# Tropical South America East of the Andes

José Antonio Marengo, Jhan Carlo Espinoza, Josyane Ronchail, L. M. Alves

► To cite this version:

José Antonio Marengo, Jhan Carlo Espinoza, Josyane Ronchail, L. M. Alves. Tropical South America East of the Andes. Bulletin of the American Meteorological Society, 2016, State of the Climate in 2015, 97 (8), pp.S185. 10.1175/2016BAMSStateoftheClimate.1. hal-01644275

HAL Id: hal-01644275

https://hal.science/hal-01644275

Submitted on 14 Jan 2021

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
STATE OF THE CLIMATE
IN 2015

<figure><img src="image_2.png" /></figure>

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

**Editors**

Jessica Blunden

Derek S. Arndt

**Chapter Editors**

Howard J. Diamond
A. Johannes Dolman
Robert J. H. Dunn
Dale F. Hurst
Gregory C. Johnson

Jeremy T. Mathis
Ademe Mekonnen
A. Rost Parsons
James A. Renwick

Jacqueline A. Richter-Menge
Ahira Sánchez-Lugo
Carl J. Schreck III
Sharon Stammerjohn
Kate M. Willett

**Technical Editors**

Kristin Gilbert
Tom Maycock
Susan Osborne
Mara Sprain

**AMERICAN METEOROLOGICAL SOCIETY**
COVER CREDITS:

FRONT: Reproduced by courtesy of Jillian Pelto Art/University of Maine Alumnus, Studio Art and Earth Science —
*Landscape of Change* © 2015 by the artist.

BACK: Reproduced by courtesy of Jillian Pelto Art/University of Maine Alumnus, Studio Art and Earth Science —
*Salmon Population Decline* © 2015 by the artist.

*Landscape of Change* uses data about sea level rise, glacier volume decline, increasing global temperatures, and the increas-
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-population-decline; 2015.)

How to cite this document:

Citing the complete report:

Blunden, J. and D. S. Arndt, Eds., 2016: State of the Climate in 2015. *Bull. Amer. Meteor. Soc.*, **97** (8), S1–S275, DOI:10.1175/2016BAMSStateoftheClimate.I

Citing a chapter (example):

Mekonnen, A., J. A. Renwick, and A. Sánchez-Lugo, Eds., 2016: Regional climates [in “State of the Climate in 2015”]. *Bull. Amer. Meteor. Soc.*, **97** (8), S173–S226.

Citing a section (example):

Tsidu, M., 2016: Southern Africa between 5° and 30°S [in “State of the Climate in 2015”]. *Bull. Amer. Meteor. Soc.*, **97** (8), S192–S193.
EDITOR AND AUTHOR AFFILIATIONS (ALPHABETICAL BY NAME)

**Aaron-Morrison, Arlene P.**, Trinidad & Tobago
Meteorological Service, Piarco, Trinidad

**Ackerman, Steven A.**, CIMSS, University of Wisconsin–
Madison, Madison, Wisconsin

**Adams, Nicolaus G.**, NOAA/NMFS Northwest Fisheries
Science Center, Seattle, Washington

**Adler, Robert F.**, Earth System Sciences Interdisciplinary
Center, University of Maryland, College Park, College
Park, Maryland

**Albanil, Adelina**, National Meteorological Service of
Mexico, Mexico

**Alfaro, E.J.**, Center for Geophysical Research and School
of Physics, University of Costa Rica, San José, Costa Rica

**Allan, Rob**, Met Office Hadley Centre, Exeter, United
Kingdom

**Alves, Lincoln M.**, Centro de Ciencias do Sistema
Terrestre, Instituto Nacional de Pesquisas Espaciais,
Cachoeira Paulista, Sao Paulo, Brazil

**Amador, Jorge A.**, Center for Geophysical Research and
School of Physics, University of Costa Rica, San José,
Costa Rica

**Andreassen, L. M.**, Section for Glaciers, Ice and Snow,
Norwegian Water Resources and Energy Directorate,
Oslo, Norway

**Arendt, A.**, Applied Physics Laboratory, University of
Washington, Seattle, Washington

**Arévalo, Juan**, Instituto Nacional de Meteorología e
Hidrología de Venezuela, Caracas, Venezuela

**Arndt, Derek S.**, NOAA/NESDIS National Centers for
Environmental Information, Asheville, North Carolina

**Arzhanova, N. M.**, Russian Institute for
Hydrometeorological Information, Obninsk, Russia

**Aschan, M. M.**, UiT The Arctic University of Norway,
Tromsø, Norway

**Azorin-Molina, César**, Instituto Pirenaico de Ecología,
Consejo Superior de Investigaciones Científicas,
Zaragoza, Spain

**Banzon, Viva**, NOAA/NESDIS National Centers for
Environmental Information, Asheville, North Carolina

**Bardin, M. U.**, Institute for Global Climate and Ecology,
Roshydromet and Russian Acad. Sci., Institute of
Geography, Russian Acad. Sci., Moscow, Russia

**Barichivich, Jonathan**, Instituto de Conservación,
Biodiversidad y Territorio, Universidad Austral de Chile, and
Center for Climate and Resilience Research (CR)², Chile

**Baringer, Molly O.**, NOAA/OAR Atlantic Oceanographic
and Meteorological Laboratory, Miami, Florida

**Barreira, Sandra**, Argentine Naval Hydrographic Service,
Buenos Aires, Argentina

**Baxter, Stephen**, NOAA/NWS Climate Prediction
Center, College Park, Maryland

**Bazo, Juan**, Servicio Nacional de Meteorología e
Hidrología de Perú, Lima, Perú

**Becker, Andreas**, Global Precipitation Climatology
Centre, Deutscher Wetterdienst, Offenbach, Germany

**Bedka, Kristopher M.**, NASA Langley Research Center,
Hampton, Virginia

**Behrenfeld, Michael J.**, Oregon State University,
Corvallis, Oregon

**Bell, Gerald D.**, NOAA/NWS Climate Prediction Center,
College Park, Maryland

**Belmont, M.**, Seychelles National Meteorological Services,
Pointe Larue, Mahé, Seychelles

**Benedetti, Angela**, European Centre for Medium-Range
Weather Forecasts, Reading, United Kingdom

**Bernhard, G.**, Biospherical Instruments, San Diego,
California

**Berrisford, Paul**, European Centre for Medium-Range
Weather Forecasts, Reading, United Kingdom

**Berry, David I.**, National Oceanography Centre,
Southampton, United Kingdom

**Bettolli, María L.**, Departamento Ciencias de la
Atmósfera y los Océanos, Facultad de Ciencias Exactas y
Naturales, Universidad de Buenos Aires, Argentina

**Bhatt, U. S.**, Geophysical Institute, University of Alaska
Fairbanks, Fairbanks, Alaska

**Bidegain, Mario**, Instituto Uruguayo de Meteorología,
Montevideo, Uruguay

**Bill, Brian D.**, NOAA/NMFS Northwest Fisheries Science
Center, Seattle, Washington

**Billheimer, Sam**, Scripps Institution of Oceanography,
University of California, San Diego, La Jolla, California

**Bissolli, Peter**, Deutscher Wetterdienst, WMO RA VI
Regional Climate Centre Network, Offenbach, Germany

**Blake, Eric S.**, NOAA/NWS National Hurricane Center,
Miami, Florida

**Blunden, Jessica**, NOAA/NESDIS National Centers for
Environmental Information, Asheville, North Carolina

**Bosilovich, Michael G.**, Global Modelling and Assimilation
Office, NASA Goddard Space Flight Center, Greenbelt,
Maryland

**Boucher, Olivier**, Laboratoire de Météorologie
Dynamique, Institut Pierre Simon Laplace, CNRS/UPMC,
Paris, France

**Boudet, Dagne**, Climate Center, Institute of Meteorology
of Cuba, Cuba

**Box, J. E.**, Geological Survey of Denmark and Greenland,
Copenhagen, Denmark

**Boyer, Tim**, NOAA/NESDIS National Centers for
Environmental Information, Silver Spring, Maryland

**Braathen, Geir O.**, WMO Atmospheric Environment
Research Division, Geneva, Switzerland

**Bromwich, David H.**, Byrd Polar and Climate Research
Center, The Ohio State University, Columbus, Ohio

**Brown, R.**, Climate Research Division, Environment and
Climate Change Canada, Montreal, Quebec, Canada

**Bulygina, Olga N.**, Russian Institute for
Hydrometeorological Information, Obninsk, Russia

**Burgess, D.**, Geological Survey of Canada, Ottawa,
Ontario, Canada
**Calderón, Blanca**, Center for Geophysical Research,
University of Costa Rica, San José, Costa Rica

**Camargo, Suzana J.**, Lamont-Doherty Earth
Observatory, Columbia University, Palisades, New York

**Campbell, Jayaka D.**, Department of Physics, The
University of the West Indies, Jamaica

**Cappelen, J.**, Danish Meteorological Institute,
Copenhagen, Denmark

**Carrasco, Gualberto**, Servicio Nacional de Meteorología
e Hidrología de Bolivia, La Paz, Bolivia

**Carter, Brendan R.**, Joint Institute for the Study of the
Atmosphere and Ocean, University of Washington, and
NOAA/OAR Pacific Marine Environmental Laboratory,
Seattle, Washington

**Chambers, Don P.**, College of Marine Science, University
of South Florida, St. Petersburg, Florida

**Chandler, Elise**, Bureau of Meteorology, Melbourne,
Victoria, Australia

**Christiansen, Hanne H.**, Arctic Geology Department,
UNIS-The University Centre in Svalbard, Longyearbyen,
Norway

**Christy, John R.**, University of Alabama in Huntsville,
Huntsville, Alabama

**Chung, Daniel**, Department of Geodesy and
Geoinformation, Vienna University of Technology,
Vienna, Austria

**Chung, E.-S.**, Rosenstiel School of Marine and
Atmospheric Science, University of Miami, Miami, Florida

**Cinque, Kathy**, Melbourne Water, Melbourne, Australia

**Clem, Kyle R.**, School of Geography, Environment, and
Earth Sciences, Victoria University of Wellington,
Wellington, New Zealand

**Coelho, Caio A.S.**, CPTEC/INPE Center for Weather
Forecasts and Climate Studies, Cachoeira Paulista, Brazil

**Cogley, J. G.**, Department of Geography, Trent University,
Peterborough, Ontario, Canada

**Coldewey-Egbers, Melanie**, German Aerospace Center
(DLR) Oberpfaffenhofen, Wessling, Germany

**Colwell, Steve**, British Antarctic Survey, Cambridge,
United Kingdom

**Cooper, Owen R.**, Cooperative Institute for Research in
Environmental Sciences, University of Colorado Boulder,
and NOAA/OAR Earth System Research Laboratory,
Boulder, Colorado

**Copland, L.**, Department of Geography, University of
Ottawa, Ottawa, Ontario, Canada

**Cosca, Catherine E.**, NOAA/OAR Pacific Marine
Environmental Laboratory, Seattle, Washington

**Cross, Jessica N.**, NOAA/OAR Pacific Marine
Environmental Laboratory, Seattle, Washington

**Crotwell, Molly J.**, Cooperative Institute for Research in
Environmental Sciences, University of Colorado Boulder,
and NOAA/OAR Earth System Research Laboratory,
Boulder, Colorado

**Crouch, Jake**, NOAA/NESDIS National Centers for
Environmental Information, Asheville, North Carolina

**Davis, Sean M.**, Cooperative Institute for Research in
Environmental Sciences, University of Colorado Boulder,
and NOAA/OAR Earth System Research Laboratory,
Boulder, Colorado

**de Eyto, Elvira**, Marine Institute, Newport, Ireland

**de Jeu, Richard A. M.**, Transmissivity, and VanderSat,
Noordwijk, Netherlands

**de Laat, Jos**, Royal Netherlands Meteorological Institute
(KNMI), DeBilt, Netherlands

**DeGasperi, Curtis L.**, King County Water and Land
Resources Division, Seattle, Washington

**Degenstein, Doug**, University of Saskatchewan,
Saskatoon, Saskatchewan, Canada

**Demircan, M.**, Turkish State Meteorological Service,
Ankara, Turkey

**Derksen, C.**, Climate Research Division, Environment and
Climate Change Canada, Toronto, Ontario, Canada

**Destin, Dale**, Antigua and Barbuda Meteorological Service,
St. John's, Antigua

**Di Girolamo, Larry**, University of Illinois at Urbana-
Champaign, Urbana, Illinois

**Di Giuseppe, F.**, European Centre for Medium-Range
Weather Forecasts, Reading, United Kingdom

**Diamond, Howard J.**, NOAA/NESDIS National Centers
for Environmental Information, Silver Spring, Maryland

**Dlugodencky, Ed J.**, NOAA/OAR Earth System Research
Laboratory, Boulder, Colorado

**Dohan, Kathleen**, Earth and Space Research, Seattle,
Washington

**Dokulil, Martin T.**, Research Institute for Limnology,
University of Innsbruck, Mondsee, Austria

**Dolgov, A. V.**, Knipovich Polar Research Institute of Marine
Fisheries and Oceanography, Murmansk, Russia

**Dolman, A. Johannes**, Department of Earth Sciences,
Earth and Climate Cluster, VU University Amsterdam,
Amsterdam, Netherlands

**Domingues, Catia M.**, Institute for Marine and Antarctic
Studies, University of Tasmania, and Antarctic Climate
and Ecosystems Cooperative Research Centre, and
Australian Research Council's Centre of Excellence for
Climate System Science, Hobart, Tasmania, Australia

**Donat, Markus G.**, Climate Change Research Centre,
University of New South Wales, Sydney, New South
Wales, Australia

**Dong, Shenfu**, NOAA/OAR Atlantic Oceanographic and
Meteorological Laboratory, and Cooperative Institute
for Marine and Atmospheric Science, Miami, Florida

**Dorigo, Wouter A.**, Department of Geodesy and
Geoinformation, Vienna University of Technology,
Vienna, Austria

**Dortch, Quay**, NOAA/NOS National Centers for Coastal
Ocean Science, Center for Sponsored Coastal Ocean
Research, Costal Ocean Program, Silver Spring, Maryland
**Doucette, Greg**, NOAA/NOS National Centers for Coastal Ocean Science, Center for Coastal Environmental Health and Biomolecular Research, Charleston, South Carolina

**Drozdov, D. S.**, Earth Cryosphere Institute, Tyumen, and Tyumen State Oil and Gas University, Tyumen, Russia

**Ducklow, Hugh**, Lamont-Doherty Earth Observatory, Columbia University, New York, New York

**Dunn, Robert J. H.**, Met Office Hadley Centre, Exeter, United Kingdom

**Durán-Quesada, Ana M.**, Center for Geophysical Research and School of Physics, University of Costa Rica, San José, Costa Rica

**Dutton, Geoff S.**, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, and NOAA/OAR Earth System Research Laboratory, Boulder, Colorado

**Ebrahim, A.**, Egyptian Meteorological Authority, Cairo, Egypt

**ElKharrim, M.**, Direction de la Météorologie Nationale Maroc, Rabat, Morocco

**Elkins, James W.**, NOAA/OAR Earth System Research Laboratory, Boulder, Colorado

**Espinoza, Jhan C.**, Instituto Geofísico del Perú, Lima, Perú

**Etienne-LeBlanc, Sheryl**, Meteorological Department of St. Maarten, St. Maarten

**Evans III, Thomas E.**, NOAA/NWS Central Pacific Hurricane Center, Honolulu, Hawaii

**Famiglietti, James S.**, Department of Earth System Science, University of California, Irvine, California

**Farrell, S.**, Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

**Fateh, S.**, Islamic Republic of Iranian Meteorological Organization, Iran

**Fausto, Robert S.**, Geological Survey of Denmark and Greenland, Copenhagen, Denmark

**Fedaeff, Nava**, National Institute of Water and Atmospheric Research, Ltd., Auckland, New Zealand

**Feely, Richard A.**, NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, Washington

**Feng, Z.**, Pacific Northwest National Laboratory, Richland, Washington

**Fenimore, Chris**, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

**Fettweis, X.**, University of Liège, Liège, Belgium

**Fioletov, Vitali E.**, Environment and Climate Change Canada, Toronto, Ontario, Canada

**Flemming, Johannes**, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

**Fogarty, Chris T.**, Canadian Hurricane Centre, Environment and Climate Change Canada, Dartmouth, Nova Scotia, Canada

**Fogt, Ryan L.**, Department of Geography, Ohio University, Athens, Ohio

**Folland, Chris**, Met Office Hadley Centre, Exeter, United Kingdom

**Fonseca, C.**, Climate Center, Institute of Meteorology of Cuba, Cuba

**Fossheim, M.**, Institute of Marine Research, Bergen, Norway

**Foster, Michael J.**, Department of Geology, CIMSS, University of Wisconsin–Madison, Madison, Wisconsin

**Fountain, Andrew**, Portland State University, Portland, Oregon

**Francis, S. D.**, Nigerian Meteorological Agency, Abuja, Nigeria

**Franz, Bryan A.**, NASA Goddard Space Flight Center, Greenbelt, Maryland

**Frey, Richard A.**, CIMSS, University of Wisconsin–Madison, Madison, Wisconsin

**Frith, Stacey M.**, NASA Goddard Space Flight Center, Greenbelt, Maryland

**Froidevaux, Lucien**, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

**Ganter, Catherine**, Bureau of Meteorology, Melbourne, Victoria, Australia

**Garzoli, Silvia**, NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory, and Cooperative Institute for Marine and Atmospheric Science, Miami, Florida

**Gerland, S.**, Norwegian Polar Institute, Fram Centre, Tromsø, Norway

**Gobron, Nadine**, Land Resources Monitoring Unit, Institute for Environment and Sustainability, Joint Research Centre, European Commission, Ispra, Italy

**Goldenberg, Stanley B.**, NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida

**Gomez, R. Sorbonne**, Sorbonne Universités (UPMC-Paris 6), LOCEAN-IPSL, CNRS-IRD-MNHN, Paris, France

**Goni, Gustavo**, NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida

**Goto, A.**, Japan Meteorological Agency, Tokyo, Japan

**Grooß, J.-U.**, Forschungszentrum Jülich, Jülich, Germany

**Gruber, Alexander**, Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria

**Guard, Charles “Chip”**, NOAA/NWS Weather Forecast Office, Guam

**Gugliemin, Mauro**, Department of Theoretical and Applied Sciences, Insubria University, Varese, Italy

**Gupta, S. K.**, Science Systems and Applications, Inc., Hampton, Virginia

**Gutiérrez, J. M.**, Instituto de Física de Cantabria (CSIC-UC), Santander, Spain

**Hagos, S.**, Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

**Hahn, Sebastian**, Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria
**Haimberger, Leo**, Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria

**Hakkarainen, J.**, Finnish Meteorological Institute, Helsinki, Finland

**Hall, Brad D.**, NOAA/OAR Earth System Research Laboratory, Boulder, Colorado

**Halpert, Michael S.**, NOAA/NWS Climate Prediction Center, College Park, Maryland

**Hamlington, Benjamin D.**, Center for Coastal Physical Oceanography, Old Dominion University, Norfolk, Virginia

**Hanna, E.**, Department of Geography, University of Sheffield, Sheffield, United Kingdom

**Hansen, K.**, Danish Meteorological Institute, Copenhagen, Denmark

**Hanssen-Bauer, I.**, Norwegian Meteorological Institute, Blindern, Oslo, Norway

**Harris, Ian**, Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom

**Heidinger, Andrew K.**, NOAA/NESDIS Center for Satellite Applications and Research, University of Wisconsin–Madison, Madison, Wisconsin

**Heikkilä, A.**, Finnish Meteorological Institute, Helsinki, Finland

**Heil, A.**, Max Planck Institute for Chemistry, Mainz, Germany

**Heim Jr., Richard R.**, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

**Hendricks, S.**, Alfred Wegener Institute, Bremerhaven, Germany

**Hernández, Marieta**, Climate Center, Institute of Meteorology of Cuba, Cuba

**Hidalgo, Hugo G.**, Center for Geophysical Research and School of Physics, University of Costa Rica, San José, Costa Rica

**Hilburn, Kyle**, Remote Sensing Systems, Santa Rosa, California

**Ho, Shu-peng (Ben)**, COSMIC, UCAR, Boulder, Colorado

**Holmes, R. M.**, Woods Hole Research Center, Falmouth, Massachusetts

**Hu, Zeng-Zhen**, NOAA/NWS National Centers for Environmental Prediction, Climate Prediction Center, College Park, Maryland

**Huang, Boyin**, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

**Huelsing, Hannah K.**, State University of New York, Albany, New York

**Huffman, George J.**, NASA Goddard Space Flight Center, Greenbelt, Maryland

**Hughes, C.**, University of Liverpool, and National Oceanography Centre, Liverpool, United Kingdom

**Hurst, Dale F.**, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, and NOAA/OAR Earth System Research Laboratory, Boulder, Colorado

**Ialongo, I.**, Finnish Meteorological Institute, Helsinki, Finland

**Ijampy, J. A.**, Nigerian Meteorological Agency, Abuja, Nigeria

**Ingvaldsen, R. B.**, Institute of Marine Research, Bergen, Norway

**Inness, Antje**, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

**Isaksen, K.**, Norwegian Meteorological Institute, Blindern, Oslo, Norway

**Ishii, Masayoshi**, Japan Meteorological Agency, Tsukuba, Japan

**Jevrejeva, Svetlana**, National Oceanography Centre, Liverpool, United Kingdom

**Jiménez, C.**, Estellus, and LERMA, Observatoire de Paris, Paris, France

**Jin, Xiangze**, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts

**Johannesen, E.**, Institute of Marine Research, Bergen, Norway

**John, Viju**, EUMETSAT, Darmstadt, Germany, and Met Office Hadley Centre, Exeter, United Kingdom

**Johnsen, B.**, Norwegian Radiation Protection Authority, Østerås, Norway

**Johnson, Bryan**, NOAA/OAR Earth System Research Laboratory, Global Monitoring Division, and University of Colorado Boulder, Boulder, Colorado

**Johnson, Gregory C.**, NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, Washington

**Jones, Philip D.**, Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom

**Joseph, Annie C.**, Dominica Meteorological Service, Dominica

**Jumaux, Guillaume**, Météo France, Réunion

**Kabidi, Khadija**, Direction de la Météorologie Nationale Maroc, Rabat, Morocco

**Kaiser, Johannes W.**, Max Planck Institute for Chemistry, Mainz, Germany, and European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

**Kato, Seiji**, NASA Langley Research Center, Hampton, Virginia

**Kazemi, A.**, Islamic Republic of Iranian Meteorological Organization, Iran

**Keller, Linda M.**, Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin

**Kendon, Mike**, Met Office Hadley Centre, Exeter, United Kingdom

**Kennedy, John**, Met Office Hadley Centre, Exeter, United Kingdom
**Kerr, Kenneth**, Trinidad & Tobago Meteorological Service,
Piarco, Trinidad

**Kholodov, A. L.**, Geophysical Institute, University of
Alaska Fairbanks, Fairbanks, Alaska

**Khoshkam, Mahbobeh**, Islamic Republic of Iranian
Meteorological Organization, Iran

**Killick, Rachel**, Met Office Hadley Centre, Exeter, United
Kingdom

**Kim, Hyungjun**, Institute of Industrial Science, University
of Tokyo, Japan

**Kim, S.-J.**, Korea Polar Research Institute, Incheon,
Republic of Korea

**Kimberlain, Todd B.**, NOAA/NWS National Hurricane
Center, Miami, Florida

**Klotzbach, Philip J.**, Department of Atmospheric Science,
Colorado State University, Fort Collins, Colorado

**Knaff, John A.**, NOAA/NESDIS Center for Satellite
Applications and Research, Fort Collins, Colorado

**Kobayashi, Shinya**, Japan Meteorological Agency, Tokyo,
Japan

**Kohler, J.**, Norwegian Polar Institute, Tromsø, Norway

**Korhonen, Johanna**, Freshwater Centre, Finnish
Environment Institute (SYKE), Helsinki, Finland

**Korshunova, Natalia N.**, All-Russian Research Institute of
Hydrometeorological Information - World Data Center,
Obninsk, Russia

**Kovacs, K. M.**, Norwegian Polar Institute, Tromsø, Norway

**Kramarova, Natalya**, Science Systems and Applications,
Inc., NASA Goddard Space Flight Center, Greenbelt,
Maryland

**Kratz, D. P.**, NASA Langley Research Center, Hampton,
Virginia

**Kruger, Andries**, South African Weather Service, Pretoria,
South Africa

**Kruk, Michael C.**, ERT, Inc., NOAA/NESDIS National
Centers for Environmental Information, Asheville, North
Carolina

**Kudela, Raphael**, University of California, Santa Cruz,
Santa Cruz, California

**Kumar, Arun**, NOAA/NWS National Centers for
Environmental Prediction, Climate Prediction Center,
College Park, Maryland

**Lakatos, M.**, Hungarian Meteorological Service, Budapest,
Hungary

**Lakkala, K.**, Finnish Meteorological Institute, Arctic
Research Centre, Sodankylä, Finland

**Lander, Mark A.**, University of Guam, Mangilao, Guam

**Landsea, Chris W.**, NOAA/NWS National Hurricane
Center, Miami, Florida

**Lankhorst, Matthias**, Scripps Institution of Oceanography,
University of California, San Diego, La Jolla, California

**Lantz, Kathleen**, Cooperative Institute for Research in
Environmental Sciences, University of Colorado Boulder,
and NOAA/OAR Earth System Research Laboratory,
Boulder, Colorado

**Lazzara, Matthew A.**, Space Science and Engineering
Center, University of Wisconsin-Madison, and
Department of Physical Sciences, Madison Area
Technical College, Madison, Wisconsin

**Lemons, P.**, U.S. Fish and Wildlife Service, Anchorage,
Alaska

**Leuliette, Eric**, NOAA/NESDIS NCWCP Laboratory for
Satellite Altimetry, College Park, Maryland

**L'Heureux, Michelle**, NOAA/NWS Climate Prediction
Center, College Park, Maryland

**Lieser, Jan L.**, Antarctic Climate and Ecosystems
Cooperative Research Centre, University of Tasmania,
Hobart, Tasmania, Australia

**Lin, I.-I.**, National Taiwan University, Taipei, Taiwan

**Liu, Hongxing**, Department of Geography, University of
Cincinnati, Cincinnati, Ohio

**Liu, Yinghui**, Cooperative Institute for Meteorological
Satellite Studies, University of Wisconsin-Madison,
Madison, Wisconsin

**Locarnini, Ricardo**, NOAA/NESDIS National Centers for
Environmental Information, Silver Spring, Maryland

**Loeb, Norman G.**, NASA Langley Research Center,
Hampton, Virginia

**Lo Monaco, Claire**, Sorbonne Universités (UPMC-Paris
6), LOCEAN-IPSL, CNRS-IRD-MNHN, Paris, France

**Long, Craig S.**, NOAA/NWS National Centers for
Environmental Prediction, Camp Springs, Maryland

**López Álvarez, Luis Alfonso**, Instituto de Hidrología
de Meteorología y Estudios Ambientales de Colombia
(IDEAM), Bogotá, Colombia

**Lorrey, Andrew M.**, National Institute of Water and
Atmospheric Research, Ltd., Auckland, New Zealand

**Loyola, Diego**, German Aerospace Center (DLR)
Oberpfaffenhofen, Wessling, Germany

**Lumpkin, Rick**, NOAA/OAR Atlantic Oceanographic and
Meteorological Laboratory, Miami, Florida

**Luo, Jing-Jia**, Bureau of Meteorology, Melbourne, Victoria,
Australia

**Luojus, K.**, Finnish Meteorological Institute, Helsinki,
Finland

**Lydersen, C.**, Norwegian Polar Institute, Tromsø, Norway

**Lyman, John M.**, NOAA/OAR Pacific Marine
Environmental Laboratory, Seattle, Washington, and
Joint Institute for Marine and Atmospheric Research,
University of Hawaii, Honolulu, Hawaii

**Maberly, Stephen C.**, Lake Ecosystems Group, Centre for
Ecology and Hydrology, Lancaster, United Kingdom

**Maddux, Brent C.**, AOS/CIMSS University of Wisconsin-
Madison, Madison, Wisconsin

**Malheiros Ramos, Andrea**, Instituto Nacional de
Pesquisas Espaciais, Brasília, Brazil

**Malkova, G. V.**, Earth Cryosphere Institute, Tyumen, and
Tyumen State Oil and Gas University, Tyumen, Russia
**Manney, G.,** NorthWest Research Associates, and New Mexico Institute of Mining and Technology, Socorro, New Mexico

**Marcellin, Vernie,** Dominica Meteorological Service, Dominica

**Marchenko, S. S.,** Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska

**Marengo, José A.,** Centro Nacional de Monitoramento e Alertas aos Desastres Naturais, Cachoeira Paulista, São Paulo, Brazil

**Marra, John J.,** NOAA/NESDIS National Centers for Environmental Information, Honolulu, Hawaii

**Marszelewski, Włodzimierz,** Department of Hydrology and Water Management, Nicolaus Copernicus University, Toruń, Poland

**Martens, B.,** Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium

**Martínez-Güingla, Rodney, CIIFEN** Centro Internacional para la Investigación del Fenómeno de El Niño, Guayaquil, Ecuador

**Massom, Robert A.,** Australian Antarctic Division, and Antarctic Climate and Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, Tasmania, Australia

**Mata, Mauricio M.,** Laboratório de Estudos dos Oceanos e Clima, Instituto de Oceanografia, Universidade Federal do Rio Grande-FURG, Rio Grande, Brazil

**Mathis, Jeremy T.,** NOAA/OAR Arctic Research Program, Climate Observation Division, Silver Spring, Maryland

**May, Linda,** Centre for Ecology and Hydrology, Edinburgh, United Kingdom

**Mayer, Michael,** Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria

**Mazloff, Matthew,** Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California

**McBride, Charlotte,** South African Weather Service, Pretoria, South Africa

**McCabe, M. F.,** Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

**McCarthy, M.,** Met Office Hadley Centre, Exeter, United Kingdom

**McClelland, J. W.,** Marine Science Institute, University of Texas at Austin, Port Aransas, Texas

**McGree, Simon,** Bureau of Meteorology, Melbourne, Victoria, Australia

**McVicar, Tim R.,** CSIRO Land and Water Flagship, Canberra, Australian Capital Territory, and Australian Research Council Centre of Excellence for Climate System Science, Sydney, New South Wales, Australia

**Mears, Carl A.,** Remote Sensing Systems, Santa Rosa, California

**Meier, W.,** NASA Goddard Space Flight Center, Greenbelt, Maryland

**Meinen, Christopher S.,** NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida

**Mekonnen, A.,** Department of Energy and Environmental Systems, North Carolina A & T State University, Greensboro, North Carolina

**Menéndez, Melisa,** Environmental Hydraulic Institute, Universidad de Cantabria, Cantabria, Spain

**Mengistu Tsidu, G.,** Department of Earth and Environmental Sciences, Botswana International University of Science and Technology, Palapye, Botswana, and Department of Physics, Addis Ababa University, Addis Ababa, Ethiopia

**Menzel, W. Paul,** Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin

**Merchant, Christopher J.,** Department of Meteorology, University of Reading, Reading, United Kingdom

**Meredith, Michael P.,** British Antarctic Survey, NERC, Cambridge, United Kingdom

**Merrifield, Mark A.,** Joint Institute for Marine and Atmospheric Research, University of Hawaii, Honolulu, Hawaii

**Metzl, N.,** Sorbonne Universités (UPMC-Paris 6), LOCEAN-IPSL, CNRS-IRD-MNHN, Paris, France

**Minnis, Patrick,** Science Directorate, NASA Langley Research Center, Hampton, Virginia

**Miralles, Diego G.,** Department of Earth Sciences, VU University Amsterdam, Amsterdam, Netherlands

**Mistelbauer, T.,** Department of Geodesy and Geoinformation, Vienna University of Technology, and EODC, Vienna, Austria

**Mitchum, Gary T.,** College of Marine Science, University of South Florida, St. Petersburg, Florida

**Monselesan, Didier, CSIRO** Oceans and Atmosphere, Hobart, Tasmania, Australia

**Monteiro, Pedro, CSIR** Natural Resources and the Environment, Stellenbosch, South Africa

**Montzka, Stephen A.,** NOAA/OAR Earth System Research Laboratory, Boulder, Colorado

**Morice, Colin, Met Office** Hadley Centre, Exeter, United Kingdom

**Mote, T.,** Department of Geography, The University of Georgia, Athens, Georgia

**Mudryk, L.,** Department of Physics, University of Toronto, Toronto, Ontario, Canada

**Mühle, Jens, Scripps Institution of Oceanography,** University of California, San Diego, La Jolla, California

**Mullan, A. Brett, National Institute of Water and Atmospheric Research, Ltd.,** Wellington, New Zealand

**Nash, Eric R.,** Science Systems and Applications, Inc., NASA Goddard Space Flight Center, Greenbelt, Maryland

**Naveira-Garabato, Alberto C.,** University of Southampton, National Oceanography Centre, Southampton, United Kingdom
**Nerem, R. Steven**, Colorado Center for Astrodynamics Research, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado

**Newman, Paul A.**, NASA Goddard Space Flight Center, Greenbelt, Maryland

**Nieto, Juan José**, CIIFEN Centro Internacional para la Investigación del Fenómeno de El Niño, Guayaquil, Ecuador

**Noetzli, Jeannette**, WSL Institute for Snow and Avalanche Research, Davos, Switzerland

**O'Neel, S.**, USGS, Alaska Science Center, Anchorage, Alaska

**Osborn, Tim J.**, Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom

**Overland, J.**, NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, Washington

**Oyunjargal, Lamjav**, Hydrology and Environmental Monitoring, Institute of Meteorology and Hydrology, National Agency for Meteorology, Ulaanbaatar, Mongolia

**Parinussa, Robert M.**, School of Civil and Environmental Engineering, Water Research Centre, University of New South Wales, Sydney, New South Wales, Australia

**Park, E-hyung**, Korea Meteorological Administration, Republic of Korea

**Parker, David**, Met Office Hadley Centre, Exeter, United Kingdom

**Parrington, M.**, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

**Parsons, A. Rost**, NOAA/NESDIS National Centers for Environmental Information, Silver Spring, Maryland

**Pasch, Richard J.**, NOAA/NWS National Hurricane Center, Miami, Florida

**Pascual-Ramírez, Reynaldo**, National Meteorological Service of Mexico, Mexico

**Paterson, Andrew M.**, Dorset Environmental Science Centre, Ontario Ministry of the Environment and Climate Change, Dorset, Ontario, Canada

**Paulik, Christoph**, Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria

**Pearce, Petra R.**, National Institute of Water and Atmospheric Research, Ltd., Auckland, New Zealand

**Pelto, Mauri S.**, Nichols College, Dudley, Massachusetts

**Peng, Liang**, UCAR COSMIC, Boulder, Colorado

**Perkins-Kirkpatrick, Sarah E.**, Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

**Perovich, D.**, USACE, ERDC, Cold Regions Research and Engineering Laboratory, and Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire

**Petropavlovskikh, Irina**, NOAA/OAR Earth System Research Laboratory, Global Monitoring Division, and University of Colorado Boulder, Boulder, Colorado

**Pezza, Alexandre B.**, Greater Wellington Regional Council, Wellington, New Zealand

**Phillips, David**, Environment and Climate Change Canada, Toronto, Ontario, Canada

**Pinty, Bernard**, Land Resource Management Unit, Institute for Environment and Sustainability, European Commission Joint Research Centre, Ispra, Italy

**Pitts, Michael C.**, NASA Langley Research Center, Hampton, Virginia

**Pons, M. R.**, Agencia Estatal de Meteorología, Santander, Spain

**Porter, Avalon O.**, Cayman Islands National Weather Service, Grand Cayman, Cayman Islands

**Primicerio, R.**,UiT The Arctic University of Norway, Tromsø, Norway

**Proshutinsky, A.**, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts

**Quegan, Sean**, University of Sheffield, Sheffield, United Kingdom

**Quintana, Juan**, Dirección Meteorológica de Chile, Chile

**Rahimzadeh, Fatemeh**, Atmospheric Science and Meteorological Research Center, Tehran, Iran

**Rajeevan, Madhavan**, Earth System Science Organization, Ministry of Earth Sciences, New Delhi, India

**Randriamarolaza, L.**, Service de la Climatologie et du Changement Climatique, Direction Générale de la Météorologie, Madagascar

**Razuvaev, Vyacheslav N.**, All-Russian Research Institute of Hydrometeorological Information, Obninsk, Russia

**Reagan, James**, NOAA/NESDIS National Centers for Environmental Information, Silver Spring, Maryland, and Earth System Science Interdisciplinary Center/ Cooperative Institute for Climate and Satellites– Maryland, University of Maryland, College Park, Maryland

**Reid, Phillip**, Australian Bureau of Meteorology, and ACE CRC, Hobart, Tasmania, Australia

**Reimer, Christoph**, Department of Geodesy and Geoinformation, Vienna University of Technology, and EODC, Vienna, Austria

**Rémy, Samuel**, Laboratoire de Météorologie Dynamique, Paris, France

**Renwick, James A.**, Victoria University of Wellington, Wellington, New Zealand

**Revadekar, Jayashree V.**, Indian Institute of Tropical Meteorology, Pune, India

**Richter-Menge, J.**, USACE Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire

**Riffler, Michael**, GeoVille Information Systems, Innsbruck, Austria, and Institute of Geography, University of Bern, Bern, Switzerland
**Rimmer, Alon**, Kinneret Limnological Laboratory, Israel
Oceanographic and Limnological Research, Migdal, Israel

**Rintoul, Steve**, CSIRO-CMAR/CAWCR/ACE-CRC,
Hobart, Tasmania, Australia

**Robinson, David A.**, Department of Geography, Rutgers
University, Piscataway, New Jersey

**Rodell, Matthew**, Hydrological Sciences Laboratory,
NASA Goddard Space Flight Center, Greenbelt,
Maryland

**Rodríguez Solís, José L.**, National Meteorological Service
of Mexico, Mexico

**Romanovsky, Vladimir E.**, Geophysical Institute,
University of Alaska Fairbanks, Fairbanks, Alaska

**Ronchail, Josyane**, Université Paris Diderot (UPMC-Paris
7), LOCEAN-IPSL, CNRS-IRD-MNHN, Paris, France

**Rosenlof, Karen H.**, NOAA/OAR Earth System Research
Laboratory, Boulder, Colorado

**Roth, Chris**, University of Saskatchewan, Saskatoon,
Saskatchewan, Canada

**Rusak, James A.**, Dorset Environmental Science Centre,
Ontario Ministry of the Environment and Climate
Change, Dorset, Ontario, Canada

**Sabine, Christopher L.**, NOAA/OAR Pacific Marine
Environmental Laboratory, Seattle, Washington

**Sallée, Jean-Bapiste**, Sorbonne Universités (UPMC-Paris
6), LOCEAN-IPSL, CNRS-IRD-MNHN, Paris, France,
and British Antarctic Survey, NERC, Cambridge, United
Kingdom

**Sánchez-Lugo, Ahira**, NOAA/NESDIS National Centers
for Environmental Information, Asheville, North Carolina

**Santee, Michelle L.**, NASA Jet Propulsion Laboratory,
Pasadena, California

**Sawaengphokhai, P.**, Science Systems and Applications,
Inc., Hampton, Virginia

**Sayouri, Amal**, Direction de la Météorologie Nationale
Maroc, Rabat, Morocco

**Scambos, Ted A.**, National Snow and Ice Data Center,
University of Colorado Boulder, Boulder, Colorado

**Schemm, Jae**, NOAA/NWS Climate Prediction Center,
College Park, Maryland

**Schladow, S. Geoffrey**, Tahoe Environmental Research
Center, University of California, Davis, Davis, California

**Schmid, Claudia**, NOAA/OAR Atlantic Oceanographic
and Meteorological Laboratory, Miami, Florida

**Schmid, Martin**, Eawag, Swiss Federal Institute of Aquatic
Science and Technology, Dübendorf, Switzerland

**Schmidtko, Sunke**, GEOMAR Helmholtz Centre for
Ocean Research Kiel, Kiel, Germany

**Schreck III, Carl J.**, Cooperative Institute for Climate and
Satellites, North Carolina State University, Asheville,
North Carolina

**Selkirk, H. B.**, Universities Space Research Association,
NASA Goddard Space Flight Center, Greenbelt, Maryland

**Send, Uwe**, Scripps Institution of Oceanography,
University of California, San Diego, La Jolla, California

**Sensoy, Serhat**, Turkish State Meteorological Service,
Kalaba, Ankara, Turkey

**Setzer, Alberto**, National Institute for Space Research,
São José dos Compos-SP, Brazil

**Sharp, M.**, Department of Earth and Atmospheric Sciences,
University of Alberta, Edmonton, Alberta, Canada

**Shaw, Adrian**, Meteorological Service, Jamaica, Kingston,
Jamaica

**Shi, Lei**, NOAA/NESDIS National Centers for
Environmental Information, Asheville, North Carolina

**Shiklomanov, A. I.**, University of New Hampshire,
Durham, New Hampshire, and Shirshov Institute of
Oceanology, Moscow, Russia

**Shiklomanov, Nikolai I.**, Department of Geography,
George Washington University, Washington, D.C.

**Siegel, David A.**, University of California, Santa Barbara,
Santa Barbara, California

**Signorini, Sergio R.**, Science Application International
Corporation, Beltsville, Maryland

**Sima, Fatou**, Division of Meteorology, Department of
Water Resources, Banjul, The Gambia

**Simmons, Adrian J.**, European Centre for Medium-Range
Weather Forecasts, Reading, United Kingdom

**Smeets, C. J. P. P.**, Institute for Marine and Atmospheric
Research Utrecht, Utrecht University, Utrecht,
Netherlands

**Smith, Sharon L.**, Geological Survey of Canada, Natural
Resources Canada, Ottawa, Ontario, Canada

**Spence, Jaqueline M.**, Meteorological Service, Jamaica,
Kingston, Jamaica

**Srivastava, A. K.**, India Meteorological Department,
Jaipur, India

**Stackhouse Jr., Paul W.**, NASA Langley Research Center,
Hampton, Virginia

**Stammerjohn, Sharon**, Institute of Arctic and Alpine
Research, University of Colorado Boulder, Boulder,
Colorado

**Steinbrecht, Wolfgang**, German Weather Service
(DWD), Hohenpeissenberg, Germany

**Stella, José L.**, Servicio Meteorológico Nacional, Buenos
Aires, Argentina

**Stengel, Martin**, Deutscher Wetterdienst, Offenbach,
Germany

**Stennett-Brown, Roxann**, Department of Physics, The
University of the West Indies, Jamaica

**Stephenson, Tannecia S.**, Department of Physics, The
University of the West Indies, Jamaica

**Strahan, Susan**, Universities Space Research Association,
NASA Goddard Space Flight Center, Greenbelt,
Maryland

**Streletskiy, D. A.**, Department of Geography, George
Washington University, Washington, D.C.

**Sun-Mack, Sunny**, Science Systems and Applications, Inc.,
Hampton, Virginia
**Swart, Sebastiaan**, CSIR Southern Ocean Carbon & Climate Observatory, Stellenbosch, South Africa

**Sweet, William**, NOAA/NOS Center for Operational Oceanographic Products and Services, Silver Spring, Maryland

**Talley, Lynne D.**, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California

**Tamar, Gerard**, Grenada Airports Authority, St. George's, Grenada

**Tank, S. E.**, University of Alberta, Edmonton, Alberta, Canada

**Taylor, Michael A.**, Department of Physics, The University of the West Indies, Jamaica

**Tedesco, M.**, Lamont-Doherty Earth Observatory, Columbia University Palisades, New York, and NASA Goddard Institute of Space Studies, New York, New York

**Teubner, Katrin**, Research Institute for Limnology, University of Innsbruck, Mondsee, Austria

**Thoman, R. L.**, NOAA/NWS, Alaska Region, Fairbanks, Alaska

**Thompson, Philip**, Joint Institute for Marine and Atmospheric Research, University of Hawaii, Honolulu, Hawaii

**Thomson, L.**, Department of Geography, University of Ottawa, Ottawa, Ontario, Canada

**Timmermans, M.-L.**, Yale University, New Haven, Connecticut

**Tirnanes, Joaquin A.**, Laboratory of Systems, Technological Research Institute, Universidad de Santiago de Compostela, Santiago de Compostela, Spain

**Tobin, Skie**, Bureau of Meteorology, Melbourne, Victoria, Australia

**Trachte, Katja**, Laboratory for Climatology and Remote Sensing, Philipps-Universität, Marburg, Germany

**Trainer, Vera L.**, NOAA/NMFS Northwest Fisheries Science Center, Seattle, Washington

**Tretiakov, M.**, Arctic and Antarctic Research Institute, St. Petersburg, Russia

**Trewin, Blair C.**, Bureau of Meteorology, Melbourne, Victoria, Australia

**Trotman, Adrian R.**, Caribbean Institute for Meteorology and Hydrology, Bridgetown, Barbados

**Tschudi, M.**, Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, Colorado

**van As, D.**, Geological Survey of Denmark and Greenland, Copenhagen, Denmark

**van de Wal, R. S. W.**, Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, Netherlands

**van der A, Ronald J.**, Royal Netherlands Meteorological Institute (KNMI), DeBilt, Netherlands

**van der Schalie, Robin**, Transmissivity, and VanderSat, Noordwijk, Netherlands

**van der Schrier, Gerard**, Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands

**van der Werf, Guido R.**, Faculty of Earth and Life Sciences, VU University Amsterdam, Netherlands

**Van Meerbeeck, Cedric J.**, Caribbean Institute for Meteorology and Hydrology, Bridgetown, Barbados

**Velicogna, I.**, University of California, Irvine, California

**Verburg, Piet**, National Institute of Water and Atmospheric Research, Ltd., Hamilton, New Zealand

**Vigneswaran, Bala**, Water Quality and Spatial Science Section, WaterNSW, Penrith, New South Wales, Australia

**Vincent, Lucie A.**, Environment and Climate Change Canada, Toronto, Ontario, Canada

**Volkov, Denis**, NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory, and Cooperative Institute for Marine and Atmospheric Science, Miami, Florida

**Vose, Russell S.**, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

**Wagner, Wolfgang**, Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria

**Wählin, Anna**, Department of Earth Sciences, University of Gothenburg, Göteborg, Sweden

**Wahr, J.**, Department of Physics and Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado

**Walsh, J.**, International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, Alaska

**Wang, Chunzai**, NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida

**Wang, Junhong**, State University of New York, Albany, New York

**Wang, Lei**, Department of Geography and Anthropology, Louisiana State University, Baton Rouge, Louisiana

**Wang, M.**, Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, Washington

**Wang, Sheng-Hung**, Byrd Polar and Climate Research Center, The Ohio State University, Columbus, Ohio

**Wanninkhof, Rik**, NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida

**Watanabe, Shohei**, Tahoe Environmental Research Center, University of California, Davis, Davis, California

**Weber, Mark**, University of Bremen, Bremen, Germany

**Weller, Robert A.**, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts

**Weyhenmeyer, Gesa A.**, Department of Limnology, Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden

**Whitewood, Robert**, Environment and Climate Change Canada, Toronto, Ontario, Canada

**Wijffels, Susan E.**, CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
**Wilber, Anne C.**, Science Systems and Applications, Inc.,
Hampton, Virginia

**Wild, Jeanette D.**, INNOVIM, NOAA/NWS National
Centers for Environmental Prediction, Climate
Prediction Center, College Park, Maryland

**Willett, Kate M.**, Met Office Hadley Centre, Exeter,
United Kingdom

**Williams, Michael J.M.**, National Institute of Water and
Atmospheric Research, Ltd., Wellington, New Zealand

**Willie, Shem**, St. Lucia Meteorological Service, St. Lucia

**Wolken, G.**, Alaska Division of Geological and Geophysical
Surveys, Fairbanks, Alaska

**Wong, Takmeng**, NASA Langley Research Center,
Hampton, Virginia

**Wood, E. F.**, Department of Civil and Environmental
Engineering, Princeton University, Princeton, New Jersey

**Woolway, R. Iestyn**, Department of Meteorology,
University of Reading, Reading, United Kingdom

**Wouters, B.**, School of Geographical Sciences, University
of Bristol, Bristol, United Kingdom

**Xue, Yan**, NOAA/NWS National Centers for
Environmental Prediction, Climate Prediction Center,
College Park, Maryland

**Yamada, Ryuji**, Japan Meteorological Agency, Tokyo, Japan

**Yim, So-Young**, Korea Meteorological Administration,
Republic of Korea

**Yin, Xungang**, ERT, Inc., NOAA/NESDIS National
Centers for Environmental Information, Asheville, North
Carolina

**Young, Steven H.**, Independent Researcher, Long Beach,
California

**Yu, Lisan**, Woods Hole Oceanographic Institution, Woods
Hole, Massachusetts

**Zahid, H.**, Maldives Meteorological Service, Maldives

**Zambrano, Eduardo**, Centro Internacional para la
Investigación del Fenómeno El Niño, Guayaquil, Ecuador

**Zhang, Peiqun**, Beijing Climate Center, Beijing, China

**Zhao, Guanguo**, University of Illinois at Urbana-
Champaign, Urbana, Illinois

**Zhou, Lin**, Cold and Arid Regions Environmental and
Engineering Research Institute, Lanzhou, China

**Ziemke, Jerry R.**, Goddard Earth Sciences Technology
and Research, Morgan State University, Baltimore,
Maryland, and NASA Goddard Space Flight Center,
Greenbelt, Maryland

EDITORIAL AND PRODUCTION TEAM

**Love-Brotak, S. Elizabeth**, Lead Graphics Production,
NOAA/NESDIS National Centers for Environmental
Information, Asheville, North Carolina

**Gilbert, Kristin**, Bulletin of the American Meteorological
Society, Boston, Massachusetts

**Maycock, Tom**, Technical Editor, Cooperative Institute
for Climate and Satellites–NC, North Carolina State
University, Asheville, North Carolina

**Osborne, Susan**, Technical Editor, TeleSolv Consulting,
NOAA/NESDIS National Centers for Environmental
Information, Asheville, North Carolina

**Sprain, Mara**, Technical Editor, LAC Group, NOAA/
NESDIS National Centers for Environmental
Information, Asheville, North Carolina

**Veasey, Sara W.**, Visual Communications Team Lead,
NOAA/NESDIS National Centers for Environmental
Information, Asheville, North Carolina

**Ambrose, Barbara J.**, Graphics Support, Riverside
Technology, Inc., NOAA/NESDIS National Centers
for Environmental Information, Stennis Space Center,
Mississippi

**Griffin, Jessica**, Graphics Support, Cooperative Institute
for Climate and Satellites–NC, North Carolina State
University, Asheville, North Carolina

**Misch, Deborah J.**, Graphics Support, TeleSolv Consulting,
NOAA/NESDIS National Centers for Environmental
Information, Asheville, North Carolina

**Riddle, Deborah B.**, Graphics Support, NOAA/NESDIS
National Centers for Environmental Information,
Asheville, North Carolina

**Young, Teresa**, Graphics Support, STG, Inc., NOAA/
NESDIS National Centers for Environmental
Information, Asheville, North Carolina
TABLE OF CONTENTS

<table><tr><td>List of authors and affiliations</td><td>i</td></tr><tr><td>Abstract</td><td>xvi</td></tr></table>

<table><tr><td>1. INTRODUCTION</td><td>1</td></tr><tr><td>SIDEBAR 1.1: THE 2015/16 EL NIÑO COMPARED WITH OTHER RECENT EVENTS</td><td>5</td></tr></table>

<table><tr><td>2. GLOBAL CLIMATE</td><td>7</td></tr></table>

<table><tr><td>a. Overview</td><td>7</td></tr></table>

<table><tr><td>b. Temperature</td><td>12</td></tr></table>

<table><tr><td>1. Surface temperature</td><td>12</td></tr></table>

<table><tr><td>2. Lower and midtropospheric temperatures</td><td>13</td></tr></table>

<table><tr><td>3. Lower stratospheric temperature</td><td>15</td></tr></table>

<table><tr><td>4. Lake surface temperatures</td><td>17</td></tr></table>

<table><tr><td>5. Land surface temperature extremes</td><td>19</td></tr></table>

<table><tr><td>c. Cryosphere</td><td>20</td></tr></table>

<table><tr><td>1. Permafrost thermal state</td><td>20</td></tr></table>

<table><tr><td>2. Northern Hemisphere continental snow cover extent</td><td>22</td></tr></table>

<table><tr><td>3. Alpine glaciers and ice sheets</td><td>23</td></tr></table>

<table><tr><td>d. Hydrological cycle</td><td>24</td></tr></table>

<table><tr><td>1. Surface humidity</td><td>24</td></tr></table>

<table><tr><td>2. Total column water vapor</td><td>25</td></tr></table>

<table><tr><td>3. Upper tropospheric humidity</td><td>27</td></tr></table>

<table><tr><td>4. Precipitation</td><td>27</td></tr></table>

<table><tr><td>5. Cloudiness</td><td>28</td></tr></table>

<table><tr><td>6. River discharge</td><td>29</td></tr></table>

<table><tr><td>7. Groundwater and terrestrial water storage</td><td>30</td></tr></table>

<table><tr><td>8. Soil moisture</td><td>31</td></tr></table>

<table><tr><td>9. Monitoring global drought using the self-calibrating Palmer drought severity index</td><td>32</td></tr></table>

<table><tr><td>SIDEBAR 2.1: GLOBAL LAND EVAPORATION</td><td>34</td></tr></table>

<table><tr><td>e. Atmospheric circulation</td><td>36</td></tr></table>

<table><tr><td>1. Mean sea level pressure and related modes of variability</td><td>36</td></tr></table>

<table><tr><td>2. Surface winds</td><td>38</td></tr></table>

<table><tr><td>3. Upper air winds</td><td>40</td></tr></table>

<table><tr><td>f. Earth radiation budget</td><td>41</td></tr></table>

<table><tr><td>1. Earth radiation budget at top-of-atmosphere</td><td>41</td></tr></table>

<table><tr><td>2. Mauna Loa clear-sky “apparent” solar transmission</td><td>43</td></tr></table>

<table><tr><td>g. Atmospheric composition</td><td>44</td></tr></table>

<table><tr><td>1. Long-lived greenhouse gases</td><td>44</td></tr></table>

<table><tr><td>2. Ozone-depleting gases</td><td>47</td></tr></table>

<table><tr><td>3. Aerosols</td><td>47</td></tr></table>

<table><tr><td>4. Stratospheric ozone</td><td>49</td></tr></table>

<table><tr><td>5. Stratospheric water vapor</td><td>51</td></tr></table>

<table><tr><td>6. Tropospheric ozone</td><td>53</td></tr></table>

<table><tr><td>7. Carbon monoxide</td><td>55</td></tr></table>

<table><tr><td>SIDEBAR 2.2: ATMOSPHERIC COMPOSITION CHANGES DUE TO THE EXTREME 2015 INDONESIAN FIRE SEASON TRIGGERED BY EL NIÑO</td><td>56</td></tr></table>

<table><tr><td>h. Land surface properties</td><td>58</td></tr></table>

<table><tr><td>1. Land surface albedo dynamics</td><td>58</td></tr></table>

<table><tr><td>2. Terrestrial vegetation dynamics</td><td>59</td></tr></table>

<table><tr><td>3. Biomass burning</td><td>60</td></tr></table>

<table><tr><td>3. GLOBAL OCEANS</td><td>63</td></tr></table>

<table><tr><td>a. Overview</td><td>63</td></tr></table>

<table><tr><td>b. Sea surface temperatures</td><td>63</td></tr></table>

<table><tr><td>c. Ocean heat content</td><td>66</td></tr></table>

<table><tr><td>SIDEBAR 3.1: A WIDESPREAD HARMFUL ALGAL BLOOM IN THE NORTHEAST PACIFIC</td><td>66</td></tr></table>

<table><tr><td>d. Salinity</td><td>70</td></tr></table>

<table><tr><td>1. Introduction</td><td>70</td></tr></table>

<table><tr><td>2. Sea surface salinity</td><td>71</td></tr></table>
<table><tr><td>3. Subsurface salinity</td><td>72</td></tr><tr><td>e. Ocean surface heat, freshwater, and momentum fluxes</td><td>74</td></tr><tr><td>1. Surface heat fluxes</td><td>75</td></tr><tr><td>2. Surface freshwater fluxes</td><td>76</td></tr><tr><td>3. Wind stress</td><td>77</td></tr><tr><td>4. Long-term perspective</td><td>77</td></tr></table>

<table><tr><td>SIDEBAR 3.2: EXTRAORDINARILY WEAK EIGHTEEN DEGREE WATER PRODUCTION CONCURS WITH STRONGLY POSITIVE NORTH ATLANTIC OSCILLATION IN LATE WINTER 2014/15</td><td>78</td></tr><tr><td>f. Sea level variability and change</td><td>80</td></tr><tr><td>g. Surface currents</td><td>82</td></tr><tr><td>h. Meridional overturning circulation observations in the North Atlantic Ocean</td><td>84</td></tr><tr><td>i. Global ocean phytoplankton</td><td>87</td></tr><tr><td>j. Global ocean carbon cycle</td><td>89</td></tr><tr><td>1. Air-sea carbon dioxide fluxes</td><td>90</td></tr><tr><td>2. Carbon inventories from the GO-SHIP surveys</td><td>91</td></tr></table>

<table><tr><td>4. THE TROPICS</td><td>93</td></tr><tr><td>a. Overview</td><td>93</td></tr><tr><td>b. ENSO and the tropical Pacific</td><td>93</td></tr><tr><td>1. Oceanic conditions</td><td>94</td></tr><tr><td>2. Atmospheric circulation: tropics and subtropics</td><td>96</td></tr><tr><td>3. Rainfall impacts</td><td>97</td></tr><tr><td>c. Tropical intraseasonal activity</td><td>98</td></tr><tr><td>d. Intertropical convergence zones</td><td>101</td></tr><tr><td>1. Pacific</td><td>101</td></tr><tr><td>2. Atlantic</td><td>102</td></tr><tr><td>e. Tropical cyclones</td><td>104</td></tr><tr><td>1. Overview</td><td>104</td></tr><tr><td>2. Atlantic basin</td><td>105</td></tr><tr><td>3. Eastern North Pacific and central North Pacific basins</td><td>108</td></tr><tr><td>4. Western North Pacific basin</td><td>110</td></tr><tr><td>5. North Indian Ocean</td><td>114</td></tr><tr><td>6. South Indian Ocean</td><td>115</td></tr><tr><td>7. Australian basin</td><td>116</td></tr><tr><td>8. Southwest Pacific basin</td><td>118</td></tr><tr><td>f. Tropical cyclone heat potential</td><td>120</td></tr><tr><td>g. Atlantic warm pool</td><td>123</td></tr><tr><td>h. Indian Ocean dipole</td><td>124</td></tr><tr><td>SIDEBAR 4.1: THE RECORD-SHATTERING 2015 NORTHERN HEMISPHERE TROPICAL CYCLONE SEASON</td><td>127</td></tr><tr><td>SIDEBAR 4.2: A SOUTHEAST PACIFIC BASIN SUBTROPICAL CYCLONE OFF THE CHILEAN COAST</td><td>129</td></tr></table>

<table><tr><td>5. THE ARCTIC</td><td>131</td></tr><tr><td>a. Introduction</td><td>131</td></tr><tr><td>b. Air temperature</td><td>132</td></tr><tr><td>c. Sea ice cover</td><td>134</td></tr><tr><td>SIDEBAR 5.1: WALRUSES IN A TIME OF CLIMATE CHANGE</td><td>136</td></tr><tr><td>d. Sea surface temperature</td><td>137</td></tr><tr><td>SIDEBAR 5.2: CLIMATE CHANGE IS PUSHING BOREAL FISH NORTHWARD TO THE ARCTIC: THE CASE OF THE BARENTS SEA</td><td>139</td></tr><tr><td>e. Greenland Ice Sheet</td><td>140</td></tr><tr><td>f. Glaciers and ice caps outside Greenland</td><td>142</td></tr><tr><td>g. Terrestrial snow cover</td><td>145</td></tr><tr><td>h. River discharge</td><td>147</td></tr><tr><td>i. Terrestrial permafrost</td><td>149</td></tr><tr><td>j. Ozone and UV radiation</td><td>152</td></tr></table>

<table><tr><td>6. ANTARCTICA</td><td>155</td></tr><tr><td>a. Overview</td><td>155</td></tr><tr><td>b. Atmospheric circulation</td><td>156</td></tr><tr><td>c. Surface manned and automatic weather station observations</td><td>157</td></tr><tr><td>d. Net precipitation (P - E)</td><td>159</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>e. Seasonal melt extent and duration</td>
      <td>161</td>
    </tr>
    <tr>
      <td>SIDEBAR 6.1: EL NIÑO AND ANTARCTICA</td>
      <td>162</td>
    </tr>
    <tr>
      <td>f. Sea ice extent, concentration, and duration</td>
      <td>163</td>
    </tr>
    <tr>
      <td>g. Southern Ocean</td>
      <td>166</td>
    </tr>
    <tr>
      <td>h. Antarctic ozone hole</td>
      <td>168</td>
    </tr>
    <tr>
      <td>SIDEBAR 6.2: POLAR ECOSYSTEMS AND THEIR SENSITIVITY TO CLIMATE PERTURBATION</td>
      <td>170</td>
    </tr>
    <tr>
      <td><strong>7. REGIONAL CLIMATES</strong></td>
      <td>173</td>
    </tr>
    <tr>
      <td>a. Overview</td>
      <td>173</td>
    </tr>
    <tr>
      <td>b. North America</td>
      <td>173</td>
    </tr>
    <tr>
      <td>1. Canada</td>
      <td>173</td>
    </tr>
    <tr>
      <td>2. United States</td>
      <td>175</td>
    </tr>
    <tr>
      <td>3. Mexico</td>
      <td>176</td>
    </tr>
    <tr>
      <td>c. Central America and the Caribbean</td>
      <td>178</td>
    </tr>
    <tr>
      <td>1. Central America</td>
      <td>178</td>
    </tr>
    <tr>
      <td>2. Caribbean</td>
      <td>181</td>
    </tr>
    <tr>
      <td>d. South America</td>
      <td>182</td>
    </tr>
    <tr>
      <td>1. Northern South America and the tropical Andes</td>
      <td>183</td>
    </tr>
    <tr>
      <td>2. Tropical South America east of the Andes</td>
      <td>184</td>
    </tr>
    <tr>
      <td>3. Southern South America</td>
      <td>185</td>
    </tr>
    <tr>
      <td>e. Africa</td>
      <td>187</td>
    </tr>
    <tr>
      <td>1. Northern Africa</td>
      <td>187</td>
    </tr>
    <tr>
      <td>2. West Africa</td>
      <td>188</td>
    </tr>
    <tr>
      <td>3. Eastern Africa</td>
      <td>189</td>
    </tr>
    <tr>
      <td>4. Southern Africa between 5° and 30°S</td>
      <td>192</td>
    </tr>
    <tr>
      <td>5. South Africa</td>
      <td>193</td>
    </tr>
    <tr>
      <td>6. Western and central Indian Ocean island countries</td>
      <td>195</td>
    </tr>
    <tr>
      <td>f. Europe and the Middle East</td>
      <td>197</td>
    </tr>
    <tr>
      <td>1. Overview</td>
      <td>198</td>
    </tr>
    <tr>
      <td>2. Central and western Europe</td>
      <td>200</td>
    </tr>
    <tr>
      <td>3. The Nordic and the Baltic countries</td>
      <td>201</td>
    </tr>
    <tr>
      <td>4. Iberian Peninsula</td>
      <td>202</td>
    </tr>
    <tr>
      <td>SIDEBAR 7.1: UNUSUALLY STRONG AND LONG-LASTING HEAT WAVE IN EUROPE</td>
      <td>204</td>
    </tr>
    <tr>
      <td>5. Mediterranean and Balkan States</td>
      <td>205</td>
    </tr>
    <tr>
      <td>6. Eastern Europe</td>
      <td>206</td>
    </tr>
    <tr>
      <td>7. Middle East</td>
      <td>207</td>
    </tr>
    <tr>
      <td>g. Asia</td>
      <td>209</td>
    </tr>
    <tr>
      <td>1. Overview</td>
      <td>209</td>
    </tr>
    <tr>
      <td>2. Russia</td>
      <td>209</td>
    </tr>
    <tr>
      <td>3. East Asia</td>
      <td>212</td>
    </tr>
    <tr>
      <td>SIDEBAR 7.2: EXTREMELY WET CONDITIONS IN JAPAN IN LATE SUMMER 2015</td>
      <td>213</td>
    </tr>
    <tr>
      <td>4. South Asia</td>
      <td>215</td>
    </tr>
    <tr>
      <td>5. Southwest Asia</td>
      <td>216</td>
    </tr>
    <tr>
      <td>h. Oceania</td>
      <td>217</td>
    </tr>
    <tr>
      <td>1. Overview</td>
      <td>217</td>
    </tr>
    <tr>
      <td>2. Northwest Pacific and Micronesia</td>
      <td>217</td>
    </tr>
    <tr>
      <td>3. Southwest Pacific</td>
      <td>219</td>
    </tr>
    <tr>
      <td>4. Australia</td>
      <td>221</td>
    </tr>
    <tr>
      <td>5. New Zealand</td>
      <td>223</td>
    </tr>
    <tr>
      <td>SIDEBAR 7.3: AUSTRALIA'S WARM RIDE TO END 2015</td>
      <td>224</td>
    </tr>
    <tr>
      <td><strong>APPENDIX I: Relevant Datasets and Sources</strong></td>
      <td>227</td>
    </tr>
    <tr>
      <td><strong>ACKNOWLEDGMENTS</strong></td>
      <td>237</td>
    </tr>
    <tr>
      <td><strong>ACRONYMS AND ABBREVIATIONS</strong></td>
      <td>239</td>
    </tr>
    <tr>
      <td><strong>REFERENCES</strong></td>
      <td>241</td>
    </tr>
  </tbody>
</table>
In 2015, the dominant greenhouse gases released into Earth's atmosphere—carbon dioxide, methane, and nitrous oxide—all continued to reach new high levels. At Mauna Loa, Hawaii, the annual CO₂ concentration increased by a record 3.1 ppm, exceeding 400 ppm for the first time on record. The 2015 global CO₂ average neared this threshold, at 399.4 ppm. Additionally, one of the strongest El Niño events since at least 1950 developed in spring 2015 and continued to evolve through the year. The phenomenon was far reaching, impacting many regions across the globe and affecting most aspects of the climate system.

Owing to the combination of El Niño and a long-term upward trend, Earth observed record warmth for the second consecutive year, with the 2015 annual global surface temperature surpassing the previous record by more than 0.1°C and exceeding the average for the mid- to late 19th century—commonly considered representative of preindustrial conditions—by more than 1°C for the first time. Above Earth's surface, lower troposphere temperatures were near-record high.

Across land surfaces, record to near-record warmth was reported across every inhabited continent. Twelve countries, including Russia and China, reported record high annual temperatures. In June, one of the most severe heat waves since 1980 affected Karachi, Pakistan, claiming over 1000 lives. On 27 October, Vredendal, South Africa, reached 48.4°C, a new global high temperature record for this month.

In the Arctic, the 2015 land surface temperature was 1.2°C above the 1981–2010 average, tying 2007 and 2011 for the highest annual temperature and representing a 2.8°C increase since the record began in 1900. Increasing temperatures have led to decreasing Arctic sea ice extent and thickness. On 25 February 2015, the lowest maximum sea ice extent in the 37-year satellite record was observed, 7% below the 1981–2010 average. Mean sea surface temperatures across the Arctic Ocean during August in ice-free regions, representative of Arctic Ocean summer anomalies, ranged from ~0°C to 8°C above average. As a consequence of sea ice retreat and warming oceans, vast walrus herds in the Pacific Arctic are hauling out on land rather than on sea ice, raising concern about the energetics of females and young animals. Increasing temperatures in the Barents Sea are linked to a community-wide shift in fish populations: boreal communities are now farther north, and long-standing Arctic species have been almost pushed out of the area.

Above average sea surface temperatures are not confined to the Arctic. Sea surface temperature for 2015 was record high at the global scale; however, the North Atlantic southeast of Greenland remained colder than average and colder than 2014. Global annual ocean heat content and mean sea level also reached new record highs. The Greenland Ice Sheet, with

the capacity to contribute ~7 m to sea level rise, experienced melting over more than 50% of its surface for the first time since the record melt of 2012.

Other aspects of the cryosphere were remarkable. Alpine glacier retreat continued, and preliminary data indicate that 2015 is the 36th consecutive year of negative annual mass balance. Across the Northern Hemisphere, late-spring snow cover extent continued its trend of decline, with June the second lowest in the 49-year satellite record. Below the surface, record high temperatures at 20-m depth were measured at all permafrost observatories on the North Slope of Alaska, increasing by up to 0.66°C decade⁻¹ since 2000.

In the Antarctic, surface pressure and temperatures were lower than the 1981–2010 average for most of the year, consistent with the primarily positive southern annular mode, which saw a record high index value of +4.92 in February. Antarctic sea ice extent and area had large intra-annual variability, with a shift from record high levels in May to record low levels in August. Springtime ozone depletion resulted in one of the largest and most persistent Antarctic ozone holes observed since the 1990s.

Closer to the equator, 101 named tropical storms were observed in 2015, well above the 1981–2010 average of 82. The eastern/central Pacific had 26 named storms, the most since 1992. The western north Pacific and north and south Indian Ocean basins also saw high activity. Globally, eight tropical cyclones reached the Saffir–Simpson Category 5 intensity level.

Overlaying a general increase in the hydrologic cycle, the strong El Niño enhanced precipitation variability around the world. An above-normal rainy season led to major floods in Paraguay, Bolivia, and southern Brazil. In May, the United States recorded its all-time wettest month in its 121-year national record. Denmark and Norway reported their second and third wettest year on record, respectively, but globally soil moisture was below average, terrestrial groundwater storage was the lowest in the 14-year record, and areas in “severe” drought rose from 8% in 2014 to 14% in 2015. Drought conditions prevailed across many Caribbean island nations, Colombia, Venezuela, and northeast Brazil for most of the year. Several South Pacific countries also experienced drought. Lack of rainfall across Ethiopia led to its worst drought in decades and affected millions of people, while prolonged drought in South Africa severely affected agricultural production. Indian summer monsoon rainfall was just 86% of average. Extremely dry conditions in Indonesia resulted in intense and widespread fires during August–November that produced abundant carbonaceous aerosols, carbon monoxide, and ozone. Overall, emissions from tropical Asian biomass burning in 2015 were almost three times the 2001–14 average.
# I. INTRODUCTION—D. S. Arndt, J. Blunden, and K. M. Willett

This is the 26th edition of the annual assessment now known as *State of the Climate*. The year 2015 saw the toppling of several symbolic mileposts: notably, it was 1.0°C warmer than preindustrial times, and the Mauna Loa observatory recorded its first annual mean carbon dioxide concentration greater than 400 ppm. Beyond these more recognizable markers, changes seen in recent decades continued.

The year's exceptional warmth was fueled in part by a nearly year-round mature El Niño event, which is an omnipresent backdrop to the majority of the sections in this edition.

The ENSO phenomenon is perhaps the most visible reminder of connections across regions, scales, and systems. It underscores the circumstance that the climate system's components are intricately connected, to each other and to the world's many natural and human systems.

To that end, this year's SoC has an emphasis on ecosystems; several chapters have dedicated a sidebar to the complex relationship between a changing climate and its impact on living systems. This notion of connectedness—between climate, landscape, and life; between our daily work and the expression of its meaning; between planetary-scale drivers and humble living things; between the abstraction and rigor of data and the reality and complexity of their importance; and especially between one generation and the next—inspires and informs much of the work within this volume.

Our cover images this year reflect these intimate connections. Many of the shapes in the images are drawn, quite literally, from time series represented in this volume. The artist, Jill Pelto, is a practicing Earth scientist whose work reflects her field experience and her interpretation of the connection between global change, landscape, and life. Her father, Mauri, is both a longtime contributor to the *State of the Climate* series and a steward of a prominent global glacier dataset.

To convey these connections so beautifully and generously is a gift; we are thankful to artist and scientist alike, for sharing their talents and disciplines with the community.

Finally, we wish one of our dearest and most valuable connections, our technical editor, Mara Sprain, a speedy recovery from an unexpected health challenge. Her consistency and diligence continue to be a model for this series.

An overview of findings is presented in the Abstract, Fig. 1.1, and Plate 1.1. Chapter 2 features global-scale climate variables; Chapter 3 highlights the global oceans; and Chapter 4 includes tropical climate phenomena including tropical cyclones. The Arctic and Antarctic respond differently through time and are reported in separate chapters (5 and 6, respectively). Chapter 7 provides a regional perspective authored largely by local government climate specialists. Sidebars included in each chapter are intended to provide background information on a significant climate event from 2015, a developing 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.
Time series of major climate indicators are
again presented in this introductory chapter. Many
of these indicators are essential climate variables
(ECVs), originally defined in GCOS 2003 and up-
dated again by GCOS in 2010.

The following ECVs, included in this edition,
are considered “fully monitored,” in that they are
observed and analyzed across much of the world,
with a sufficiently long-term dataset that has peer-
reviewed documentation:

* Atmospheric Surface: air temperature, precipitation, air pressure, water vapor, wind speed and direction.

* Atmospheric Upper Air: earth radiation budget, temperature, water vapor, wind speed and direction.

* Atmospheric Composition: carbon dioxide, methane, other long-lived gases, ozone.

* Ocean Surface: temperature, salinity, sea level, sea ice, current, ocean color, phytoplankton.

* Ocean Subsurface: temperature, salinity.

* Terrestrial: snow cover, albedo.

ECVs in this edition that are considered “par-
tially monitored,” meeting some but not all of the
above requirements, include:

* Atmospheric Upper Air: cloud properties.

* Atmospheric Composition: aerosols and their precursors.

* Ocean Surface: carbon dioxide, ocean acidity.

* Ocean Subsurface: current, carbon.

* Terrestrial: soil moisture, permafrost, glaciers and ice caps, river discharge, groundwater, ice sheets, fraction of absorbed photosynthetically active radiation, biomass, fire disturbance.

Remaining ECVs that are desired for the future
include:

* Atmospheric Surface: surface radiation budget.

* Ocean Surface: sea state.

* Ocean Subsurface: nutrients, ocean tracers, ocean acidity, oxygen.

* Terrestrial: water use, land cover, lakes, leaf area index, soil carbon.

PLATE I. I. Global (or representative) average time series for essential climate variables. Anomalies are shown 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 reanalysis (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 (October) [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 humidity-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].
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>
<figure><img src="image_6.png" /></figure>

Please Note: Material provided in this map was compiled from NOAA's NCEI State of the Climate Reports, the WMO Provisional Status of the Climate in 2015, and authorship for this report.
For more information please visit: http://www.ncdc.noaa.gov/sotc

<caption>Fig. 1.1. Geographical distribution of notable climate anomalies and events occurring around the world in 2015.</caption>
SIDEBAR 1.1: **THE 2015/16 EL NIÑO COMPARED WITH OTHER 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 2015/16 El Niño. This sidebar places the event, still ongoing as of May 2016, into context by comparison to recent El Niños 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 SBI.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. SBI.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 almost 2°C weaker than the two strongest events: 1982/83 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 pressure 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 influence on climate.

El Niño events tend to elevate global mean surface temperatures and, indeed, 2015 reached record warmth (section 2b1). The history of these events since the mid-20th century in

relation to global surface temperature suggests that the ongoing event will likely have a slightly greater effect on the global surface temperature of 2016 than on that of 2015.

<figure><img src="image_7.png" /></figure>

FIG. SBI.1. Sea surface temperature (relative to 1961-90 base period) averaged between 5°S and 5°N over the Pacific from 120°E to 80°W, based on HadISST1.1 (Rayner et al. 2003).
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 eastward as in late 1997, and the patterns were much less organized in the Indian and Atlantic Oceans (Online Fig. S2.22).

During El Niño events, cooling (warming) of the ocean surface and subsurface in the western (eastern) tropical Pacific, in addition to reduced drag on the ocean surface by weakened easterly winds, drives sea level fall (rise) in the western (eastern) tropical Pacific. The net effect is an increase in global sea level (section 4f; Fig. 3.17), evident in both 1997/98 and 2015/16.

Similar to other major El Niños, the 2015/16 event affected many parts of the global climate. Tropical cyclone activity, with respect to accumulated cyclone energy (ACE), was suppressed in the Atlantic Ocean (section 4e2) but enhanced across the North Pacific regarding both ACE and number of storms (sections 4e3, 4e4) The central Pacific was particularly active, setting several records. Global rainfall patterns were also greatly impacted (Section 4d1). The equatorial Pacific, Gulf of Mexico, and South America saw enhanced rainfall. Meanwhile, southern Africa, Australia, the Amazon, Caribbean, and Central America saw decreased rainfall. These patterns led to a substantial increase in the global land area covered by severe or extreme drought in 2015, similar to 1982/83 but not 1987/88 or 1997/98, possibly owing to countervailing influences such as extratropical atmospheric circulation patterns (section 2d9; Fig. 2.28; Plate 2.1f; Fig. 2.29).

The warmth in 2015 enabled an increase in total column water vapor (TCWV) of ~1 mm globally over both land and ocean (section 2d2; Figs. 2.16, 2.17). There were broadly similar increases following 1987/88, 1997/98, and 2009/10. Over the Pacific, 2015 lacked the dry anomaly north of the equator present in 1997 (Online Fig. S2.13). The dry anomaly over the Maritime Continent extended much farther west in 1997.

<figure><img src="image_8.png" /></figure>

FIG. SBI.2. Time series of various ENSO indicators: (a) Niño-3: 5°S-5°N, 150°-90°W; (b) Niño-4: 5°S-5°N, 160°E-150°W; (c) Niño-3.4: 5°S-5°N, 170°-120°W; (d) Niño-1+2: 10°S-0°, 90°-80°W; (e) oceanic Niño index (ONI); (f) Southern Oscillation index (SOI); (g) Equatorial Southern Oscillation index (EQ-SOI). The Niño region time series are from HadISST1.1 (Rayner et al. 2003). The ONI and EQ-SOI are from the NOAA Climate Prediction Center (www.cpc.ncep.noaa.gov/data/indices/). The SOI is from the Australian Bureau of Meteorology.

Although global average total cloudiness did not change in 2015 and shows no clear ENSO signal (Fig. 2.20), there was a dramatic shift of ice cloud from the warm pool region of the western Pacific to the central Pacific during 2015, and likewise during 1997 (section 2d4; Fig. 2.21). This shift followed the displacement of convection during the events. The eastward displacement was greater in 1997/98, matching that event's more eastward peak SST anomaly. Related regional features are apparent in 2015 annual averages of many hydrological cycle ECVs (Plate 2.1).

The tendency for increased drought in the tropics during El Niño leads to increased release of CO₂ from increased tropical wildfires. In 2015, out-of-control agricultural biomass burning was exacerbated in Indonesia (see Sidebar 2.2) by ignition of the subsurface peat. These changes in terrestrial carbon storage likely contributed to the record 3.1 ppm increase in atmospheric CO₂ at Mauna Loa Observatory from 1 January 2015 to 1 January 2016. The previous highest annual increase of 2.9 ppm occurred in 1998.

Biomass burning in Indonesia also led to regional increases in atmospheric carbon monoxide, aerosols, and tropospheric ozone in 2015 (Sidebar 2.2). Huijnen et al. 2016 suggest that the 2015 carbon emissions from the Indonesian fires were the largest since those during the El Niño year of 1997 (section 2g7; Fig. 2.60), although still only 25% of the 1997 emissions.
**2. GLOBAL CLIMATE**—K. M. Willett, D. F. Hurst, R. J. H. Dunn, and A. J. Dolman

a. Overview—K. M. Willett, D. F. Hurst, R. J. H. Dunn, and A. J. Dolman

Following the warmest year on record in 2014 according to most estimates, 2015 reached record warmth yet again, surpassing the previous record by more than 0.1°C. The considerable warmth, protracted strong El Niño, and new record levels of greenhouse gases provided climatological highlights for the year.

The progressing El Niño is a common theme woken throughout the essential climate variables (ECVs) presented here; its characteristic signature in temperature and water-related ECVs is clear across the maps in Plate 2.1. Having appeared in some indicators in 2014, and maturing in early 2015, this now-protracted event became the strongest since the 1997/98 El Niño. Indeed, many sections in this chapter compare the two events. Although strength-wise there are similarities, their characteristics are quite different (see Sidebar 1.1).

Atmospheric burdens of the three dominant greenhouse gases (CO₂, CH₄, N₂O) all continued to increase during 2015. The annual average CO₂ mole fraction at Mauna Loa, Hawaii (MLO), exceeded 400 ppm, a milestone never before surpassed in the MLO record or in measurements of air trapped in ice cores for up to 800 000 years. The 2015 global CO₂ average at Earth's surface was not far below, at 399.4 ± 0.1 ppm. The 3.1 ppm (0.76%) increase in CO₂ at Mauna Loa during 2015 was the largest annual increase observed in the 56-year record. Global average surface methane increased 11.5 ± 0.9 ppb (0.6%) from 2014 to 2015, the largest annual increase since 1997–98. Many ozone-depleting substances (ODS) continued to decline, lowering the stratospheric loading of halogen and the radiative forcing associated with ODS. Recent ozone measurements in the extra-polar upper stratosphere (~40 km) show a small increase that may be a first sign of long-term ozone layer recovery. Despite this, the 2015 Antarctic ozone hole was near-record in terms of size and persistence. Stratospheric water vapor just above the tropical tropopause increased 30% from December 2014 to December 2015, likely due to the combined changes in phase of the quasi-biennial oscillation (QBO) (cold to warm) and the El Niño–Southern Oscillation (ENSO) during 2015. The strong El Niño in 2015 produced extremely dry conditions in Indonesia, contributing to intense and widespread fires during August–November that produced anomalously high abundances of carbonaceous aerosols, carbon monoxide, and ozone in the tropical troposphere (Sidebar 2.2).

Significant forest fires were noted in many of the terrestrial variables, with emissions from tropical Asian biomass burning almost three times the 2001–14 average. Drier-than-average conditions were also evident over the global landmass. Soil moisture was below average for the entire year, and terrestrial groundwater storage was lower than at any other time during the record, which began in 2002. Areas in “severe” drought greatly increased, from 8% at the end of 2014 to 14% by the end of 2015. In keeping with the prevailing theme of warmer/drier, the global average surface air temperature record was accompanied by record high frequency of warm days and record low frequency of cool days. The lower troposphere was also close to record warmth.

Despite drier conditions on the ground, there was generally more moisture in the air as shown by the peaks in surface specific humidity and total column water vapor. These peaks were especially high over oceans, consistent with the generally warmer air. These warmer, moister conditions tend to lag El Niño by a few months, and the event was ongoing at year end.

In the cryosphere, Northern Hemisphere snow cover extent was slightly below average. However, alpine glacier retreat continued unabated and, with an update to the now 41-reference glacier dataset, 2015 became the 36th consecutive year of negative mass balance.

In addition to the strong El Niño, 2015 saw mostly positive Antarctic Oscillation (AAO) conditions throughout the year, contributing to stronger wind speed anomalies both at the surface and aloft (850 hPa). This typically leads to reduced west Antarctic Peninsula (WAP) sea ice extent, but it was opposed in 2015 by the El Niño, which is more often associated with a weaker polar jet stream. The North Atlantic Oscillation (NAO) was broadly positive for the fifth year in a row. Land wind speed continued a slight increase, similar to 2014, following a long, steady decline over the entire record from 1973.

The lake temperatures section returns this year after two years of unavailability. Additionally, two sidebars are included: Sidebar 2.1 explores our ability to monitor evaporation over land, a crucial missing link for studying the hydrological cycle; Sidebar 2.2 provides an overview of atmospheric chemical composition changes in 2015 as a result of El Niño–related forest fires.

Time series and anomaly maps for many EVCs are shown in Plates 1.1 and 2.1 respectively. Supplementary online figures can be found at: http://journals.ametsoc.org/doi/suppl/10.1175/2016BAMSStateoftheClimate.1.
<figure><img src="image_9.png" /><figcaption>(b) Lower Tropospheric Temperature</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(c) Surface Temperature</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(d) Warm Days</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(e) Cool Days</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(f) Soil Moisture</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>(g) Terrestrial Water Storage</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>(h) Precipitation</figcaption></figure>

<figure><img src="image_16.png" /></figure>
<figure><img src="image_17.png" /><figcaption>(m) Cloudiness</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(j) River Discharge</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>(n) Upper Tropospheric Humidity</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>(k) Surface Specific Humidity</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>(o) Total Column Water Vapor</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>(l) Surface Relative Humidity</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>(p) Drought (self-calibrating PDSI)</figcaption></figure>

<figure><img src="image_24.png" /></figure>
<figure><img src="image_25.png" /><figcaption>(u) OMI/MLS Tropospheric Column Ozone</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>(r) Upper Air (850-hPa) Winds</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>(v) Total Aerosol</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>(s) Surface Winds</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>(w) Dust Aerosol</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>(t) Sea Level Pressure</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>(x) Biomass Burning Aerosol</figcaption></figure>

<figure><img src="image_32.png" /></figure>
<figure><img src="image_33.png" /><figcaption>(ab) Carbon Emissions from Biomass Burning</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>(z) Land Surface Albedo in the Visible</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>(ac) Carbon Monoxide</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>(aa) Land Surface Albedo in the Near-Infrared</figcaption></figure>

<figure><img src="image_37.png" /></figure>

PLATE 2.1. (a) ERA-Interim lower stratospheric temperature; (b) ERA-Interim lower tropospheric temperature; (c) NOAA/NCEI surface temperature (contoured) and lake temperatures (circles); (d) GHCNDEX warm day threshold exceedance (TX90p); (e) GHCNDEX cool day threshold exceedance (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 broadband; (aa) Surface near-infrared albedo from MODIS White Sky broadband; (ab) GFASv1.3 carbonaceous emissions from biomass burning; (ac) CAMS total column CO anomalies.
## b. Temperature

1) SURFACE TEMPERATURE—A. Sánchez-Lugo, C. Morice, and P. Berrisford

The 2015 global land and ocean temperature set new records, exceeding the previous records set in 2014 (and 2010 depending on the in situ dataset) by a wide margin of 0.13°–0.18°C. Much-warmer-than-average conditions across much of the world’s surface and a strong El Niño contributed to the highest temperature since records began in the mid- to late 1800s, according to four independent in situ analyses (NASA-GISS, Hansen et al. 2010; HadCRUT4, Morice et al. 2012; NOAA-GlobalTemp, Smith et al. 2008; JMA, Ishihara 2006). The 2015 globally averaged surface temperature was 0.42°–0.46°C (Table 2.1) above the 1981–2010 average. Note that ranges of temperature anomalies provided in this summary are ranges of best estimates for the assessed in situ datasets. These ranges do not include additional uncertainty information from each in situ analysis, which can be found in Table 2.1.

The last time a record high temperature surpassed the previous record by such a wide margin was 1998, which surpassed the previous 1997 record by 0.12°–0.16°C. Similar to 2015, a strong El Niño developed during the latter half of 1997, reaching its maturity during the first part of 1998 (see Sidebar 1.1). The presence of El Niño typically increases concurrent global temperatures and those in the year following its onset.

The year 2015 also marked the first time the global average surface temperature reached more than 1°C above the average of the mid- to late 19th century, a period in which temperatures are commonly taken to be representative of pre industrial conditions. The best-estimate global average surface temperatures were 1.03°–1.09°C above the mid- to late 19th century average in assessed datasets. Fourteen of the 15 warmest years on record have occurred since the beginning of the 21st century, with 1998 the only exception (ranking between third and eighth warmest year, depending on the dataset).

Every estimate of global average temperature has inherent uncertainty. The main sources of un-

certainty in observational datasets tend to be associated with changes in measurement practices and with sparse spatial sampling, both of which can vary with time. When taking into consideration the estimated uncertainty of the global land and ocean annual temperature in the annual ranking, following the method of Arguez et al. (2013), it is virtually certain that 2015 was the warmest year since records began, with a probability >99%, according to the NOAA-GlobalTemp dataset (see Arguez and Applequist 2015).

The near-surface temperature analyses assessed here are derived from air temperatures observed at weather stations over land and sea surface temperatures (SST) observed from ships and buoys. While each analysis differs in methodology, all four analyses are in close agreement (Fig. 2.1). Plate 2.1c and Online Figs. S2.1, S2.2, and S2.3 show the differences between the datasets, which are mainly due to how each methodology treats areas with little to no data and how each analysis accounts for changes in measurement methods [for more details see Kennedy et al. (2010); Hansen et al. (2010); and Huang et al. (2015)].

Global average surface air temperatures are also estimated using reanalyses, which blend information from a numerical weather prediction model with observations. Reanalysis produces datasets with uniform spatial and temporal coverage, but suffers from model biases and problems arising from time variations in amount and/or quality of assimilated observations. Surface temperatures from reanalyses are consistent with observations in regions of good observational coverage at the surface, due in part to the large volumes of assimilated observations (e.g., more than 40 billion to date in the ERA-Interim reanalysis).

According to ERA-Interim (Dee et al. 2011), the 2015 globally averaged, analyzed 2-m temperature was the highest since records began in 1979. The

<table><caption>TABLE 2.1. Temperature anomalies (°C) and uncertainties (where available) for 2015 (base period: 1981–2010). The uncertainties indicate the scope of the range around the central value. For ERA-Interim, the values shown are the analyzed 2-m temperature anomalies (uncorrected). Note that the land values computed for HadCRUT4 used the CRUTEM.4.4.0.0 dataset (Jones et al. 2012), the ocean values were computed using the HadSST.3.1.1.0 dataset (Kennedy et al. 2011a, 2011b), and the global land and ocean values used the HadCRUT4.4.0.0 dataset. Uncertainty ranges are represented in terms of a 95% confidence interval, with the exception of JMA which has a 90% confidence interval.</caption><thead><tr><th>Global</th><th>NASA-GISS</th><th>HadCRUT4</th><th>NOAA-<br>Global Temp</th><th>JMA</th><th>ERA-Int</th></tr></thead><tbody><tr><td>Land</td><td>+0.64</td><td>+0.66±0.14</td><td>+0.72±0.18</td><td>+0.70</td><td>+0.65</td></tr><tr><td>Ocean</td><td>+0.36</td><td>+0.39±0.07</td><td>+0.37±0.16</td><td>+0.33</td><td>+0.28</td></tr><tr><td>Land and<br>Ocean</td><td>+0.44±0.05</td><td>+0.45±0.08</td><td>+0.46±0.08</td><td>+0.42±0.13</td><td>+0.38</td></tr></tbody></table>
temperature was 0.38°C above the 1981–2010 average (Table 2.1) and 0.10°C above its previous record set in 2005. The magnitude of the anomaly would be larger had the temperature analyses been corrected for changes in the source of the prescribed SST, which was uniformly cooler by about 0.1°C relative to HadCRUT4 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, Greenland, 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, depending 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 Pacific 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).

## 2) LOWER AND MIDTROPOSPHERIC TEMPERATURES—
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 remaining datasets (Fig. 2.2).

Direct measurement of the LT bulk temperature utilizes radiosonde datasets since 1958, complemented by satellites since late 1978. The datasets are described in Christy (2015) with new additions of the UNSW radiosonde dataset from Sherwood and Nishant (2015) and, for use in the tropical midtroposphere, two satellite datasets with similar construction

methods [NOAA (Zhou and Wang 2011) and UW (Po-Chedley et al. 2015)]. Previously utilized datasets from UAH and RSS have been updated (UAHv6.0, Spencer et al. 2016 submitted to *Asia-Pacific J. Atmos. Sci.*, RSSv4.0 for the midtroposphere, Mears and

<figure><img src="image_38.png" /><figcaption>**Fig. 2.1.** Global average surface temperature anomalies (°C, 1981–2010 base period). In situ estimates are shown from NOAA/NCEI (Smith et al. 2008), NASA-GISS (Hansen et al. 2010), HadCRUT4 (Morice et al. 2012), CRUTEM4 (Jones et al. 2012), HadSST3 (Kennedy et al. 2011a, b), JMA (Ishihara 2006), and Berkeley Earth (Rohde et al. 2013). Reanalyses estimates are shown from ERA-Interim (Dee et al. 2011), MERRA-2 (Gelaro et al. 2016; Bosilovich et al. 2015), and JRA-55 (Ebita et al. 2011).</figcaption></figure>
<figure><img src="image_39.png" /></figure>

**FIG. 2.2.** Global average lower tropospheric temperature annual anomalies (°C; 1981–2010 base period) for the MSU LT equivalent layer. (a) Radiosonde: RATPAC (Free et al. 2005; 85 stations), RAOBCORE and RICH (Haimberger et al. 2012; 1184 stations), and UNSW (Sherwood and Nishant 2015, 460 stations). (b) Satel- lites: UAHv6.0 (Spencer et al. 2016 submitted to Asia- Pacific J. Atmos Sci) and RSSv3.3 (Mears and Wentz 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 the far Southern Ocean (Plate 2.1b). The latitude–time depiction of the LT temperatures beginning in 1979 indicates tropical warming that is particularly strong during 2015, associated with the ongoing El Niño (Online Fig. S2.4).

The long-term global LT trend based on radiosondes (starting in 1958) is +0.15° ± 0.02°C decade⁻¹ and based on both radiosondes and satellites (starting in 1979) is +0.13° ± 0.03°C decade⁻¹. The range represents the variation among the different datasets, 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 is a statistical confidence range of ± 0.06°C decade⁻¹, meaning that the trends are significantly positive. Major volcanic events in 1963, 1982, and 1991 contributed 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 midtropospheric temperature (MT, surface to around 70 hPa) product for the tropical atmosphere (Fig. 2.3). The MT profile extends higher than that of LT, entering the stratosphere, but only slightly in the tropics where the tropopause is at approximately 16-km altitude. The dominant signal of this product is in the mid- to-upper troposphere, thus capturing the layer in the tropics which represents the maximum response to forcing (e.g., increased greenhouse gases, warm surface waters from El Niño, volcanic cooling, etc.). MT is constructed from the Microwave Sound- ing Unit (MSU) channel 2 and the Advanced MSU

<figure><img src="image_40.png" /></figure>

**FIG. 2.3.** Tropical (20°S–20°N) anomalies of midtropospheric temperature relative to the 1981–2010 base period. Data sources are as described in Figs. 2.1 and 2.2 with the addition of NOAA (Zhou and Wang 2011), UW (Po-Chedley et al. 2015) and RSSv4.0 (Mears and Wentz 2016).
channel 5 (Christy et al. 2003). MT tropical trends are influenced by lower stratospheric cooling by approximately 0.03°–0.04°C decade⁻¹, 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: +0.08°C decade⁻¹ (most radiosonde datasets and satellite UAH) and +0.12°C decade⁻¹ (RICH radiosonde 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 in past *State of the Climate* reports that observed MT 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 suggests amplification by a factor of 1.4 ± 0.2 of the mid tropospheric trend over the surface trend. The range of trends for 1979–2015 from the different radiosonde datasets is +0.07° to +0.11°C decade⁻¹ and from satellites is +0.07° to +0.14°C decade⁻¹ compared with the tropical surface trend (average of NOAAGlobalTemp and HadCRUT4) of +0.12°C decade⁻¹. The median trend of all observational datasets examined

<figure><img src="image_41.png" /><figcaption>FIG. 2.4. Global mean annual temperature anomalies of the lower-stratosphere temperatures derived from (a) radiosonde, (b) satellite, and (c) reanalysis. Anomalies are from the 1981–2010 mean. Data sources are as described in Figs. 2.1 and 2.2, additional data sources: NOAA (Zhou and Wang 2010).</figcaption></figure>

here is between +0.09 and 0.10°C decade⁻¹. Thus, the current tropical MT/surface ratio from observations since 1979 (0.8 ± 0.3) continues to be less than theory.

<table><caption>TABLE 2.2. Linear trends (°C decade⁻¹) in lower tropospheric (LT) and midtropospheric (MT) temperatures. The tropics region spans 20°S–20°N.</caption><thead><tr><th rowspan="2">Start Year</th><th colspan="2">Global LT</th><th colspan="2">Tropics MT</th></tr><tr><th>1958</th><th>1979</th><th>1958</th><th>1979</th></tr></thead><tbody><tr><th colspan="5">Radiosondes</th></tr><tr><td>RAOBCORE</td><td>+0.15</td><td>+0.13</td><td>+0.13</td><td>+0.08</td></tr><tr><td>RICH</td><td>+0.15</td><td>+0.15</td><td>+0.10</td><td>+0.11</td></tr><tr><td>RATPAC</td><td>+0.15</td><td>+0.15</td><td>+0.09</td><td>+0.07</td></tr><tr><td>UNSW</td><td>+0.17</td><td>+0.16</td><td>+0.10</td><td>+0.07</td></tr><tr><th colspan="5">Satellites</th></tr><tr><td>UAHv6.0</td><td>x</td><td>+0.11</td><td>x</td><td>+0.07</td></tr><tr><td>RSSv3.3</td><td>x</td><td>+0.12</td><td>x</td><td>+0.09</td></tr><tr><td>RSSv4.0</td><td>x</td><td>x</td><td>x</td><td>+0.14</td></tr><tr><td>NOAAv3.0</td><td>x</td><td>x</td><td>x</td><td>+0.13</td></tr><tr><td>UWv1.0</td><td>x</td><td>x</td><td>x</td><td>+0.12</td></tr><tr><th colspan="5">Reanalyses</th></tr><tr><td>ERA-I</td><td>x</td><td>+0.12</td><td>x</td><td>+0.08</td></tr><tr><td>JRA-55</td><td>+0.16</td><td>+0.15</td><td>x</td><td>+0.08</td></tr><tr><td>MERRA</td><td>x</td><td>+0.19</td><td>x</td><td>+0.16</td></tr></tbody></table>

3) LOWER STRATOSPHERIC TEMPERATURE—C. S. Long and J. R. Christy

The globally averaged temperature in the lower stratosphere (TLS) for 2015, as measured by radiosonde and satellite and analyzed by reanalyses, ranged from slightly above to approximately 0.5°C below the 1981–2010 climatology (Fig. 2.4). All TLS estimates agree that globally 2015 was about the same as 2014. This year’s persistence of last year’s annual temperatures only slightly impacted the near-neutral to very gradual warming trend observed from 1995 to present.
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 latitudes, 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 and warm anomalies for the first five months of 2015 (Online Fig. S2.5). The southern polar region was anomalously cold from August through December. These negative temperature anomalies coincided with the large and persistent ozone hole for 2015 (section 2g4). The tropical warm anomalies were a result of the thermal response to the descending quasi-biennial oscillation (QBO) westerlies during 2015 and the upper troposphere warming from the El Niño in the latter half of 2015.

A cooler stratosphere is consistent with a warmer troposphere in the case of rising greenhouse gases, as more outgoing energy is trapped in the troposphere. The TLS is a weighted layer-mean temperature of the part of the atmosphere observed by specific channels from satelliteborne microwave sounding instruments. It ranges from around 200–20 hPa (12–27 km) and is entirely in the lower stratosphere polewards of 35°. But equatorward of this, it extends into the upper troposphere, which needs to be accounted for when assessing latitudinal trends. For further details see Long and Christy (2015).

All radiosonde datasets (RATPAC, RAOBCORE, RICH, NSW) show a cooling trend in the lower stratosphere from 1958 to 1995. However, after 1995 there is not much of a trend to the present (Fig. 2.4). The pre-1995 cooling trend is only interrupted by several volcanoes [Agung (1963), El Chichón (1982), and Mt. Pinatubo (1991)], which imparted a warm pulse for about two years following each eruption. The satellite MSU channel 4 datasets (RSS, NOAA, and UAH) and four recent reanalysis datasets (CFSR; ERA-Interim; JRA-55; MERRA-2) also show general agreement with the radiosonde time series. Table 2.3 provides the trends for various time periods for the radiosondes, satellites, and reanalyses. There is variability among the datasets in the cooling trend from 1979 to 1995, with RATPAC having the greatest

cooling of the radiosonde datasets and MERRA-2 having the greatest cooling of the reanalyses, while ERA-Interim and JRA-55 have the least cooling. The post-1995 trends also vary considerably. All three satellite, JRA-55, RATPAC, and NSW trends are near neutral. The ERA-Interim and MERRA-2 reanalyses, RICH, and RAOBCORE have a slightly positive trend. As shown in Long and Christy (2015), the trends discussed above are not uniformly distributed across all latitudes, rather there is considerable variability with latitude.

Figure 2.5 shows time series of daily TLS anomalies for the 60°–90°N and 60°–90°S bands for 2015. The southern high latitudes were exceptionally cold from August through December 2015. Monthly mean height analyses show that the polar circulation in late 2015 was centered over the South Pole (not shown). Additionally, wave activity was minimal, keeping the circulation very zonal and cold. The low temperatures and persistent circulation aided the destruction of ozone, resulting in a larger ozone hole than in recent years (section 2g4). In the northern high latitudes, a few midwinter warmings affected the upper stratosphere but did not propagate down into the middle or lower stratosphere. A final warming in mid-March propagated down to the TLS region and increased the temperatures in the polar zone (Fig. 2.5). This warming is classified as a “final” warming as the atmospheric temperatures and circulation did not return to a winter pattern but continued to transition to a summer pattern. During the boreal autumn and early winter, Arctic TLS temperatures were well below normal.

<figure><img src="image_42.png" /><figcaption>**FIG. 2.5.** Daily time series (blue lines) for 2015 lower stratosphere temperatures from the CFSR (Saha et al. 2010a) for the (a) northern high latitudes (60°–90°N) and (b) southern high latitudes (60°–90°S). The 1979–2015 daily maximum and minimum temperatures for each latitude region are shown in black.</figcaption></figure>
<table><caption>TABLE 2.3. Computed trends (°C decade⁻¹) for radiosonde, satellite, and re-analysis data for the periods: 1958–95, 1979–95, 1995–2015, and 1979–2015. 1995 is chosen as an inflection point distinguishing the earlier downward trend from the near-neutral trend of recent years.</caption><thead><tr><th colspan="5">Global (82.5°N–82.5°S) TLS Temperature Anomaly Trends<br>(1981–2010 base period)</th></tr><tr><th>Dataset</th><th>1958–95</th><th>1979–95</th><th>1995–2015</th><th>1979–2015</th></tr></thead><tbody><tr><td colspan="5"><strong>Radiosonde</strong></td></tr><tr><td>RAOBCORE</td><td>-0.117</td><td>-0.306</td><td>0.260</td><td>-0.208</td></tr><tr><td>RICH</td><td>-0.282</td><td>-0.484</td><td>0.219</td><td>-0.278</td></tr><tr><td>RATPAC</td><td>-0.257</td><td>-0.649</td><td>-0.010</td><td>-0.467</td></tr><tr><td>UNSW</td><td>-0.330</td><td>-0.474</td><td>0.039</td><td>-0.317</td></tr><tr><td colspan="5"><strong>Satellite</strong></td></tr><tr><td>RSS</td><td>x</td><td>-0.336</td><td>-0.012</td><td>-0.261</td></tr><tr><td>STAR</td><td>x</td><td>-0.364</td><td>-0.002</td><td>-0.262</td></tr><tr><td>UAH</td><td>x</td><td>-0.399</td><td>-0.046</td><td>-0.312</td></tr><tr><td colspan="5"><strong>Reanalysis</strong></td></tr><tr><td>CFSR</td><td>x</td><td>-0.652</td><td>0.106</td><td>-0.348</td></tr><tr><td>ERA-Interim</td><td>x</td><td>-0.187</td><td>0.199</td><td>-0.119</td></tr><tr><td>JRA-55</td><td>x</td><td>-0.235</td><td>0.018</td><td>-0.217</td></tr><tr><td>MERRA-2</td><td>x</td><td>-0.300</td><td>0.171</td><td>-0.199</td></tr></tbody></table>

4) LAKE SURFACE TEMPERATURES—R. I. Woolway, K. Cinque, E. de Eyto, C. L. DeGasperi, M. T. Dokulil, J. Korhonen, S. C. Maberly, W. Marszelewski, L. May, C. J. Merchant, A. M. Paterson, M. Riffler, A. Rimmer, J. A. Rusak, S. G. Schladow, M. Schmid, K. Teubner, P. Verburg, B. Vigneswaran, S. Watanabe, and G. A. Weyhenmeyer

Lake summer surface water temperatures (LSSWT) in 2015 strongly reflected the decadal patterns of warming noted in the scientific literature. Northern Hemisphere summer refers to July–September whereas Southern Hemisphere summer refers to January–March. A recent worldwide synthesis of lake temperatures (O’Reilly et al. 2015) found that LSSWTs rose by, on average, 0.034°C yr⁻¹ between 1985 and 2009, ~1.4 times that of the global surface air temperature (SAT) in general. Data from lakes in various regions collated here show that during 2009–15 lake temperatures continued to rise.

During 2015, LSSWT of many lakes exceeded their 1991–2010 averages by 1°C or more (Online Fig. S2.6; Plate 2.1c). Strong warm anomalies in LSSWT were most prominent in central Europe [Austria, Switzerland, and Poland (data from the Institute of Meteorology and Water Management, Poland)], where anomalies above 1°C were recorded. The hot central European summer (JJA) of 2015 (sections 2b6 7f, and Sidebar 7.1) is reflected in relatively high

mean LSSWTs in three Austrian lakes (Mondsee, Neusiedler See, Wörthersee; Fig. 2.6; Online Fig. 2.6) with anomalies up to +1.6°C. Similarly, satellite-based LSSWT anomalies of 25 European lakes in and near the Alps were in excess of 1.0°C in 2015 (Fig. 2.7a), the second warmest anomaly year since the record summer of 2003 (Beniston 2004). High LSSWTs were also observed in other regions of the world (Plate 2.1c; Online Fig. 2.6), with anomalies for lakes in Seattle [Washington (state), U.S.], for example, up to +1°C in 2015.

LSSWTs are influenced by a combination of broad climatic vari-

ability and local characteristics, so regional and subregional differences in LSSWTs are common. LSSWTs in Britain and Ireland during 2015 were ~0.6°C below average, in contrast to central Europe. This likely reflects cool anomalies in SAT in early and mid-2015 (e.g., www.met.ie/climate/Monthly Weather/clim-2015-ann.pdf).

Although the Great Lakes (United States and Canada) have warmed faster than SAT in recent decades, the 2015 LSSWTs were relatively cool. This is attributable to above-average winter ice cover during 2014/15, which shortened the warming season. The annual maxima of percent ice cover (Great Lakes Environmental Research Laboratory; www.glerl.noaa.gov/) in 2014 (92.5%) and 2015 (88.8%) were substantially above the 1973–2015 average (53.2%). These were the first consecutive high-ice-cover years since the 94.7% maximum ice coverage recorded in 1979. The strong El Niño conditions of 2015 lessen the chance that 2016 will imitate 2014 and 2015.

Despite these recent cooler LSSWTs, the average warming rate for the Great Lakes is approximately 0.05°C yr⁻¹ (1979–2015). This rate contrasts with the Dorset lakes in Ontario, Canada (surface areas <100 ha), which do not show a statistically significant trend in LSSWT between 1980 and 2015. In 2015, LSSWT anomalies in these lakes were ~+0.6°C. These
lakes display large interannual variation in LSSWT, mainly reflecting interannual differences in SAT, with strong agreement in high and low years.

The relationship between SAT and LSSWT can be complicated by several processes. For Lake Erken, Sweden, LSSWT is strongly influenced by water col-

umn mixing and precipitation, leading to a relatively weak relationship between SAT and LSSWT. The LSSWT of New Zealand's largest lake, Lake Taupo, is thought to be influenced by interannual variation in geothermal heating (de Ronde et al. 2002) and shows 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 period (~0.036°C yr⁻¹). Two factors explain most of the variability (r² = 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 average warming rate for lakes in Britain and Ireland is substantially higher during winter (0.028°C yr⁻¹; Fig. 2.7b) than in summer (0.018°C yr⁻¹; Fig. 2.7d). Future assessments that focus on all seasons will provide a more complete picture.

<figure><img src="image_43.png" /></figure>

**FIG. 2.6.** Lake summer (Jul-Sep in Northern Hemisphere, 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 Water, Esthwaite Water, Lough Feeagh, Grasmere, Loch Leven, and Windermere (North and South bays)]; (e) Scandinavia (Erken, Inarijärvi, Kitusjärvi, Lappajärvi, Päijänne, Pielinen, and Saimaa); (f) central Europe (Charzykowskie, Jeziorak, Lubie, Mondsee, Neusiedler See, Wörthersee, and Zurich); (g) Israel (Kinneret); and (h) Australia and New Zealand (Burragorang, Cardinia, Sugarloaf, Taupo, and Upper Yarra). Gray lines indicate the temperature for each individual lake and the thick black line indicates the average lake temperature for the specified region. The trend for 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.

<figure><img src="image_44.png" /></figure>

**FIG. 2.7.** Satellite-derived lake surface water temperature anomalies for (a) summer (Jul-Sep; 1991-2015) for European Alpine lakes (all natural water bodies in or near the Alps larger than 14 km²; Riffler et al. 2015) and (b) winter (Jan-Mar, 1961-2015) for Britain and Ireland (base period: 1991-2010). Gray lines indicate the temperature for each individual lake and the thick black line indicates the average lake temperature for the region. The trend for the regionally averaged temperatures is shown in red, and the equation describing the change is presented. The lakes included are the same as those shown in Online Fig. 2.6 and Plate 2.1c.
The year 2015 not only set the highest global annual mean temperature on record, it also brought some extreme temperature events, most anomalously warm. Regionally, the frequencies of warm days and warm nights were the highest on record in western North America, parts of central Europe, and central Asia (Plates 2.1d,e). The GHCNDEX dataset (Donat et al. 2013) is used to monitor temperature extremes for 2015. GHCNDEX is a quasi-global gridded dataset of land-based observed temperature and precipitation extremes. A suite of temperature and precipitation extremes indices (Zhang et al. 2011) is first calculated for daily station time series from the GHCN-Daily archive (Menne et al. 2012), before interpolating the indices on global grids. At the time of writing, and similar to Dunn et al. (2015), some of the indices fields have limited spatial coverage for 2015, especially those derived from minimum temperatures across central and eastern Asia, compared to those calculated from maximum temperatures. This limited spatial coverage is related to an excessive number of missing values throughout the year, whereas monthly indices fields are more complete. For more details on the completeness requirements see Zhang et al. (2011).

Here, results for TX90p (frequency of warm days, defined as number of days above the seasonal 90th percentile of daily maximum temperatures over the 1961-90 base period), TX10p (frequency of cool days, defined as number of days with maximum temperatures below the seasonal 10th percentile), TXx (the hottest daily maximum temperature) and TNn (the coldest daily minimum temperature) are presented.

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, experienced strong warm anomalies, i.e., high frequencies of warm days and low frequencies of cool days, throughout much of the year (Plates 2.1d,e). As GHCNDEX has limited spatial coverage, the ERA-Interim reanalysis product (Dee et al. 2011) is used to provide a more complete picture. ERA-Interim also

shows anomalously high numbers of warm days and low numbers of cool days in Africa and large parts of South America, where GHCNDEX lacks coverage, suggesting that most global land areas saw warm anomalies in 2015 (see Online Fig. S2.8).

The first half of the year had some strong cold anomalies over the eastern United States, persisting after the cold winter 2014/15 into spring and even early summer. This resulted in comparatively lower values of warm extremes, though some cold extremes indices only showed cold anomalies during boreal winter (December–February; Fig. 2.9a). Similar behavior was observed during 2013 and 2014.

Notable extreme temperature events included the European summer heat waves (late June–early July and early August); a number of Asian heat waves in, for example, India, Pakistan, and Indonesia; and the warm spring and autumn in Australia, Alaska, and western Russia. Winter (December–February) showed strong warm anomalies over much of the Northern Hemisphere, including large parts of Europe, Asia, and western North America. Most of these events are evident in higher frequencies of warm days and lower frequencies of cool nights (TN10p) and they mainly occurred during the shoulder seasons. The heat waves of Pakistan, India, and Indonesia could not be monitored from GHCNDEX due to lack of coverage. However, results from the ERA-Interim reanalysis (see Online Fig. S2.8) indicate anomalously high annual frequencies of warm days and low frequencies of cold nights over these areas during 2015.

The European heat wave is clearly evident in June–August hottest days (TXx), with anomalies of 4°–5°C, and to a lesser extent in corresponding

<figure><img src="image_45.png" /><figcaption>FIG. 2.8. Global average time series of the number of (a) warm days (TX90p) and (b) cool days (TX10p) over land. The dashed line shows a 5-year binomial smoothed time series. (Source: GHCNDEX.)</figcaption></figure>
coldest nights (TNn), with anomalies of 1°–2°C (see Online Fig. S2.9). The frequency of both warm days and nights were also about double the normal for this period (see Fig. 2.9 and Online Fig. S2.7).

The Australian spring (September–November) experienced frequencies of cool nights and warm days well below and above average, respectively (Fig. 2.9), although anomalies in the hottest day and coldest night were not as extreme. The Russian and western North American springs (March–May) were also notably warm, similarly manifested in high frequencies of warm days and nights (Fig. 2.9).

The European autumn (September–November) also had anomalously high frequencies of warm days, whereas over northern North America and Greenland high frequencies of warm nights were more notable. Northern Russia and central Europe experienced warm days 3°–5°C warmer than normal

during autumn. Interestingly, the northern central Asia autumn was relatively cold for both warm and cold extremes (Fig. 2.9).

### c. Cryosphere

1) PERMAFROST THERMAL STATE—J. Noetzli, H. H. Christiansen, M. Gugliemin, V. E. Romanovsky, N. I. Shiklomanov, S. L. Smith, and L. Zhao

The Global Terrestrial Network for Permafrost (GTN-P) brings together long-term records on permafrost from permafrost regions worldwide (Smith and Brown 2009; Biskaborn et al. 2015). The two current observation elements are permafrost temperatures and active layer thickness (ALT). The ALT is the layer that thaws and freezes over the seasonal cycle; it generally increases in warmer conditions.

Permafrost has warmed over the past 2–3 decades, and generally continues to warm across the circum-

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 permafrost is provided in section 5i. In this section, results from the European Alps, central Asia, and continental Antarctica are summarized.

Mountain permafrost in the European Alps is patchy and its character and thermal conditions are spatially heterogeneous. The majority of permafrost 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 elevation 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 4450 m a.s.l. experience annual mean temperatures near the surface as low as –10°C (Paolo Pogliotti, Environmental

<figure><img src="image_46.png" /><figcaption>**Fig. 2.9.** Seasonal anomalies of the frequency of (a–d) warm days (TX90p) and (e–h) cool nights (TN10p) for 2015 relative to the 1961–90 base period. There must be at least two months of data present within each season. (Source: GHCNDEX.)</figcaption></figure>
Protection Agency of Valle d'Aosta, 20 February 2015, personal communication). Records measured within the Swiss Permafrost Monitoring Network (PERMOS) during the past 10 to 25 years show a general warming trend at depths to 10 and 20 m, especially over the past seven years (Figs. 2.10a,b). The recent warming is accentuated in 2015, when the highest permafrost temperatures were recorded at most PERMOS sites. This is a cumulative effect of the continuously warm weather conditions in recent years rather than a result of the extremely warm summer 2015. ALT reached new record values in 2015 at many PERMOS sites. Absolute ALT changes depend strongly on surface processes—mainly snow cover duration and thickness—and subsurface ice content (PERMOS 2013). The recent warming of permafrost

in the Swiss Alps since 2009 has been accompanied by an increase of rock glacier velocities, as observed at multiple sites within Switzerland.

In the warm permafrost of the higher elevations of central Asia, ground temperatures have increased by up to 0.5°C decade⁻¹ since the early 1990s, and a general increase in ALT has been observed (e.g., Zhao et al. 2010). The ground temperature at sites along the Qinghai–Xizang Highway increased between 2004 and 2014 by 0.04°–0.5°C decade⁻¹ at 10-m depth, and about 0.01°–0.29°C decade⁻¹ at 20-m depth (Fig. 2.10c,d). Based on monitoring results extended by a freezing–thawing index model, the average increase of ALT was about 28 cm decade⁻¹ from 1981 to 2015 along the Qinghai–Xizang Highway (Fig. 2.11). The average ALT from 2011 to 2015 in Fig. 2.11 was about 15 cm more than the 2001–10 average. The mean annual air temperature in the Tibetan Plateau region increased at an average rate of 0.68°C decade⁻¹ over the past 35 years (Fig. 2.11).

Permafrost temperature at 20-m depth along the latitudinal 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. 2.10e). This increase 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, Fig. 2.10e) despite recorded air warming in the area. ALT is strongly increasing in the coastal areas of continental Antarctica, between 5 cm year⁻¹ at Marble Point (Balks et al. 2016) and 0.8 cm year⁻¹ at Boulder Clay (Guglielmin et al. 2014a). In maritime Antarctica, at Signy Island, the active layer has ranged between 124 and 185 cm since 2006 (Guglielmin et al. 2012), while at Livingstone Island between 124 and 145 cm (De Pablo et al. 2014), both without any trends despite air temperatures having increased in this area.

<figure><img src="image_47.png" /><figcaption>**FIG. 2.10.** Temperatures measured in permafrost boreholes. Boreholes for central and northern Europe at approximately (a) 10-m and (b) 20-m depth, with actual depths shown in parentheses; along Qinghai–Xizang Highway on the Tibetan Plateau at (c) 10-m and (d) 20-m depth; and (e) in Antarctica at 20-m depth: WV = Wright Valley; MP = Marble Point; OASI in Continental Antarctica; and Rothera in Maritime Antarctica. (Sources: Swiss Permafrost Monitoring Network PERMOS; Norwegian Meteorological Institute and the Norwegian Permafrost Database, NORPERM; EDYTEM/University of Savoie; Cryosphere Research Station on Qinghai–Xizang Plateau, CAS.)</figcaption></figure>
<figure><img src="image_48.png" /><figcaption>FIG. 2.11. Annually-averaged ALTs and MAATs along Qinghai-Xizang Highway on the Tibetan Plateau (modified after Li et al. 2012 based on new data). (Sources: Cryosphere Research Station on Qinghai-Xizang Plateau, CAS.)</figcaption></figure>

## 2) NORTHERN HEMISPHERE CONTINENTAL SNOW COVER EXTENT—D. A. Robinson

Annual snow cover extent (SCE; Table 2.4; Fig. 2.12) over Northern Hemisphere (NH) lands averaged 24.6 million km² in 2015. This is 0.5 million km² less than the 46-year average and ranks 2015 as having the 36th most extensive (or 10th least extensive) cover on record. This evaluation considers snow over NH continents, including the Greenland ice sheet. SCE in 2015 ranged from 47.1 million km² in January to 3.0 million km² in August. Monthly SCE is calculated at the Rutgers Global Snow Lab from daily SCE maps produced by meteorologists at the National Ice Center (a U.S. joint NOAA, Navy, and Coast Guard facility), who rely primarily on visible satellite imagery to construct the maps.

SCE across the NH was close to average in January 2015, a balance between above-average cover in Eurasia (EU) and below-average over North America (NA). This reversed in Feb-

ruary, with SCE 1.1 million km² 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 month ranking seventh most extensive. December saw the brakes put on this rapid expansion, with coverage 0.2 million km² below average, or 32nd most extensive.

SCE over the contiguous United States was at the boundary of the middle and lower tercile in January 2015. It was within the middle tercile but nearer the above-normal side in February. The situation changed considerably in spring, with March SCE the fifth lowest on record and April ninth least extensive. Autumn 2015 SCE began building slowly in October, ranking ninth lowest. This changed in November and December, which ranked 19th and 22nd most extensive, respectively.

<table><caption>TABLE 2.4. Monthly and annual climatological statistics on Northern Hemisphere and continental snow extent between November 1966 and December 2015. Included are: number of years with data used in the calculations, means, standard deviations, 2015 values, and ranks. Areas are in km² (millions). 1968, 1969, and 1971 have 1, 5, and 3 missing months, respectively, thus are not included in the annual calculations. North America (N. Am.) includes Greenland. Ranks are from most extensive (1) to least (ranges from 46 to 50, depending on the month).</caption><thead><tr><th></th><th>Years</th><th>Mean</th><th>Std. Dev.</th><th>2015</th><th>2015 N. Hem rank</th><th>Eurasia rank</th><th>N. Am. rank</th></tr></thead><tbody><tr><td>Jan</td><td>49</td><td>47.1</td><td>1.6</td><td>47.3</td><td>22</td><td>18</td><td>32</td></tr><tr><td>Feb</td><td>49</td><td>46.1</td><td>1.8</td><td>45.0</td><td>36</td><td>41</td><td>20</td></tr><tr><td>Mar</td><td>49</td><td>40.6</td><td>1.8</td><td>38.5</td><td>43</td><td>41</td><td>40</td></tr><tr><td>Apr</td><td>49</td><td>30.6</td><td>1.7</td><td>30.1</td><td>28</td><td>21</td><td>35</td></tr><tr><td>May</td><td>49</td><td>19.3</td><td>1.9</td><td>17.0</td><td>44</td><td>38</td><td>47</td></tr><tr><td>Jun</td><td>48</td><td>9.7</td><td>2.4</td><td>5.4</td><td>47</td><td>47</td><td>47</td></tr><tr><td>Jul</td><td>46</td><td>4.0</td><td>1.2</td><td>2.5</td><td>42</td><td>39</td><td>44</td></tr><tr><td>Aug</td><td>47</td><td>3.0</td><td>0.7</td><td>2.6</td><td>34</td><td>39</td><td>23</td></tr><tr><td>Sep</td><td>47</td><td>5.4</td><td>1.0</td><td>5.9</td><td>14</td><td>18</td><td>8</td></tr><tr><td>Oct</td><td>48</td><td>18.3</td><td>2.6</td><td>21.4</td><td>7</td><td>6</td><td>11</td></tr><tr><td>Nov</td><td>50</td><td>34.0</td><td>2.1</td><td>36.2</td><td>7</td><td>7</td><td>19</td></tr><tr><td>Dec</td><td>50</td><td>43.7</td><td>1.9</td><td>43.5</td><td>32</td><td>30</td><td>22</td></tr><tr><td>Ann</td><td>46</td><td>25.1</td><td>0.8</td><td>24.6</td><td>36</td><td>29</td><td>39</td></tr></tbody></table>
<figure><img src="image_49.png" /><figcaption>FIG. 2.12. Twelve-month running anomalies of monthly snow cover extent over Northern Hemisphere lands as a whole and Eurasia and North America (including Greenland) separately between Nov 1966 and Dec 2015. Anomalies are calculated from NOAA snow maps (http://snowcover.org) relative to 1981–2010. Monthly means for the period of record are used for 9 missing months between 1968 and 1971 in order to create a continuous series of running means. Missing months fall between Jun and Oct; no winter months are missing.</figcaption></figure>

Maps depicting daily, weekly, and monthly conditions, daily and monthly anomalies, and monthly climatologies for the entire period of record may be viewed at the Rutgers Global Snow Lab website (http://snowcover.org). Monthly SCE for the NH, EU, NA, contiguous U.S., Alaska, and Canada are also posted, along with information on how to access weekly areas and weekly and monthly gridded products.

### 3) ALPINE GLACIERS AND ICE SHEETS—M. S. Pelto

The World Glacier Monitoring Service (WGMS) record of mass balance and terminus behavior provides a global index for alpine glacier behavior. The WGMS dataset for terminus change contains 42 000 observations from 2000 glaciers extending from the mid-19th century. There are 5200 geodetic and glaciological mass balance observations in this dataset. Annual mass balance is the annual change in volume due to snow and ice accumulation and snow and ice losses. Here, WGMS mass balance is reported in mm of water 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 consecutive 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 have reported to date. The preliminary data indicate 2015 mass balance will be one of the two most negative along with 2003, with 2003 at -1268 mm for reference glaciers and -1198 mm for all glaciers.

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. The Randolph Glacier Inventory version 3.2 (RGI) was completed in 2014, compiling digital outlines of alpine glaciers using satellite imagery from 1999 to 2010. The inventory identified 198 000 glaciers, with a total extent estimated at 726 800 ± 34 000 km² (Pfeffer et al. 2014). This inventory was crucial for glacier run-off modelling that indicates 11 of 13 alpine regions are experiencing decreased runoff (Bliss et al. 2014). This is due to a greater loss of glacier area than increased rate of glacier melt. The volume loss of alpine glaciers has led to a current sea level rise equivalent of approximately 0.8–1.0 mm year⁻¹ (Marzeion et al. 2012).

The cumulative mass balance loss from 1980 to 2015 is 18.8 m, the equivalent of cutting a 20.5 m thick slice off the top of the average glacier (Fig. 2.13). The trend is remarkably consistent from region to region (WGMS 2015a). The decadal mean annual mass balance was -261 mm in the 1980s, -386 mm in the 1990s, -727 mm for 2000s, and -818 mm from 2010 to 2015. The declining mass balance trend during a period of glacier retreat indicates alpine glaciers are not approaching equilibrium and retreat will continue to be the dominant terminus response (Zemp et al. 2015). The recent rapid retreat and prolonged negative balances have led to many glaciers disappearing and others fragmenting (Pelto 2010; Carturan et al. 2015).

In South America, seven glaciers in Colombia, Argentina, and Chile reported mass balance in 2015. All seven glaciers had losses greater than 1200 mm,

<figure><img src="image_50.png" /><figcaption>FIG. 2.13. Mean annual (red bars) and cumulative (red line) annual balance reported for the 41 reference glaciers to the WGMS (1980–2015). The data for 2015 are preliminary, only including 27 reference glaciers at the time of publication.</figcaption></figure>
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, rivaling 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 Columbia to Washington the accumulation season temperature was exceptional with the mean for November–April being the highest observed (Fig. 2.14).

In the high mountains of central Asia, seven glaciers from China, Russia, Kazakhstan, and Kyrgyzstan reported data; all were negative with a mean of –705 mm.

### d. Hydrological cycle

1) SURFACE HUMIDITY—K. M. Willett, D. I. Berry, M. G. Bosilovich, and A. Simmons

Surface moisture values in 2015 were at their highest level since the last El Niño event in 2010 (Fig. 2.15). Over land, levels of water vapor in the air (specific humidity) were well above the 1981–2010 average and approaching those of 1998 and 2010. Over oceans, annual average specific humidity values were higher than at any other point in the record that began in the early 1970s. The ability of the atmosphere to carry water vapor is limited by its temperature. The extra warmth associated with the El Niño, ongoing in some respects since 2014, together with generally above-average global temperatures, is consistent with the high atmospheric humidity seen in 2015. Similar anomalously high humidity levels are seen in the years following previous El Niño events, with the atmospheric humidity typically lagging the temperature changes by a few months.

Relative humidity levels in 2015 remained well below average, continuing an apparent declining trend since the early 2000s. While the land in situ data

<figure><img src="image_51.png" /><figcaption>FIG. 2.14. Columbia Glacier, Washington: 1 of 41 WGMS reference glaciers, viewed on 4 Aug 2015 from (a) below the terminus and (b) above the head of the glacier. Note the lack of retained snowcover with seven weeks left in the melt season. Numerous annual firn and ice layers exposed. (Photo credit: M. Pelto)</figcaption></figure>

(HadISDH.2.1.0.2015p) are in broadscale agreement with the ERA-Interim and JRA-55 reanalyses in terms of overall behavior, HadISDH presents 2015 as slightly more moist than 2014 whereas both reanalyses present 2015 as slightly more arid. However, for HadISDH at least, the 2015–2014 difference is smaller than the annual uncertainty estimate for 2015 (±0.2% rh)

All estimates contain uncertainty. Arguably the largest sources of uncertainty, generally, are the gaps in sampling both in space and time. There is also uncertainty stemming from systematic errors in the data and the different methods for dealing with these by bias correction or homogeneity detection and adjustment. Over the ocean (Berry and Kent 2009, 2011), ship heights have increased over time, requiring height adjustment to avoid erroneously decreasing specific and relative humidity. Systematic biases have also been found between psychrometers housed within screens versus those that are hand held. Over land (Willett et al. 2013b, 2014b), changes to observing instruments, locations, or processes have been common and poorly documented, requiring statistical methods to account for them. Measurement uncertainty also plays a role. Reanalyses (Simmons et al. 2010; Simmons and Poli 2014) have the benefit
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)].
The new MERRA-2 reanaly-
sis (R. Gelaro et al. 2016
unpublished manuscript;
Bosilovich et al. 2015) shows
better agreement than the
previously used MERRA,
owing to improved data se-
lection, inclusion of mod-
ern data, and model and
data assimilation advances.
MERRA-2 uses observation-
corrected precipitation for

forcing the land surface, which helps constrain the
near-surface temperature and moisture over land
(Reichle and Lui 2015). While the year-to-year vari-
ability is similar to the other estimates, there are
some deviations around 2002 and 2007–09 (Fig. 2.15).
These are thought to be linked to variability in the
precipitation forcing at those times. All agree on the
most recent period having the highest specific humid-
ity levels on record while also being the most arid in
relative humidity terms (Fig. 2.15).

Spatially, specific humidity was anomalously high
over much of the land, especially over India and
Southeast Asia, which was also common to 1998 and
2010 (Plate 2.1k; Online Figs. S2.10, S2.11). In contrast
to 2014, the United States experienced almost entirely
above-average specific humidity. Southern Africa
was particularly dry. Over oceans, data quality sig-
nificantly impacts the spatial coverage of the in situ
data, meaning that the key El Niño–Southern Oscil-
lation (ENSO) region of the Pacific Ocean is not well
observed. ERA-Interim and MERRA-2 show strong
moist anomalies there, in good agreement with the
other hydrological cycle ECVs and the very warm
SSTs (Plate 2.1c, Online Figs. S2.1 to S2.3).

<figure><img src="image_52.png" /><figcaption>FIG. 2.15. Global average surface humidity annual anomalies (base period: 1979–2003). For in situ datasets, 2-m surface humidity is used over land and ~10-m over the oceans. For the reanalysis, 2-m humidity is used across the 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 unreliable use of ship data in producing the analysis. All data have been adjusted to have a mean of zero over the common period 1979–2003 to allow direct comparison, with HOAPS given a zero mean over the 1988–2003 period. ERA values over land are from ERA-40 prior to 1979 and ERA-Interim thereafter. [Sources: HadISDH (Willett et al. 2013a, 2014a); HadCRUH (Willett et al. 2008); Dai (Dai 2006); HadCRUHext (Simmons et al. 2010); NOCSv2.0 (Berry 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.]</figcaption></figure>

Relative humidity was anomalously low over much
of the land (Plate 2.1l; Online Fig. S2.12). Interestingly,
some regions, such as southern Africa and Australia,
experienced both below-average water vapor amounts
(specific humidity) and levels of saturation (relative
humidity), while other regions, such as the United
States and southern India, experienced above-average
water vapor but below-average saturation. The regions
of low relative humidity are broadly, but not exactly,
consistent with below-average precipitation (Plate
2.1h). Over the oceans there was a strong dipole along
the equatorial Pacific with much lower-than-average
values to the south. This was slightly farther north
than the specific humidity dipole associated with the
El Niño warm pool.

2) TOTAL COLUMN WATER VAPOR—C. Mears, S. Ho, J. Wang, H. Huelsing, and L. Peng

Total column water vapor (TCWV) rapidly
increased during 2015 in response to the 2015/16
El Niño event (Fig. 2.16), with the annual average
anomaly lying well above the long-term average.
Estimates come from satelliteborne microwave ra-
diometers over ocean (Wentz 1997, 2015), COSMIC
<figure><img src="image_53.png" /></figure>

**FIG. 2.16.** Global average total column water vapor anomalies (mm; 1981–2010 reference period) for (a,b) ocean only and (c,d) land only for observations and reanalyses (see Fig. 2.1 for reanalyses references) averaged over 60°S–60°N. The shorter time series have been given a zero mean over the period of overlap with ERA-Interim (1988–2015 for RSS Satellite, 1995–2015 for GNSS, 2007–15 for COSMIC).

GPS-RO (Global Positioning System–Radio Occultation) over land and ocean (Ho et al. 2010; Teng et al. 2013; Huang et al. 2013), and ground-based GNSS (Global Navigation Satellite System) stations (Wang 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 Amazon 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 radiometers show maxima in 1983/84, 1987/88, 1997/98, 2009/10, and late 2015, each associated with El Niño events. The December 2015 anomaly is the largest recorded for any month, particularly in the satellite radiometer data. This is a result of the large wet

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 reanalysis 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 throughout the record and all show a subtle increase in TCWV, similar to over ocean.

A land-and-ocean time-latitude plot derived from JRA-55 (Fig. 2.17) indicates that the long-term increase in TCWV is occurring at all latitudes, with less variability outside the tropics. The El Niño events are clear, especially the 1997/98 event. The previous strong El Niño events during 1983/84 and 1997/98 showed pronounced drying in the northern tropics that accompanied moistening on the equator and the southern subtropics. Although similar in strength in terms of sea surface temperature, the TCWV response to the current El Niño does not show this feature (see Sidebar 1.1; Online Fig. S2.13).

<figure><img src="image_54.png" /></figure>

**FIG. 2.17.** Hovmöller plot of total column water vapor anomalies (mm; base period 1981–2010) including land and ocean derived from JRA-55 reanalysis.
### 3) UPPER TROPOSPHERIC HUMIDITY—V. O. John, L. Shi, and E.-S. Chung

Global scale monitoring of upper tropospheric relative humidity (UTH) was first reported last year, using one dataset of satellite origin and one reanalysis. However, the reanalysis data showed drying of the upper troposphere since 2001 that was not present in the satellite data. Therefore, for this year, two independent UTH satellite datasets are used. One is the infrared-based HIRS dataset (Shi and Bates 2011) which was used last year, and the other is the microwave-based UTH dataset (Chung et al. 2013). UTH represents a weighted average of relative humidity in a broad layer, roughly between 500 and 200 hPa. Humidity distribution at these levels of the atmosphere is a key climate variable due to its strong control on the outgoing longwave radiation (OLR) which makes a strong feedback factor in the climate system.

Area-weighted anomaly time series of UTH for the 60°N–60°S latitude belt are shown in Fig. 2.18. The anomalies are computed relative to 2001–10 because the microwave-based UTH dataset begins only in 1999. A slightly below-average 2015 anomaly is observed. A near-zero trend in the UTH time series indicates an increase in specific humidity in the warming upper troposphere and is consistent with a positive water vapor feedback (Chung et al. 2016). It is encouraging to see good agreement between the two independent datasets despite their differences in sampling: microwave data have an almost all-sky sampling whereas HIRS data samples mainly clear-sky areas. The annual average of UTH for 2015 (Plate 2.1n; Online Fig. S2.14) shows large moist anomalies over the central and eastern tropical Pacific and dry anomalies over the Maritime Continent, which results 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).

<figure><img src="image_55.png" /><figcaption>**FIG. 2.18.** Anomaly time series of upper tropospheric humidity using HIRS (black) and microwave (blue) datasets. The anomalies are computed based on 2001–10 average, and the time series is smoothed to remove variability on time scales shorter than 3 months.</figcaption></figure>

### 4) PRECIPITATION—R. S. Vose, A. Becker, K. Hilburn, G. Huffman, M. Kruk, and X. Yin

Precipitation over the global land surface in 2015 was far below the long-term average (Fig. 2.19). In fact, 2015 was the driest year on record in two prominent global products: the Global Precipitation Climatology Centre (GPCC) dataset (Schneider et al. 2011; Becker et al. 2013), which is based on surface stations, and the Global Precipitation Climatology Project (GPCP) version 2.3 (Adler et al. 2003), which is based on both satellite data and surface stations. Last year was also among the five driest years on record in a new (experimental) version of another prominent global product, the Global Historical Climatology Network (GHCN) dataset (Peterson and Vose 1997; Menne et al. 2012), which contains about five times as many surface stations as its operational counterpart (version 2).

From a spatial perspective, coherent anomaly patterns were evident across the global land surface in 2015 (Plate 2.1h). El Niño affected precipitation in many areas; in particular, below-average precipitation fell over much of northeastern South America, southern Africa, the Maritime Continent, and northern Australia, while above-average precipitation fell over the southeastern quadrants of North and South America. Relative to 2014, northern and eastern Asia became much wetter while western Europe became much drier.

In contrast to global land areas, precipitation over the global ocean surface in 2015 was much above the long-term average, continuing the general increase of the last five years (Fig. 2.19). Above-normal precipitation over the ocean served as a counterpoint to below-normal precipitation over land, and thus the global value for 2015 was slightly above the long-term

<figure><img src="image_56.png" /><figcaption>**FIG. 2.19.** Globally averaged precipitation anomalies (mm) for (a) four in situ datasets over land (1961–90 base period) and (b), (c) one satellite-based dataset over ocean (1988–2010 base period). Ocean averages are for the global ocean equatorward of 60° latitude using a common definition of “ocean” and the annual cycle has been removed.</figcaption></figure>
average. The ongoing El Niño, which was particularly
dominant in the tropics in the latter half of the year,
resulted in several distinct anomaly patterns over the
ocean (Plate 2.1h). In particular, an intense positive
anomaly of rainfall stretched across the Pacific Ocean
along the Inter-Tropical Convergence Zone, just north
of the equator. The western Pacific experienced two
distinct anomaly maxima (a larger one slightly to
the south of the equator and a secondary one to the
north). In addition, there was a strong negative rain-
fall anomaly over the seas of the Maritime Continent.
Other large anomalies for 2015 include above-normal
rainfall over the central Indian Ocean and the eastern
Pacific Ocean (east of the Hawaiian Islands) as well as
below-normal precipitation in parts of the northern
and southern Pacific Oceans.

5) CLOUDINESS—M. J. Foster, S. A. Ackerman, K. Bedka, R. A. Frey, L. Di Girolamo, A. K. Heidinger, S. Sun-Mack, B. C. Maddux, W. P. Menzel, P. Minnis, M. Stengel, and G. Zhao

Based on the longest continuous record of cloud
cover, PATMOS-x (Pathfinder Atmospheres Extend-
ed; Heidinger et al. 2013), 2015 was 1.4% less cloudy
than the 35-year average, making it the 10th least
cloudy year on record. Global mean annual cloudi-
ness anomalies from eight satellite records are shown
in Fig. 2.20. Four of the records—MISR (Multiangle
Imaging Spectroradiometer; Di Girolamo et al. 2010),
Aqua MODIS C6 (Moderate Resolution Imaging
Radiospectrometer Collection 6; Ackerman et al.
2008), CALIPSO (Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observation; Winker et al. 2007),
and CERES (Clouds and the Earth's Radiant Energy
System) Aqua MODIS (CERES-MODIS; Minnis et al.
2008; Trepte et al. 2010) show little change in global
cloudiness from 2014 to 2015 (<0.1%). PATMOS-x and
HIRS (High Resolution Infrared Sounder; Wylie et
al. 2005; Menzel et al. 2014)—show modest increases
of 0.30% and 0.29%, respectively, while SatCORPS
(Satellite ClOud and Radiative Property retrieval
System; Minnis et al. 2015) shows an increase of 1.1%,

and CLARA-A2 (Cloud, ALbedo and RAdiation dataset; Karlsson et al. 2013) shows a modest decrease of 0.25%. Three of the records—PATMOS-x, CLARA-A2, and SatCORPS—are derived from the AVHRR (Advanced Very High Resolution Radiometer) on the NOAA Polar Orbiter Environmental Satellite series and more recently the EUMETSAT Polar System Metop series. SatCORPS is the most recent addition and was developed through the NOAA Climate Data Record program. CLARA-A2 is the successor to CLARA-A1 and includes several changes, the most notable to impact global cloudiness being improvements in cloud detection over semiarid regions. In addition to instrument sensitivity and calibration, several factors contribute to differences among the records. For example, the AVHRR-derived records use different methods to account for satellite diurnal drift, while HIRS is primarily focused on detection of cirrus cloud.

The satellite records are in good agreement post-
2000, but prior to 2000, global cloudiness was more
variable among the records and was relatively higher
for all series with the exception of SatCORPS. Four of
the records in Fig. 2.20 are derived from instruments
flown on the NASA Earth Observing System (EOS)
satellite missions, beginning in 1999 with the launch
of *Terra*. MISR is flown on *Terra*, while CERES and
MODIS are flown on *Terra* and *Aqua* (launched in
2002). Recent calibration issues with IR channels
on *Terra*/MODIS have resulted in artificial positive
trends in cloudiness, noticeable from around 2010,
so only *Aqua*/MODIS is included here. *CALIPSO* was
launched in 2006.

One explanation posited for the discrepancy be-
tween pre- and post-2000 is the lack of strong El Niño
events in recent years. The last strong El Niño event
was observed in 1997/98. Thus 2015 is significant in
that it is the first year with a strong El Niño event
during the NASA EOS/post-2000 era. The close
agreement between cloud records and lack of a large
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
observations during strong El Niño months while the
1997 and 2015 images show the same month of the
previous year (preceding the El Niño). Both El Niño
events see a dramatic shift of ice clouds in January
from the Warm Pool region of the western Pacific to

<figure><img src="image_57.png" /><figcaption>FIG. 2.20. Annual global cloudiness anomalies for 1981–2015 (base period, 2003–14, a period common to the satellite records excluding CALIPSO, where the entire record was used instead). Datasets include PATMOS-x, HIRS, MISR, AQUA MODIS C6, CALIPSO, CERES, SatCORPS and CLARA-A2.</figcaption></figure>
<figure><img src="image_58.png" /><figcaption>FIG. 2.21. Mean ice cloud fractions for Jan 1997, 1998, 2015 and 2016. Data from 1997 and 1998 are from NOAA-14/AVHRR and data from 2015 and 2016 from SNPP/VIIRS.</figcaption></figure>

central Pacific. This caused statistically significant (at the 5% level) lower cloud cover over the Maritime Continent and the equatorial western Pacific and correspondingly significant higher cloud cover across the central and eastern Pacific (see Plate 2.1m). As the warm SSTs shift to the east, so do the tropical convection and the associated ice cloud. The 1997/98 data were taken from the AVHRR PATMOS-x record, which spans 1982-present. The 2015/16 data were generated using the SNPP/VIIRS instrument, which is the successor to the AVHRR.

Smaller, but still statistically significant, anomalies were observed across the globe, with many regional anomalies being attributable to teleconnections associated with El Niño. For example, the Amazon basin experienced significant drought, which corresponded to a reduction in cloud cover. The cloud cover over the southeast Pacific off the coast of Peru and the stratocumulus deck off the coast of southwest North America were both anomalously low. Interestingly, other atmospheric oscillations such as the record positive Pacific decadal oscillation (PDO) showed little impact on the annual and monthly cloud cover.

Other anomalies, not statistically significant for the entire year, were observed in the first half of the year, prior to El Niño (see Online Figs. S2.15, S2.16). Some of these anomalies corresponded with a record positive PDO, but were not directly attributable to it. Large positive anomalies in cloud cover over the

southern United States coincided with its wettest month on record in May (see section 7b2).

### 6) RIVER DISCHARGE—H. Kim

Runoff is one of the key components of the terrestrial 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, playing a significant role in the freshwater balance and the salinity of the ocean.

Because of the lack of an observational methodology for real-time global long-term monitoring (Fekete et al. 2012), offline 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 boundary condition has been updated to use the second Japanese global atmospheric reanalysis (JRA-55; Kobayashi et al. 2015) and the Monitoring Product version 5 (Schneider et al. 2015) monthly observational precipitation by the Global Precipitation Climatology Centre (GPCC). The other parts of the simulation framework remain as described by Kim and Oki (2015). ELSE has been validated against the GRDC and also terrestrial water storage from GRACE (Kim et al. 2009).

The global distributions of runoff and river discharge anomalies in 2015 (relative to the 1958–2015 base period) are illustrated in Plates 2.1i and 2.1j, respectively. Most river basins in the tropics, such as the Congo, Zambezi, Tocantins, and São Francisco, show anomalously dry conditions. Among the major river basins in the subtropics and temperate regions, the Danube, La Plata, Indus, and Yangtze were wetter than the climate normal. The Mississippi, Nile, and Volga were drier than their long-term mean states. Many of the basins in northern latitudes (e.g., Ob, Yenisei, and Lena) were wetter than their climatological mean.

The 58-year time series of total terrestrial runoff anomalies and of the ENSO intensity are shown in Fig. 2.22. Variations of annual mean runoff and the oceanic Niño index (ONI) time series, smoothed by 12-month window moving average, are significantly anticorrelated with each other ($R = -0.63$). Because a strong El Niño developed through 2015, at the global scale the mean anomaly turned into a dry phase. However, it still remained close to the climate normal because of the lingering effect of La Niña
<figure><img src="image_59.png" /></figure>

**FIG. 2.22.** (a) Interannual variability of global runoff anomalies relative to the 1958–2015 base period (thick line for 12-month window moving average) and (b) the oceanic Niño index (ONI) [red and blue shades for positive and negative phases, respectively, with lighter (darker) shades representing weaker (stronger) phases in (a)].

<figure><img src="image_60.png" /></figure>

**FIG. 2.23.** Seasonal variations of global and continental runoff. Gray bars show the 58-year climatology (1958–2015) with error bars for 2σ and colored lines for the most recent 4 yrs.

after the 2009/10 El Niño. As shown in Fig. 2.23, seasonal variations of global runoff remained near the long-term mean during the boreal spring and summer and turned into a strong dry phase (~2σ) from the boreal autumn onwards. Regionally, North America, Asia, and Europe experienced significantly dry conditions during the latter half of 2015, and the dry condition was persistent in Africa through the entire year. During the most recent four years, large interannual variability affected South America, Europe, and Australia during February–July while the seasonal fluctuations in Africa were weak.

### 7) GROUNDWATER AND TERRESTRIAL WATER STORAGE—
M. Rodell, D. P. Chambers, and J. S. Famiglietti

Terrestrial water storage (TWS) comprises groundwater, soil moisture, surface water, snow, and ice. Groundwater varies more slowly than the TWS components that are more proximal to the atmosphere, but often it is more dynamic on multianual timescales (Rodell and Famiglietti 2001). In situ groundwater data are archived and made public by only a few countries. However, since 2002 the Gravity Recovery and Climate Experiment (GRACE; Tapley et al. 2004) satellite mission has been providing ob-

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 variations at climatic scales.

Changes in mean annual TWS from 2014 to 2015 are plotted in Plate 2.1g as equivalent heights of water in cm. TWS can be thought of as an integrator of other hydroclimatic variables (see Plates 2.1f–p). In addition to being very warm, 2015 was a dry year in terms of water in the ground, particularly in the southern tropics. TWS decreased in central and eastern South America, in southern Africa, and in central 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 diminished water levels across a swath of central Europe, from France across to western Russia. A combination of drought and water consumption most likely continued to diminish groundwater in the Middle East (Voss et al. 2013), northern India (Rodell et al. 2009; Panda and Wahr 2016), and the North China Plain (Feng et al. 2013). On the other hand, Turkey recovered from a major drought, and TWS also increased in a longitudinal band from Pakistan north through Afghanistan, Kazakhstan, and west central Russia. TWS also increased appreciably in Morocco (heavy rainfall in August), Texas and northern Mexico (continued recovery from a deep drought), central Argentina (heavy rains in February and August), and Peru and western Brazil. Northern Africa and eastern Asia were a mosaic of increasing and decreasing TWS, as seen in Plate 2.1g. Significant reductions in TWS in Greenland, Antarctica, and southern coastal Alaska represent ongoing ice sheet and glacier ablation, not groundwater depletion.

Figures 2.24 and 2.25 show time series of zonal mean and global, deseasonalized monthly TWS anomalies from GRACE, excluding Greenland and Antarctica. Data gaps occur in months when the satellites were powered down during certain parts of the orbital cycle to conserve battery life. Relative dryness in the southern tropics and northern equatorial zone in 2015 is clear in Fig. 2.24, while the northern midlatitudes maintained their low TWS conditions. All told, Earth's nonpolar TWS hit a new GRACE-period low in 2015, -2.3 cm equivalent height of water (Fig. 2.25), equivalent to about 9 mm of sea level rise across the global oceans.
<figure><img src="image_61.png" /><figcaption>FIG. 2.24. GRACE zonal mean terrestrial water storage anomalies (cm equivalent height of water; base period: 2005–10). Gray areas indicate months when data were unavailable.</figcaption></figure>

<figure><img src="image_62.png" /><figcaption>FIG. 2.25. GRACE global average terrestrial water storage anomalies (cm equivalent height of water, base period: 2005–10).</figcaption></figure>

8) **SOIL MOISTURE**—W. A. Dorigo, D. Chung, A. Gruber, S. Hahn, T. Mistelbauer, R. M. Parinussa, C. Paulik, C. Reimer, R. van der Schalie, R. A. M. de Jeu, and W. Wagner

Satellite-mounted microwave instruments can measure the moisture content of the upper few centimeters of the unsaturated soil column. Dedicated soil moisture missions, such as the Soil Moisture Active Passive (SMAP) mission launched in 2015 by NASA, are able to provide nearly contiguous global spatial coverage at daily time scales but, as stand-alone missions, are too short for assessing soil moisture variability and change in the context of a changing climate. To bridge this gap, the ESA Climate Change Initiative (CCI) developed the first multisatellite surface soil moisture dataset (ESA CCI SM), which combines observations from a large number of historical and present-day passive and active microwave instruments (De Jeu et al. 2012b; Liu et al. 2012; Wagner et al. 2012). The current version of the dataset combines nine different sensors (SMMR, ERS-1/2, TMI, SSM/I, AMSR-E, ASCAT, WindSat, AMSR2, and SMOS) between late 1978 and December 2015. It has been used for a wide range of applications (e.g., Dorigo and De Jeu 2016) and has been benchmarked against a large number of land surface models and in situ datasets (Albergel et al. 2013; Dorigo et al. 2015b;

Loew et al. 2013), revealing a good performance across the globe except for densely vegetated areas. The surface soil moisture content sensed by the microwave satellites is closely linked to that of the root zone (Paulik et al. 2012), except for very dry conditions 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 excessively warm and dry summer and autumn in this region (http://edo.jrc.ec.europa.eu/; ZAMG 2016, see October monthly anomalies; Online Fig. S2.17j). 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 conditions in southern Africa resulted from a dry episode in the southwest in early 2015, in combination with aggravating drought conditions towards the end of the year in the southeastern part of the continent (Online Fig. S2.17), increasing the risk of crop failure and food shortage in South Africa, Mozambique, Madagascar, Malawi, and Zimbabwe. For parts of Queensland, Australia, negative anomalies were a continuation of drought conditions observed in this region over the past three years (BoM 2016; Dorigo et al. 2014, 2015a). Even though most parts of Indonesia and Papua New Guinea are masked as missing because of dense vegetation, which is impenetrable for the microwave sensors used in ESA CCI SM, strong negative anomalies were still observed in the agricultural areas. Dry conditions promoted deforestation and biomass burning practices in this area, causing severe air quality problems during several months (sections 2g3, 2h3; Sidebar 2.2).

Prevailing wet soil moisture anomalies were observed for most of the United States, including the southwest, which was previously plagued by a persistent drought for several years (Dorigo et al. 2014, 2015a). Large parts of the United States experienced their wettest May on record (see section 7b2), which is reflected by the strong positive soil moisture anomalies (Online Figs. S2.17e,f). The shift from dry to wet conditions from October through November was remarkable, following the passage of Hurricane Patricia (Online Fig. S2.17). Anomalous wet
soil moisture conditions were
also observed in eastern China
with reported severe floods in
May–June. The southern part of
South America also experienced
wetter-than-usual conditions,
including severe flooding in Ar-
gentina and heavy precipitation
in the Chilean Atacama Desert
in March (see section 7c3).

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
tropics. However, even though El Niño conditions in
2015 were almost as strong as in 1997/98, its impact
up to the end of 2015 on global soil moisture was not
as strong. This suggests that other climate oscilla-
tions may have partly counterbalanced the effects of
El Niño during 2015 at least.

No evident large-scale, long-term global soil
moisture trends can be observed (Figs. 2.26, 2.27).
However, this does not exclude the existence of long-
term trends at the regional or local scale (Dorigo et al.
2012). Trends in average global soil moisture should
be treated with caution owing to dataset properties
changing over time and the inability to observe
beneath dense vegetation, for mountain areas, or
frozen soils (cf. gray regions in Plate 2.1f and Online
Fig. S2.17).

9) **MONITORING GLOBAL DROUGHT USING THE SELF-CALIBRATING PALMER DROUGHT SEVERITY INDEX**—T. J. Osborn, J. Barichivich, I. Harris, G. van der Schrier, and P. D. Jones

Hydrological drought results from a period of abnormally low precipitation, sometimes exacerbated by additional evapotranspiration (ET), and its occurrence can be apparent in reduced river discharge, soil moisture, and/or groundwater storage, depending on season and duration of the event. Here, an esti-

<figure><img src="image_63.png" /><figcaption>FIG. 2.26. Time series of average global soil moisture anomalies for 1991–2015 (base period: 1991–2014). Data were masked as missing where retrievals were either not possible or of very low quality (dense forests, frozen soil, snow, ice, etc.). (Source: ESA CCI.)</figcaption></figure>

<figure><img src="image_64.png" /><figcaption>FIG. 2.27. Time-latitude diagram of soil moisture anomalies (base period: 1991–2014). Data were masked as missing where retrievals are either not possible or of very low quality (dense forests, frozen soil, snow, ice, etc.). (Source: ESA CCI.)</figcaption></figure>

mate of drought called the self-calibrating Palmer drought severity index is presented (scPDSI; Palmer 1965; Wells et al. 2004; van der Schrier et al. 2013a) using precipitation and Penman-Monteith potential ET from an early update of the CRU TS 3.24 dataset (Harris et al. 2014). Moisture categories are calibrated over the complete 1901–2015 period to ensure that “extreme” droughts and pluvials relate to events that do not occur more frequently than in approximately 2% of the months. This affects direct comparison with 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).

van der Schrier et al. (2015) noted that 2014 ap-
peared to have a remarkably small global area affected
by drought, but the updated analysis (Fig. 2.28, with
additional precipitation data that was not available at
the time) now suggests that 2014 was affected by more
extensive droughts (8% of land in severe drought at
the end of 2014, compared with only 5% previously
estimated). See Online Fig. S2.18 for a comparison
with last year's analysis.
<figure><img src="image_65.png" /><figcaption>FIG. 2.28. Percentage of global land area (excluding ice sheets and deserts) with scPDSI indicating moderate (< -2), severe (< -3) and extreme (< -4) drought for each month of 1950-2015. Inset: 2015 monthly values.</figcaption></figure>

There was a large expansion in the overall area of drought across the globe in 2015 (Fig. 2.28, inset), with 14% of global land seeing severe drought conditions (scPDSI < -3) by the end of the year. The areas where scPDSI indicates moderate (30%), severe (14%), or extreme (5%) droughts by the end of 2015 are among the highest in the post-1950 record, exceeded only by some years in the mid-1980s. The 2015 peak should be interpreted cautiously, given that more observations for the final months of 2015 will become available in due course (see Online Fig. S2.18).

The regional patterns of drought (Plate 2.1p) are partly associated with the strong El Niño event that developed during 2015. The full effect of this event may not be apparent until 2016, and other factors dominate in regions where the influence of the tropical Pacific is weak. Averaged over 2015, almost no regions of Africa experienced wet spells, and indeed most land areas south of 20°N across all continents were either near-normal (31% with scPDSI within ±1) or subject to some degree of drought (56% with scPDSI < -1).

Extensive severe or extreme drought affected many countries in southern Africa, intensifying as the 2015 El Niño progressed. These areas had been slowly recovering since a dry spell that began with the previous El Niño in 2010. In the Horn of Africa, severe drought affected Ethiopia and some neighboring regions in 2015, with significant impacts despite being apparent only over a relatively small region in the scPDSI data (Plate 2.1p). Very few areas of Africa exhibited wet spells in the 2015 mean scPDSI.

The effects of the 2014 drought in southeastern Brazil continued to be felt in 2015, though high rainfall farther south over the Paraná basin (consistent with previous strong El Niño events) replaced drought with wet conditions. New regions of drought emerged

in the El Niño-sensitive regions of northeastern Brazil, 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/megasequia).

Drought conditions developed in some Central American and Caribbean nations, such as Guatemala and Haiti, contributing to food insecurity in the region. California continued to experience severe or extreme drought conditions, while most of the U.S. Midwest, South, and East were moderately or very wet, extending into Ontario, Canada.

Dry conditions were widespread across Australia, continuing from 2014. Severe or extreme drought conditions were apparent along the west coast, the southeast, and parts of Queensland, a region particularly susceptible to drought during protracted El Niño events, like the current one (section 2e1). Farther north, dry conditions were established across many parts of the Maritime Continent and parts of Southeast Asia, especially Myanmar and southwestern China (Plate 2.1p). Drought also affected parts of northern China and Mongolia in 2015 according to the scPDSI metric. In contrast with 2014, drought conditions were not evident in India despite a dry monsoon season. This was due to heavy out-of-season rainfall both early and late in the year. Dry conditions were, however, apparent over many Middle East countries.

In Europe, there was a strong contrast between the wet conditions of the southeast and Turkey and the severe drought indicated by scPDSI in eastern Europe and western Russia, affecting important crop production regions. Though not apparent in the annual-mean scPDSI (Plate 2.1p), July to December was very dry in Turkey, consistent with the strong positive North Atlantic Oscillation in late 2015 (sections 2e1, 7f).

The expansion in drought-affected areas during 2015 is similar to the earlier expansion during 1982 (Figs. 2.28, 2.29a), also a year when a strong El Niño developed, and is consistent with the reduction in the atmospheric transport of moisture from oceans to land during El Niño events (Dai 2013). The patterns of scPDSI drought (Plate 2.1p) correspond partly to those regions where El Niño events are associated with reduced rainfall (southeastern Africa, northeastern Australia, the Maritime Continent, and northeastern Brazil). There is weaker agreement with the 1997 pattern (Fig. 2.29b), which had less extensive droughts than in 2015, contributing to the absence of a clear signal in drought-affected area during the
Evaporation of water from soils, snow-covered surfaces, continental water bodies, and vegetation (either via transpiration or interception loss) accounts for approximately two-thirds of continental precipitation. As such, land evaporation represents a key mechanism governing the distribution of hydrological resources, spanning catchment to planetary scales. The ability to monitor land evaporation dynamics is also critical in climatological applications, since evaporation 1) represents the exchange of latent energy from land to atmosphere, directly affecting air temperature; 2) influences air humidity and cloud formation, playing a strong role in driving atmospheric feedbacks; and 3) is intrinsically connected to photosynthesis, echoing changes in vegetation carbon fixation. A number of recent studies have highlighted the impact of evaporation on climate trends (e.g., Douville et al. 2013; Sheffield et al. 2012) and hydrometeorological 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 international 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, with the remaining models showing a higher degree of spatial agreement.

Records of observation-based global evaporation only span the satellite era. This has not prevented a handful of studies from attempting to disentangle the impact of climate change on trends in evaporation. Jung et al. (2010) suggested a reversal in the rise of evaporation since the late 1990s, which was later shown to be a temporary anomaly caused by ENSO (Miralles et al. 2014b). Nonetheless, these studies, together with more recent contributions (Zhang et al. 2015, 2016), have indicated the existence of a slight positive trend over the last few decades, in agreement with expectations derived from temperature trends and global greening, and the theory of an accelerating hydrological cycle.

Although many of the models used for global flux estimation were originally intended for climatological-scale studies, some have evolved to provide estimates of evaporation in operational

<figure><img src="image_66.png" /><figcaption>FIG. SB2.1. Mean land evaporation patterns for different datasets. The right panel illustrates the latitudinal averages over the 2005–07 period. Adapted after Miralles et al. (2016).</figcaption></figure>
mode, with ongoing efforts aiming to reduce product latency and improve spatial resolution. This opens up a range of possible applications, from global drought monitoring to irrigation management. Some examples of evaporation datasets targeting near-real-time simulation at continental scales include the Land Surface Analysis Satellite Applications Facility (LSA SAF) product (Ghilain et al. 2011) and the Atmosphere–Land Exchange Inverse (ALEXI) datasets (Anderson et al. 1997, 2011). While GLEAM was not deliberately designed with an operational intent, the current version 3 dataset has been updated to include 2015, using observations from the Soil Moisture and Ocean Salinity (SMOS) mission (www.gleam.eu). Figure SB2.2 shows the anomalies in evaporation for 1980–2015 based on this new dataset.

Periods of global decline in evaporation typically coincide with El Niño conditions, and are associated with drought in the water-limited ecosystems of the Southern Hemisphere (Miralles et al. 2014b). The year 2015 was no exception: despite El Niño conditions intensifying only in the second half of 2015, Fig. SB2.2 shows anomalously low evaporation in central Australia, eastern South America, Amazonia, and southern Africa. Considering the entire multidecadal record, the continental evaporation in 2015

does not seem particularly anomalous, as climate variability is superimposed on a positive trend of ~0.4 mm yr⁻¹. For most of the Northern Hemisphere, evaporation was above the multidecadal mean, with the notable exception of California, which experienced extraordinary drought conditions.

With the development of improved algorithms dedicated to estimating evaporation from satellite observations, global operational monitoring of land evaporation is becoming a realistic proposition. While discrepancies amongst current models are still large (Michel et al. 2016; McCabe et al. 2016), several of the existing datasets compare well against each other and against in situ measurements. These datasets open new pathways to diagnose large-scale drought and irrigation needs, and to improve water resources management and the characterization of hydrological cycles. Satellite-based evaporation estimates respond to long-term changes in Earth's water and energy budgets and are able to capture fluctuations due to internal climate variability. The mean distribution of evaporation anomalies in 2015 (Fig. SB2.2) is a clear example of the underlying effects of multidecadal climate trends and climate oscillations on the terrestrial water cycle.

<figure><img src="image_67.png" /></figure>

FIG. SB2.2. (a) 2015 land evaporation anomalies. (Source: GLEAM). (b) Mean continental evaporation 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.
<figure><img src="image_68.png" /><figcaption>FIG. 2.29. Mean scPDSI for (a) 1982 and (b) 1997, years in which a strong El Niño developed. No calculation is made (gray areas) where a drought index is meaningless (e.g., ice sheets and deserts with approximately zero mean precipitation).</figcaption></figure>

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.

### e. Atmospheric circulation

#### 1) MEAN SEA LEVEL PRESSURE AND RELATED MODES OF VARIABILITY—R. Allan and C. K. Folland

Mean sea level pressure (MSLP) provides diagnostics of the major modes of variability that drive significant weather and climate events (Kaplan 2011). Arguably, the most globally impactful mode is the El Niño–Southern Oscillation (ENSO), for which the sea level pressure-derived Southern Oscillation index [SOI; Allan et al. 1996; normalized MSLP difference between Tahiti and Darwin (various other indices are also commonly used); Kaplan 2011; section 4b] is an indicator. For 2015, the SOI was negative, indicating the presence of the strongest El Niño since 1997/98 (see Sidebar 1.1).

The SOI trace since 2009 highlights the shift from El Niño to strong La Niña conditions around mid-

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 episode: “...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 period....” Figure 2.30 shows the presence of these 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-global in their influence on world weather patterns, owing to ocean–atmosphere interactions across the Indo-Pacific region, with teleconnections to higher latitudes in both hemispheres. Protracted El Niño and La Niña episodes tend to be more regional in

<figure><img src="image_69.png" /><figcaption>FIG. 2.30. Time series for modes of variability described using sea level pressure for the (left) complete period of record and (right) 2006–15. (a),(b) Southern Oscillation index (SOI) provided by the Australian Bureau of Meteorology; (c),(d) Arctic Oscillation (AO) provided by NCEP Climate Prediction Center; (e),(f) Antarctic Oscillation (AAO) provided by NCEP Climate Prediction Center; (g),(h) Winter (Dec–Feb) North Atlantic Oscillation (NAO) average provided by NCAR (presented for early winter of each year so winter 2015/16 is not shown); (i),(j) Summer (Jul–Aug) North Atlantic Oscillation (SNAO) average (Folland et al. 2009).</figcaption></figure>
their impacts (Allan and D'Arrigo 1999; Allan et al. 2003). The main influence appears to be periods of persistent drought (widespread flooding) 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 reflects 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 circulation 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 positive 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 boreal 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 Peninsula (WAP) region, owing to enhanced westerly wind conditions (Stammerjohn et al. 2008). During the current situation, however, the WAP sea ice margins were extended (http://nsidc.org/data/seaice_index/), because in the interplay between the protracted El Niño, which should favor a weaker polar jet stream, and the positive AAO mode, with stronger westerly winds, the former appeared to have dominated. Related positive wind speed anomalies were noted at 850 hPa (section 2e3) over the midlatitude Southern Ocean.

<figure><img src="image_70.png" /><figcaption>**FIG. 2.31.** Boreal winter sea level pressure anomalies (hPa, base period: 1981–2010) averaged over Dec–Feb for (a) 2013/14, (b) 2014/15, and (c) 2015/16. NAO daily time series (hPa) for winter (d) 2013/14, (e) 2014/15, and (f) 2015/16. The 5-day running mean is shown by the solid black line. The data are from HadSLP2r (Allan and Ansell 2006).</figcaption></figure>

In 2015, the summer NAO (SNAO), defined over July and August as in Folland et al. (2009), continued its marked tendency to a more negative state in the last decade. Only 2013 was a prominent exception. The