Global Climate in 2023

R. Dunn, J. Blannin, N. Gobron, J. Miller, K. Willett, Melanie Ades, Robert Adler, Mihai Alexe, Richard Allan, John Anderson, et al.

► To cite this version:

R. Dunn, J. Blannin, N. Gobron, J. Miller, K. Willett, et al. Global Climate in 2023. Bulletin of the American Meteorological Society, 2024, 105 (8), pp.S12-S155. 10.1175/BAMS-D-24-0116.1. hal-04846853

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# STATE OF THE CLIMATE IN 2023

## GLOBAL CLIMATE

R. J. H. Dunn, J. Blannin, N. Gobron, J. B. Miller, and K. M. Willett, Eds.

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

Special Online Supplement to the *Bulletin of the American Meteorological Society*, Vol. 105, No. 8, August 2024

https://doi.org/10.1175/BAMS-D-24-0116.1

Corresponding author: Robert Dunn / robert.dunn@metoffice.gov.uk

©2024 American Meteorological Society

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.
# STATE OF THE CLIMATE IN 2023
## Global Climate

**Editors**

Jessica Blunden
Tim Boyer

**Chapter Editors**

Anthony Arguez
Josh Blannin
Peter Bissolli
Kyle R. Clem
Howard J. Diamond
Matthew L. Druckenmiller
Robert J. H. Dunn
Catherine Ganter
Nadine Gobron
Gregory C. Johnson
Rick Lumpkin
Rodney Martinez
Ademe Mekonnen
John B. Miller
Twila A. Moon
Marilyn N. Raphael
Carl J. Schreck III
Laura Stevens
Richard L. Thoman
Kate M. Willett
Zhiwei Zhu

**Technical Editor**

Lukas Noguchi

**BAMS Special Editor for Climate**

Timothy DelSole

**American Meteorological Society**
**Cover Credit:**

Aerial photo of smoke from wildfires in Alberta, Canada, on 5 May 2023. (Photo credit: Alberta Forestry and Parks)

**How to cite this document:**

Global Climate is one chapter from the *State of the Climate in 2023* annual report and is available from https://doi.org/10.1175/BAMS-D-24-0116.1. Compiled by NOAA's National Centers for Environmental Information, *State of the Climate in 2023* is based on contributions from scientists from around the world. It provides a detailed update on global climate indicators, notable weather events, and other data collected by environmental monitoring stations and instruments located on land, water, ice, and in space. The full report is available from https://doi.org/10.1175/2024BAMSStateoftheClimate.1.

**Citing the complete report:**

Blunden, J. and T. Boyer, Eds., 2024: "State of the Climate in 2023". *Bull. Amer. Meteor. Soc.*, **105** (8), Si-S483 https://doi.org/10.1175/2024BAMSStateoftheClimate.1.

**Citing this chapter:**

Dunn, R. J. H., J. Blannin, N. Gobron, J. B. Miller, and K. M. Willett, Eds., 2024: Global Climate [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S12-S155, https://doi.org/10.1175/BAMS-D-24-0116.1.

**Citing a section (example):**

Azorin-Molina, C., R. J. H. Dunn, L. Ricciardulli, C. A. Mears, J. P. Nicolas, T. R. McVicar, Z. Zeng, and M. G. Bosilovich, 2024: Land and ocean surface winds [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S76-S79, https://doi.org/10.1175/BAMS-D-24-0116.1.
Editor and Author Affiliations (alphabetical by name)

Ades, Melanie, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

Adler, Robert, University of Maryland, College Park, Maryland

Alexe, Mihai, European Centre for Medium-Range Weather Forecasts, Bonn, Germany

Allan, Richard P., Department of Meteorology and National Centre for Earth Observation, University of Reading, Reading, United Kingdom

Anderson, John, Hampton University, Hampton, Virginia

Anneville, Orlane, National Research Institute for Agriculture, Food and Environment (INRAE), CARRTEL, Université Savoie Mont Blanc, Thonon les Bains, France

Aono, Yasuyuki, Graduate School of Agriculture, Osaka Metropolitan University, Sakai, Japan

Arguez, Anthony, NOAA/NESDIS National Centers for Environmental Information, Asheville, North Carolina

Armenteras Pascual, Dolores, Science Faculty, Universidad Nacional de Colombia, Bogotá, Colombia

Arosio, Carlo, University of Bremen, Bremen, Germany

Asher, Elizabeth, NOAA Global Monitoring Laboratory, Boulder, Colorado

Augustine, John A., NOAA Global Monitoring Laboratory, Boulder, Colorado

Azorin-Molina, Cesar, Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA), Valencia, Spain

Baez-Villanueva, Oscar M., Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium

Barichivich, J., Laboratoire des Sciences du Climat et de l'Environnement (LSCE), LSCE/IPSL, CEA-CNRS-UVSQ, Gif-sur-Yvette, France; Instituto de Geografia, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

Beck, Hylke E., Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

Bellouin, Nicolas, University of Reading, Reading, United Kingdom; Institute Pierre Simon Laplace (IPSL), Guyancourt, France

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

Blannin, Josh, Met Office Hadley Centre, Exeter, United Kingdom

Blenkinsop, Stephen, School of Engineering, Newcastle University, Newcastle-upon-Tyne, United Kingdom

Bock, Olivier, Université Paris Cité, Institut de physique du globe de Paris, CNRS, IGN, F-75005 Paris, France; Univ Gustave Eiffel, ENSG, IGN, F-77455 Marne-la-Vallée, France

Bodin, Xavier, Laboratoire EDYTEM, CNRS/Université Savoie Mont-Blanc, Le Bourget-du-Lac, France

Bonte, Olivier, Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium

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

Boucher, Olivier, Institute Pierre Simon Laplace (IPSL), Guyancourt, France

Buehler, Stefan A., Universität Hamburg, Hamburg, Germany

Byrne, Michael P., Climate Dynamics Lab, University of St Andrews, St Andrews, United Kingdom

Campos, Diego, Earth Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain

Cappucci, Fabrizio, European Commission Joint Research Centre, Ispra, Italy

Carrea, Laura, University of Reading, Reading, United Kingdom

Chang, Kai-Lan, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; NOAA Chemical Sciences Laboratory, Boulder, Colorado

Christiansen, Hanne H., Arctic Geophysics Department, University Centre in Svalbard, Longyearbyen, Norway; Geology Department, University Centre in Svalbard, Longyearben, Norway

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

Chung, Eui-Seok, Korea Polar Research Institute, Incheon, South Korea

Ciasto, Laura M., NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center, College Park, Maryland

Clingan, Scott, Cooperative Institute for Research in Earth Science, University of Colorado, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

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

Cooper, Owen R., NOAA Chemical Sciences Laboratory, Boulder, Colorado

Cornes, Richard C., National Oceanography Centre, Southampton, United Kingdom

Covey, Curt, Lawrence Livermore National Laboratory, Livermore, California

Crétaux, Jean-Francois, LEGOS (CNES/CNRS/IRD/UPS), Université de Toulouse, Toulouse, France

Crimmins, Theresa, USA National Phenology Network, School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona.

Crotwell, Molly, Cooperative Institute for Research in Earth Science, University of Colorado, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

Culpepper, Joshua, Department of Biology, York University, Toronto, Canada

Cusicanqui, Diego, Institut de Géosciences de l'Environnement (IGE), Université Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, ISTerre, Grenoble, France

Davis, Sean, NOAA Chemical Sciences Laboratory, Boulder, Colorado

de Jeu, Richard A. M., Planet Labs, Haarlem, The Netherlands

Degenstein, Doug, University of Saskatchewan, Saskatoon, Canada

Delaloye, Reynald, Department of Geosciences, University of Fribourg, Fribourg, Switzerland

DiGangi, Elizabeth, AEM R&D, Germantown, Maryland

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

Donat, Markus G., Barcelona Supercomputing Center, Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain

Dorigo, Wouter A., Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria

Duchemin, Diane, CESBIO, Université de Toulouse, CNES/CNRS/INRAe/IRD/UPS, Toulouse, France

Dugan, Hilary, Department of Integrative Biology, University of Wisconsin-Madison, Madison, Wisconsin

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

Durre, Imke, NOAA/NESDIS National Center for Environmental Information, Asheville, North Carolina

Dutton, Geoff, Cooperative Institute for Research in the Earth Sciences, NOAA Global Monitoring Laboratory, Boulder, Colorado

Duveiller, Gregory, Max Planck Institute for Biogeochemistry, Jena, Germany

Estilow, Thomas W., Rutgers University, Piscataway, New Jersey

Estrella, Nicole, Ecoclimatology, Department of Life Science Systems, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.

Fereday, David, Met Office Hadley Centre, Exeter, United Kingdom

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

Flemming, Johannes, European Centre for Medium-Range Weather Forecasts, Bonn, Germany

Foster, Michael J., Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin-Madison, Madison, Wisconsin

Frederikse, Thomas, Planet Labs, Haarlem, The Netherlands

Frith, Stacey M., Science Systems and Applications, Inc., Lanham, Maryland; NASA Goddard Space Flight Center, Greenbelt, Maryland

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

Füllekrug, Martin, University of Bath, Bath, United Kingdom

Garforth, Judith, Woodland Trust, Grantham, United Kingdom.
Editor and Author Affiliations (continued)

Garg, Jay, ADNET Systems, Inc., Hampton, Virginia

Gobron, Nadine, European Commission Joint Research Centre, Ispra, Italy

Godin-Beekmann, Sophie, Laboratoire Atmosphères, Observations Spatiales (LATMOS), UVSQ, Sorbonne Université, CNRS, IPSL, Guyancourt, France

Goodman, Steven, Thunderbolt Global Analytics, Huntsville, Alabama

Goto, Atsushi, World Meteorological Organization, Geneva, Switzerland

Grimm, Alice, Federal University of Parana, Curitiba, Brazil

Gruber, Alexander, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria

Gu, Guojun, University of Maryland, College Park, Maryland

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

Hahn, Sebastian, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria

Haimberger, Leopold, University of Vienna, Vienna, Austria

Hall, Brad D., NOAA Global Monitoring Laboratory, Boulder, Colorado

Harlan, Merritt E., U.S. Geological Survey, Denver, Colorado

Harris, I., National Centre for Atmospheric Science (NCAS), University of East Anglia, Norwich, United Kingdom; Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom

Hemming, Deborah L., Met Office Hadley Centre, Exeter, United Kingdom.;
Birmingham Institute of Forest Research, Birmingham University, Birmingham, United Kingdom.

Hirschi, Martin, Institute for Atmospheric and Climate Science, ETH Zurich, Zürich, Switzerland

Ho, Shu-peng (Ben), NOAA/NESDIS Center for Satellite Applications and Research, College Park, Maryland

Holzworth, Robert, University of Washington, Seattle, Washington

Horton, Radley M., Columbia Climate School, Columbia University, New York, New York

Hrbáček, Filip, Department of Geography, Masaryk University, Brno, Czech Republic

Hu, Guojie, Cryosphere Research Station on Qinghai-Tibet Plateau, Northwestern Institute of Eco-Environment and Resources, CAS, Beijing, China

Hurst, Dale, NOAA Global Monitoring Laboratory, Boulder, Colorado

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

Isaksen, Ketil, Norwegian Meteorological Institute, Oslo, Norway

John, Viju O., EUMETSAT, Darmstadt, Germany

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

Junod, Robert, Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama

Kääb, Andreas, Department of Geosciences, University of Oslo, Norway

Kaiser, Johannes W., Norwegian Institute for Air Research (NILU), Kjeller, Norway

Kaufmann, Viktor, Institute of Geodesy, Working Group Remote Sensing and Photogrammetry, Graz University of Technology, Graz, Austria

Kellerer-Pirklbauer, Andreas, Institute of Geography and Regional Science, Cascade - The Mountain Processes and Mountain Hazards Group, University of Graz, Graz, Austria

Khaykin, Sergey, Laboratoire Atmosphères, Observations Spatiales (LATMOS), UVSQ, Sorbonne Université, CNRS, IPSL, Guyancourt, France

Kidd, Richard, EODC GmbH, Vienna, Austria

King, Tyler V., U.S. Geological Survey, Boise, Idaho

Kipling, Zak, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

Koppa, Akash, Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium

Kraemer, Benjamin M., University of Freiburg, Freiburg, Germany

La Fuente, R. Sofia, Vrije Universiteit Brussel, Brussels, Belgium

Laas, Alo, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartumaa, Estonia

Lan, Xin, Cooperative Institute for Research in the Earth Sciences, NOAA Global Monitoring Laboratory, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

Lantz, Kathleen O., NOAA Global Monitoring Laboratory, Boulder, Colorado

Lapierre, Jeff, AEM R&D, Germantown, Maryland

Lavers, David A., European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

Leblanc, Thierry, Jet Propulsion Laboratory, California Institute of Technology, Wrightwood, California

Leibensperger, Eric, Department of Physics and Astronomy, Ithaca College, Ithaca, New York

Lennard, Chris, Department of Environmental and Geographical Science, University of Cape Town, Cape Town, South Africa

Liley, Ben, National Institute of Water & Atmospheric Research (NIWA), Lauder, New Zealand

Liu, Yakun, Massachusetts Institute of Technology, Cambridge, Massachusetts

Lo, Y. T. Eunice, Cabot Institute for the Environment, University of Bristol, Bristol, United Kingdom

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

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

Magnin, Florence, Laboratoire EDYTEM, CNRS/Université Savoie Mont-Blanc, Le Bourget-du-Lac, France

Matsuzaki, Shin-Ichiro, National Institute for Environmental Studies, Tsukuba, Japan

Matthews, Tom, Department of Geography, King's College London, London, United Kingdom

Mayer, Michael, University of Vienna, Vienna, Austria; European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

McCarthy, Michael, University of Washington, Seattle, Washington

McVicar, Tim R., CSIRO Environment, Canberra, Australian Capital Territory; Australian Research Council Centre of Excellence for Climate Extremes, Sydney, Australia

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

Menzel, Annette, Ecoclimatology, Department of Life Science Systems, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Institute for Advanced Study, Technical University of Munich, Garching, Germany

Merchant, Christopher J., University of Reading, Reading, United Kingdom

Merio, Leo-Juhani, Finnish Environment Institute (SYKE), Oulu, Finland

Meyer, Michael F., U.S. Geological Survey, Madison, Wisconsin

Miller, John B., NOAA Global Monitoring Laboratory, Boulder, Colorado

Miralles, Diego G., Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium

Montzka, Stephan A., NOAA Global Monitoring Laboratory, Boulder, Colorado

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

Morino, Isamu, National Institute for Environmental Studies, Tsukuba, Japan

Mrekaj, Ivan, Technical University in Zvolen, Zvolen, Slovakia

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

Nance, D., Cooperative Institute for Research in Earth Science, University of Colorado, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

Nicolas, Julien P., European Centre for Medium-Range Weather Forecasts, Bonn, Germany

Noetzli, Jeannette, WSL Institute for Snow and Avalanche Research SLF Climate Change, Davos Dorf, Switzerland; Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland

Noll, Ben, National Institute of Water and Atmospheric Research (NIWA), Auckland, New Zealand
Editor and Author Affiliations (continued)

O'Keefe, John, The Harvard Forest, Harvard University, Petersham, Massachusetts

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

Parrington, Mark, European Centre for Medium-Range Weather Forecasts, Bonn, Germany

Pellet, Cécile, Department of Geosciences, University of Fribourg, Fribourg, Switzerland

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

Petersen, Kyle, Cooperative Institute for Research in Earth Science, University of Colorado, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

Phillips, Coda, Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin-Madison, Madison, Wisconsin

Pierson, Don, Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden

Pinto, Izidine, Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands

Po-Chedley, Stephen, Lawrence Livermore National Laboratory, Livermore, California

Pogliotti, Paolo, Environmental Protection Agency of Valle d'Aosta, Saint Christophe, Italy

Polvani, Lorenzo, Columbia University, New York, New York

Preimesberger, Wolfgang, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria

Price, Colin, Tel Aviv University, Tel Aviv, Israel

Randel, William J., National Center for Atmospheric Research, Boulder, Colorado

Raymond, Colin, University of California, Los Angeles, Los Angeles, California

Rémy, Samuel, HYGEOS, Lille, France

Ricciardulli, Lucrezia, Remote Sensing Systems, Santa Rosa, California

Richardson, Andrew D., School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona; Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, Arizona

Robinson, David A., Rutgers University, Piscataway, New Jersey

Rodell, Matthew, Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland

Rodríguez-Fernández, Nemesio, CESBIO, Université de Toulouse, CNES/CNRS/ INRAe/IRD/UPS, Toulouse, France

Rogers, Cassandra D. W., Bureau of Meteorology, Melbourne, Australia

Rohini, P., India Meteorological Department, Ministry of Earth Sciences, Pune, India

Rosenlof, Karen, NOAA Chemical Sciences Laboratory, Boulder, Colorado

Rozanov, Alexei, University of Bremen, Bremen, Germany

Rozkošný, Jozef, Slovak Hydrometeorological Institute, Bratislava, Slovakia

Rusanovskaya, Olga O., Irkutsk State University, Institute of Biology, Irkutsk, Russia

Rutishauser, This, Swiss Academy of Sciences (SCNAT), Bern, Switzerland

Sabeerali, C. T., India Meteorological Department, Ministry of Earth Sciences, Pune, India

Said, Ryan, Vaisala R&D, Louisville, Colorado

Sakai, Tetsu, Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan

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

Sawaengphokhai, Parnchai, ADNET Systems, Inc., Hampton, Virginia

Schenzinger, Verena, Medical University of Innsbruck, Innsbruck, Austria

Schlegel, Robert W., Laboratoire d'Océanographie de Villefranche, Sorbonne Université, Villefranche-sur-mer, France

Schmid, Martin, Eawag, Department Surface Waters - Research and Management, Kastanienbaum, Switzerland

Seneviratne, Sonia I., ETH Zurich, Department of Environmental Systems Science, Zürich, Switzerland

Sezaki, Fumi, Japan Meteorological Agency, Tokyo, Japan

Shao, Xi, NOAA/NESDIS Center for Satellite Applications and Research, College Park, Maryland

Sharma, Sapna, Department of Biology, York University, Toronto, Canada

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

Shimaraeva, Svetlana V., Irkutsk State University, Institute of Biology, Irkutsk, Russia

Shinohara, Ryuichiro, Regional Environment Conservation Division, National Institute for Environmental Studies, Tsukuba, Japan

Silow, Eugene A., Irkutsk State University, Institute of Biology, Irkutsk, Russia

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

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

Soden, Brian J., University of Miami, Miami, Florida

Sofieva, Viktoria, Finnish Meteorological Institute, Helsinki, Finland

Soldo, Logan, Cooperative Institute for Research in the Environmental Sciences, University of Colorado, Boulder, Boulder, Colorado

Sreejith, O. P., India Meteorological Department, Ministry of Earth Sciences, Pune, India

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

Stauffer, Ryan, Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

Steinbrecht, Wolfgang, Deutscher Wetterdienst, Hohenpeissenberg, Germany

Steiner, Andrea K., Wegener Center for Climate and Global Change, University of Graz, Graz, Austria

Stoy, Paul, C., Biological Systems Engineering, College of Agricultural and Life Sciences, University of Wisconsin-Madison, Madison, Wisconsin

Stradiotti, Pietro, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria

Streletskiy, Dmitry A., Department of Geography, George Washington University, United Kingdom

Taha, Ghassan, Morgan State University, Baltimore, Maryland; NASA Goddard Flight Center, Greenbelt, Maryland

Thackeray, Stephen J., United Kingdom Centre for Ecology & Hydrology, Lancaster, United Kingdom.

Thibert, Emmanuel, Institut de Géosciences de l'Environnement (IGE), Université Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, ISTerre, Grenoble, France

Timofeyev, Maxim A., Institute of Biology, Irkutsk State University, Irkutsk, Russia

Tourpali, Kleareti, Aristotle University, Thessaloniki, Greece

Tronquo, Emma, Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium

Tye, Mari R., National Center for Atmospheric Research, Boulder, Colorado

van der A, Ronald, Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands

van der Schalie, Robin, Planet Labs, Haarlem, The Netherlands

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

van Vliet, Arnold J. H., Environmental Systems Analysis Group, Wageningen University & Research, The Netherlands

Verburg, Piet, National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand

Vernier, Jean-Paul, NASA Langley Research Center, Hampton, Virginia

Vimont, Isaac J., NOAA Global Monitoring Laboratory, Boulder, Colorado

Virts, Katrina, University of Huntsville in Alabama, Huntsville, Alabama

Vivero, Sebastián, Department of Geosciences, University of Fribourg, Fribourg, Switzerland

Vömel, Holger, National Center for Atmospheric Research, Boulder, Colorado
Editor and Author Affiliations (continued)

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

**Wang, Ray H. J.**, Georgia Institute of Technology, Atlanta, Georgia

**Warnock, Taran**, University of Saskatchewan, Saskatoon, Canada

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

**Wiese, David N.**, Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, California

**Wild, Jeannette D.**, Earth System Science Interdisciplinary Center, University
of Maryland, College Park, Maryland; NOAA/NESDIS Center for Satellite
Applications and Research, College Park, Maryland

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

**Williams, Earle**, Massachusetts Institute of Technology, Cambridge, Massachusetts

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

**Woolway, Richard Iestyn**, School of Ocean Sciences, Bangor University, Bangor,
United Kingdom

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

**Zeng, Zhenzhong**, School of Environmental Science and Engineering, Southern
University of Science and Technology, Shenzhen, China

**Zhao, Lin**, School of Geographical Sciences, Nanjing University of Information
Science and Technology, Nanjing, China

**Zhong, Feng**, Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent,
Belgium

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

**Ziese, Markus**, Global Precipitation Climatology Centre, Deutscher Wetterdienst,
Offenbach, Germany

**Zotta, Ruxandra M.**, TU Wien, Vienna, Austria

**Zou, Cheng-Zhi**, NOAA/NESDIS Center for Satellite Applications and Research,
College Park, Maryland

Editorial and Production Team

**Allen, Jessica**, Graphics Support, Cooperative Institute for Satellite Earth System
Studies, North Carolina State University, Asheville, North Carolina

**Camper, Amy V.**, Graphics Support, Innovative Consulting and Management
Services, LLC, NOAA/NESDIS National Centers for Environmental Information,
Asheville, North Carolina

**Haley, Bridgette O.**, Graphics Support, NOAA/NESDIS National Centers for
Environmental Information, Asheville, North Carolina

**Hammer, Gregory**, Content Team Lead, Communications and Outreach,
NOAA/NESDIS National Centers for Environmental Information, Asheville,
North Carolina

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

**Ohlmann, Laura**, Technical Editor, Innovative Consulting and Management Services,
LLC, NOAA/NESDIS National Centers for Environmental Information, Asheville,
North Carolina

**Noguchi, Lukas**, Technical Editor, Innovative Consulting and Management Services,
LLC, 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

**Veasey, Sara W.**, Visual Communications Team Lead, Communications and
Outreach, NOAA/NESDIS National Centers for Environmental Information,
Asheville, North Carolina
## 2. Table of Contents

<table><tr><td><strong>g. Atmospheric composition</strong></td><td>S87</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;1. Long-lived greenhouse gases</td><td>S87</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;2. Ozone-depleting substances</td><td>S90</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;3. Tropospheric aerosols</td><td>S91</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;4. Tropospheric ozone</td><td>S94</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;5. Stratospheric aerosols</td><td>S96</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;6. Stratospheric ozone</td><td>S99</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;7. Stratospheric water vapor</td><td>S101</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;8. Carbon monoxide</td><td>S103</td></tr><tr><td><strong>h. Land surface properties</strong></td><td>S105</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;1. Terrestrial surface albedo dynamics</td><td>S105</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;2. Terrestrial vegetation dynamics</td><td>S106</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;3. Biomass burning</td><td>S107</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;4. Phenology of primary producers</td><td>S109</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;5. Vegetation optical depth</td><td>S113</td></tr><tr><td><strong>Acknowledgments</strong></td><td>S115</td></tr><tr><td><strong>Appendix 1: Acronyms</strong></td><td>S120</td></tr><tr><td><strong>Appendix 2: Datasets and sources</strong></td><td>S124</td></tr><tr><td><strong>Appendix 3: Supplemental materials</strong></td><td>S135</td></tr><tr><td><strong>References</strong></td><td>S141</td></tr></table>
## 2. GLOBAL CLIMATE

R. J. H. Dunn, J. Blannin, N. Gobron, J. B. Miller, and K. M. Willett, Eds.

### a. Overview

—R. J. H. Dunn, J. Blannin, N. Gobron, J. B. Miller, and K. M. Willett

Globally, 2023 was the warmest year since records began in the mid-1800s to mid-1900s, according to all seven global temperature datasets. The prolonged La Niña that began in 2020 faded at the start of 2023 and was replaced by a strong El Niño by the end of the year. The change to El Niño conditions contributed to exceptionally high temperatures worldwide, especially in the latter part of the year as the El Niño strengthened. The pervasive warmth was highlighted by widespread and intense temperature extremes, with record numbers of warm days globally and the third-highest land fraction experiencing record numbers of warm days. Also, globally averaged lake surface temperatures in the warm season were the highest since records began in 1995. Over the oceans, night-time air temperatures likewise reached record values. Not only was the near-surface affected, but the lower troposphere average had record temperatures, with particularly exceptional values over the tropics in the latter part of the year. The stratosphere, which usually cools in response to anthropogenic carbon dioxide (CO₂) increases, also warmed this year, reflecting a recovery from the Hunga Tonga–Hunga Haʻapai (HTHH) eruption in 2022.

The unprecedented temperature anomalies over recent years and decades have had world-wide impacts on many essential climate variables covered in this chapter. A sidebar on these impacts in 2023 is included to link the sections and domains where the high global temperatures have driven important changes in Earth's climate system (Sidebar 2.1). The warm temperatures drove consistent changes in the hydrological cycle with greater quantities of water in the atmosphere but also record areas under extreme drought. Glaciers continued to lose mass for the 36th consecutive year, and land surface variables also showed substantial or record-breaking changes.

The fading La Niña in the early part of the year contributed to destructive flooding in New Zealand (e.g., post-Cyclone Gabrielle in February). Later on, the growing El Niño had regional impacts, with increased rainfall in South America leading to flooding in Chile in August, and, conversely, Australia experiencing its driest three-month period on record (August–October). Globally, upper-air winds were also reduced in the second half of the year in a manner typical of El Niño conditions. The impact of the El Niño can even be seen in the high number of thunder hours—a proxy for lightning activity, and which this year replaces the section on lightning flashes—across the eastern Pacific Ocean and southeastern South America.

Atmospheric composition changes both in 2023 and in general are characterized mainly by continued record-breaking atmospheric abundances of long-lived greenhouse gases (LLGHGs). These are the result of continued anthropogenic GHG emissions and year-to-year variability in short-lived species such as water vapor, aerosols, and carbon monoxide related to annual anomalies in emissions and circulation. Globally averaged atmospheric concentrations of CO₂, methane (CH₄), and nitrous oxide (N₂O) for 2023 each reached new annual record-high values of 419.3 ppm, 1922.6 ppb, and 336.7 ppb, respectively. The gases that destroy stratospheric ozone most effectively (i.e., chlorofluorocarbons [CFCs]) continue to decline, and their initial replacements (i.e., hydrochlorofluorocarbons [HCFCs]) may have peaked, although the current replacement compounds (i.e., hydrofluorocarbons [HFCs]) continue to increase, albeit with minimal impact on stratospheric ozone.
Large fires, especially those in eastern and western Canada, led to large anomalies in both carbon monoxide and tropospheric aerosols. In the stratosphere, the HTHH eruption that started in late 2021 still appeared to be impacting levels of water vapor, ozone, and aerosols in 2023. However, stratospheric ozone anomalies are mainly linked to circulation changes, including those related to the onset of El Niño in 2023; the long-term recovery of stratospheric ozone is consistent with model predictions given the decrease in CFCs and related compounds.

Earth's radiation budget at the top-of-atmosphere (approximately 20 km) continued to show a net imbalance. Anomalies for all components were greater than their interannual variability for the first time in the CERES (Clouds and the Earth's Radiant Energy System) record (starting March 2000). The Mauna Loa atmospheric transmission record resumed in July 2023, after being interrupted by the eruption of its volcano in late November 2022.

This year we welcome two new sections to the report: 1) Humid-heat extremes over land (previously introduced in Sidebar 2.1 in *State of the Climate* in 2022), a timely regular addition to the report closely linked to human heat health, and 2) Stratospheric aerosols (previously introduced in Sidebar 2.2 in *State of the Climate* in 2019), which shows the ongoing effects of the HTHH eruption in January 2022.

Another sidebar (Sidebar 2.2) this year discusses near-surface equivalent temperature as a metric to measure changes in the total energy content of the near-surface atmosphere, comprising sensible and latent heat components. A large fraction of the change in near-surface total energy content has been in the latent heat component, which is not captured when measuring surface air temperature. Using the equivalent temperature, this sidebar shows how near-surface atmospheric heating reached record levels in 2023.

As usual, Plate 2.1 shows maps of global annual anomalies for many of the variables and metrics presented herein. Many of these variables are also presented as time series in Plate 1.1. Many sections now use the 1991-2020 climatological reference period, in line with the World Meteorological Organization recommendations, although this newer reference period is not possible for all datasets, depending on their length of record or legacy processing methods.

## Sidebar 2.1: Impacts of extreme global temperatures and events in 2023

—R. J. H. DUNN, N. GOBRON, AND K. M. WILLETT

The year 2023 saw record-breaking global surface temperatures (section 2b1), especially during the latter half of the calendar year. Record frequencies of extreme temperature days (e.g., by numbers of warm days [TX90p]) were experienced. Near-record spatial extents (in percent of land gridboxes) experienced record warm-day frequencies, while marine heatwaves (in sea-surface temperature) covered the largest total area (in percent of ocean gridboxes) on record (section 2b4). Although significant in its own right, this record warmth resulted in widespread impacts across the essential climate variables (GCOS 2022) presented in this chapter. And, despite being numerically exceptional, this warmth is consistent with globally increasing temperatures over the last decades that are unequivocally the result of human activities (IPCC 2021).

High temperatures and the transition from La Niña to El Niño conditions during 2023 resulted in large quantities of water in the atmospheric column. Evaporation over land reached record levels for the globe, dominated by record values for the Northern Hemisphere (section 2d12). The water vapor content of the near-surface atmosphere was record or near-record high

globally, exceeding 2022 levels by a large margin (section 2d1). The energy contribution from the related latent heat component contributed to a record-high global anomaly in equivalent temperature, a measure of the total energy content of the atmosphere (Sidebar 2.2). It was also a record-breaking year for humid-heat indices, as humid-heat intensity doubled the previous record anomaly in 1998, reaching +0.6°C (section 2d2). Three datasets of total column water vapor showed the wettest year on record globally, as well as over the oceans, for all five datasets (section 2d3), with over 1 kg of water vapor extra per square meter across Earth's surface. Despite this increased moisture aloft, 2023 had the lowest cloud area fraction since records began in 1980 (section 2d7) with skies clearer globally. Consequently, the clouds reflected away to space a record small amount of shortwave radiation, but also blocked a record small amount of longwave radiation from leaving Earth. The overall effect was the weakest cooling effect of clouds on record. The clearer skies may have contributed to a lower global mean precipitation total over land surfaces for the year, with 2023 being one of the driest years since 1979 (section
2d5), but global one-day maximum accumulations (Rx1day) were close to average, demonstrating increased intensity of the rain that did fall (section 2d6), as expected under warmer conditions (e.g., Fowler et al. 2021). Global soil moisture, which on average has increased since around 2012, returned to 2020 levels. This return was due to a combination of little change in the Northern Hemisphere and a strong drying in the Southern Hemisphere, likely the result of the shift from La Niña to El Niño conditions (section 2d10). And overall, 2023 saw terrestrial water storage measures reach their second-lowest point since 2002 (section 2d9), leading to 7.9% of global land area being under extreme drought (self-calibrating Palmer Drought Severity Index, scPDSI ≤-4) in July, the first time 7% has been surpassed for this most-severe drought category (section 2d11).

The land surface responded to the elevated temperatures, with a near-record negative anomaly for the visible albedo in the Northern Hemisphere (section 2h1) as the surface darkened. The surface was notably darker for a substantial fraction of Earth's surface (17%), linked to rapid snowmelt in Canada and Siberia. The darkening was also linked to increased plant growth (which causes the absorption of radiation) in other parts of the world, and there were also record positive anomalies for the fraction of absorbed photosynthetically active radiation in the Northern Hemisphere (section 2h2). Plants directly responded to the warmth; early in the year, the full bloom for the cherry trees in the Arashiyama district of Kyoto, Japan, occurred on the earliest date in the over-1200-year-long record, and there was an early start of season in North America. In the latter half of the year, leaf fall in boreal autumn was delayed in North America and Europe as above-average temperatures prevailed (section 2h4).

Glaciers in mountainous regions have continued to lose mass, with the 36th consecutive year of global mass balance loss and the 15th with losses of more than 500-mm water equivalent. This year also marked the highest ratio of negative to positive mass balance observations of any year in the record. In the European Alps, a second consecutive very-warm summer resulted in a 10% decrease in remaining ice volume for Swiss glaciers since 2021 (GLAMOS 2023; WMO 2024; section 2c3). The effects of the warmth penetrated into the ground, with permafrost temperatures at record levels at 10-m and even 20-m depth in the same region. In the Arctic, permafrost temperatures were record high at 9 of the 17 reporting sites, and active-layer thicknesses (the layers in the ground which freeze and thaw each year) also set records for all sites in Svalbard, in some places by up to 5 m (section 2c1).

The exceptional wildfire season in Canada (see Sidebar 7.1 for details), where large-scale fires burned continually from May to September, consumed three times more biomass than the previous record and pushed the global emissions of carbon from biomass burning to the highest annual total since 2015 (section 2h3). Plumes of smoke from these fires elevated aerosol optical depth at 550 nm and increased particulate matter at 2.5 µm in diameter (PM2.5) across North America during 2023, dominating the number of days with extreme (>99.9th percentile) aerosol optical depth globally (section 2g3). Low precipitation amounts and the subsequent drought in central and southern Canada were also contributing factors to the wildfires in those areas (sections 2d5, 2d10). The warm, dry spring resulted in the lowest May snow cover in the satellite record for Canada, and also globally (section 2b5). And finally, above-average thunder hours in the western United States and Canada in 2023 contributed to a greater number of fires ignited by lightning during the year (section 2e4).
<figure><img src="image_3.png" /><figcaption>(b) Lake Temperature</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(c) Night Marine Air Temperature</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(d) Warm Days (TX90p)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(e) Cool Nights (TX10p)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(f) Lower Tropospheric Temperature</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(g) Surface Specific Humidity</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Plate 2.1. (a) NOAA NCEI Global land and ocean surface annual temperature anomalies (°C); (b) Satellite-derived lake surface water temperature anomalies, from European Space Agency (ESA) Climate Change Initiative (CCI) LAKES/ Copernicus Climate Change Service (C3S) /Earth Observation Climate Information Service (EOCIS) (°C); (c) CLASSnmat night marine air temperature annual average anomalies (°C); (d) ERA5 warm day threshold exceedance (TX90p); (e) ERA5 cool night threshold exceedance (TN10p); (f) Average of Remote Sensing Systems (RSS) and UAH lower tropospheric temperature anomalies (°C). Hatching denotes regions in which 2023 was the warmest year on record; (g) ERA5 surface specific humidity anomalies (g kg⁻¹);</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>(i) Humid-heat days (TwX90p)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(j) Maximum T<sub>wet</sub></figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(k) Total Column Water Vapor</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(l) Upper Tropospheric Humidity</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>(m) Precipitation</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>(n) Maximum 5 Day Precipitation Amount</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Plate 2.1 (cont.) (h) ERA5 surface relative humidity anomalies (%rh); (i) HadISDH/extremes humid heat frequency anomalies (T<sub>w</sub>X90p), measured by the number of days where the daily maximum wet-bulb temperature exceeds the local daily 90th percentile (days yr<sup>-1</sup>). White gridboxes (over land) represent regions with insufficient data.; (j) HadISDH. extremes humid heat intensity (T<sub>w</sub>X), measured by the annual median anomaly of daily maximum wet-bulb temperature (°C). White gridboxes (over land) represent regions with insufficient data. (k) ERA5 TCWV anomalies (%). Data from GNSS stations are plotted as filled circles; (l) Annual microwave-based upper tropospheric humidity (UTH) anomalies (%rh); (m) GPCP v2.3 annual mean precipitation anomalies (mm yr<sup>-1</sup>); (n) CHIRPS maximum 5-day (Rx5day) annual precipitation anomalies (mm);</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>(q) Terrestrial Water Storage</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(s) Drought</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>(u) Sea Level Pressure</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>(p) Lake Water Level</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>(r) Soil Moisture</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>(t) Land Evaporation</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>Plate 2.1 (cont.) (o) PATMOS-x 6.0 cloud fraction annual anomalies (%); (p) GloLakes lake water storage anomalies (%); (q) GRACE-FO difference in annual-mean terrestrial water storage between 2022 and 2023 (cm); (r) Copernicus Climate Change Service (C3S) average surface soil moisture anomalies (m3 m⁻³). Data are masked where no retrieval is possible or where the quality is not assured and flagged, for example due to dense vegetation, frozen soil, or radio frequency interference; (s) Mean self-calibrating Palmer Drought Severity Index (scPDSI) for 2021. Droughts are indicated by negative values (brown), wet episodes by positive values (green). No calculation is made where a drought index is meaningless (gray areas: ice sheets or deserts with approximately zero mean precipitation); (t) GLEAM land evaporation anomalies (mm yr⁻¹); (u) ERA5 mean sea level pressure anomalies (hPa);</figcaption></figure>
<figure><img src="image_24.png" /><figcaption>(x) Total Aerosol</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>(z) Extreme Aerosol Days</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>(ab) Stratospheric (Total Column) Ozone</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>(w) Upper Air (850-hPa) Eastward Winds</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>(y) PM2.5</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>(aa) OMI/MLS Tropospheric Column Ozone</figcaption></figure>

<figure><img src="image_30.png" /></figure>

Plate 2.1 (cont.) (v) Surface wind speed anomalies (m s⁻¹) from the observational HadISD3 dataset (land, circles), the ERA5 reanalysis output (land, shaded areas), and Remote Sensing Satellite (RSS) satellite observations (ocean, shaded areas); (w) ERA5 850-hPa eastward wind speed anomalies for Sep–Dec (m s⁻¹); (x) CAMS reanalysis total aerosol optical depth (AOD) anomalies at 550 nm; (y) CAMS reanalysis PM2.5 anomalies (µg m⁻³); (z) Number of days with AOD above the 99.9th percentile from CAMS reanalysis. Areas with zero days appear as the white/gray background; (aa) OMI/MLS tropospheric ozone column anomalies for 60°S–60°N (DU); (ab) total column ozone anomalies determined from TROPOMI aboard Sentinel-5 Precursor (S5P; DU);
<figure><img src="image_31.png" /><figcaption>(ae) Land Surface Albedo in the Near-Infrared</figcaption></figure>

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

<figure><img src="image_33.png" /><figcaption>(ad) Land Surface Albedo in the Visible</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>(af) Fraction of Absorbed Photosynthetically Active Radiation</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>(ah) Vegetation Optical Depth</figcaption></figure>

<figure><img src="image_36.png" /></figure>

Plate 2.1 (cont.) (ac) CAMS reanalysis total column carbon monoxide anomalies (x 10¹⁸ molecules cm⁻²); (ad) VIIRS land surface visible broadband albedo anomalies (%); (ae) VIIRS land surface near-infrared albedo anomalies (%); (af) FAPAR anomalies; (ag) GFASv1.4 carbonaceous emission anomalies (g C m⁻² yr⁻¹) from biomass burning; (ah) VODCA CXKu-band VOD anomalies.
## b. Temperature

# 1. GLOBAL SURFACE TEMPERATURE

—A. Sanchez-Lugo, C. Morice, J. P. Nicolas, A. Arguez, F. Sezaki, and A. Goto

The global surface temperature for 2023 was 0.55°C–0.60°C above the 1991–2020 average, according to seven global temperature datasets (Table 2.1; Fig. 2.1). This was the highest value since global records began in the mid-1800s to mid-1900s (with the length of record depending on the dataset), surpassing the previous warmest year on record in 2016 (equal with 2020 in the GISTEMP dataset) by a large margin (+0.13°C to +0.17°C). The datasets consist of four global in situ surface temperature analyses (GISTEMP, Lenssen et al. 2019; HadCRUT5, Morice et al. 2021; NOAAGlobalTemp, Vose et al. 2021; Berkeley Earth, Rhode and Hausfather 2020) and three global atmospheric reanalyses (ERA5, Hersbach et al. 2020; Bell et al. 2021; JRA-55, Kobayashi et al. 2015; JRA-3Q, Kosaka et al. 2024).

<table><caption>Table 2.1. Global temperature anomalies (°C; 1991–2020 base period) for 2023. Note that for the Had-CRUT5 column, land values were computed using the CRUTEM.5.0.2.0 dataset (Osborn et al. 2021), ocean values were computed using the HadSST.4.0.1.0 dataset (Kennedy et al. 2019), and global land and ocean values were computed using the HadCRUT.5.0.2.0 dataset (Morice et al. 2021).</caption><thead><tr><th>Global</th><th>NASA-GISTEMPv4</th><th>HadCRUT5</th><th>NOAA GlobalTemp</th><th>Berkeley Earth</th><th>ERA5</th><th>JRA-55</th><th>JRA-3Q</th></tr></thead><tbody><tr><td>Land</td><td>+0.78</td><td>+0.73</td><td>+0.83</td><td>+0.73</td><td>+0.85</td><td>+0.76</td><td>+0.79</td></tr><tr><td>Ocean</td><td>+0.43</td><td>+0.47</td><td>+0.43</td><td>–</td><td>+0.50</td><td>+0.48</td><td>+0.50</td></tr><tr><td>Land and Ocean</td><td>+0.56</td><td>+0.56</td><td>+0.55</td><td>+0.57</td><td>+0.60</td><td>+0.56</td><td>+0.58</td></tr></tbody></table>

All seven datasets agree that the last nine years (2015–23) were the nine warmest years since global records began, and the global trends at the short-term (1981–2023; 0.19°C–0.20°C decade⁻¹) and long-term (1880–2023; 0.08°C–0.09°C decade⁻¹) periods for each dataset are comparable to one another. On a trend-adjusted basis, following the Arguez et al. (2020) approach, 2023 was well above the trend in all seven datasets, exceeding the 90th percentile in each. In fact, 2023 registered the highest departure above the trend line (computed for the period 1975–2023) in all four in-situ analyses and the second-highest departure in each reanalysis product, eclipsed only by 1981 in ERA5 and 2016 in JRA-55 and JRA-3Q.

The global surface temperature for 2023 was also 1.35°C–1.54°C above the 1850–1900 average (a period commonly used to represent pre-industrial conditions). The pre-industrial temperature anomaly range was computed using the three datasets that extend back to 1850 (NOAAGlobalTemp, HadCRUT5, Berkeley Earth) using each dataset’s own 1850–1900 baseline. The year 2023 marked the ninth consecutive year with a temperature more than 1°C above this average.

After three consecutive years (mid-2020 to early 2023) of La Niña across the tropical

<figure><img src="image_37.png" /><figcaption>Fig. 2.1. Global average surface-air temperature anomalies (°C; 1991–2020 base period). In situ estimates are shown from NOAAGlobalTemp (Vose et al. 2021), NASA GISTEMPv4 (Lenssen et al. 2019), HadCRUT5 (Morice et al. 2021), CRUTEM5 (Osborn et al. 2021), HadSST4 (Kennedy et al. 2019), and Berkeley (Rhode and Hausfather 2020). Reanalysis estimates are shown from ERA5 (Hersbach et al. 2020; Bell et al. 2021), JRA-55 (Kobayashi et al. 2015), and JRA-3Q (Kosaka et al. 2024).</figcaption></figure>
Pacific Ocean, 2023 quickly transitioned to ENSO-neutral and then to El Niño conditions by May. Monthly global ocean surface temperatures were unusually high (Plate 2.1a; Appendix Figs. A2.1–A2.5), with new global ocean temperature records set each month from June through December 2023. This sustained warmth resulted in a record-high annual global ocean surface temperature that was 0.43°C–0.50°C above the 1991–2020 average. The unusually warm oceans across many basins, along with the presence of El Niño and the long-term warming trend caused by anthropogenic greenhouse gas emissions, were key contributors to the high monthly global surface temperature records observed during the latter half of the year (see also section 3b and Sidebar 3.1 for details). The annual global land surface temperature was also record high, at 0.73°C–0.85°C above average.

Much-warmer-than-average conditions were observed across most of the world's surface, with the largest positive temperature anomalies occurring across parts of the higher northern latitudes (Plate 2.1a; Appendix Figs. A2.1–A2.5). Even with record warmth for the globe as a whole, below-average annual temperatures were observed across parts of Greenland, the southwestern contiguous United States, and parts of the Southern Ocean and Antarctica.

## Sidebar 2.2: Near-surface equivalent temperature as a key climate change metric

—T. MATTHEWS, M. BYRNE, P. C. STOY, AND K. M. WILLETT

Only ~1% of the accumulating heat in Earth's system is being stored in the atmosphere (von Schuckmann et al. 2023) and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere, and the atmosphere. According to the Sixth Assessment Report by Working Group I of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and has resulted in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance (EEI), yet air temperature has been the de facto metric for communicating climate change. It has therefore been relatively straightforward to estimate global mean temperature change since the pre-industrial period (Hansen et al. 2010; Morice et al. 2021; Rohde and Hausfather 2020). Air temperature trends are also highly relevant to society, not least due to the universal temperature sensitivity observed in the biosphere (Gillooly et al. 2001) and because of the fundamental control of temperature on the hydrological cycle (Held and Soden 2006).

Air temperature alone, however, provides an incomplete perspective of atmospheric heat accumulation (Matthews et al. 2022; Pielke 2003). The total energy content (TEC) of the atmosphere is mostly comprised of sensible heat (~97%) with a minor contribution from latent heat (~3%; Peixoto and Oort

1992). In tracking sensible heat content, air temperature is therefore a good proxy for TEC; however, it is less appropriate for monitoring changes in TEC because almost half of the recent gain in global mean near-surface energy has been channeled into latent heat (Matthews et al. 2022; Stoy et al. 2022), exceeding 75% in some tropical regions. The potential for this increase to be “hidden” by air temperature trends is concerning, as latent heat plays a key role in determining maximum intensities for precipitation extremes (Ali et al. 2018; O’Gorman 2012; Song et al. 2022), near-surface air temperature, and human heat stress (Matthews 2018). Hotspots and hot moments of societally relevant heat accumulation are therefore at risk of being missed by using air temperature alone to track climate change. A metric proportional to TEC could also help constrain assessments of Earth’s changing energy budget (von Schuckmann et al. 2023).

$$Teq = T + \frac{L_v}{c_p} q$$

In response to these concerns, the equivalent temperature (Teq) has been suggested as an important additional metric for use in climate communications (Matthews et al. 2022; Pielke 2003; Song et al. 2022): in which T is the (dry-bulb) air temperature, $L_v$ is the latent heat of vaporization, $c_p$ is the
specific heat capacity of air at constant pressure, and *q* is the specific humidity. Teq therefore tracks the sensible heat content through *T* and the latent heat through *L*<sub>v</sub>/*c*<sub>p</sub> *q*. It represents the air temperature that could be reached if all latent heat was converted to sensible heat. A related quantity—the equivalent potential temperature—is conserved under reversible moist adiabatic processes and has been used to explore the land-ocean warming contrast and to help understand the maximum possible intensity of heatwaves (Byrne and O’Gorman 2013; Zhang and Boos 2023).

*T*, Teq, and the "latent temperature" (Tq = Teq - *T*) were computed using near-surface air temperature, dewpoint, and surface pressure from ERA5 (Hersbach et al. 2020) following Raymond et al. (2021) to evaluate *L*<sub>v</sub> and *c*<sub>p</sub>. For comparison, Teq from the HadISDH near-surface in situ humidity product (land only; Willett et al. 2014, 2013) was also assessed, processing it from hourly observations through to the quality-controlled and homogenized monthly mean gridded fields. Note that both ERA5 and HadISDH data were assessed for the overlapping 1973–2023 period. The 1991–2020 ERA5

climatology illustrates that Teq is more variable over Earth's surface than T. Both share a minimum slightly below 220 K in Antarctica, while Teq climbs to more than 30 K above T in the tropics due to the much greater contribution from Tq at low latitudes (Figs. SB.2.1a-c). Teq therefore exhibits a much steeper poleward reduction (Fig. SB.2.1d).

The ERA5 trends in *T* highlight the familiar warming enhancement over land (Byrne and O’Gorman 2018) and in the Arctic (Figs. SB.2.2a,b). However, *Tq* trends are different, being generally greatest in the northern tropics and subtropics and more similar between land and ocean (Figs. SB.2.2c,d). The weaker trends over subtropical land agree with Simpson et al. (2024) and highlight that latent heat content can fall even as *T* (and hence saturation vapor pressure) climbs due to reductions in relative humidity. The Teq trends reflect the combined response of *T* and *Tq* (Figs. SB.2.2e,f), with perhaps the most significant difference from *T* being that Arctic amplification appears more subdued relative to the (densely populated) northern low latitudes. HadISDH agrees with this pattern but suggests even larger low-latitude trend amplification

<figure><img src="image_38.png" /><figcaption>Fig. SB.2.1. The 1991–2020 ERA5 climatology for (a) air temperature (Ta), (b) latent temperature (Tq = Teq – Ta), and (c) equivalent temperature (Teq). (d) Zonal-mean profiles (Tx) normalized to the mean of each series plotted (|Tx|). Note that Tq is plotted on the bottom x-axis due to its greater variability.</figcaption></figure>
(Fig. SB.2.2f), likely driven by undersampling in those tropical regions (e.g., East Africa) where ERA5 suggests lower Teq trends due to drying (Figs. SB.2.2c,e; see Willett et al. 2023).

The ERA5 trend (1973–2023) in global-mean near-surface Teq is 0.36±0.03 K decade⁻¹ (Fig. SB.2.3). The comparison series from HadISDH indicates a larger trend of 0.55±0.03 K decade⁻¹, likely due to a combination of its land-only nature

and incomplete spatial sampling. Disaggregating the Teq trend for ERA5 indicates that approximately 58% is explained by the *T* trend (0.21±0.01 K decade⁻¹) and 42% by *Tq* (0.15±0.02 K decade⁻¹). However, because climatological *Tq* is much lower than *T* (Fig. SB.2.1), the *Tq* trend represents a much more significant relative increase. This was well illustrated in 2023: the hottest year on record for Teq (in ERA5 and HadISDH:

<figure><img src="image_39.png" /><figcaption>Fig. SB.2.2. The 1973–2023 ERA5 trends and their zonal means (K decade⁻¹; right-hand panels, smoothed with 5° running mean) in (a), (b) air temperature (Ta), (c), (d) latent temperature (Tq = Teq – T), and (e), (f) equivalent temperature (Teq). In panels (d), (f), (land only) zonal-mean trends for HadISDH are also shown as the dashed line.</figcaption></figure>
Fig. SB.2.3) and for Ta and Tq individually (Table SB.2.1). The latter both recorded anomalies of 0.6 K relative to the 1991–2020 climatology (see sections 2b1, 2d), but the much lower baseline in Tq translates the 0.6 K anomaly to a relative increase of 2.5%—over an order of magnitude larger than for T (0.21%). This carries through to Teq to some extent, with the relative anomaly in 2023 of 1.2 K representing a rise of 0.38% (Table SB.2.1). As measured by Teq, the climate has therefore departed even further from the reference points of human history.

Although Teq is a complete physical descriptor of atmospheric heating, its unfamiliarity may present a challenge in climate communications, not least because its absolute values (Fig. SB.2.2) and its variability (Fig. SB.2.3) are much higher than for T. However, presenting relative changes as above may be a simple and intuitive solution for overcoming this communication barrier. Such efforts are worth pursuing, as Teq is a key indicator of changes to the atmospheric state that are of critical relevance to society.

<figure><img src="image_40.png" /><figcaption>Fig. SB.2.3. 1973–2023 ERA5 and HadISDH trends in global mean air temperature (Ta; ERA5 only), latent temperature (Tq = Teq – Ta), and equivalent temperature (Teq). Trend lines were computed with simple linear regression, and shading spans 95% confidence intervals. The trends presented on the plot are for ERA5, with ±1 sigma standard error.</figcaption></figure>

<table><caption>Table SB.2.1. Top-10 years for annual mean values in ERA5 (1973–2023). Note that relative anomalies are computed from the 1991–2020 baseline.</caption><thead><tr><th>Rank</th><th>Ta year</th><th>Ta (K)</th><th>Ta (%)</th><th>Tq year</th><th>Tq (K)</th><th>Tq (%)</th><th>Teq year</th><th>Teq (K)</th><th>Teq (%)</th></tr></thead><tbody><tr><td>1</td><td>2023</td><td>0.60</td><td>0.21</td><td>2023</td><td>0.60</td><td>2.51</td><td>2023</td><td>1.20</td><td>0.38</td></tr><tr><td>2</td><td>2016</td><td>0.44</td><td>0.15</td><td>2016</td><td>0.50</td><td>2.12</td><td>2016</td><td>0.94</td><td>0.30</td></tr><tr><td>3</td><td>2020</td><td>0.43</td><td>0.15</td><td>2019</td><td>0.44</td><td>1.85</td><td>2019</td><td>0.84</td><td>0.27</td></tr><tr><td>4</td><td>2019</td><td>0.40</td><td>0.14</td><td>2020</td><td>0.38</td><td>1.62</td><td>2020</td><td>0.82</td><td>0.26</td></tr><tr><td>5</td><td>2017</td><td>0.34</td><td>0.12</td><td>2017</td><td>0.34</td><td>1.41</td><td>2017</td><td>0.68</td><td>0.22</td></tr><tr><td>6</td><td>2022</td><td>0.30</td><td>0.10</td><td>1998</td><td>0.27</td><td>1.14</td><td>2015</td><td>0.52</td><td>0.17</td></tr><tr><td>7</td><td>2021</td><td>0.27</td><td>0.10</td><td>2015</td><td>0.27</td><td>1.13</td><td>2018</td><td>0.52</td><td>0.17</td></tr><tr><td>8</td><td>2018</td><td>0.26</td><td>0.09</td><td>2018</td><td>0.25</td><td>1.07</td><td>2021</td><td>0.41</td><td>0.13</td></tr><tr><td>9</td><td>2015</td><td>0.26</td><td>0.09</td><td>2010</td><td>0.15</td><td>0.63</td><td>2022</td><td>0.40</td><td>0.13</td></tr><tr><td>10</td><td>2010</td><td>0.13</td><td>0.05</td><td>2021</td><td>0.13</td><td>0.56</td><td>1998</td><td>0.29</td><td>0.09</td></tr></tbody></table>

## 2. LAKE SURFACE WATER TEMPERATURE

—L. Carrea, C. J. Merchant, R. I. Woolway, J.-F. Crétaux, T. M. Dokulil, H. Dugan, A. Laas, E. Leibensperger, S.-I. Matsuzaki, L. J. Merio, D. Pierson, O. O. Rusanovskaya, S. V. Shimaraeva, E. A. Silow, M. Schmid, M. A. Timofeyev, and P. Verburg

The globally averaged satellite-derived lake surface water temperature (LSWT) anomaly during the 2023 warm season was +0.46°C with respect to the 1995–2020 baseline, the highest since the record began in 1995 (Fig. 2.2a). The mean LSWT trend during 1995–2023 was 0.20±0.01°C decade⁻¹, broadly consistent with previous analyses even though the number of lakes analyzed has doubled since 2022 (Woolway et al. 2017, 2018; Carrea et al. 2019, 2020, 2021, 2022a; Fig. 2.2a). The 2023 warm-season anomalies for each lake are shown in Plate 2.1b; of the 1949 studied lakes that were not dry, 79% of these were warmer than average and 21% were colder. For 33 lakes, no anomalies could be computed since they were found to be dry.

Large coherent regions of high LSWT were identified in 2023, with 44% of all observed lakes experiencing LSWT anomalies in excess of +0.5°C (Plate 2.1b). The highest anomalies occurred
<figure><img src="image_41.png" /><figcaption>(a) Europe</figcaption></figure>

<figure><img src="image_42.png" /><figcaption>(c) Canada</figcaption></figure>

<figure><img src="image_43.png" /><figcaption>(b) Africa</figcaption></figure>

<figure><img src="image_44.png" /><figcaption>(d) Tibetan Plateau</figcaption></figure>

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /><figcaption>**Fig. 2.3.** Lake temperature anomalies (°C, colored dots) and 2-m air temperature anomalies (°C) in 2023 for lakes in (a) Europe, (b) Africa, (c) Canada, and (d) the Tibetan Plateau. These values were calculated for the warm season (Jul–Sep in the extratropical Northern Hemisphere; Jan–Mar in the extratropical Southern Hemisphere; Jan–Dec in the tropics) with reference to the 1995–2020 base period.</figcaption></figure>

in lakes situated in northern Canada, eastern China, Japan, and Europe. Negative LSWT anomalies were consistently observed in Patagonia, Greenland, Alaska, Australia, northern South America, and southeast Asia.

Four regions were studied in more detail: Europe (number of lakes, n = 268, Figs. 2.2b, 2.3a), Canada (n = 496, Figs. 2.2d, 2.3c), Tibet (n = 144, Figs. 2.2e, 2.3d), and Africa (n = 145, Figs. 2.2c, 2.3b). In these regions, the warm-season LSWT anomalies are consistent with the corresponding air temperature anomalies, as compiled by NASA's Goddard Institute for Space Studies (GISS; Lenssen et al. 2019; GISTEMP Team 2023), with an average warming trend of +0.31±0.03°C decade⁻¹ in Europe (Fig. 2.2b) and +0.18±0.03°C decade⁻¹ in Canada (Fig. 2.2d). In Canada, where the mean LSWT anomaly was +0.83°C in 2023, 92% of observed lakes had positive anomalies. In Europe, the

<caption>**Fig. 2.2.** Annual time series of satellite-derived warm-season lake surface water temperature anomalies (°C; 1995–2020 base period) from 1995 to 2023 for lakes distributed (a) globally, and regionally in (b) Europe, (c) Africa, (d) Canada, and (e) the Tibetan Plateau.</caption>
decade⁻¹ and +0.10±0.02°C decade⁻¹, respectively (Figs. 2.2c,e). In Africa, 81% of the 145 lakes had positive LSWT anomalies, and the average anomaly in 2023 was +0.33°C. In Tibet, the average 2023 anomaly was +0.09°C, with 70% of the lakes experiencing positive anomalies.

In situ observations of (single-point) warm season temperature anomalies from 38 lakes are shown in Fig 2.4, 23 of which have measurements for the year 2023, with an average of +0.78°C. The anomalies calculated here differ from those derived from satellite data, which represent lake-wide averages. Five lakes experienced negative anomalies (average of -0.76°C) and 18 lakes had positive anomalies (average of +1.21°C) in 2023. The time series in Fig. 2.4 clearly show that lakes are warming.

The period 1995–2020 is used as a baseline for both in situ (unless no data were available) and satellite temperatures to compute anomalies. The warm-season averages for midlatitude lakes were computed for summers (July–September in the Northern Hemisphere and January–March in the Southern Hemisphere), and January–December averages are presented for tropical lakes (within 23.5° of the equator).

Lake surface water temperature time series were derived from the European Space Agency Climate Change Initiative (ESA CCI) LAKES/Copernicus Climate Change Service (C3S) /Earth Observation Climate Information Service (EOCIS) climate data record (Carrea et al. 2022b, 2023).

The LSWT time series has been derived using ATSR2, AATSR, MODIS, AVHRR and SLSTR sensors. For 2023, satellite observations from SLSTR on Sentinel3A and 3B were used. The retrieval method of MacCallum and Merchant (2012) was applied on image pixels filled with water according to both the inland water dataset of Carrea et al. (2015) and a reflectance-based water detection scheme (Carrea et al. 2023).

The satellite-derived LSWT data are spatial averages for each of a total of 1949 lakes. The satellite-derived LSWT data were validated with in situ measurements with an average satellite-minus-in situ temperature difference of less than 0.5°C and standard deviation (robust) of less than 0.7°C (Carrea et al. 2023). Lake-wide average surface temperatures have been shown to give a more representative picture of LSWT responses to climate change compared to single-point measurements (Woolway and Merchant 2018).

The average surface air temperature was calculated from GHCN v4 (250-km smoothing radius) data of the NASA GISS surface temperature analysis (Lenssen et al. 2019; GISTEMP Team 2024).

### 3. NIGHT MARINE AIR TEMPERATURE

—R. C. Cornes and R. Junod

<figure><img src="image_47.png" /><figcaption>Fig. 2.4. In situ lake surface water temperature observations from 38 globally distributed lakes, showing the annually averaged warm season (Jul–Sep in the Northern Hemisphere; Jan–Mar in the Southern Hemisphere) anomalies (°C; 1995–2020 base period or the available base period).</figcaption></figure>

Two night marine air temperature (NMAT) datasets are routinely updated and used for analysis in this section: UAHNMAT (Junod and Christy 2020) and CLASSnmat (Cornes et al. 2020). These datasets are evaluated in combination with the HadSST4 dataset (Kennedy et al. 2019). Since these datasets are not spatially interpolated, they each have slightly different spatial coverage. In this evaluation the data have been masked to allow comparisons to be made over the common coverage areas, and to the common period of 1900–2023. NMAT and sea-surface temperature
(SST) data are expected to follow each other closely across large spatial scales and over longer time periods. As such, the NMAT data are used here to evaluate the more widely used SST data, which are considered in more detail in section 3b.

In all three datasets, 2023 was the warmest year in the series, with exceptional global annual average temperatures that were 0.50°C, 0.40°C, and 0.47°C above the 1991–2020 baseline in the CLASSnmat, UAHNMAT, and HadSST4 series, respectively (Fig. 2.5a). During the latter half of the year, consistent positive anomalies of >+0.5°C were recorded in the CLASSnmat and HadSST4 datasets (Fig. 2.6). The anomalies in the UAHNMAT were slightly lower, although global average anomalies were consistently above +0.4°C from June to December.

Large-scale averages of the NMAT/SST data are plotted in Figs. 2.5b–d. These results indicate that 2023 was the warmest year in each of the three regions. The largest anomalies were recorded in the northern extratropics (north of 30°N), with average annual anomalies of >+0.7°C in the CLASSnmat and HadSST4 datasets and +0.6°C in UAHNMAT. These results reflect the high frequency of marine heatwaves that occurred globally throughout 2023 (see sections 2b4, 3b, and Sidebar 3.1), although individual events are not evident in those results due to the consideration of annual averages. Examination of the grid-cell values (Plate 2.1c) indicates three areas of marked positive anomalies, which are also present in the UAHNMAT and HadSST data (not shown): the eastern tropical Pacific, the northern Pacific, and the North Atlantic. The pattern across the tropical Pacific reflects the strong El Niño that developed during the latter half of the year.

Marine air temperature data recorded on board ships have been used for many years for climate monitoring purposes and to evaluate land air temperature and SST datasets (Rayner et al. 2003). However, two main biases exist in these observations: artificial diurnal heating due to the superstructure of the ships (Cropper et al. 2023) and variable

temperature observing heights (Kent et al. 2013). To reduce the effect of diurnal heating biases, the daytime observations are removed from the data, resulting in night marine air temperature data. As such, the processing is designed to remove this artefact from the data rather than for

<figure><img src="image_48.png" /><figcaption>Fig. 2.5. Annual average marine temperature anomalies (°C; 1991–2020 base period) calculated from the CLASSnmat, UAHNMAT, and HadSST4 datasets averaged over the (a) globe, (b) northern extratropics, (c) tropics, and (d) southern extratropics. The tropics is defined as the latitude range 30°S–30°N and the northern (southern) extra-tropics as >30°N (<30°S). The averages only include values that are common to all three datasets for a given year; since UAHNMAT starts in 1900, only values for the period 1900–2023 are plotted.</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>Fig. 2.6. Global monthly average marine temperature anomalies (°C; 1991–2020 base period) in the (a) CLASSnmat, (b) UAHNMAT, and (c) HadSST4 datasets. Each line represents a year of data, and the results for 2023 are shown in red.</figcaption></figure>
quantifying night temperature per se (c.f. terrestrial nighttime temperature extremes in section 2b4). Inhomogeneities arising from changing observation height are removed by adjusting the temperature readings to a common reference height, which is typically 10 m above the sea surface. The UAHNMAT and CLASSnmat datasets examined in this section use different methods to implement the height correction, and they also differ in the quality-control of the data and the approach taken to addressing other biases in the data, notably for the warm bias that exists in the data during the World War II period (Cornes et al. 2020).

## 4. SURFACE TEMPERATURE EXTREMES

—R. J. H. Dunn, M. G. Donat, R. W. Schlegel, and M. G. Bosilovich

The record-breaking global surface temperatures of 2023 (section 2b1) also translated to record numbers of “warm days” (TX90p; Table 2.2) and, as in the last few years, well-below-average numbers of “cool nights” (TN10p; Table 2.2) over land.

The GHCNDEX dataset of gridded in situ observations (Donat et al. 2013) had a globally averaged number of warm days of 70±7. As this dataset has limited spatial coverage for 2023, several reanalysis products are used (ERA5, Hersbach et al. 2020; Bell et al. 2021; JRA-55, Kobayashi et al. 2015; MERRA-2, Gelaro et al. 2017) to give a globally complete assessment of the land surface extreme temperatures. As shown in Fig. 2.7c and Table 2.2, all reanalysis products reached record values in 2023 for the number of warm days, values which were substantially more than the previous record set in 2016. There is a wide spread in values from these globally complete products, from 70 to 81 warm days in the year (relative to the value over the 1981–2010 reference period of 36.5), with ERA5 showing greater warming than MERRA-2 and JRA-55.

Many areas of the world had their highest number of warm days on record in 2023 (Fig. 2.8a). Globally averaged, 2023 had the third-highest land fraction experiencing record numbers of warm days, following the strong El Niño years of 2010 and 1998 (Fig. 2.8b). Regionally, most of Canada had record numbers of warm days, with a large fraction of South America, and substantial areas of Africa, Europe, and Asia also showing record numbers of warm days (see Chapter 7 for details on regional temperatures). In contrast, Australia had almost no areas of record high values for TX90p (Plate 2.1d). A similar pattern is seen in the anomalies of cool nights (Plate 2.1e). Below-average numbers of warm days occurred in the western United States, Alaska, parts of the Indian subcontinent, and northern Australia.

<figure><img src="image_50.png" /></figure>

Fig. 2.7. (a),(b) Time series of the (a) annual number of warm days (TX90p) and (b) cool nights (TN10p) averaged over global land regions based on gridded station data from the GHCNDEX dataset (smoothed shown by dashed lines) and ERA5 using 1961–90 as the reference period. The spatial coverage in GHCNDEX is limited, the black dashed lines show the percentage of land area covered (right y-axis). The 2-σ coverage uncertainty (following Brohan et al. 2006; Dunn et al. 2020) is shown by the light red bands in (a),(b), (c), (d) As in (a),(b), for three atmospheric reanalyses (ERA5, MERRA-2, and JRA-55) using 1991–2020 as the reference period.
<table><caption>Table 2.2. Definitions of indices used for land surface temperature extremes, their globally averaged values (days) for 2023, and ranks from the four datasets. Coverage uncertainties are shown for GHCNDEX.</caption><thead><tr><th>Index</th><th>Name</th><th>Definition</th><th>GHCNDEX<br>(1951–2023)<br>Value, [rank]<br>Reference Period<br>1961–90</th><th>ERA5<br>(1940–2023)<br>Value, [rank]<br>Reference Period<br>1961–90</th><th>ERA5<br>(1940–2023)<br>Value, [rank]<br>Reference Period<br>1991–2020</th><th>MERRA-2<br>(1980–2023)<br>Value, [rank]<br>Reference Period<br>1991–2020</th><th>JRA-55<br>(1970–2023)<br>Value, [rank]<br>Reference Period<br>1991–2020</th></tr></thead><tbody><tr><td>TX90p</td><td>Warm<br>days</td><td>The annual count of days when the daily maximum temperature exceeds the 90th percentile</td><td>22<br>[third lowest]</td><td>102<br>[highest]</td><td>81<br>[highest]</td><td>70<br>[highest]</td><td>72<br>[highest]</td></tr><tr><td>TN10p</td><td>Cool<br>night</td><td>The annual count of nights when the daily minimum temperature falls below the 10th percentile</td><td>21±8<br>[seventh<br>lowest]</td><td>17<br>[fourth<br>lowest]</td><td>21<br>[third lowest]</td><td>21<br>[lowest]</td><td>--</td></tr></tbody></table>

Indices recommended by the former World Meteorological Organization Expert Team on Climate Change Detection and Indices (Zhang et al. 2011) characterize temperature extremes using observations of daily maximum and minimum temperatures. These indices are calculated from the daily maximum and minimum temperatures for stations in the GHCN-daily dataset (Menne et al. 2012) and interpolated on to a regular 2.5° grid to form GHCNDEX (Donat et al. 2013). As in previous years' assessments, spatial coverage falls off in recent years, so we use reanalysis products to provide globally complete fields for these indices. A recent comparison of reanalysis and observational products for these indices shows good agreement, especially for the indices presented here (Dunn et al. 2022). These indices use a fixed reference period (1961–90 for GHCNDEX and ERA5; 1991–2020 for ERA5, MERRA-2, and JRA-55), and intercomparison between these is not trivial (Dunn et al. 2020; Yosef et al. 2021; Dunn and Morice 2022).

A marine heatwave (MHW) is detected when five or more consecutive days of temperature are above a 90th-percentile daily climatology (Hobday et al. 2016). Marine heatwaves are categorized as moderate when the greatest temperature anomaly during the event is less than double the difference between the 90th percentile and the seasonal anomaly. When this value is more than double, triple, or quadruple the difference, the MHW is categorized as strong, severe, or extreme, respectively (see Fig. 2 in Hobday et al. 2018). The direct inverse is used to detect and categorize marine cold spells (MCSs; i.e., days below the 10th percentile). The baseline period used to detect events in this report is 1982–2011, because 1982 is the first full year of the NOAA OISST product (Huang et al. 2021).

The 2023 analysis of NOAA OISST daily v2.1 revealed that 94% of the global ocean surface experienced at least one marine heatwave (Hobday et al. 2016; Figs. 2.9a,b),

<figure><img src="image_51.png" /><figcaption>Fig. 2.8. (a) Map indicating grid cells where the warm day index (TX90p) for 2023 ranked in the three highest (orange to red) or three lowest (blue) values based on ERA5 since 1940 using the 1991–2020 reference period. (b) Time series of the percent of land area ranked as the highest value for TX90p in each year for ERA5 (from 1960), JRA-55 (from 1990), and MERRA-2 (from 2000). The ranks from the first 20 years of each reanalysis are not calculated.</figcaption></figure>
and 27% experienced at least one MCS (Figs. 2.9c,d). The most common MHW category (Hobday et al. 2018) in 2023 was Category 2 Strong (49%), with the coverage of Category 3 Severe events reaching 10%. Category 1 Moderate MCSs have remained the most common (16%) cool events in all years since 1987. The ocean experienced a new global average record of 116 MHW days in 2023. This is far more than the 2016 MHW record of 86 days (Fig. 2.9a). This equates to a daily average MHW coverage of 32%. In 2023, the global ocean experienced 13 MCS days, far below the record of 37 days in 1982, equating to a daily average coverage of 4% (Fig. 2.9c).

## MHW category summaries: 1982 – 2023

<figure><img src="image_52.png" /></figure>

## MCS category summaries: 1982 – 2023

<figure><img src="image_53.png" /></figure>

Fig. 2.9. Annual global marine heatwave (MHW; [a],[b]) and marine cold spell (MCS; [c],[d]) occurrence from NOAA OISST v2.1 using a climatology base period of 1982–2011. (a),(c) The average count of MHW/MCS days experienced over the surface of the ocean each year (left y-axis), also expressed as the percent of the surface of the ocean experiencing a MHW/MCS on any given day (right y-axis) of that year. (b),(d) Total percent of the surface area of the ocean that experienced an MHW/MCS at some point during the year. The values shown are for the highest category of MHW/MCS experienced at any point.

## 5. TROPOSPHERIC TEMPERATURE

—S. Po-Chedley, J. R. Christy, L. Haimberger, C. A. Mears, and C.-Z. Zou

The annual average lower tropospheric temperature (LTT) was record high for 2023, with particularly exceptional values in the tropics during the second half of the year (Fig. 2.10). The annual average LTT was 0.43°C–0.65°C above the 1991–2020 average, depending on the dataset (Fig. 2.11). In the annual average, LTT was above average over approximately 90% of Earth with
record-setting temperatures over 17%–20% of the globe (Plate 2.1d). While La Niña conditions during late 2022 into early 2023 depressed global LTT in the first half of the year, El Niño developed in May and strengthened through the year. The El Niño conditions, paired with the underlying global warming trend (Table 2.3), contributed to the record observed tropospheric warmth in 2023 (Fig. 2.10). Continued research is needed to quantify other factors that may have enhanced the exceptional global tropospheric warmth in 2023 (Perkins-Kirkpatrick et al. 2024; Rantanen and Laaksonen 2024; Schmidt 2024).

Long-term records of tropospheric temperature are derived from in situ balloon-borne radiosonde measurements, microwave measurements from satellites, and atmospheric reanalysis models. In this section, we focus on LTT, which represents a weighted vertical average of atmospheric temperature with weight concentrated in the lower troposphere (c.f., Fig. 1 in Christy et al. 2003). Other measures of tropospheric temperature yield broadly consistent results (Po-Chedley et al. 2023). Despite differences in geographic sampling and observation type, the records show excellent agreement on interannual timescales (the minimum correlation coefficient between pairs of annually averaged, global mean, detrended LTT time series is 0.84; Fig. 2.11). While global trend differences are non-negligible (approximately ±0.04°C per decade across datasets, depending on the start date), all datasets exhibit substantial lower tropospheric warming ranging from 0.14°C to 0.22°C per decade. Satellite and reanalysis datasets indicate that 2023 was the warmest year on record for global LTT. 2016 and 2023 were tied as the warmest year in the RATPAC-A radiosonde dataset, and 2023 ranked fourth in the RICH and RAOBCORE radiosonde datasets. Differences in the relative ordering of annual mean global LTT anomalies are due in part to sampling. For example, if we sample reanalysis and satellite LTT values to match RAOBCORE data availability, then 2023 falls behind 2016 as the warmest year on record.

<table><caption>Table 2.3. Global lower-tropospheric temperature (LTT) trends (°C decade⁻¹) over the periods 1958–2023 and 1979–2023. NASA MERRA-2 data begins in 1980 and NOAA STAR v5.0 begins in 1981.</caption><thead><tr><th>Method</th><th>Source</th><th>Start Year 1958</th><th>Start Year 1979</th></tr></thead><tbody><tr><td>Radiosonde</td><td>NOAA RATPAC vA2 (Free et al. 2005)</td><td>0.19</td><td>0.22</td></tr><tr><td>Radiosonde</td><td>RAOBCORE v1.9 (Haimberger et al. 2012)</td><td>0.16</td><td>0.18</td></tr><tr><td>Radiosonde</td><td>RICH v1.9 (Haimberger et al. 2012)</td><td>0.18</td><td>0.20</td></tr><tr><td>Satellite</td><td>UAH v6.0 (Spencer et al. 2017)</td><td>—</td><td>0.14<sup>[1]</sup></td></tr><tr><td>Satellite</td><td>RSS v4.0 (Mears and Wentz 2016)</td><td>—</td><td>0.22</td></tr><tr><td>Satellite</td><td>NOAA STAR v5.0 (Zou et al. 2023)</td><td>—</td><td>0.14<sup>[1]</sup></td></tr><tr><td>Reanalysis</td><td>ERA5 (Hersbach et al. 2020)</td><td>0.16</td><td>0.18</td></tr><tr><td>Reanalysis</td><td>JRA-55 (Kobayashi et al. 2015)</td><td>0.17</td><td>0.19</td></tr><tr><td>Reanalysis</td><td>NASA MERRA-2 (Gelaro et al. 2017)</td><td>—</td><td>0.20</td></tr><tr><td>Median</td><td>N/A</td><td>0.17</td><td>0.19</td></tr></tbody></table>

<footnote>[1] The retrieval algorithm in UAH and STAR LTT is different from other datasets and results in vertical sampling that is slightly higher in the troposphere (Spencer et al. 2017). As a result, temperature trends are approximately 0.01°C decade⁻¹ smaller in UAH and STAR LTT.</footnote>
<figure><img src="image_54.png" /><figcaption>Fig. 2.10. Seasonal cycle of five-day average lower tropospheric temperature (LTT; K) for the (a) global and (b) tropical (20°S–20°N) mean. Each year (1979–2023) is plotted as a different gray line, except for select years that experienced the onset of El Niño (1982, 1997, 2015, and 2023 in blue, cyan, orange, and red, respectively). The full seasonal cycle is shown (shaded blue background) along with data from the preceding three months and following six months. Pentad values are from the UAH LTT dataset.</figcaption></figure>

<figure><img src="image_55.png" /></figure>

Over the Southern Hemisphere extratropics, record-high annual average LTT values were recorded over parts of the Southern Ocean into the South Atlantic, South Pacific, and South Indian Oceans. Over the Northern Hemisphere extratropics, record-high values occurred over northwest Canada into the Arctic Ocean, along the northwest coast of Africa and western Europe, and over Central and East Asia. In the tropics, all-time highs were concentrated over tropical South America, eastern Africa, and the tropical Atlantic and eastern tropical Pacific. Most of the tropics (20°S–20°N) set record-high LTT values for the months of July through December (not shown).

In some ways, 2023 appears to be following aspects of the 1997/98 El Niño event (Bell and Halpert 1998), which produced record-high tropospheric temperatures (Figs. 2.10, 2.11). Twenty-five years later, 1998 still ranks as one of the 10 warmest years in most tropospheric temperature datasets. In both 1997 and 2023, El Niño conditions were established by the early summer and strengthened through December. Since there is generally a three- to five-month lag between the warm sea-surface temperatures that accompany an El Niño event and tropical and global tropospheric temperature, record tropospheric warmth occurred in 1998 and, similarly, will likely continue to persist into 2024.

## 6. STRATOSPHERIC TEMPERATURE

—W. J. Randel, C. Covey, L. Polvani, and A. K. Steiner

Global mean temperatures in the lower, middle, and upper stratosphere increased slightly during 2023, mainly reflecting a recovery from anomalous cooling due to the Hunga Tonga–Hunga Haʻapai (HTHH) volcanic eruption in early 2022 (Davis et al. 2023). The long-term trends, however, show multi-decadal cooling of the stratosphere due to anthropogenic carbon dioxide (CO₂) increases and ozone (O₃) loss. The Arctic stratospheric polar vortex was disturbed by a major stratospheric warming in February 2023, while the Antarctic polar vortex was strong and persistent during the winter and

<caption>Fig. 2.11. Monthly average global lower tropospheric temperature (LTT) anomalies (°C) for (a) radiosonde, (b) satellite, and (c) reanalysis datasets. Annual averages are displayed for the RATPAC-A dataset. Anomalies are with respect to a 1991–2020 base period.</caption>
spring. The stratospheric quasi-biennial oscillation progressed normally in 2023, with equatorial easterly zonal wind shears and cold temperatures descending from the middle to lower stratosphere during the year.

Time series of global monthly temperature anomalies from the middle troposphere to the upper stratosphere based on satellite measurements are shown in Fig. 2.12. In addition to long-term stratospheric cooling and tropospheric warming due to greenhouse gas increases, transient variations arise from a variety of causes, including large volcanic eruptions (e.g., in 1982 and 1991), El Niño–Southern Oscillation (e.g., tropospheric warming events in 1997, 2016, and 2023), and large-scale wildfires (e.g., Australia in 2019/20). The middle stratosphere was anomalously cold in 2022 and early 2023 due to radiative effects of large water vapor (H₂O) anomalies injected by the January 2022 HTHH volcanic eruption (Millan et al. 2022; Wang et al. 2023; Davis et al. 2023; Flemming et al. 2024; Stocker et al. 2024). The HTHH stratospheric H₂O anomalies diffused and propagated upwards during 2023, resulting in smaller stratospheric radiative impacts and leading to a recovery from the anomalous cooling. The 11-year solar cycle was also increasing during 2023 (https://www.swpc.noaa.gov/products/solar-cycle-progression), which may contribute to slightly higher temperatures in the middle and upper stratosphere (Randel et al. 2009).

<figure><img src="image_56.png" /></figure>

Fig. 2.12. Monthly global temperature anomalies (°C) from the middle troposphere to upper stratosphere (bottom to top). Middle and upper stratosphere data are from the Stratospheric Sounding Unit (SSU) updated with microwave measurements, representing thick-layer averages centered near 30 km, 38 km, and 45 km (SSU1, SSU2, and SSU3, respectively). Lower-stratosphere temperatures (TLS) are ~13-km–22-km layer averages from satellite microwave measurements. Middle troposphere (TMT) data are ~0-km–10-km layer averages and are included for comparison. Satellite data sources and details are discussed in Steiner et al. (2020). Each time series has been normalized to zero for the period 1995–2005, and curves are offset for clarity.
## 1. PERMAFROST TEMPERATURE AND ACTIVE-LAYER THICKNESS

—J. Noetzli, H. H. Christiansen, M. Guglielmin, F. Hrbáček, G. Hu, K. Isaksen, F. Magnin, P. Pogliotti, S. L. Smith, L. Zhao, and D. A. Streletskiy

In recent decades, permafrost in cold regions worldwide have undergone widespread and persistent change, but the process is mostly slow and not directly visible. Permafrost is ground material with a maximum temperature of 0°C for at least two consecutive years. Its strongest warming was observed in cold high-latitude and high-elevation permafrost, where decadal rates of permafrost temperature increase by up to 1.0°C decade⁻¹ at the depth of zero annual amplitude (DZAA, the depth where annual temperature fluctuations become negligible; e.g., Smith et al. 2023; Noetzli et al. 2021; Smith et al. 2022; Etzelmüller et al. 2023; Magnin et al. 2023; PERMOS 2023). In ice-bearing ground in warmer permafrost regions, latent heat effects due to phase change can significantly reduce temperature changes to below 0.1°C decade⁻¹. The layer above the permafrost that thaws during summer is called the active layer. Its annual thickness (active-layer thickness; ALT) has generally increased in all regions as a result of higher air temperatures. ALT increased by a few centimeters per decade in continuous permafrost in the Arctic in sediments and by decimeters per decade in discontinuous permafrost in bedrock in the Arctic, Antarctica, Scandinavia (e.g., Smith et al. 2022; Noetzli et al. 2023; section 5j), and the Qinghai-Tibet Plateau (QTP, Zhao 2024; Hu 2024). ALT changes of several meters were recorded during the past 20 years in the European Alps in bedrock as well as in degrading permafrost sites in talus slopes (e.g., PERMOS 2023, 2024; Magnin et al. 2023; Pogliotti et al. 2023).

Permafrost temperatures in 2023 were the highest on record for 9 of the 17 Arctic sites and higher than those in 2022 for most sites (see section 5j). However, for six sites in northwestern North America, permafrost temperatures were lower in 2023 compared to those in 2022, resulting from the delayed reaction at depth to the lower air temperatures during 2020–22. Similarly, the effect of higher air temperatures in 2023 is not yet observed at depths of 15 m–20 m. In the cold permafrost in Svalbard, the ground temperatures at 20 m were the fifth highest on record in 2023, based on measurements since 1999. Permafrost temperature at 10 m increased only slightly compared to 2022 and 2021 (Fig. 2.13, Janssonhaugen) and was still above the long-term average.

Active-layer thickness observed in 2023 in the Arctic differs between regions (Fig. 2.14; see section 5j): In high-Arctic Svalbard, record ALTs were documented after the record-warm

summer of 2023 (see section 5b) for all sites, with values in bedrock of up to nearly 5 m in extreme cases. In 2023, above-average values were measured in Greenland and at sites from the Barents Sea region to West Siberia. In Central, East Siberia, and Chukotka in the Russian Arctic, in Arctic Alaska, western Alaska, and Northwest Canada (in 2022), ALT was close to the long-term average. In North America, the largest positive ALT anomaly in 2023 was observed in interior Alaska.

Mountain permafrost temperatures near the DZAA in mainland Norway were the highest on record in 2023, meaning reported warming continues (Noetzli et al. 2023; Etzelmüller et al. 2023). In the European Alps, permafrost temperatures at 10-m and 20-m depth were at record levels, particularly for bedrock sites, due to two consecutive hot summers in 2022 and 2023 (as yet, the full effect of the 2023 summer heat cannot be observed at greater depth). In contrast, permafrost temperatures decreased at 10-m depth for several rock glacier stations in

<figure><img src="image_57.png" /><figcaption>Fig. 2.13. Mean annual ground temperatures (°C) measured in European permafrost boreholes in the Alps, Scandinavia, and Svalbard at a depth of ca. 10 m. Maximum values for each time series are highlighted by a square. (Data sources: Norway: Norwegian Meteorological Institute and the Norwegian Permafrost Database [NORPERM]; Switzerland: Swiss Permafrost Monitoring Network [PERMOS]; France: Magnin et al. 2023; Italy: Pogliotti et al. 2023 and Guglielmin, M. unpublished data.)</figcaption></figure>
<figure><img src="image_58.png" /><figcaption>Fig. 2.14. Standardized active-layer thickness (ALT) index relative to 2000–20. (a) Arctic regions: Beaufort Chukchi Sea–Arctic Alaska and Mackenzie Delta region (BCS), Interior Alaska and central Mackenzie Valley, Northwest Territories (IAK_CMV), Barents Sea region–West Siberia (BAR_WS), Central Siberia (CENT_SIB), East Siberia (EAST_SIB); (b) Mountain regions: Norwegian mountains (MNT_NOR), Swiss Alps (MNT_SWI), Qinghai–Tibet Plateau (MNT_QTP); and (c) Antarctic: southern Victoria Land (ANT_SVL), Antarctic Peninsula (ANT_PEN), East Antarctic (ANT_EAST). (Source: Circumpolar Active Layer Monitoring [CALM].)</figcaption></figure>

2023 after a snow-poor winter (Fig. 2.13; PERMOS 2024). The ALT for 2023 at the majority of sites in the Norwegian mountains and in the European Alps were at or close to their previous maximum, or set a new maximum (Fig. 2.14). Degraded permafrost in the upper part of the ground can be observed at several sites in Europe, e.g., by talik formation or active layers that no longer freeze during winter (Etzelmüller 2023; PERMOS 2023, 2024).

Permafrost temperatures at depths of 10 m and 20 m at six sites in the QTP in central Asia (Kunlun mountain pass to Liangdaohe) warmed significantly between 2005 and 2022, with many record values observed in 2021 (Fig. 2.15). For ALT in this region, a large increase was observed at 10 sites from 1981 to 2022 (Fig. 2.14), associated with a significant increase in air temperature.

Active-layer thickness in the Antarctic Peninsula region has increased since 2014, with the 2023 value being the maximum for 2006–23 (Fig. 2.14). Permafrost temperatures at DZAA at Rothera Station and Signy Island have remained stable since 2013 (Grifoni et al., accepted). In East Antarctica and Victoria Land, ALT remains relatively stable without clear detectable trends (Hrbáček et al. 2023).

Permafrost observation relies on field measurements at the national or institutional level and is globally collected in the framework of the Global Terrestrial Network for Permafrost (GTN-P; Streletskiy et al. 2021) as an essential climate variable of the Global Climate Observation System. The global coverage of permafrost monitoring sites is

sparse and is mainly available in the Northern Hemisphere. Coverage is particularly limited in regions such as Siberia, central Canada, Antarctica, and the mountains in Central Asia, the Himalayas, and the Andes.

<figure><img src="image_59.png" /><figcaption>Fig. 2.15. Ground temperatures (°C) measured at 10-m depth in the Qinghai–Tibet Plateau during the period 2005–22. (Source: Cryosphere Research Station on Qinghai–Xizang Plateau, Chinese Academy of Sciences.)</figcaption></figure>

## 2. ROCK GLACIER VELOCITY

— C. Pellet, X. Bodin, D. Cusicanqui, R. Delaloye, A. Kääb, V. Kaufmann, E. Thibert, S. Vivero, and A. Kellerer-Pirklbauer

Rock glaciers are debris landforms generated by the creep of perennially frozen ground (permafrost) whose velocity changes are indicative of changes in the thermal state of permafrost and associated ground hydrological changes (i.e., increasing temperatures lead to increase in velocity and vice-versa; RGIK 2023a; Staub et al. 2016). Rock glacier velocity (RGV) is a time
series of annualized surface velocity values measured/computed on a rock glacier or a part of it (RGIK 2023b). Rock glacier velocities observed in different mountain ranges worldwide have been increasing since the 1950s, with large regional and inter-annual variability. These changes are consistent with the evolution of permafrost temperatures (section 2c1).

Although the hydrological year 2023 (October 2022 to September 2023) was the warmest on record in the European Alps (Fig. 2.16a), RGVs slightly increased in the western part of the Alps and continued to decrease in the east. Compared to 2022, velocity increased in the French Alps (+4% at Laurichard) and western Swiss Alps (+11% at Grosses Gufer and +15% at Gemmi/Furggentälti), whereas velocities continued to decrease in the Austrian Alps (-8% at Dösen and -22% at Hinteres Langtalkar; Fig. 2.16b). These regional evolutions are consistent with different snow conditions, namely exceptionally late onset of the snow cover and low snow depth in the east, which enabled marked cooling of the ground (as confirmed by the permafrost temperature decrease at 10-m depth observed on rock glacier Murtèl-Corvatsch in eastern Switzerland, Fig. 2.16). In the west, slightly later-than-average onset of the snow cover and slightly below-average snow depth were observed (PERMOS 2024). The reported RGV observations in 2023 in the European Alps are part of a general acceleration trend observed at all sites since the 1950s (Cusicanqui et al. 2021; Kellerer-Pirklbauer et al. 2024; PERMOS 2024).

In the Dry Andes in South America, RGVs reconstructed on three rock glaciers showed low velocities from 1950 to 2000, followed by a steady acceleration since the 2000s (Fig. 2.16c), consistent with the slight air temperature increase observed in the region since 1976 (Vivero et al. 2021). The potential effects of the above-average snow depth and longer snow cover duration in this region, associated with the strong El Niño event in 2023, have yet to be quantified.

Rock glacier velocities observed in Central Asia during the period of around 2018–23 show overall high values. Maximum velocities have been observed at Karakoram and Moreenny, and

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

Fig. 2.16. Rock glacier velocity and climate: (a) air and ground temperature (°C) in the European Alps, (b)–(d) rock glacier velocities (m yr⁻¹) at selected sites in the (b) European Alps, (c) Dry Andes (updated from Vivero et al. 2021), and (d) Central Asia (updated from Kääb et al. 2021). Rock glacier velocities are based on in situ geodetic surveys or photogrammetry in the context of long-term monitoring. In situ hydrological mean annual permafrost temperature measured at 10-m depth (blue line) at Murtèl Corvatsch (black triangle on Europe map) and air temperature: composite anomaly to the 1981–2010 base period (bars) and composite 20-year running mean (solid line) at Besse (FR), Grand Saint-Bernard (CH), Saentis (CH), Sonnblick (AT), and Zugspitze (D, black diamonds on Europe map). (Sources: Météo-France, Deutscher Wetterdienst [DWD], MeteoSwiss, GeoSphere Austria, Swiss Permafrost Monitoring Network [PERMOS], University of Fribourg, University of Graz, Graz University of Technology, Université Grenoble Alpes [INRAE], University of Oslo.)
velocities on Kugalan Tash and Ordzhonikidze remain at a high level, although velocity slightly decreased at the latter (Fig. 2.16d; Kääb et al. 2021). This evolution is consistent with increasing air temperatures reported in the region since 1900 (Azisov et al. 2022; Sorg et al. 2015) and with the RGV evolution reported in the European Alps and Dry Andes.

Rock glacier velocity refers to velocities related to permafrost creep, which is a generic term referring to the combination of both internal deformation within the crystalline structure of the frozen ground (creep stricto sensu) and shearing in one or more discrete layers at depth (shear horizon; RGIK 2023b). RGVs are mostly related to the evolution of ground temperature and liquid water content between the upper surface of permafrost (i.e., permafrost table) and the layer at depth of the shear horizon (Cicoira et al. 2019; Frauenfelder et al. 2003; Kenner et al. 2017; Staub et al. 2016). Despite variable size, morphology, topographical and geological settings, and velocity ranges, consistent regional RGV evolutions have been highlighted in several studies (e.g., Pellet et al. 2023; Kellerer-Pirklbauer et al. 2024). Multi-annual long-term RGV time series are reconstructed using repeated aerial or optical satellite images. Horizontal displacements are computed based on cross-correlation feature tracking on multi-temporal ortho-images or digital elevation model matching (Kääb et al. 2021; Vivero et al. 2021). The resulting accuracy strongly depends on the spatial resolution of the images and on the image quality (i.e., presence of snow and shadows). Surface displacements are averaged for a cluster of points/pixels selected within areas considered as representative of the downslope movement of the rock glacier (RGIK 2023b). Annual rock glacier velocities are commonly measured using terrestrial geodetic surveys performed each year at the same time (usually at the end of summer). The positions of selected boulders (10–100 per landform) are measured with an average accuracy in the range of mm to cm (Lambiel and Delaloye 2004; Kellerer-Pirklbauer et al. 2024; PERMOS 2024; Thibert and Bodin 2022).

### 3. ALPINE GLACIERS

—M. S. Pelto

Mountain-region (i.e., alpine) glacier annual mass balance (sum of accumulation and ablation) observations are reported to the World Glacier Monitoring Service (WGMS). The WGMS reference glaciers each have at least 30 continuous years of mass balance observation, and benchmark glaciers have at least a 10-year mass balance record and are in regions that lack sufficient reference glaciers. In 2023, all 35 reporting reference glaciers had a negative balance, along with all 18 benchmark glaciers. This is the first year that all reference glaciers have

had a negative balance. The 2023 dataset includes 109 glaciers from six continents, with 108 having a negative balance and 1 glacier reporting a positive mass balance. This makes 2023 the 36th consecutive year with a global alpine glacier mass balance loss, the 15th consecutive year with a mean global mass balance below –500 mm water equivalent (w.e.), and the year with the highest ratio of negative-to-positive mass balance observations of any year in the record (Fig. 2.17).

The combination of benchmark and reference glaciers is used to generate regional averages (WGMS 2023). Global values are calculated using a single averaged value for each of 19 mountain regions, limiting bias towards well-observed regions (WGMS 2023). In 2023, the mean annual mass balance of the 35 reference glaciers was –1568 mm w.e., and –1590 mm w.e. for all 109 reporting glaciers regardless of record length. In a similar result, 2022 mean annual mass balance was –1475 mm w.e. for 37 reporting reference glaciers and –1568 mm w.e. for all 116 reporting glaciers. The regionally averaged global mass balance was –1090 mm w.e. in 2022; a final value for 2023 has not yet been determined, but the preliminary value is –1219 mm w.e.

The result of the melt in several regions has been an increasing complete loss of glaciers (see below; Huss and Fischer 2016; Fountain et al. 2023). This led to the Global Land Ice Measurements

<figure><img src="image_61.png" /><figcaption>Fig. 2.17. Time series of global mean annual glacier mass balance (mm w.e.) of alpine glaciers from 1970 to 2023 as determined by the World Glacier Monitoring Service, using 19 regional averages from 53 glaciers in total.</figcaption></figure>
from Space (GLIMS) initiative adding an extinct glacier layer to its Glacier Viewer in 2023 to indicate glaciers that have been lost (GLIMS 2023). The sustained mass balance loss this century is also reducing the drought-buffering capacity of alpine glaciers in most midlatitude mountain ranges (Ultee et al. 2022). In 2023, we continued to see many glaciers across the globe with minimal to no retained snow cover, leading to surface darkening and even greater mass losses (Fig. 2.18).

In the European Alps, all 21 reporting glaciers had annual mass balances below -1300 mm w.e., with an average of -2311 mm w.e. In the Pyrenees, mass balances were also strongly negative. The combination of a snow drought and warm summer temperatures led to this sharp loss in glacier volume.

In High Mountain Asia, 22 of 23 glaciers had negative mass balances across seven nations: China, India, Kazakhstan, Kyrgyzstan, Nepal, Tajikistan, and Uzbekistan. The mean annual mass balance was -1048 mm w.e.

In North America, all 16 glaciers outside of the Arctic had negative mass balances averaging -2700 mm w.e. The combination of limited winter snowpack and a prolonged ablation season across the Pacific Northwest was the most significant contributor to this loss.

In South America, all 10 glaciers had significant negative annual mass balance averaging -1715 mm w.e. Continued drought in the central Andes and a warm melt season across the entire region led to negative mass balances from Ecuador southward to Argentina and Chile. In the central Andes, many glaciers from 32°S to 36°S lost all snow cover.

In Sweden and Norway, all 14 glaciers had negative annual mass balances averaging -1364 mm w.e. Across the Arctic in the Canadian Arctic Islands, Iceland, and Svalbard, all 19 glaciers had negative mass balances averaging -976 mm w.e. (see section 5h).

The rapid volume loss from 2021 to 2023 led to the complete loss of two glaciers in the WGMS mass balance dataset: St. Anna Glacier, Switzerland (reported 2011-23), and Ice Worm Glacier in the United States (reported 1984-2023). These glaciers are indicative of the increasing rate of glacier disappearance.

<figure><img src="image_62.png" /><figcaption>Fig. 2.18. Many alpine glaciers across the globe lost all or nearly all their snow cover in 2023, as illustrated by (a) the 63°S Antarctic Peninsula region, (b) the Andes, (c) North Cascades, and (d) 53°N in the Canadian Rockies. To be in equilibrium, a glacier needs to have at least 50% of its area snow covered throughout the year.</figcaption></figure>
## 4. LAKE ICE

—S. Sharma, R. I. Woolway, and J. Culpepper

In the 2022/23 winter season (November 2022–April 2023), lake ice phenology (timing of ice-on and ice-off) across the Northern Hemisphere (NH) generally revealed that some lakes had later-than-normal ice-on dates and earlier-than-normal ice-off dates, although most lakes had shorter-than-normal seasonal ice cover. Notably, in situ phenological records revealed that ice-off dates were later for over half (55.8%) of the studied lakes, although the ice season remained shorter.

Across the NH, based on the ERA5 reanalysis data, lakes froze on average four days later and thawed five days earlier, with ice duration nine days shorter relative to the 1991–2020 base period (Fig. 2.19). The ice-on date was the third latest, the ice-off date was the third earliest, and the duration of lake ice cover was the fifth shortest since the start of the record in 1980.

Further, in situ lake ice observations from 157 lakes revealed that, on average, during the 2022/23 winter, ice-on was 2.1 days later, ice-off was 1.6 days later, and ice duration was 1.7 days

<figure><img src="image_63.png" /></figure>

Fig. 2.19. Anomalies (days) for (a) the start of ice cover, (b) end of ice cover, and (c) duration of ice cover for lakes across the Northern Hemisphere (NH), with negative (positive) values being earlier (later) in the year. (d) Surface air temperature anomalies (°C) for the NH cold-season (Nov–Apr average) in 2023. The base period is 1991–2020. (Sources: ERA5, GISTEMPv4.)
shorter relative to the 1991–2020 base period (Fig. 2.20). Notably, Lake Suwa in Japan froze on 26 January 2023, marking the third year in a row this lake froze; three such consecutive freezes have not occurred in at least the past decade. In the last several decades, it has become increasingly rare for Lake Suwa to freeze.

Mountain lakes continue to be underrepresented in global studies of ice phenology (Christianson et al. 2021), owing to challenges in obtaining data and unsafe winter conditions (Block et al. 2019). During winter 2023, the 18 mountain lakes in our dataset (>1000 m a.s.l.) froze 7.8 days later and thawed 3.2 days later on average. Low-temperature anomalies during this winter likely led to later breakup (Fig. 2.20) as well as increased snowfall in the western United States from nine atmospheric rivers throughout December 2022 and January 2023 that impacted the area (NOAA/NCEI 2023c). Castle Lake in northern California, for example, broke up 34.4 days later than its 32-year mean. Despite the overall later breakup dates, ice cover duration continued to show signs of decline, with 10 mountain lakes having shorter ice duration and one lake having a near-zero anomaly. The continued decline in ice-cover duration suggests that generally later formation counteracts the later breakup date.

In North America, the Laurentian Great Lakes had 24.1% less maximal ice coverage during the 2022/23 winter, relative to the winters of 1991–2020. Both Lakes Erie and Superior had approximately 35% less ice coverage in 2023, followed by Lakes Huron (25.5%), Michigan (15%), and Ontario (9.8%; Fig. 2.21). Ice coverage was highest on 4 February—20 days earlier than average—across all of the Great Lakes.

The ERA5 reanalysis product (Hersbach et al. 2020) was used to calculate ice-on and ice-off dates, in addition to ice-duration dates across NH lakes, following the methodology of Grant et al. (2021). Many citizen scientists, in addition

to established monitoring networks, contributed in situ observations for 157 lakes across Canada, the United States, Norway, Finland, and Japan. Citizen scientist networks have been instrumental in sharing their local ice records and can offer extensive, efficient, and cost-effective local in situ environmental monitoring across vast spatial and temporal scales (Fritz et al. 2019; Lopez et al. in press). Furthermore, in situ ice phenology data for eight mountain lakes in the United States and 10 lakes in Europe were obtained and updated through personal correspondence with the data authors (Caine et al. 2023; Chandra et al. 2022; Kainz et al. 2017). Annual maximum ice coverage (%) data for each of the Laurentian Great Lakes were acquired for the period 1973–2023 from the NOAA Great Lakes Environmental Research Laboratory, in addition to the surface air temperature data for the NH cold season (November–April average) from the NASA Goddard Institute for Space Studies’ surface temperature analysis (GISTEMP Team 2024). Anomalies for each of our ice metrics were calculated for the 2022/23 winter relative to the 1991–2020 normal base period.

<figure><img src="image_64.png" /><figcaption>Fig. 2.20. Anomalies (days) in the timing of (a) ice-on, (b) ice-off, and (c) ice duration from 1980 to 2023 derived from ERA5 reanalysis, in situ observations, and mountain lakes. Base period is 1991–2020.</figcaption></figure>

<figure><img src="image_65.png" /><figcaption>Fig. 2.21. Anomalies in the Laurentian Great Lakes maximum ice cover extent (%) for the period 1973–2023 (base period is 1991–2020) for individual lakes (Erie, Huron, Michigan, Ontario, Superior) and the Great Lakes average.</figcaption></figure>
# 5. NORTHERN HEMISPHERE CONTINENTAL SNOW COVER EXTENT

—D. A. Robinson and T. W. Estilow

Annual snow cover extent (SCE) over NH lands averaged 24.3 million km² in 2023. This was 0.8 million km² (3.2%) below the full period-of-record (November 1966–December 2023) mean, marking the seventh-least-extensive cover on record (Table 2.4; Fig. 2.22a). Monthly SCE in 2023 ranged from a maximum of 47.1 million km² in January to a minimum of 2.9 million km² in August.

Northern Hemisphere SCE in January and March ranked in the lower tercile of the 57-year record (1.8% and 2.8% below normal,

Fig. 2.22. (a) Twelve-month running anomalies of monthly snow cover extent (SCE; x 10⁶ km², or million km²) over Northern Hemisphere (NH, black) lands as a whole and Eurasia (red) and North America (blue) separately plotted on the seventh month using values from Nov 1966 to Dec 2023. Anomalies relative to the full period are calculated from NOAA snow maps. Mean NH SCE is 25.1 million km² for the full period of record. Monthly means for the period of record are used for nine missing months during 1968, 1969, and 1971 to create a continuous series of running means. Missing months fall between Jun and Oct. (b) Weekly NH SCE time series (x 10⁶ km²) for 2023 (black) plotted with the mean (gray dashed line), maximum (purple), and minimum (green) SCE for each week. Mean weekly SCE and extremes are calculated using the 57-year record from Jan 1967–Dec 2023. Weekly data granules represent SCE for each seven-day period ending on Monday.

<figure><img src="image_66.png" /></figure>

<figure><img src="image_67.png" /><figcaption>Table 2.4. Monthly and annual climatological information on Northern Hemisphere (NH), Eurasia (EUR), and North America (NA) snow cover extent (SCE) between Nov 1966 and Dec 2023. Included are the numbers of years with data used in the calculations, NH anomalies, NH means (Nov 1966–Dec 2023), standard deviations (Nov 1966–Dec 2023), 2023 values, and rankings (highest and lowest). Areas are in millions (x 10⁶) of square kilometers (km²). The years 1968, 1969, and 1971 have one, five, and three missing months, respectively, thus are not included in the annual calculations. NA includes Greenland. Ranks are from most (1) to least extensive (least to most in parentheses)</figcaption></figure>

<table><thead><tr><th>Month</th><th>Yrs</th><th>2023 NH<br>Anomaly<br>(x 10<sup>6</sup> km<sup>2</sup>)</th><th>NH Mean<br>(x 10<sup>6</sup> km<sup>2</sup>)</th><th>NH Std.<br>Dev.</th><th>2023 NH rank</th><th>2023 EUR rank</th><th>2023 NA rank</th></tr></thead><tbody><tr><td>Jan</td><td>57</td><td>-0.9</td><td>47.1</td><td>1.5</td><td>41 (17)</td><td>48 (10)</td><td>27 (31)</td></tr><tr><td>Feb</td><td>57</td><td>-0.4</td><td>45.9</td><td>1.8</td><td>32 (26)</td><td>37 (21)</td><td>24 (34)</td></tr><tr><td>Mar</td><td>57</td><td>-1.1</td><td>40.4</td><td>1.8</td><td>43 (15)</td><td>52 (6)</td><td>4 (54)</td></tr><tr><td>Apr</td><td>57</td><td>-0.2</td><td>30.5</td><td>1.6</td><td>28 (30)</td><td>43 (15)</td><td>16 (42)</td></tr><tr><td>May</td><td>57</td><td>-2.3</td><td>19.0</td><td>2.0</td><td>50 (8)</td><td>35 (23)</td><td>57 (1)</td></tr><tr><td>Jun</td><td>56</td><td>-3.3</td><td>9.3</td><td>2.5</td><td>50 (7)</td><td>46 (11)</td><td>53 (4)</td></tr><tr><td>Jul</td><td>54</td><td>-1.0</td><td>3.8</td><td>1.2</td><td>44 (11)</td><td>42 (13)</td><td>41 (14)</td></tr><tr><td>Aug</td><td>55</td><td>-0.4</td><td>2.9</td><td>0.7</td><td>39 (17)</td><td>39 (17)</td><td>29 (27)</td></tr><tr><td>Sep</td><td>55</td><td>-0.1</td><td>5.4</td><td>0.9</td><td>28 (28)</td><td>22 (34)</td><td>33 (23)</td></tr><tr><td>Oct</td><td>56</td><td>+0.2</td><td>18.6</td><td>2.6</td><td>25 (32)</td><td>24 (33)</td><td>28 (29)</td></tr><tr><td>Nov</td><td>58</td><td>+1.2</td><td>34.4</td><td>2.1</td><td>17 (42)</td><td>16 (43)</td><td>28 (31)</td></tr><tr><td>Dec</td><td>58</td><td>-1.2</td><td>43.7</td><td>1.8</td><td>48 (11)</td><td>18 (41)</td><td>56 (3)</td></tr><tr><td><strong>Annual<br>Calculations</strong></td><td><strong>54</strong></td><td><strong>-0.8</strong></td><td><strong>25.1</strong></td><td><strong>0.8</strong></td><td><strong>48 (7)</strong></td><td><strong>45 (10)</strong></td><td><strong>45 (10)</strong></td></tr></tbody></table>
respectively; Fig. 2.22b), while February (–0.9%) and April (–0.7%) were in the middle. North America (NA) had more extensive snow cover compared to normal than Eurasia (EUR) during each of these four months. In particular, NA had its fourth-most-extensive SCE in March (+7.6%) and its 16th most extensive in April (+4.8%). Thereafter, melt quickly occurred across NA, with May having its least-extensive SCE of the satellite era (–19.5%). Both continents contributed to June having the sixth-least SCE across the NH (–35.6%; Fig 2.23). Across the NH, September and October began the new snow season with SCE in the middle tercile (–1.0% and +0.9%, respectively). SCE was above normal for both continents in November (+3.6%; Fig 2.23). While SCE remained above normal over EUR in December (+2.1%), SCE in NA declined to its third least extensive for the month (–10.8%), contributing to the seventh-least-extensive SCE overall for the NH (–2.8%).

The contiguous United States' (US) SCE (not shown) was close to normal at the beginning of 2023, then became well above normal in March (+45.6%; fourth most extensive) and April (+62.3%, seventh most extensive). In May, the US SCE was below average (–17.6%) while Canadian SCE (not shown) was the lowest on record (–30.7%). Autumn snow cover began early over the US and was the 11th most extensive on record for October (+68.6%), but for the remainder of the year, US SCE was below normal, with December having the sixth-least-extensive SCE on record (–43.4%).

SCE is calculated at the Rutgers Global Snow Lab (GSL) from daily SCE maps produced by meteorologists at the US National Ice Center, who rely primarily on visible satellite imagery to construct the maps (Estilow et al. 2015). Maps depicting daily, weekly, and monthly conditions, anomalies, and climatologies may be viewed at the GSL website (https://snowcover.org).

<figure><img src="image_68.png" /><figcaption>Fig. 2.23. Monthly snow cover extent (SCE) departure (%) maps for (a) Jun and (b) Nov 2023. Jun exhibited the lowest SCE anomaly (–2.17 million km²) during 2023, while Nov was the highest above normal (+0.53 million km²). Mean monthly SCE calculated using the 30-yr span from 1991–2020. Negative departures indicate less SCE than normal (green) with positive departures (purple) showing areas of SCE above the 30-yr mean.</figcaption></figure>
### d. Hydrological cycle—atmosphere

#### 1. SURFACE HUMIDITY

—K. M. Willett, A. J. Simmons, M. Bosilovich, and D. A. Lavers

The near-surface atmosphere had record or near-record water vapor content for 2023, with global specific humidity (q) anomalies reaching 0.17 g kg⁻¹–0.42 g kg⁻¹ across the various datasets (Figs. 2.24a–d; Table 2.5). These anomalies exceeded 2022 q levels by a large margin of 0.14 g kg⁻¹ to 0.28 g kg⁻¹. As has been the case since 2011, saturation (relative humidity, RH) remained below average over land, being –0.46%rh to –1.05%rh across datasets. This decline reflects the fact that the temperature over land is rising so quickly that the water-holding capacity is outpacing the actual water vapor content, which is governed largely by local water availability and slower ocean warming rates. Relative humidity over oceans was close to average in 2023, within ±0.1%rh.

Global annual mean q anomalies (relative to 1991–2020; Figs. 2.24a–d; Table 2.5) from HadISDH, MERRA2, JRA-55, and the new JRA-3Q surpassed previous records, with HadISDH and MERRA2 reaching 0.33 g kg⁻¹ over land and 0.4 g kg⁻¹ and 0.42 g kg⁻¹, respectively, over ocean. Years of previous records differed among datasets. ERA5 had similarly large increases from 2022 (Table 2.5) and record-high q over ocean, reaching 0.24 g kg⁻¹. Masking to HadISDH coverage resulted in higher 2023 anomalies, especially over ocean where HadISDH spatial coverage is very limited. Global annual mean anomalies of RH (Figs. 2.24.e–h; Table 2.5) were lower than those of 2022 over land by between 0.12%rh for ERA5 to 0.32%rh for JRA-3Q. JRA-55 had 2023 as

<table><caption>Table 2.5. Global mean surface-specific (q) and relative humidity (RH) anomalies for 2023 and comparison with previous values. Note that no previous record is reported for ocean RH because a long-term trend has not been robustly established. RH values for MERRA-2 are not included in this report. Values in bold type identify new records.</caption><thead><tr><th>Dataset</th><th>q (g kg<sup>-1</sup>)<br>2023 global mean<br>anomaly</th><th>q (g kg<sup>-1</sup>)<br>2022 global mean<br>anomaly</th><th>q (g kg<sup>-1</sup>)<br>Previous record<br>high (year of<br>previous record)</th><th>RH (%rh)<br>2023 global mean<br>anomaly</th><th>RH (%rh)<br>2022 global mean<br>anomaly</th><th>RH (%rh)<br>Previous record<br>low (year of<br>previous record)</th></tr></thead><tbody><tr><td>HadISDH.land</td><td>0.33</td><td>0.14</td><td>0.27<br>(1998)</td><td>-0.46</td><td>-0.24</td><td>-0.79<br>(2019)</td></tr><tr><td>ERA5 over land</td><td>0.17</td><td>-0.01</td><td>0.21<br>(2016)</td><td>-1.05</td><td>-0.93</td><td>-1.32<br>(2021)</td></tr><tr><td>ERA5 over land<br>masked</td><td>0.22</td><td>0.02</td><td>0.25<br>(2016)</td><td>-1.01</td><td>-0.88</td><td>-1.28<br>(2021)</td></tr><tr><td>MERRA-2<br>over land</td><td>0.33</td><td>0.19</td><td>0.27<br>(2020)</td><td>--</td><td>--</td><td>--</td></tr><tr><td>JRA-55<br>over land</td><td>0.25</td><td>0.06</td><td>0.21<br>(2016)</td><td>-0.87</td><td>-0.62</td><td>-0.83<br>(2021)</td></tr><tr><td>JRA-3Q<br>over land</td><td>0.26</td><td>0.08</td><td>0.21<br>(1998/2016)</td><td>-0.91</td><td>-0.59</td><td>-0.93<br>(2021)</td></tr><tr><td>HadISDH.marine</td><td>0.4</td><td>0.12</td><td>0.27<br>(2020)</td><td>0.06</td><td>-0.23</td><td>--</td></tr><tr><td>ERA5 over ocean</td><td>0.24</td><td>0.03</td><td>0.20<br>(2019)</td><td>-0.08</td><td>-0.12</td><td>--</td></tr><tr><td>ERA5 over ocean<br>masked</td><td>0.40</td><td>0.19</td><td>0.34<br>(2016)</td><td>-0.12</td><td>-0.06</td><td>--</td></tr><tr><td>MERRA-2 over<br>ocean</td><td>0.42</td><td>0.18</td><td>0.25<br>(2019)</td><td>--</td><td>--</td><td>--</td></tr><tr><td>JRA-55<br>over ocean</td><td>0.34</td><td>0.09</td><td>0.19<br>(2016/2020)</td><td>0.25</td><td>0.21</td><td>--</td></tr><tr><td>JRA-3Q<br>over ocean</td><td>0.34</td><td>0.09</td><td>0.19<br>(2020)</td><td>0.10</td><td>0.10</td><td>--</td></tr></tbody></table>
record low for land RH but by a very small margin (0.04%rh). Over ocean, where agreement between datasets is much poorer, 2023 values were only slightly higher than in 2022 for ERA5 and JRA-55 (+0.04%rh and +0.03%rh), identical for JRA-3Q, and much larger for HadISDH.marine (+0.29%rh). ERA5 2023 anomalies were slightly drier than average (-0.08%rh) whereas the other datasets were above average (0.06%rh to 0.25%rh). Masking ERA5 to HadISDH coverage resulted in even drier anomalies.

The increases in q relative to 2022 were characteristic of El Niño—this can be seen clearly for 1998, 2010, and 2016 in Figs. 2.24a–d. The La Niña during 2021 and 2022 also likely contributed through its tendency to depress the near-surface water content. Plate 2.1g shows the largest positive q anomalies lying mostly within ±30° latitude over typical El Niño-related wet regions to a large degree. For example, the southern United States, northwestern and southeastern South America, east Africa, and eastern China are broadly positive; the January to December averaging likely dampens these seasonal-scale anomalies. India, Southeast Asia, and northern Australia also show strong positive q anomalies despite El Niño favoring dry conditions over these regions. Over oceans, strong positive q anomalies were present over the typical El Niño–Southern Oscillation (ENSO) region in the tropical Pacific and also over the North Pacific, tropical North Atlantic, and southern Indian Ocean. The latter is associated with the positive phase of the Indian Ocean dipole. These features correspond well to anomalously warm sea-surface temperatures (Plate 2.1a). Figure 2.25a shows that the temporal and latitudinal extent of positive q anomalies in 2023 were unusual in historical context. It also shows that although long-term trends (Fig. 2.25b) are positive over the entire Northern Hemisphere and tropics, there is considerable intra- and inter-annual variability in addition to latitudinal variability.

For RH, the fingerprint of ENSO was less clear in the global and latitudinal mean time series (Figs. 2.24e–h; 2.25c) and the 2023 annual anomaly map (Plate 2.1h). Dry anomalies dominated

<figure><img src="image_69.png" /></figure>

Fig. 2.24. Global average surface humidity annual anomalies (g kg⁻¹ for [a]–[d] and %rh for [e]–[h]; 1991–2020 base period). For the in situ datasets, 2-m surface humidity is used over land and ~10-m surface humidity is used over the oceans. For the reanalysis, 2-m humidity is used over the whole globe. For ERA5, ocean series-only points over open sea are selected. ERA5 mask is a version of ERA5 limited to the spatial coverage of HadISDH. Two-sigma uncertainty is shown for HadISDH, capturing the observation, gridbox sampling, and spatial coverage uncertainty. (Sources: HadISDH [Willett et al. 2013, 2014, 2020]; ERA5 [Hersbach et al. 2020]; JRA-55 [Kobayashi et al. 2015]; JRA-3Q [Kosaka et al., 2024]; MERRA-2 [Gelaro et al. 2017].)
over land, despite the strong positive anomalies over the western United States, northwest India/ Pakistan, and northern Australia. Dry anomalies were broadly similarly located compared to 2022. Over ocean, the Indian Ocean dipole is apparent with positive RH anomalies spatially matching positive q anomalies. The eastern tropical Pacific had a band of strong negative RH anomalies despite this being a region of strong positive q anomalies, indicating that the warming—and thus water-holding capacity—here (see Plate 2.1a) outpaced the actual water vapor increase.

The lack of in situ data, particularly over oceans, continues to limit our ability to robustly monitor near-surface humidity. The spatial coverage from HadISDH in the Southern Hemisphere is poor, especially over ocean. Figure 2.24 includes ERA5 masked to the lower coverage of HadISDH. This shows improved agreement and that the more positive q anomalies and less negative RH anomalies in HadISDH are partly artifacts of HadISDH undersampling regions where, according to ERA5, drying is stronger. The lower anomalies in ERA5 from 2020 onwards remain substantially lower than in HadISDH and other reanalyses in the masked version, suggesting that this feature is not related to coverage differences. ERA5 suffers from in situ data gaps similar to HadISDH but uses information from satellites and the background model to derive estimates for these regions. All datasets have their own strengths and weaknesses.

<figure><img src="image_70.png" /><figcaption>Fig. 2.25. Latitudinal monthly mean anomalies of (a) specific humidity (g kg⁻¹) and (c) relative humidity (%rh; from HadISDH.blend). (b),(d) Decadal trends for each gridbox (dots) and latitude band mean (line), fitted using an ordinary least-squares linear regression following Santer et al. (2008), with gray shading representing the percentage of globe covered by observations (in gridboxes) at each latitude band. Latitude band means are only calculated where there are at least five gridboxes (5° × 5°) at that latitude band.</figcaption></figure>
## 2. HUMID-HEAT EXTREMES OVER LAND

—K. M. Willett, R. M. Horton, Y. T. E. Lo, C. Raymond, and C. D. W. Rogers

The year 2023 emerged as a record-breaking year by a considerable margin for humid heat over global land, based on daily maximum wet-bulb temperatures ($T_w$X), for all but one ($T_w$X31) of the six indices presented herein (see Table 2.6 for definitions of these indices). Humid-heat intensity, measured as the annual anomaly of $T_w$X, was 0.6°C above the 1991–2020 average (Fig. 2.26a), doubling the previous record of 0.3°C in 1998. Humid-heat frequency also increased by a large margin. The annual $T_w$X90p anomaly (Fig. 2.26b), a measure of local extremes, was 26.4 days year–1 above average, far exceeding the previous record of 16.2 days year–1 in 1998.

Annual occurrence anomalies for $T_w$X25, $T_w$X27, and $T_w$X29 (Table 2.6) were 6.1, 9.3, and 1.3 days year–1 above average, respectively (Fig. 2.26c). These exceeded the previous records of 4.4, 6.7, and 0.9 days year–1 set in 2020 (equal with 2022 for $T_w$X25), respectively. For $T_w$X31, 2023 was equal with the previous record in 1998 at 0.2 days year–1.

Fig. 2.26. Global land mean annual anomaly time series of various daily maximum wet-bulb temperature indices from HadISDH/extremes relative to a 1991–2020 base period. Decadal trends (significant at p<0.01) are also shown. Trends were fitted using an ordinary least-squares linear regression with an autoregressive (1) correction following Santer et al. (2008). (a) Anomaly of the annual median of the monthly maximum wet-bulb temperature (°C). (b) Anomaly of the annual sum of the daily maximum wet-bulb temperature exceedances of the locally defined daily 90th percentile (days yr⁻¹). (c) Anomaly of the annual sums of the daily maximum wet-bulb temperature ≥ 25°C, 27°C, 29°C, and 31°C thresholds (days yr⁻¹). Note that coverage is skewed towards the northern extratropical latitudes with large data gaps over Africa and considerable gaps over South America, Australia, and parts of Central Asia (see Plates 2.1a and 2.1b for spatial coverage).

<figure><img src="image_71.png" /><figcaption>Table 2.6. Definitions of six humid-heat indices and their respective 2023 global land annual anomalies (1991–2020 base period). The 2023 global annual anomaly for the exceedance indices (not $T_w$X) is the sum of the monthly spatial mean over the globe. For $T_w$X, the median is used as a more robust measure in the presence of outliers, averaging first over space for each month and then over time.</figcaption></figure>

<table><thead><tr><td>Index</td><td>Description</td><td>Meaning</td><td>2023 Global Anomaly</td></tr></thead><tbody><tr><td>T<sub>w</sub>X</td><td>Annual median of monthly maximum wet-bulb temperature</td><td>Intensity of humid-heat extremes</td><td>0.6°C</td></tr><tr><td>T<sub>w</sub>X90p</td><td>Days per year exceeding the 90th percentile of the climatological daily maximum wet-bulb temperature (seasonally varying)</td><td>Frequency of humid-heat extremes relative to local climatology</td><td>26.4 days</td></tr><tr><td>T<sub>w</sub>X25</td><td>Days per year where the daily maximum wet-bulb temperature ≥25°C</td><td>Frequency of moderately high humid-heat extremes</td><td>6.1 days</td></tr><tr><td>T<sub>w</sub>X27</td><td>Days per year where the daily maximum wet-bulb temperature ≥27°C</td><td>Frequency of high humid-heat extremes</td><td>9.3 days</td></tr><tr><td>T<sub>w</sub>X29</td><td>Days per year where the daily maximum wet-bulb temperature ≥29°C</td><td>Frequency of very high humid-heat extremes</td><td>1.3 days</td></tr><tr><td>T<sub>w</sub>X31</td><td>Days per year where the daily maximum wet-bulb temperature ≥31°C</td><td>Frequency of severe humid-heat extremes</td><td>0.2 days</td></tr></tbody></table>
The exceptionally large humid-heat index anomalies are in concert with record anomalies across many other variables in 2023. El Niño, present from May onwards and becoming strong by autumn (see section 4b for details), contributed to record humid-heat anomalies by influencing both atmospheric and sea-surface temperatures. In conjunction with significant positive trends in humid-heat intensity and frequency (Fig. 2.26), these record anomalies are clear indicators of a rapidly changing climate that is impacting society across the globe. Humid heat presents challenges to maintaining comfortable and safe temperatures for humans (Saeed et al. 2022; Wolf et al. 2022; Kjellstrom et al. 2017) and other large mammals (Buzan and Huber 2020) as evaporative cooling (including sweating) is less efficient in hot and humid conditions (Baldwin et al. 2023).

Thresholds of 25°C, 27°C, 29°C, and 31°C T<sub>w</sub>X represent moderately high to severe humid-heat extremes. They may be exceeded during midlatitude warm seasons or year-round in the tropics but rarely or never at higher latitudes and elevations. Figure 2.27 shows 2023 anomalies for all indices in historical context, using deciles to identify “unusual” humid heat. Analysis excludes gridboxes with both no 2023 exceedances and <15 years with an exceedance within the 1991–2020 climatological period. “Very unusually high” (10th decile) occurrences for T<sub>w</sub>X25 and

<figure><img src="image_72.png" /><figcaption>Fig. 2.27. Humid-heat extremes of 2023 as deciles over the period 1973–2023 for various indices. Number of days in 2023 with maximum wet-bulb temperature (T<sub>w</sub>) ≥ (a) 25°C (T<sub>w</sub>X25), (b) 27°C (T<sub>w</sub>X27), (c) 29°C (T<sub>w</sub>X29), and (d) 31°C (T<sub>w</sub>X31). Gridboxes bounded in pink indicate that <15 years within the 1991–2020 period had an exceedance. These panels are annotated with the percentage of observed area where an exceedance occurred in 2023 and climatologically (≥15 years of at least one exceedance between 1991 and 2020). (e) Annual median anomaly of monthly maximum T<sub>w</sub> (T<sub>w</sub>X). (f) Number of days in 2023 with maximum T<sub>w</sub> > local daily 90th percentile (T<sub>w</sub>X90p) relative to a 1991–2020 base period. For (a)–(d) only, gridboxes bounded in pink indicate <15 years within the 1991–2020 period. For (e) and (f), the gray ‘never’ and ‘no 2023 exceedance’ categories are not relevant. Data have been screened to remove gridboxes where temporal completeness is less than 70% (<35 yrs in 51). All valid years have data present for all months.</figcaption></figure>
$T_w$X27 in 2023 (Figs. 2.27a,b) covered 20.4% and 28.0% of analysis-relevant gridboxes, respectively. This is in contrast to 9.6% and 10.4%, respectively, for those indices in the “no 2023 exceedance” and “very unusually low” (1st decile) categories combined. These “very unusually high” exceedances were mostly over the southeastern United States ($T_w$X27 only), eastern China, Japan, and Southeast Asia. In some higher latitudes (e.g., eastern North America, the Caspian Sea), exceedances were “unusually low” (2nd to 3rd deciles), pointing to the inherent interannual variability in rare events and potential differences between dry-bulb and wet-bulb measures of extremes. Sparse occurrences of $T_w$X29 and $T_w$X31 (Figs. 2.27c,d) with “very unusually high” frequencies covered 21.9% and 39.4% of analysis-relevant gridboxes, respectively. For $T_w$X29, these occurred over parts of the tropics, northern India, eastern China, and as far north as Japan and the central United States. “Very unusually high” $T_w$X31 occurrences were mostly outside the tropics, including over the Mediterranean, near Sicily, and the central United States. The Persian Gulf, a region with climatologically exceptionally high humid heat (Raymond et al. 2020), experienced “normal” (4th to 7th deciles) to “unusually high” (8th to 9th deciles) frequencies in 2023 for $T_w$X25 to $T_w$X31. For all absolute threshold indices ($T_w$X25 to $T_w$X31, Figs. 2.27a–d), 2023 saw a larger global land area experiencing ≥1 exceedance of +2.1 to +6.7 percent of gridboxes compared to the climatological mean.

The globally applicable measures of $T_w$X90p and $T_w$X (Plates 2.1i,j) had positive intensity and frequency anomalies over most of the observed land in 2023. “Unusually high” to “very unusually high” (8th to 10th deciles) local humid-heat intensity ($T_w$X) and frequency ($T_w$X90p) covered 57% and 53% of the observed land, respectively (Figs. 2.27e,f), with Europe and eastern Asia standing out. Few gridboxes had “unusually low” or “very unusually low” (1st to 3rd decile) intensities (2.5%) or frequencies (10%). Humid-heat events with notable impacts included those near Rio de Janeiro in November, in Florida and the United States Gulf Coast in June–July, South and Southeast Asia in April, and China in July. A lack of in situ data precludes confident statements about humid heat in many tropical, desert, high-elevation, and high-latitude areas. Absences are most prominent for Africa. The drying out of wet-bulb temperature ($T_w$) thermometers results

in erroneously high readings. Although this is less common now due to the increasing prevalence of humidity probes, its influence in these high-value threshold exceedances cannot be ruled out.

Here, humid heat is explored using the in situ-based monitoring product HadISDH. extremes (Willett 2023a,b) for the period 1973–2023. This product calculates $T_w$ using the Noniterative Evaluation of Wet-bulb Temperature method (Rogers and Warren 2024). Indices are calculated using daily maximum wet-bulb temperature following methods established for dry-bulb indices (section 2b4; https://climpact-sci.org).

### 3. TOTAL COLUMN WATER VAPOR

—O. Bock, C. A. Mears, S. P. Ho, and X. Shao

In 2023, global (60°S–60°N) mean total column water vapor (TCWV) was between 0.89 kg m⁻² and 1.12 kg m⁻² above the 1991–2020 average, according to five datasets (Fig. 2.28a; Table 2.7). Three of the datasets (MERRA2, JRA55, and GPS-RO) determined that 2023 was the wettest year on record. Over oceans (Fig. 2.28b), the moisture excess was even larger, with record-high anomalies between 1.01 kg m⁻² and 1.23 kg m⁻² in all five datasets (ERA5, MERRA2, JRA55, RSS TPW

<figure><img src="image_73.png" /><figcaption>Fig. 2.28. Global mean total column water vapor (TCWV) annual anomalies (kg m⁻²) over (a) land and ocean, (b) ocean-only, and (c) land-only from observations and reanalyses. The shorter time series from the observations have been adjusted so there is zero mean difference relative to the ERA5 results during their respective periods of record.</figcaption></figure>
<table><caption>Table 2.7. Global mean (60°S–60°N) total column water vapor (TCWV) anomalies (kg m⁻²; 1991–2020 base period) for 2023 and linear trends (kg m⁻² decade⁻¹) over the period 1991–2023 (2006–23 for GPS-RO, 1995–2023 for GNSS). The GNSS data include 197 stations over land and ocean islands and 143 stations over land.</caption><caption>TCWV Anomalies (kg m<sup>-2</sup>)</caption><thead><tr><th>Dataset</th><th>ERA5</th><th>MERRA2</th><th>JRA-55</th><th>MWR</th><th>GPS-RO</th><th>GNSS</th></tr></thead><tbody><tr><td>Global</td><td>0.89</td><td>1.12</td><td>1.10</td><td>--</td><td>1.01</td><td>0.77</td></tr><tr><td>Ocean</td><td>1.02</td><td>1.21</td><td>1.23</td><td>1.18</td><td>1.01</td><td>--</td></tr><tr><td>Land</td><td>0.46</td><td>0.81</td><td>0.85</td><td>--</td><td>0.50</td><td>0.73</td></tr></tbody></table>

<table><caption>TCWV Trends (kg m<sup>-2</sup> decade<sup>-1</sup>)</caption><thead><tr><th>Dataset</th><th>ERA5</th><th>MERRA2</th><th>JRA-55</th><th>MWR</th><th>GPS-RO</th><th>GNSS</th></tr></thead><tbody><tr><td>Global</td><td>0.38±0.06</td><td>0.36±0.05</td><td>0.37±0.05</td><td>--</td><td>0.44±0.14</td><td>0.39±0.14</td></tr><tr><td>Ocean</td><td>0.45±0.06</td><td>0.39±0.05</td><td>0.38±0.05</td><td>0.43±0.06</td><td>0.44±0.16</td><td>--</td></tr><tr><td>Land</td><td>0.19±0.06</td><td>0.30±0.08</td><td>0.32±0.07</td><td>--</td><td>0.29±0.16</td><td>0.33±0.16</td></tr></tbody></table>

v7.0, and GPS-RO). Over land (Fig. 2.28c), TCWV was well above average, but ranked as the second- to fourth-wettest year in four datasets (ERA5, JRA55, GNSS, and GPS-RO) where 2016 holds the record. MERRA-2 ranked 2023 as the wettest. All global anomalies exceeded the linear trend estimate for 2023, coinciding with the El Niño–Southern Oscillation (ENSO) transition to a strong El Niño (see section 4b for details). This contrasts with 2021 and 2022 anomalies, when La Niña conditions prevailed, which were below the linear trend.

On average, moist anomalies were mainly located along the equatorial Pacific Ocean and in a C-shaped pattern extending from the east coast of equatorial Africa to China in the north and into the southern Indian Ocean to the south (Plate 2.1k; Fig. 2.29a). These patterns are typical of El Niño states (Fig. 2.29b; Timmermann et al. 2018), where 2023 resembled other strong October–December El Niños (e.g., 1997 [Fig. 2.29c] and 2015 [Fig. 2.29d]). Similar to 2015, several regions experienced wetter-than-average conditions in 2023 (central Africa, northern North America, Europe, and the Middle East), while other regions experienced drier-than-average conditions, leading to rainfall deficiencies and droughts (Australia and Indonesia, northwest and southwest Africa, and Brazil).

Global mean TCWV variations are strongly constrained by lower tropospheric temperature (LTT) variations following the Clausius–Clapeyron relation, with a typical rate of change in water vapor of ~7% per 1°C (O’Gorman and Muller 2010). This tight relation holds at inter-annual and longer time scales. The global TCWV trends lie between 0.36 kg m⁻² decade⁻¹ and 0.44 kg m⁻² decade⁻¹ (Table 2.7) or 1.30% decade⁻¹ and 1.67% decade⁻¹, considering a global mean TCWV of 26.3 kg m⁻². When related to the median LTT trend of ~0.18°C decade⁻¹ to 0.22°C decade⁻¹ reported in Table 2.3, the estimated rate of change of water vapor is in the range 7.6%–9.3% per °C, which is consistent with the Clausius–Clapeyron relation given the uncertainty in the trend estimates (Po Chedley et al. 2023). Superposed on the long-term trend are positive and negative excursions, which coincide with the warm (e.g., 1998 and 2016) and cold (e.g., 2021 and 2022) phases of ENSO.

This assessment used observations from satellite-borne microwave radiometers over the oceans (RSS TPW v7.0; Mears et al. 2018), GPS-RO observations from several satellite missions (Ho et al. 2020; Shao et al. 2023), both over land and oceans, and ground-based GNSS observations over land and islands (Bock 2022). Three global reanalysis products were used: ERA5 (Hersbach et al. 2020), MERRA-2 (Gelaro et al. 2017), and JRA-55 (Kobayashi et al. 2015). All three reanalyses assimilate satellite microwave radiometer data (in the form of radiances) and GPS-RO data (in the form of bending angles), but not ground-based GNSS measurements, which serve as an independent validation dataset.
There is reasonable agreement across datasets, both on interannual timescales and trends in the base period (1991–2020), but this deteriorates prior to 1991 (Figs. 2.28a–c) and in some extreme El Niño years (1997/98, 2010, 2015/16; Figs. 2.28b,c). The divergence from the linear trend and significant scatter between reanalyses prior to 1993 may be due to changes in the global observing system (Allan et al. 2014). Differences in the observations assimilated, in the assimilation systems, as well as in model physics, may all contribute to differences in the reanalysis products, especially in data-sparse regions and in the pre- and early-satellite era (before 1980). Few validation datasets are available prior to 1993. The microwave radiometers included here do not diverge from the linear trend (Fig. 2.28b), suggesting common structural inhomogeneities in the reanalyses. Furthermore, TCWV over land in ERA5 has been low since 2020 (Fig. 2.28c), almost halving the linear trend estimate compared to other datasets (Table 2.8). Comparison with ground-based GNSS data (Plate 2.1k) reveals that ERA5 has a widespread dry bias of 0.5 kg m⁻²–1 kg m⁻² in the tropical land areas and smaller wet anomalies (~0.5 kg m⁻²) in the midlatitudes, consistent with a previous version of the reanalysis (Bock and Parracho 2019). MERRA2 also exhibits a dry bias of ~1 kg m⁻², mainly located over the Maritime Continent, which is compensated in the global mean by small wet biases in other regions (not shown).

<figure><img src="image_74.png" /><figcaption>Fig. 2.29. (a) Oct–Dec 2023 mean total column water vapor (TCWV) anomaly from ERA5, compared to (b) Oct–Dec composite for six strong (1957, 1965, 1972, 1987, 1991, 2023) and three very-strong (1982, 1997, 2015) El Niño events (according to the Oceanic Niño Index from NOAA's Climate Prediction Center) and two individual recent very-strong events in (c) 1997 and (d) 2015. Units are given by kg m⁻².</figcaption></figure>

4. UPPER-TROPOSPHERIC HUMIDITY

—V. O. John, L. Shi, E.-S. Chung, R. P. Allan, S. A. Buehler, and B. J. Soden

In 2023, the global mean upper-tropospheric humidity (UTH) anomaly, shown using relative humidity in Fig 2.30a, was slightly above normal in the first half of the year but below in the second half. The UTH exhibited expected behavior during El Niño, with regions of large drier-than-average relative humidity anomalies at subtropical latitudes over the Pacific Ocean. These were only partly balanced by more-than-humid anomalies near the equator (McCarthy and Toumi 2004), as depicted in Plate 2.1l. The mean and standard deviation of the global monthly anomalies in 2023 were –0.08±0.32%rh for the microwave-based data (Chung et al. 2013), 0.03±0.32%rh for the infrared-based data (Shi and Bates 2011), and –0.24±0.59%rh for ERA5 reanalysis data (Hersbach et al. 2020).
All datasets show near-zero and statistically insignificant global mean trends, which is in line with the theoretical consideration that the large-scale relative humidity in the upper troposphere remains approximately constant (Ingram 2010); however, this does not mean that the absolute humidity (amount of water vapor) in the upper troposphere remains unchanged in a warming upper troposphere. This is illustrated in Fig 2.30b, which shows the difference between

mid-to-upper-tropospheric mean layer temperature (MSU T2; Zou et al. 2023) and the measured brightness temperature of the 6-µm water vapor channel (HIRS T12), which is sensitive to the upper-tropospheric relative humidity. The mid-to-upper-tropospheric mean layer temperature is derived from the brightness temperature of the 60-GHz oxygen channel. As the change of oxygen concentration is insignificant, the emission level in the troposphere of the oxygen channel remains constant and, therefore, the measurements correctly reflect tropospheric warming (Simmons 2022), and the time series of the measurements shows a positive trend (not shown, see tropospheric mean temperature in section 2b5). If there were no change in water vapor amount in the mid-to-upper troposphere, the time series of the upper-tropospheric water vapor channel would have a similar positive warming trend, and the time series of the difference between the two should have a nearly zero trend. On the contrary, the difference time series shows a positive trend. This is because as the amount of water vapor in the upper troposphere (UT) increases, the emission level of the water vapor channel shifts higher in the troposphere and measures water vapor emissions with a lower temperature, diverging from the oxygen emission levels. Therefore, the trend in the difference time series is from the moistening of the UT (Soden et al. 2005; Chung et al. 2014). The 2023 differences were larger than any other points within the record, pointing to record-high UT absolute humidity (water vapor).

Plate 2.11 shows the annual average anomaly map of relative UTH in 2023 derived

from the microwave data, and the infrared equivalent is shown in Fig 2.31. The UTH anomalies reflect the large-scale circulation patterns. El Niño-like features were clearly represented, with a large positive anomaly in the eastern Pacific. The strong positive phase of the Indian Ocean dipole can also be seen. Here, the cooler-than-normal eastern Indian Ocean and warmer-than-normal western Indian Ocean led to reduced convection in the east and enhanced convection into the west. There were generally dry conditions over the North and South American continents, and moistening signatures in the UT over Africa.

<figure><img src="image_75.png" /><figcaption>Fig. 2.30. Time series of (a) global monthly mean anomaly upper-tropospheric humidity (UTH) for the three datasets (%rh; see text for details) and (b) the difference between upper-tropospheric temperature (T2) and water vapor channel (T12) brightness temperatures (K). Anomalies are with respect to the 2001–20 base period.</figcaption></figure>

<figure><img src="image_76.png" /><figcaption>Fig. 2.31. Upper-tropospheric humidity anomaly map (%rh) for 2023 from the infrared data record (2001–20 base period).</figcaption></figure>
## 5. PRECIPITATION

—R. S. Vose, R. Adler, G. Gu, X. Yin, and M. Ziese

Precipitation over global land areas in 2023, as estimated from two different monitoring products, was much below the 1991–2020 long-term average (Fig. 2.32a). In particular, the gauge-based product from the Global Precipitation Climatology Centre (GPCC; Becker et al. 2013) had an anomaly of –31.5 mm for 2023 (GPCC land mean is 780 mm), and the blended gauge–satellite product from the Global Precipitation Climatology Project (GPCP; Adler et al. 2018) had an anomaly of –28.1 mm. Both products indicate that mean global land precipitation in 2023 was much lower than in 2022 and that 2023 was one of the driest years on record (i.e., from 1979 to present). In contrast, precipitation over the global ocean (Fig. 2.32b) was 9.7 mm above the long-term average, according to the GPCP product, which resulted in near-average precipitation for the globe as a whole (Fig. 2.32c).

Over the global land surface, the highest positive precipitation anomalies in 2023 were concentrated in relatively small areas, including equatorial Africa and eastern Asia, though larger regions such as Europe and northern Asia were also wetter than the long-term average. The biggest negative precipitation anomalies over land were spread across a broad swath of the Americas (especially over the Amazon basin) as well as parts of southern Europe, southern Africa, southern Asia, and most of Australia (Plate 2.1m). Over the global oceans, high positive precipitation anomalies were apparent across the northern Indian Ocean, the western Pacific Ocean, and along the Pacific Intertropical Convergence Zone (ITCZ, the narrow band of heavy precipitation across the tropical Pacific just north of the equator). In contrast, large negative precipitation anomalies were evident over much of the southern Indian Ocean and the eastern Pacific Ocean (except along the ITCZ as noted above).

Consistent with the transition from La Niña to El Niño, anomaly patterns evolved substantially over the course of the year. The pattern for January–March contained

a number of typical La Niña features (e.g., a large rainfall deficit over the central equatorial Pacific, a V-shaped positive anomaly over the Maritime Continent, a mostly dry Indian Ocean), but other typical features were absent (e.g., the Amazon was not wet), likely because La Niña was weakening. The pattern for April–June included positive anomalies along the Pacific ITCZ and in the western Pacific, a reflection of the emergence of El Niño. This trend continued to develop from July through December, with the strong El Niño pattern arising by the last three months of the year (e.g., negative anomalies in northern South America, southern Africa, and Australia). The establishment of the El Niño pattern was associated with occurrences of floods and landslides, for example, over Somalia and eastern Africa, and the continuation of the drought over the Amazon.

<figure><img src="image_77.png" /><figcaption>Fig. 2.32. Globally averaged precipitation anomalies (mm yr⁻¹) relative to the 1991–2020 base period over (a) land areas, (b) ocean areas, and (c) the globe. Land and ocean time series were created using a proportional land/sea mask at the 1° × 1° scale.</figcaption></figure>
# 6. LAND SURFACE PRECIPITATION EXTREMES

—M. R. Tye, S. Blenkinsop, M. G. Bosilovich, M. G. Donat, I. Durre, C. Lennard, I. Pinto, A. J. Simmons, and M. Ziese

The year 2023 transitioned from La Niña to strong El Niño conditions (see section 4b), intensifying one-day/accumulated five-day maxima (Rx1day/Rx5day) in regions surrounding the Pacific and Indian Oceans. Meanwhile, severe drought coincided with a reduction in extreme precipitation over the Amazon basin. While global mean land precipitation was below the long-term (1991–2020) average (section 2d5), global mean Rx1day was close to average (Fig. 2.33). Positive one-day and/or five-day extremes covered large areas of Asia, Europe, northeastern Africa, and isolated locations in North and South America (Fig. 2.33; Plate 2.1n). Other heavy precipitation events were anomalous within regions surrounded by low precipitation (e.g., Brazil) or after long-lasting drought (e.g., Somalia). Some notable local meteorological extremes are listed below and in Appendix Table A2.1 but are not necessarily those with the greatest impact.

Here, Rx1day and Rx5day are derived from gauge-based (GHCNDEX; Donat et al. 2013; HadEX3, Dunn et al. 2020; GPCC, Ziese et al. 2022) and reanalysis (ERA5, Hersbach et al. 2020) data.

Late (January–March) and early (December) summers in Australasia brought notable precipitation extremes. Post-Cyclone Gabrielle crossed New Zealand in February, bringing the

wettest start to the year since records began (Murray 2023). In one location, 24-hour accumulations of 175.8 mm were recorded, more than three times the average February total. Northwestern Australia received record-breaking Rx5day in January and March. In December, ex-tropical Cyclone Jasper resulted in a concentration of record-breaking Rx1day and Rx5day over Queensland, with Rx5day exceeding 1000 mm at several locations, nearly triple previous records (Fig. 2.34), making Jasper the wettest tropical cyclone on record to affect Australia (Bureau of Meteorology 2024; Bowen et al. 2024; section 7h4).

Several notable events occurred over East Asia despite fewer-than-average western Pacific typhoons. Super Typhoon Betty (also named Mawar) brought flooding to the Philippines, Guam, and Japan in June, with record precipitation over Japan exceeded again in September. In July, Typhoon Doksuri generated intense precipitation over Beijing with many stations breaking records, while September's Typhoon Haikui generated record one-hour precipitation over Hong Kong.

The dominant modes of variability resulted in high storm and cyclone activity over the Indian Ocean (section 2e1). April–June monsoon rains included isolated exceptionally heavy events causing flash

floods in Pakistan. Flood conditions were then exacerbated in India by Tropical Storm Biparjoy (see section 7g4). Cyclone Mocha brought flooding to Myanmar in May, while Tropical Cyclone Tej made landfall in Yemen in October. Long-term drought over East Africa was ended by exceptional flooding during October and November. Long-lived Tropical Cyclone Freddy compounded the effects from January's storm Cheneso over Madagascar, also bringing catastrophic flooding to Mozambique, Zimbabwe, and Malawi after causing damage and fatalities in Mauritius and

<figure><img src="image_78.png" /><figcaption>Fig. 2.33. (a) Global mean anomaly of Rx1day (mm) over land from HadEX3 (Dunn et al. 2020) and GHCNDEX gridded observations. (b) Global Rx1day anomalies (mm day⁻¹) in 2023 with respect to the 1991–2020 mean from the Global Precipitation Climatology Centre (GPCC).</figcaption></figure>
La Reunion (see Sidebar 4.2 for details). An extreme cut-off low-pressure system that was isolated from the main atmospheric flow over the Western Cape province of South Africa resulted in widespread flooding, with many stations reporting record daily rainfall amounts during the event, as well as record September totals.

In contrast, intense precipitation events around the Atlantic were isolated within exceptionally dry regions. In Africa, eastern Ghana experienced severe flooding during October. The area around São Paulo, Brazil, recorded its highest 24-hour precipitation totals in February, with high cyclone activity in September affecting Rio Grande do Sul. The percentage of the northeast United States with a much-greater-than-normal proportion of precipitation derived from extreme one-day precipitation was in the top 10th percentile of a 122-year record (NOAA NCEI 2024), although few events were record breakers. On the opposite coast, atmospheric rivers brought record precipitation to California in January–March (section 2d9), while Tropical Storm Hillary also brought persistent heavy rain to the southwest United States in August. In South America, Chile was affected by extreme precipitation in February, June, and, most significantly, August when a frontal system and atmospheric river coincided over the Ñuble region (section 2e1).

Storm Daniel formed in the eastern Mediterranean in September, causing flooding in Greece, Türkiye, and Bulgaria, resulting in the loss of at least 27 lives before making landfall in Libya. Here, 414.1 mm of

rain was recorded over a 24-hour period in Bayda with an estimated 150 lives lost across the country. Other parts of Europe also experienced summer flooding and fatalities, including Italy in May, followed by a total of 60 new Rx1day and Rx5day records across Scandinavia, the Baltic States, Russia, and Slovenia in August.

# 7. CLOUDINESS

— C. Phillips and M. J. Foster

Global cloud area fraction in 2023 was 0.16% less than in 2022, the lowest fraction measured in the entire PATMOS-x record, which starts in 1980. A trend of -0.62% decade⁻¹ has been observed since the start of the record, increasing the likelihood of record minimum years like 2023. This lower-than-average cloudiness (Plate 2.1o) was distributed globally, with the Indian Ocean, Arctic, and Northern Hemisphere land being especially low in cloudiness in 2023. In 2022, there was a notable increase over the equatorial western Pacific associated with La Niña (Phillips and Foster 2022) that did not appear in 2023 as La Niña ended and El Niño conditions emerged in Northern Hemisphere spring.

These PATMOS-x observations are consistent with independent measurements of cloud radiative effect (CRE) from CERES EBAF 4.2 (Loeb et al. 2018), which started in March 2000. Note

(a)

<figure><img src="image_79.png" /></figure>

(b)

<figure><img src="image_80.png" /><figcaption>Fig. 2.34. (a) Ratio of new Rx1day (circles) and Rx5day (squares) records set over Australia in 2023 with respect to the previous record. (b) Regional mean anomaly of Rx1day (mm) over north Australia from HadEX3 and GHCNDEX gridded observations.</figcaption></figure>
that CRE is the “all sky” value minus the “clear sky only” value in this analysis. This shows a record-high global shortwave CRE annual mean anomaly (SWCRE, 1.20 W m⁻²) and record-low global longwave CRE annual mean anomaly (LWCRE, -0.62 W m⁻²) in 2023, relative to a 2000–20 base period (Fig. 2.35).

In most cases, the shortwave effect of clouds is to reflect sunlight, which results in cooling, whereas the longwave effect of clouds is to insulate the lower atmosphere, resulting in heating. The sign convention here is that positive anomalies imply heating, so the decrease of clouds in 2023 caused both heating by reflecting less sunlight (+1.20 W m⁻²) and cooling by insulating less (-0.62 W m⁻²). In absolute terms, the SWCRE is negative and the LWCRE is positive. The annual mean SWCRE of -44.53 W m⁻² was the least negative on record, and the annual mean LWCRE of 27.28 W m⁻² was the least positive on record. Hence, the effect of clouds could be considered weaker than average as both shortwave (negative) and longwave (positive) were closer to zero. This represents the continuation of a decade-long trend (Phillips and Foster 2022), leading to five of the weakest SWCRE and LWCRE years occurring in the past six years.

Adding the shortwave and longwave CRE together, the annual mean total CRE in 2023 was record high at -17.25 W m⁻² (0.58 W m⁻² greater than the 2000–20 average). This means that, globally, clouds still had an overall cooling effect, but it was the weakest global cooling effect of any year measured. Unlike its components, total CRE does not exhibit a significant trend—the global long-term deviations SWCRE and LWCRE appear balanced. Related analysis of the radiative flux and energy budget can be found in section 2f1.

The El Niño–Southern Oscillation transitioned from La Niña in January to El Niño in May. Hence, for analysis, 2023 is split into two periods: January–April and May–December. Figures 2.36a–d show the average anomalies (relative to 1991–2020; deseasonalized) for PATMOS-x cloud area fraction compared to a composite of all La Niñas and El Niños. Composites use the thresholds of Multivariate ENSO Index version 2 <-1 for La Niña and >1 for El Niño. The January–April and May–December averages show good agreement with the La Niña and El Niño composites, respectively. The La Niña cloud climate is characterized by about 5% more cloud cover over the Maritime Continent (MC) and about 5% less cloud cover directly

to the east. During El Niño, anomalies are typically stronger, with up to 10% less MC cloud cover and 10% more cloud cover over the rest of the equatorial Pacific.

These anomalies in the PATMOS-x cloud area fraction are mirrored by CRE anomalies (from CERES EBAF Ed4.2, Figs. 2.36e–h). Regions with decreased cloud fraction are correlated with negative LWCRE anomalies and positive SWCRE anomalies, meaning that in absolute terms, these CRE quantities are closer to zero. For the most part, these large anomalies are balanced such that total CRE is unaffected. The strongest anomalies for the total CRE (not shown) are located off the coast of Ecuador, where both the SWCRE and LWCRE are positive (heating) during the El Niño period from May to December 2023.

PATMOS-x v6.0 provides twice-daily observed cloud products, including cloud area fraction, from each satellite from the set of NOAA Polar Orbiting Environmental Satellite series and

<figure><img src="image_81.png" /><figcaption>Fig. 2.35. Cloud radiative effect anomalies (W m⁻²; 2000–20 base period) from CERES EBAF Ed4.2 (Loeb et al. 2018) representing the changes in top-of-atmosphere radiative forcing that are attributable to clouds (which could include both changes to clouds themselves and surface changes masked by clouds). Positive values indicate cloudiness-related warming through more radiation reaching the surface and less being reflected back out to space (shortwave cloud radiative effect [SWCRE]) or more being trapped close to the surface rather than escaping out to space (longwave cloud radiative effect [LWCRE]). Negative values indicate cloudiness-related cooling. Note that these are monthly anomalies whereas annual mean anomalies and absolute values are quoted in the main text.</figcaption></figure>
EUMETSAT Polar System (Foster et al. 2023). Producing a global cloud area fraction anomaly necessitates combining these individual observations into a single value representing the diurnal average. This is done by averaging the data for every observed local hour, month of the year, and gridbox to produce a joint diurnal-seasonal-regional climate average. This multidimensional average can be indexed for any individual observation to find the expected bias compared to the desired reference, which is then subtracted out. CERES EBAF Ed4.2 is an energy-balanced-and-filled dataset with top-of-atmosphere radiative flux derived from the CERES instruments onboard the Aqua, Terra, and NOAA-20 satellites (Loeb et al. 2018).

<figure><img src="image_82.png" /><figcaption>Fig. 2.36. (a) La Niña and (b) El Niño cloud area fraction anomaly composite compared to time averages for (c) Jan–Apr 2023 and (d) May–Dec 2023, respectively (%). PATMOS-x v6.0 composite cloud area fraction anomaly from 1991 to 2022. (e),(f) CERES EBAF-TOA Ed4.2 shortwave cloud radiative effect (SWCRE) anomalies and (g),(h) longwave cloud radiative effect (LWCRE) anomalies (W m⁻²) for 2023 relative to 2000–20. All anomalies are implicitly deseasonalized.</figcaption></figure>
# 8. LAKE WATER STORAGE

—M. E. Harlan, B. M. Kraemer, T. V. King, R. S. La Fuente, and M. F. Meyer

Water storage data for 5764 of the world's lakes, provided by a recently published dataset (GloLakes; Hou et al. 2024), reveals a complex picture of hydrological shifts in 2023 (Plate 2.1p). Cumulative lake water storage (LWS) was 1.2% higher in 2023 compared to the baseline period of 1991–2020, demonstrating a slight increase over average historical conditions. Collectively, the lakes with rising LWS increased by a total of 4828 million cubic meters (MCM) whereas those with declining LWS decreased by 2624 MCM. This led to a net increase of 2204 MCM in 2023 (Fig. 2.37). Notably, 64% of the lakes analyzed exhibited higher-than-average water levels relative to their 1991–2020 baseline, reflecting a partial reversal of the recently reported decline in global LWS (Yao et al. 2023; Fig. 2.38). These global, yet heterogenous anomalies underscore the influence of varying climatic and anthropogenic factors on LWS, including precipitation patterns, evaporation rates, and water management practices (Yao et al. 2023; Zhao et al. 2022).

<figure><img src="image_83.png" /><figcaption>Fig. 2.37. Volumetric water level anomalies (x 10⁶ m³) relative to 1991–2020. The latitudinal and longitudinal insets show the cumulative increase (teal), cumulative decrease (brown), and the net change (gray) across one-degree latitudinal and longitudinal bins.</figcaption></figure>

Excessive LWS fluctuations caused by droughts or floods can have major implications for the availability of essential resources like drinking water, irrigation, food, energy, and transportation, and they pose significant socio-economic challenges (Zohary and Ostrovsky 2011). Importantly, excessive fluctuations in lake volume can also cause considerable ecosystem disturbances, affecting key physical processes, community composition, and biodiversity (Jeppesen et al. 2015), underscoring the need for sustainable water management and conservation strategies in the face of changing global conditions.

While most LWS anomalies were positive, some countries, including Argentina, Algeria, Morocco, Spain, and Türkiye, experienced widespread decreases in lake water volume, with reductions ranging from 10% to 35%. Conversely, Mali, Cambodia, Australia,

<figure><img src="image_84.png" /><figcaption>Fig. 2.38. Long-term change in volumetric water level anomalies (x 10⁶ m³) relative to the 1991–2020 mean for each one-degree latitudinal bin. Values are smoothed with a General Additive Model to aid in visualization where the cumulative lake storage anomaly was modeled as a function of an interactive smoothing function between both year and latitude.</figcaption></figure>
South Africa, and India saw their lake volumes increase on average by 43%. The positive LWS anomalies at 60°N observed here contrast with reported water losses at these latitudes due to permafrost thaw (Webb et al. 2022), although previous analyses focused on 2000–20 trends rather than 2023 relative to a baseline of 1991–2020.

To identify changes in lake levels, we used the “GloLakes” lake and reservoir storage dataset (Hou et al. 2024), which combines altimetry and reflectance satellite data across Landsat (Pekel et al. 2016), ICESat2 (Jasinski et al. 2023), the Global Reservoirs and Lakes Monitor (Birkett et al. 2011), and Sentinel-2 (https://www/blue-dot-observatory.com/) to estimate LWS over recent decades. The data were generated using a geostatistical model (Messager et al. 2016) paired with innovative gap-filling methods (Hou et al. 2022). Here, we relied on a subset of the lakes from GloLakes (5764) with data from at least 20 years in the period of 1991 through 2023, with no more than a three-year gap of observations and at least three observations of lake storage in 2023. While the GloLakes dataset allowed us to expand our analysis from altimetry-based water levels in previous reports that focused solely on altimetry data (Kraemer et al. 2022) to include lake water storage, and to include many more lakes (5764 lakes compared to 264 in 2022), the dataset is still restricted in its spatiotemporal coverage. Additionally, the incorporation of optical remote sensing adds challenges such as those posed by clouds, atmospheric interferences, and vegetation, potentially reducing the accuracy of water detection. These challenges could be further mitigated in future years using technologies like passive microwave sensors, synthetic-aperture radars, and wide-swath altimetry (e.g., the Surface Water and Ocean Topography mission).

# 9. GROUNDWATER AND TERRESTRIAL WATER STORAGE

— M. Rodell and D. N. Wiese

Various regions of the world experienced large increases or decreases in terrestrial water storage (TWS) in 2023, with the global mean approaching a 21-year low. Changes in mean annual TWS between 2023 and 2022 are plotted in Plate 2.1q. Europe, which has been in a state of drought more often than not since 2019, experienced a partial respite in 2023, with TWS increasing slightly across much of the continent while remaining below the long-term average. TWS in parts of southeastern Asia declined from above normal to below normal, while wetness across the rest of Asia, excluding the ever-receding Caspian Sea, remained fairly stable.

Heavy rains in March caused flooding and contributed to TWS increases that exceeded 12 cm across a large area of northern Australia, with some parts experiencing record highs. Total water storage has been well above normal across most of sub-Saharan Africa since 2019, and 2023 was no different, with wet weather raising TWS, especially in the Congo River basin. This multi-year wet event is by far the most intense worldwide (in terms of extent, duration, and TWS anomaly) since satellite observations of TWS changes began in 2002 (Rodell and Li 2023). To the south, drought caused water-level declines across a region centered near the southeastern corner of Angola.

In North America at the start of the year, atmospheric rivers delivered heavy rains to California and parts of adjacent states, resulting in floods and reservoirs being filled to capacity. Despite this, TWS remained near or slightly below the long-term mean in southern California because the slowly recharging aquifers have not fully recovered after years of drought and an associated heavy reliance on groundwater for crop irrigation (Liu et al. 2022). Drought caused TWS to decrease to record lows in central and southern Canada and contributed to their worst year for wildfires on record. Drought also affected TWS in southern Mexico and the central Mississippi River basin, the former which continued into a second year. In South America, a major drought caused TWS to decline by more than 12 cm over a large area of the Amazon River basin, leading to record lows for both that basin and South America as a whole. Northern Argentina and Uruguay also saw water levels decline. On the other hand, a swath of southern Brazil gained a large amount of water.

Deseasonalized time series of monthly zonal-mean and global-mean TWS anomalies are plotted in Figs. 2.39 and 2.40. Data gaps occur when satellite observations are not available. Excluded from the calculation of these means are regions where TWS declines are dominated by
ice sheet and glacier ablation: Antarctica, Greenland, the Gulf Coast of Alaska, polar islands, High Mountain Asia, alpine western Canada, and the southern Andes. Land in the zone between about 25°N and 45°N has been drying gradually (roughly 0.5 cm yr⁻¹ to 1.0 cm yr⁻¹ on average) since the early 2000s, if not before, and that tendency seems to have gained momentum in recent years. This drying aligns with droughts in the central United States and Europe and the long-term declines of the Caspian Sea and groundwater levels in northern India. The latter two are ulti-

mately attributable to agricultural irrigation (Rodell et al. 2018). A zone of elevated TWS between about 8°S and 15°N first appeared around 2019 and persisted in 2023. At its root is the ongoing wet event in sub-Saharan Africa, while contributions from excess TWS in eastern Brazil and southern India abated in 2023. Just south of that latitude band, TWS returned to normal levels within a ~10° latitude zone after having been low during the preceding four years. That zone includes wetting regions of Argentina and Uruguay, southern Africa, and northern Australia as seen in Plate 2.1q. Figure 2.40 shows that in 2023, global mean TWS, excluding ice sheet and glacier losses, reached its second lowest level since 2002, as declines in northern Brazil, Canada, Mexico, southeastern Asia, the Caspian Sea, and elsewhere outweighed gains in Africa, California, northern Australia, and southern Brazil. The three lowest levels of global mean, non-ice TWS—in 2016, 2023, and 2019—all occurred during El Niño events.

Since 2002, TWS anomalies have been derived from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite observations of Earth's time-varying gravity field (Tapley et al. 2004; Landerer et al. 2020). In situ networks do not observe the components of TWS (groundwater, soil moisture, surface waters, snow, and ice) with sufficient density to infer regional to global changes, hence the reliance on remote sensing. Uncertainty in the monthly TWS anomaly observations is about 1 cm–2 cm equivalent height of water over a 500,000 km² region at midlatitudes (Wiese et al. 2016). Groundwater is commonly the largest component of variations in TWS over periods longer than a year and outside of the humid tropics (surface water) and high-latitude and alpine regions (ice and snow; Getirana et al. 2017).

<figure><img src="image_85.png" /><figcaption>Fig. 2.39. Zonal means of monthly terrestrial water storage anomalies, excluding those in Antarctica, Greenland, the Gulf Coast of Alaska, polar islands, and major glacier systems (e.g., High Mountain Asia, alpine western Canada, and the southern Andes), in cm equivalent height of water, based on gravity observations from the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO). The anomalies are relative to a 2003–20 base period.</figcaption></figure>

<figure><img src="image_86.png" /><figcaption>Fig. 2.40. Global average terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment (GRACE; gray) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO; black), excluding those in Antarctica, Greenland, the gulf coast of Alaska, polar islands, and major glacier systems (e.g., High Mountain Asia, alpine western Canada, and the southern Andes), in cm equivalent height of water, relative to a 2003–20 base period.</figcaption></figure>
# 10. SOIL MOISTURE

—M. Hirschi, P. Stradiotti, W. Preimesberger, R. van der Schalie, T. Frederikse, D. Duchemin, N. Rodriguez-Fernandez, A. Gruber, S. Hahn, W. A. Dorigo, R. A. M. de Jeu, S. I. Seneviratne, and R. Kidd

Due to its importance in the water, energy, and carbon cycles, soil moisture plays a crucial role in the land-atmosphere interaction (Seneviratne et al. 2010), with impacts on surface air temperature, precipitation generation, and extreme events such as heatwaves and forest fires. The increase in global soil moisture observed over the previous four years (van der Schalie et al. 2022; Stradiotti et al. 2023) reversed in 2023, and the soil moisture values declined back to 2020 levels (Fig. 2.41). While soil moisture in the Northern Hemisphere remained at a similar

level to 2022, soil moisture in the Southern Hemisphere strongly decreased after the recent pronounced wetting tendency that began in 2020. This may be a sign of the transition of the ENSO from La Niña to El Niño conditions that occurred in 2023 (see section 4b). This transition is consistent with the occurrence of more widespread below-average soil moisture conditions in the Southern Hemisphere in 2023 (Fig. 2.42; e.g., Zhang et al. 2023). Accordingly, soil moisture in the Southern Hemisphere shifted from a wet to a dry anomaly in 2023, while soil moisture in the Northern Hemisphere remained slightly wetter than normal. Overall, the global soil moisture conditions were close to the 1991–2020 average.

Wetter-than-normal conditions were present throughout most of the year in northern Australia, with wet anomalies that were particularly widespread in January, April, and July (up to 200% of normal in some places; Plate 2.1r, Appendix Fig. A2.6). Similarly strong wet anomalies were also observed in southern and northwestern India, particularly from January to July. Also, parts of East Asia experienced noticeable wetter-than-normal conditions in 2023, similar to 2022. In the Horn of Africa, the drought conditions of 2022 gave way to wet anomalies around March, which intensified again in November due to heavy rain (e.g., Kimutai et al. 2023; section 2d6). This change from a long-term drought in the region to flooding coincided with a switch of the Indian Ocean dipole (IOD) from a negative to positive mode in 2023 (see section 4f). This mode is associated with above-average rainfall in East Africa (Nicholson 2017; Marchant et al. 2007; see also section 2d5). Further areas of above-average soil moisture were also noticeable in parts of eastern Europe and Central Asia, in Türkiye, and in the western United States. Northeast Brazil

<figure><img src="image_87.png" /><figcaption>Fig. 2.41. (top) Time series of global (black), Northern Hemisphere (blue), and Southern Hemisphere (red) monthly surface soil moisture anomalies (m³ m⁻³) for the period 1991–2023 (1991–2020 base period) and (bottom) the valid observations as a percentage (%) of total global land surface. Data are masked where no retrieval is possible or where the quality is not assured and flagged, for example due to dense vegetation, frozen soil, permanent ice cover, or radio frequency interference. (Source: Copernicus Climate Change Service [C3S] Soil Moisture.)</figcaption></figure>

<figure><img src="image_88.png" /><figcaption>Fig. 2.42. Time-latitude diagram of monthly surface soil moisture anomalies (m³ m⁻³; 1991–2020 base period). Data are masked where no retrieval is possible or where the quality is not assured and flagged, for example due to dense vegetation, frozen soil, permanent ice cover, or radio frequency interference. (Source: C3S Soil Moisture).</figcaption></figure>
started the year with strong wet anomalies that persisted for most of the year, while in the central and southeast part of the country, severe below-normal conditions started to emerge at the end of 2023 (e.g., Clarke et al. 2024; see section 7d2).

In contrast to these regionally confined wet soil moisture anomalies of 2023, dry conditions were observed in numerous regions (Plate 2.1r). The most pronounced dry anomaly was observed in southern South America, especially in the River Plate basin and Patagonia (below 50% of normal soil moisture in some areas). This region has been suffering from a multi-year drought since 2019 (Naumann 2021). Pronounced dry conditions also persisted in the Canadian Prairies for the third consecutive year (see section 7b1; van der Schalie et al. 2022; Stradiotti et al. 2023). Although soil moisture remained below normal, drought conditions in the Great Plains of central North America weakened in 2023 compared to 2022. Mexico experienced drier-than-normal conditions during June–September (Appendix Fig. A2.6). Similarly, below-normal soil moisture was observed in southwestern Africa (including South Africa and Namibia), with the most pronounced dry anomalies recorded from February to May. Many of the regions around the Mediterranean Sea (including Spain, northern Morocco, Algeria, and Tunisia) also experienced moderately dry conditions in 2023. In addition, widespread mild-to-moderate negative soil moisture anomalies were observed over much of inland China, southern Central Asia, northern Asia, and in the higher latitudes in general. In southeast Australia, the strong positive soil moisture anomalies of 2022 (Stradiotti et al. 2023) turned into widespread dry anomalies covering most of the southern part of the continent (except for parts of Victoria), but with intermittent periods of wetter-than-normal conditions in January, April, June, and July.

Soil moisture was observed by microwave satellite remote sensing of the surface soil layer down to approximately 5-cm depth, as provided by the COMBINED product of the Copernicus Climate Change Service (C3S) version 202212 (Dorigo et al. 2023). C3S combines multi-sensor data in the 1978–2023 period through statistical merging (Dorigo et al. 2017; Gruber et al. 2017, 2019). Wet and dry anomalies here refer to the deviation from the 1991–2020 climatological average. Note that changes in spatiotemporal coverage (also between product versions, e.g., resulting from the inclusion of additional sensors) can introduce uncertainties in the domain-averaged soil moisture time series (e.g., Bessenbacher et al. 2023).

# 11. MONITORING GLOBAL DROUGHT USING THE SELF-CALIBRATING PALMER DROUGHT SEVERITY INDEX

—J. Barichivich, T. J. Osborn, I. Harris, G. van der Schrier, and P. D. Jones

The self-calibrating Palmer Drought Severity Index (scPDSI; Wells et al. 2004; van der Schrier et al. 2013) over the period 1950–2023 shows that the increasing trend in severity and extent of global drought, which has been ongoing since mid-2019 (Barichivich et al. 2020, 2021, 2022),

reached a new historical peak during the second half of 2023 (Fig. 2.43). During June–September, extreme drought conditions (scPDSI ≤-4) surpassed 7% of the global land area for the first time in the record, peaking at a new historical maximum of 7.9% in July. Similarly, the extent of severe plus extreme drought conditions (scPDSI ≤-3) in 2023 exceeded 16% of the global land area for the first time during the same period, reaching a historical maximum of 16.8% in July. Moderate or worse drought conditions (scPDSI ≤-2) peaked in September at a historical maximum of 29.7% of the global land area.

The global pattern of regional droughts seen in 2022 largely persisted through 2023, with the most extensive severe-to-extreme

<figure><img src="image_89.png" /></figure>

Fig. 2.43. Percentage of global land area (excluding ice sheets and deserts) with self-calibrating Palmer Drought Severity Index (scPDSI) indicating moderate (≤-2), severe (≤-3), and extreme (≤-4) drought for each month during the period 1950–2023. Inset: each month of 2023.
drought conditions occurring over South America, parts of North America, the Mediterranean, and the midlatitudes of Asia (Plate 2.1s). Drought severity eased through western North America and parts of northern and eastern Europe but worsened in tropical South America and the midlatitudes of Asia (Fig. 2.44). In western North America, California experienced a shift from dry to wet conditions, but the overall west-east moisture contrast observed across the United States since 2017 continued as Arizona and New Mexico were under moderate drought (Plate 2.1s). Moderate drought conditions also affected Mexico and Central America. In South America, El Niño conditions during the latter half of 2023 led to extremely wet conditions in coastal areas of Peru and extreme drought through the Amazon basin to the La Plata basin and central Chile. By the end of October, the Rio Negro at Manaus, a major tributary of the Amazon River (Barichivich et al. 2018), fell to its lowest water level since records began in 1902. The megadrought of central Chile reached its 14th consecutive year in 2023, but an increase in winter rainfall broke the drought in the south-central part of the country (section 2d5).

Although precipitation was above normal in parts of northern, central, and eastern Europe in 2023 (section 2b5), most of the southern part of the continent, particularly countries around the Mediterranean, continued under severe-to-extreme drought (Plate 2.1s). In northern Africa, previous extreme drought conditions along the Mediterranean coast from Morocco to Tunisia continued through 2023 (Plate 2.1s). Most of the Middle East from eastern Türkiye to Pakistan also saw a continuation of severe-to-extreme drought conditions.

Although uncertain due to sparse in situ data, moisture patterns in Africa did not change much in 2023 (Plate 2.1s). Tropical Africa saw a continuation of moderate wet conditions that were observed since 2019. Southern Africa saw a continuation of drought conditions that began in 2018, and its severity remained mostly as moderate. In Australia, drought eased in many northern regions, was sustained in the southwest, and worsened in the easternmost parts during 2023; some parts of the country continued under moderate drought (Plate 2.1s). Wet conditions seen through most of India and southeast Asia in 2022 continued during 2023.

In contrast, severe-to-extreme drought conditions extended farther through China, Mongolia, and Kazakhstan. Previous severe-to-extreme drought continued through part of northeastern Siberia (Plate 2.1s).

Hydrological drought results from a period of abnormally low precipitation, sometimes exacerbated by a concurrent increase in evapotranspiration (ET). Its occurrence can be apparent in reduced river discharge, soil moisture, and/or groundwater storage, depending on the season and duration of the event. Here, the scPDSI is calculated, using gridded global precipitation and Penman-Monteith Potential ET from an early update of the CRU TS 4.08 dataset (Harris et al. 2020). A simple water balance at the core of the scPDSI estimates actual evapotranspiration, soil moisture content, and runoff based on the input precipitation and potential loss of moisture to the atmosphere. Estimated soil moisture categories are calibrated over the complete 1901-2023 period to ensure that “extreme” droughts and pluvials (wet periods) relate to events that do not occur more frequently than in approximately 2% of the months. This calibration affects direct comparison with other hydrological cycle variables in Plate 2.1s that use a different baseline period.

<figure><img src="image_90.png" /><figcaption>Fig. 2.44. Change in drought categories from 2022 to 2023 (mean self-calibrating Palmer Drought Severity Index [scPDSI] for 2023 minus mean scPDSI for 2022). Increases in drought severity are indicated by negative values (brown) and decreases by positive values (green). No calculation is made where a drought index is meaningless (gray areas: ice sheets or deserts with approximately zero mean precipitation).</figcaption></figure>
12. LAND EVAPORATION

—D. G. Miralles, O. M. Baez-Villanueva, A. Koppa, O. Bonte, E. Tronquo, F. Zhong, and H. E. Beck

Understanding the spatial and temporal dynamics of evaporation is crucial for agriculture and water management, as well as for diagnosing the influence of short-term climate variability and long-term climate trends on water resources. The year 2023 saw a mix of positive and negative evaporation anomalies across different regions, reflecting a complex interplay of meteorological variables and land surface processes (Plate 2.1t). Notably, semiarid regions of Australia, South America, and North America experienced negative anomalies, particularly towards the end of the year, consistent with the reversal of La Niña to El Niño conditions (Martens et al. 2018; Miralles et al. 2014). Meanwhile, positive anomalies were prevalent over the U.S. East Coast, most of Europe, boreal latitudes, tropical forests, and large parts of India and China. These anomalies mostly reflected regional climatic conditions, with high temperatures driving increased evaporation rates over the U.S. East Coast and Europe (section 2b1), while in tropical forests, positive anomalies were mostly attributed to enhanced precipitation (section 2d4), which increased interception loss, a primary component of evaporation in forested regions. Similarly, in northern India, positive anomalies correlated with higher-than-usual precipitation volumes. The high regional heterogeneity underscores the need for continued monitoring of evaporation for agriculture and water management applications. For example, in semiarid regions experiencing negative anomalies, such as parts of Australia and the Americas, decreased evaporation reflects reduced water availability (section 2d9), with potential implications for crop yields and freshwater security. Conversely, in regions with positive anomalies, like central Europe and parts of Asia, higher-than-usual evaporation rates may contribute to decreased water resources in following dry seasons.

Despite El Niño conditions usually being associated with lower-than-usual global mean evap-
oration due to the occurrence of persistent droughts in several global regions (Miralles et al.
2014), the average evaporation values in 2023 reached unprecedented high levels due to the high
air temperatures (section 2b1), marking the highest on record for the Northern Hemisphere and
the globe as a whole (Fig. 2.45). The global mean evaporation in 2023 was above the linear trend
of +0.5 mm yr⁻¹, which can be attributed to positive anomalies in both hemispheres. The positive
multidecadal trend has been attributed to the ongoing rise in global temperatures (Brutsaert
2017) and terrestrial greening (Yang et al. 2023; see also section 2h2). Arguably due to the positive

temperature anomalies in 2023 in the
Northern Hemisphere and tropics, particu-
larly towards the end of the year (section
2b1), evaporation was consistently higher
than usual in those latitudes (Fig. 2.46).
Meanwhile, in the Southern Hemisphere,
drought conditions led to negative anoma-
lies in semiarid regions at latitudes between
25°S to 40°S during the second half of the
year (Fig. 2.46).

In recent years, land evaporation has
been gaining recognition as an essential
climate variable by the World Meteorological
Organization, and today multiple
satellite-based approaches are advancing
global evaporation monitoring. The results
shown here correspond to the latest version
of GLEAM, a set of algorithms dedicated to
estimating evaporation based on satellite
and reanalysis data (Miralles et al. 2011).

<figure><img src="image_91.png" /><figcaption>Fig. 2.45. Land evaporation anomaly (mm yr⁻¹; 1991–2020 base period) for the Northern Hemisphere, Southern Hemisphere, and the entire globe (blue, red, and black solid lines, respectively). Linear trends in evaporation (dashed lines) and the Southern Oscillation Index (SOI) from the Climatic Research Unit (right axis, shaded area) are also shown. (Source: Global Land Evaporation Amsterdam Model [GLEAM].)</figcaption></figure>
Despite improvements in global evaporation monitoring in recent years, challenges persist, particularly those related to the accurate depiction of ecosystem responses to drought stress and the representation of interception loss in forests (Fisher et al. 2017; McCabe et al. 2019). Efforts to further advance land evaporation monitoring are ongoing, with future advancements expected to leverage emerging technologies from thermal missions like ECOSTRESS (Fisher et al. 2020) and TRISHNA (Lagouarde et al. 2018), as well as hyper-resolution optical remote sensing facilitated by cubesat constellations (McCabe et al. 2017). These innovations hold promise for enhancing our understanding of evaporation dynamics and their implications for water resources, climate, and ecosystems.

<figure><img src="image_92.png" /></figure>

Fig. 2.46. Zonal-mean terrestrial evaporation anomalies (mm month⁻¹; 1991–2020 base period). (Source: Global Land Evaporation Amsterdam Model [GLEAM].)
### e. Atmospheric circulation

#### 1. MEAN SEA LEVEL PRESSURE AND RELATED MODES OF VARIABILITY

##### —B. Noll, D. Fereday, and D. Campos

Mean sea-level pressure (MSLP) variability is characterized by large-scale modes that drive weather and climate anomalies and extremes. These modes include the Arctic Oscillation, the North Atlantic Oscillation (NAO), and the Pacific/North American (PNA) in the Northern Hemisphere (NH) as well as the Southern Annular Mode (SAM)/Antarctic Oscillation in the Southern Hemisphere (SH; Kaplan 2011). Because of its direct impact in the tropics and important extratropical teleconnections to both hemispheres (Capotondi et al. 2015), the El Niño–Southern Oscillation (ENSO) is one of the most significant and well-tracked global climate drivers. ENSO can be described by the Southern Oscillation Index (SOI), the normalized MSLP difference between Tahiti and Darwin (Allan et al. 1996; Kaplan 2011).

The SOI was variable in early 2023, coinciding with a decaying La Niña, before turning consistently negative from July onward as El Niño became established (see section 4b). Early in the year, La Niña contributed to destructive floods and ex-tropical cyclones in New Zealand (see sections 4g8, 7h5), while the emerging El Niño contributed to seven consecutive months of record-breaking global warmth from June to December (section 2b1; C3S 2024), elevated wildfire activity in Canada (see section 7b1, Sidebar 7.1), and caused record-low annual maximum and minimum sea-ice extents in Antarctica (see section 6d). From June to August, the development of anomalous low pressure in the subtropical South Pacific, closely related to the strengthening El Niño, allowed an enhanced atmospheric river season across southern South America (e.g., Campos and Rondanelli 2023) and led to flooding in central Chile (DMC 2023a).

The Indian Ocean dipole (IOD) is an important mode of climatic variability in the Eastern Hemisphere (Saji et al. 1999). The positive IOD phase, which developed in September 2023 (see section 4f), comprises warm ocean temperatures in the tropical western Indian Ocean and cool ocean temperatures in the east. The IOD is often driven by ENSO via the Walker Circulation (Behera et al. 2006). The concurrent strongly positive IOD and El Niño contributed to Australia's driest three-month period of record from August to October 2023 (see section 7h4). It also strengthened the descending branch of the Walker Circulation over Indonesia and western Australia, which likely delayed the northern Australian monsoon (BoM 2024b; Lisonbee and Ribbe 2021).

In the NH, the NAO index was positive in January and February, consistent with mild winter conditions in Europe. Summer saw low pressure over the eastern United States, contributing to increased rainfall there (see section 7b2). A pressure dipole was seen over northwestern Europe, strongly projecting onto the summer NAO pattern (Fig 2.47c). This pattern is defined as the leading empirical orthogonal function (EOF) mode of variability in the North Atlantic/European region in July/August (Folland et al. 2009). The 2023 summer NAO index was the lowest in the time series back to 1959, in contrast to the second-highest value seen in 2022 (Fig 2.47e). Low pressure persisted across the North Atlantic, Europe, and east into Asia in autumn, and was associated with increased rainfall in these regions (see sections 2d5, 7f, 7g). Consistent with the positive ENSO phase in late 2023, the December PNA and NAO were both positive (Livezey et al. 1997; Ayarzagüena et al. 2018).

The SAM, which explains 22%–34% of the variability in extratropical SH atmospheric circulation (Fogt and Marshall 2020), was positive for 53% of days during 2023, fewer days than each year from 2020 to 2022. Annual MSLP was much above normal in the midlatitude South Pacific east of New Zealand and southwest of Australia, and below normal in the Bellingshausen Sea (Plate 2.1u; Fig. 2.48). Overall, this resembled the negative phase of the Pacific–South American (PSA) pattern (Irving and Simmonds 2016). The atmospheric circulation anomalies and blocking (e.g., Renwick and Revell 1999) associated with this pattern likely contributed to wetter conditions in northern New Zealand and the country's second-warmest year on record (see section 7h5) as well as to wetter conditions in late winter and spring over south-central Chile (see section 7d3). They also likely contributed to drier conditions in western and southern Western Australia (see section 7h4), as well as extremely low Antarctic sea-ice extent for most of the year (see