and northern parts of India during the second half of May. Maximum temperatures were more than - (ii) Precipitation 5°C above normal at many eastern and central sta tions for several days. Some stations in Odissa and Generally, in 2015, Iran experienced drier-than- coastal Andhra Pradesh reported temperatures of normal conditions in winter and spring, while sum - near 47°C during 23–26 May. Overall, the intense mer and autumn were wetter than normal (Fig. 7.50). - During winter 2014/15, 30%–90% of normal pre heat over central and peninsular parts of the country cipitation fell across most parts of the country. Areas during May took a toll of around 2500 lives, and more than 2000 deaths were reported in the south Indian with average or above-average rainfall (up to 170% of states of Telangana and Andhra Pradesh. - normal) were confined to a small part in the north One of the most severe heat waves since 1980 affected west of the country adjacent to the Turkish border and - a small part in the southeast. During spring, precipi Karachi, Pakistan, during the second half of June and took a toll of more than 1000 lives. Temperatures reached tation amounts were 30%–90% of normal across most - of the country. The middle of the country received 44°C for two days during the period. The heat wave coin more than 90% of normal precipitation. cided with the beginning of the holy month of Ramadan, In summer, most of the country experienced when many Muslims do not eat or drink during daylight normal or above-normal precipitation (90%–170% hours, increasing susceptibility to heat stroke. During 25–26 June, heavy rain and f loods associ of normal). During autumn, precipitation was more - than 90% of normal in much of the northern and ated with a deep depression over the Arabian Sea took southern regions, while the rest of country received a toll of more than 80 lives in Gujarat in western India. 30%–90% of normal. Floods caused about 70 deaths in West Bengal (eastern India) during 30 July–5 August. Many parts of Bangladesh experienced severe (iii) Notable events Significant dust storms during spring and summer f loods from late June through the first week of Au - spread over many parts of the country, especially gust. An estimated 30 people were killed and around one million were affected. southern and southwestern Iran. Very heavy rainfall during an active period of the NEM during 9–17 November and 2–5 December led to more than 350 fatalities in Tamil Nadu (southern - | S216 AUGUST 2016

237 . 7.49. Seasonal mean surface temperature ig F ig 7.50. Observed precipitation over Iran (% of normal) F . anomalies (°C) in (a) summer (Jun–Aug) and for (a) winter (Dec–Feb 2014/15), (b) spring (Mar–May), (c) (b) autumn (Sep–Nov). (Source: I.R. of Iran Me - summer (Jun–Aug), and (d) autumn (Sep–Nov). (Source: teorological Organization & National Center for I.R. of Iran Meteorological Organization.) Drought and Disaster Risk Management.) Oceania h. the year (www.cpc.ncep.noaa.gov/products/precip W J. A. Renwick — vervie 1) o /CWlink/daily_ao_index/aao/month_aao_index - During the first half of 2015, substantial warm .shtml). The base period used throughout this section - is 1981–2010, unless otherwise indicated. ing of the equatorial Pacific sea surface and subsur face waters clearly signaled the arrival of El Niño. and Extremes typical of El Niño onset were observed M. A. Lander — Sia iCrone M 2) n orth WeSt p aCifi C across the region, including rainfall extremes and an and C. P. Guard abundance of early-season tropical cyclones. This assessment covers the area from the interna - tional date line west to 130°E, between the equator Following warm SSTs in the central and eastern equatorial Pacific in 2014 that almost reached El Niño and 20°N. It includes the U.S.-affiliated islands of Micronesia, but excludes the western islands of Kiri - thresholds (defined by NOAA as +0.5°C SST anomaly bati and nearby northeastern islands of Indonesia. in the Niño-3.4 region for three consecutive months), El Niño became established in spring (March–May) 2015 and evolved into one of the strongest such events (i) Temperature on record (alongside 1972/73, 1982/83, and 1997/98; Temperatures across Micronesia in 2015 were mostly above average. The warmth was persistent, see section 4b). El Niño–associated air temperature and rainfall patterns were observed across most of - with above-average temperatures occurring dur ing most or all of the year. Only Yap Island had a the South Pacific in 2015. A number of South Pacific substantial negative departure for any of the time - countries experienced agricultural and/or hydrologi periods summarized in Table 7.5. At islands located cal drought. Temperatures were generally above normal in in the west of the region (e.g., Palau, Yap, Guam, and Saipan) there was a tendency for daytime maximum Australasia, with Australia having another warm year, especially in the spring (Sidebar 7.3). Precipitation temperature anomalies to be greater than those of nighttime minima. In the east (Chuuk to Kosrae totals for 2015 were generally near-normal for both and Majuro), the reverse pattern was observed, as Australia and New Zealand. The southern annular also seen in 2014. Average monthly maximum and mode (SAM) was generally positive through much of 2015, becoming strongly positive at the end of minimum temperatures across most of Micronesia | S217 AUGUST 2016 STATE OF THE CLIMATE IN 2015

238 have gradually increased for several decades, with in the year. The 2015 fourth quarter rainfall totals at a total rise in average temperature on par with the Yap Island and at Palau were the lowest and second lowest in their ~65-year post-World War II historical global average increase of +0.74°C in the last century (Guard and Lander 2012). record, respectively. By late December 2015, persistent dry conditions were becoming established at most of the islands of Micronesia. The 6-month and annual (ii) Precipitation Dryness was observed across the Republic of the rainfall values for selected locations across Micronesia are summarized in Table 7.5. Marshall Islands (RMI) during early 2015, with very low rainfall totals reported at Utirik and Wotje in (iii) Notable events the northern RMI during January and February. Micronesia was the overwhelming focus of the However, rainfall throughout the RMI had a dramatic 2015 western North Pacific typhoon track distribu - rebound to very wet conditions during March and tion, with Guam at the primary nexus, by virtue of April, even at the normally driest of the atolls in the the passage of 12 named tropical cyclones within 550 north (e.g., Kwajalein, Utirik, and Wotje). Very wet km (see section 4e8 for more detail). conditions in the Marshall Islands typically occur in After nearly a decade of high values, sea level late winter and spring during years of El Niño onset. - Dryness associated with El Niño typically begins ear across Micronesia began to fall in 2014 and continued lier in the western bounds of Micronesia (e.g., Palau) to fall dramatically in 2015 (Fig. 7.51). The maximum and spreads eastward later in the year to the RMI. drop in monthly mean sea level (since 2013) at both Meanwhile, locations in the far west of Micronesia Guam and at Kwajalein was approximately 40 cm experienced an early onset of dry conditions that (the drop in 12-month means was around 25 cm). A sharp drop in mean sea level typically occurs dur - became extreme late in the year. ing El Niño, with the lowest sea level occurring in Annual totals during 2015 were mostly higher than December of the year of the El Niño peak. average, with early wetness outweighing dryness later able 7.5. Temperature and rainfall anomalies for selected Micronesia locations during 2015, (base T period: 1981–2010). Latitudes and longitudes are approximate. “Kapinga” stands for Kapingamarangi Atoll in Pohnpei State, Federated States of Micronesia. Max/Min Temp Precipitation Anomaly Location Jan –Jun Ye ar Jul–Dec Jan–Jun Jul–Dec –Jun Jan Jul–Dec Ye ar mm mm % °C °C % % of avg. mm Saipan +1.92 +1. 83 85.2 570.0 126.9 939.3 71.0 1509.3 +1.46 +1.0 4 15°N ,146 °E +0.58 +0.40 Guam 127. 5 881.6 2058.4 115 .1 2940.1 118 . 5 °E 13°N ,145 − +0.37 0.06 − 0.30 Ya p –1.44 102.2 1319. 5 112 . 8 1818.9 95.6 3138.4 –0.29 °E 9°N ,138 +0.25 +0.96 Palau +1.0 0 2451.1 62.3 1265.9 118 5 . 2 69.0 65.4 7 ° +0.31 e ° 134 n , +0.03 +0.31 +0.28 Chuuk 3970.8 116 . 2 99.4 2147.8 135.6 1823.0 °E 7°N,152 +0.97 +0.48 +0.18 –0.10 Pohnpei 5510. 3 119. 7 105.8 3039.4 134.1 2470.9 °E 7°N,158 +0.78 +0.10 Kapinga N/A 119. 5 2 411. 7 137.7 1486.9 98.4 3261.4 N/A 1°N,155 °E +0.38 – 0.21 Kosrae 2007.6 4560.1 2552.5 99.4 92.9 85.7 5°N,163°E +1. 34 +1.29 +0.01 –0.03 Majuro 3568.2 110 . 2 91.7 1854.7 135. 5 1713. 5 7 ° N ,171° E +1.17 +0.91 +0.37 +0.22 Kwajalein 3330.7 139.9 100.9 1737.1 216.8 1593.6 9°N,168°E +0.32 +0.09 | S218 AUGUST 2016

239 strengthening in the south (Fig. 7.52c). By the end of September, the characteristic El Niño signal was established: positive anomalies dominated the equatorial region, southwest of which was a band of negative anomalies aligned northwest–southeast. A narrow strip of near-average temperatures was sand - wiched between the two major anomaly features. - Below-normal air temperatures near the PNG Is lands persisted into the last three months of the year, although the band of negative anomalies stretching . 7.51. Observed sea level rise/fall (12-month moving F ig southeast from PNG through Fiji to the southern average) over the period 1945–2015 at Kwajalein (black, Cook Islands weakened considerably in the last left vertical axis) and Guam (gray, right vertical axis). quarter (Fig. 7.52d). In contrast, positive anomalies intensified along the equator and expanded south - E. Chandler and S. McGree 3) oUth p S WeSt aCifi C ward to encompass northern French Polynesia. — Countries considered in this section include: (ii) Precipitation American Samoa, Cook Islands, Fiji, French Polyne - In addition to ENSO, key climate features in the sia, Kiribati, New Caledonia, Niue, Papua New Guin - southwest Pacific are the west Pacific Monsoon ea (PNG), Samoa, Solomon Islands, Tokelau, Tonga, (WPM), which lies over the west Pacific warm pool, Tuvalu, and Vanuatu. Air temperature and rainfall anomalies are relative to the 1981–2010 period. the South Pacific convergence zone (SPCZ) aligned northwest–southeast in the southwest Pacific, and (i) Temperature the subtropical high pressure belt which is part of the Hadley Circulation. Mean air temperatures in 2015 (derived from Due mainly to enhanced activity in the WPM and NCEP–NCAR reanalysis) were strongly inf luenced SPCZ, the year began with above-normal rainfall by El Niño, which dominated the climate of the South Pacific during the year. Temperatures were recorded during January–March in many western near normal or above normal between January and - places and the Cook Islands (Table 7.6). High rain fall in central Vanuatu was associated with Tropical March (Fig. 7.52a) across much of the southwest Pacific. Positive anomalies peaked at around +1.3°C near the equatorial date line. Below-average temperatures oc - curred near PNG, with anomalies up to –1.5°C over a small region covering the PNG Islands. Positive temperature anomalies cen - tered on the equator expanded west - ward towards the Solomon Islands and strengthened during the second quarter (Fig. 7.52b). The largest positive anomalies over central Kiribati exceeded +1.2°C. Negative anomalies persisted over the PNG Islands, while a large area of nega - tive anomalies covered Vanuatu, Fiji, Tonga, and Niue during April–June, as - sociated with cool surrounding ocean. Temperatures were within 0.3°C of aver - age around the Solomon Islands, New Caledonia, Samoa, Tuvalu, and parts of French Polynesia. The temperature anomaly pattern F ig . 7.52. 2015 Southwest Pacific surface air temperature anomalies from April to June persisted into the from NCEP–NCAR reanalysis (°C; 1981–2010 base period); for (a) third quarter with negative anomalies Jan–Mar, (b) Apr–Jun, (c) Jul–Sep, and (d) Oct–Dec. | S219 AUGUST 2016 STATE OF THE CLIMATE IN 2015

240 Table 7.6. Observed 2015 rainfall relative to base period at capital towns/cities in the South Pacific. Jan Jul Jun May Apr Mar Feb Aug Oct Nov Dec Sep Port Moresby, PNG Honiara, Solomon Is Noumea, N. Caledonia Port Vila, Vanuatu Suva, Fiji Nuku‘alofa, Tonga Alofi, Niue Apia, Samoa Pago Pago, A. Samoa Rarotonga, Cook Is Funafuti, Tuvalu Tarawa, Kiribati ≥ < 40% 120% to < 160% ≥ 80% to < 120% 40 to < 80% ≥ > 160% Cyclone Pam (see Notable events and section 4e8 for the region of enhanced rainfall extending to the - northern Cook Islands and northern French Poly more details). Below-normal rainfall was recorded in the New Guinea Islands, northern and southern nesia in November. Most islands between PNG and Vanuatu, southern Tuvalu, Fiji, northern Tonga, Niue, southern French Polynesia, with the exception of the northern French Polynesia, and parts of Samoa. At Solomon Islands and Samoa, received below-normal Pekoa and Lamap in northern Vanuatu, January– rainfall. Rainfall for October–December at Garoka March was second (out of 45 years of record) and in the PNG highlands was the lowest in 45 years fourth (out of 54 years of record) driest, respectively. and second and third lowest at Momote and Wewak, In the second quarter the SPCZ was displaced to respectively. Very low rainfall was also observed in the northeast. Rainfall was below normal in parts of western and southeastern Fiji, southern Cook Islands, and central Tonga. PNG, Vanuatu, Fiji, Tonga, Niue, the Cook Islands, and French Polynesia. In contrast to typical El Niño (iii) Notable events conditions, the northern Cook Islands were drier On 6 March, Tropical Disturbance 11F developed than normal. At Suva (Fiji), April–June was the driest - about 1140 km to the northwest of Nadi, Fiji. The dis since 1942. Kiritimati (eastern) and Tarawa (western) Kiribati recorded their wettest and third wettest turbance was upgraded to a tropical depression two April–June respectively, with rainfall in excess of days later, then named Pam on 9 March. Located in an area of favorable conditions, Pam gradually intensified 1100 mm received across the country. The extent of suppressed rainfall in the south - and became a Category 5 severe tropical cyclone on west Pacific expanded over the third quarter (July– 12 March. Pam’s 10-min maximum sustained winds -1 September) to include most of PNG and most of the peaked at 135 kt (69 m s ), along with a minimum islands southwest of the SPCZ. Above-normal rainfall pressure of 896 hPa, making Pam the most intense TC of the southwest Pacific basin since Zoe in 2002 (and continued in the Kiribati, Tuvalu, and Tokelau region. Rainfall was strongly suppressed in the far western third most intense storm in the Southern Hemisphere, after Zoe in 2002 and Gafilo in 2004). In addition, Pacific, with enhanced convection in the equatorial Pam had the highest 10-minute sustained wind speed Pacific east of the Solomon Islands, a pattern typical of El Niño. recorded of any South Pacific TC. The center of Pam passed just east of Efate where the capital Port Vila is In the fourth quarter, the SPCZ continued to located (Fig. 7.53), and Erromango and Tanna suffered be displaced to the northeast. The central Pacific remained wetter or much wetter than average, with a direct hit, making Pam the single worst natural di - | S220 AUGUST 2016

241 South Wales all observed one of their ten warmest years on record. - The Australian annual mean maximum tempera ture (Fig. 7.54) was 0.96°C above average, and annual mean minimum temperature (Fig. 7.55) was 0.69°C above average; both sixth highest on record. Several exceptional warm spells occurred during 2015, with an especially warm October–December (see Notable events and Sidebar 7.3 for more details). April and May were the only months in which national mean temperatures were below average. Annual maxima were in the highest decile (top 10%) of the historical distribution (since 1900) for the north of the Northern Territory, most of Queensland and Victoria, southeast and western South Australia, and large areas of Western Australia (highest on record for part of southwest Western Australia). An - nual anomalies of +1.5°C to +2.0°C were observed F . ig 7.53. Tropical Rainfall Measuring Mission (TRMM) in the southwest and southern interior of Western satellite over Cyclone Pam on 13 March 2015 UTC. The Australia and over a large area of southwestern to image shows the cyclone track and a rainfall analysis central Queensland. from TRMM’s Microwave Imager (TMI) and Precipi - Annual minima were also in the highest 10% of tation Radar (PR) instruments. Rainfall in part of the historical observations for most of Western Australia, cyclone was measured by TRMM PR at more than –1 large parts of Queensland, western South Australia, 119 mm h . (Source: trmm.gsfc.nasa.gov/trmm_rain/Events /pam_trmm_tmi_pr_13_march_2015_0923_utc.jpg.) areas of New South Wales, and far eastern Victoria. Annual minima were near-average for most of the Northern Territory, northeastern Western Australia, saster in the history of Vanuatu. The cyclone crippled infrastructure, with an estimated 90% of Vanuatu’s - other smaller areas in western Tasmania, the north ern Cape York Peninsula and near Rockhampton buildings impacted by the storm. Communications in Queensland, and pockets of the southern half of were devastated and there was a shortage of water 00 0 for several days following the storm. At least 132 people were affected by Pam, including 54 0 00 chil - dren. There were at least 15 fatalities. 4) a tralia C. Ganter and S. Tobin US — The information presented here has been prepared using the homogenized Australian temperature da - taset (ACORN-SAT) for area-averaged temperature values and the observational dataset (AWAP) for area-averaged rainfall values and mapped analyses for both temperature and rainfall. See www.bom.gov .au/climate/change/acorn-sat/ and www.bom .gov.au/climate/maps/#tabs=About-maps-and-data for more information. (i) Temperature Australia’s annual mean temperature for 2015 was 0.83°C above the 1961–90 average, making it the fifth warmest year since national observations commenced in 1910. Eight of Australia's ten warmest years have F ig . 7.54. Maximum temperature anomalies (°C) occurred since 2002, with the most recent three years for Australia, averaged over 2015, relative to a among the five warmest. In 2015, Western Australia, 1961–90 base period. (Source: Australia Bureau of Meteorology.) Queensland, Victoria, South Australia, and New | S221 AUGUST 2016 STATE OF THE CLIMATE IN 2015

242 states had below-average rainfall, with Victoria 14th driest and Tasmania 8th driest; both experiencing - their driest year since the 2006 El Niño year. For Vic toria, 16 of the last 19 years (1997–2015) have brought below-average rainfall with similar, though not quite as persistent, runs in other parts of southern Australia (e.g., southeastern Australia, 13 of the last 19 years). Large parts of eastern Australia commenced the year with continuing long-term rainfall deficiencies (on the two- to three-year scale). These deficiencies persisted across much of inland Queensland in 2015, while drought increased through Victoria and south - east South Australia, and also emerged in Tasmania and southwest Western Australia. The deficiencies echo long-term declines in cool-season rainfall across southern Australia and poor wet-season rainfall in Queensland over three successive years. - After a wet January, much of northern and cen tral Australia was very dry from February onwards, F i g . 7.55. Minimum temperature anomalies (°C) marking a dry end to the northern Australian wet for Australia, averaged over 2015, relative to a season (October–April). 1961–90 base period. (Source: Australia Bureau of The combination of a strong El Niño and a record Meteorology.) warm Indian Ocean (see section 4b) is an unusual set of climate drivers, and for Australia the presence of South Australia. They were cooler than average for a very warm Indian Ocean appears to have limited some areas of the Northern Territory and northern Western Australia. Large areas of Western Australia the broad-scale rainfall anomalies in the cooler part - of the year in inland southern and eastern Australia. and the western half of Queensland observed anoma However, southwest Western Australia recorded its lies in excess of +1.0°C, rising to more than +2.0°C second driest May–July while Victoria and southern in the southeastern interior of Western Australia. South Australia were also dry, but to a lesser extent. Cool anomalies within 1°C of average were observed over the northern Kimberley and large parts of the Northern Territory. (ii) Precipitation Rainfall averaged across Australia for 2015 was 445.8 mm, or 96% of the 1961–90 average, the 59th driest year since records commenced in 1900 and close to the median. The near-average national total masks some regional differences. Notable areas of below-average rainfall were recorded in the southwest of Western Australia, large areas of southwest to central Queensland, and large areas of the southeast, extending from Tasmania through Victoria and into South Australia. Above-average precipitation was recorded in the Pilbara and Gascoyne regions of Western Australia, and across most of the Northern Territory extending into northern South Australia. Scattered parts of the eastern seaboard, extending from Victoria to southern Queensland, also had above-average precipitation for the year (Fig. 7.56). State-wise, only Western Australia and the North - ig . 7.56. Rainfall deciles for Australia for 2015, based F ern Territory had above-average precipitation for the - on the 1900–2015 distribution. (Source: Australia Bu year, but within 20% of their annual total. All other reau of Meteorology.) | S222 AUGUST 2016

243 A late-developing positive Indian Ocean dipole was a fifth, Quang, weakening below cyclone intensity associated with a very dry September–October, which just prior to landfall. Marcia was the strongest at had significant impacts on agricultural production in landfall (Category 5) and the most intense known tropical cyclone so far south on the east coast [ maxi - southern areas. December closed the year with heavy –1 ), rainfall over large parts of the north. mum 10-minute sustained winds of 110 kt (57 m s crossing near Yeppoon, and causing damage as far (iii) Notable events south as Bundaberg]. Lam made landfall in the eastern Top End on the same day, 20 February—the An exceptional heat wave affected large parts of northern and central Australia during March, with first time in recorded history that two severe tropical prolonged heat peaking on the 19th and 20th. The cyclones made landfall in Australia on the same day other most notable heat waves occurred during the (see also section 4e7). last three months of the year—record early-season For further detail on these and other significant heat across southern Australia in early October, con - events please see the Monthly Weather Reviews, tributing to Australia’s warmest October on record Annual Climate Statement, and Annual Climate and extreme heat in much of southeastern Australia Report available from www.bom.gov.au/climate in the third week of December (see Sidebar 7.3 for /current/ . more detail). eW 5) n — N. Fedaeff Z ealand In the following discussion, the base period is Many significant bushfires occurred during the year. The most destructive, in terms of property loss 1981–2010 for all variables, unless otherwise noted. The nation wide average temperature is based upon or total area burned, are described below: Early January, South Australia’s Mount Lofty the National Institute of Water and Atmospheric • Research (NIWA) seven-station temperature series 0 00 hectares Ranges, 27 houses destroyed and 20 burned; that begins in 1909 (www.niwa.co.nz/our-science - /climate/information-and-resources/nz-temp-record • Late January and early February, southwest West 00 hectares burned—the most ern Australia, 150 0 /seven-station-series-temperature-data). All statistics are based on data available as of 8 January 2016. significant fires for the region in many decades; • 15–21 November, around Esperance in Western 00 hectares burned; (i) Temperature 0 Australia, 145 New Zealand had a relatively mild 2015, with 25–27 November, South Australia’s Mid North, • annual mean temperatures within 0.5°C of the an at least 87 houses at Pinery (north of Adelaide) - 0 nual average across much of the country (Fig. 7.57). severely damaged or destroyed and 90 - 00 hect ares burned; • 25 December, near Lorne on Victoria’s southwest coast, 116 homes and holiday houses destroyed at Wye River and Separation Creek. Two east coast lows brought significant damage. The first caused severe weather and f looding in coastal New South Wales between 20 and 23 April, with 12 regions declared natural disaster areas and several deaths reported due to f lash f looding at Dungog. The second low produced heavy rain and damaging winds over southeast Queensland and parts of New South Wales between 1 and 4 May. A significant, but far from record-breaking, cold outbreak over southeastern Australia during 11–17 July brought widespread snow along the Great Dividing Range, extending from the hills east of Melbourne into southern Queensland. This was the most significant snow event in Queensland since 1984. F 7.57. 2015 annual mean temperature anomalies ig . Four tropical cyclones made landfall in Australia (°C) relative to 1981–2010 normal. Dots show observing during 2015: Lam, Marcia, Nathan, and Olwyn with station locations. (Source: NIWA.) | S223 AUGUST 2016 STATE OF THE CLIMATE IN 2015

244 S I D E B A R 7. 3: AUSTRALIA’S WARM RIDE TO END 2015 The last three months of 2015 saw a very warm end Overall, October–December was the warmest such to the year for Australia. It was the warmest October period on record, with a mean temperature anomaly of on record with respect to both maximum and minimum +1.93°C. It also tied with July–September 2013 for highest temperatures, with the October mean temperature positive anomaly for any three month period. anomaly of +2.89°C the largest anomaly on record for Australia for any month in 106 years of records. Maximum temperatures for October in Victoria, South Australia, and New South Wales were close to values typical of an average December, with monthly anomalies of more than +5°C for the three states (Fig. SB7.6). October's most significant daily extremes occurred in the first half of the month. Significantly high daytime temperatures occurred in southwest Western Australia beginning 1 October, spreading eastwards and peaking in extent during 4–6 October in the southeast; each day, some part of southern Australia had daily anomalies in excess of +12°C. Another bout of extreme heat occurred over southern Western Australia from 8 to 13 October. Later in the month, there were several other periods which had temperatures well above average, but no indi - vidual event in the latter part of October surpassed the extremes of the first 10 days (www.bom.gov.au/climate /current/statements/scs52.pdf). - November mean temperatures were the third warm i g F . SB7.6. Maximum temperature anomalies est on record and, overall, spring 2015 was second for Oct 2015 for Australia (1961–90 base period). warmest on record. The most recent three springs were (Source: Australia Bureau of Meteorology.) the three warmest, with 2014 remaining the warmest on record. The last notable warm period for the year occurred in December. Following a consistently warm first half of December for the southeast interior of Australia, a burst of more extreme warmth occurred in mid-December over South Australia. Adelaide reached 40°C each day during 16–19 December—the first time this has occurred in Adelaide in December (previous earliest run of four or more days of at least 40°C was 3–6 January in 1906). Heat peaked for this event on 19 December in South Australia and western Victoria ahead of a front, with the cool - change passing through southeast Australia on 20 Decem ber. Individual daytime and nighttime December records were set on the 19th and 20th across South Australia, Victoria, New South Wales, and Tasmania (Fig. SB7.7). Mildura measured a minimum of 31.9°C on 20 December. This was a new record high minimum temperature for a Victorian site for any month, surpassing 30.9°C also at Mildura on 24 January 2001. A number of other locations in northern Victoria experienced their hottest night on SB7.7. Daily minimum temperature percentiles F ig . record for any month (www.bom.gov.au/climate/current for 20 Dec 2015 (1961–90 base period). (Source: /statements/scs53.pdf). Australia Bureau of Meteorology.) | S224 AUGUST 2016

245 The nation wide average temperature for 2015 was 12.7°C (0.1°C above average). According to NIWA’s seven-station temperature series, 2015 was the 27th warmest year for New Zealand in the 107-year period of record. Above-average temperature anomalies were observed throughout many regions of the country in January and March, while below-average temperature anomalies were prominent in September. (ii) Precipitation Annual rainfall totals for 2015 were below normal (50%–79% of the annual normal) in the north and east of the country: Northland, Tasman, Nelson, and Canterbury as well as parts of eastern Waikato, Bay of Plenty, Gisborne, and Wellington—a pattern typically observed during El Niño. Rainfall was within 20% of the annual normal for the remainder of New Zealand (Fig. 7.58). It was the driest year on record for Kaitaia and Kerikeri (both located in Northland), which re - 7.58. 2015 annual total rainfall (%) relative to . ig F corded 75% and 63% of their normal annual rainfall, 1981–2010 normal. Distribution of observing station locations is as in 7. 57. (Source: NIWA .) . ig F respectively. There were no high total rainfall records or near-records set in 2015. January was a particularly dry month for New Zealand with rainfall totals well ships as high seas were expected to cause f looding and damage. below normal (less than 50% of the January normal) or below normal (50%–79% of the January normal) On 3 June, Dunedin (Otago) was inundated by - heavy and prolonged rainfall, which resulted in sig for most parts of the country. Parts of Northland, nificant f looding, loss of electricity, evacuations, and Auckland, Taranaki, Manawatu-Whanganui, Kapiti Coast, Wellington, Marlborough, north Canterbury, road closures throughout the city and nearby areas. and Central Otago each received less than 10% of Dunedin (Musselburgh) received 113 mm of rainfall in the 24 hours to 9 a.m. on 4 June—its second-highest their respective January normal rainfall. Conversely, 1-day rainfall total on record for all months (records rainfall during April and June was well above normal began in 1918). (greater than 149% of normal) in Taranaki and large Another sigificant f looding event occurred dur parts of Manawatu-Whanganui. - ing 20–21 June in Whanganui. Heavy and prolonged Of all of the regularly reporting gauges, the wettest rainfall caused evacuation of more than 100 house - location in 2015 was Cropp River, in the Hokitika holds and the Whanganui River breached its banks, River Catchment (West Coast, South Island, 975 m - 6 32 mm. The spilling f loodwaters into Whanganui’s central busi a.s.l.) with an annual rainfall total of 11 ness district. This event was the worst f lood on record driest of the regularly reporting rainfall sites in 2015 for the area and led to the declaration of a state of was Clyde (Central Otago), which recorded 267 mm of rainfall for the year. North Egmont (Taranaki) emergency. From 23 to 26 June, record-low temperatures experienced the highest 1-day rainfall total in 2015 of 466 mm on 19 June. were observed in many regions of the country. A high pressure system over and west of New Zealand (iii) Notable events combined clear skies with a southerly f low, result - ing in very cold temperatures for many parts of the See Fig. 7.59 for a schematic of notable events. On 16 and 17 March, ex-Tropical Cyclone Pam passed country. In particular, sites in the Mackenzie Country east of New Zealand and was associated with strong and Central Otago dropped to well below 0°C. The lowest recorded air temperature for 2015 (excluding winds and heavy rain in northern and eastern parts of - the North Island. About 2200 Auckland and North high elevation alpine sites) was −21.0°C, observed at Tara Hills (Mackenzie Country) on 24 June. This land properties lost power as strong winds brought was the fourth coldest temperature ever recorded in down trees onto power lines. Over 100 people in the New Zealand. East Cape area were evacuated from their homes as - a precaution, particularly in low lying coastal town | S225 AUGUST 2016 STATE OF THE CLIMATE IN 2015

246 F ig . 7.59. Notable weather events and climate extremes for New Zealand in 2015. (Source: NIWA.) | S226 AUGUST 2016

247 APPENDIX 1: RELEVANT DATASETS AND SOURCES General variable or Specific dataset Source Section phenomenon or variable http://apps.ecmwf.int/datasets/data/macc-reanalysis SB2.2 Aerosol products Aerosols CAMS Reanalysis 2g3, SB2.2 http://macc.copernicus-atmosphere.eu/catalogue/ Woods Hole 3e http://oaflux.whoi.edu Oceanographic Air-sea fluxes Institute OAFlux project MODIS http://ladsweb.nascom.nasa.gov 2h1, 5e Albedo GFAS http://atmosphere.copernicus.eu/documentation 2h3, SB2.2 -fire-emissions Biomass burning GFEDv4 https://daac.ornl.gov/VEGETATION/guides/fire 2h3 _emissions_v4.html CALIPSO http://eosweb.larc.nasa.gov/PRODOCS/calipso 2d5 /table_calipso.html 2d5 CLARA-A2 https://climatedataguide.ucar.edu/climate-data /clara-a1-cloud-properties-surface-albedo-and -surface-radiation-products-based-avhrr 2d5 HIRS www.ssec.wisc.edu/~donw/PAGE/CLIMATE .HTM Clouds, cloudiness MISR http://eosweb.larc.nasa.gov/PRODOCS/misr/level3 2d5 /overview.html MODIS C6 http://ladsweb.nascom.nasa.gov 2d5 d5 http://cfs.ncep.noaa.gov/cfsr/ NCEP CFSR PATM OS -x 2d5 www.ncdc.noaa.gov/cdr/operationalcdrs.html SatCORPS No public archive 2d5 www.ecmwf.int/en/research/climate-reanalysis SB2.1 ERA-Interim /era-interim SB2.1 www.gleam.eu/ GLEAM Evaporation, evapotranspiration, http://oaflux.whoi.edu 3e2 Woods Hole sublimation Oceanographic Institute OAFlux project FAPAR http://fapar.jrc.ec.europa.eu 2h2 https://earth.esa.int/web/guest/missions/esa MERIS 2h2 FAPAR -operational-eo-missions/envisat/instruments/meris MODIS -TIP http://modis.gsfc.nasa.gov/about/ 2h2 ERA-Interim www.ecmwf.int/en/research/climate-reanalysis 6b /era-interim Geopotential height JRA-55 http://jra.kishou.go.jp/JRA-55/index_en.html 7g NCEP–NCAR www.esrl.noaa.gov/psd/data/gridded/data 5b, 7f .ncep.reanalysis.pressure.html reanalysis-1 pressure Glacier mass balance http://dx.doi.org/10.5904/wgms-fog-2015-11 5f Randolph Glacier www.glims.org/RGI/ 2c3 Glacier mass or volume Inventory v3.2 www.wgms.ch/mbb/sum12.html 2c3, 5f World Glacier Monitoring Service | S227 AUGUST 2016 STATE OF THE CLIMATE IN 2015

248 General variable or Specific dataset Source Section phenomenon or variable By email to [email protected] Dai 2d1 ERA-Interim www.ecmwf.int/research/era 2d1 HadCRUH www.metoffice.gov.uk/hadobs/hadcruh 2d1 www.metoffice.gov.uk/hadobs/hadisdh 2d1 HadISDH Humidity, (near) surface HOAPS wui.cmsaf.eu/safira/action 2d1 /viewDoiDetails?acronym=HOAPS_V001 http://jra.kishou.go.jp/JRA-55/index_en.html JRA-55 2d1 MERRA-2 http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/ 2d1 www.noc.soton.ac.uk/noc_flux/noc2.php 2d1 NOCS 2.0 HIRS www.ssec.wisc.edu/~donw/PAGE/CLIMATE 2d3 Humidity, upper .HTM atmosphere By email to [email protected] 2d3 UTH DMSP-SSMIS http://nsidc.org/data/docs/daac/nsidc0001_ssmi_tbs 5e, 6e .gd.html GRACE http://podaac.jpl.nasa.gov/datasetlist?ids 5e, 5f Ice sheet characteristics =Platform&values=GRACE PROMICE www.promice.dk/home.html 5e (Greenland) 2b4 www.imgw.pl Institute of Meteorology and Water Management Lake temperature (Poland) www.glerl.noaa.gov 2b4 NOAA/GLERL 3h, 4e2 AMO www.esrl.noaa.gov/psd/data/timeseries/AMO/ AO 2e1 www.cpc.ncep.noaa.gov/products/precip /CWlink/daily_ao_index/teleconnections.shtml www.cpc.ncep.noaa.gov/data/indices 4b, 6d EQ-SOI www.esrl.noaa.gov/psd/enso/mei/ MEI 3f http://monitor.cicsnc.org/mjo/current/rmm/ 4c MJO, real-time multivariate ftp://ftp.cpc.ncep.noaa.gov/wd52dg/data/indices SB3.2, 3h NAO /tele_index.nh NAO (summer) 2e1 Courtesy of Chris K. Folland 2e1 NAO (winter) https://climatedataguide.ucar.edu/climate-data Modes of variability /hurrell-north-atlantic-oscillation-nao-index-station -based ONI www.cpc.ncep.noaa.gov/products/analysis 4b _monitoring/ensostuff/ensoyears.shtml PDO http://research.jisao.washington.edu/pdo/ 3b PDO 3b www.cpc.ncep.noaa.gov/products/GODAS/ SAM www.antarctica.ac.uk/met/gjma/sam.html 6b, 6d SAM, AAO www.cpc.ncep.noaa.gov/products/precip/CWlink 2e1 /daily_ao_index/aao/aao.shtml ftp://ftp.bom.gov.au/anon/home/ncc/www/sco/soi 2e1 SOI /soiplaintext.html | S228 AUGUST 2016

249 General variable or Specific dataset Section Source phenomenon or variable CLIVAR /CO2 www.go-ship.org 3j Repeat Hydrography Global Ocean Ship- Based Hydrographic Ocean carbon Investigations Program pCO www.socat.info 3j, 6g 2 Atlantic Meridional 3h www.noc.soton.ac.uk/rapidmoc Ocean circulation Overturning Circulation Antarctic Bottom https://portal.aodn.org.au/ 6g Water CSIRO/ACE CRC/ 3c www.cmar.csiro.au/sealevel/thermal - IMAS-UTAS esti _expansion_ocean_heat_timeseries.html mate MRI/JMA www.data.jma.go.jp/gmd/kaiyou/english/ohc/ohc 3c _global_en.html 3c NCEI www.nodc.noaa.gov/OC5/3M_HEAT Ocean heat content and _CONTENT/ temperature NCEP ocean www.cpc.ncep.noaa.gov/products/GODAS/ 4h reanalysis http://oceans.pmel.noaa.gov PMEL/JPL/JIMAR 3c Roemmich and http://sio-argo.ucsd.edu/RG_Climatology.html 3c, SB3.2 Gilson (2009) Argo monthly climatology Met Office EN4.0.2 3c www.metoffice.gov.uk/hadobs/en4 /download-en4-0-2-l09.html 6g https://portal.aodn.org.au/ Antarctic Bottom Water Argo www.argo.ucsd.edu/ 3d Blended Analysis for ftp://ftp.cpc.ncep.noaa.gov/precip/BASS 3d2 Surface Salinity NCEI global salinity 3d3 www.nodc.noaa.gov/OC5/3M_HEAT Ocean salinity anomalies _CONTENT Roemmich and http://sio-argo.ucsd.edu/RG_Climatology.html SB3.2 Gilson (2009) Argo monthly climatology World Ocean Atlas 3d2, 3d3 www.nodc.noaa.gov/OC5/WOA09/pr_woa09 .html 2009 3e CERES FLASHflux https://eosweb.larc.nasa.gov/project/ceres/ebaf Ocean surface heat flux _surface_table CERES FLASHFlux http://flashflux.larc.nasa.gov 3e, 4b2, 4c Project Outgoing longwave radiation 4e3, 4e6 https://www.ncdc.noaa.gov/cdr/atmospheric Daily OLR /outgoing-longwave-radiation-daily | S229 AUGUST 2016 STATE OF THE CLIMATE IN 2015

250 General variable or Specific dataset Source Section phenomenon or variable www.bodekerscientific.com/data/total-column Bodeker Scientific 5j -ozone CALIPSO (polar http://eosweb.larc.nasa.gov/PRODOCS/calipso 6h stratospheric /table_calipso.html clouds) GOME/ www.iup.uni-bremen.de/gome/wfdoas/ 2g4 SCIAMACHY/ GOME2 (GSG) merged total ozone GOME/ 2g4 http://atmos.eoc.dlr.de/gome/gto-ecv.html SCIAMACHY/ www.esa-ozone-cci.org GOME2 (GTO) merged total ozone https://gozcards.jpl.nasa.gov 2g4 GOZCARDS ozone profiles http://mirador.gsfc.nasa.gov Ozone, total column and KNMI OMI http://ozoneaq.gsfc.nasa.gov 6h stratospheric www.temis.nl 2g4 Multisensor reanalysis of total ozone Aura MLS http://mls.jpl.nasa.gov/index-eos-mls.php 5j, 6h NASA http://acdb-ext.gsfc.nasa.gov/Data_services/merged 2g4 NASA BUV/SBUV v8.6 (MOD v8.6) merged ozone NOAA BUV/SBUV 2g4 ftp://ftp.cpc.ncep.noaa.gov/SBUV_CDR v8.6 (MOD v8.6) merged ozone Ozonesonde 6h www.esrl.noaa.gov/gmd/dv/spo_oz 2g4 SAGE II/OSIRIS Dataset linked to Bourassa et al. (2014) WOUDC ground- 2g4 ftp://ftp.tor.ec.gc.ca/pub/woudc/Project-Campaigns based ozone /ZonalMeans Aura OMI/MLS http://acd-ext.gsfc.nasa.gov/Data_services 2g6, SB2.2 Ozone, tropospheric /cloud_slice/new_data.html Active layer http://nsidc.org/data/docs/fgdc/ggd313_calm/ 5i thickness GTN-P http://gtnpdatabase.org 2c1 Permafrost 5i http://permafrost.gi.alaska.edu/sites_map temperature http://edytem.univ-savoie.fr/ 2c1 Permafrost temperature at Permafrost French sites www.tspnorway.com, www.met.no 2c1 Permafrost temperature at Norwegian sites www.permos.ch 2c1 Permafrost temperature at Swiss sites Aqua MODIS- http://oceancolor.gsfc.nasa.gov/cms/reprocessing/ 3i Reprocessing R2014.0 Phytoplankton, ocean color SeaWiFS R2014.0 http://oceancolor.gsfc.nasa.gov/cms/reprocessing/ 3i VIIRS R2014.0 3i http://oceancolor.gsfc.nasa.gov/cms/reprocessing/ | S230 AUGUST 2016

251 General variable or Specific dataset Source Section phenomenon or variable www.cpc.ncep.noaa.gov/products/janowiak 4b3, 4d CMORPH /cmorph_description.html www.ncdc.noaa.gov/temp-and-precip 2d4 GHCN /ghcn-gridded-products.php GPCC 2d4, 7f www.gpcc.dwd.de Precipitation http://precip.gsfc.nasa.gov 2d4, 3e, 4h GPCPv23 NCEP–NCAR www.esrl.noaa.gov/psd/data/gridded/data 7e .ncep.reanalysis.html reanalysis http://pmm.nasa.gov/TRMM/products-and 7h TRMM MI/PR -applications JRA-55 6d http://jra.kishou.go.jp/JRA-55/index_en.html Precipitation (net) 6c http://amrc.ssec.wisc.edu/data Antarctic Meteorological Research Center AWS 6b, SB6.1 ERA-Interim www.ecmwf.int/en/research/climate-reanalysis Pressure, sea level or near- /era-interim surface HadSLP2r www.metoffice.gov.uk/hadobs 2e1 JRA-55 http://jra.kishou.go.jp/JRA-55/index_en.html 6d NCEP–NCAR www.esrl.noaa.gov/psd/data/gridded/data 7f reanalysis .ncep.reanalysis.html ELSE River discharge No public archive 2d6 Near-Real-Time http://nsidc.org/data/nsidc-0081.html 6f DMSP SSM/I-SSMIS Daily Polar Gridded Sea ice concentration Nimbus-7 SMMR http://nsidc.org/data/docs/daac/nsidc0079 6f _bootstrap_seaice.gd.html and DMSP SSM/I (Bootstrap) 5c ESA CryoSat-2 https://earth.esa.int/web/guest/missions/esa -operational-eo-missions/cryosat https://espo.nasa.gov/oib/content/OIB_1 5c NASA Operation IceBridge http://nsidc.org/data/nsidc-0081.html 6f Near-Real-Time Sea ice duration DMSP SSM/I-SSMIS Daily Polar Gridded http://nsidc.org/data/nsidc-0079.html 6f Nimbus-7 SMMR and DMSP SSM/I (Bootstrap) 5c,6f http://nsidc.org/data/docs/daac/nsidc0079 Nimbus-7 SMMR _bootstrap_seaice.gd.html Sea ice extent and DMSP SSM/I (Bootstrap) 5c CryoSat-2 https://earth.esa.int/web/guest/-/how-to Sea ice freeboard/thickness -access-cryosat-data-6842 Ssalto/Duacs www.aviso.altimetry.fr 3f, 6g Multimission Altimeter Products Sea level/sea surface height 3f Tide gauge http://uhslc.soest.hawaii.edu/ TOPEX/Jason 3f http://sealevel.colorado.edu/ | S231 AUGUST 2016 STATE OF THE CLIMATE IN 2015

252 General variable or Specific dataset Source Section phenomenon or variable www.esrl.noaa.gov/psd/data/gridded 3b, 4e2, ERSST.v3b and v4 /data.noaa.ersst.html 4e4 ,4g www.metoffice.gov.uk/hadobs/hadisst 3b HadISST1 www.metoffice.gov.uk/hadobs/hadsst3 2b1 HadSST3 Sea surface temperature www.esrl.noaa.gov/psd/data/gridded N OA A O I S S Tv2 3b, 4b1, 4d2, 4e3, /data.ncep.oisst.v2.html 4e6, 4h, 5d, 7d NOAA daily http://nsidc.org/data/g02156 5g Interactive Multisensor Snow and Ice Mapping Snow cover System Snow cover extent www.snowcover.org 2c2, 5g and duration 5g http://nsidc.org/data/nsidc-0447 Canadian Meteorological Centre daily gridded Snow depth global snow depth analysis Soil moisture ESA CCl SM www.esa-soilmoisture-cci.org/node?page=3 2d8 www.esrl.noaa.gov/gmd/grad/mloapt.html Mauna Loa solar 2f2 Solar transmission transmission ftp://aftp.cmdl.noaa.gov/data/ozwv/WaterVapor 2g5 Frost Point Hygrometer Data (Boulder, Hilo, Lauder) http://physics.valpo.edu/ozone/ticosonde.html 2g5 Frost Point Stratospheric water vapor Hygrometer Data (San Jose) MLS data http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/MLS 2g5 /index.shtml Aura http://aura.gsfc.nasa.gov/instruments/mls.html 2g5 MLS NASA Brazil-Malvina 3g www.aoml.noaa.gov/phod/altimetry/cvar/mal Region Confluence /BM_anm.php Region www.aoml.noaa.gov/phod/altimetry/cvar/agu/ 3g Long-term time series of surface currents: Agulhas Current 3g www.aoml.noaa.gov/phod/altimetry/cvar/nbc Long-term time Surface current series of surface currents: North Brazil Current www.aoml.noaa.gov/phod/altimetry/cvar/yuc 3g Long-term time /transport.php series of surface currents: Yucatan Current 3h OSCAR www.oscar.noaa.gov | S232 AUGUST 2016

253 Specific dataset General variable or Specific dataset General variable or Section Source Section Source or variable phenomenon or variable phenomenon Antarctic 6c http://amrc.ssec.wisc.edu/data ERSST.v3b and v4 www.esrl.noaa.gov/psd/data/gridded 3b, 4e2, Meteorological 4e4 ,4g /data.noaa.ersst.html Research Center www.metoffice.gov.uk/hadobs/hadisst 3b HadISST1 AWS 2b1 www.metoffice.gov.uk/hadobs/hadsst3 HadSST3 Sea surface temperature CRUTEM4 www.metoffice.gov.uk/hadobs/crutem4 2b1, 5b, 7f N OA A O I S S Tv2 www.esrl.noaa.gov/psd/data/gridded 3b, 4b1, www.cru.uea.ac.uk/cru/data/temperature 4d2, 4e3, /data.ncep.oisst.v2.html www.ecmwf.int/en/research/climate-reanalysis 2b1, 2b5, ERA-Interim 4e6, 4h, 6b /era-interim 5d, 7d EURO4m E-obs www.ecad.eu/download/ensembles 7f NOAA daily 5g http://nsidc.org/data/g02156 /ensembles.php Interactive GHCNDEX www.climdex.org/datasets.html 2b5 Multisensor Snow and Ice Mapping 2b1 www.metoffice.gov.uk/hadobs/hadcrut4 HadCRUT4 global Snow cover Temperature, (near) surface System temperature 2c2, 5g www.snowcover.org Snow cover extent http://ds.data.jma.go.jp/tcc/tcc/products/gwp/temp JMA global 2b1, 7g and duration temperature /map/download.html http://nsidc.org/data/nsidc-0447 5g Canadian JRA-55 http://jra.kishou.go.jp/JRA-55/index_en.html 2b1 Meteorological MERRA-2 http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/ 2b1 Snow depth Centre daily gridded NASA/GISS global 2b1 http://data.giss.nasa.gov/gistemp global snow depth temperature analysis www.esrl.noaa.gov/psd/data/gridded/data NCEP–NCAR 5b, 5i, 7e, www.esa-soilmoisture-cci.org/node?page=3 2d8 Soil moisture ESA CCl SM .ncep.reanalysis.html reanalysis 7h Mauna Loa solar www.esrl.noaa.gov/gmd/grad/mloapt.html 2f2 Solar transmission www.ncdc.noaa.gov/monitoring-references NOAA/NCEI global 2b1 transmission /faq/anomalies.php temperature 2g5 ftp://aftp.cmdl.noaa.gov/data/ozwv/WaterVapor Frost Point 2b1 www.berkeleyearth.org Berkeley Earth Hygrometer Data surface temperature (Boulder, Hilo, Lauder) ERA-Interim www.ecmwf.int/en/research/climate-reanalysis 2b1, 2b2, 2b3, 6b /era-interim http://physics.valpo.edu/ozone/ticosonde.html 2g5 Frost Point Stratospheric water vapor Hygrometer Data 2b2, 2b3 http://jra.kishou.go.jp/JRA-55/index_en.html JRA-55 (San Jose) http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/ 2b2, 2b3 MERRA-2 http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/MLS MLS data 2g5 2b3 http://cfs.ncep.noaa.gov/cfsr/ NCEP CFSR /index.shtml 6h www.esrl.noaa.gov/psd/data/gridded NCEP–DOE NASA Aura MLS http://aura.gsfc.nasa.gov/instruments/mls.html 2g5 Reanalysis 2 /data.ncep.reanalysis2.html 3g www.aoml.noaa.gov/phod/altimetry/cvar/mal Brazil-Malvina NCEP–NCAR www.esrl.noaa.gov/psd/data/gridded 7f Region Confluence /BM_anm.php /data.ncep.reanalysis.html reanalysis Temperature, upper Region atmosphere 2b2, 2b3 www.star.nesdis.noaa.gov/smcd/emb/mscat/ NOAA/NESDIS/ www.aoml.noaa.gov/phod/altimetry/cvar/agu/ 3g Long-term time S TAR series of surface 2b2, 2b3 RAOBCORE, RICH www.univie.ac.at/theoret-met/research currents: Agulhas /raobcore Current www.ncdc.noaa.gov/oa/climate/ratpac 2b2, 2b3 R ATPAC www.aoml.noaa.gov/phod/altimetry/cvar/nbc 3g Long-term time Surface current series of surface 2b2, 2b3 www.remss.com RSS currents: North http://vortex.nsstc.uah.edu/public/msu UAH MSU 2b2, 2b3 Brazil Current 2b2 University of New web.science.unsw.edu.au/~stevensherwood www.aoml.noaa.gov/phod/altimetry/cvar/yuc 3g Long-term time South Wales /radproj/index.html /transport.php series of surface University of www.atmos.uw.edu/~pochedls/nobackup 2b2, 2b3 currents: Yucatan /share/ Washington Current GRACE http://podaac.jpl.nasa.gov/star/index.php 2d7 Terrestrial groundwater 3h www.oscar.noaa.gov OSCAR storage http://ceres.larc.nasa.gov/products 2f1 CERES EBAF Ed2.8 .php?product=EBAF-TOA TOA earth radiation budget https://eosweb.larc.nasa.gov/project/ceres/ebaf CERES FLASHFlux 2f1 _toa_table | S233 AUGUST 2016 STATE OF THE CLIMATE IN 2015

254 General variable or Specific dataset Source Section phenomenon or variable COSMIC GPS-RO www.cosmic.ucar.edu/ro.html 2d2 www.ecmwf.int/en/research/climate-reanalysis ERA-Interim 2d2 /era-interim GNSS ground-based 2d2 http://rda.ucar.edu/datasets/ds721.1/ total column water vapor Total column water vapor http://jra.kishou.go.jp/JRA-55/index_en.html 2d2 JRA-55 http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/ 2d2 MERRA-2 RSS SSM/I AMSR-E www.remss.com 2d2 ocean total column water vapor Total solar irradiance http://science.nasa.gov/missions/sorce/ 2f SORCE/TIM AGGI www.esrl.noaa.gov/gmd/aggi 2g1 Carbon dioxide www.esrl.noaa.gov/gmd/dv/iadv 2g1 2g7, S B2 . 2 Carbon monoxide https://www2.acom.ucar.edu/mopitt http://mls.jpl.nasa.gov/products/clo_product.php 6h Chlorine monoxide, Aura MLS Hydrogen chloride, 6h http://disc.sci.gsfc.nasa.gov/datacollection/ML2HCL MLS Aura _V004.html Trace gases Methane www.esrl.noaa.gov/gmd/dv/iadv 2g1 Nitrous oxide www.esrl.noaa.gov/gmd/hats/combined 2g1 /N2O.html www.esrl.noaa.gov/gmd/odgi ODGI 2g2 Perfluorocarbons http://agage.eas.gatech.edu 2g1, 2g2 www.esrl.noaa.gov/gmd/hats/combined Sulfur hexafluoride 2g1 /SF6.html I BTrAC S www.ncdc.noaa.gov/oa/ibtracs 4e JTWC best- 4e4, 4e5, www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc track data (2011 4e6 /best_tracks preliminary) Tropical cyclone data www.jma.go.jp/jma/jma-eng/jma-center/rsmc RSMC-Tokyo, JMA 4e4 best-track data -hp-pub-eg/besttrack.html S PE A rTC http://apdrc.soest.hawaii.edu/projects/speartc 4e7, 4e8 2e2 Australian (McVicar) http://doi.org/10.4225/08/56A85491DDED2 www.ecmwf.int/en/research/climate-reanalysis 2e2, SB6.1 ERA-Interim /era-interim www.metoffice.gov.uk/hadobs/hadisd/ HadISD 2e2 Wind, (near) surface JRA-55 http://jra.kishou.go.jp/JRA-55/index_en.html 2e2, 4h MERRA-2 http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/ 2e2 winds.jpl.nasa.gov/missions/RapidScat/ SB4.2 RapidScat | S234 AUGUST 2016

255 Specific dataset General variable or Section Source or variable phenomenon 4b1 Climate Forecast http://cfs.ncep.noaa.gov/ System 2e3, 6b www.ecmwf.int/en/research/climate-reanalysis ERA-Interim /era-interim GRASP http://doi.pangaea.de/10.1594/PANGAEA.823617 2e3 Wind, upper atmosphere http://jra.kishou.go.jp/JRA-55/index_en.html JRA-55 2e3 MERRA http://gmao.gsfc.nasa.gov/research/merra/ 2e3 4e3, 4e4, NCEP–NCAR www.esrl.noaa.gov/psd/data/gridded/data .ncep.reanalysis.html reanalysis 4e6, 4g | S235 AUGUST 2016 STATE OF THE CLIMATE IN 2015

256 | S236 AUGUST 2016

257 ACKNOWLEDGMENTS We wish to thank the AMS Journals’ editorial S. Dong, S. Garzoli, and D. Volkov were supported • staff, in particular Melissa Fernau, for facilitating the - by NOAA/AOML, NOAA/CPO, and the Coopera document. We thank the NCEI visual communica - tive Institute for Marine and Atmospheric Studies, tions team for laying the document out and executing University of Miami. the countless number of technical edits needed. We • S. Billheimer and L. D. Tally acknowledge funding also wish to express our sincere and deep gratitude to from US CLIVAR CLIMODE, NSF OCE-0960928. Dr. Rick Rosen, who served as the AMS special editor • M. Ishii’s work was supported by ERTDF [2-1506] for this report. Dr. Rosen’s handling of the reviews of the Ministry of Environment, Japan. was at the same time rigorous and responsive, and G. C. Johnson and J. M. Lyman were supported • greatly improved the document. by NOAA/PMEL and the Climate Observations Division of the NOAA/CPO. Chapter 2 • R. Killick was supported by the joint U.K. DECC/ • We thank David Parker for his excellent internal Defra Met Office Hadley Centre Climate Pro - rev iew. gramme (GA01101). S. W. Wijffels and D. Monselesan were supported Kate Willett, Robert Dunn, Rob Allan, David • • by the Australian Climate Change Science Pro - Parker, Chris Folland, and Colin Morice were sup - gram. ported by the Joint U.K. DECC/Defra Met Office • C. M. Domingues was supported by an Aus Hadley Centre Climate Programme (GA01101). - tralian Research Council Future Fellowship • Markus Donat received funding from Australian (FT130101532). Research Council Grant DE150100456. • Iestyn Woolway and Chris Merchant received Computational resources and support from the • NASA Advanced Supercomputing Division are funding from the European Union’s Horizon 2020 gratefully acknowledged. Programme for Research and Innovation under Grant Agreement 640171. • Chapter 4 Sarah Perkins-Kirkpatrick was funded by Austra - lian Research Council Grant DE140100952. We thank Brenden Moses (NOAA/National • • Hurricane Center, Miami, Florida) for his timely The datasets used for sections 2d2 and 2d5 were inputs to sidebar 4.2. provided from the JRA-55 project carried out by We thank Bill Ward (NOAA/NWS/Pacific Region • the Japan Meteorological Agency. We thank Paul Berrisford (European Centre • Headquarters) who was involved with the internal for Medium-Range Weather Forecasts), Mike review of the chapter. We thank Mark Lander (University of Guam) • Bosilovich (NASA), and Shinya Kobayashi (Japan and Charles ”Chip” Guard (NWS/Guam Weather Meteorological Agency) for timely provision of reanalysis data. Forecast Office) for providing valuable inputs related to section 4e4. Chapter 3 Sandra Bigley (NOAA/Pacific Marine Environ • - Chapter 5 mental Laboratory) provided outstanding editorial • For support in coediting the chapter, Jackie assistance. Richter-Menge and Jeremy Mathis thank the NOAA/Arctic Research Office. • Scott Cross, Toby Garfield, Jon Hare, Boyin Huang, Liqing Jiang, Kelly Kearney, and Dan • We thank the authors for their contributions and Seidov imparted useful comments on an early the reviewers for their thoughtful and construc - draft of the chapter. tive comments. Jim Overland’s contribution to section 5b was • Comments from three anonymous reviewers • helped to improve the chapter. supported by the NOAA/Arctic Research Project of the Climate Program Office and by the Office M. Baringer, G. Goni, R. Lumpkin, C. Meinen, and • C. Schmid were supported by NOAA/AOML and of Naval Research, Code 322. the Climate Observation Division of NOAA/CPO. | S237 AUGUST 2016 STATE OF THE CLIMATE IN 2015

258 • - Kit M. Kovacs and Christian Lydersen acknowl Chapter 6 • edge the support of the Norwegian Polar Institute, Special thanks to Dr. Marilyn Raphael and while Patrick Lemons acknowledges the U.S. Fish Dr. Florence Fetterer for their internal reviews of the chapter. and Wildlife Service, for the research programs • The work of Rob Massom, Phil Reid, and Jan Lieser that supported the creation of sidebar 5.1. was supported by the Australian Government’s For section 5f, B.Wouters was supported by the • Cooperative Research Centre program through Netherlands Polar Program and the Marie Curie the Antarctic Climate and Ecosystems CRC, and International Outgoing Fellowship within the 7th European Community Framework Programme contributes to AAS Project 4116. Ted Scambos was supported under NASA Grant • (FP7-PEOPLE-2011-IOF-301260), and M. Sharp is supported by a Discovery Grant from NSERC NNX10AR76G and NSF ANT 0944763, the Ant - Canada. arctic Glaciological Data Center. • Sharon Stammerjohn was supported under NSF Max Holmes and the coauthors of section 5h • PLR 0823101. thank the USGS (Yukon), Water Survey of Canada (Mackenzie), and Roshydromet (Severnaya Dvina, Chapter 7 Pechora, Ob’, Yenisey, Lena, and Kolyma) for the • We thank Peter Bissolli (Deutsche Wetterdienst) discharge data. • Vladimir Romanovsky and coauthors of section and David Parker (Met Office) for their excellent 5i acknowledge the support of the state of Alaska, help with section 7f. the National Science Foundation (Grants PLR- Samson Hagos and Zhe Feng are supported by • the U.S. Department of Energy Office of Science 0856864 and PLR-1304271 to the University of Alaska, Fairbanks, as well as PLR-1002119 and Biological and Environmental Research as part of the Regional and Global Climate Modeling PLR-1304555 to the George Washington Univer - Program; their institution, Pacific Northwest sity), and the Geological Survey of Canada and National Laboratory, is operated by Battelle for Natural Resources Canada. • the U.S. Department of Energy under Contract Support for section 5i was also provided by the DE-AC05-76RLO1830. Russian Science Foundation (Projects RNF 16-17-00102, 13-05-41509 RGO, 13-05-00811, 13-08-91001, 14-05-00956, 14-17-00037, and 15- 55-71004) and by the government of the Russian Federation. • Germar Bernhard and coauthors of section 5j ac - knowledge the support of the U.S. National Science Foundation (Grant ARC-1203250), a Research Council of Norway Centres of Excellence award (Project 223268/F50) to the Norwegian Radiation Protection Authority, and the Academy of Finland for UV measurements by the FARPOCC and SAARA projects in Finland. | S238 AUGUST 2016

259 ACRONYMS AND ABBREVIATIONS obal Climate Observing System An tarctic Oscillation AAO Gl GCOS lobal Historical Climatology G GHCN ACE N OAA’s Accumulated Cyclone Network Energy Index reenhouse gas OAA’s Annual Greenhouse Gas N AGGI GHG g GISS N ASA’s Goddard Institute of Space Index Studies ALT a ctive layer thickness tlantic multidecadal oscillation lobal Ozone Monitoring G GOME AMO A dvanced Microwave Scanning A AMSR-E Experiment G Radiometer for Earth Observing GPCC lobal Precipitation Climatology Centre System AMSU G lobal Precipitation Climatology dvanced Microwave Sounding A GPCP Project Unit AO rctic Oscillation GRACE ravity Recovery and Climate G A Experiment AOD a erosol optical depth GTN-P long-Track Scanning Radiometers lobal Terrestrial Network on G ATSR A AVHRR dvanced Very High Resolution A Permafrost HadAT Radiometer M et Office Hadley Centre’s AVISO radiosonde temperature product rchiving, Validating, and A HadCRUT Interpretation of Satellite M et Office Hadley Centre/CRU gridded monthly temperatures Oceanographic data CAMS limate Anomaly Monitoring C dataset M et Office Hadley Centre's sea ice System HadISST and SST dataset CDR limate data record c C CERES igh Resolution Infrared Sounder HIRS-W H louds and the Earth’s Radiant nternational Best Track Archive IBTrACS I Energy System N for Climate Stewardship OAA’s Climate Prediction Center CPC N CPHC i ICD ce cover duration OAA’s Central Pacific Hurricane ndian Ocean dipole I IOD Center ISCCP I niversity of East Anglia's Climate U CRU nternational Satellite Cloud Research Unit Climatology Project DU JMA obson Unit apanese Meteorological Agency J D et Propulsion Laboratory J JPL e vaporation minus precipitation E–P JRA apanese Reanalysis ECMWF E uropean Centre for Medium- J .S. Navy’s Joint Typhoon Warning U JTWC Range Weather Forecasts Center ECV e ssential climate variable LHF EECl atent heat f lux l ffective equivalent chlorine e ffective equivalent stratospheric EESC e ong-lived greenhouse gas l LLGHG MDR ain Development Region M chlorine arth Observatory System E ultivariate ENSO index MEI m EOS MERIS edium Resolution Imaging M arth radiation budget E ERB ERBE Spectrometer arth Radiation Budget E M MISR ultiangle Imaging Experiment SpectroRadiometer xtended Reconstructed Sea ERSST E MLS Surface Temperature M icrowave Limb Sounder ESA m MOC E uropean Space Agency eridional overturning current ESRL MOCHA eridional Overturning M E arth System Research Laboratory Circulation Heat Transport Array FAPAR F raction of Absorbed Photosynthetically Active MODIS M oderate Resolution Imaging Spectroradiometer Radiation F ast Longwave and Shortwave FLASHf lux MSLP m ean sea level pressure Radiative Fluxes MSU M icrowave Sounding Unit | S239 AUGUST 2016 STATE OF THE CLIMATE IN 2015

260 NAO N orth Atlantic Oscillation adiosonde Innovation Composite R RICH Homogenization NASA N ational Aeronautics and Space RSS emote Sensing Systems R Administration S NCAR ational Center for Atmospheric N SAM outhern annular mode SCD s now covered duration Research SCE OAA’s National Climatic Data s now cover extent NCDC N Center canning Imaging Absorption S SCIAMACHY N NCEP OAA’s National Center for Spectrometer for Atmospheric Environmental Prediction Chartography ational Environmental Research N NERC SeaWiFS S ea-viewing Wide Field of View ensible heat f lux S SHF Council S SLP ea level pressure ational Oceanic and Atmospheric NOAA N outhern Oscillation index SOI S Administration outh Pacific convergence zone SPCZ ational Snow and Ice Data Center NSIDC N S Sp ecial Sensor Microwave Imager O OAFlux bjectively Analyzed Air-Sea SSM/I S SSH ea surface height Fluxes ea surface salinity S SSS O zone-depleting Gas Index ODGI SSTA ea surface temperature anomaly S ODS o zone-depleting substance now water equivalent SWE OHCA S o cean heat content anomaly OISST ropical cyclone heat potential T TCHP O ptimal Interpolation SST OLR TCWV T otal column water vapor ou tgoing longwave radiation T O TOA zone Monitoring Instrument OMI op of atmosphere OAA’s Oceanic Niño index otal Ozone Mapping Spectrometer ONI TOMS T N LR precipitation index O OPI T TRMM ropical Rainfall Measuring P–E Mission p recipitation minus evaporation W ater Balance Model WBM PATMOS (-x) P athfinder Atmospheres (Extended ater equivalent Product) w.e. w P WGMS W PDO orld Glacier Monitoring Service acific decadal oscillation WMO W orld Meteorological olar stratospheric clouds p PSC Organization p ractical salinity scale PSS orld Ocean Atlas Q uasi-biennial oscillation WOA QBO W W WOCE ick Scatterometer Qu QuikSCAT orld Ocean Circulation RAOBCORE adiosonde Observation R Experiment Correction adiosonde Atmospheric R RATPAC Temperature Products for Assessing Climate | S240 AUGUST 2016

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