Global Climate

Robert Dunn, John Miller, Kate Willett, Nadine Gobron, Melanie Ades,
Robert Adler, Mihai Alexe, Richard Allan, John Anderson, Orlane Anneville,
et al.

► To cite this version:

Robert Dunn, John Miller, Kate Willett, Nadine Gobron, Melanie Ades, et al. Global Climate. Bulletin of the American Meteorological Society, 2023, 104 (9), pp.S11-S145. 10.1175/BAMS-D-23-0090.1. hal-04846861

HAL Id: hal-04846861

https://hal.science/hal-04846861v1

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<figure><img src="image_1.png" /></figure>
# STATE OF THE CLIMATE IN 2022

## GLOBAL CLIMATE

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

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

Special Online Supplement to the *Bulletin of the American Meteorological Society* Vol. 104, No. 9, September, 2023

https://doi.org/10.1175/BAMS-D-23-0090.1

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

©2023 American Meteorological Society

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.
# STATE OF THE CLIMATE IN 2022
## Global Climate

**Editors**

Ellen Bartow-Gillies
Jessica Blunden
Tim Boyer

**Chapter Editors**

Peter Bissolli
Kyle R. Clem
Howard J. Diamond
Matthew L. Druckenmiller
Robert J. H. Dunn
Catherine Ganter
Nadine Gobron
Gregory C. Johnson
Rick Lumpkin
Ademe Mekonnen
John B. Miller
Twila A. Moon
Marilyn N. Raphael
Ahira Sánchez-Lugo
Carl J. Schreck III
Richard L. Thoman
Kate M. Willett
Zhiwei Zhu

**Technical Editor**

Lukas Noguchi

**BAMS Special Editor for Climate**

Michael A. Alexander

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

Photo by Taaniela Kula, Tongan Geological Services (TGS).

Eruption at Hunga Tonga-Hunga Haʻapai, witnessed by TGS observer team, a 5-kilometer wide plume rises over 18 kilometers above sea level, 14 January 2022, 5:14 pm local time.

**How to cite this document:**

Global Climate is one chapter from the *State of the Climate in 2022* annual report and is available from https://doi.org/10.1175/BAMS-D-23-0090.1. Compiled by NOAA's National Centers for Environmental Information, *State of the Climate in 2022* 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/10.1175/2023BAMSStateoftheClimate.1.

**Citing the complete report:**

Blunden, J., T. Boyer, and E. Bartow-Gillies, Eds., 2023: "State of the Climate in 2022". Bull. Amer. Meteor. Soc., 104 (9), Si-S501 https://doi.org/10.1175/2023BAMSStateoftheClimate.1.

**Citing this chapter:**

Dunn, R. J. H., J. B Miller, K. M. Willett, and N. Gobron, Eds., 2023: Global Climate [in "State of the Climate in 2022"). Bull. Amer. Meteor. Soc., 104 (9), S11-S145, https://doi.org/10.1175/BAMS-D-23-0090.1.

**Citing a section (example):**

Füllekrug, M., E. Williams, C. Price, S. Goodman, R. Holzworth, K. Virts, D. Buechler, T. Lang, and Y. Liu, 2023: Lightning [in "State of the Climate in 2022"). Bull. Amer. Meteor. Soc., 104 (9), S76-S78, https://doi.org/10.1175/BAMS-D-23-0090.1.
Editor and Author Affiliations (alphabetical by name)

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

Adler, Robert, CMNS-Earth System Science Interdisciplinary Center, 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, Chambéry, France

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

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

Arosio, Carlo, University of Bremen, Bremen, Germany

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

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

Barnes, John E., retired, NOAA Global Monitoring Laboratory, Boulder, Colorado

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

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

Blagrave, Kevin, Department of Biology, York University, Toronto, Canada

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; ENSG-Géomatique, IGN, F-77455 Marne-la-Vallée, France

Bodin, Xavier, Laboratoire EDYTEM, CNRS/Université Savoie Mont-Blanc, Chambéry, France

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

Boucher, Olivier, Sorbonne Université, Paris, France

Buechler, Dennis, University of Alabama in Huntsville, Huntsville, Alabama

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

Campos, Diego, Dirección Meteorológica de Chile (DMC), Santiago, Chile

Carrea, Laura, Department of Meteorology, 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., Geophysics Department, University Centre in Svalbard, Longyearbyen, 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 the Earth Sciences, NOAA Global Monitoring Laboratory, Boulder, Colorado

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

Cooper, Owen R., Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; NOAA Chemical Sciences Laboratory, Boulder, Colorado

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

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

Créatux, Jean-François, LEGOS (CNES/CNRS/IRD/UPS), University of Toulouse, Toulouse, France

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

Cropper, Thomas, National Oceanography Centre, Southampton, United Kingdom

Crotwell, Molly, Cooperative Institute for Research in the Earth Sciences, NOAA Global Monitoring Laboratory, Boulder, Colorado

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

Cusicanqui, Diego, Université Grenoble Alpes, Institut de Géosciences de l'Environnement (IGE), Grenoble, France

Davis, Sean M., 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

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

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

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

Dugan, Hilary A., Center for Limnology, University of Wisconsin-Madison, Madison, Wisconsin

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

Durre, Imke, NOAA/NESDIS National Centers 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, Reading, United Kingdom

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

Franz, Bryan, NASA Goddard Space Flight Center, Greenbelt, Maryland

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

Garg, Jay, Science Systems and Applications, Inc. (SSAI), Hampton, Virginia

Gibbes, Badin, School of Civil Engineering, The University of Queensland, Brisbane, Australia

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

Goodman, Steven, Thunderbolt Global Analytics, Huntsville, Alabama

Goto, Atsushi, World Meteorological Organization, Geneva, Switzerland
Editor and Author Affiliations (continued)

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

**Gu, Guojun**, CMNS-Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland

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

**Haimberger, Leopold**, University of Vienna, Vienna, Austria

**Hall, Bradley D.**, NOAA Global Monitoring Laboratory, Boulder, Colorado

**Harris, Ian**, 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**, ETH Zurich, Department of Environmental Systems Science, Zürich, Switzerland

**Ho, Shu-peng (Ben)**, Center for Satellite Applications and Research, NOAA, College Park, Maryland; Remote Sensing Systems, Santa Rosa, California

**Holzworth, Robert**, University of Washington, Seattle, Washington

**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 F.**, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; 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, Philip D.**, Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom

**Junod, Robert**, Earth System Science Center (ESSC), University of Alabama in Huntsville, Huntsville, Alabama

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

**Kaiser, Johannes W.**, SatFire Kaiser, Hofheim am Taunus, Germany

**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

**Kent, Elizabeth C.**, National Oceanography Centre, Southampton, United Kingdom

**Kidd, Richard**, EODC GmbH, Vienna, Austria

**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 Konstanz, Konstanz, Germany

**Kramarova, Natalya**, NASA Goddard Space Flight Center, Greenbelt, Maryland

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

**La Fuente, Sofia**, Dundalk Institute of Technology, Dundalk, Ireland

**Laas, Alo**, Estonian University of Life Sciences, Tartumaa, Estonia

**Lan, Xin**, CIRES, University of Colorado Boulder & NOAA Global Monitoring Laboratory, Boulder, Colorado

**Lang, Timothy**, NASA Marshall Space Flight Center, Huntsville, Alabama

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

**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 M.**, 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

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

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

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

**Maberly, Stephen C.**, UK Centre for Ecology & Hydrology, Lancaster, United Kingdom

**Madelon, Remi**, CESBIO, Université de Toulouse, CNES/CNRS/INRAe/IRD/UPS, Toulouse, France

**Magnin, Florence**, Laboratoire EDYTEM, CNRS/Université Savoie Mont-Blanc, Chambéry, France

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

**May, Linda**, UK Centre for Ecology & Hydrology, Edinburgh, United Kingdom

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

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

**McVicar, Tim R.**, CSIRO Environment, Canberra, Australia; 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.**, Department of Meteorology, University of Reading, Reading, United Kingdom

**Meyer, Michael F.**, United States 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

**Moesinger, Leander**, TU Wien, Vienna, Austria

**Monet, Ghislaine**, Université Savoie Mont Blanc, INRAE, CARRTEL, Chambéry, France

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

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

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

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

**Nance, David**, Cooperative Institute for Research in the Earth Sciences, 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, Davos-Dorf, Switzerland; Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland

**Noll, Ben**, National Institute of Water and Atmospheric Research, Auckland, New Zealand

**O'Keeffe, John**, The Harvard Forest, Harvard University, Petersham, Massachusetts

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

**Park, Taejin**, NASA Ames Research Center, Mountain View, California; Bay Area Environmental Research Institute, Mountain View, California
Editor and Author Affiliations (continued)

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 the Earth Sciences, 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, TU Wien, Department of Geodesy and Geoinformation, Vienna, Austria

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

Pulkkanen, Merja, Finnish Environment Institute (SYKE), Jyvaskyla, Finland

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

Rémy, Samuel, HYGEOS, Lille, France

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

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

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

Rocha, Willy, Servicio Nacional de Meteorologia e Hidrologia, Bolivia

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

Rodriguez-Fernandez, Nemesio, CESBIO, Université de Toulouse, CNES/CNRS/IRAE/IRD/UPS, Toulouse, France

Rosenlof, Karen H., 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

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

Sawaengphokhai, Parnchai, Science Systems and Applications, Inc. (SSAI), Hampton, Virginia

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

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

Schmid, Martin, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Surface Waters - Research and Management, Kastanienbaum, Switzerland

Schneider, Udo, Global Precipitation Climatology Centre, Deutscher Wetterdienst, Offenbach, Germany

Sezaki, Fumi, Japan Meteorological Agency, Tokyo, Japan

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

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, FMI (Finnish Meteorological Institute), Helsinki, Finland

Sparks, Tim H., Poznań University of Life Sciences, Poznań, Poland

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

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

Stauffer, Ryan, NASA Goddard Space Flight Center, Greenbelt, Maryland

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

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

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

Streletskiy, Dmitry A., George Washington University, Washington DC

Surendran, Divya E., India Meteorological Department, Ministry of Earth Sciences, Pune, India

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

Thibert, Emmanuel, Université Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, IGE, Grenoble, France.

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

Tourpali, Kleareti, Aristotle University, Thessaloniki, Greece

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 and Research, The Netherlands

Verburg, Piet, National Institute of Water and Atmospheric Research, 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 Alabama in Huntsville, Huntsville, Alabama

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

Vömel, Holger, Earth Observing Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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

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

Wang, Xinyue, National Center for Atmospheric Research, Boulder, Colorado

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., ESSIC/University of Maryland, College Park, Maryland; NOAA NESDIS/STAR, College Park, Maryland

Willett, Kate M., Met Office Hadley Centre, Exeter, United Kingdom
Editor and Author Affiliations (continued)

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, Menai Bridge, Anglesey, Wales

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

Zhou, Xinjia, Center for Satellite Applications and Research, NOAA, College Park, Maryland

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

**List of authors and affiliations**...................................................................................................................S14

**a. Overview**...................................................................................................................................................................................S20

**b. Temperature**...................................................................................................................................................................................S27

1. Global surface temperature...................................................................................................................................................S27

2. Lake surface water temperature...................................................................................................................................................S28

3. Night marine air temperature...................................................................................................................................................S31

4. Surface temperature extremes...................................................................................................................................................S33

5. Tropospheric temperature...................................................................................................................................................S36

6. Stratospheric temperature...................................................................................................................................................S38

**c. Cryosphere**...................................................................................................................................................................................S39

1. Permafrost temperature and active layer thickness...................................................................................................................S39

2. Rock glacier velocity...................................................................................................................................................................................S41

3. Alpine glaciers...................................................................................................................................................................................S43

4. Lake ice...................................................................................................................................................................................................................S45

5. Northern Hemisphere continental snow-cover extent...................................................................................................................S47

**d. Hydrological cycle**...................................................................................................................................................................................S49

1. Surface humidity...................................................................................................................................................................................S49

**Sidebar 2.1: Assessing humid heat extremes over land**...................................................................................................................S51

2. Total column water vapor...................................................................................................................................................................................S54

3. Upper-tropospheric humidity...................................................................................................................................................................................S55

4. Precipitation...................................................................................................................................................................................................................S57

5. Land surface precipitation extremes...................................................................................................................................................S58

6. Cloudiness...................................................................................................................................................................................................................S60

7. Lake water levels...................................................................................................................................................................................................................S61

8. Groundwater and terrestrial water storage...................................................................................................................................................S63

9. Soil moisture...................................................................................................................................................................................................................S65

10. Monitoring global drought using the self-calibrating Palmer Drought Severity Index...................................................................................................................................................................................S66

11. Land evaporation...................................................................................................................................................................................................................S68

**e. Atmospheric circulation**...................................................................................................................................................................................S69

1. Mean sea-level pressure and related modes of variability...................................................................................................................S69

2. Land and ocean surface winds...................................................................................................................................................................................S72

3. Upper air winds...................................................................................................................................................................................................................S74

4. Lightning...................................................................................................................................................................................................................................................S76

**f. Earth radiation budget**...................................................................................................................................................................................S79

1. Earth radiation budget at top-of-atmosphere...................................................................................................................................................S79

2. Mauna Loa apparent transmission record...................................................................................................................................................S81
## 2. Table of Contents

<table><tr><td><strong><em>Sidbar 2.2: Hunga Tonga-Hunga Ha'apai eruption</em></strong></td><td>S82</td></tr><tr><td><strong><em>g. Atmospheric composition</em></strong></td><td>S85</td></tr><tr><td>1. Long-lived greenhouse gases</td><td>S85</td></tr><tr><td>2. Ozone-depleting substances and their substitutes</td><td>S87</td></tr><tr><td>3. Aerosols</td><td>S91</td></tr><tr><td>4. Stratospheric ozone</td><td>S94</td></tr><tr><td>5. Stratospheric water vapor</td><td>S96</td></tr><tr><td>6. Tropospheric ozone</td><td>S98</td></tr><tr><td>7. Carbon monoxide</td><td>S101</td></tr><tr><td><strong><em>h. Land surface properties</em></strong></td><td>S102</td></tr><tr><td>1. Terrestrial surface albedo dynamics</td><td>S102</td></tr><tr><td>2. Terrestrial vegetation dynamics</td><td>S103</td></tr><tr><td>3. Biomass burning</td><td>S105</td></tr><tr><td>4. Phenology of primary producers</td><td>S107</td></tr><tr><td>5. Vegetation optical depth</td><td>S110</td></tr><tr><td><strong><em>Acknowledgments</em></strong></td><td>S113</td></tr><tr><td><strong><em>Appendix 1: Chapter 2 - Acronyms</em></strong></td><td>S118</td></tr><tr><td><strong><em>Appendix 2: Chapter 2 - Supplemental Material</em></strong></td><td>S122</td></tr><tr><td><strong><em>References</em></strong></td><td>S130</td></tr></table>

Please refer to Chapter 8 (Relevant Datasets and Sources) for a list of all climate variables and datasets used in this chapter for analyses, along with their websites for more information and access to the data.
## 2. GLOBAL CLIMATE

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

### a. Overview

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

Throughout 2022, the “triple-dip” La Niña (three consecutive years) showed its hand in a large number of the essential climate variables and metrics that are covered in this chapter. La Niña conditions tend to have a cooling effect on global temperatures in comparison to neutral or El Niño years and impact precipitation patterns around the globe. Upper-level wind patterns at 200 hPa across the globe for 2020–22 showed a striking similarity with the last triple-dip La Niña that occurred in 1998–2000.

Yet, despite the cooling effect of the ongoing La Niña, 2022 was still among the six warmest years since global records began in the mid-to-late 1880s, according to six datasets of global surface temperatures. It was also the warmest La Niña year on record, surpassing 2021.

Exceptional heatwaves occurred across the globe in 2022, boosted by above-average temperatures that continue their relentless long-term rise. In Europe, the “unweather”—an Old English term for weather so severe that it appears to come from a different climate or world—shattered records across the continent during the summer months, while rivers and reservoirs fell to critically low levels. Meanwhile China experienced its hottest summer on record and at Wuhan, the Yangtze River reached record-low values.

The extreme high summer temperatures over Europe resulted in unprecedented melting of glaciers in the Alps, with over 6% of their volume lost in Switzerland this year alone, a record loss. Globally, 2022 was the 35th consecutive year of glacier mass loss and the 14th consecutive year of exceptional loss (more than 500 mm water equivalent). Ice cover on lakes was almost nine days shorter than average, the fourth shortest since 1980; the five shortest ice seasons have all occurred since 2016. The average temperature anomaly for more than 1950 lakes across the Northern Hemisphere was the second highest since the beginning of the record in 1995.

Drought conditions were pervasive, occurring across Europe (linked to the extreme summer temperatures), as well as the American West, China, and most of Southern Hemisphere South America. Globally, record-high areas of land experiencing extreme drought (6.2%) were reached in August 2022; overall, 29% of land experienced moderate or worse categories of drought. Low values of terrestrial water storage also occurred in Europe and parts of China (linked to the heatwaves), but La Niña influenced high values in southeastern Australia. Extreme rainfall was observed in southeastern and eastern Australia as well as in Pakistan, which received around three times its normal August monsoon rainfall. However, precipitation amounts for the globe as a whole and over the ocean was much lower than normal, but close to normal over land. Total column water vapor and surface humidity were close to normal over the ocean. Despite ‘normal’ rainfall amounts over land and lower terrestrial water storage, 2022 saw continued above-average soil moisture values, which are approaching the level of the previous global record in 2011. Lake water levels were higher than normal overall, but cloudiness was below normal. Overall, 2022 was a mixed year for the hydrological variables presented in this chapter.

In addition to the ongoing La Niña, other climate modes of variability in 2022 included a negative Indian Ocean dipole, a positive winter North Atlantic Oscillation (NAO), and the second highest summer NAO on record. The Southern Annular Mode was positive for a record-equaling
76% of days in 2022; it was the sixth time 60% of days has been exceeded since 2015, compared to only 10 years between 1979 and 2014 where high positive rates of >60% of days occurred.

The most significant long-term changes in atmospheric composition continue to be record levels of long-lived greenhouse gases (LLGHGs). Globally averaged carbon dioxide, methane, and nitrous oxide levels in 2022 continued to increase rapidly by 2.2 ppm, 14.4 ppb, and 1.3 ppb to 417.1 ppm, 1911.8 ppb, and 335.7 ppb, respectively. Collectively, all LLGHGs contributed 3.4 W m⁻² of all radiative forcing, with the main three LLGHGs accounting for 98% of the increase in the last five years. As measured by equivalent effective stratospheric chlorine, the gases that destroy stratospheric ozone continue to decline nearly linearly. Since the 2018 discovery of post-2011 renewed trichlorofluoromethane (CFC-11) emissions, in violation of the Montreal Protocol, illegal emissions have mostly disappeared.

Considering short-lived atmospheric components, global mean aerosol optical depth in 2022 was the lowest on record, as was carbon monoxide, reflecting fewer fires in 2022 on top of a long-term decreasing trend likely reflecting global improvements in fossil fuel combustion efficiency. Tropospheric ozone has continued to trend upwards with an 8% increase since 2004. The most remarkable impact on short-lived species in 2022 resulted from the Hunga Tonga-Hunga Ha'apai underwater volcanic eruption (HTHH) in January 2022, discussed in Sidebar 2.2. Stratospheric aerosols registered their largest perturbation since the Mt. Pinatubo eruption of 1991. The HTHH eruption injected ~50 Tg–150 Tg of water vapor into the stratosphere, an amount unprecedented in the satellite record which represents more than 10% of the entire stratospheric water vapor burden, an anomaly that will persist for several years.

Low amounts of early summer snow cover in the Northern Hemisphere, along with increased plant growth and associated greening resulting from La Niña-induced rainfall decreased the global albedo in 2022. Carbon emissions from biomass burning during 2022 were 22% below the long-term average, making it the lowest fire year on record. However, considerable fire activity was still observed in boreal North America, parts of Europe, and central South America.

This year, a new measure of humid heat events is introduced in Sidebar 2.1, using equivalent heat indices based on the wet bulb temperature rather than air temperature. Humid heat extremes have increased in both magnitude and frequency since 1973, and 2022 was above average for both.

Time series and anomaly maps for 2022 from many of the variables described in this chapter can be found in Plates 1.1 (Chapter 1) and 2.1. Most sections now use the 1991–2020 climatological reference period, in line with World Meteorological Organization recommendations. This was not possible for all datasets depending on their length of record or legacy processing methods at the time of writing and is noted accordingly.

Finally, already looking towards the next reports, we welcome expressions of interest from those who wish to propose new sections for this chapter or wish to bring their expertise to existing author teams.
<figure><img src="image_3.png" /><figcaption>(c) Night Marine Air Temperature</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(e) Cool Nights</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(g) Surface Specific Humidity</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(b) Lake Temperature</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(d) Warm Days</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(f) Lower Tropospheric Temperature</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 ESA CCI LAKES/Copernicus C3S (°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 RSS and UAH lower-tropospheric temperature anomalies (°C). Hatching denotes regions in which 2022 was the warmest year on record; (g) ERA5 surface specific humidity anomalies (g kg⁻¹);</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>(j) Upper Tropospheric Humidity</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(l) Maximum 1 Day Precipitation Amount</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(n) Lake Water Level</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(i) Total Column Water Vapor</figcaption></figure>

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

<figure><img src="image_15.png" /><figcaption>(m) Cloudiness</figcaption></figure>

<figure><img src="image_16.png" /></figure>

Plate 2.1 (cont.) (h) ERA5 surface relative humidity anomalies (%rh); (i) ERA5 TCWV anomalies (%). Data from GNSS stations are plotted as filled circles; (j) Annual microwave-based UTH anomalies (%rh); (k) GPCP v2.3 annual mean precipitation anomalies (mm yr⁻¹); (l) CHIRPS maximum 1-day (Rx1day) annual precipitation anomalies (mm); (m) PATMOS-x 6.0 cloud fraction annual anomalies (%); (n) Goshen lake water level anomalies. Triangles pointing upward indicate positive anomalies, and triangles pointing down indicate negative anomalies;
<figure><img src="image_17.png" /><figcaption>(q) Drought</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(s) Sea Level Pressure</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>(u) Upper Air (850-hPa) Eastward Winds</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>(p) Soil Moisture</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>(r) Land Evaporation</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>(t) Surface Winds</figcaption></figure>

<figure><img src="image_23.png" /></figure>

Plate 2.1 (cont.) (o) GRACE and GRACE-FO difference in annual-mean terrestrial water storage between 2020 and 2021 (cm); (p) 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; (q) Mean scPDSI for 2021. Droughts are indicated by negative values (brown), wet episodes by positive values (green); (r) GLEAM land evaporation anomalies (mm yr⁻¹); (s) ERA5 mean sea-level pressure anomalies (hPa); (t) Surface wind speed anomalies (m s⁻¹) from the observational HadISD3 dataset (land, circles), the ERA5 reanalysis output (land, shaded areas), and RSS satellite observations (ocean, shaded areas); (u) ERA5 850-hPa eastward wind speed anomalies (m s⁻¹);
<figure><img src="image_24.png" /><figcaption>(x) Extreme Aerosol Days</figcaption></figure>

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

<figure><img src="image_26.png" /><figcaption>(ab) Land Surface Albedo in the Visible</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>(w) AOD Percentage Anomaly</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>(y) Stratospheric (Total Column) Ozone</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>(aa) Carbon Monoxide</figcaption></figure>

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

Plate 2.1 (cont.) (v) Total aerosol optical depth (AOD) anomalies at 550 nm; (w) Percent difference of total AOD at 550 nm in 2022 relative to 2003–21; (x) Number of days with AOD above the 99.9th percentile. Areas with zero days appear as the white/gray background; (y) TROPOMI aboard Sentinel-5 Precursor (S5P) measurements of total column ozone anomalies relative to the 1998–2008 mean from GSG merged dataset (DU); (z) OMI /MLS tropospheric ozone column anomalies for 60°S–60°N (DU); (aa) CAMS reanalysis total column CO anomalies (x 10¹⁸ molecules cm⁻²); (ab) Land surface visible broadband albedo anomalies (%);
<figure><img src="image_31.png" /><figcaption>(ae) Carbon Emissions from Biomass Burning</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>(ag) Humid-heat days (TwX90p)</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>(ad) Fraction of Absorbed Photosynthetically Active Radiation</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>(af) Vegetation Optical Depth</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>(ah) Maximum T<sub>wet</sub></figcaption></figure>

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

Plate 2.1 (cont.) (ac) Land surface near-infrared albedo anomalies (%); (ad) FAPAR anomalies; (ae) GFASv1.4 carbonaceous emission anomalies (g C m⁻² yr⁻¹) from biomass burning; (af) VODCA Ku-band VOD anomalies; (ag) HadISDH extremes daily maximum wet bulb temperature 90th percentile exceedances (days yr⁻¹); (ah) HadISDH/extremes annual mean anomaly in daily maximum wet bulb of the month (°C).
## b. Temperature

# 1. GLOBAL SURFACE TEMPERATURE

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

The year 2022 secured its place as one of the 10 warmest years since global records began (in the mid-1800s to mid-1900s, with the length of record depending on the dataset), with a global surface temperature between 0.25° and 0.30°C above the 1991–2020 average, according to six global temperature datasets (Table 2.1). Depending on the dataset, 2022 was either the fifth-warmest (equal with 2015 in some datasets) or sixth-warmest year on record. Despite these minor differences in anomalies and ranks between datasets, all six datasets agree that the last eight years (2015–22) were the eight warmest years on record (Fig. 2.1), and the global trends at the short- (1980–2022) and long-term (1880–2022) periods for each dataset are consistent with each other. The annual global average surface temperature has increased at an average rate of 0.08°C decade⁻¹ to 0.09°C decade⁻¹ since 1880 and at a rate more than twice that since 1980 (0.19°C decade⁻¹ to 0.20°C decade⁻¹). The datasets consist of four global in situ surface temperature analyses (NASA GISS Surface Temperature Analysis version 4 [NASA-GISSTEMP v4], Lenssen et al. 2019; Hadley Centre/Climatic Research Unit Temperature version 5 [HadCRUT5], Morice et al. 2021; NOAA Merged Land Ocean Global Surface Temperature Analysis version 5.1.0 [NOAAGlobalTemp v5.1.0], Vose et al. 2021; Berkeley Earth, Rhode and Hausfather 2020) and two global atmospheric reanalyses (European Centre for Medium-Range Weather Forecasts Reanalysis version 5 [ERA5], Hersbach et al. 2020, Bell et al. 2021; Japanese 55-year Reanalysis [JRA-55], Kobayashi et al. 2015).

Even though 2022 ranked as one of the six warmest years on record, the presence of

<figure><img src="image_37.png" /><figcaption>Fig. 2.1. Global average surface air temperature anomalies (°C; 1991–2020 base period) for (a) land and ocean, (b) land only, and (c) ocean only. In situ estimates are shown from the datasets NOAAGlobalTemp (Vose et al. 2021), NASA-GISS (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). The 95% confidence ranges are also shown for HadCRUT5, CRUTEM5, and HadSST4. Reanalyses estimates are shown from the datasets ERA5 (Hersbach et al. 2020) and JRA-55 (Kobayashi et al. 2015).</figcaption></figure>

<table><caption>Table 2.1. Temperature anomalies (°C; 1991–2020 base period) for 2022. Note that for the HadCRUT5 column, land values were computed using the CRUTEM.5.0.1.0 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 used the HadCRUT.5.0.1.0 dataset (Morice et al. 2021).</caption><thead><tr><th>Global</th><th>NASA-GISS</th><th>HadCRUT5</th><th>NOAA GlobalTemp</th><th>Berkeley Earth</th><th>ERA5</th><th>JRA-55</th></tr></thead><tbody><tr><td>Land</td><td>+0.40</td><td>+0.30</td><td>+0.49</td><td>+0.34</td><td>+0.41</td><td>+0.34</td></tr><tr><td>Ocean</td><td>+0.19</td><td>+0.23</td><td>+0.19</td><td>–</td><td>+0.26</td><td>+0.22</td></tr><tr><td>Land and Ocean</td><td>+0.28</td><td>+0.26</td><td>+0.28</td><td>+0.27</td><td>+0.30</td><td>+0.25</td></tr></tbody></table>
La Niña—the cool phase of the El Niño–Southern Oscillation (ENSO)—in the Pacific Ocean had a dampening effect on the global temperatures, in comparison to years characterized by El Niño or ENSO-neutral conditions. The year began with La Niña conditions, which first developed in August 2020 and persisted throughout most of 2021 and all of 2022 (see section 4b for details). 2022 was also the warmest La Niña year on record, surpassing the previous record set in 2021.

While it is common, and arguably expected, for each newly completed year to rank as a top 10 warmest year (see Arguez et al. 2020), the global annual temperature for 2022 was lower than we would expect due to the secular warming trend alone, with trend-adjusted anomalies regitering between the 20th and 40th percentiles (depending on the dataset) following the Arguez et al. (2020) approach. Trend-adjusted anomalies for 2022 are consistent with the typical slight cooling influence of La Niña and similar to the trend-adjusted anomalies recorded over the relatively cool years from 2011 to 2014, as well as 2021, years that also predominantly exhibited cooler-than-normal ENSO index values.

Above-normal temperatures were observed across much of the world's land and ocean surfaces during 2022 (Plate 2.1a; Appendix Figs. A2.1–A2.4). Notably, record-high annual temperatures were present across Europe, northern Africa, and parts of the Middle East, central Asia, and China, as well as the northern and southwestern Pacific, Atlantic, and Southern Oceans. Below-normal annual temperatures were present across parts of northern North America, South America, Africa, Australia, and the southeastern, central, and eastern tropical Pacific Ocean. The global land-only surface temperature was 0.30°C–0.49°C above normal, the fifth to seventh highest on record, depending on the dataset. The annual global sea-surface temperature was also fifth or sixth highest on record, at 0.19°C–0.26°C above normal.

## 2. LAKE SURFACE WATER TEMPERATURE

—L. Carrea, C. J. Merchant, J.-F. Creatux, T. M. Dokulil, H. A. Dugan, B. Gibbes, A. Laas, E. M. Leibensperger, S. Maberly, L. May, S.-I. Matsuzaki, G. Monet, D. Pierson, M. Pulkkanen, O. O. Rusanovskaya, S. V. Shimaraeva, E. A. Silow, M. Schmid, M. A. Timofeyev, P. Verburg, and R. I. Woolway

<figure><img src="image_38.png" /></figure>

In 2022, the worldwide averaged satellite-derived lake surface water temperature (LSWT) warm-season anomaly was +0.33°C with respect to the 1995–2020 baseline, the second highest since the record began in 1995. The mean LSWT trend between 1995 and 2022 was 0.20±0.01°C decade⁻¹, broadly consistent with previous analyses (e.g., Carrea et al. 2020, 2021, 2022a; Fig. 2.2a). Warm-season anomalies for each lake are shown in Plate 2.1b. The lake-mean temperature anomalies were positive for 70% and negative for 30% of the 1951 globally distributed lakes. For about 30 other lakes, no anomalies could be computed since no water was found in 2022.

Large regions of coherently high LSWT anomalies were identified in 2022, with 40% of the observed lakes experiencing LSWT

**Fig. 2.2.** Annual time series of satellite-derived warm-season lake surface water temperature anomalies (°C; 1995–2020 base period) from 1995 to 2022 for lakes distributed (a) globally, and regionally in (b) Europe, (c) Africa, (d) the Tibetan Plateau, and (e) Canada.
anomalies in excess of +0.5°C (Plate 2.1b). The highest anomalies were for lakes situated in the northwestern contiguous United States and Canada. Negative LSWT anomalies were consistently observed throughout most of South America (except Patagonia), parts of Africa, and in Alaska and Greenland.

Four regions of interest were studied in more detail: Canada (number of lakes, *n* = 556, Figs. 2.2e, 2.3c), Europe (*n* = 268, Figs. 2.2b, 2.3a), Tibet (*n* = 145, Figs. 2.2d, 2.3d), and Africa (*n* = 147, Figs. 2.2c, 2.3b). In these regions, the warm season LSWT anomalies are consistent with the corresponding 2-m air temperature anomalies, as measured by NASA GISS (Hansen et al. 2010; GISS Surface Temperature Analysis [GISTEMP] Team 2022) and show an average warming trend of +0.31±0.03°C decade⁻¹ in Europe (Fig. 2.2b) and +0.15±0.03°C decade⁻¹ in Canada (Fig. 2.2e). In Africa, long-term change in LSWT is comparatively smaller at +0.10±0.01°C decade⁻¹ (Fig. 2.2c), while in Tibet the warming tendency has increased relative to previous reports with the largest positive anomaly in 2022. The warming rate of LSWT in Tibet from 1995 to 2022 was +0.15±0.02°C decade⁻¹ (Fig. 2.2d). Moreover, in Tibet, all the observed lakes, except one, experienced positive LSWT anomalies in 2022 with an average of +0.6°C, which is more than double the standard deviation of mean anomalies from 1995 to 2022 and confirmed by high anomalies for the air temperature (Fig. 2.3d). In Europe, below-normal LSWT in northern Europe (80 lakes) was less prevalent than above-normal LSWT (188 lakes), resulting in an average of +0.35°C. In Africa, 60% of the 147 lakes experienced negative LSWT anomalies, and the average anomaly in 2022 was -0.11°C. In Canada, 91% of the observed lakes experienced positive anomalies, with only 9% experiencing negative anomalies for an average of +0.67°C in 2022.

<figure><img src="image_39.png" /><figcaption>**Fig. 2.3.** Lake temperature anomalies (°C, colored dots) and 2-m air temperature anomalies (°C) in 2022 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 situ observations of warm season LSWT anomalies from the 1995–2020 mean for 40 lakes, 18 of which have measurements for the year 2022, are shown in Fig. 2.4 with an average anomaly of –0.03°C. Fourteen lakes experienced positive anomalies (average: +0.70°C) and four lakes negative anomalies (average: –2.60°C) in 2022. At the in situ measurement site on Lake Baikal in Siberia, a temperature anomaly of –6.9°C was recorded, which is very different from the satellite lake-mean anomaly of –0.47°C, suggesting a within-lake variation of the LSWT anomalies (see Carrea et al. 2022a; Toffolon et al. 2020) on Lake Baikal. At the in situ site, such a large negative anomaly suggests a potential intrusion of colder water resembling upwellings in ocean waters; this has been recorded on the lake for more than 20 years at different depths. Overall, the time series of the 18 lakes show clearly that lakes are warming, especially after the year 2000.

In North America, the anomalies recorded from the in situ data for Lakes Superior, Erie, Michigan, and Huron are –2.98°C, +0.67°C, +0.69°C, and +0.55°C, respectively, which are noticeably larger (in absolute terms) than those estimated from satellite measurements (–0.61°C, +0.20°C, +0.28°C, and +0.18°C, respectively). The difference is largely because in situ data are point measurements whereas satellite data represent lake-wide averages, suggesting spatial patterns of the LSWT anomalies (see Carrea et al. 2022a; Toffolon et al. 2020). In Europe, all the lakes with in situ data had positive anomalies, except Lake Balaton (Hungary) which was 0.36°C below its 1995–2020 average (–0.01°C with satellite). Mondsee (Austria) was 1.51°C warmer than average in 2022 and the highest recorded value for the in situ data. In New Zealand, Lake Taupō had a slight negative anomaly of –0.15°C (+0.98°C from satellites) while Rotorua had a positive anomaly of +0.51°C (with reference period 2011–2020) compared to the anomaly from satellite of +0.6°C (with reference period 1995–2020).

The LSWT warm-season averages for midlatitude lakes are computed for summers (July–September in the Northern Hemisphere and January–March in the Southern Hemisphere), and whole-year averages are presented for tropical lakes (within 23.5° of the equator).

LSWT time series were derived from the European Space Agency Climate Change Initiative LAKES/Copernicus C3S climate data record (Carrea et al. 2022b, 2023). For 2022, satellite observation from the Sea and Land Surface Temperature Radiometer on Sentinel3B and MODIS on Terra 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 1951 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 (Carrea et al. 2023). Lake-wide average surface temperatures have been shown to give a more representative picture of LSWT responses to climate change than single-point measurements (Woolway and Merchant 2018).

<figure><img src="image_40.png" /><figcaption>**Fig. 2.4.** In situ lake surface water temperature (LSWT) observations from 40 globally distributed lakes (the name is reported for the lakes mentioned in the text), showing the annually averaged warm season (Jul–Sep in the Northern Hemisphere; Jan–Mar in the Southern Hemisphere) anomalies (°C; 1995–2020 base period).</figcaption></figure>
### 3. NIGHT MARINE AIR TEMPERATURE

—R. C. Cornes, T. Cropper, R. Junod, and E. C. Kent

Air temperature measurements have been made onboard ships for centuries and continue to be collected today thanks to the Voluntary Observing Ship initiative (https://www.ocean-ops.org/reportcard2022/). Gridded datasets of marine air temperature

(MAT) are constructed from the individual measurements, and two such datasets that are routinely updated are used in this section: University of Alabama in Huntsville night-time MAT (UAHNMAT; Junod and Christy 2020) and Climate Linked Atlantic Sector Science night MAT (CLASSnmat; Cornes et al. 2020). Since daytime MAT observations are biased warm due to heating from the ship superstructure, only night-time values are currently used in these datasets and, hence, they are referred to as night marine air temperature (NMAT). These NMAT datasets provide comparison against the more widely used sea-surface temperature (SST) datasets. In keeping with this theme, we also include SST statistics from The Met Office Hadley Centre's sea-surface temperature dataset (HadSST4; Kennedy et al. 2019) in this section. Note, however, that the large-scale average values from HadSST4 presented in this section (Fig. 2.5 and Table 2.2) may differ slightly from other estimates from the dataset presented in this report because the data have been masked such that the spatial coverage is the same across the three datasets in order to ensure a fair comparison.

Evidence from the NMAT datasets and HadSST4 indicates that across global ocean regions, 2022 was the fifth-warmest year since 1900 (Table 2.2). As with the global estimates of temperature discussed in section 2b1, the “triple-dip” La Niña conditions (see Sidebar 3.1 for details) suppressed

<figure><img src="image_41.png" /><figcaption>Fig. 2.5. Annual mean night marine air and sea-surface temperature anomalies (°C; 1991–2020 base period) calculated from the CLASSnmat, UAHNMAT, and HadSST4 datasets averaged over the (a) globe, (b) northern extra-tropics, (c) tropics, and (d) southern extra-tropics. 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 and since UAHNMAT starts in 1900, only values for the period 1900–2022 are plotted.</figcaption></figure>

<table><caption>Table 2.2. Average anomalies (°C; 1991–2020 base period) for 2022 calculated from two NMAT datasets (CLASSnmat and UAHNMAT) and HadSST4. The regions are defined as in Fig. 2.5. The values in parentheses indicate the ranking of 2022 values within the period 1900–2022.</caption><thead><tr><td>Dataset</td><td>Global</td><td>Northern Extra-Tropics</td><td>Tropics</td><td>Southern Extra-Tropics</td></tr></thead><tbody><tr><td>CLASSnmat</td><td>0.16 (5)</td><td>0.52 (1)</td><td>0.03 (16)</td><td>0.25 (3)</td></tr><tr><td>UAHNMAT</td><td>0.15 (5)</td><td>0.47 (1)</td><td>0.03 (20)</td><td>0.28 (1)</td></tr><tr><td>HadSST4</td><td>0.26 (5)</td><td>0.71 (1)</td><td>0.12 (8)</td><td>0.29 (1)</td></tr></tbody></table>
the global average NMAT values during the year. This feature is apparent in the maps of temperature anomalies (Fig. 2.6) and is reflected in the average anomalies for the tropics, where 2022 ranked as only the 16th- or 20th-warmest year on record (Table 2.2).

Across the northern extra-tropics (north of 30°N), 2022 was the warmest year in the 123-year record according to all three datasets. Monthly anomalies were more than 0.3°C above the 1991–2020 average throughout all months of the year, with particularly large anomalies greater than +0.7°C recorded from August to November. The annual average anomaly was greatest in HadSST4 (+0.7°C) compared to the NMAT datasets where it was approximately +0.5°C. The highest positive temperature anomalies in this region were recorded across the northern Pacific Ocean (Fig. 2.6). Across all datasets, relatively high anomalies were also recorded in the north-east Atlantic Ocean, particularly in the seas around western Europe and the Mediterranean, and across the western boundary current region of the North Atlantic.

Across the southern extra-tropics, NMAT anomalies were also high in 2022, with relatively high anomalies recorded in the western South Pacific/Coral Sea region. For CLASSnmat, the year ranked as third warmest for the region whereas both UAHNMAT and HadSST4 ranked 2022 as the warmest year in the series. Due to the incorporation of drifting buoy data in HadSST4, the spatial coverage is generally better than for the NMAT datasets, which only use ship-based measurements of air temperature, and this is most apparent across the Southern Ocean. This sparser coverage results in a greater uncertainty in the NMAT datasets in this region.

In previous *State of the Climate* reports (e.g., Cornes et al. 2022), the discrepancy in trends between the NMAT and SST datasets has been discussed. While the global average trend between 1900 and 2022 is slightly higher in HadSST4 compared to the NMAT datasets (c.f. 0.09°C decade⁻¹ in UAHNMAT and 0.11°C decade⁻¹ in CLASSnmat compared to 0.16°C decade⁻¹ in HadSST4) there is a much larger discrepancy in temperature trends between SST and NMAT after around 1990. This is particularly the case in the tropics where temperature increased at a rate of 0.05°C decade⁻¹ or 0.07°C decade⁻¹ in NMAT compared to 0.13°C decade⁻¹ in HadSST4. The reason for this discrepancy remains unclear and a wider discussion of this feature of the data, which also considers trends in Coupled Model Intercomparison Project (CMIP) simulations, is provided by Cross-chapter box 2.3 in Gulev et al. (2021).

<figure><img src="image_42.png" /></figure>

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

<figure><img src="image_44.png" /><figcaption>**Fig. 2.6.** Average annual night marine air and sea-surface anomalies (°C; 1991–2020 base period) for 2022 in the (a) CLASSnmat, (b) UAHNMAT, and (c) HadSST4 datasets. Averages were calculated for a grid-cell where more than six months of data are present. This calculation has been done separately for each dataset and results in a different spatial coverage in the three datasets.</figcaption></figure>
## 4. SURFACE TEMPERATURE EXTREMES

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

In 2022, the globally averaged frequency of warm days (TX90p; see Table 2.3 for the definition) in reanalyses products was about 49 (ERA5, Hersbach et al. 2020), 40 (MERRA-2, Gelaro et al. 2017) and 44 (JRA-55, Kobayashi et al. 2015), and 66 based on observational data from the Global Historical Climatology Network Index (GHCNDEX; which has more limited spatial coverage and uses a different reference period; Donat et al. 2013), slightly lower than in the previous year (Fig. 2.7). While the global average warm-day frequency in 2022 cannot be regarded as extraordinary compared to previous years, the frequencies are substantially above the average value of 36 days per year (Fig 2.7).

Large areas of the globe were affected by strong, and in some places record-breaking hot extremes in 2022. In particular, large parts of Europe, Asia, and South America were affected by anomalously frequent warm days (in many areas, more than double the average frequency; Plate 2.1d). The frequency of warm days was highest on record in large parts of China and Western and southwestern Europe (and northwestern Africa, for ERA5; Fig. 2.8; Supp. Fig. A2.5b). This high frequency of warm days was accompanied by heat events of record-breaking intensity. Large parts of China, Central Asia, and Central and Western Europe show the highest TXx values (annual maximum of daily maximum temperatures) in the GHCNDEX record (Supp. Fig. A2.5a). These extreme

<figure><img src="image_45.png" /><figcaption>Fig. 2.7. Time series of the annual number of (a),(c) warm days and (b),(d) cool nights averaged over global land regions based on gridded station data from the GHCNDEX dataset (a),(b) and three atmospheric reanalyses (ERA5, MERRA-2, JRA-55; (c),(d)). The spatial coverage in GHCNDEX is limited; the black dotted lines show the percentage of land area covered (right y-axis in (a),(b)), and the coverage uncertainty (2-σ, following Brohan et al. 2006, Dunn et al. 2020) is shown as the light red bands in (a),(b).</figcaption></figure>

<table><caption>Table 2.3. Definitions of temperature extremes indices, along with 2022 value and ranks from the four datasets. Reference period for GHCNDEX (1961–90) is different to that used for the reanalyses products (1991–2020).</caption><thead><tr><th>Index</th><th>Name</th><th>Definition</th><th>GHCNDEX<br>(1951–2022)<br>Value, Rank</th><th>ERA5<br>(1979–2022)<br>Value, Rank</th><th>MERRA-2<br>(1980–2022)<br>Value, Rank</th><th>JRA-55<br>(1970–2022)<br>Value, Rank</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>66.2 days,<br>3rd highest</td><td>48.6 days,<br>8th highest</td><td>40.0 days,<br>14th highest</td><td>44.0 days,<br>10th 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>23.0 days,<br>11th lowest</td><td>31.1 days,<br>8th lowest</td><td>32.3 days,<br>9th lowest</td><td>31.2 days,<br>6th lowest</td></tr><tr><td>TXx</td><td>Hottest<br>Day</td><td>Annual highest value of daily maximum temperature</td><td>—</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>
temperature values include the first-ever occurrence of temperatures exceeding 40°C in the United Kingdom and other parts of northwestern Europe (see section 7f for more details). In contrast, likely due to the persisting La Niña conditions, southeastern Australia recorded its lowest TXx values in 2022 in the ERA5 and GHCNDEX records (Fig. 2.8a and Supp. Fig. A2.6a, respectively).

The frequency of cool nights (TN10p; see definition in Table 2.3) was the eighth lowest on record for ERA5 and 16th lowest based on the GHCNDEX global average. Regions affected by an above-average frequency of cool nights, reflecting relatively low temperatures in general, include parts of Australia, South America, and northwestern North America (Plate 2.1e). Parts of South America had minimum night-time temperatures that were among the lowest on record based on ERA5 (Fig. 2.8c). In contrast, most other land regions showed below-average frequency of cold extremes (Plate 2.1e).

Analysis of NOAA Optimum Interpolation Sea Surface Temperature (NOAA OISST v2.1; Huang et al. 2021) showed that, in 2022, 58% of the ocean surface experienced at least one marine heatwave (MHW; Hobday et al. 2016; Figs. 2.9a,b), and 25% experienced at least one marine cold spell (MCS; Figs. 2.9c,d). Category 2 Strong MHWs (Hobday et al. 2018) were the most common (26%) warm events for the ninth consecutive year, whereas Category 1 Moderate MCSs have remained the most common (20%) cool events in all years since 1985. The ocean experienced a global average of 57 MHW days (18 MCS days) in 2022, which is greater than the 2021 average of 48 days (13 days), but less than the 2016 record of 61 days (1982 record of 27 days; Figs. 2.9a,c). This daily average equates to 16% (5%) of the surface of the ocean experiencing a MHW (MCS) on any given day (Figs. 2.9a,c).

Land surface temperature extremes are characterized by indices developed by the former World Meteorological Organization Expert Team in Climate Change Detection and Indices (Zhang et al. 2011). The observations-based GHCNDEX (Donat et al. 2013) uses daily maximum and minimum temperatures from the GHCND dataset (Menne et al. 2012) to calculate these indices for each station, which are then interpolated onto a regular 2.5° grid. Spatial coverage for 2022 is, as in previous years, limited to primarily the Northern

<figure><img src="image_46.png" /><figcaption>**Fig. 2.8.** Maps indicating grid cells where the temperature indices for 2022 ranked in the three highest or three lowest values based on ERA5 since 1979: (a) hottest day of the year (TXx), (b) annual number of warm days (TX90p), and (c) annual number of cool nights (TN10p).</figcaption></figure>
Hemisphere extra-tropics and Australia, with very little coverage in Africa and South America (Fig. A2.5). We use the ERA5 reanalysis (Hersbach et al. 2020) to provide globally complete fields of these indices covering 1979–2022, which performed well in a recent intercomparison between observation and reanalyses datasets (Dunn et al. 2022). The indices quantifying exceedances of percentile-based thresholds use a fixed reference period, and intercomparison between these is complex in a strongly warming climate (Dunn et al. 2020; Yosef et al. 2021; Dunn and Morice 2022). The percentile period in GHCNDEX is 1961–90, whereas for the index calculations with ERA5 the percentiles are calculated for the 1991–2020 period.

An MHW is detected when five or more consecutive days of temperature are above a 90th percentile daily climatology (Hobday et al. 2016). MHWs are categorized as moderate when the greatest temperature anomaly during the event is less than double the 90th percentile for the seasonal anomaly. When this value is more than double, triple, or quadruple the distance, the MHW is categorized as strong, severe, or extreme, respectively (Hobday et al. 2018). The direct inverse is used to detect and categorize 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.

<figure><img src="image_47.png" /><figcaption>Fig. 2.9. (a),(c) Average annual number of global marine heatwave (MHW) and marine cold-spell (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 an 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. The base period is 1982–2011. (Source: NOAA OISST v2.1.)</figcaption></figure>
## 5. TROPOSPHERIC TEMPERATURE

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

The 2022 globally averaged lower-tropospheric temperature (LTT) anomaly was 0.26°C (0.17°C to 0.37°C) above the 1991–2020 average, ranking among the nine warmest years on record (fourth to ninth warmest, depending on the dataset). Long-term warming of the troposphere is consistent with our understanding of greenhouse warming. Other factors, such as volcanic eruptions, decadal variability, and solar activity also modulate the long-term warming trend (Christy and McNider 2017; Po-Chedley et al. 2022). Interannual variations in global LTT are dominated by the El Niño–Southern Oscillation, which has largely been in a La Niña state since August 2020 (see section 4b and Sidebar 3.1 for details; Figs. 2.10a,b). As with the year 2021, the depression of atmospheric temperature due to La Niña combined with the background warming trend (Table 2.4) produced a year that was warmer than average, but not record breaking.

La Niña events are accompanied by a distinct pattern of tropospheric temperature anomalies, which are evident in the annual average departures in both 2022 (Plate 2.1f) and 2021 (see Plate 2.1f)

<table><caption>Table 2.4. Temperature trends (units of °C decade⁻¹) for global lower-tropospheric temperature (LTT) and tropical tropospheric temperature (TTT) over the periods 1958–2022 and 1979–2022. NASA MERRA-2 data begins in 1980 and NOAA STAR v5.0 TLT begins in 1981. UW does not produce an LTT product.</caption><thead><tr><th>Method</th><th>Source</th><th>LTT<br>(90°S–90°N)<br>1958–2022</th><th>LTT<br>(90°S–90°N)<br>1979–2022</th><th>TTT<br>(20°S–20°N)<br>1958–2022</th><th>TTT<br>(20°S–20°N)<br>1979–2022</th></tr></thead><tbody><tr><td>Radiosonde</td><td>NOAA RATPAC vA2<br>(Free et al. 2005)</td><td>0.18</td><td>0.22</td><td>0.17</td><td>0.18</td></tr><tr><td>Radiosonde</td><td>RAOBCORE v1.9<br>(Haimberger et al. 2012)</td><td>0.16</td><td>0.17</td><td>0.13</td><td>0.15</td></tr><tr><td>Radiosonde</td><td>RICH v1.9<br>(Haimberger et al. 2012)</td><td>0.18</td><td>0.20</td><td>0.18</td><td>0.19</td></tr><tr><td>Satellite</td><td>UAH v6.0<br>(Spencer et al. 2017)</td><td>—</td><td>0.13<sup>[1]</sup></td><td>—</td><td>0.12</td></tr><tr><td>Satellite</td><td>RSS v4.0<br>(Mears and Wentz, 2016)</td><td>—</td><td>0.21</td><td>—</td><td>0.16</td></tr><tr><td>Satellite</td><td>UW v1.0<br>(Po-Chedley et al. 2015)</td><td>—</td><td>—</td><td>—</td><td>0.16</td></tr><tr><td>Satellite</td><td>NOAA STAR v5.0<br>(Zou et al. 2023)</td><td>—</td><td>0.13<sup>[1]</sup></td><td>—</td><td>0.10</td></tr><tr><td>Reanalysis</td><td>ERA5<br>(Hersbach et al. 2020)</td><td>—</td><td>0.18</td><td>—</td><td>0.15</td></tr><tr><td>Reanalysis</td><td>JRA-55<br>(Kobayashi et al. 2015)</td><td>0.17</td><td>0.18</td><td>0.16</td><td>0.14</td></tr><tr><td>Reanalysis</td><td>NASA MERRA-2<br>(Gelaro et al. 2017)</td><td>—</td><td>0.19</td><td>—</td><td>0.17</td></tr><tr><td>Median</td><td>Calculated from previous values</td><td>0.17</td><td>0.18</td><td>0.16</td><td>0.15</td></tr></tbody></table>

<footnote>[1] The vertical sampling in UAH and NOAA STAR LTT is slightly different from other datasets and results in temperature trends that are approximately 0.01°C decade⁻¹ smaller than other datasets.</footnote>
in Blunden and Boyer 2022). La Niña conditions over late 2021 through 2022 contributed to record-breaking LTT values over the North and South Pacific Ocean, China, and parts of South Asia. Persistent summertime heatwaves during June to August contributed to record-breaking tropospheric temperatures over Europe in 2022. Large positive total column water vapor anomalies were collocated with the anomalous tropospheric warmth (Plate 2.1i; section 2d2). Overall, the global LTT was above average across 70% of the globe, with 6% of Earth experiencing the highest temperatures since the start of the record in 1979 (Plate 2.1f; Fig. 2.10c). In contrast, 1% of Earth experienced its coldest year on record.

Atmospheric temperature data are derived from balloon-borne radiosonde measurements, satellite-based microwave soundings, and atmospheric reanalyses (Table 2.4). Each dataset employs different strategies to remove biases and drifts from sources of atmospheric temperature data. Across datasets and measurement techniques, there is good agreement on interannual timescales (Fig. 2.10a; Supp. Fig. A2.7), but structural uncertainty leads to non-negligible difference in long-term warming trends (Table 2.4).

One issue in the construction of tropospheric temperature microwave records is that short-term trends from overlapping satellites do not always agree after estimated biases are removed. For example, tropospheric warming inferred from the Microwave Sounding Unit (MSU) onboard the NOAA-14 satellite exceeds that from Advanced MSU (AMSU) data from NOAA-15. Reliance on data from NOAA-14 (NOAA-15) results in larger (smaller) estimates of tropospheric warming (Mears and Wentz 2016; Santer et al. 2021). A new version of the NOAA STAR dataset treats data from the latest microwave sounding instruments (AMSU and the Advanced Technology Microwave Sounder) as a reference with which to calibrate earlier data from earlier MSU instruments (Zou et al. 2021, 2023). This decision has the effect of substantially reducing global tropical tropospheric temperature (TTT) warming over 1979 to 2021 (from 0.23°C decade⁻¹ to 0.14°C decade⁻¹; Zou et al. 2023). Changes in the estimated rate of warming of a few hundredths of a degree per decade are common when tropospheric temperature datasets are updated. These changes illustrate the challenges and pronounced structural uncertainty in constructing records of tropospheric warming.

General circulation models (GCMs) tend to simulate greater tropospheric warming than satellite observations over 1979 to present, particularly in the tropics (McKittrick and Christy 2020; Po-Chedley et al. 2021; Zou et al. 2023). Observed tropical sea-surface warming, which is closely coupled to tropospheric warming, is also smaller than the average warming in GCMs (Eyring et al. 2021). Two factors likely contribute to faster-than-observed model warming: biases in

<figure><img src="image_48.png" /><figcaption>**Fig. 2.10.** Time series of (a) global average lower-tropospheric temperature (LTT; °C) anomalies, (b) central Pacific (Niño-3.4 region) sea-surface temperature anomalies (°C), and (c) percentage of Earth experiencing record-high (red) and low (blue) LTT values, according to RSS and UAH LTT datasets for the period 1979–2022. Bold lines in (a) represent the annual average values (across datasets) for sondes (yellow), reanalysis (red), and satellite (blue) data. Monthly values for individual datasets are also plotted with thinner and lighter lines for context. The climatological base period for (a) and (b) is 1991–2020. Niño-3.4 anomalies are calculated using the HadISST1 dataset (Rayner et al. 2003). STAR data are not included in the satellite LTT time series because the time series begins in 1981 (versus 1979 for RSS and UAH data).</figcaption></figure>
the prescribed model forcing and biases in the GCM response to greenhouse gas forcing. For example, Fasullo et al. (2022) shows that a discontinuity in the biomass-burning aerosol forcing prescribed to models in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) results in inflated warming in version 2 of the Community Earth System Model—an issue that may affect other CMIP6 GCMs. Several GCMs also exceed the likely range of estimates of climate sensitivity (Forster et al. 2021)—the global surface warming response to a doubling of atmospheric carbon dioxide—which in turn contributes to overestimates of historical warming (Scafetta 2023). Multidecadal internal variability has also reduced observed warming since 1979 (Po-Chedley et al. 2022), which contributes to the difference between observed and simulated warming. Aside from these factors, it is also possible that observational biases may affect observed tropospheric warming (Santer et al. 2021).

# 6. STRATOSPHERIC TEMPERATURE

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

Global mean temperatures in the lower, middle, and upper stratosphere were anomalously low during 2022, a result of the large volcanic eruption of Hunga Tonga–Hunga Ha‘apai (HTHH) in January 2022 (Sidebar 2.2). These cold anomalies were primarily observed in the Southern Hemisphere (SH), and these volcanic effects accentuated the multi-decadal global-scale cooling of the stratosphere due to increases of anthropogenic carbon dioxide concentrations in the atmosphere. The Antarctic polar vortex was strong and persistent in 2022, while the Arctic polar vortex was disturbed by a major stratospheric warming in March. The stratospheric quasi-biennial oscillation (QBO) progressed normally, with equatorial westerly winds and positive temperature anomalies descending from the middle stratosphere to the lower stratosphere during the year.

The HTHH eruption (~20°S, 175°W) on 15 January 2022 injected extreme amounts of water vapor (H₂O) and a moderate amount of the aerosol precursor sulfur dioxide into the stratosphere. The amount of stratospheric H₂O injected from HTHH is unprecedented in the continuous satellite record beginning in the middle 1980s (Davis et al. 2016; Milan et al. 2022; Vömel et al. 2022). The H₂O and aerosol perturbations persisted throughout 2022 (e.g., Schoeberl et al. 2022; Mishra et al. 2022), and the radiative effects of enhanced H₂O resulted in large-scale cooling of the SH stratosphere, in contrast to aerosol-induced warming of the stratosphere observed from past large volcanic eruptions (e.g., Labitzke and McCormick 1992). Additional H₂O cools the stratosphere because of enhanced longwave emission to space (e.g., Forster and Shine 1999). Observations show low temperatures in 2022 that are well outside the range of previous variability (Fig. 2.11a), with corresponding anomalies in stratospheric winds and circulation in balance

<figure><img src="image_49.png" /></figure>

**Fig. 2.11.** Evolution of Southern Hemisphere midlatitude (10°S–50°S) stratospheric temperature anomalies (°C) in 2022 from Aura MLS measurements (https://aura.gsfc.nasa.gov/). (a) Time series of temperatures at 25 km for each individual year, highlighting anomalously cold temperatures in 2022 (red) compared to previous years 2004–21. (b) Height-time evolution of temperature anomalies in 2022 (2022 minus 2004–21 average). Hatching denotes 2022 anomalies that are outside of all previous variability during 2004–21.
with the anomalous temperatures (Coy et al. 2022; Wang et al. 2022). Low anomalies were largest during SH winter (Fig. 2.11b), with a corresponding equatorward shift of the Antarctic polar vortex and circulation-induced midlatitude ozone losses (Wang et al. 2022). While the HTHH H₂O plume is slowly dispersing throughout the global stratosphere, it is expected to persist for a number of years as H₂O is chemically inert, and the main loss processes are due to transport in the slow overturning stratospheric circulation. Hence the HTHH H₂O anomalies will continue to influence stratospheric temperatures beyond 2022.

The Antarctic polar vortex was strong and characterized by anomalously low temperatures during spring 2022, persisting through December (see section 6b for details). Springtime polar temperatures and vortex persistence are closely linked with springtime polar ozone amounts, due to ozone radiative forcing after the sun returns in October. Springtime polar ozone was also relatively low in 2022 (section 2g4), likely contributing to the observed low temperatures.

The Arctic polar vortex was stable and relatively cold during winter but was disturbed by a major stratospheric warming event in March (Vargin et al. 2022), with polar temperature increases over a few days of about 30K. The vortex did not recover, and this event thus corresponded to the ‘final warming’ for that winter. The stratospheric QBO in 2022 continued its usual regular progression (as observed since the 1950s) in contrast to the anomalous disruption events of 2016 and 2020 (section 2e3).

### c. Cryosphere

#### 1. PERMAFROST TEMPERATURE AND ACTIVE LAYER THICKNESS

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

Permafrost is a subsurface phenomenon in polar and high mountain regions and defined as ground with a maximum temperature of 0°C throughout the year. Permafrost temperatures close to the depth where annual fluctuations become minimal (the depth of zero annual amplitude) increased across all permafrost regions in the past decades with rates ranging from below 0.3°C decade⁻¹ in warm permafrost (with temperatures close to 0 °C) to above 0.8°C decade⁻¹ in cold permafrost (Biskaborn et al. 2019; Smith et al. 2022; Etzelmüller et al. 2020; Zhao et al. 2020; Fig. 2.12; see also section 5i). The thickness of the active layer (ALT), the layer above the permafrost that thaws during summer, increased in the Arctic by a few centimeters per decade in cold continuous permafrost and by more than 10 cm decade⁻¹ in discontinuous permafrost. ALT increased by 19.6 cm decade⁻¹ over the past 40 years in the Qinghai-Tibet Plateau (Fig. 2.13) and has increased by a few meters in the past 20 years at several sites in the European Alps.

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

<figure><img src="image_51.png" /></figure>

**Fig. 2.12.** Permafrost temperatures (°C) measured in boreholes in the European Alps and the Nordic countries at a depth of (a) ~10 m (monthly means) and (b) 20 m (annual means). (Sources: Switzerland: Swiss Permafrost Monitoring Network; Norway: Norwegian Meteorological Institute and the Norwegian Permafrost Database; France: updated from Magnin et al. 2015; Italy: updated from Pogliotti et al. 2015.)
Permafrost temperatures in 2022 were the highest on record at 11 of the 25 sites monitored in the Arctic (section 5i), while they were lower than in 2021 in northern Alaska, the northern Mackenzie region in northwestern Canada, and the Canadian high Arctic. This is partly associated with lower air temperatures in those regions over the past two to three years. ALT in Arctic Alaska was one of the lowest since 1995 where most of the sites were established. ALT was lower than 2021 but above the long-term averages in Interior Alaska, northwestern Canada, Greenland, and northern European Russia. ALT in West Siberia was on average 5 cm higher in 2022 than in 2021, while in Central Siberia it was

6 cm lower, but 13 cm higher than average. In East Siberia and Chukotka, ALT was 2 cm–3 cm higher than in 2021, but close to the long-term mean. In high-Arctic Svalbard, permafrost temperatures were the fourth highest on record. ALT was not at maximum due to lower air temperatures in April and early May, and despite record air temperatures in summer 2022 in western and northern Svalbard.

Several countries in Europe recorded extremely dry and warm conditions in summer 2022 (see section 7f; sections 2b4, 2d11; Copernicus 2023). In northern Norway, the permafrost degradation continued, with permafrost thaw down to 20-m depth at Iskoras, and in southern Norway the permafrost temperature was the highest on record at Juvvasshøe (Fig. 2.12). Nearby, on Dovrefjell, since 2021 the active layer has not completely frozen down to the underlying permafrost during winter, resulting in a talik (unfrozen zone; Isaksen et al. 2022). In the European Alps, mean annual ground surface temperature increased in 2022 by more than 1°C compared to 2021 at the majority of the 30 Swiss sites due to higher air temperatures and early snow melt (section 2c5; MeteoSwiss 2023; Pielmeier et al. 2023). The active layer was the thickest on record at most monitoring sites in the Swiss, French, and Italian Alps. In contrast, permafrost temperatures at 10-m depth decreased in 2022 at many sites (update from the Swiss Permafrost Monitoring Network [PERMOS] 2022; Pogliotti et al. 2015; Magnin et al. 2015; Fig. 2.12) reflecting the colder conditions of 2021 (Noetzli et al. 2022). Permafrost temperatures at 20-m depth—where they react to longer-term trends—continued to increase in 2022 at most sites and were close to record levels.

Permafrost temperatures in the Qinghai-Tibet Plateau continued to increase from 2005 to 2021 at 10- and 20-m depth at six sites, with stronger warming in colder permafrost. At the 10 ALT sites along the Qinghai-Tibet Highway (Kunlun mountain pass), ALT increased from the start of the measurements in 1981 to a new maximum of 250 cm in 2021 (the latest value available; Fig. 2.12).

On James Ross Island in the northern Antarctic Peninsula, 2022 was the warmest of the instrumental records since 2004. The mean annual near-surface temperature (–3.2°C) was 2.2°C above the 2011–20 mean (reference site AWS-JGM), leading to a mean annual temperature at the permafrost table (i.e., the top of permafrost) 1.6°C above average. The ALT was 71 cm in 2022 and 22 cm above the mean during 2011–20 (Kaplan-Pastirikova et al. 2023). ALT has been increasing at all Antarctic Peninsula monitoring sites since 2015, whereas it has remained stable in the other regions of Antarctica.

International field data of ALT, permafrost temperatures, and rock glacier velocity (Streletskiy et al. 2021; section 2c2) are collected by the Global Terrestrial Network for Permafrost (GTN-P). Permafrost temperatures are manually recorded or continuously logged in boreholes with a

<figure><img src="image_52.png" /><figcaption>**Fig. 2.13.** Active layer thickness (cm) and air temperature anomaly (°C; 1991–2020 base period) in the permafrost zone along the Qinghai-Tibet Highway for the period 1981–2021. (Source: Cryosphere Research Station on Qinghai-Xizang Plateau, CAS.)</figcaption></figure>
measurement accuracy of ~0.1°C (Biskaborn et al. 2019; Noetzli et al. 2021; Streletskiy et al. 2021). ALT is either determined by mechanical probing (with an accuracy of ~1 cm) or interpolated from borehole temperature measurements. The global coverage of permafrost monitoring sites is sparse and biased to the Northern Hemisphere. Permafrost data are particularly limited in regions such as Siberia, central Canada, Antarctica, and the Himalayan and Andes Mountains.

## 2. ROCK GLACIER VELOCITY

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

Rock glaciers are debris landforms generated by the creep of frozen ground (permafrost) whose velocity changes are indicative of changes in the thermal state of permafrost (RGIK 2022a,b). Rock glacier velocities (RGV) observed in different mountain ranges worldwide have been increasing since the 1950s, with large regional and inter-annual variability. In 2022, RGVs in the European Alps decreased at all monitoring sites. For some rock glaciers this was the second consecutive year of decreasing velocities. These changes are consistent with the evolution of permafrost temperatures (section 2c1) to which rock glacier surface velocities respond synchro-nously (e.g., Kenner et al. 2017; Staub et al. 2016).

Although summer was marked by exceptionally high air temperatures (Fig. 2.14a; section 2b4), RGVs in the European Alps decreased at all sites in 2022, which contrasts with the general

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

Fig. 2.14. (a) Air and ground temperatures (°C) in the European Alps, (b) rock glacier velocities (m yr⁻¹) at selected sites in the European Alps, (c) the Dry Andes (adapted from Vivero et al. 2021), and (d) Central Asia (adapted 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 average (bars) and composite 20-yr 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, MeteoSwiss, GeoSphere Austria, Swiss Permafrost Monitoring Network, University of Fribourg, University of Graz, Graz University of Technology, Université Grenoble Alpes, University of Oslo.)
acceleration trend observed since the 1950s (Pellet et al. 2022; PERMOS 2022). Maximum velocity decrease compared to 2021 was observed in the Swiss Alps (e.g., Grosses Gufer: -49% and Gemmi/Furggentälti: -37%), whereas a smaller decrease was reported in the French (e.g., Laurichard: -14%) and Austrian (e.g., Dösen: -15% and Hinteres Langtalkar: -5%) Alps (Fig. 2.14b). The velocity decrease is consistent with a decrease in permafrost temperatures observed at 10-m depth (section 2c1), which reflects the comparatively cold year of 2021. The relatively dry winter of 2021/22 and dry and warm spring and summer of 2022 affected the geohydrological conditions at all sites (i.e., reduced the amount of water available in the terrain) and also contributed to velocity decrease (i.e., reduced shearing due to reduced pore water pressure; see Cicoira et al. 2019).

There are only a few long-term in situ RGV measurements outside of the European Alps. However, RGVs have been increasingly observed and reconstructed using (archival) aerial photographs and high-resolution satellite data (e.g., Cusicanqui et al. 2021; Eriksen et al. 2018). In the Dry Andes, RGVs reconstructed on three rock glaciers show low velocities from 1950 to 2000, followed by a steady acceleration since the 2000s (Fig. 2.14c), consistent with the climatic conditions in the region (Vivero et al. 2021).

RGVs observed in Central Asia have increased overall since the first available measurements in the 1950s, although their inter-annual evolution differs (Fig. 2.14d; Kääb et al. 2021). This general trend is consistent with increasing air temperatures in the region and with the acceleration reported in the European Alps and Dry Andes.

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 where most of the deformation occurs (the so-called 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., Delaloye et al. 2010; Kääb et al. 2021; Kellerer-Pirklbauer et al. 2018). Given the global occurrence of rock glaciers and the sensitivity of their surface velocity to ground temperatures and, by extension, to climate change, RGV was adopted in 2021 as a new associated product to the essential climate variable permafrost by the Global Climate Observing System (GCOS 2022a,b) and the GTN-P (Streletskiy et al. 2021). 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., snow-free and shadows). Surface displacements are averaged for a cluster of points/pixels selected within areas representative of the downslope movement of the rock glacier (RGIK 2022a). Annual rock glacier velocities are 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 (Delaloye et al. 2008; Kellerer-Pirklbauer and Kaufmann. 2012; PERMOS 2022; Thibert and Bodin 2022).
### 3. ALPINE GLACIERS

—M. S. Pelto

In 2022 heat events in the European Alps, Svalbard, High Mountain Asia, and the central Andes of Argentina and Chile resulted in a global mean annual mass balance of -1433 mm w.e. (water equivalent) for all 108 reporting alpine (mountain-region) glaciers, with data reported from 20 nations on five continents. In the hydrological year 2021/22, the preliminary regionally averaged annual mass balance based on the World Glacier Monitoring Service (WGMS 2021) reference glaciers was -1179 mm w.e. compared to the 1970-2020 average of -490 mm w.e. This makes 2022 the 35th consecutive year with a global alpine mass balance loss and the 14th consecutive year with a mean global mass balance below -500 mm w.e. (Fig. 2.15). This acceleration in mass loss from global alpine glaciers in the twenty-first century matches the findings of Huggenot et al. (2021). Since the start of the record in 1970, 9 of the 10 most negative mass balances have occurred since 2013.

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

**Fig. 2.15.** Global average annual (left axis, red bars) and cumulative (right axis, black line) mass balance (1000 mm w.e.) of alpine glaciers for the period 1970-2022. (Source: WGMS regionally averaged reference glacier network.)

In 2022, a negative annual mass balance was reported from 34 of the 37 reference glaciers reported to WGMS. The mean annual mass balance of the 37 reference glaciers was -1547 mm w.e. Reference glaciers each with at least 30 continuous years of observation are used to generate regional averages. Global values are calculated using a single value (averaged) for each of 19 mountain regions in order to avoid a bias toward well-observed regions.

More frequent and intense heatwaves impacting glaciated ranges continued to take a toll on alpine glaciers in 2022. Heatwaves reduce snow cover extent earlier in the melt season, exposing ice surfaces earlier and enhancing surface darkening, both of which cause higher melt rates on alpine glaciers (Shaw et al. 2021; Pelto et al. 2022; Cremona et al. 2023).

All 32 reporting glaciers in the Alps, Pyrenees, and Caucasus Mountains had a negative mass balance averaging -3100 mm w.e. in 2022. In the European Alps, the combination of low winter snowpack and several summer heatwaves generated unprecedented mass loss (sections 2b4, 7f3). In Switzerland, the 25 days of heatwaves in 2022 are estimated to have melted 1.27±0.10 km³ w.e., equivalent to 35% of the overall glacier mass loss that occurred during the summer, a period that led to a 6.2% overall glacier volume loss (Cremona et al. 2023).

In Norway and Sweden, the average balance of 11 reporting glaciers was -443 mm w.e., with three glaciers in Norway having a positive balance. Iceland completed surveys of nine glaciers; five had a positive balance and four a negative balance, with a mean mass balance of -7 mm w.e., close to equilibrium.
On Svalbard, the mean loss of the four reporting glaciers was -1102 mm w.e. The negative mass balances were due to several summer heat events (see section 5b, Sidebar 5.1), which led to many glaciers and ice caps losing all or most of their snow cover, further accelerating mass loss (Fig. 2.16).

In Alberta and British Columbia, Canada, and in Alaska and Washington, United States, 19 glaciers had a negative mass balance, averaging -965 mm w.e. The Alberta, British Columbia, and Washington regions experienced several prolonged heatwaves as they did in 2021. Daily glacier ablation in this region was noted as increasing by 30%-40% during heatwave periods (Pelto et al. 2022).

In South America, mass balance data, reported from five Andean glaciers in Ecuador, Argentina, and Chile were negative, with a mean of -1465 mm w.e. The combination of drought and heat events left many central Andean glaciers snow free by mid-summer in early 2022. Shaw et al. (2021) noted a significant decline in surface albedo (section 2h1) due to decreased fractional snow cover that further enhances melt.

In High Mountain Asia, mass balance measurements were completed on glaciers in China, Kazakhstan, Kyrgyzstan, Russia, and Tajikistan. All

20 glaciers reported negative balances, with an average of -1040 mm w.e. The negative balances were driven by above-average melting during the May-July period.

In New Zealand, the mass balance assessed on Brewster and Rolleston Glaciers were strongly negative at -1125 mm and -1065 mm w.e., respectively. The end of year snowline observations on 50 glaciers was one of the five highest of the last 45 years.

Annual mass balance is reported in mm water equivalent (w.e.). A value of -1000 m w.e. per year represents a mass loss of 1000 kg m⁻² of ice, or an annual glacier-wide thickness loss of about 1100 mm yr⁻¹.

<figure><img src="image_55.png" /><figcaption>Fig. 2.16. Langjokulen (La), Kvitisen (Kv), Bergfonna (Be), and Blaisen (BI) ice caps on the northeastern island of Edgeøya, Svalbard, in Copernicus Sentinel-2 MSI image (RGB) on 20 Aug 2022 illustrating the lack of snow cover, limited firn areas, and numerous annual layers. This pattern of annual layers due to glaciers being stripped of snow cover is becoming increasingly frequent.</figcaption></figure>
## 4. LAKE ICE

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

In winter 2021/22, many, but not all lakes across the Northern Hemisphere (NH) had later ice-on dates, earlier ice-off dates, and shorter seasonal ice cover, thus continuing the pattern observed in recent decades (Sharma and Woolway 2021; Sharma et al. 2022).

NH lakes froze on average 5.6 days later and thawed 3.2 days earlier, with 8.9 days shorter ice duration relative to the 1991–2020 base period based on ERA5 reanalysis data (Figs. 2.17, 2.18). The duration of lake ice cover was the fourth shortest since the start of the record in 1980 (Fig. 2.18c). The regional variations in ice duration were consistent with NH winter air temperature anomalies. Some regions in North America, such as western Canada and Alaska, experienced below-average air temperatures, which resulted in longer-than-average ice duration. Conversely, many regions in Eurasia, in particular Siberia, experienced warmer-than-average conditions that resulted in shorter-than-average ice duration (Fig. 2.17).

In situ ice records from 118 lakes distributed across North America, Europe, and Asia revealed mixed patterns for the 2021/22 winter relative to 1991–2020. On average, across this set of 118 lakes,

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

**Fig. 2.17.** Anomalies (days) in 2022 for (a) ice-on, (b) ice-off, and (c) ice duration for lakes across the Northern Hemisphere (NH), and (d) surface air temperature anomalies (°C) for the NH cold season (Nov–Apr average), the time of year in which lakes typically freeze. The base period is 1991–2020. In (a)–(c), green colors represent higher ice loss (i.e., later ice-on, earlier ice-off, and shorter ice duration), and purple colors represent higher ice coverage (i.e., earlier ice-on, later ice-off, and shorter ice duration). (Sources: ERA5, GISTEMP.)
ice-on was 0.5 days later, ice-off was 3.5 days later, and ice duration was 3.7 days longer (Fig. 2.18). For North American lakes on average, ice duration was 1 day longer, whereas for European lakes, ice-on was 7.1 days longer in 2021/22 relative to 1991–2020. In Asia, Lake Suwa in Japan froze on 7 January 2022. This lake has frozen two years in a row, an event that has not occurred in a decade, though historically the lake froze most years (Sharma et al. 2016, 2021).

We further collated in situ records from 18 mountain lakes (>1000 m a.s.l.) as Pepin et al. (2015) suggests that high-elevation regions will experience more rapid warming than lower elevations.

On average, these mountain lakes froze 11 days later and thawed 5.3 days earlier than the 1991–2020 baseline period (Fig. 2.18). Lake Lunz in Austria showed particularly strong reductions in ice cover as it only froze for 1 day in January and 2 days in February, freezing 41.3 days later, thawing 21.1 days earlier, and losing 58.2 days of duration. This lake also showed multiple freeze and breakup events during the last two years, which had not occurred previously in its continuous 102-year record (Kainz et al. 2017).

The Laurentian Great Lakes had 10.9% more maximal ice coverage, relative to the winters of 1991–2020. Lake Superior was the most anomalous with 25% more ice coverage in 2022, followed by Lake Ontario which had 17.8% more ice coverage (Fig. 2.19). Ice formation was quite late in the Great Lakes owing to warmer autumn water temperatures in late 2021.

We used ice simulations from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) reanalysis product (Hersbach et al. 2020) 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). We obtained in situ data for 118 lakes: Canada (4), United States (74), Estonia (1), Finland (27), Norway (10), Sweden (1), and Japan (1). We also obtained in situ data for mountain lakes for the United States (8) and Europe (10; Benson et al. 2000, updated 2022). Furthermore, we acquired annual maximum ice cover (%) data for each of the Laurentian Great Lakes from 1973–2022 (https://www.glerl.noaa.gov/data/ice/). Surface air temperature data for the NH cold season (November–April average) were downloaded from the NASA GISS surface temperature analysis (GISTEMP Team 2023). Anomalies for each of our ice metrics were calculated for the 2021/22 winter relative to the 1991–2020 normal base period.

<figure><img src="image_57.png" /><figcaption>**Fig. 2.18.** Lake (a) ice-on, (b) ice-off, and (c) ice duration anomalies (days) from 1980 to 2022, relative to the 1991–2020 base period, derived from ERA5 reanalysis, in situ observations, and mountain lakes. Positive values for ice-on suggest later freezing, whereas negative values for ice-off and ice-duration indicate earlier ice-thaw and shorter ice duration.</figcaption></figure>

<figure><img src="image_58.png" /><figcaption>**Fig. 2.19.** Anomalies in Great Lakes maximum ice cover extent (%) for the period 1973–2022, relative to the 1991–2020 base period. The black line shows the average anomaly for all of the Great Lakes, whereas the lines in color show individual lakes (Erie, Michigan, Superior, Ontario, and Huron).</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 24.9 million km² in 2022. This is 0.04 million km² less than the 1991–2020 mean and 0.23 million km² below the full period of record (1967–2022) mean (Fig. 2.20; Table 2.5). This ranks 2022 as having the 24th least-extensive cover (33rd most) on record. Monthly SCE in 2022 ranged from 47.3 million km² in January to 2.4 million km² in August (Fig. 2.21).

<figure><img src="image_59.png" /></figure>

Fig. 2.20. Twelve-month running anomalies of monthly snow-cover extent (SCE; x 10⁶ km²) over Northern Hemisphere (NH) lands as a whole and Europe (EUR) and North America (NA) separately plotted on the seventh month using values from Nov 1966 to Dec 2022. Anomalies from the 1991–2020 mean are calculated from NOAA snow maps. Mean NH SCE is 25.1 x 10⁶ 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. The missing months fall between Jun and Oct.

<table><caption>Table 2.5. Monthly and annual climatological information on Northern Hemisphere (NH), Eurasian (EUR), and North American (NA) snow cover extent (SCE) between Nov 1966 and Dec 2022. Included are the numbers of years with data used in the calculations, NH means, standard deviations (std. dev.), 2022 values, and rankings. Areas are in millions of square kilometers (km²). The years 1968, 1969, and 1971 have 1, 5, and 3 missing months respectively, thus are not included in the annual (Ann) calculations. NA includes Greenland. Ranks are from most (1) to least extensive.</caption><thead><tr><th>Month</th><th>Yrs</th><th>NH Mean</th><th>Std. Dev.</th><th>2022</th><th>2022 NH rank</th><th>2022 EUR rank</th><th>2022 NA rank</th></tr></thead><tbody><tr><td>Jan</td><td>56</td><td>47.1</td><td>1.5</td><td>47.3</td><td>26</td><td>24</td><td>32</td></tr><tr><td>Feb</td><td>56</td><td>46.0</td><td>1.8</td><td>45.8</td><td>27</td><td>22</td><td>37</td></tr><tr><td>Mar</td><td>56</td><td>40.4</td><td>1.8</td><td>40.0</td><td>31</td><td>33</td><td>32</td></tr><tr><td>Apr</td><td>56</td><td>30.5</td><td>1.7</td><td>30.9</td><td>23</td><td>32</td><td>13</td></tr><tr><td>May</td><td>56</td><td>19.1</td><td>2.0</td><td>18.0</td><td>40</td><td>44</td><td>23</td></tr><tr><td>Jun</td><td>55</td><td>9.3</td><td>2.5</td><td>5.5</td><td>53</td><td>54</td><td>51</td></tr><tr><td>Jul</td><td>53</td><td>3.9</td><td>1.2</td><td>2.7</td><td>45</td><td>51</td><td>43</td></tr><tr><td>Aug</td><td>54</td><td>3.0</td><td>0.7</td><td>2.4</td><td>43</td><td>45</td><td>38</td></tr><tr><td>Sep</td><td>54</td><td>5.4</td><td>0.9</td><td>5.9</td><td>14</td><td>6</td><td>45</td></tr><tr><td>Oct</td><td>55</td><td>18.6</td><td>2.6</td><td>18.7</td><td>25</td><td>22</td><td>34</td></tr><tr><td>Nov</td><td>57</td><td>34.3</td><td>2.1</td><td>37.7</td><td>4</td><td>11</td><td>3</td></tr><tr><td>Dec</td><td>57</td><td>43.7</td><td>1.8</td><td>43.6</td><td>37</td><td>48</td><td>13</td></tr><tr><td>Annual Calculations</td><td>53</td><td>25.1</td><td>0.8</td><td>24.9</td><td>33</td><td>34</td><td>30</td></tr></tbody></table>
The first four months of 2022 saw NH SCE rank in the middle tercile of the 56-year record. This dropped to the lower tercile in May, and June was the third least extensive on record (Fig. 2.22a). When snowfall resumed in September the SCE over Eurasia (EUR) quickly climbed to sixth most extensive, while North America (NA) SCE grew more slowly. NH October SCE was in the middle tercile before both continents experienced above-normal November SCE (NA ranking third most extensive and EUR 11th; Fig. 2.22b). However, the pace of seasonally advancing SCE slowed considerably across EUR in December (10th least extensive on record), while NA continued to see above-normal SCE (13th most extensive).

Compared to normal, monthly SCE over the contiguous United States varied considerably in 2022. Rankings were in the middle tercile in January, the lowest tercile in February, and was the seventh least extensive on record in March. Melt slowed considerably in April, with the 19th most extensive cover occurring, followed by a middle tercile ranking in May. End-of-year SCE for the United States was above normal, ranking 11th, 6th, and 14th most extensive in October, November, and December, respectively.

SCE is calculated at the Rutgers Global Snow Lab (GSL) from daily SCE maps produced by meteorologists at the U.S. 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_60.png" /><figcaption>Fig. 2.21. Weekly Northern Hemisphere snow-cover extent (SCE; × 10⁶ km²) for 2022 (black) plotted with the mean (gray dashed line), maximum (red), and minimum (blue) SCE for each week. Mean weekly SCE and extremes are calculated using the 56-yr record from Jan 1967 to Dec 2022. Weekly data granules represent SCE for each seven-day period ending on Monday.</figcaption></figure>

<figure><img src="image_61.png" /><figcaption>Fig. 2.22. Monthly snow-cover extent (SCE) departure (%; 1991–2020 base period) maps showing (a) Jun 2022 and (b) Nov 2022. The monthly percent anomaly for a grid cell is based on the percent of days that cell was snow covered in that month compared to the long-term climatological average of snow cover days. Jun exhibited the lowest SCE anomaly (–3.91 million km²) during 2022, while Nov was the highest above normal (+3.79 million km²). Negative departures indicate less SCE than normal (green) with positive departures (purple) showing areas of SCE above the 30-year mean.</figcaption></figure>
In 2022, surface humidity exhibited similar levels of water vapor to 2021, as measured by specific humidity (q). Saturation levels, as measured by relative humidity (RH), were slightly higher than in 2021 over land, yet remained drier than the 1991–2020 average (Figs. 2.23e,f). This finding is common to all products shown here, which comprise the in situ Hadley Centre Integrated Surface Database Humidity (HadISDH [v4.5.1.2022f]) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), and Japanese 55-year Reanalysis (JRA-55) reanalysis products. It is consistent with the similarity of global near-surface temperatures to those in 2021 and the continuing La Niña conditions that were mostly present throughout both years. Relative humidity over oceans remained highly uncertain, as represented by the spread across the two reanalysis estimates (ERA5 and JRA-55; Figs. 2.23h). Note that this year HadISDH.marine is not included while a discrepancy linked to reduced data coverage in the updated version is investigated.

Despite overall agreement between products for much of the more-than-40-year record, 2022 saw a continued widening of the divergence in anomaly estimates apparent from around 2019. HadISDH showed 2022 as having a slightly higher water vapor content compared to 2021 with the specific humidity anomaly over land (q<sub>land</sub>) remaining wetter than the 1991–2020 average at 0.13 (0.09 to 0.17 2-sigma uncertainty range) g kg<sup>-1</sup>. ERA5 placed q<sub>land</sub> much lower at –0.01 g kg<sup>-1</sup>, identical to its estimate in 2021. ERA5 q<sub>ocean</sub> was slightly drier than 2021 at 0.03 g kg<sup>-1</sup>. MERRA-2 and

<figure><img src="image_62.png" /></figure>

Fig. 2.23. Global average surface humidity annual anomalies (1991–2020 base period). For the in situ datasets 2-m surface humidity is used over land and ~10 m over the oceans. For the reanalysis, 2-m humidity is used over the whole globe. For ERA5 ocean series-only points over open sea are selected. 2-σ uncertainty is shown for the HadISDH dataset capturing the observation, gridbox sampling, and spatial coverage uncertainty. (Sources: HadISDH [Willett et al. 2013, 2014]; ERA5 [Hersbach et al. 2020]; JRA-55 [Kobayashi et al. 2015]; MERRA-2 [Gelaro et al. 2017].)
JRA-55 remained wetter-than-average over land and ocean, with MERRA-2 anomalies reaching the highest at 0.19 g kg⁻¹ and 0.18 g kg⁻¹ for $q_{\text{land}}$ and $q_{\text{ocean}}$, respectively.

In terms of saturation, all products show that $RH_{\text{land}}$ remained drier-than-average but was more humid than in 2021. Anomalies ranged from -0.93 %rh for ERA5 to -0.33 (-0.53 to -0.13 2-sigma uncertainty range) %rh for HadISDH. ERA5 has consistently presented drier $RH_{\text{land}}$ anomalies than HadISDH since 2019. Over ocean, ERA5 and JRA-55 had $RH_{\text{ocean}}$ anomalies drier than average at -0.12 %rh and more humid than average at 0.21 %rh, respectively.

Interestingly, the divergence in products is also apparent in the global land average for total column water vapor (TCWV; section 2d2; Fig. 2.23c). ERA5 and GPS radio occultation (GPS-RO) estimates show TCWV close to average for 2022 whereas MERRA-2, JRA-55, and the more spatially limited ground-based global navigation satellite system (GNSS) record place 2022 much wetter at levels comparable with the previous five years.

At least part of the reason behind the ERA5-HadISDH land divergence is driven by spatial coverage differences. Plates 2.1g,h and Fig. 2.24 show that central South America and western and central Africa are regions of dry anomalies in ERA5 but mostly missing gridboxes in HadISDH. Over Africa, MERRA-2 shows mostly wet anomalies, contributing to its wetter $q_{\text{land}}$ anomaly for 2022 which appears more comparable with HadISDH. This is similar to 2021, where La Niña was also present, with near-identical spatial patterns of anomalies. Plate 2.1g (using ERA5) shows expansive dry $q$ anomalies spreading across the central Pacific, surrounded by expansive strong wet anomalies. These are characteristic of La Niña and are respectively drier and wetter in 2022 compared to 2021. Over land, wet anomalies over India extended farther west over Pakistan in 2022 (when Pakistan had severe flooding, section 2d5) compared to 2021, and they were also more expansive over eastern Australia and southern Africa in 2022. Dry anomalies over the northern midlatitudes were similar but located farther east compared to 2021. The

<figure><img src="image_63.png" /><figcaption>**Fig. 2.24.** Annual average surface humidity anomalies from the 1991–2020 average for (a),(b) specific humidity (g kg⁻¹) and (c),(d) relative humidity (%rh). Maps for (a) and (c) are from the HadISDH in situ product that uses weather station observations. Maps for (b) and (d) are from the MERRA-2 reanalysis product.</figcaption></figure>
south-central United States and subtropical South America experienced very dry anomalies in 2022. These were also regions suffering from drought.

Li et al. (2020) and Freychet et al. (2020) demonstrate a possible dry bias in ERA5 (and HadISDH.land [Willett 2023a,b]) over China from the early 2000s onward when manual wet bulb thermometers were replaced with automated RH sensors nationwide. All products contain some degree of uncertainty. For HadISDH, this is dominated by incomplete coverage and remaining inhomogeneity from changes to the observing system over time, despite considerable efforts to homogenize the data (Willett et al. 2013, 2014). For reanalyses, observation sparseness and quality and the drop in/out of data platforms over time are all sources of uncertainty. These do not undermine the conclusion of generally increasing water vapor alongside decreasing saturation levels.

## Sidebar 2.1: Assessing humid heat extremes over land

—K. M. WILLETT

Extremes of heat based solely on temperature have been routinely monitored for some time using a wide range of Climbact indices (which includes those from the World Meteorological Organization Expert Team on Climate Change Detection and Indices; section 2b4; https://climbact-sci.org/). Although temperature is likely the dominant factor for heat-related mortality (Armstrong et al. 2019; Lo et al. 2023), humidity can also play an important role. In fact, even at more moderate temperatures, high humidity increases the overall 'heat' loading on the body and can therefore lead to negative impacts on health. Physical and even mental tasks can become more difficult to complete, slowing the rate at which people function and increasing the amount of rest required. The resulting decreased productivity can have a negative economic impact in addition to health and wellbeing impacts (Parsons et al. 2022).

Relative humidity is the level of water vapor saturation in the atmosphere. As this depends both on the water vapor content and the temperature of the air, knowing the relative humidity alone is not a useful measure in terms of heat stress. The wet bulb temperature, $T_w$, was until recently a commonly observed meteorological variable and can be relatively easily calculated from standard meteorological variables in most conditions. The lower the level of saturation, the lower the $T_w$ will be compared to the air temperature. If the air is completely saturated (relative humidity [RH]=100%rh), then the $T_w$ will

equal the air temperature. This becomes important for human health when the $T_w$ approaches skin temperature, which is on average about 35°C. At this point the air closest to the skin is then saturated, meaning that sweat can no longer evaporate from the body and therefore it is no longer an effective cooling mechanism. This is then a theoretical critical threshold above which humans cannot survive, as we have no other biophysical cooling mechanisms available and so would overheat rapidly even if inactive. In practice, the critical level of $T_w$ for the human body to function is below 32°C (Vecellio et al. 2022).

With this in mind, quantifying the current exposure to high $T_w$ and monitoring change over time is important, as is looking at future potential changes given further warming. Building on the existing surface-humidity monitoring product HadISDH.land (Willett et al. 2013, 2014), a new dataset of gridded, monthly, wet bulb and air temperature extremes indices from 1973 to present has been developed: HadISDH.extremes (v1.0.0.2022f, Willett 2023a,b). By utilizing the existing framework of the Climbact indices we can assess the different exposures to dry versus humid heat, exploring the concept of 'stealth heat events', where the temperature may not be considered extreme but the humidity is high. Such events may not be sufficient to cause fatalities but could still impact productivity and health. Table SB2.1 describes the core indices used here; more indices are available from the HadISDH.extremes.

<table><caption>Table SB2.1. Heat extreme indices for wet bulb temperature ($T_w$). A bigger range of indices are available at HadISDH.extremes dataset pages on HadOBS and CEDA (HadOBS: www.metoffice.gov.uk/hadobs/hadisdh CEDA: https://catalogue.ceda.ac.uk/uuid/2d1613955e1b4cd1b156e5f3edbd7e66).</caption><thead><tr><td>Index</td><td>Long name</td><td>Description</td></tr></thead><tbody><tr><td>T<sub>w</sub>X</td><td>Maximum wet bulb temperature</td><td>Gridbox mean of station month maxima of daily maximum T<sub>w</sub></td></tr><tr><td>T<sub>w</sub>X90p</td><td>90th percentile maximum wet bulb temperature exceedance</td><td>Gridbox mean of station percentages of days where the daily maximum T<sub>w</sub> exceeds the climatological 90th percentile of daily maxima for the month</td></tr><tr><td>T<sub>w</sub>X29</td><td>29°C maximum wet bulb temperature exceedance</td><td>Gridbox mean of station percentages of days where the daily maximum T<sub>w</sub> ≥29°C</td></tr></tbody></table>
HadISDH/extremes uses hourly weather station observations of wet bulb temperature that have been quality controlled from the HadISD dataset (Dunn et al. 2012, 2014, 2016; Dunn 2019; Smith et al. 2011). Wet bulb temperature is calculated from dew point temperature and air temperature using the Stull (2011) formula. To ensure a high-quality final product, only stations with sufficient data completeness are included and the final grid boxes are filtered to remove those stations where large inhomogeneities are present (see Willett 2023a,b for more details). Importantly, the high-variability nature of extremes and dependence on a single daily observation ultimately means that uncertainty is larger than for monthly mean

quantities such as those provided by HadISDH.land (section 2d1).

There are many regions of the globe for which high $T_w$ is rare or non-existent. Fig. SB.2.1 shows the percentage of days where the $T_w$X exceeded 25, 29, and 31°C over the 1973–2022 record (panels a,c,e) and the number of days for 2022 as an anomaly compared to the 1991–2020 baseline (panels b,d,f). Even the lower midlatitudes experience ‘moderate’ $T_w$ ($T_w$X25, Figs. SB.2.2a,b), but ‘high’ $T_w$ ($T_w$X29 and $T_w$X31, Figs. SB.2.1c–f) are so far mostly limited to the lower latitudes. In the Middle East, the Persian Gulf and Red Sea regions typically experience the most frequent ‘high’ $T_w$, but

<figure><img src="image_64.png" /><figcaption>Fig. SB2.1. Number of days where the daily maximum wet bulb temperature is equal to or exceeds set thresholds from HadISDH/extremes. Data have been screened to remove grid boxes where temporal completeness is less than 70% (<35 of 50 yrs). (a), (c), (e) show the percentage of days over the 1973–2022 period and (b), (d), (f) show 2022 annual anomalies compared to the 1991–2020 base period as number of days per year. Panels (a), (c), and (e) identify land regions where there are no exceedances specifically as white grid boxes. This is different from the gray “missing data” regions.</figcaption></figure>
did not during 2022. Interestingly, the 2022 heatwave in the United Kingdom (UK; sections 2b4, 7f2), despite record-breaking dry bulb temperatures, remained below the T<sub>w</sub>X25 threshold and relatively dry in terms of humidity. When averaged globally (Fig. SB2.2a), the T<sub>w</sub>X25 and T<sub>w</sub>X29 indices show significant trends in days at 0.13±0.03 days yr<sup>-1</sup> decade<sup>-1</sup> and 0.02±0.01 days yr<sup>-1</sup> decade<sup>-1</sup>, respectively.

The T<sub>w</sub>X90p index is more globally applicable, though the current (1991–2020) 90th percentiles for higher latitude grid boxes are less likely to be at levels sufficient to cause significant health impacts. Plate 2.1ag shows eastern North America, the UK/Europe, India, China, Japan, much of Southeast Asia, and eastern Australia with widespread higher-than-average exceedances in 2022. When averaged globally (Fig. SB2.2b) it is clear that the frequency of these ‘moderate’ humid heat extremes are increasing, and 2022 saw a near-record-high number of day counts, surpassed only by 1998, 2016, 2020, and 2021. The long-term trend is 4.6±1.08 days yr<sup>-1</sup> decade<sup>-1</sup>. This time series also shows clearly that while high-humidity heat events are more common in El Niño years, with peaks occurring in 1998, 2010, and 2016, the more neutral and La Niña years of 2020 and 2021, respectively, were actually comparable.

The T<sub>w</sub>X index shows that humid heat extremes are also becoming more severe. The global average T<sub>w</sub>X has significantly increased since the 1970s at 0.13±0.04 °C decade<sup>-1</sup> and was higher than average for 2022 (Fig.SB2.2c). The spatial anomalies (Plate 2.1ah) for 2022 follow the pattern of T<sub>w</sub>X90p generally, demonstrating that many regions are experiencing both more frequent and more extreme humid heat events.

This new dataset, HadISDH/extremes, will be updated annually and so can be used to track changes in the frequency and severity of humid heat events. HadISDH/extremes also provides equivalent dry bulb temperature indices that have been identically processed, therefore uniquely enabling analysis of the varying contributions of dry and humid heat to a range of societal impacts.

<figure><img src="image_65.png" /><figcaption>Fig. SB2.2. Global 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 are also shown. These were fitted using an ordinary least squares regression with AR(1) correction following Santer et al. (2008). (a) Annual sums of the daily maximum wet bulb temperature (T<sub>w</sub>X) ≥25°C, ≥29°C, and ≥31°C thresholds. (b) Annual sum of the daily maximum wet bulb temperature exceedances of the 90th percentile (T<sub>w</sub>X90p). (c) Annual mean of the daily maximum wet bulb temperature (T<sub>w</sub>X) of the month.</figcaption></figure>
## 2. TOTAL COLUMN WATER VAPOR

—C. A. Mears, J. P. Nicolas, O. Bock, S. P. Ho, and X. Zhou

In 2022, the global land and ocean averages of total column water vapor (TCWV) were near or slightly above the 1991–2020 climatological averages, despite the ongoing presence of La Niña

conditions in the tropical Pacific Ocean, which usually reduces the TCWV due to lower tropospheric temperatures. In reanalysis output, 2022 was the 8th (MERRA-2), 10th (JRA-55), and 14th (ERA5) highest/wettest vapor year since 1980. Time series of annual vapor anomalies from different products (Fig. 2.25) agree well for combined land and ocean averages and ocean-only averages. Over land, there is a considerable discrepancy between ERA5 and GPS-RO observations, which show a substantial decrease over land for the last two years, and ground-based GNSS observations and JRA-55 and MERRA-2, which do not show such a drop. The differences arise mainly over Africa and South America (not shown). Similar discrepancies are observed between ERA5 and surface-specific humidity and relative humidity for the last two to three years, where ERA5 is very dry compared to MERRA-2 and HadISDH (section 2d1).

The global map of TCWV anomalies (presented as percent of annual mean values to more clearly show extratropical changes) for 2022 (Plate 2.1i) shows a strong low vapor (or dry) anomaly in the central equatorial Pacific, with a strong high vapor (or wet) anomaly directly to the south and west, including much of Australia and the eastern Indian Ocean south of the equator. Much of the extratropical Northern Hemisphere shows high (wet) anomalies, which are most pronounced in the North Pacific, northern India, and the Tibetan Plateau.

Several regions had record-high or record-low vapor in the annual mean during 2022. Figure 2.26 shows a global map of the number of the three reanalysis products that indicated high or low records by evaluating annual means for the years 1980–2022. All three products agree that the central and eastern equatorial Pacific Ocean experienced a record-low anomaly; the general pattern

<figure><img src="image_66.png" /><figcaption>Fig. 2.25. Global mean total column water vapor annual anomalies (kg m⁻²) over (a) land and ocean, (b) ocean only, and (c) land only from observations and reanalyses (ERA5, MERRA-2, JRA-55). The shorter time series from the observations have been adjusted so that there is zero mean difference relative to the ERA5 results during their respective periods of record.</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>Fig. 2.26. Global map of the number of reanalysis products (out of three) that indicated a record-low or record-high annual mean total column water vapor (TCWV) anomaly during 2022 relative to the 1980–2022 period.</figcaption></figure>
is consistent with typical La Niña behavior (Mears et al. 2022), but the reasons for the strength of the signal in 2022 are not yet understood. The dry anomaly extends into southern South America with slightly less agreement, where it is associated with a multiyear drought in Chile and Argentina (Heath 2022). Several regions of record-high vapor occurred over the midlatitude oceans in both hemispheres, as well as southeast Australia, which also experienced anomalously high rainfall (section 2d4), the north of New Zealand, the Bay of Bengal, and eastern Siberia.

This assessment used three global reanalysis products: ERA5 (Hersbach et al. 2020), MERRA-2 (Gelaro et al. 2017), and JRA-55 (Kobayashi et al. 2015). Measurements made over the oceans by satellite-borne microwave radiometers were used (Remote Sensing Systems Satellite; Mears et al. 2018). GPS-RO observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), Metop-A, -B, and -C, COSMIC2 (Ho et al. 2020a, b, 2010; Teng et al. 2013; Huang et al. 2013) and Spire satellite missions were used over both land and ocean. The approach to merge the RO data products from multiple RO missions into consistent climate data records is detailed in Shao et al. (2023). The ground-based GNSS dataset (Bock 2022) used in this analysis counts 240 stations, located mainly on continental land and a few islands, with more than 10 years of measurements (Plate 2.1i) among which 207 are located within 60°S–60°N (Fig. 2.25). All three reanalyses assimilate satellite microwave radiometer and GPS-RO data and are, therefore, not entirely independent from these two datasets. Ground-based GNSS measurements are not assimilated and serve as a completely independent dataset.

### 3. UPPER-TROPOSPHERIC HUMIDITY

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

Upper-tropospheric humidity (UTH) in 2022 was close to, or slightly below, the 2001–2020 average (Fig. 2.27a). The mean and standard deviation of 2022 anomalies was –0.25±0.28 %rh for the satellite microwave humidity sounder dataset (Chung et al. 2013), –0.15±0.60 %rh for the satellite High Resolution Infrared Sounder (HIRS) dataset (Shi and Bates 2011), and –0.17±0.33 %rh for the ERA5 reanalysis (Hersbach et al. 2020). Over the 44-year record, the HIRS and ERA5 time series have statistically insignificant trends of 0.007±0.023 %rh decade⁻¹ and 0.008±0.020 %rh decade⁻¹, respectively. This is consistent with the theoretical consideration that the large-scale relative humidity in the upper troposphere remains approximately constant (Ingram 2010) and implies that the absolute amount of water vapor in the upper troposphere has increased over time.

Increased upper-tropospheric water vapor is also depicted in Fig. 2.27b by a significant positive trend (+0.105±0.008 K decade⁻¹) in the difference between mid-to upper-tropospheric brightness temperature data from satellite microwave sounding unit (MSU) and advanced microwave sounding unit (AMSU) instruments (Zou et al. 2023) and the HIRS upper-tropospheric (UT) water vapor brightness temperatures. MSU instruments measure the radiation emitted by oxygen molecules in the atmosphere. As the concentration of oxygen is not changing, the emission level of the

<figure><img src="image_68.png" /></figure>

Fig. 2.27. Time series of 60°S–60°N monthly mean anomaly of (a) upper-tropospheric relative humidity (%rh) for the three datasets (see text for details) and (b) the difference between mid to upper-tropospheric temperature (MSU T2) and water vapor channel (HIRS T12) brightness temperatures (K). Anomalies are with respect to the 2001–2020 base period.
oxygen channel (measuring UT temperature) is not changing, but as the temperature of the UT increases with time, there is a positive trend in the measurement of this channel (not shown). If there were no increase in water vapor, the emission level of the HIRS water vapor channel would also stay constant in time, which, due to the UT temperature increase, would result in a positive trend in the measurement of this channel and no trend in the difference between the two time series. However, as the water vapor in the UT increases, the water vapor emission level of the HIRS channel shifts higher in the troposphere and measures water vapor emissions from a colder temperature, diverging from the oxygen emission level of MSU. Differencing the time series removes the effect of the temperature increase and shows only the impact of the water vapor changes (e.g., see Soden et al. 2005; Chung et al. 2014).

Monitoring of upper-tropospheric water vapor is crucial to determining one of the strongest positive (amplifying) feedbacks contributing to anthropogenic warming due to its powerful greenhouse effect (see Coleman and Soden 2021 for a detailed description of water vapor feedback).

The agreement among the three UTH datasets is reasonable; the correlations of HIRS and ERA5 with the microwave series during their common period (1999–2022) are 0.6 and 0.5, respectively, despite their structural differences. For example, satellites represent a layer-average UTH with one satellite sampling the same location over Earth only twice a day while ERA5 represents the 400-hPa level RH with hourly sampling. The microwave data have almost all-sky sampling while the HIRS data sample only clear-sky conditions; this sampling difference is one reason for the higher interannual variability in the HIRS data as illustrated in John et al. (2011). Water vapor increases in the upper and lower troposphere since 1979 are captured by climate models when observed sea-surface temperatures are prescribed but smaller than those simulated by coupled climate simulations (Allan et al. 2022). This can be explained by the unusual spatial pattern and resulting magnitude of observed warming compared to that simulated by coupled climate models in the recent period, which included a number of strong La Niña events (Andrews et al. 2022) such as the extended event that affected 2022.

The spatial anomaly patterns (Plate 2.1j for microwave UTH; Fig. 2.28 for HIRS) relate to large-scale weather conditions, with positive (negative) anomalies associated with wetter (drier)

conditions at the surface. This is because one of the main drivers of UTH is convection; therefore, UTH is useful for monitoring changes in large-scale dynamics in the atmosphere. Clear La Niña patterns are visible, with positive anomalies over the Maritime Continent and a strong dry signal in the western equatorial Pacific (centered near the date line). Prevailing drought conditions over the western United States, central Europe, and southern China are also reflected in lower-than-average UTH. Drought conditions affecting parts of South America and Angola/Namibia in Africa in 2021 may be associated with low UTH in these regions during 2022, yet severe drought affecting East Africa

in 2022 is not, implying that the link between UTH and dry conditions is complex. Higher-than-average UTH over Pakistan, Nigeria, eastern Australia, and northern Brazil are associated with a series of substantial rainfall events with associated flooding in 2022.

<figure><img src="image_69.png" /><figcaption>Fig. 2.28. Upper-tropospheric humidity anomaly map (%rh) for 2022 for the HIRS data record with respect to the 2001–20 base period.</figcaption></figure>
## 4. PRECIPITATION

—R. S. Vose, R. Adler, G. Gu, U. Schneider, and X. Yin

Precipitation over global land areas in 2022, as estimated from two different monitoring products, was near or slightly below the 1991–2020 long-term average (Fig. 2.29a). In particular, the gauge-based product from the Global Precipitation Climatology Centre (GPCC; Becker et al. 2013) had an anomaly of –4.34 mm for 2022, and the blended gauge–satellite product from the Global Precipitation Climatology Project (GPCP; Adler et al. 2018) had an anomaly of –0.11 mm. Both products indicate that mean global land precipitation in 2022 was less negative than in 2021.

According to the GPCP product, the precipitation anomaly over the global ocean (Fig. 2.29b) was –18.03 mm, and the overall global (i.e., land plus ocean) anomaly (Fig. 2.29c) was –13.02 mm. Both anomalies are less negative than the previous year. The negative ocean and global anomalies are typical of La Niña, wherein below-normal sea-surface temperatures in the eastern tropical Pacific Ocean are associated with suppressed convection and rainfall. Overall, the GPCP product ranks 2022 as the fourth-driest year in the global record (Fig. 2.29c), which begins in 1979.

Over global land areas, the highest positive precipitation anomalies in 2022 were across northern South America and the Maritime Continent, and the greatest negative precipitation anomalies were over western and central North America, central South America, western Europe, and parts of southern and eastern Africa (Plate 2.1k). Over the global oceans, high positive precipita-

tion anomalies extended from the eastern Indian Ocean southeastward to the tropical western Pacific Ocean. Parts of the equatorial Atlantic Ocean and the Southern Ocean near South America were also much wetter than average. In contrast, large negative precipitation anomalies were apparent over much of the central Pacific Ocean and the western Indian Ocean and, to a somewhat lesser extent, over parts of the north Atlantic Ocean.

La Niña, along with the Indian Ocean dipole, influenced precipitation patterns across the globe again in 2022, particularly in the tropics. The current La Niña began in 2020, and while it has varied in intensity since that time, there is a notable resemblance between the annual precipitation anomaly patterns of the past two years. For example, wetter-than-normal condi- tions once again extended from the Maritime Continent into the South Pacific Ocean and from northern South America into the equatorial Atlantic Ocean. Likewise, much of the central Pacific Ocean near and south of the equator were drier than normal again in 2022, as was much of the North Atlantic Ocean. The largest anomalies at the core of these wet and dry features exceeded 500 mm per year. These tropical La Niña features in 2022 helped to fuel frequent flood and land- slide conditions (section 2d5), for example, in Indonesia and Malaysia, Indochina, southern India, Pakistan, and the southeast quadrant of Australia. Floods and landslides were also more prevalent across northern South America and eastern Brazil.

<figure><img src="image_70.png" /><figcaption>Fig. 2.29. Globally averaged precipitation anomalies (mm yr⁻¹; 1991–2020 base period) over (a) land, (b) ocean, and (c) the globe (land and ocean). Land and ocean time series were created using a proportional land/sea mask at the 1° × 1° scale.</figcaption></figure>
# 5. 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

Continuing La Niña conditions (see Sidebar 3.1 and section 4b for details) contributed to lower maximum-intensity rainfall than the 1991–2020 mean in South America and southern Africa and higher maximum-intensity rainfall in eastern Australia (Plate 2.1l; Figs. 2.30, 2.31), continuing the pattern from recent years. Some regions with less intense rainfall extremes than average, such as Bangladesh, China, southern Europe, and the central and southwestern United States, also experienced wide-spread drought, exacerbating the resultant floods and landslides when extreme precipitation occurred over dry land.

Here, we focus on rainfall intensity indices: Rx1day (maximum rainfall in 24 hours) and Rx5day (maximum accumulated rainfall over five consecutive days). These metrics reflect strong potential for societal impacts from flooding. We use a combination of gauge-based (Global Historical Climatology Network daily [GHCND], Menne et al. 2012; GPCC, Ziese et al. 2022), reanalysis (ERA5, Hersbach et al. 2020; MERRA-2, Gelaro et al. 2017) and satellite (Climate Hazards Group InfraRed Precipitation with Station data [CHIRPS], Funk et al. 2015) data. Records demonstrate that a proportion of the globe experiences extreme precipitation in any given year. The reported events were exceptional in terms of Rx1Day and Rx5Day but are not a comprehensive list. Details on extreme flooding events can also be found in Chapter 7.

Pakistan received around three times its normal volume of monsoon precipitation in August (ECMWF 2022), with some regions receiving up to eight times their expected monthly totals (PMDNWFC 2022). It was the wettest August since Pakistan records began in 1961. Figures 2.30 and 2.31 (and Plate 2.1l) show widespread strong positive Rx5day anomalies over the region. The monsoon axis was farther south than normal and was accompanied by a strong land/sea heat contrast (PMCDPC 2022). Approximately 15% of the South Asia region (as defined in Iturbide et al. 2020) received Rx1day and Rx5day precipitation totals up to 2.5 times greater than their previous records. Recent analysis indicates that Rx5day over the Sindh and Balochistan provinces is now about 75% more intense than it would have been without climate change (Otto et al. 2022). Similar to

<figure><img src="image_71.png" /><figcaption>Fig. 2.30. Annual maximum five-day precipitation (Rx5day) as a percentage of the 1991–2020 average for (a) the globe and over Europe and South Asia in (b) 2010 and (c) 2022. (Source: ERA5.)</figcaption></figure>
the 2010 La Niña, the duration and intensity of the most extreme rains over Pakistan in 2022 were abnormally high, while net monsoon rains over Bangladesh were below average (Rajeevan et al. 2011; Figs. 2.39b,c). Heavy rains in south and east China in June exceeded decades-old records (NCEI 2023; Fig. 2.30a), continuing a pattern of above-average Rx1day observed in this region in 2020 and 2021.

Australian floods in Queensland and eastern New South Wales between February and March caused 22 fatalities and were the costliest on record for the insurance sector (Aon 2023). Several locations had Rx5day >1000 mm during February, with Brisbane receiving 677 mm over three days and some locations doubling their previous five-day annual record (Fig. 2.31). Further flooding occurred in eastern Australia during October and November, with around 11% of Victoria gauges and 13% of New South Wales gauges reporting record Rx1day totals over the two months and around 67% of the area in the top 1% of recorded daily totals (Bureau of Meteorology [BOM] 2023; Plate 2.1l; Fig. 2.30a).

Floods and landslides occurred following heavy rainfall between 11 and 13 April over south-eastern South Africa (OCHA 2022a). Rx1day at Pennington South (307.2 mm) and at King Shaka Airport (221.2 mm) were more than three and four times their previous daily records, respectively (SAWS 2022). This resulted in more than $3.6 billion in economic loss and 455 fatalities (Aon 2023).

<figure><img src="image_72.png" /><figcaption>Fig. 2.31. Annual maximum five-day precipitation record-breaking totals from GHCN over southeastern Australia in Feb 2022 for (a) absolute values (mm) and (b) ratios to the previous record; (c) GPCC global Rx5day anomalies from 1981–2022 mean (mm); and (d) MERRA-2 global Rx5day anomalies from 1991–2020 mean (mm).</figcaption></figure>
Heavy rainfall associated with a succession of four tropical cyclones between January and February 2022 over Madagascar, Mozambique, and Malawi caused severe humanitarian impacts and infrastructure destruction (see sections 4g6 and 7e5 for details).

Despite a wetter-than-average autumn over some parts of western and northern Europe, drier-than-average conditions persisted over most of Europe (section 7f) and were accompanied by ~67% of the Mediterranean and southern Europe region experiencing Rx1day up to 70% lower than normal (Plate 2.1l; Fig.2.30a). Exceptions to this pattern were Spain and Portugal during December. NOAA's Climate Extremes Index component 4 (CEI4; Gleason et al. 2008) reported a slightly above-average year (12% compared to the long-term mean of 10.4%) for the percentage of the contiguous United States experiencing extreme one-day precipitation. While some regions had record-breaking values of CEI4 in summer and autumn, they do not include those affected by Hurricanes Fiona and Ian. Hurricane Ian was the second-costliest disaster on record (see Sidebar 4.1), bringing exceptional Rx1day and Rx5day to Florida as evidenced in Fig. 2.30a and Plate 2.1l.

# 6. CLOUDINESS

—C. Phillips and M. J. Foster

Cloudiness in 2022 was at its lowest coverage since the Pathfinder Atmospheres – Extended (PATMOS-x v6.0; Foster et al. 2023) satellite record began in 1980, with a mean global cloud area fraction of 65% (Figure 2.32). Dataset reliability is lower this year (see below), but the Clouds and the Earth's Radiant Energy System Energy Balance and Filled (CERES EBAF) record, beginning in 2000 supports this by showing 2022 as having the second-lowest amount of solar

radiation reflected by clouds relative to clear-sky. The eruption of the large Hunga Tonga–Hunga Ha‘apai volcano in the South Pacific, which ended on 15 January, had the potential to impact the global or regional cloud amount, and a sharp decrease in global cloudiness was observed afterwards. However, Plate 2.1m and closer spatial analysis did not indicate any definitive connection to the eruption.

During 2022, there were several important satellite transitions, launches, and changes affecting cloud observation from space. Most relevant here, the *Aqua* satellite used previously (Platnick et al. 2015; Phillips and Foster 2022) started to drift significantly in orbit. The effective

sampling time-of-day is no longer stable, and systematic diurnal variation of cloudiness must be accounted for. The PATMOS-x v6.0 cloud climate dataset (Foster et al. 2023) is used instead. Its constituent satellites also suffer from severe orbital drift and intersatellite differences, but the record starts in 1980 and is thus much longer than *Aqua* Moderate Resolution Imaging Spectroradiometer (MODIS), which begins in 2002.

Differences in sensors can make comparison difficult, as the errors in cloud area fraction are likely to be systematic and conditional on the cloud type, time of day, scan angle, surface type, etc. This is exemplified by the differing trends between the PATMOS-x cloudiness (Fig. 2.32) and the *Aqua* MODIS-C6.1 cloudiness. PATMOS-x shows a long-term decline in cloud area fraction, whereas *Aqua* MODIS-C6.1 previously showed an increase (Phillips and Foster 2022), and the drifting *Aqua* MODIS-C6.1 recorded the most cloud coverage on record for 2022 (not shown). For context, Fig. 2.33 shows the cloud radiative effects from CERES EBAF-top-of-atmosphere

<figure><img src="image_73.png" /><figcaption>**Fig. 2.32.** Global mean cloud area fraction (%). (Source: PATMOS-x v6.0 [Foster et al. 2023].)</figcaption></figure>
Ed4.2 through December 2022 (Loeb et al. 2018). Cloud radiative effect in this case is defined as the difference between average clear-sky observed radiative flux and average all-sky observed radiative flux. Shortwave cloud radiative effect (SWCRE) in 2022 was the second highest since the record began in 2000. This means that clouds had an anomalously large warming effect (less cooling) in the shortwave as a result of reflecting less radiation back out to space and permitting more to reach the surface. Conversely, the longwave cloud radiative effect (LWCRE) in 2022 was the second lowest. This corresponds to clouds having a stronger cooling effect (less warming) in the longwave as a result of permitting more radiation out into space and trapping less close to the surface. Added together, the shortwave and longwave cancel out such that the cloud radiative effect in 2022 was close to the mean value (+0.05 Wm⁻²). More details on radiative flux and energy budget can be found in section 2f1.

Note that the cloud radiative effect can vary as the surface warms and/or changes albedo (section 2h1) without any change in cloud properties at all. However, if the cloud area fraction is indeed decreasing (as measured by PATMOS-x), this is consistent with the observed positive shortwave and negative longwave cloud radiative effect. Fewer clouds mean more

absorbed solar radiation as opposed to reflected and also more longwave emission to space from the warm surface. Cloudiness has long been a difficult essential climate variable to quantify, and we conclude that uncertainty remains large.

<figure><img src="image_74.png" /><figcaption>Fig. 2.33. Cloud radiative effect (CRE) anomaly (W m⁻²) from the dataset 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 (a) cloudiness-related warming through more radiation reaching the surface and less being reflected back out to space (SWCRE, blue line) or (b) more being trapped close to the surface rather than escaping out to space (LWCRE, orange line). Gray line is total CRE. Negative values indicate cloudiness-related cooling.</figcaption></figure>

# 7. LAKE WATER LEVELS

—B. M. Kraemer, H. A. Dugan, S. La Fuente, and M. F. Meyer

For 264 of the world's largest lakes, the 2022 mean water-level anomaly was 1.59 m above the 1992–2002 baseline, with 67% having higher-than-average levels (data from Birkett et al. [2022]; Birkett and Beckley [2010]; Crétaux et al. [2011]). Water-level anomalies ranged widely from –65.75 m to +157.02 m, with the most extreme anomalies due to reservoir filling and drainage. The median water-level anomaly was +0.28 m with an interquartile range of –0.14 m to +1.12 m. These measurements support understanding of global hydrological changes, water availability, drought, and the impact of human water diversions on lake water levels. Climate change affects these water levels by altering global precipitation patterns (Konapala et al. 2020) and increasing atmospheric water loss from inland waters due to higher evaporation rates (Zhao et al. 2022; Sharma et al. 2019).

Water-level anomalies in lakes varied greatly but with some regional consistency (Plate 2.1n). Lakes in the western United States, southern South America, the Middle East, and the Caucasus in eastern Europe had consistently below-normal water levels, due to a combination of a surface-vapor pressure deficit (as seen in Plate 2.1 and section 2d1), enhanced water evaporative loss, reduced precipitation, increasing human water use, and other factors (Friedrich et al. 2018;
Khazaei et al. 2019; Pisano et al. 2020). The largest negative volumetric anomalies (water-level anomalies multiplied by static lake surface area from HydroLAKES; Messager et al. 2016) were all in the Middle East, including the Caspian Sea, Aral Sea, and Lake Urmia (Fig. 2.34). Lakes in Canada, the tropics, and southern Asia tended to have positive water-level anomalies. The six largest positive volumetric anomalies were all found in tropical Africa: Lakes Victoria, Tanganyika, Malawi/Nyasa, Turkana, Volta, and Nasser (Fig. 2.34) due to years of above-average precipitation (although the tropical African precipitation anomaly for 2022 was not above average, as seen in Plate 2.1k). Positive water-level anomalies can be explained in general by climate variability and change through increasing heavy precipitation, as well as by dam management and reductions in human water demand (Vanderkelen et al. 2018). Snowmelt may also contribute to higher water levels in northern and high-altitude regions (Zhang et al. 2019; Woolway et al. 2020; Kraemer et al. 2019). In general, the global patterns in water-level anomalies with radar altimetry match those observed using data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission (as seen in Fig. 2.34 and section 2d8; Landerer and Swenson 2012) with exceptions in East Africa and northern North America where local lake conditions may cause water levels to diverge from terrestrial water storage patterns.

To detect water-level anomalies for 2022, we used radar altimeter measurements obtained from the NASA/CNES Topex/Poseidon and Jason satellite missions through the Global Reservoir and Lake Monitoring (G-REALM; https://ipad.fas.usda.gov/cropexplorer/global_reservoir/) project

<figure><img src="image_75.png" /><figcaption>Fig. 2.34. Lake water-level anomaly (m) time series for 264 globally distributed lakes ranked by their 2022 anomaly relative to the 1992–2002 mean (based on data from Birkett et al. 2022; Birkett and Beckley 2010; Crétaux et al. 2011). Of the 264 water level time series, 106 had substantial data gaps from 2003 to 2008 due to changes in the orbital pathways of the satellite altimeters during that time period. The subset of lakes that are named on the y-axis of (a) and plotted in (b) are those with the 10 largest anomalies (either positive or negative) when water levels anomalies were weighted by the surface area of each lake. The time series plots (b) are sorted by volumetric anomalies with the largest positive anomaly on the top and the largest negative anomaly on the bottom.</figcaption></figure>
version 2.5 (Birkett et al. 2022; Birkett and Beckley 2010) and Theia's Hydroweb database (down-
loaded 16 January 2022; https://hydroweb.theia-land.fr/; Crétaux et al. 2011). Lake surface areas
are from the HydroLAKES database (Messager et al. 2016). The 264 lakes in this analysis contain
more than 90% of Earth's liquid surface freshwater (Messager et al. 2016), have the longest (31+
years) and highest resolution time series, and are updated in near real-time. Water levels are
usually measured every 10 days, but with some lake-to-lake variation. To ensure consistency, the
lake time series were linearly interpolated to daily timescales. Due to changes in orbital pathways
of the satellite altimeters, 103 of the 264 lakes had substantial data gaps from 2003 to 2008,
thus a period before these gaps (1992–2002) was used as the baseline for calculating anomalies.
Monitoring lake water levels on-site is important for verifying and adjusting satellite estimates
of long-term water level changes. However, the lack of readily available, machine readable, and
near-real-time data limits our ability to monitor global-scale changes using only on-site data.
Comparisons between satellite altimeter and on-site measurements have a root mean square
error of ~5 cm for large lakes (Birkett and Beckley 2010).

Satellite radar altimeters are an effective tool for monitoring volumetric change in inland waters, as they provide frequent coverage regardless of cloudiness. However, most satellite altimeters were designed for mapping ocean heights and are best used to monitor the largest lakes (Crétaux et al. 2011; >1000 km²). By chance, 155 smaller lakes (10 km²–1000 km²) are also included here because they had enough overpasses. Multispectral satellites like LandSat-8/9 and Sentinel-2 can detect changes in lake area (Khandelwal et al. 2022; Pekel et al. 2016; Meyer et al. 2020) at high-resolution (30 m) and frequent coverage (10 days–16 days), but require cloud-free conditions. The December 2022 launch of the Surface Water and Ocean Topography (SWOT) satellite is anticipated to revolutionize lake water-level monitoring due to its high spatial resolution (50 m) and frequent coverage (≤21 days) (Biancamaria et al. 2016). Meanwhile, efforts to harmonize existing lake water-level data sources and develop tools to improve accessibility will enhance our understanding of water cycle variations.

8. GROUNDWATER AND TERRESTRIAL WATER STORAGE

— M. Rodell and D. N. Wiese

Terrestrial Water Storage (TWS: the sum of groundwater, soil moisture, surface water, snow, and ice) reached 20-year highs or lows in several regions in 2022. Changes in mean annual TWS between 2022 and 2021 are plotted in Plate 2.10 as equivalent heights of water (cm). Drought and heat continued to affect southern Europe in 2022, with TWS reaching 20-year lows in the Danube River basin and in the Alps. Drought in western Iran also worsened with another year of large TWS losses (some >9 cm). Above-normal precipitation provided some relief to western Russia, but TWS remained below average. Large increases in TWS (some >12 cm) were seen in Southeast Asia, due in part to the heaviest rain in 60 years falling in southern China in June and Typhoon Noru causing flooding in Vietnam and Laos in September (see section 7g for details). TWS in central and southern India approached 20-year highs as well. Extreme drought struck the Yangtze River basin in the summer of 2022, causing the river to reach record-low levels and severely depressing TWS in the region. Southeastern Australia had a very wet year, with multiple episodes of extreme rain and flooding contributing to 20-year high TWS in the Murray-Darling River basin by the end of the year. Conversely, north-central Australia became notably drier (section 7h4). Wet weather caused TWS increases in the northern portion of sub-Saharan Africa and in South Africa, with the latter experiencing record-breaking rainfall in April. Drought affected a large area of south-central Africa centered on Lake Tanganyika, diminishing TWS, yet TWS remained above normal in almost all of sub-Saharan Africa. In North America, dry weather caused TWS declines up and down the U.S. Central Plains, while previously elevated TWS dropped closer to normal levels in the eastern United States. As a result, TWS in the Mississippi River basin dropped to a 20-year low, as water levels in October in the lower Mississippi River itself were the lowest since at least 1988. TWS in the southwestern United States was nearly unchanged from the low levels in that region in 2021. TWS increased in south-central Canada
and decreased in north-central Canada. In South America, eastern Brazil gained a huge amount of water, exceeding 12 cm over a large area, while northern Brazil added to already elevated TWS levels. Much of the western half of the continent's TWS experienced declines due to subpar rainfall.

Figures 2.35 and 2.36 depict zonal-mean and global-mean TWS anomalies, respectively, since April 2002 after removing the seasonal cycle. Data gaps occur during the interim between the GRACE and GRACE-FO missions and when onboard instruments were shut down for various reasons. In addition to excluding from these averages TWS declines associated with ice sheet and glacier losses in Antarctica, Greenland, the Gulf Coast of Alaska, and polar islands (as per Rodell and Wiese 2022), more areas where glacier ablation caused long-term trends in High Mountain Asia, western Canada, and in the southern Andes were also excluded this year. Most notably, 2022 witnessed intensification of both a dry zone between about 25°N and 45°N and a wet zone between about 8°S and 15°N (Fig. 2.35). The former is attributable to TWS declines in

the central and eastern United States, southern Europe, the Caspian Sea and adjacent lands, and eastern China. The latter was caused by TWS gains in eastern Brazil, northern sub-Saharan Africa, southern India, and southeastern Asia. South of that zone of wetness, dryness that began in 2019 or earlier appears to be abating, also owing to TWS gains in eastern Brazil. At the global scale (Fig. 2.36), TWS, excluding ice sheets and glaciers, fluctuated by about 1 cm throughout the year, remaining stable with respect to 2021 and somewhat low overall.

TWS is a useful indicator of hydroclimatic variability because it reflects the integrated effects of weather over months to years. Groundwater and terrestrial water storage are not well monitored at regional and larger scales using conventional approaches, but the GRACE and GRACE Follow-On satellite missions have provided global, monthly time series of TWS anomalies (departures from the long-term mean) since 2002 (Tapley et al. 2004; Landerer et al. 2020). On a monthly scale, uncertainties are typically around 1 cm to 2 cm equivalent height of water over a 500,000 km² region at midlatitudes (Wiese et al. 2016). Groundwater typically dominates the interannual variations in TWS except in the wet tropics (dominated by surface water) and high latitude and alpine regions (dominated by ice and snow; Getirana et al. 2017).

<figure><img src="image_76.png" /><figcaption>**Fig. 2.35.** Zonal means of monthly terrestrial water storage anomalies, excluding those in Antarctica, Greenland, the Gulf Coast of Alaska, polar islands, and major glacier systems, in cm equivalent height of water (cm), based on gravity observations from GRACE and GRACE-FO. Anomalies are relative to a 2003–20 base period.</figcaption></figure>

<figure><img src="image_77.png" /><figcaption>**Fig. 2.36.** Global average terrestrial water storage anomalies from GRACE (gray) and GRACE-FO (black), in cm equivalent height of water (cm), relative to a 2003–20 base period.</figcaption></figure>
## 9. SOIL MOISTURE

—P. Stradiotti, W. Preimesberger, R. van der Schalie, R. Madelon, N. Rodriguez-Fernandez, M. Hirschi, A. Gruber, S. Hahn, W. A. Dorigo, R. A. M. de Jeu, and R. Kidd

The year 2022 saw a global increase in average soil moisture for the fourth consecutive year, with conditions close to the previous wet record of 2011 and of similar magnitude in both the Southern and Northern Hemisphere (SH and NH; Fig. 2.37). The year was generally a continuation of 2021 (van der Schalie et al. 2022), the largest difference between the two consisting of a transition from below- to above-average soil moisture (compared with the 1991–2020 base period) in parts of the SH (Fig. 2.38).

The third consecutive La Niña year (2020–22; see Sidebar 3.1 for details) brought about distinct patterns of precipitation (section 2d4) resulting regionally in above-average soil moisture. This

was the case for eastern Australia, where wetter-than-normal conditions persisted throughout the year and turned into localized strong positive anomalies (above 0.1 m³ m⁻³; Supp. Fig. A2.8). Widespread strong positive anomalies of similar magnitude also characterized most of South and mainland Southeast Asia since the start of the year, linked to the effect of La Niña on rainfall in the wet season (Hrudya et al. 2021). With the onset of the Indian summer monsoon, these conditions shifted from central and southern India to northwestern India and Pakistan, coinciding with severe floods (Smiljanic et al. 2022). Consistent with 2021, southeastern Africa (including southern Mozambique and South Africa) experienced a wetter-than-usual phase starting in April and characterizing the whole dry season until November. Northeast Brazil started the year with strong wet anomalies that dried to average conditions by April, only to strengthen again in November and December. Areas of above-average soil moisture were also noticeable in eastern Europe and in the coastal regions surrounding the Yellow Sea for most of 2022.

While on average 2022 recorded wetter-than-normal soil moisture conditions, dry conditions were dominant in several regions. The Great Plains of central North America experienced notable below-average conditions (consistent with the deeper layers; section 2d8) that worsened during the year. These likely intensified the summer heatwaves affecting the region through local land–atmosphere interactions (Benson and Dirmeyer 2021). The strong (below –0.1 m³ m⁻³) dry anomalies developed in the southern Great Plains and moved northward, reaching Montana and the Canadian Prairies

<figure><img src="image_78.png" /><figcaption>Fig. 2.37. Time series of global (black), Northern Hemisphere (blue), and Southern Hemisphere (red) monthly surface soil moisture anomalies for the period 1991–2022 (upper, m³ m⁻³; 1991–2020 base period) and the valid observations as a percentage of total global land surface (lower, %). 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. (Source: C3S Soil Moisture.)</figcaption></figure>

<figure><img src="image_79.png" /><figcaption>Fig. 2.38. Time–latitude diagram of monthly surface soil moisture anomalies (m³ m⁻³; 1991–2020 base period) for the period 1991–2022. 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. (Source: C3S Soil Moisture.)</figcaption></figure>
in autumn. This aggravated the water deficit of the region for the second consecutive year (van der Schalie et al. 2022). Persistent dry conditions also continued in southern South America and were especially pronounced in the Rio Paranà basin and Patagonia, now in a four-year-long drought spell (Naumann et al. 2021). In eastern Africa, the Indian Ocean dipole (IOD) is one of the main drivers of intra-annual climatic variability along with the El Niño–Southern Oscillation (Nicholson 2017; Marchant et al. 2007; Anderson et al. 2022). The negative IOD mode, which lasted until October 2022 (see section 4f for details), is consistent with the below-average soil moisture observed for most of the Horn of Africa, northern Mozambique, and Madagascar, developing into very dry conditions toward the end of the year. Negative anomalies for the region are a continuation of the severe droughts in recent years (Anderson et al. 2022). Mild negative anomalies remained steady throughout 2022 around the Mediterranean Sea regions (Spain, northern Morocco, Libya, and Tunisia). In large parts of China and northern Asia, widespread negative anomalies persisted and intensified in the eastern Siberian tundra region at the end of the boreal autumn. However, the strong negative water deficit in the Yangtze River basin (section 2d8) is not as visible in the surface layer.

A strong intra-annual variation was observed in western and northern Australia, with average to very dry conditions (below −0.1 m³ m⁻³) in the first part of the year giving way to slightly positive anomalies from mid-year. A similar progression was observed for the Arabian Peninsula and the Persian plateau, northern Europe (Scandinavian peninsula), and the southern Sahel regions. In contrast, the Pacific Northwest region started 2022 with above-average conditions, which subsided toward the boreal summer, turning to below-average soil moisture by the end of the year.

Soil moisture was observed by microwave satellite remote sensing of the upper few centimeters of the soil layer, as provided by the COMBINED product of the Copernicus Climate Change Service (C3S) v202012 (Dorigo et al. 2017). C3S combines multi-sensor data in the 1978–2022 period through statistical merging (Gruber et al. 2017, 2019). Wet and dry anomalies here refer to the positive and negative deviations respectively from the 1991–2020 climatological average.

# 10. 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) over the period 1950–2022 shows that the ongoing increase in global drought since mid-2019 (Barichivich et al. 2020, 2021) reached

a new historical peak in October 2022 (Fig. 2.39), surpassing the peak in August 2021 (Barichivich et al. 2022). A historical maximum of 6.2% of the global land area experienced extreme drought conditions (scPDSI≤-4) in August 2022, slightly greater than the previous maximum in October 1984 (6.1%). The extent of severe plus extreme drought conditions (scPDSI≤-3) in 2022 exceeded 15% of the global land area between July and November, reaching a historical maximum of 15.8% in August. Similarly, moderate or worse drought conditions (scPDSI≤-2) peaked in October at a historical maximum of 29% of the global land area.

The global pattern of regional droughts in 2021 largely persisted through 2022 (Plate 2.1q). Drought severity through western

<figure><img src="image_80.png" /><figcaption>Fig. 2.39. Percentage of global land area (excluding ice sheets and deserts) with the self-calibrating Palmer Drought Severity Index (scPDSI) indicating moderate (<-2), severe (<-3), and extreme (<-4) drought for each month during 1950–2022. Inset: each month of 2022.</figcaption></figure>
North America remained mostly unchanged from 2021 to 2022, but worsened in Europe, parts of South America, and the midlatitudes of Asia (Fig. 2.40). Despite persistent drought conditions in western North America, California experienced a milder fire season than in 2021 (section 2h3)

but the west-east moisture contrast observed across the United States since 2017 persisted (Plate 2.1q). In South America, earlier drought hot spots through most of Chile and around the El Gran Chaco region in northern Argentina intensified (Barichivich et al. 2022). The record-breaking megadrought of central Chile reached its 13th consecutive year in 2022, and 80-year record-low river levels in northern Argentina and Paraguay (e.g., Bermejo and Paraná) disrupted fluvial transport.

A persistent lack of precipitation in large areas of Europe from winter to summer, together with warmer-than-usual conditions and a sequence of heatwaves (sections 2b4, 7f) triggered a severe-to-extreme drought (Plate 2.1q). At its peak, the drought affected more than two-thirds of Europe, becoming one

of the worst historical droughts in France, Spain, Germany, and Italy. In northern Italy, the Po River and canals in Venice reached record-low levels. The drought did not extend into northern Europe, where wet conditions across Fennoscandia continued through 2022. In northern Africa, previous moderate drought intensified to extreme drought along the Mediterranean coast from Morocco to Tunisia (Plate 2.1q). Most of the Middle East from eastern Turkey to Pakistan also saw an intensification of drought to severe or extreme conditions.

Although changes in moisture anomalies through tropical Africa are uncertain due to the sparse coverage of meteorological station data, this region largely saw a continuation of the wet conditions that began in 2019 (Plate 2.1q). In southern Africa, drought conditions seen since 2018 continued through 2022 but eased slightly compared to 2021 (Fig. 2.40). In Australia, previous drought eased in the east but most of the country continued under moderate drought during 2022 (Plate 2.1q). In contrast, India and Southeast Asia experienced predominantly wet conditions. The Yangtze River basin in central-eastern China saw severe drought as a result of precipitation deficit combined with an extreme heatwave, though most of northern China saw wet conditions (see section 7g and Sidebar 7.2 for details). Previous moderate-to-severe drought in parts of northeastern Siberia and the Russian Far East continued in 2022 (Plate 2.1q).

The update of the scPDSI (Wells et al. 2004; van der Schrier et al. 2013) for this year uses global precipitation and Penman-Monteith Potential Evapotranspiration (ET) from an early update of the Climatic Research Unit gridded Time Series (CRU TS) 4.07 dataset (Harris et al. 2020). It incorporates new estimates of some variables in CRU TS4.07 compared with CRU TS4.06 used last year, affecting potential ET via an improved baseline climatology for cloud cover. These revisions modify the scPDSI drought index values throughout, notably a small reduction in the global areas of moderate and severe drought that is consistent throughout the time series.

<figure><img src="image_81.png" /><figcaption>Change in categories from 2021 to 2022 (scPDSI)</figcaption></figure>

<caption>**Fig. 2.40.** Change in drought (self-calibrating Palmer Drought Severity Index [scPDSI]) from 2021 to 2022 (mean scPDSI for 2022 minus mean scPDSI for 2021). Increases in drought severity are indicated by negative values (brown), decreases by positive values (green). No calculation is made where a drought index is not physically meaningful (gray areas: ice sheets or deserts with approximately zero mean precipitation).</caption>
# 11. LAND EVAPORATION

—D. G. Miralles, A. Koppa, H. E. Beck, and M. F. McCabe

Around two-thirds of the precipitation that falls over land is returned to the atmosphere through evaporation (Dorigo et al. 2021). Understanding the spatial and temporal dynamics of evaporation is of key importance for agriculture and water management, as well as for diagnosing the influence of short-term climate variability and long-term climate changes on water resources (Miralles et al. 2014). In 2022, most land regions experienced positive (more than normal) evaporation anomalies relative to the 1991–2020 reference period (Plate 2.1r). Abnormally high values were observed in Amazonia, Southeast Asia and India, southern Africa, and eastern Australia. Most of these anomalies were linked to high precipitation (section 2d4) and coincided with reports of extreme-intensity events and floods (section 2d5). In the Amazon, precipitation enhances evaporation mainly through its influence on interception loss—the vaporization of rain stored on tree canopies and understory vegetation. For drier regions, such as eastern Australia or southern Africa, positive soil evaporation and transpiration anomalies occurred mostly in response to increased soil moisture availability (section 2d9). Conversely, anomalously low evaporation was observed across the Horn of Africa, the U.S. Central Plains, and parts of Brazil and Argentina. In water-limited regions, negative anomalies in evaporation are mainly caused by below-average precipitation (Orimoloye et al. 2022). In the Horn of Africa, the abnormally low evaporation can be linked to a prolonged meteorological drought that has already persisted for five consecutive rainfall seasons (section 7e; Anderson et al. 2023). Interestingly, despite the summer drought and heatwave events striking western Europe (sections 2d10, 7f2), no anomalously low evaporation was recorded in the region.

The global mean land evaporation in 2022 was above the 1991–2020 mean, and even above the expectation based on the positive 1980–2021 trend. This 28 mm yr⁻¹ global mean anomaly is in fact the largest on record (Fig. 2.41). The geographical patterns shown in Plate 2.1r are typical of La Niña conditions (Miralles et al. 2014; Martens et al. 2018), which tend to cause an increase in global mean evaporation (see Southern Oscillation Index [SOI] in Fig. 2.41). This El Niño–Southern Oscillation-induced variability in evaporation is superimposed on a long-term trend of 0.78 mm yr⁻¹, which falls towards the high-end of trend estimates reported in the recent literature (Zhang et al. 2016; Brutsaert et al. 2017; Anabalón and Sharma 2017). This positive trend has been attributed to increasing global temperatures (Miralles et al. 2014) and terrestrial greening (Cheng et al. 2017). Both hemispheres experienced positive evaporation anomalies

<figure><img src="image_82.png" /><figcaption>**Fig. 2.41.** Land evaporation anomalies (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 CRU (right axis, shaded area) are also shown. (Sources: GLEAM; https:// rudata.uea.ac.uk/cru/data/soi/.)</figcaption></figure>
throughout the year, with the Southern Hemisphere in particular (Fig. 2.42) reflecting behavior consistent with La Niña conditions.

The evaporation results are based on version 3.7 of the Global Land Evaporation Assessment Model (GLEAM; Miralles et al. 2011). This version is driven by satellite observations of soil and vegetation water content (Dorigo et al. 2017; Moesinger et al. 2019), a blend of gauge, satellite, and reanalysis data for precipitation (Beck et al. 2019), and reanalysis data for radiation and air temperature (Beck et al. 2022). The reported long-term trends are affected by the indirect representation of the influence of carbon dioxide and atmospheric aridity on vegetation stress in GLEAM v3 (Martens et al. 2017). The unbiased

root-mean square error is approximately 0.7 mm day⁻¹ and the temporal correlation against in situ eddy-covariance measurements is around 0.8 on average (Martens et al. 2017).

<figure><img src="image_83.png" /><figcaption>Fig. 2.42. Zonal mean terrestrial evaporation anomalies (mm month⁻¹; 1991–2020 base period). (Source: GLEAM.)</figcaption></figure>

### 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) can be used to derive indices that describe globally important modes of atmospheric variability, which provide context to weather and climate anomalies and extremes. One of the most globally impactful modes is the El Niño–Southern Oscillation (ENSO), not only owing to its direct effects in the Indo-Pacific region but also to its teleconnections across the mid and high latitudes of both hemispheres (Capotondi et. al. 2015). 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 mostly positive from late 2020 through 2022, coinciding with an impactful, protracted La Niña event (following Allan and D'Arrigo 1999), which has been associated with heavy rainfall and floods in Australia and New Zealand (see sections 7h4 and 7h5, respectfully, for details), a wet summer and dry winter in south-central Chile (section 7d4), and a persistence of drought in the western United States (section 7b2).

In the tropical Indian Ocean, the Indian Ocean dipole (IOD) also influences MSLP patterns (Saji et al. 1999). The negative phase of the IOD, which developed during austral winter 2022, is associated with above-normal ocean temperatures in the tropical eastern Indian Ocean and below-normal ocean temperatures in the west (see section 4f for details). The development of an IOD event is correlated with ENSO by way of variations in the Walker Circulation (Behera et al. 2006). The combined effect of La Niña and a negative IOD contributed to a stronger-than-normal rising branch of the Walker Circulation in the eastern Indian Ocean, western Pacific, and across Australasia, which influenced regional moisture availability and global atmospheric circulation patterns during the year.

Variability in MSLP is also expressed at the regional scale by modes including the Arctic Oscillation (AO), 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 (AAO) in the Southern Hemisphere (SH; Kaplan 2011).

In the NH, the winter NAO is the leading winter mode of variability in the North Atlantic/ European (NAE) region, comprising an MSLP dipole with centers over Iceland and the Azores. The NAO was positive in January and February (Fig. 2.43a), consistent with the strong
stratospheric polar vortex, while (as in 2021) the PNA index was mostly negative throughout the year, in line with the persistent La Niña event (Yeh et al. 2018). The winter NAO and PNA are generally independent (Soulard and Lin 2017). A prolonged high-pressure anomaly occurred over Europe in spring and summer (Figs. 2.43b,c) driving extreme dry and hot conditions (see section 7f for details). The summer NAO (the leading NAE-region mode of variability in July and August; Folland et al. 2009) is an MSLP dipole with centers over Greenland and northern Europe. The 2022 summer NAO index was the second highest in the series dating to 1959, underlining the strength of the MSLP anomaly (Fig. 2.43e). In December, the winter NAO was once again negative, as is weakly favored by La Niña in early winter (e.g., Moron and Plaut 2003).

The SAM, which contributes to up to 34% of the variability in the extratropical SH atmospheric circulation (Fogt and Marshall 2020), was positive for a record-tying 76% of days during 2022 (Fig. 2.44e). This matched the record set in 1998 and was the sixth time since 2015 that the SAM was positive for more than 60% of days. Positive SAM events often occur during La Niña, and the SOI and SAM show a positive correlation starting around 1990 (Clem and Fogt 2013). The upward SAM trend in recent decades, particularly during austral summer, has been associated with a poleward shift of the westerly wind belt in the SH (Fogt and Marshall 2020), which also

<figure><img src="image_84.png" /><figcaption>Fig. 2.43. Northern Hemisphere circulation in 2022. (a)–(d) seasonal mean sea-level pressure (MSLP) anomalies with respect to the 1991–2020 base period, shown as percentiles based on the 1959–2022 period. (e) Jul/Aug summer North Atlantic Oscillation (NAO) index for the period 1959–2022. (Source: ERA5 [Hersbach et al. 2020].)</figcaption></figure>
occurred in 2022. This was associated with a prominent belt of higher-than-normal MSLP from the sub-Antarctic Indian Ocean, across New Zealand, and into southern South America (Fig. 2.44; Plate 2.1s), contributing to New Zealand's warmest year on record (section 7h5). The combined effect of the SAM, ENSO, and IOD teleconnections contributed to lower-than-normal MSLP across Australia and the Maritime Continent. 2022 was Australia's ninth-wettest year on record, with parts of New South Wales experiencing its wettest year on record (section 7h4). In association with higher-than-normal sea-surface temperatures (SSTs), annual precipitable water values were above normal in the tropical eastern Indian Ocean, across northern and eastern Australia, and into the southwestern Pacific, contributing to greater moisture availability for low-pressure systems in the region and culminating in some impactful atmospheric river events, such as New Zealand's strongest August atmospheric river on record (see section 7h5; NIWA 2022b). In the South Pacific, a prominent pressure dipole was observed, especially during the winter, in response to La Niña, a positive SAM, and the presence of above-normal SST anomalies in the southwestern Pacific (Garreaud et al. 2021).

<figure><img src="image_85.png" /><figcaption>**Fig. 2.44.** Southern Hemisphere circulation in 2022. Seasonal mean sea-level pressure (MSLP) anomalies (hPa; 1991–2020 base period) for (a) DJF 2021/22, (b) MAM 2022, (c) JJA 2022, and (d) SON 2022. (Source: ERA5 reanalysis.) (e) Daily Antarctic Oscillation (AOO) index time series. (Source: NOAA Climate Prediction Center.)</figcaption></figure>
## 2. LAND AND OCEAN SURFACE WINDS

—C. Azorin-Molina, R. J. H. Dunn, L. Ricciardulli, C.A. Mears, J. P. Nicolas, T. R. McVicar, Z. Zeng, and M. G. Bosilovich

Relative to the 1991–2020 climatology, land surface wind-speed anomalies at ~10 m above the ground in 2022 were dominated by positive values (Table 2.6). North and South America showed the highest positive anomalies relative to the climatology (+0.070 m s⁻¹ and +0.112 m s⁻¹, respectively), followed by East and Central Asia (+0.012 m s⁻¹ and +0.007 m s⁻¹, respectively). One exception was Europe, where annual mean wind speeds below the climatology persisted in 2022 (−0.072 m s⁻¹; Plate 2.1t). The positive anomalies in 2022 agree with the recent reversal or stabilization of surface winds observed since the 2010s (Zeng et al. 2019) after decades of decrease, denoted as “stilling” (Roderick et al. 2007; McVicar et al. 2012; Fig. 2.45a). The changes in the frequency of wind intensities still show long-term trends since the 1970s, with no trends or weak declines for moderate winds (>3 m s⁻¹; Fig. 2.45c) and clear slowdowns for the strongest winds (>10 m s⁻¹; Fig. 2.45d).

Changes and variability of land surface winds were assessed using: 1) anemometer observations from the Hadley Centre Integrated Surface Database version 3 (HadISD3) dataset (1973–2022; Dunn et al. 2012, 2016; Dunn 2019) and 2) two reanalyses: European Centre for Medium-Range Weather Forecasts Reanalysis version 5

<figure><img src="image_86.png" /><figcaption>Fig. 2.45. Land surface Northern Hemisphere (20°N–70°N) and regional surface wind-speed anomaly time series (m s⁻¹; 1991–2020 base period). Panel (a) shows the HadISD3 observational dataset (1973–2022) and (b) ERA5 (1979–2022) and MERRA-2 (1980–2022) reanalyses. HadISD3 occurrence frequencies (% yr⁻¹) are shown for wind speeds (c) >3 m s⁻¹, and (d) >10 m s⁻¹.</figcaption></figure>

<table><caption>Table 2.6. Northern Hemisphere (20°N–70°N) and regional statistics for land surface wind speed (m s⁻¹) using the observational HadISD3 dataset for the period 1979–2022.</caption><thead><tr><th>Region</th><th>Mean 1991–2020 (m s<sup>-1</sup>)</th><th>Anomaly 2022 (m s<sup>-1</sup>)</th><th>Trend 1979–2022 (m s<sup>-1</sup> decade<sup>-1</sup>), and 5th to 95th percentile confidence range</th><th>Number of stations</th></tr></thead><tbody><tr><td>Northern Hemisphere</td><td>3.308</td><td>+0.009</td><td>-0.055 (-0.070 → -0.041)</td><td>2877</td></tr><tr><td>North America</td><td>3.643</td><td>+0.070</td><td>-0.068 (-0.085 → -0.051)</td><td>842</td></tr><tr><td>Europe</td><td>3.648</td><td>-0.072</td><td>-0.050 (-0.071 → -0.035)</td><td>934</td></tr><tr><td>Central Asia</td><td>2.738</td><td>+0.007</td><td>-0.072 (-0.012 → -0.046)</td><td>304</td></tr><tr><td>East Asia</td><td>2.715</td><td>+0.012</td><td>-0.028 (-0.044 → -0.015)</td><td>537</td></tr><tr><td>South America</td><td>3.452</td><td>+0.112</td><td>+0.051 (+0.033 → +0.069)</td><td>101</td></tr></tbody></table>
(ERA5; 1979–2022, Hersbach et al. 2020; Bell et al. 2021) and Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2; 1980–2022, Gelaro et al. 2017). Note that

surface wind-speed anomalies and trends differ between observations and reanalyses due to the difficulty that reanalysis systems have in reproducing long-term variability (Fig. 2.45b; e.g., Torralba et al. 2017; Ramon et al. 2019; Wohland et al. 2019).

Overall, observed trends of land surface winds for the past 44 years (i.e., since 1979) show a dominance of negative values (Fig. 2.46). In situ wind speeds declined by 0.055 m s⁻¹ decade⁻¹ across the NH over that time (Table 2.6). Over the last decade there has been a stabilization or reversal of wind speed trends globally (e.g. Zeng et al. 2019), which has also been observed regionally (e.g., Utrabo-Carazo et al. 2022). The exception is South America, where both the few observations and ERA5 reanalysis (Fig. 2.46) show positive trends, which support interhemispheric asymmetry of surface wind changes (Deng et al. 2022; Yu et al. 2022).

The recent reversal of the long-term surface wind speed decline over land has resulted in an increase in wind energy production over the last decade (e.g., over China; Liu et al. 2022). Observed surface wind speed changes are likely to be associated with internal decadal ocean–atmosphere oscillations (Zeng et al. 2019) along with temperature gradient variations arising from global warming (Zhang et al. 2021) but local-to-regional land use changes (Minola et al. 2022), instrumentation (Azorin-Molina et al. 2018), and encoding issues (Dunn et al. 2022) are also factors.

Compared to a 1991–2020 climatology, 2022 had positive anomalies over oceans recorded by satellite radiometers (Remote Sensing Systems [RSS]: +0.11 m s⁻¹), slightly larger than satellite scatterometers (Advanced Scatterometer [ASCAT]: +0.037 m s⁻¹) and reanalysis (ERA5: +0.036 m s⁻¹; Fig. 2.47). The most prominent anomalies were recorded in the Pacific Ocean, with strong positive anomalies (>+1.2 m s⁻¹) in the central tropical Pacific and negative (<-1.2 m s⁻¹) in the western tropical Pacific and Maritime Continent, extending well into the eastern equatorial Indian Ocean—a strong signature of a persistent La Niña phase.

<figure><img src="image_87.png" /></figure>

<figure><img src="image_88.png" /><figcaption>**Fig. 2.46.** Wind speed trends (m s⁻¹ decade⁻¹) from the (a) ERA5 reanalysis output over land/ice and Remote Sensing Systems (RSS) satellite radiometers (SSM/I, SSMIS, TMI, AMSR2, ASMR-E, and WindSat) over ocean for the period 1988–2022 (shaded areas) and (b) observational HadISD3 dataset over land (circles) for the period 1979–2022.</figcaption></figure>

<figure><img src="image_89.png" /><figcaption>**Fig. 2.47.** Annual global mean wind speed anomalies (m s⁻¹; 1991–2020 base period) over the ocean from satellite radiometers and scatterometers.</figcaption></figure>
Similar to 2021, the mid-to-high latitude South Pacific Ocean experienced a strong positive anomaly (>+1.2 m s⁻¹), consistent with the observed strengthening of the Southern Hemisphere westerlies (e.g., Deng et al. 2022; section 2e1). Strong positive anomalies were also seen in the Northern Hemisphere high-latitude regions (e.g., the Gulf of Alaska and the Greenland Sea).

Changes in ocean surface winds show widespread midlatitude negative trends for the period 1988–2022 and a strong negative trend in the Indian Ocean (Fig. 2.46). In contrast, positive trends prevail in the Pacific trade winds, the Southern Ocean, the Bering Sea, and near the coastlines (e.g., North America). The overall global ocean wind trend for 1988–2022 over 60°S–60°N is close to zero (RSS Radiometers: <-+0.01 m s⁻¹ decade⁻¹; ERA5: +0.036 m s⁻¹ decade⁻¹).

Over the ocean, surface winds were evaluated over the period 1988–2022 by 1) ERA5; and 2) satellite-based products: merged radiometer winds (including Special Sensor Microwave/Imager [SSM/I], the Special Sensor Microwave Imager/Sounder [SSMIS], the Advanced Microwave Scanning Radiometer-Earth Observing System [AMSRE], and the Advanced Microwave Scanning Radiometer 2 [AMSR2], Tropical Rainfall Measuring Mission [TRMM], Microwave Imager [TMI], and WindSat), and scatterometer winds Quick Scatterometer [QuikSCAT] and ASCAT (Wentz 1997, 2015; Wentz et al. 2007; Ricciardulli and Wentz 2015; Ricciardulli and Manaster 2021).

### 3. UPPER AIR WINDS

—M. Mayer, L. Haimberger, C. T. Sabeerali, V. Schenzinger, D. E. Surendran, and O. P Sreejith

The 2022 global mean wind-speed anomaly at 850 hPa was about 0.1 m s⁻¹ above the 1991–2020 climatology (Fig. 2.48a). The linear trend for the period 1991–2022 of 0.05 m s⁻¹ decade⁻¹ in the case of ERA5 and 0.04 m s⁻¹ decade⁻¹ for the Japanese 55-year Reanalysis (JRA55) is significant (p-value <0.01) but not statistically significant in MERRA-2. It is worth noting that the time series has been extended to the 1940s due to a recent backward extension of ERA5 (Hersbach et al. 2023).

Figure 2.48b together with Plate 2.1u indicate the mostly strongly positive zonal 850-hPa wind-speed anomalies at 50°S–70°S in 2022. The increase in wind speed in this latitude belt

has therefore continued, with 2022 close to the absolute maxima of the shown time series. The linear trends of the annual means are highly significant (p-value <0.002) for the period 1991–2022, between 0.20 and 0.26 m s⁻¹ decade⁻¹. This result is consistent with the high positive SAM (Marshall 2003) that continued to increase from the already high annual value of 1.2 in 2021 to 1.5 in 2022 (see also section 2e1). The AAO index, which is closely related to the SAM, was also strongly positive (0.79 in 2021 and 0.71 in 2022).

Plate 2.1u shows the annual zonal wind anomaly speed map at 850 hPa, averaged over September–December (SOND) 2022. The high (3 m s⁻¹ near 60°S) wind speed anomaly in the Southern Ocean is the most prominent feature there. It is consistent with higher-than-average baroclinicity that was caused by record-high lower-tropospheric temperatures just north of the 50°S–70°S belt (section 2b5).

<figure><img src="image_90.png" /></figure>

**Fig. 2.48.** Annual anomalies of (a) global mean and (b) 70°S–50°S belt mean eastward wind speed (m s⁻¹; 1991–2020 base period) at 850 hPa from four reanalyses (ERA5 [Hersbach et al. 2020], ERA-Interim [Dee et al. 2011], MERRA-2 [Gelaro et al. 2017], and JRA-55 [Kobayashi et al. 2015]). The numbers in parentheses are linear trends in m s⁻¹ decade⁻¹ for the period 1991–2020. The ERA-Interim time series ends in 2019.