# STATE OF THE CLIMATE IN 2021

## GLOBAL CLIMATE

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

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

Special Online Supplement to the *Bulletin of the American Meteorological Society*, Vol. 103, No. 8, August 2022

doi:10.1175/BAMS-D-22-0092.1

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

©2022 American Meteorological Society

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.
# STATE OF THE CLIMATE IN 2021
## Global Climate

### Editors

Jessica Blunden
Tim Boyer

### Chapter Editors

Freya Aldred
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

Laura Ohlmann

### BAMS Special Editor for Climate

Michael A. Alexander

### American Meteorological Society
**Cover credit:**

Caption: Lightning discharges appear in various colours depending on the scatter of light inside the thundercloud and in the atmosphere. The intracloud lightning discharges in the centre of the thundercloud appear to be white with a bluish tint, and the cloud-to-ground discharge below appears to be orange. The right hand side of the thundercloud exhibits a green tint that is attributed to the unique composition of hydrometeors inside the thundercloud.

The photo was taken in the late evening of 10 September 2013, near Tarragona in northeastern Spain.

© Oscar van der Velde, http://www.lightningwizard.com/index.php?type=sets&setId=72157624159585244&page=2, accessed 10 June 2016.

Global Climate is one chapter from the State of the Climate in 2021 annual report and is available from https://doi.org/10.1175/BAMS-D-22-0092.1. Compiled by NOAA's National Centers for Environmental Information, *State of the Climate in 2021* 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/10.1175/2022BAMSStateoftheClimate.1

**How to cite this document:**

**Citing the complete report:**

Blunden, J. and T. Boyer, Eds., 2022: "State of the Climate in 2021". *Bull. Amer. Meteor. Soc.*, **103** (8), S1–S465 https://doi.org/10.1175/2022BAMSStateoftheClimate.1

**Citing this chapter:**

Dunn, R. J. H., F. Aldred, N. Gobron, J. B Miller, and K. M. Willett, Eds., 2022: Global Climate [in "State of the Climate in 2021"). *Bull. Amer. Meteor. Soc.*, **103** (8), S11–S142, https://doi.org/10.1175/BAMS-D-22-0092.1.

**Citing a section (example):**

Cornes, R. C., T. Cropper, R. Junod, and E. C. Kent, 2022: Night marine air temperature [in "State of the Climate in 2021"). *Bull. Amer. Meteor. Soc.*, **103** (8), S31–S33, https://doi.org/10.1175/BAMS-D-22-0092.1.
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

Aldred, Freya, Met Office Hadley Centre, Exeter, United Kingdom

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

Anderson, John, Department of Atmospheric and Planetary Science, Hampton
University, Hampton, Virginia

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

Aono, Yasuyuki, Graduate School of Life and Environmental Sciences, Osaka
Prefecture University, Sakai, Osaka, Japan

Árgüez, 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 (CIDE, CSIC-UV-Generalitat Valenciana), Climate,
Atmosphere and Ocean Laboratory (Climatoc-Lab), Moncada, Valencia, Spain

Barichivich, Jonathan, Instituto de Geografia, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile and Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom

Basu, Aman, York University, Toronto, Canada

Beck, Hylke E., Joint Research Centre of the European Commission, Ispra, Italy
and GloH2O, Almere, the Netherlands

Bellouin, Nicolas, University of Reading, Reading, United Kingdom

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

Blagrave, Kevin, York University, Toronto, Canada

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

Bock, Olivier, Université de Paris Cité, Institut de physique du globe de Paris,
CNRS, IGN, F-7500 Paris, France

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

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

Boucher, Olivier, Sorbonne Université, Paris, France

Bove, Gerald, University of the Virgin Islands, St. Thomas, U.S. Virgin Islands

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

Buehler, Stefan A., Meteorologisches Institut, Centrum für Erdsystem- und
Nachhaltigkeitsforschung (CEN), Fachbereich Erdsystemwissenschaften,
Universität Hamburg, Hamburg, Germany

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; and NOAA Chemical Sciences Laboratory,
Boulder, Colorado

Christiansen, Hanne H., Geology Department, University Centre in Svalbard,
Longyearbyen, Norway

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

Chung, Eui-Seok, IBS Center for Climate Physics, South Korea

Ciasto, Laura M., NOAA Climate Prediction Center, College Park, Maryland

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

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

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

Covey, Curt, Lawrence Livermore National Laboratory, Livermore California

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

Crotwell, Molly, Cooperative Institute for Research in Environmental Sciences,
University of Colorado, Boulder, Colorado; and NOAA Global Monitoring
Laboratory, Boulder, Colorado

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,
Saskatchewan, Canada

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

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

Dorigo, Wouter A., TU Wien, Vienna, Austria

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 S., Cooperative Institute for Research in Environmental Sciences,
University of Colorado, Boulder, Colorado; and NOAA Global Monitoring
Laboratory, Boulder, Colorado

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

Elkins, James W., NOAA Global Monitoring Laboratory, Boulder, Colorado

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

Fedaeff, Nava, National Institute of Water and Atmospheric Research (NIWA),
New Zealand

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

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

Flemming, Johannes, Copernicus Department, 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

Frith, Stacey M., Science Systems and Applications, Inc., Lanham, Maryland;
and 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, SSAI, Hampton, Virginia

Gentry, Matthew, Cooperative Institute for Research in Environmental
Sciences, University of Colorado, Boulder, Colorado; and NOAA Global
Monitoring Laboratory, Boulder, Colorado

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

Goodman, Steven, Thunderbolt Global Analytics, Huntsville, Alabama

Gou, Qiqi, Hydro-Climate Extremes Lab (H-CEL), Ghent University,
Ghent, Belgium

Granin, Nikolay, Siberian Branch of Russian Academy of Sciences,
Irkutsk, Russia

Guglielmin, Mauro, Department of Theoretical and Applied Science, Insubria
University, Italy

Hahn, Sebastian, TU Wien, Vienna, Austria

Haimberger, Leopold, Department of Meteorology and Geophysics, University
of Vienna, Vienna, Austria
Hall, Brad D., NOAA Global Monitoring Laboratory, Boulder, Colorado

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

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

Hirschi, Martin, ETH Zürich, Zürich, Switzerland

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

Holzworth, Robert, University of Washington, Seattle, Washington

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

Hubert, Daan, Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium

Hulsman, Petra, Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium

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., Centre for Agrometeorological Research (ZAMF), German Meteorological Service (DWD), Braunschweig, 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, Earth Observation Data Centre (EODC), Vienna, Austria

Kim, Hyungiun, Moon Soul Graduate School of Future Strategy and Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea

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

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

L'Abée-Lund, Jan Henning, Norwegian Water Resources and Energy Directorate, Oslo, Norway

Lan, Xin, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado; NOAA Global Monitoring Laboratory, Boulder, Colorado

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

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

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

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

Madelon, Remi, CESBIO (Université Toulouse 3, CNES, CNRS, INRAE, IRD), Toulouse, France

Malmquist, Hilmar J., Icelandic Museum of Natural History, Reykjavík, Iceland

Marszelewski, Wlodzimierz, Nicolaus Copernicus University in Toruń, Toruń, Poland

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

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

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

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

Menzel, Annette, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; and Institute for Advanced Study, Technical University of Munich, Garching, Germany

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

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

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

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

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

Mösinger, Leander, TU Wien, Vienna, Austria

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

Nicolas, Julien P., European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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

Nõges, Tiina, Estonian University of Life Sciences, Tartu, Estonia

Noll, Ben, National Institute of Water and Atmospheric Research (NIWA), New Zealand

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

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

Park, Taejin, NASA Ames Research Center, Moffett Field, California; Bay Area Environmental Research Institute, Moffett Field, California

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

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

Perkins-Kirkpatrick, Sarah E., University of New South Wales, Canberra, Australia

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

Po-Chedley, Stephen, Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California

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

Preimesberger, Wolfgang, TU Wien, Vienna, Austria

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

Pulkkanen, Merja, Finnish Environment Institute SYKE, Helsinki, Finland

Rains, Dominik G., Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium

Randel, William J., National Center for Atmospheric Research (NCAR), 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 and Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, Arizona

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

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

Rodríguez-Fernández, Nemesio J., CESBIO, Université Toulouse 3, CNES, CNRS, INRAE, IRD, Toulouse, France

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

Roth, Chris, University of Saskatchewan, Saskatoon, Canada

Rozanov, Alexei, University of Bremen, Bremen, Germany

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

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., Sorbonne Université, CNRS, Villefranche-sur-mer, France

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

Sharma, Sapna, York University, Toronto, Ontario, Canada

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

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

Siso, Carolina, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder CO USA, and NOAA Global Monitoring Laboratory, Boulder, Colorado

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

Soden, Brian J., University of Miami Rosenstiel School of Marine and Atmospheric Science (RSMAS), Key Biscayne, Florida

Sofieva, Viktoria, Finnish Meteorological Institute (FMI), Helsinki, Finland

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

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

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

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

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

Stewart, Kenton, State University of New York at Buffalo, Buffalo, New York

Stradiotti, Pietro, TU Wien, Vienna, Austria

Streletskiy, Dimitri A., George Washington University, Washington, D.C.

Tchg, Hagen, Cooperative Institute for Research in the Environmental Sciences, University of Colorado, Boulder, Colorado

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

Thibert, Emmanuel, Université Grenoble Alpes, INRAE, UR ETGR, Grenoble, France

Todt, Michael, Cooperative Institute for Research in the Environmental Sciences, University of Colorado, Boulder

Tokuda, Daisuke, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

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), DeBilt, The Netherlands

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

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

van der Vliet, Mendy, Planet Labs, Haarlem, The Netherlands

van der Werf, Guido R., Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

van Vliet, Arnold., Environmental Systems Analysis Group, Wageningen University & Research, the Netherlands

Vernier, Jean-Paul, National Institute of Aerospace/NASA Langley Research Center, Hampton, Virginia

Vimont, Isaac J., Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder; and NOAA Global Monitoring Laboratory, Boulder, Colorado

Virts, Katrina, University of Huntsville, Huntsville, Alabama

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

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

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

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

Weber, Markus, University of Bremen, Bremen, Germany

Wiese, David, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

Wild, Jeanette D., ESSIC/University of Maryland, College Park, Maryland; and NOAA Climate Prediction Center, College Park, Maryland

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

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

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

Woolway, R. I., School of Ocean Sciences, Bangor University, Menai Bridge, Anglesey, Wales

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

Yuan, Ye, TUM School of Life Sciences, Technical University of Munich, Freising, Germany

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; and NASA Goddard Space Flight Center, Greenbelt, Maryland

Ziese, Markus, Global Precipitation Climatology Centre, Deutscher Wetterdienst, Offenbach am Main, Germany

Zotta, Ruxandra M., TU Wien, Vienna, Austria
**Allen, Jessica**, Graphics Support, Cooperative Institute for Satellite Earth System Studies, North Carolina State University, Asheville, North Carolina

**Camper, Amy**, Graphics Support, Innovative Consulting and Management Services, LLC, 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

**Misch, Deborah J.**, Graphics Support, Innovative Consulting and Management Services, LLC, 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

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

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

<table><tr><td>List of authors and affiliations</td><td>S14</td></tr><tr><td>a. Introduction</td><td>S20</td></tr><tr><td>b. Temperature</td><td>S27</td></tr><tr><td>1. Global surface temperature</td><td>S27</td></tr><tr><td>2. Lake surface water temperature</td><td>S28</td></tr><tr><td>3. Night marine air temperature</td><td>S31</td></tr><tr><td>4. Land and surface marine temperature extremes</td><td>S33</td></tr><tr><td>5. Tropospheric temperature</td><td>S36</td></tr><tr><td>6. Stratospheric temperature</td><td>S39</td></tr><tr><td>c. Cryosphere</td><td>S41</td></tr><tr><td>1. Permafrost temperature and active layer thickness</td><td>S41</td></tr><tr><td>2. Rock glacier velocity</td><td>S43</td></tr><tr><td>3. Alpine glaciers</td><td>S45</td></tr><tr><td>4. Lake ice</td><td>S47</td></tr><tr><td>5. Northern Hemisphere continental snow cover extent</td><td>S49</td></tr><tr><td>d. Hydrological cycle</td><td>S50</td></tr><tr><td>1. Surface humidity</td><td>S50</td></tr><tr><td>2. Total column water vapor</td><td>S52</td></tr><tr><td>3. Upper tropospheric humidity</td><td>S54</td></tr><tr><td>4. Precipitation</td><td>S55</td></tr><tr><td>5. Land-based precipitation extremes</td><td>S57</td></tr><tr><td>6. Cloudiness</td><td>S59</td></tr><tr><td>7. River discharge and runoff</td><td>S61</td></tr><tr><td>8. Groundwater and terrestrial water storage</td><td>S63</td></tr><tr><td>9. Soil moisture</td><td>S64</td></tr><tr><td>10. Monitoring global drought using the self-calibrating Palmer Drought Severity Index</td><td>S66</td></tr><tr><td>11. Land evaporation</td><td>S68</td></tr><tr><td>e. Atmospheric circulation</td><td>S69</td></tr><tr><td>1. Mean sea level pressure and related modes of variability</td><td>S69</td></tr><tr><td>2. Land and ocean surface winds</td><td>S71</td></tr><tr><td>3. Upper air winds</td><td>S74</td></tr></table>
## 2. Table of Contents

<table><tr><td><strong>f. Earth radiation budget</strong></td><td>S75</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;1. Earth radiation budget at top-of-atmosphere</td><td>S75</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;2. Mauna Loa apparent transmission</td><td>S77</td></tr><tr><td><strong>Sidbar 2.1: Lightning</strong></td><td>S79</td></tr><tr><td><strong>g. Atmospheric composition</strong></td><td>S81</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;1. Long-lived greenhouse gases</td><td>S81</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;2. Ozone-depleting substances</td><td>S84</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;3. Aerosols</td><td>S86</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;4. Stratospheric ozone</td><td>S90</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;5. Stratospheric water vapor</td><td>S93</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;6. Tropospheric ozone</td><td>S96</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;7. Carbon monoxide</td><td>S99</td></tr><tr><td><strong>h. Land surface properties</strong></td><td>S100</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;1. Land surface albedo dynamics</td><td>S100</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;2. Terrestrial vegetation dynamics</td><td>S101</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;3. Biomass burning</td><td>S103</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;4. Phenology of primary producers</td><td>S105</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;5. Vegetation optical depth</td><td>S108</td></tr><tr><td><strong>Acknowledgments</strong></td><td>S110</td></tr><tr><td><strong>Appendix 1: Chapter 2 – Acronyms</strong></td><td>S115</td></tr><tr><td><strong>Appendix 2: Supplemental Materials</strong></td><td>S118</td></tr><tr><td><strong>References</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, F. Aldred, N. Gobron, J. B. Miller, and K. M. Willett, Eds.

### a. Introduction—R. J. H. Dunn, F. Aldred, N. Gobron, J. B Miller, and K. M. Willett

In 2021, both social and economic activities began to return towards the levels preceding the COVID-19 pandemic for some parts of the globe, with others still experiencing restrictions. Meanwhile, the climate has continued to respond to the ongoing increase in greenhouse gases and resulting warming. La Niña, a phenomenon which tends to depress global temperatures while changing rainfall patterns in many regions, prevailed for all but two months of the year. Despite this, 2021 was one of the six-warmest years on record as measured by global mean surface temperature with an anomaly of between +0.21° and +0.28°C above the 1991–2020 climatology.

Lake surface temperatures were their highest on record during 2021. The number of warm days over land also reached a new record high. Exceptional heat waves struck the Pacific Coast of North America, leading to a new Canadian maximum temperature of 49.6°C at Lytton, British Columbia, on 29 June, breaking the previous national record by over 4°C. In Death Valley, California, the peak temperature reached 54.4°C on 9 July, equaling the temperature measured in 2020, and the highest temperature recorded anywhere on the globe since at least the 1930s. Over the Mediterranean, a provisional new European record of 48.8°C was set in Sicily on 11 August. In the atmosphere, the annual mean tropospheric temperature was among the 10 highest on record, while the stratosphere continued to cool.

While La Niña was present except for June and July, likely influencing Australia's coolest year since 2012 and wettest since 2016, other modes of variability played important roles. A negative Indian Ocean dipole event became established during July, associated with a warmer east and cooler west Indian Ocean. Northern Hemisphere winters were affected by a negative phase of the North Atlantic Oscillation at both the beginning and end of 2021. In the Southern Hemisphere, a very strong positive Southern Annular Mode (also known as the Antarctic Oscillation) contributed to New Zealand's record warm year and to very cold temperatures over Antarctica. Land surface winds continued a slow reversal from the multi-decadal stilling, and over the ocean wind speeds were at their highest in almost a decade.

La Niña conditions had a clear influence on the regional patterns of many hydrological variables. Surface specific humidity and total column water vapor over land and ocean were higher than average in almost all datasets. Relative humidity over land reached record or near-record low saturation depending on the dataset, but with mixed signals over the ocean. Satellite measurements showed that 2021 was the third cloudiest in the 19-year record. The story for precipitation was mixed, with just below-average mean precipitation falling over land and below-average mean precipitation falling over the ocean, while extreme precipitation was generally more frequent, but less intense, than average. Differences between means and extremes can be due to several factors, including using different indices, observing periods, climatological base reference periods, and levels of spatial completeness. The sharp increase in global drought area that began in mid-2019 continued in 2021, reaching a peak in August with 32% of global land area experiencing moderate or worse drought, and declining slightly thereafter.

Arctic permafrost temperatures continued to rise, reaching record values at many sites, and the thickness of the layer which seasonally thaws and freezes also increased over 2020 values
in a number of regions. It was the 34th-consecutive year of mass balance loss for alpine glaciers in mountainous regions, with glaciers on average 25 m thinner than in the late 1970s. And the duration of lake ice in the Northern Hemisphere was the fourth lowest in situ record dating back to 1991.

The atmospheric concentrations of the major long-lived greenhouse gases, CO₂, CH₄, and N₂O, all reached levels not seen in at least the last million years and grew at near-record rates in 2021. La Niña conditions did not appear to have any appreciable impact on their growth rates. The growth rate for CH₄, of 17 ppb yr⁻¹, was similar to that for 2020 and set yet another record, although the causes for this post-2019 acceleration are unknown presently. Overall, CO₂ growth continues to dominate the increase in global radiative forcing, which increased from 3.19 to 3.23 W m⁻² (watts per square meter) during the year. In 2021, stratospheric ozone did not exhibit large negative anomalies, especially near the poles, unlike 2020, where large ozone depletions appeared, mainly from dynamical effects. The positive impact of reductions in emissions of ozone depleting substances can be seen most clearly in the upper stratosphere, where such dynamical effects are less pronounced.

It was the fourth-lowest fire year since global records began in 2003, though extreme regional fire activity was again seen in North America and also in Siberia; as in 2020, the effects of wildfires in these two regions led to locally large regional positive anomalies in tropospheric aerosol and carbon monoxide abundance.

Vegetation is responding to the higher global mean temperatures, with the satellite-derived measures for the Northern Hemisphere for 2021 rated among the earliest starts of the growing season and the latest end of the season on record. The first bloom date for cherry trees in Kyoto, Japan, broke a 600-year record set in 1409.

This year we welcome a sidebar on the global distribution of lightning, which has been recently declared an essential climate variable (ECV) by the Global Climate Observing System (GCOS).

Time series and anomaly maps from many of the variables described in this chapter can be found in Plates 1.1 and 2.1. As with other chapters, many of the sections have moved from the previous 1981–2010 to the new 1991–2020 climatological reference period, in line with WMO recommendations (see Chapter 1). This is not possible for all datasets, as it is dependent on their length of record or legacy processing methods. While anomalies from the new climatology period are not so easily comparable with previous editions of this report, they more clearly highlight deviations from more recent conditions.
<figure><img src="image_2.png" /><figcaption>(c) Night Marine Air Temperature</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>(e) Cool Nights</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(g) Surface Specific Humidity</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(b) Lake Temperature</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(d) Warm Days</figcaption></figure>

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

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

Plate 2.1. (a) NOAA NCEI Global land and ocean surface annual temperature anomalies (°C); (b) Satellite-derived warm season lake surface water temperature anomalies (°C); (c) CLASSnmat night marine air temperature annual average anomalies (°C); (d) GHCNDEX warm day threshold exceedance (TX90p); (e) GHCNDEX cool night threshold exceedance (TN10p); (f) Average of RSS and UAH lower tropospheric temperature anomalies (°C). Gray contours represent the value of the linear regression between the monthly Niño 3.4 index and the monthly local lower tropospheric temperature anomaly over 1979–2019; (g) HadISDH surface specific humidity anomalies (g kg⁻¹);
<figure><img src="image_9.png" /><figcaption>(j) Upper Tropospheric Humidity</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(l) Rx1day Anomalies</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(n) Runoff</figcaption></figure>

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

<figure><img src="image_13.png" /><figcaption>(k) Precipitation</figcaption></figure>

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

<figure><img src="image_15.png" /></figure>

Plate 2.1. (cont.) (h) HadISDH surface relative humidity anomalies (%rh); (i) MERRA2 TCWV anomalies (%). Data from GNSS stations are plotted as filled circles; (j) "All sky" microwave-based UTH anomalies (%rh); (k) GPCP v2.3 annual mean precipitation anomalies (mm yr⁻¹); (l) GPCC maximum 1-day (Rx1day) annual precipitation anomalies (mm); (m) MODIS Aqua C6.1 cloud fraction annual anomalies (%); (n) ELSE (Ensemble Land State Estimator) global distribution of runoff anomalies (mm yr⁻¹);
<figure><img src="image_16.png" /><figcaption>(q) Soil Moisture</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>(s) Land Evaporation</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(p) Terrestrial Water Storage</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>(r) Drought</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>(t) Sea Level Pressure</figcaption></figure>

<figure><img src="image_21.png" /></figure>

Plate 2.1. (cont.) (o) ELSE global distribution of river discharge anomalies (m³ s⁻¹); (p) GRACE and GRACE-FO difference in annual-mean terrestrial water storage between 2021 and 2020 (cm); (q) ESA CCI average surface soil moisture anomalies (m³ m⁻³); (r) Mean scPDSI for 2021. Droughts are indicated by negative values (brown), wet episodes by positive values (green); (s) GLEAM land evaporation anomalies (mm yr⁻¹); (t) ERA5 sea level pressure anomalies (hPa);
<figure><img src="image_22.png" /><figcaption>(w) Total Aerosol</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>(y) Extreme Aerosol Days</figcaption></figure>

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

<figure><img src="image_25.png" /><figcaption>(v) Upper Air (850-hPa) Eastward Winds</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>(x) AOD Ratio</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>(z) Stratospheric (Total Column) Ozone</figcaption></figure>

<figure><img src="image_28.png" /></figure>

Plate 2.1. (cont.) (u) 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); (v) ERA5 850-hPa eastward wind speed anomalies (m s⁻¹); (w) Total aerosol optical depth (AOD) anomalies at 550 nm; (x) Ratio of total AOD at 550 nm in 2021 relative to 2003–20; (y) Number of days with AOD above the 99.9th percentile. Areas with zero days appear as the white/gray background; (z) GOME-2C total column ozone anomalies relative to the 1998–2008 mean from GSG merged dataset (DU); (aa) OMI/MLS tropospheric ozone column anomalies for 60°S–60°N (DU);
<figure><img src="image_29.png" /><figcaption>(ad) Land Surface Albedo in the Near-Infrared</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>(af) Carbon Emissions from Biomass Burning</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>(ac) Land Surface Albedo in the Visible</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>(ae) Fraction of Absorbed Photosynthetically Active Radiation</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>(ag) Vegetation Optical Depth</figcaption></figure>

<figure><img src="image_34.png" /></figure>

Plate 2.1. (cont.) (ab) CAMS reanalysis total column CO anomalies (%); (ac) Land surface visible broadband albedo anomalies (%); (ad) Land surface near-infrared albedo anomalies (%); (ae) FAPAR anomalies; (af) GFASv1.4 carbonaceous emission anomalies (g C m⁻² yr⁻¹) from biomass burning; (ag) VODCA Ku-band VOD anomalies.
## b. Temperature

### 1. GLOBAL SURFACE TEMPERATURE—A. Sánchez-Lugo, C. Morice, J. P. Nicolas, and A. Argüez

The year 2021 was among the seven warmest years since global records began in the mid-to-late 1800s, with a temperature departure between +0.21° and +0.28°C, according to six global temperature datasets (Table 2.1). Depending on the dataset, 2021 was either the fifth, sixth, or equal with 2018 as sixth-warmest year on record (Fig. 2.1). These datasets consist of four independent global in situ surface temperature analyses (NASA-GISS, Lenssen et al. 2019; HadCRUT5, Morice et al. 2021; NOAA-GlobalTemp, Zhang et al. 2019; Berkeley Earth, Rhode and Hausfather 2020) and two global atmospheric reanalyses (ERA5, Hersbach et al. 2020, Bell et al. 2021; JRA-55, Kobayashi et al. 2015).

<table><caption>Table 2.1. Temperature anomalies (°C) and uncertainties (where available) for 2021 (1991–2020 base period). Where uncertainty ranges are provided, the temperature anomalies correspond to the central values of a range of possible estimates. Uncertainty ranges represent a 95% confidence interval. 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.</caption><thead><tr><th>Global</th><th>NASA-GISS</th><th>HadCRUT5</th><th>NOAA GlobalTemp</th><th>Berkley Earth</th><th>ERA5</th><th>JRA-55</th></tr></thead><tbody><tr><td>Land</td><td>+0.40</td><td>+0.32 ± 0.11</td><td>+0.41 ± 0.14</td><td>+0.35 ± 0.03</td><td>+0.41</td><td>+0.33</td></tr><tr><td>Ocean</td><td>+0.14</td><td>+0.19 ± 0.06</td><td>+0.15 ± 0.16</td><td>—</td><td>+0.22</td><td>+0.16</td></tr><tr><td>Land and Ocean</td><td>+0.24 ± 0.05</td><td>+0.22 ± 0.03</td><td>+0.22 ± 0.15</td><td>+0.24 ± 0.03</td><td>+0.28</td><td>+0.21</td></tr></tbody></table>

<figure><img src="image_35.png" /><figcaption>Fig. 2.1. Global average surface air temperature anomalies (°C; 1991–2020 base period). In situ estimates are shown for NOAA/NCEI (Zhang et al. 2019), NASA-GISS (Lenssen et al. 2019), Berkeley Earth (Rhode and Hausfather 2020), HadCRUT5 (Morice et al. 2021), CRUTEM5 (Osborn et al. 2021), and HadSST4 (Kennedy et al. 2019). Reanalysis estimates are shown from ERA5 (Hersbach et al. 2020), including from the preliminary ERA5 back-extension (Bell et al. 2021) for 1967–2021, and JRA-55 (Kobayashi et al. 2015). Please note change in x-axis scale pre/post 2000.</figcaption></figure>
The six datasets all agree that the last seven years (2015–21) were the seven warmest years on record. Similarly, the datasets agree that the global average surface temperature has increased at an average rate of 0.08°–0.09°C decade⁻¹ since 1880 and at a rate more than twice as high since 1981 (0.18°–0.20°C decade⁻¹, depending on the dataset).

The year began with a cold phase of the El Niño-Southern Oscillation (ENSO; see section 4b), also known as La Niña, across the eastern and central tropical Pacific Ocean, helping cool global temperatures slightly. The month of February had the smallest temperature anomaly of the year for the globe and was the coldest February since 2014, with global temperatures close to the 1991–2020 base period. La Niña dissipated by June, but re-emerged in August. With the exception of Febru-ary, each month during 2021 had a global temperature that was above the 1991–2020 average.

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 2021 was considerably lower than we would expect due to the secular upward trend alone, with trend-adjusted anomalies registering between the 10th and 40th percentiles (depending on the dataset) following the Arguez et al. (2020) approach. These relatively cool conditions observed in 2021 are generally consistent with the typical cooling influence of La Niña. Moreover, trend-adjusted anomalies for 2021 are similar to the values recorded over the relatively cool years from 2011 to 2014, a period that also predominantly exhibited cooler-than-normal ENSO index values.

The year 2021 was characterized by above-average temperatures across much of the globe (Plate 2.1a; Appendix Figs. A2.1–A2.4), in particular across a large swath of North America (from the western United States to far northeastern Canada), as well as a region spanning northern Africa, western and central Asia, and into eastern Asia. Average to below-average temperatures prevailed across the central and eastern tropical Pacific Ocean and across parts of northwestern North America, Scandinavia, northern Russia, southern Africa, southern Australia, and parts of the southern oceans. Averaged as a whole, the global land-only surface air temperature for 2021 ranked among the sixth highest in the six datasets with a temperature departure of +0.32° to +0.41°C. The globally averaged SST was either sixth or seventh highest on record at +0.14° to +0.22°C, depending on the dataset.

Even though each dataset might differ slightly on the annual rankings and anomalies, it is worth noting that these differences are small and that, overall, temperature anomalies for each dataset are in close agreement (for more details see Simmons et al. 2017, 2021; Morice et al. 2021). Global atmospheric reanalyses use a weather prediction model to combine information from a range of satellite, radiosonde, aircraft, and other in situ observations to reconstruct historical weather and climate across the whole globe. While reanalyses may show regional differences from in situ surface temperature analyses because of regional model biases and changes in the observation network, they have been shown to agree well in well-observed regions (Simmons et al. 2017, 2021). Here, the data from ERA5 and JRA-55 are processed as described in Sanchez-Lugo et al. (2021).

## 2. LAKE SURFACE WATER TEMPERATURE—L. Carrea, C. J. Merchant, and R. I. Woolway

In 2021, the worldwide averaged satellite-derived lake surface water temperature (LSWT) warm-season anomaly was +0.60°C with respect to the 1996–2016 baseline, the highest since the beginning of the record in 1995, comparable with 2016. The mean LSWT trend during 1995–2021 is +0.24° ± 0.01°C decade⁻¹ (Fig. 2.2a), broadly consistent with previous analyses (Woolway et al. 2017, 2018; Carrea et al, 2019, 2020, 2021). The warm-season anomalies for each lake are shown in Plate 2.1b, with 78% having positive (i.e., above-average temperature) and 22% negative (i.e., below-average) anomalies.

Globally, distinct regions of coherent above- and below-average LSWT anomaly can be seen. Almost half (45%) of the observed lakes show LSWT anomalies exceeding +0.5°C, with 3% having anomalies higher than +3°C. The highest positive anomalies were located across the Tibetan Plateau, in the northwest United States, and in the Middle East and Pakistan. Negative anomalies
<figure><img src="image_36.png" /><figcaption>Fig. 2.2. Annual time series of satellite-derived warm-season lake surface water temperature anomalies (°C; 1996–2016 base period) from 1995 to 2021 for (a) more than 900 lakes distributed globally, (b) Europe, (c) Africa, (d) Tibet, and (e) Canada.</figcaption></figure>

were mostly in South America (except Patagonia), Australia, and in high northern latitudes, including Alaska, Greenland, and eastern Russia (Plate 2.1b). Two lakes in the latter region had the most negative anomalies (below –3°C).

Four regions are considered in more detail: Canada, Europe, Tibet, and Africa (Fig. 2.3). The warm-season (July–September) LSWT anomalies calculated from the satellite data are consistent with the averaged surface air temperature (SAT) anomalies and show a warming tendency (from 1995) of +0.38° ± 0.03°C decade⁻¹ in Europe and +0.18° ± 0.03°C decade⁻¹ in Canada (Figs. 2.2b,e). In Africa, the tendency is closer to neutral (+0.08° ± 0.01°C decade⁻¹), while in Tibet the warming tendency has increased with respect to the previous years due to the exceptionally positive 2021 anomaly (Figs. 2.2c,d). In the Tibetan area, all but two lakes had positive anomalies in 2021, with an average of +1.3°C. This is exceptionally high for the region, being 3.8 times the standard deviation of the average anomalies (1996–2016) and the highest on record. In Europe, below-average anomalies in northern Europe (29 lakes) are less prevalent than above-average anomalies (97 lakes), with an average anomaly of +0.31°C. In Africa, 74% of the 70 lakes recorded positive anomalies. Several of the highest anomalies occurred north of the equator, contributing to an average continental anomaly of +0.33°C. In Canada, 80% of the lakes had positive anomalies, with an average LSWT anomaly of +0.34°C. The 2021 warm-season anomalies for Lakes Superior, Michigan, and Huron were computed using both in situ measurements and satellite data. The 2021 in situ anomalies were +3.36°C, +1.47°C, and +1.00°C, and the satellite were +2.49°C (the highest on record), +0.85°C, and +0.99°C, respectively. The differences are mostly because the in situ measurements are taken at only some sites on the lake while the satellite observations
<figure><img src="image_37.png" /><figcaption>Fig. 2.3. Individual lake temperature anomalies (°C, colored dots) and 2-m air temperature anomalies (°C) in 2021 in (a) Europe, (b) Africa, (c) Canada, and (d) Tibet. These values were calculated for the warm season (Jul–Sep in the extratropi-cal NH; Jan–Mar in the extratropical SH; Jan–Dec in the tropics) with reference to the 1996–2016 base period.</figcaption></figure>

cover the whole lake. The spatial distribution of 2021 anomalies for these lakes (Appendix Fig. A2.5) all have positive values, but with strong variation across each of the lakes. The LSWT warm-season averages for midlatitude lakes are computed for summers, (July–September [JAS] in the extratropi-cal Northern Hemisphere and January–March [JFM] in the extratropical Southern Hemisphere) and whole-year averages (January–December) are presented for tropical lakes (within 23.5° of the equator). LSWT time series were derived from satellite observations from the series of Along Track Scanning Radiometers (ATSRs), the Advanced Very High Resolution Radiometers (AVHRRs) on MetOp A and B (1996–2019), and the Sea and Land Surface Temperature Radiometers (SLSTRs) on Sentinel3A and 3B (2019–2021). 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. The satellite-derived LSWT data are spatial averages for each of a total of 963 lakes, for which high quality temperature records were available in 2021. The satellite-derived LSWT data were validated with in situ measurements with a good agreement (average satellite minus in situ temperature difference less than 0.5°C). 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). The averaged surface air temperature was calculated from the GHCN v4 (250-km smoothing radius) data of the NASA GISS surface temperature analysis (Hansen et al. 2010; GISTEMP Team 2022). The in situ data for the Great Lakes were collected by the NOAA National Data Buoy Center.
### 3. NIGHT MARINE AIR TEMPERATURE—R. C. Cornes, T. Cropper, R. Junod, and E. C. Kent

Globally gridded datasets of night marine air temperature (NMAT) provide a useful and independent comparison against sea surface temperature (SST) data. Air temperature values have been recorded onboard vessels for centuries and continue to the present through the Voluntary Observing Ship (VOS) network. These observations can be used to construct global datasets of marine air temperature back to at least 1900. While temperature values are recorded throughout the day and night on these ships, only the nighttime observations are currently used to construct the gridded values due to the heating bias that arises in the daytime data, as a result of the superstructure of the ship.

Global mean temperature anomalies calculated from the two gridded NMAT datasets (CLASSnmat, Cornes et al. 2020 and UAHNMAT, Junod and Christy 2020) show a marked cooling in 2021 compared to 2020 but also relative to values over the last decade (Fig. 2.4). For CLASSnmat, the average global temperature anomaly (relative to the 1961–90 average) during 2021 was +0.38°C, a value comparable to that recorded in 2006. As such, 2021 was only the 13th highest in the series, which dates back to 1880. For UAHNMAT, the global mean anomaly value was +0.33°C in 2021; this is also the 13th highest annual mean temperature in its 1900–2021 record. These values contrast with the HadSST4 sea surface temperature dataset (Kennedy et al. 2019), which shows that 2021 was the fifth highest in the 1850–2021 period, with a global average anomaly of

<figure><img src="image_38.png" /><figcaption>Fig. 2.4. Annual mean temperature anomalies (°C; 1961–90 base period) calculated from the CLASSnmat, UAHNMAT, and HadSST4 datasets averaged over four domains. The tropics is defined as the latitude range 20°S–20°N and the Northern (Southern) Hemisphere is defined as north (south) of 0°. The averages only include values that are common to all three datasets for a given year, and since UAHNMAT are only available after 1900, only values for the period 1900–2021 are plotted.</figcaption></figure>
<table><caption>Table 2.2. Annual average temperature anomalies (°C) across four regions for 2021 for the CLASSnmat, UAHNMAT, and HadSST4 datasets (1961–90 base period). The values in parentheses indicate the ranking (1 = highest) of these values over the period 1900–2021.</caption><thead><tr><th></th><th>Global</th><th>Northern Hemisphere</th><th>Southern Hemisphere</th><th>Tropics</th></tr></thead><tbody><tr><td>CLASSnmat2.1</td><td>+0.38 (13)</td><td>+0.46 (11)</td><td>+0.27 (17)</td><td>+0.16 (31)</td></tr><tr><td>UAHNMAT</td><td>+0.33 (13)</td><td>+0.42 (11)</td><td>+0.22 (25)</td><td>+0.11 (38)</td></tr><tr><td>HadSST4</td><td>+0.67 (5)</td><td>+0.77 (4)</td><td>+0.54 (7)</td><td>+0.54 (10)</td></tr></tbody></table>

+0.67°C. A similar difference of about -0.3°C in the 2021 NMAT averages, relative to HadSST4, is seen across other large-scale regions (Table 2.2). In this comparison of the three datasets, co-located gridded data are used to avoid any complication to the analysis from differing global coverage. In particular, the NMAT datasets have poor coverage south of 40°S.

The principal reason for the lower temperature anomalies during 2021, compared to recent years, is the extended La Niña conditions that were present during the year (see section 4b). Historically, the NMAT data show a clear response to La Niña conditions, with a pattern across the Pacific that is similar in form and magnitude to that observed in the SST data (Figs. 2.5a-c). However, differences are apparent in the NMAT data compared to SST beyond the typical La Niña pattern and is the reason for the lower global average anomalies in the two NMAT datasets for 2021 compared to 2020 (Figs. 2.5d-f). The negative temperature anomalies in the central Pacific are more widespread in NMAT compared to SST, and the two nodes of positive anomalies in the North and southwestern Pacific are not clearly defined in either NMAT dataset. In addition, while positive anomalies for 2021 are evident in most grid cells in the SST data across the Atlantic and Indian Oceans, these are much weaker in the NMAT datasets, and there are many more grid cells in these regions with negative anomalies. Some of these regional differences can be explained by different data availability in the SST and NMAT datasets. Most notably, across the southern Pacific there is greater uncertainty in the NMAT values because there are few ship observations in the region; this is mitigated in the SST through the use of drifting buoy measurements in the grid cell averages. However, this does not fully explain the differences seen in these results as there is relatively good observational coverage in the NMAT datasets across the North Atlantic.

The data used in Fig. 2.5 were detrended, prior to averaging, in order to remove the long-term trend from the data by calculating the residuals from a linear regression fit to the data per grid cell. This detrending allows the response of the SST and NMAT data to La Niña conditions to be compared more directly. Over approximately the last 50 years, global mean NMAT has increased at a slower rate than SST (Fig. 2.4; Cornes et al. 2021); this accounts for the difference in rankings in the NMAT datasets compared to HadSST4. The reasons for this trend-differential are not well understood, and work is ongoing to determine if this is a true feature of the data or an artifact in the NMAT and/or SST datasets. It is also not clear if the interannual variability, and notably the relatively cool conditions observed during 2021, is related to this difference in long-term trends since taking into account this trend differential, the -0.3°C offset between NMAT and SST in 2021 was unusually large.
<figure><img src="image_39.png" /></figure>

Fig. 2.5. Average detrended temperature anomalies (°C) in the HadSST4, CLASSnmat, and UAHNMAT datasets calculated as (a–c) the mean of nine years between 1955 and 2000 in the year following a La Niña event and (d–f) for the year 2021. The years used in (a–c) are 1955, 1956, 1965, 1971, 1974, 1976, 1989, 1999, and 2000. Grid-cell values are marked missing if there are fewer than five months complete per year. In (a–c), grid-cells are marked as missing if there are fewer than five of the nine years complete.

## 4. LAND AND SURFACE MARINE TEMPERATURE EXTREMES—R. J. H. Dunn, M. G. Donat,

R. W. Schlegel, and S. E. Perkins-Kirkpatrick

The average number of warm days (TX90p, Table 2.3) over land was the second most on record, according to the observational dataset GHCNDEX (Donat et al. 2013; Fig. 2.6, using the 1961–90 reference period), with 68 days, though we note the incomplete spatial coverage. The average number of cool nights (TN10p) was 22, which was 14 fewer than the expected average, but not at record values. The expected values for these two indices are 36.5 days over the reference period, which is used in their calculation. The average values of the spatially complete reanalysis dataset ERA5 (Hersbach et al. 2020, using the 1981–2010 reference period) over land are similar to those from GHCNDEX, and TX90p was at its third highest value at 65.6 days, after 2016 and 2020 (Fig. 2.6). The spatial patterns of these indices (Plates 2.1d,e; Appendix Fig. A2.6) show especially high numbers of warm days over Africa and Asia and relatively low numbers over Australia, which also had high numbers of cool nights in 2021. Below we describe some of the low and high temperature events of 2021 in more detail (see also WMO 2022), primarily from the global north where these details were more readily available.
<table><caption>Table 2.3. WMO Extremes indices from the Expert Team for Climate Change Detection and Indices (ETCCDI) used in this section and their definitions (Zhang et al. 2011). In GHCNDEX, these indices are calculated using 1961–90 as the reference period, and thus these anomalies use the same period. As a result of their construction, comparison, or conversion to other base periods is not simple (Dunn and Morice 2022).</caption><thead><tr><td>Index</td><td>Name</td><td>Definition</td></tr></thead><tbody><tr><td>TX90p</td><td>Number of warm days</td><td>Number of days in a year where the daily maximum temperature was above the 90th percentile of the 1961–90 values.</td></tr><tr><td>TN10p</td><td>Number of cool nights</td><td>Number of days in a year where the daily minimum temperature was below the 10th percentile of the 1961–90 values.</td></tr></tbody></table>

<figure><img src="image_40.png" /><figcaption>Fig. 2.6. Timeseries of (a),(c) number of warm days (TX90p) and (b),(d) number of cool nights (TN10p) from GHCNDEX (a),(b) and ERA5 (c),(d). The dashed lines show the smoothed behavior from a binomial filter, and the coverage uncertainty (following Brohan et al. 2006) is shown by the shading in (a) and (c). The dotted black lines in (a),(b) show the percentage of land grid boxes with sufficient temporal coverage over the record which have data in each year.</figcaption></figure>
During 2021 there were a number of notable cold periods, starting with the after effects of a major storm on the Iberian Peninsula in January, with widespread temperatures below -20°C in northeast Spain, and a new national record of -34.1°C set on 6 January at Clot del Tuc de la Llança (Appendix Table 2.1). In February, North America was affected by two major winter storms, leading to the coldest event across the continental United States in more than 30 years, reaching as far south as northern Mexico. Heating demand placed strain on the electrical power grid in Texas, resulting in up to 10 million people without power, and there were over 200 direct and indirect deaths related to this event (see section 7b2 for details).

Following a warm end to March, Europe had an abnormally cold April, with record low minimum temperatures for the month (Appendix Table 2.1). Western North America experienced exceptional heat waves during June and July. Temperatures of above 40°C occurred over a large area in June, with maximum temperatures more than 15°C above average. Lytton (British Columbia, Canada) reached 49.6°C on 29 June, a new Canadian national record 4.6°C above the previous record (Appendix Fig. A2.7; see Sidebar 7.1 for details). On 9 July, Death Valley (California) recorded 54.4°C, equaling the temperature measured in 2020 as the highest temperature on Earth since at least the 1930s. Many other stations reported new maximum records from these two events.

The Mediterranean region experienced a number of record-setting heatwaves in July and August, and a station near Syracuse, Sicily (Italy), recorded a provisional new European maximum temperature record of 48.8°C on 11 August (Appendix Table 2.1). On 5 July, Lapland (Finland) recorded 33.6°C in Kevo, the hottest day in the region for over a century; it was the second warmest summer (JJA) average in the 120-year Finnish national record (see section 7f for details). Around the Arabian Gulf, a high temperature of 50.4°C was recorded at Dammam, Saudi Arabia, on 31 July. Also, during July, and coinciding with the delayed 2020 Olympic Games, a heatwave over Japan broke all-time records at numerous stations (see Sidebar 7.4 for details). In the Southern Hemisphere, New Zealand recorded its warmest year on record, which included its warmest June on record, 2°C higher than the 1981-2010 average (see Sidebar 7.5 for details).

Analyzing data from NOAA OISST v2.1 (Huang et al. 2021), 57% of the surface of the ocean experienced at least one marine heatwave (MHW, Hobday et al. 2016) in 2021 (Fig. 2.7b), and 25% experienced at least one marine cold-spell (MCS, Fig. 2.7d). Category 2 Strong MHWs (Hobday et al. 2018) were the most common (28%) for the eighth consecutive year, whereas Category 1 Moderate MCSs have remained the most common (20%) since 1985. The ocean experienced a global average of 49 MHW days (13 MCS days), which is fewer than the 2020 average of 58 days (14 days) and the 2016 record of 61 days (1982 record of 27 days, Figs. 2.7a,c). This daily average equates to 13% (4%) of the surface of the ocean experiencing a MHW (MCS) on any given day (Figs. 2.7a,c). The equatorial Pacific and Southern Oceans showed a noticeable lack of MHWs in 2021 while experiencing nearly complete MCS coverage. Heat anomalies in the equatorial Pacific Ocean are tightly linked with ENSO, which was in a La Niña state most of the year (see section 2e1), a continuation from 2020. The relationship between extreme events in the Southern Ocean and broad climate indices is complex and still poorly understood, making this an opportune avenue of future research. Note that with the Hobday et al. (2016) MHW definition the long-term trend is not removed before detecting events. This means that warming (cooling) areas will generally experience more (fewer) MHWs in the present than in the past, with the inverse observed for MCSs.

GHCNDEX (Donat et al. 2013) is based on the daily GHCND dataset (Menne et al. 2012). The extremes indices developed by the former ETCCDI (WMO Expert Team in Climate Change Detection and Indices, Zhang et al. 2011) are calculated for each station and then interpolated onto a regular 2.5° grid. Reduced spatial coverage in the most recent years (Fig. 2.6) arises because of late arriving data; hence for complete global land coverage we also use the ERA5 reanalysis (Hersbach et al. 2020), not including the preliminary release of the 1950–78 data. The shorter temporal coverage means a different reference period needs to be used (1981–2010), which can lead to differences when comparing recent trends (Dunn et al. 2020; Yosef et al. 2021; Dunn and Morice 2022).
<figure><img src="image_41.png" /><figcaption>Fig. 2.7. Annual global marine heatwave (MHW; a,b) and marine cold-spell (MCS; c,d) occurrence from NOAA OISST using a climatology base period of 1982–2011. (a),(c) The average count of MHW/MCS days experienced over the surface of the ocean in 2021 (left y-axis), also expressed as the percent of the surface of the ocean experiencing a MHW/MCS on any given day (right y-axis). (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.</figcaption></figure>

5. TROPOSPHERIC TEMPERATURE—S. Po-Chedley, J. R. Christy, L. Haimberger, and C. A. Mears

The 2021 global lower-tropospheric temperature (LTT) was approximately 0.25°C higher (0.14°–0.34°C, depending on dataset) than the long-term climatological (1991–2020) average. This places 2021 among the 10 warmest years on record (since 1958) despite La Niña conditions during most of 2021, which typically reduce the temperature of the troposphere.

Earth's tropospheric temperature is influenced by several natural factors such as volcanic eruptions, solar variability, and internal climate variability. Anthropogenic emissions of greenhouse gases and aerosols also affect LTT and collectively contribute to long-term warming of the global troposphere (Table 2.4). Since August of 2020, the El Niño-Southern Oscillation (ENSO) has largely been in a La Niña state (Fig. 2.8; see section 4b), which corresponds to below-average central and eastern tropical Pacific Ocean SSTs and reduced tropical and global average tropospheric temperatures. The background warming trend and La Niña conditions combined in 2021 to create a global LTT that was higher-than-average, but not record-breaking (Fig. 2.9).
<table><caption>Table 2.4. Temperature trends (°C decade⁻¹) for global lower tropospheric temperature (LTT) and tropical tropospheric temperature (TTT) over the period 1958–2021 and 1979–2021, respectively. NASA MERRA-2 data begins in 1980. UW (Po-Chedley et al. 2015) and NOAA STAR (Zou and Wang 2011) data do not produce LTT products.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">LTT<br>(90°S-90°N)</th><th colspan="2">TTT<br>(20°S-20°N)</th></tr><tr><th>1958</th><th>1979</th><th>1958</th><th>1979</th></tr></thead><tbody><tr><td rowspan="3">Start Year</td><td>NOAA/RATPACvA2</td><td>0.18</td><td>0.22</td><td>0.17</td><td>0.19</td></tr><tr><td>RAOBCOREv1.9</td><td>0.168</td><td>0.18</td><td>0.14</td><td>0.16</td></tr><tr><td>RICHv1.9</td><td>0.18</td><td>0.20</td><td>0.18</td><td>0.20</td></tr><tr><td rowspan="4">Radiosonde</td><td>UAHv6.0</td><td>—</td><td>0.13*</td><td>—</td><td>0.13</td></tr><tr><td>RSS v4.0</td><td>—</td><td>0.21</td><td>—</td><td>0.17</td></tr><tr><td>UWv1.0</td><td>—</td><td>—</td><td>—</td><td>0.17</td></tr><tr><td>NOAA STAR v4.1</td><td>—</td><td>—</td><td>—</td><td>0.23</td></tr><tr><td rowspan="3">Satellite</td><td>ERA5</td><td>—</td><td>0.18</td><td>—</td><td>0.16</td></tr><tr><td>JRA-55</td><td>0.17</td><td>0.18</td><td>0.16</td><td>0.15</td></tr><tr><td>NASA/MERRA-2</td><td>—</td><td>0.19</td><td>—</td><td>0.18</td></tr><tr><td colspan="2">Median</td><td>0.18</td><td>0.19</td><td>0.17</td><td>0.17</td></tr></tbody></table>

<figure><img src="image_42.png" /><figcaption>Fig. 2.8. (a) Sea surface temperature anomalies (°C, 1991–2020 base period) in the Niño 3.4 region in the central equatorial Pacific. Prolonged positive anomalies in red are associated with El Niño events; the reverse is true for La Niña events (in blue). (b) Fraction of Earth (%) with record high (red) and low (blue) monthly LTT values. The width of the line represents the difference between the UAH and RSS datasets.</figcaption></figure>

The LTT anomaly pattern in 2021 is typical of La Niña conditions (Plate 2.1f; Yulaeva and Wallace 1994). Although La Niña events tend to reduce globally averaged LTT, some regions are associated with above-average LTT (Plate 2.1f), and approximately 5% of the planet experienced its highest annual mean LTT since 1979 (Fig. 2.8). Regions of record low annual mean LTT were sparse and represented less than 1% of global area (Fig. 2.8). Areas experiencing record high temperatures included the Pacific Ocean midlatitudes, the southwest Atlantic Ocean, the Middle East,

<footnote>*The vertical sampling in UAH 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>
<figure><img src="image_43.png" /><figcaption>Fig. 2.9. Monthly average LTT anomalies (°C) for (a) radiosonde, (b) satellite, and (c) reanalysis datasets. Time series are smoothed using a 12-month running average. Annual averages are displayed for the RATPAC dataset. Anomalies are with respect to a 1991–2020 base period.</figcaption></figure>

southwest China, and northeastern Canada. In contrast, the tropical eastern Pacific Ocean, regions along the West Antarctic coastline, and northwest Canada and Alaska exhibited below-average tropospheric temperatures in 2021.

Datasets of atmospheric temperature change are derived from balloon-based radiosonde measurements (RATPAC vA2, RICH v1.9, and RAOBCORE v1.9), satellite-based microwave sound- ing observations (RSS v4, UAH v6, UW v1, and NOAA STAR v4.1), and atmospheric reanalysis products (MERRA-2, ERA5, and JRA-55). Each dataset is constructed with different strategies to remove biases, resulting in slightly different time series. In the ERA5 and MERRA-2 reanalysis datasets, 2021 was the fifth-warmest year on record for global LTT (Hersbach et al. 2020; Gelaro et al. 2017). 2021 was the sixth-warmest year in the RATPAC, RICH, and RSS datasets (Free et al. 2005; Mears and Wentz 2016; Haimberger et al. 2012) and eighth in the JRA-55, RAOBCORE, and UAH datasets (Kobayashi et al. 2015; Spencer et al. 2017). In general, observations of the global LTT (Fig. 2.9) and tropical tropospheric temperature (TTT) have similar interannual variations, but exhibit non-negligible structural uncertainty for long-term trends, which range from 0.13° to 0.23°C decade⁻¹ since 1979 (Table 2.4). The estimated trend uncertainty for individual satellite datasets is approximately 0.04°C decade⁻¹ and the uncertainty in converting level temperatures to synthetic satellite brightness temperatures is approximately 0.02°C decade⁻¹ (Po-Chedley et al. 2021).

Efforts to intercompare and understand differences across tropospheric temperature datasets is ongoing (Steiner et al. 2020). For example, Christy et al. (2018) compares radiosonde and satellite-based measurements of tropospheric temperature and concludes that satellite datasets likely have spurious warming over the 1990s. On the other hand, this analysis also documents unexplained
cooling in satellite datasets over the early 2000s. This latter finding is consistent with recent work by Zou et al. (2021), who note that existing satellite datasets underestimate tropospheric temperature trends relative to a new post-2002 dataset, which relies on observations from the most recent generation of microwave sounding instruments and/or satellites in stable sun-synchronous orbits. Consistent with physical expectations, recent research shows that climate models exhibit a close coupling between atmospheric moistening and warming in the tropics. If model simulations are sufficiently accurate, relationships between distinct geophysical fields (e.g., water vapor and sea surface temperature observations) may be used to investigate potential observational biases in tropospheric temperature datasets (Santer et al. 2021). As a result of collective efforts to better understand and intercompare existing observational datasets, records of tropospheric warming are continually evolving and improving.

## 6. STRATOSPHERIC TEMPERATURE—W. J. Randel, C. Covey, L. Polvani, and A. K. Steiner

Global mean temperatures in the lower, middle, and upper stratosphere for 2021 were similar to 2020. The long-term trend, however, is multi-decadal cooling of the upper stratosphere and warming of the troposphere due to anthropogenic CO₂ increases. Shorter-term climate variations from both natural and anthropogenic sources (e.g., Australian bushfires in 2020) are also evident in the record but were not prominent in 2021. The Antarctic stratospheric polar vortex was strong and persistent in 2021, while the Arctic was disturbed by a major stratospheric warming event early in the year. The stratospheric quasi-biennial oscillation (QBO) progressed normally in 2021, in contrast to disruptions in 2016 and 2020.

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

Fig. 2.10. Monthly global temperature anomalies (°C) from the middle troposphere to upper stratosphere (bottom to top). Middle and upper stratosphere data are from the Stratospheric Sounding Unit (SSU), representing thick-layer averages centered near 30, 38, and 45 km (SSU1, SSU2 and SSU3, respectively). Lower stratosphere temperatures (TLS) are ~13–22-km layer averages from satellite microwave measurements. Middle troposphere (TMT) data are ~0–10-km layer averages, and are included for comparison. Each time series has been normalized to zero for the period 1995–2005, and curves are offset for clarity.
Time series of global monthly temperature anomalies from the middle troposphere to the upper stratosphere based on satellite measurements are shown in Fig. 2.10. As discussed in Steiner et al. (2020), the middle and upper stratosphere data are merged products combining infrared emission measurements (Stratospheric Sounding Unit [SSU] 1, 2, 3 from 1979 to 2006) with more recent satellite data from microwave and infrared limb sounders for a continuous record. Merged datasets from microwave emission measurements provide layer-averaged temperatures for the lower stratosphere (TLS) and for the middle troposphere (TMT). In addition to long-term stratospheric cooling (due to CO₂ increase and stratospheric ozone changes) and tropospheric warming due to greenhouse gas increases (section 2b5), transient variations arise from a variety of causes including ENSO (e.g., large El Niño events in 1997 and 2016) and large volcanic eruptions (e.g., in 1982 and 1991). Transient warming in the lower stratosphere (TLS) in early 2020 was caused by stratospheric aerosol injections from the large Australian bushfires (Yu et al. 2021; Rieger et al. 2021; Stocker et al. 2021). However, no such events are apparent in the global average time series for 2021.

Independent measurements of temperatures between altitudes of ~10–30 km are available from occultations of GPS radio signals since 2002 (Steiner et al. 2020; Gulev et al. 2021). Figure 2.11 shows the resulting temperature trends for the period 2002–21 as a function of latitude and altitude. The results clearly differentiate warming in the troposphere from weak cooling over much of the lower to middle stratosphere. A more complicated situation occurs in the Southern Hemisphere subtropics, where warming extends across the tropopause into the lower stratosphere.

The long-term stratospheric cooling caused by rising greenhouse gas concentrations has led to a substantial contraction of the stratosphere over the last decades (Pisoft et al. 2021). Moreover, an increase of the tropopause height by about 50 m decade⁻¹ was observed over the Northern Hemisphere with radiosonde data, confirmed with GPS radio occultation (Meng et al. 2021). The increase is found to be due to tropospheric warming and stratospheric cooling over the period 1980–2000, while the continuous rise after 2000 results primarily from enhanced tropospheric warming.

Unlike 2020, when the stratospheric winter polar vortices were unusually strong and undisturbed in both hemispheres, the Arctic polar vortex was disrupted by a major sudden stratospheric warming early in 2021 (Lee 2021). The Antarctic polar vortex was relatively cold and persistent in

<figure><img src="image_45.png" /><figcaption>Fig. 2.11. Latitude–height section of temperature trends (°C decade⁻¹) derived from GPS radio occultation measurements over the period 2002–21 (updated from Steiner et al. 2020). Thick dashed line denotes the time average tropopause. Hatch- ing denotes trends that are significant at 2-sigma uncertainty.</figcaption></figure>
2021, coinciding with a large ozone hole persisting until December (sections 2g4, 6h). The equa-
torial stratosphere's quasi-biennial oscillation progressed in 2021 as it usually has for more than
half a century: downward-propagating easterly and westerly wind regimes and accompanying
temperature variations, with a mean periodicity of somewhat more than two years. This regular
downward propagation from the upper to lower stratosphere was interrupted in both 2016 and
2020, but more regular evolution appeared to resume at the end of 2020 with an easterly phase
propagating downward from the middle stratosphere (https://acd-ext.gsfc.nasa.gov/Datalle-
vices/mel/qbo/qbo.html).

c. *Cryosphere*

1. PERMAFROST TEMPERATURE AND ACTIVE LAYER THICKNESS—J. Noetzli,

H. H. Christiansen, M. Guglielmin, F. Hrbáček, K. Isaksen, S. L. Smith, L. Zhao, and D. A. Streletskiy

Permafrost—ground material remaining at or below 0°C for at least two consecutive years—is a key component of the cryosphere in high-latitude and high-altitude regions. Global permafrost temperatures have increased for several decades, with regional variability in magnitude and shorter-term fluctuations related to meteorological variations (Biskaborn et al. 2019; Etzelmueller et al. 2020; Haberkorn et al. 2021). Observed warming rates close to the depth of the zero annual amplitude (DZAA)—where annual temperature fluctuations become negligible—were in the range of a few tenths °C decade⁻¹ (Smith et al. 2022). They were largest (0.3°–0.8°C decade⁻¹) at sites in continuous permafrost or at highest elevations with low permafrost temperatures. Warmer and ice-rich permafrost warms more slowly due to latent heat uptake during ice melt (< 0.3°C decade⁻¹). Changes in active layer thickness (ALT)—the thickness of the layer above the permafrost that freezes and thaws annually—relate to annual atmospheric and snow conditions. ALT was greater in 2021 than in 2020 in some polar regions and generally above average of available records for all observed permafrost regions.

Permafrost temperatures in 2021 across the Arctic regions were the highest on record at many sites (see section 5h); however, at some Arctic sites (e.g., northwestern North America, Nordic region, and northern Russia) lower permafrost temperatures than in the previous years were observed, related to lower air temperatures. ALT could not be fully reported for some Arctic regions due to continued COVID-related travel restrictions. In northern Alaska, ALT was below the 2009–18 average, while it was among the largest values on record in Alaska Interior and on average more than 30 cm greater than in 1995. Greenland also reported its greatest ALT since 1995. Northern European Russia and western and eastern Siberia had lower ALT compared to 2020, but was greater than average, while in central Siberia it was greater than 2020.

On James Ross Island, northeastern Antarctic Peninsula, permafrost temperatures in 2021 were the second highest (−5.0°C) since the record began in 2011. The ground temperature at 75 cm increased by 0.9°C over the period 2011–21 (Hrbáček et al. 2021). ALT has increased here by 12 cm decade⁻¹ reaching 66 cm in 2021, which was 6 cm above average. At Rothera Point, permafrost temperature below the DZAA has remained stable since 2009.

Mountain permafrost accounts for approximately one-third of the global area underlain by permafrost (Hock et al. 2019). Data are primarily available from the European Alps, the Nordic region, and the Qinghai-Tibetan Plateau (QTP). Ranges of permafrost temperature and warming rates are similar to those observed in the Arctic, but with high spatial variability due to the complex topography. Significant ALT increase by meters were documented at sites in the European Alps over the past two decades (Etzelmueller et al. 2020; Haberkorn et al. 2021; PERMOS 2022), with considerable loss of ground ice (Mollaret et al. 2019). Ground temperatures close to the surface were lower in 2021 than 2020 in the European Alps (PERMOS 2022; Pogliotti et al. 2015; Magnin et al. 2015) due to a long period of snow cover and lower atmospheric temperatures (e.g., MeteoSwiss 2022). This resulted in ALT that were often lower in 2020 and a general decrease in rock glacier velocity (section 2c2). For many sites, permafrost temperatures at 20-m depth—where they react
to longer term trends—continued to increase in 2021 and reached record levels (Fig. 2.12). This is also true for the Nordic mainland; on Juvvasshøe in southern Norway, 2021 was the eighth consecutive year (since 2014) with record permafrost temperatures (Noetzli et al. 2021a; Etzelmüller et al. 2020). ALT at sites in the Nordic countries in 2021 were greater than or close to the maximum of 2020. In Svalbard, however, permafrost temperatures at 10-m depth continued to decrease due to cold winters in 2019–21 (Christiansen et al. 2021), but were still above the long-term average (Fig. 2.12). Permafrost temperatures in the QTP in central Asia increased at six sites from 2005 to 2020: 0.45°C decade⁻¹ at 10-m depth and 0.24°C decade⁻¹ at 20-m depth (Fig. 2.13; Zhao et al. 2020, 2021). Along the Qinghai-Tibet Highway (Kunlun mountain pass), an ALT increase was observed with a mean of 19.4 cm decade⁻¹ for the period 1981–2020 (Fig. 2.14).

Long-term observation of permafrost relies on field observations of ALT, permafrost temperatures, and, since 2021, on rock glacier velocity (Streletskiy et al. 2021; Pellet et al. 2021). International data are collected by the Global Terrestrial Network for Permafrost (GTN-P) as part of the Global Climate Observing System (GCOS). Permafrost temperatures are logged in boreholes reaching at least the DZAA, with a measurement accuracy assumed to be 0.1°C (Biskaborn et al. 2019; Noetzli et al. 2021b; Streletskiy et al. 2021). ALT is determined by mechanical probing where possible (accuracy of ~1 cm) and otherwise interpolated from borehole temperature measurements. The global coverage of permafrost monitoring sites is sparse and particularly limited in regions such as Siberia, central Canada, Antarctica, and the Himalayan and Andes Mountains.

<figure><img src="image_46.png" /></figure>

Fig. 2.12. Permafrost temperature (°C) measured in boreholes in the European Alps and the Nordic countries at a depth of (a) ca. 10 m (monthly means) and (b) 20 m (annual means). (Data sources: Switzerland: Swiss Permafrost Monitoring Network PERMOS; Norway: Norwegian Meteorological Institute and the Norwegian Permafrost Database NORPERM; France: updated from Magnin et al. 2015; Italy: updated from Pogliotti et al. 2015.)
<figure><img src="image_47.png" /></figure>

Fig. 2.13. Permafrost temperature (°C) measured in boreholes along the Qinghai-Xizang Highway on the Tibetan Plateau at 2-m depth for the period 2005–20. (Source: Cryosphere Research Station on Qinghai-Xizang Plateau, CAS.)

<figure><img src="image_48.png" /></figure>

Fig. 2.14. The active layer thickness (cm) and air temperature anomalies (°C) in the permafrost zone along the Qinghai-Tibet Highway during the period 1981–2020. The air temperature anomaly is estimated relative to the base period 1981–2010. (Source: Cryosphere Research Station on Qinghai-Xizang Plateau, CAS.)

## 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) found in most mountain ranges worldwide (RGIK 2021). Changes in their velocities are mostly related to the evolution of ground temperature and liquid water content between the permafrost table and the shearing horizon at depth: the closer to 0°C, the faster the rock glacier is able to move (Cicoira et al. 2019; Frauenfelder et al. 2003; Staub et al. 2016). In 2021, the variable rock glacier velocity (RGV) was adopted as a new associated product to the essential climate variable (ECV) permafrost by GCOS and the Global Terrestrial Network for Permafrost (GTN-P, Streletskiy et al. 2021), given the global occurrence of active rock glaciers and their sensitivity to changes in ground temperature.

RGVs, observed in several mountain ranges across the globe, have been increasing since the 1950s, with regional variability in magnitude and marked interannual variability. Observed rates of increase are largest since 2010 and record high velocities have been recorded since 2015. These changes are consistent with interannual variations of permafrost temperatures (cf. section 2c1), to which rock glacier velocities have been shown to respond synchronously (Cusicanqui et al. 2021; Kääb et al. 2007; Kellerer-Pirklbauer and Kaufmann 2012; Staub et al. 2016; Vivero et al.
2021). Regionally, RGVs follow the same interannual behavior despite variable size, morphology, and velocity range (e.g., Delaloye et al. 2010; Kääb et al. 2021; Kellerer-Pirklbauer et al. 2018; PERMOS 2019).

RGVs in the European Alps have increased by a factor of between 2 and 10 from the 1980s to 2021 (Fig. 2.15b). This acceleration was temporarily interrupted at most sites during 2004–06 and 2016–18, coinciding with a decrease in permafrost temperatures, mainly resulting from snow-poor winters, which enabled more efficient ground cooling due to the later onset of an insulating snow cover (Noetzli et al. 2018; PERMOS 2019). Compared to 2020, RGVs decreased in 2021, e.g., at Gemmi/Furggentälti (Switzerland, -26%), Grosses Gufer (Switzerland, -24%), and Laurichard (France, -4%), whereas RGVs increased at Dösen (Austria, +19%) and Hinteres Langtalkar (Austria, +35%) to record values (Fig. 2.15b). The velocity decrease at Swiss and French sites is consistent with lower air temperatures compared to 2020 (Fig. 2.15a) as well as a long-lasting snow cover in spring and a relatively late thickening of the snow cover in autumn, which led to lower ground temperatures (cf. section 2c1). Different behaviors of the rock glaciers between the Western and Eastern Alps in 2021 might be related to differences in precipitation and temperature, particularly in December 2020 (warmer east), January 2021 (drier east), and July 2021 (warmer and less humid east), in addition to the influence of local topo-climatic factors.

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

Fig. 2.15. (a) Rock glacier velocity and climate: air and ground temperature (°C) in the European Alps. Rock glacier velocities (m yr⁻¹) at selected sites in (b) 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 based on in situ geodetic surveys or photogrammetry in the context of long-term monitoring. In-situ permafrost temperature measured at 20-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-year running mean (solid line) at Besse (FR), Grand Saint-Bernard (CH), Saentis (CH), Sonnblick (AT), and Zugspitze (D, black diamonds on Europe map). (Data sources: Météo France, Deutscher Wetterdienst [DWD], MeteoSwiss, Zentralanstalt für Meteorologie und Geodynamik [ZAMG], Swiss Permafrost Monitoring Network [PERMOS], University of Fribourg, University of Graz, Graz University of Technology, Université Grenoble Alpes [INRAE], University of Oslo.)
There are few long-term in situ measurements of RGVs outside of the European Alps. However, an increasing number of studies exploit the potential of archival aerial photographs and high-resolution satellite data to reconstruct RGVs (e.g., Cusicanqui et al. 2021; Eriksen et al. 2018). The velocities of three rock glaciers observed in the Dry Andes in South America showed slow velocities from 1950 to 2000, followed by a steady acceleration since the 2000s (Fig. 2.15c), consistent with the climatic conditions observed in the region (Vivero et al. 2021).

RGVs observed in Central Asia since the 1950s do not show a uniform picture (Fig. 2.15d; Kääb et al. 2021). The Karakoram and Kugalan Tash (Kyrgyzstan) RGVs steadily increased since the 1960s, whereas at Ordzhonikidze and Morenny (Kazakhstan) high velocities were observed during the second half of the 1960s, then low velocities until 2010, and increasing velocities in recent years. All RGVs have increased since the start of the observations and accelerated since 2010, which is consistent with increasing air temperatures and with the acceleration reported in the European Alps and Dry Andes.

Long-term RGV time series are reconstructed using multi-temporal aerial or optical satellite images. Horizontal displacements are computed based on feature tracking, 2D ortho-image matching algorithms or digital elevation model matching. The resulting accuracy strongly depends on the spatial resolution of the aerial images and on the image quality. Surface displacements are averaged for a cluster of points selected within areas, representative of the flow field and indicative of the downslope movement of the rock glacier (RGIK 2022). 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 are measured for a number of selected boulders (10–100 per landform) with an average accuracy in the range of mm to cm (Delaloye et al. 2008; PERMOS 2019).

### 3. ALPINE GLACIERS—M. Pelto

In the hydrological year 2020/21, observed World Glacier Monitoring Service (WGMS) reference glaciers experienced a mass balance loss of –900 mm water equivalent (mm w.e.), compared to –700 mm w.e. in 2019/20. From 1970 to 2021 the eight most negative mass balance years were all recorded after 2010. A value of –1000 mm 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 2.16 illustrates glacier mass balance for the WGMS global reference glaciers with more than 30 years of data for the period 1970 to 2020. Global values are calculated using a single value (averaged) for each of 19 mountain regions in order to avoid a bias to well observed regions. In 2021, a negative annual mass balance was reported from 31 of the 32 reference glaciers reported to the WGMS as of 1 June 2022. The mean annual mass balance of the 32 reference glaciers reporting

<figure><img src="image_50.png" /><figcaption>Fig. 2.16. Global annual glacier mass balance of WGMS reference glacier network in mm water equivalent (w.e.), with annual values (red bars, left axis) and cumulative amounts since 1979 (black dots, right axis). Lighter shading for 2021 is used as the final values for that year were not yet available at time of publication.</figcaption></figure>
is –900 mm w.e., which includes data from 12 nations on four continents (not final regionally-averaged global value; there are 42 reference glaciers in total). This will make 2021 the 34th consecutive year with a global alpine mass balance loss and the 13th consecutive year with a mean global mass balance below –500 mm w.e.

The rate of thinning increased from –527 mm yr⁻¹ for 2000–09 to –896 mm yr⁻¹ for 2010–19 (WGMS 2021). This agrees well with the satellite survey of 200,000 alpine glaciers by Huggonet et al. (2021) who identified a thinning rate excluding ice sheet peripheral glaciers of 360 ± 210 mm yr⁻¹ in 2000 to 690 ± 150 mm yr⁻¹ in 2019. Alpine glaciers lost a mass of 267 ± 16 Gt yr⁻¹ from 2000 to 2019, equivalent to 21 ± 3% of the observed global sea level rise (Huggonet et al. 2021). More frequent and intense heat waves continue to take a toll on alpine glaciers (Pelto et al. 2021, 2022).

All 17 reporting glaciers in the Alps had a negative mass balance averaging –682 mm in 2021. In Austria in 2020, of the 92 glaciers with annual terminus observations, 85 (93.4%) withdrew and seven remained stationary (Lieb and Kellerer-Pirklbauer 2021). This retreat trend continued in 2021, with another year of mass balance loss. In Norway, the six reporting glaciers all had a negative mass balance, yielding an average mass balance of –671 mm in 2021. On Svalbard, the mean loss of the four reporting glaciers was –227 mm. Iceland completed surveys of nine glaciers; all nine had negative balances, with a mean mass balance of –1160 mm.

In western Canada and Washington (United States) all 14 glaciers observed in 2021 had a negative mass balance averaging –1635 mm. The exceptional heat wave during late June and early July in western North America (section 2b4; Sidebar 7.1) set the stage for the large glacier mass loss (Pelto et al. 2022; Fig. 2.17). In Alaska, three of the four glaciers measured had a negative mass balance, with a mean annual balance of –528 mm. In South America, 2021 mass balance data were reported from three glaciers in Argentina, two glaciers in Chile, and one in Colombia; all were negative with a mean of –861 mm. This is greater than the 2000–18 average loss observed in the Andes of –720 ± 220 mm yr⁻¹ (Dussaillant et al. 2019). In High Mountain Asia, 15 of 18 glaciers reported negative balances in 2021. The average mass balance was –468 mm. Early winter of 2021 was warm and dry across the Himalayan region. This was capped off by record warmth in the Mount Everest region, leading to the snow line on glaciers rising and snow-free glaciers up to 6000 m (Pelto et al. 2021), illustrating that the ablation season no longer always ends when winter begins. The importance of winter conditions was further noted by Potocki et al. (2022) who reported on an ice core drilled on South Col Glacier, on Mount Everest at 8020 m a.s.l., revealing a contemporary sublimation driven thinning of ~2000 mm yr⁻¹.

<figure><img src="image_51.png" /><figcaption>Fig. 2.17. Easton Glacier in Washington state in August 2021, with less than 10% snowcover and one month left in the ablation season.</figcaption></figure>
In the 2020/21 winter, lake ice phenology (timing of ice-on and ice-off) across the Northern Hemisphere (NH, calculated from Copernicus Climate Change Service [C3S] ERA5 [Hersbach et al. 2020]) continued to exhibit 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). Relative to the 1991–2020 base period, NH lakes froze, on average, 3.8 days later, thawed 3.5 days earlier, and ice duration was over 7 days shorter (Figs. 2.18, 2.19). The regional variations in ice duration were consistent with NH winter air temperature anomalies. Most notably, some regions in North America, such as western Canada, experienced below-average air temperatures, which resulted in longer-than-average ice duration. Conversely, eastern Canada and many regions in Eurasia experienced warmer-than-average conditions that resulted in shorter-than-average ice duration (Fig. 2.18d).

<figure><img src="image_52.png" /><figcaption>Fig. 2.18. Anomalies (days) in 2021 in (a) ice-on (positive = later), (b) ice-off (negative = earlier), and (c) ice duration for lakes across the NH (negative = shorter), 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. (Sources: ERA5, GISTEMP.) The winter season for 2021 generally refers to the time between the end of autumn 2020 and the start of spring 2021.</figcaption></figure>
Based on in situ ice phenological records from 110 lakes in North America (NA), Europe, and Asia, ice-on was 11 days later, ice-off was 7.5 days earlier, and there were 19 fewer days of ice cover over the 2020/21 winter season, on average, relative to 1991–2020 (Fig. 2.19). For NA lakes, ice-on averaged 5 days later and ice-off was 11 days earlier. For European lakes, ice-on was 17 days later, ice-off was 3 days earlier, totaling 20 days less ice cover in the winter of 2021 relative to the 1991–2020 base period. Lake Erken, in Sweden, lost the most ice cover during the 2021 winter, with 61 days less ice cover compared to the 1991–2020 normal in response to an anomalously warm winter in 2021 in the region. Analysis of ice phenology trends during 1991–2021 suggest that ice-on date is 4.1 days later per decade, ice-off date is 2.2 days earlier per decade, and ice duration

<figure><img src="image_53.png" /><figcaption>Fig. 2.19. (a) Lake ice-on, (b) ice-off, and (c) ice duration anomalies from 1980 to 2021 derived from in situ observations and ERA5 reanalysis. (d) Anomalies in Great Lakes maximum ice cover extent (%) for 1973–2021. 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, Huron). Base period is 1991–2020. The winter season for 2021 generally refers to the time between the end of autumn 2020 and the start of spring 2021.</figcaption></figure>
is 6.8 shorter per decade on average for these 110 lakes. In 2021, the Laurentian Great Lakes had 2.9% less maximal ice coverage relative to 1991–2020. The deeper and most northern lakes all had less maximal ice coverage, of which Lake Huron was the most anomalous with 11.6% less ice coverage in 2021. Lake Erie, the shallowest lake, was the exception, with 9.7% more ice coverage in 2021 (Fig. 2.19d). Since 1973, the Laurentian Great Lakes have been losing on average 4.3% (95% confidence interval: 0.5, -9.1) of ice coverage per decade. Lake Superior is losing the most ice coverage per decade of all the Great Lakes (7.0% per decade). In fact, Lake Superior is one of the world's fastest warming lakes (O'Reilly et al. 2015; Sharma et al. 2021) and has lost over 60 days of ice cover since 1857 (Sharma et al. 2021; Wang et al. 2021).

To estimate the timing of ice-on and ice-off and, ultimately, the duration of winter ice cover across NH lakes, ice simulations from the ERA5 reanalysis product (Hersbach et al. 2020) were analyzed following the methods of Grant et al. (2021). We obtained in situ ice phenology for 110 lakes: Canada (5), United States (53), Estonia (2), Finland (27), Iceland (1), Norway (18), Poland (1), Sweden (1), Russia (1), and Japan (1). In addition, we obtained annual maximum ice cover (%) data for each of the Laurentian Great Lakes from 1973 to 2020 (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 (Lenssen et al. 2019; GISTEMP Team 2022). To create the time series figure, the ERA5 data were averaged across all 0.25° grids and consisted of a much larger sample size relative to the in situ data where the anomalies were averaged across 110 lakes.

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

and T. W. Estilow

Annual snow cover extent (SCE) over Northern Hemisphere (NH) lands averaged 24.3 million km² in 2021. This is 0.6 million km² less than the 1991–2020 mean and 0.8 million km² below the mean over a 52-year period of record (Fig. 2.20; Table 2.5), marking the seventh least extensive cover on record. Monthly SCE in 2021 ranged from a maximum of 46.8 million km² in January to a minimum of 2.5 million km² in August.

The year began with NH SCE ranking in the middle tercile of the 55-year record, although North America (NA) SCE was fourth most extensive on record in February. NA quickly lost SCE in March, falling to 47th most extensive, while Eurasia (EUR) remained well below average. These conditions persisted throughout spring, with May having the 52nd most extensive SCE on record and June 47th most extensive.

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

Fig. 2.20. 12-month running anomalies (million km²) of monthly SCE over NH lands as a whole (black) and EUR (red), and NA (blue) separately, plotted on the seventh month using values from Nov 1966 to Dec 2021. Anomalies are calculated from NOAA snow maps. Mean NH SCE is 25.1 million km² for the full period of record. Monthly means for the period of record are used for nine missing months during 1968, 1969, and 1971 in order to create a continuous series of running means. Missing months fall between Jun and Oct. Data from Estilow et al. (2015).
<table><caption>Table 2.5. Monthly and annual climatological information on NH, EUR, and NA SCE between Nov 1966 and Dec 2021. Included are the numbers of years with data used in the calculations, NH means, standard deviations, 2021 values, and rankings. Units: million km². 1968, 1969, and 1971 have one, five, and three missing months respectively, thus are not included in the annual (Ann) calculations. NA includes Greenland. Ranks are from most to least extensive.</caption><thead><tr><th></th><th>Years of data</th><th>NH Mean</th><th>Std. Dev.</th><th>2021</th><th>2021 NH rank</th><th>2021 Eurasia rank</th><th>2021 N Am. rank</th></tr></thead><tbody><tr><td>Jan</td><td>55</td><td>47.1</td><td>1.5</td><td>46.8</td><td>32</td><td>36</td><td>28</td></tr><tr><td>Feb</td><td>55</td><td>46.0</td><td>1.8</td><td>46.1</td><td>22</td><td>42</td><td>4</td></tr><tr><td>Mar</td><td>55</td><td>40.4</td><td>1.9</td><td>38.6</td><td>44</td><td>42</td><td>47</td></tr><tr><td>Apr</td><td>55</td><td>30.5</td><td>1.7</td><td>28.8</td><td>45</td><td>40</td><td>46</td></tr><tr><td>May</td><td>55</td><td>19.1</td><td>2.0</td><td>16.2</td><td>53</td><td>51</td><td>45</td></tr><tr><td>Jun</td><td>54</td><td>9.4</td><td>2.5</td><td>6.2</td><td>46</td><td>52</td><td>42</td></tr><tr><td>Jul</td><td>52</td><td>3.9</td><td>1.2</td><td>2.8</td><td>43</td><td>44</td><td>39</td></tr><tr><td>Aug</td><td>53</td><td>3.0</td><td>0.7</td><td>2.5</td><td>40</td><td>42</td><td>30</td></tr><tr><td>Sep</td><td>53</td><td>5.4</td><td>0.9</td><td>5.6</td><td>22</td><td>30</td><td>13</td></tr><tr><td>Oct</td><td>54</td><td>18.6</td><td>2.7</td><td>18.1</td><td>28</td><td>22</td><td>46</td></tr><tr><td>Nov</td><td>56</td><td>34.3</td><td>2.1</td><td>35.4</td><td>18</td><td>9</td><td>44</td></tr><tr><td>Dec</td><td>56</td><td>43.7</td><td>1.8</td><td>44.5</td><td>17</td><td>13</td><td>26</td></tr><tr><td>Ann</td><td>52</td><td>25.1</td><td>0.8</td><td>24.3</td><td>46</td><td>44</td><td>41</td></tr></tbody></table>

NA picked up some early autumn snow, contributing to its 13th most extensive September cover. This ranking fell to 46th most extensive in October, when EUR and the NH ranked in the middle tercile. NH November and December SCE fell near the boundary of the highest and middle tercile, mainly due to above-average SCE in EUR where November ranked ninth most extensive and December 13th most extensive. Following an average snow cover in January, SCE over the contiguous United States was fifth most extensive in February and below-average every other month thereafter in 2021.

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. Maps depicting daily, weekly, and monthly conditions, anomalies, and climatologies may be viewed at the GSL website (https://snowcover.org).

### d. Hydrological cycle

#### 1. SURFACE HUMIDITY—K. M. Willett, D. A. Lavers, M. Bosilovich, and A.J. Simmons

Global mean specific humidity ($q$) in 2021 was lower compared to 2020, although remaining well above the 1991–2020 average in all datasets (+0.07 to +0.18 $g kg^{-1}$ for $q_{land}$, +0.07 to +0.20 $g kg^{-1}$ for $q_{ocean}$) except for ERA5, which placed global $q_{land}$ just below average at –0.01 $g kg^{-1}$ (Figs. 2.21a–d). For global mean relative humidity (RH), 2021 was less saturated than 2020 in all datasets over land (–1.32 to –0.70 %rh) (Figs. 2.21e,f). Both JRA-55 and ERA5 show a large difference between 2020 and 2021, placing 2021 as the least saturated year on record (since 1958 and 1967, respectively) by a large margin (–0.23 %rh and –0.45 %rh, respectively). It was the second driest year on record after 2019 in HadISDH. Over oceans, RH remained mixed with HadISDH and JRA-55 behaving similarly, placing 2021 $RH_{ocean}$ at +0.20 and +0.16 %rh above the 1991–2020 average, respectively, which is slightly above 2020, while ERA5 dropped below average at –0.09 %rh, continuing a short drying trajectory since 2019.

Spatially, missing data regions of HadISDH (Plate 2.1g) over much of northwestern, central, and eastern Africa are regions that showed strong negative $q$ anomalies in ERA5, indicating that the water vapor content was lower than average (Appendix Fig. A2.8). This might explain the discrepancy in global land averages between ERA5 and HadISDH. Central Africa is a key region for differences between the reanalyses, with MERRA-2 (Appendix Fig. A2.9) showing strong high water vapor content anomalies there and more expansive high water vapor content anomalies generally, likely contributing to the MERRA-2 high 2021 $q_{land}$ anomaly. Other discrepancies are notable over western Australia where HadISDH shows an isolated suspect highwater vapor content anomaly, and over northern Colombia and Venezuela where MERRA-2 shows strong low water vapor content anomalies. Over the ocean, MERRA-2 and ERA5 are in good agreement. The HadISDH higher global mean $q_{ocean}$ anomaly for 2021 is missing many of the low water vapor content anomaly regions of the central eastern Pacific and Southern Hemisphere more generally, the former being associated with the La Niña conditions. Positive and negative anomalies are broadly
<figure><img src="image_55.png" /></figure>

Fig. 2.21. 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. ERA5 mask is a version of ERA5 that spatially matches the coverage of HadISDH. 2-sigma uncertainty is shown for HadISDH capturing the observation, gridbox sampling and spatial coverage uncertainty. (Sources: HadISDH [Willett et al. 2013, 2014, 2020]; ERA5 [Hersbach et al. 2020]; JRA-55 [Kobayashi et al. 2015]; MERRA-2 [Gelaro et al. 2017].)

similar between RH (Plate 2.1h; Appendix Figs. A2.10, A2.11) and *q* but with the central Eurasian and western North American anomalies more expansive for RH<sub>land</sub>, and RH<sub>ocean</sub> anomalies more muted than those for *q*<sub>ocean</sub>, *q*<sub>land</sub>, and RH<sub>land</sub> generally.

In terms of long-term trends (Table 2.6), all datasets continue to show long-term increas-

ing water vapor over land and ocean (+0.04 to +0.10 g kg⁻¹ decade⁻¹), while the air over land has become less saturated (−0.23 to −0.45 %rh decade⁻¹). This means that the water vapor content of the air has increased more slowly than the water va- por carrying capacity of the air, which increases exponentially with temperature. The trend magnitudes for *q* are similar over land and ocean for each dataset. HadISDH and MERRA-2 show similar larger trends in *q* (+0.09 to +0.10 g kg⁻¹ decade⁻¹,

<table><caption>Table 2.6. Global average decadal trends for the period 1979–2021 fitted using ordinary least squares regression. The 90th percentile confidence intervals are shown in parentheses, fitted using AR(1) correction following Santer et al. (2008). Trends shown in bold are considered significantly different from a zero trend, in which the confidence intervals do not cross the zero line. Units are g kg⁻¹ decade⁻¹ for *q* and %rh decade⁻¹ for RH.</caption><thead><tr><th>Variable</th><th>HadISDH</th><th>ERA5</th><th>ERA5 masked to HadISDH</th><th>MERRA-2 (1980–2020)</th><th>JRA-55</th></tr></thead><tbody><tr><td>Land q</td><td>0.09<br>(0.02)</td><td>0.05<br>(0.01)</td><td>0.07<br>(0.01)</td><td>0.09<br>(0.02)</td><td>0.06<br>(0.01)</td></tr><tr><td>Land RH</td><td>-0.23<br>(0.08)</td><td>-0.45<br>(0.06)</td><td>-0.44<br>(0.08)</td><td>NA</td><td>-0.33<br>(0.04)</td></tr><tr><td>Ocean q</td><td>0.09<br>(0.01)</td><td>0.04<br>(0.02)</td><td>0.08<br>(0.02)</td><td>0.10<br>(0.02)</td><td>0.04<br>(0.01)</td></tr><tr><td>Ocean RH</td><td>-0.03<br>(0.04)</td><td>-0.18<br>(0.08)</td><td>-0.18<br>(0.07)</td><td>NA</td><td>-0.04<br>(0.02)</td></tr></tbody></table>
respectively), while ERA5 and JRA-55 show more moderate trends (+0.04 to +0.06 g kg⁻¹ decade⁻¹, respectively). While all datasets show a negative trend for RH<sub>ocean</sub>, these trends are small, relatively widespread (−0.03 to −0.18 %rh decade⁻¹), and not considered to be significant for HadISDH. We conclude that there is large uncertainty in whether there is any real change in RH<sub>ocean</sub>.

Agreement between the monitoring products in terms of both long-term trajectories and year-to-year variability is generally good (Fig. 2.21), with the exception of RH<sub>ocean</sub>. Greater discrepancy over ocean is expected given the sparse observing network available from the Voluntary Observing Ships (VOS; https://www.vos.noaa.gov/vos_schema.shtml) that make up the HadISDH record and the difference in methodological approaches between datasets. For example, ERA5 does not incorporate any in situ near-surface temperature or dew point temperature measurements over ocean, whereas HadISDH, MERRA-2, and JRA-55 do. The uncertainty range estimated for HadISDH is larger over ocean than over land, reflecting both the larger observational uncertainty since 2015, when digitized ship metadata provision ended, and larger spatial coverage uncertainty compared to over land. For RH<sub>land</sub>, ERA5 is consistent with JRA-55 whereas for RH<sub>ocean</sub> it is not.

Despite the good agreement generally over land, the reanalyses have diverged since 2018. ERA5 masked to the HadISDH spatial coverage differs little from the complete ERA5 anomaly time series (dashed lines in Fig. 2.21), especially over the last 30 years. Global trends in the masked versus complete ERA5 for *q* are closer to, but still less than, those from HadISDH (Table 2.6), but practically identical for RH. Interestingly, masked and complete ERA5 RH<sub>land</sub> and RH<sub>ocean</sub> anomalies are persistently more saturated than HadISDH pre-1990. Simmons et al. (2021) note an increase in the number of temperature observations, particularly those sampling different hours of the day around between 1988 and 1990; however, it is not clear how this would lead to biases in RH.

## 2. TOTAL COLUMN WATER VAPOR—C. A. Mears, J. P. Nicolas, O. Bock, S. P. Ho, and X. Zhou

In 2021, global land and ocean averages of total column water vapor (TCWV) were above the 1991–2020 climatological averages, but were generally lower than the 2015–20 period which showed high vapor values. This is not surprising because La Niña conditions, which were present for most of the year, have a cooling effect on global surface and tropospheric temperatures, thus lowering the water-holding capacity of the global atmosphere. In reanalysis output, where time series are available for the entire 1979–2021 period, 2021 was the fourth (MERRA-2, anomaly = 0.477 kg m⁻²), seventh (ERA5, anomaly = 0.264 kg m⁻²), and eighth (JRA55, anomaly = 0.383 kg m⁻²) highest vapor year since 1979. Time series of global averages agree well in all datasets (Fig. 2.22). Part of the discrepancy between the Global Navigation Satellite System (GNSS) time series and reanalysis over land is due to the limited spatial sampling afforded by the GNSS network. While TCWV decreased sharply from 2020 to 2021 globally and over ocean in all the datasets used in this assessment, there is substantial spread in the amount of this decrease over land.

The global map of TCWV anomalies from MERRA-2 (presented as percent of annual mean values to show extratropical changes more clearly) for 2021 (Plate 2.1i) reveals large low vapor anomalies in the eastern South Pacific, with low vapor anomalies close to the equator along a northwest–southeast oriented band and high vapor anomalies farther south. This pattern denotes the southwest displacement of the South Pacific Convergence Zone (SPCZ) that is typically associated with La Niña conditions (Brown et al. 2020). Prominent low vapor anomalies also occurred over West Antarctica and the adjacent sector of the Southern Ocean. Much of the rest of the globe showed wet anomalies, with the largest relative values observed over the Maritime Continent, South and East Asia, the northern Pacific, the southwest Atlantic, and eastern Canada, which correspond to regions with positive anomalies in surface temperature and lower tropospheric temperature (Plates 2.1.a,f). While the 2020/21 and 2021/22 boreal winters were both marked by La Niña conditions, the pattern of low vapor in the tropical Pacific along the SPCZ region was significantly more pronounced than what has been typically observed during La Niña events since 1980, and during the previous La Niña winter in 2017/18 (Fig. 2.23).
<figure><img src="image_56.png" /><figcaption>Fig. 2.22. Time series of yearly-mean TCWV anomalies (kg m⁻¹) from reanalysis, GPS-RO, GNSS, and satellite radiometers. All averages are over latitudes from 60°S to 60°N.</figcaption></figure>

This assessment of global TCWV is based on data from three global reanalysis products: ERA5 (Hersbach et al. 2020), MERRA-2 (Gelaro et al. 2017), and JRA-55 (Kobayashi et al. 2015), as well as measurements made by satellite-borne microwave radiometers over the ocean (RSS Satellite; Mears et al. 2018), Global Positioning System–Radio Occultation (GPS-RO) observations from the COSMIC, Metop-A, -B, and -C and COSMIC2 satellite missions (satellite RO; Ho et al. 2020a,b, 2010a,b; Teng et al. 2013; Huang et al. 2013), and from ground-based GNSS stations (Bock 2020). The RSS satellite measurements are only available over the ocean, while GNSS stations are generally located on land. GPS-RO is available for both land and ocean. All three reanalyses assimilate satellite microwave radiometer and GPS-RO data and are therefore not fully independent from these two datasets. Ground-based GNSS measurements are not assimilated and are thus independent.
<figure><img src="image_57.png" /></figure>

Fig. 2.23. Maps of TCWV anomalies (%) during La Niña events from the MERRA2 reanalysis. (a) Mean anomaly for the previous 12 La Niña seasons (Oct–Jan for 1983, 1984, 1988, 1995, 1998, 1999, 2005, 2007, 2010, 2011, 2017, and 2020). (b) Oct–Dec 2021 and (c) Oct–Jan 2020/21 anomalies. The La Niña periods highlighted in (b) and (c) show substantial drying south of the equator in the tropical Pacific, a feature less prominent in (d) Oct–Jan 2017/18 and in the composite in (a).

<figure><img src="image_58.png" /></figure>

Fig. 2.24. (a) Time series of upper tropospheric humidity anomalies (%rh) from three datasets (see text for details). The anomalies are computed for area averaged data using the base period 1991–2020. (b) Difference (K) between the temperature and water vapor brightness temperature measurements in the upper troposphere.

### 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) was slightly below the 1991–2020 average in 2021. Figures 2.24a,b show the monthly time series of the humidity datasets and a difference time series between the temperature and water vapor brightness temperature measurements in the UT, respectively. The large-scale relative humidity in the upper troposphere remains roughly constant, as expected from theoretical considerations (Ingram 2010); however, moistening in terms of the water vapor content of the UT is clearly evident from the lower panel. Here, there is a positive trend in the difference between T2 (brightness temperature of MSU Channel 2/AMSU-A Channel 5, which is sensitive to the UT temperature) and T12 (brightness temperature of HIRS Channel 12, which is sensitive to the UT water vapor). The trend in T2 and T12 indicates that the
emission level of the HIRS water vapor channel is shifting higher due to an increase in water vapor over time, while the emission level of the temperature sounding channel located in the 60-GHz oxygen absorption band remains unchanged, because the oxygen concentration does not change over time (Chung et al. 2016).

Water vapor is the strongest greenhouse gas in the atmosphere. Therefore, monitoring of water vapor in the upper troposphere (UT), owing to the cold temperature there, is crucial to determine one of the strongest positive feedback factors to the anthropogenic warming of the climate system (Coleman and Soden 2021). Three of the four datasets used in this work to monitor changes of water vapor in the UT are satellite based: 1) infrared based upper tropospheric humidity (UTH) derived from HIRS measurements starting in the late 1970s (Shi and Bates 2011), 2) microwave-based UTH derived from AMSU-B and MHS measurements starting in the late 1990s (Chung et al. 2013), and 3) mid-to-upper tropospheric temperature derived from MSU/AMSU-A measurements starting in the late 1970s (Zou and Wang 2011). The fourth dataset is relative humidity in the UT from the ERA5 reanalysis (Hersbach et al. 2020).

The agreement among the three UTH datasets is fairly good; the correlations of the HIRS and ERA5 data with the microwave data during their common period (1999–2021), are 0.6 and 0.5, respectively, despite their differences. For example, satellite data represent a layer average UTH with one satellite sampling the same location over Earth only two times a day while ERA5 data represent the 400-hPa level RH with hourly sampling. The microwave data have almost all-sky sampling while the HIRS data sample have 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). In recent years, the HIRS data behave differently from the other two datasets: the mean value of UTH anomaly during 2021 for the HIRS data is 0.08 %rh, while for the microwave and the ERA5 data it is –0.15 and –0.18 %rh, respectively. This difference is due to the degradation in the quality of recent HIRS instruments (e.g., on board NOAA-19). The HIRS instrument era is slowly coming to an end and these instruments will soon be replaced by hyperspectral instruments, such as Infrared Atmospheric Sounding Interferometer (IASI). For the last three years (2019–21), simulated HIRS data from IASI spectra have been used in the creation of HIRS UTH data.

Plate 2.1j shows the 2021 anomaly map for the microwave UTH data. The patterns of the anomalies relate to large-scale weather conditions, with positive (negative) UTH anomalies associated with wetter (drier) conditions at the surface. This is due to the fact that 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, India, and parts of Brazil and a strong dry signal in the western equatorial Pacific (centered near the data line). Prevailing drought conditions over the western United States are also reflected in the anomaly patterns. The patterns also reveal flooding conditions in northern Brazil and continued drought conditions over much of southern Brazil, Paraguay, Uruguay, and northern Argentina. Drought patterns in parts of the Horn of Africa, including Somalia, and in southern Madagascar are also clearly depicted.

4. PRECIPITATION—R. S. Vose, R. Adler, U. Schneider, and X. Yin

Precipitation over global land areas in 2021, as estimated from two different monitoring products, was slightly below the 1991–2020 long-term average (Fig. 2.25a). In particular, the global precipitation total anomaly according to the gauge-based product from the Global Precipitation Climatology Centre (GPCC; Becker et al. 2013) was –7.55 mm for 2021, and the blended gauge-satellite product from the Global Precipitation Climatology Project (GPCP; Adler et al. 2018) was –5.87 mm. The good agreement between the two products is in contrast to 2020, when GPCC depicted less precipitation than average whereas GPCP depicted slightly more than average. Given the modest discrepancy in 2020, it is not clear whether the global land surface was wetter in 2021 compared to 2020.
<figure><img src="image_59.png" /><figcaption>Fig. 2.25. Globally averaged precipitation anomalies (mm yr⁻¹) relative to the 1991–2020 base period over (a) land areas, (b) ocean areas, and (c) the globe. Land and ocean time series were created using a proportional land/sea mask at the 1° × 1° resolution scale.</figcaption></figure>

Over the global oceans (Fig. 2.25b), the precipitation anomaly was -21.17 mm, and the global anomaly (Fig. 2.25c) was -17.07 mm, according to the GPCP product, both of which were substantial decreases from the previous year. Overall, the GPCP product ranks 2021 as the third-lowest year for precipitation in both the global and ocean records, which begin in 1979, after 1991 and 1999, respectively.

In many parts of the world, precipitation anomaly patterns in 2021 were consistent with the 'typical' La Niña pattern. For example, La Niña is often associated with more precipitation than average falling across southern Asia southeastward across the Maritime Continent and into the South Pacific Ocean. Northern South America and the equatorial Atlantic Ocean also tend to have above-average precipitation during La Niña. In contrast, much of the central Pacific Ocean near and south of the equator tends to receive much less precipitation than average, as do the eastern North Pacific and North Atlantic Oceans. However, in 2021 some areas exhibited patterns that ran counter to a 'canonical' La Niña event; for

instance, La Niña usually means additional rainfall to Australia, but the anomaly map for 2021 shows a mixed pattern with expected abundant rainfall in the southeast and along the northern coast, but general dry conditions in the outback. In fact, the global land precipitation being below average is somewhat unusual compared to recent La Niña years when it was above average.

Over global land areas, the largest high precipitation anomalies in 2021 were across northern South America and eastern China, and the largest low precipitation anomalies were over central America, southeastern South America, the Middle East, southeastern Africa, and northern Australia (Plate 2.1k). Over the global oceans, a broad swath of large high precipitation anomalies extended from the eastern Indian Ocean to the Maritime Continent and then southeastward across the tropical western Pacific Ocean. The equatorial Atlantic Ocean and parts of the equatorial Pacific Ocean also received much more precipitation than average, as did part of the Southern Ocean near South America. In contrast, large low precipitation anomalies were apparent over much of the central Pacific Ocean south of the equator as well as over the western Indian Ocean, and to a somewhat lesser extent, over parts of the North Pacific and North Atlantic Oceans. Rainfall excesses helped to fuel frequent flood conditions, such as in Indonesia, Malaysia, and surrounding countries, as well as in northern South America and eastern Brazil. The dry anomalies were associated with continuing drought conditions over much of the southern half of South America and eastern Africa. See also section 2d5 and Chapter 7 for detailed information on region-specific extreme precipitation events.
Overall, 2021 saw above-average frequencies of heavy and very heavy 24-hour precipitation (Figs. 2.26c,d) across several large regions but below-average intensities for the most extreme events in most areas (Plate 2.1). Long-term changes in global precipitation extremes are difficult to quantify due to the local nature of events and sparse observations. These difficulties, such as different observing and reference periods and assessing means vs. totals, explain some of the discrepancies between extreme precipitation indices and below-average mean precipitation in 2021 (section 2d4). Using a combination of observational and reanalysis products, we summarize national-scale temporal variability of extreme precipitation indices (Table 2.7; NOAA 2022a).

Across the contiguous United States, the area experiencing a high proportion of precipitation from the highest 10th percentile 1-day events (NOAA Climate Extremes Index component 4; NOAA 2022b) was substantially above average, at 18.5%, the sixth largest area in the 112-year record

<figure><img src="image_60.png" /><figcaption>Fig. 2.26. Anomalies of 2021 indices: (a) Rx5day and (b) R95p (mm) derived from the in situ-based GHCNDEX relative to a 1961–90 baseline (Donat et al. 2013) and (c) R10mm and (d) R20mm (GPCC) relative to a 1982–2019 baseline.</figcaption></figure>

<table><caption>Table 2.7. Indices used in this section and their definitions. Indices are expressed as anomalies relative to a baseline climatology which varies between data products.</caption><thead><tr><td>Index</td><td>Name</td><td>Definition</td></tr></thead><tbody><tr><td>Rx1day</td><td>Maximum 1-day precipitation</td><td>Annual maximum 1-day precipitation amount (mm)</td></tr><tr><td>Rx5day</td><td>Maximum 5-day precipitation</td><td>Annual maximum consecutive 5-day precipitation amount (mm)</td></tr><tr><td>R10mm</td><td>Heavy precipitation days</td><td>Count of days where daily precipitation total > 10mm (days)</td></tr><tr><td>R20mm</td><td>Very heavy precipitation days</td><td>Count of days where daily precipitation total > 20mm (days)</td></tr><tr><td>R95p</td><td>Total precipitation from very wet days</td><td>Total precipitation from days where the daily precipitation total exceeds the climatological baseline wet day 95th percentile</td></tr><tr><td>10th Percentile 1-day</td><td>NOAA Climate Extremes Index Component 4 (NOAA 2022a)</td><td>The percentage area of the United States experiencing extreme precipitation on days where the total daily precipitation exceeds the historical record (1910–present) baseline wet day 90th percentile, per year, winter (DJF), spring (MAM), summer (JJA), or autumn (SON) season</td></tr></tbody></table>
(Fig. 2.27a), while the same index for the
autumn season indicated the largest area
on record (17.4%). GHCNDEX (Donat et al.
2013; section 2b4) highlights several clus-
ters of positive Rx1day and Rx5day (Fig.
2.26a; Table 2.7) anomalies in North Amer-
ica. Several storms occurred in California
(Appendix Table A2.2), partly related to
the clustering of strong Pacific storms in
October, including a “bomb cyclone” and
an exceptional atmospheric river that
affected California and Oregon coasts
(NOAA 2021a). Parts of Washington State
experienced another atmospheric river
event in mid-November, which caused
record rainfall in around 20 locations in
British Columbia (Canada), resulting in
flooding and landslides (see section 7b1,
Sidebar 7.1). In August, Hurricane Ida (Cat-
egory 4) was the most significant storm to
affect the eastern United States during the
North Atlantic season, moving northeast
after landfall in Louisiana, breaking the
hourly observed rainfall record in New
York City (WMO 2021b; NOAA 2021b).

Rain gauges from the European Climate
and Assessment Dataset (Klein Tank et al.
2002) indicate 2021 was an average year for
R10mm, R20mm, and Rx5day for Europe,
but above average for Rx1day (Fig. 2.27b)
and R95p, the latter ranking 9th and 11th,
respectively, in the 39-year record. A cut-
off low-pressure system contributed to
severe flooding in western Germany and
neighboring countries in mid-July (section
7f3; ECMWF 2021), with 18 (12) new Rx1day
(Rx5day) records set in GHCNDEX. The
24-hour and 48-hour accumulations over
western Germany were the highest in the
140-year record (Junghänel et al. 2021) and
caused severe flooding. A rapid attribution
study calculated an increased likelihood

of such 1-day accumulations (factor of 1.2–9) in the region today compared to a 1.2°C cooler climate (Else 2021). Positive Rx1day anomalies (GPCC, Plate 2.1l) over Sweden and adjacent areas also saw six gauges in GHCNDEX with new records, including the city of Gävle (SMHI 2021).

The GPCC (Schamm et al. 2013) and GHCNDEX data show positive anomalies corresponding to a prolonged period of heavy rain in eastern parts of New South Wales, Australia, in late March, with some areas recording more than 500 mm of rain in 48 hours (Floodlist 2021b) and more than 50 new daily records for March (Bureau of Meteorology 2021a). More than 60 daily records were broken for November across New South Wales and Queensland, with widespread flooding

<figure><img src="image_61.png" /><figcaption>Fig. 2.27. (a) Annual percentage (%) of the contiguous United States with a much-greater-than-normal proportion of precipitation derived from extreme (highest 10th percentile) 1-day precipitation events for the period 1910–2021 (NOAA 2022a). The solid red line denotes a smoothed Gaussian filter, and the horizontal black line denotes the series mean. Note that methodological changes introduced after 2005 means that this series may produce higher values after this date. (b) Median normalized rank of annual Rx1day values for Europe. The annual rank of the Rx1day value at each gauge relative to its own record is derived from the European Climate and Assessment Dataset (Klein Tank et al. 2002) and is normalized by gauge length. The median of all the gauge normalized ranks is then calculated for each year, with low values denoting a higher median rank. Only gauges with at least 50 years of data were used to calculate the annual ranking statistics, and only years for which at least 5000 gauges returned an index value are shown.</figcaption></figure>
(Bureau of Meteorology 2021b,c). Combined, these contributed to considerable positive anomalies of R10mm and R20mm (Figs. 2.26c,d).

The datasets examined here are broadly consistent over southeast Asia in showing above-average frequencies of rainfall extremes, although the GPCC data show below-average intensities in Rx1day (Plate 2.1l). Cyclone Seroja produced 15 new monthly records for April over Western Australia (Bureau of Meteorology 2021d), after generating significant accumulations over Timor, Indonesia, and Singapore (WMO 2021b; Floodlist 2021c). Farther north, Cyclone Surigae brought record rainfall to the Philippines (NOAA 2021c) and parts of Cambodia and Vietnam (Floodlist 2021d,e) in April. An area of positive anomalies over northern and eastern China appears consistent across indices and datasets (Plate 2.1l, Figs. 2.26a–d), reflecting a series of extreme rainfall events; Typhoon In-fa generated several days of heavy rain in Henan Province in mid-July. Nearby Shanxi and Shaanxi Provinces also saw over 60 gauges reporting record accumulations in October (Floodlist 2021f) as moisture was transported from the South China Sea and Bay of Bengal.

Many areas with significant events in 2021 also have positive anomalies in GHCNDEX R95p (Fig. 2.26b), showing the important contribution of extremes to total precipitation. In GPCC, R95p also shows a strong contribution from extreme events over parts of Brazil and southern Africa. The R10mm and R20mm indices for GPCC (Figs. 2.26c,d) and ERA5 (Hersbach et al. 2020) show positive anomalies over northern Brazil and neighboring nations, associated with prolonged rainfall during May and June and again at the end of the year. Above-average Rx1day is also apparent over southeastern Brazil, which experienced widespread flooding early in the year (Plate 2.1l). Above-average R10mm and R20mm frequencies are also evident over several southern African nations (Figs. 2.26c,d). Tropical Cyclone Eloise was followed by several weeks of heavy rainfall at the start of 2021, with Beira, Mozambique, receiving close to its average total January precipitation in 24 hours (NOAA 2022c). Prolonged heavy rain in the summer also resulted in above-average values over southern Sudan and northern Uganda, contrasting with much of central Africa (Plate 2.1l).

Please refer to Chapter 7 (Regional Climates) for more details about precipitation events around the world.

## 6. CLOUDINESS—C. Phillips and M. J. Foster

The average global cloud fraction, as measured by MODIS Aqua C6.1 (Platnick et al. 2015), was 67.67% in 2021. This makes 2021 the third-cloudiest year in the 19-year Aqua satellite record, after 2010 and 2020. La Niña occurred over most of the past three years, though not all years with similar Niño-3.4 index values were associated with such positive cloud anomalies. Being a single satellite in a sun-synchronous orbit, MODIS Aqua data is limited to two global observations per day. Terra MODIS might have been included to add better representation, but electronics issues beginning in October 2020 forced degradation of the cloud products, so it must be omitted (Platnick 2022). Prior to those issues, the Terra global cloud anomaly had excellent correlation with Aqua, so single day and night observations seem to be sufficient. The annual anomalies for CERES-Aqua-MODIS (Trepte et al. 2010; Minnis et al. 2008), PATMOS-x v6.0 (Heidinger et al. 2013), MISR (Di Girolamo et al. 2010), CLARA-A2.1 (Karlsson et al. 2017, 2020, 2021), and PATMOD (MODIS Aqua processed with PATMOS-x cloud algorithms, unpublished) have been included for comparison; however, they have differed from MODIS Aqua C6.1 significantly in recent years (Fig. 2.28). For the sake of simplicity, the following analysis will focus on MODIS Aqua C6.1.

This MODIS record exhibits a small increasing trend in global cloudiness since the record began in 2003. The main positive contributing regions are the poles and the tropics, which are partially offset by negative trends in the subtropics and midlatitudes (Fig. 2.29). This trend makes especially cloudy years like 2021 more likely, though the rank for 2021 is unchanged in the detrended data.

Plate 2.1m shows a map of the average cloudiness anomaly in 2021. Naturally, regional anomalies are much larger than global anomalies, frequently on the order of 5%. The pattern of increased cloudiness in the western Pacific and reduced cloudiness in the central Pacific is common during
La Niña conditions, resulting from the invigoration of convection over high sea surface temperatures (SST) and suppression of convection over cool SSTs. Conversely, other cloud regimes like the marine stratocumulus of the eastern Pacific can exhibit an inverse relationship, with lower SSTs correlated with more cloudiness (Loeb et al. 2018b). Note the opposite signs of cloud and SST anomalies in the eastern Pacific contrasted with aligned signs in the western Pacific (Plate 2.1a).

In addition to the typical La Niña pattern in the Pacific in 2021, several regions experienced their highest percentage of cloudiness in the record, notably the Indian subcontinent, the Canadian Arctic, and part of East Antarctica. All of these

locations were also anomalously cloudy in 2020. On the other hand, the southeast Atlantic near the coast of Africa has had a negative anomaly since 2018 and had a record minimum in 2021. While these regional anomalies are not the largest values seen in Plate 2.1m, they are highlighted here after inspecting their time series and considering their z-score (standard deviations from the mean) to ensure that they were truly cloud fraction outliers in 2021.

<figure><img src="image_62.png" /><figcaption>Fig. 2.28. Global average cloud fraction anomaly (%) relative to the 2003–21 reference period. PATMOS-x v6.0 anomalies were computed by backfitting a grid of joint seasonal-diurnal cloudiness models for all satellites simultaneously and taking the smoothed residual.</figcaption></figure>

<figure><img src="image_63.png" /><figcaption>Fig. 2.29. (a) Zonal mean cloud fraction anomaly (%) from MODIS Aqua C6.1 with 20 latitude bands partitioned to have approximately equal area. Monthly anomalies are relative to the mean of all identical months between 1 Jul 2002 and 31 Jan 2022. Individual bands were detrended with trends computed using the complete years from 2003 through 2021. The detrended anomaly was then smoothed by a 6-month centered rolling mean filter. The smoothed detrended global anomaly is superimposed for reference as a black line, detached from any y-axis. (b) The extracted trends in cloud fraction (% decade⁻¹, aligned gray bars) for the period 2003–21 and the global mean trend (vertical dotted black line).</figcaption></figure>
Clouds reduce both net infrared (IR) emission and solar absorption, and many factors influence the relative balance that determines the sign of the overall cloud radiative effect (section 2f1). The presented regional anomaly in total cloud fraction best describes the solar absorption component, e.g., positive cloud fraction anomalies suggest negative solar absorption anomalies. Calculating the change in full radiative balance would require information, such as independently estimating the changes in height-partitioned cloud fraction, which is difficult with passive satellite observations, due to obscuration from higher clouds. Direct observations of the radiation budget at the top-of-atmosphere are examined in section 2f1.

## 7. RIVER DISCHARGE AND RUNOFF—H. Kim and D. Tokuda

For five years consecutive years (since 2017), global average runoff has been greater than the 1981–2010 average. Runoff in 2021 was the 11th greatest (~84th percentile) in the 64-year record dating to 1958. It was slightly less than the 2020 average, which had the third greatest runoff (~97th percentile; Fig. 2.30); however, there were significant differences in global distributions of runoff (Plate 2.1n) and discharge (Plate 2.1o) anomalies compared to those of 2020.

Rivers in northern South America, such as the Amazon and Orinoco, experienced a strong wet phase of above-average discharge, after a relatively dry phase (below-average discharge) the previous year. In the Amazon, such a shift was particularly notable in the Rio Negro and Rio Solimoes, while Rio Madeira and Rio Tapajos have stayed drier than the 1981–2010 reference period. In Africa, the Nile has shifted into a dry phase, while the Congo, Niger, and Zambezi have stayed in the same phase as 2020. Northern North America had less water than in 2020, and the midwest and northeastern United States became drier than average, leading to less discharge. Northern Europe and western Siberian regions were drier than in 2020. Discharge in many European rivers, including the Dnieper, Don, Loire, Northern Dvina, Rhone, Rhine, Seine, and Volga, was below average. However, it is worth nothing that, in spite of the below-normal annual discharge, there was a devastating flood disaster in Europe, including the Ahr Valley, a tributary of Rhine River, in July 2021. The Danube discharged more freshwater into the Black Sea than usual. Compared to the prior year, most regions in Asia did not show noticeable changes except for southern China (e.g., the vicinity of the Pearl River), Indochina (e.g., the Mekong and Chao Phraya) and the Indus River region, where those regions experienced severe drought and thus had less discharge.

<figure><img src="image_64.png" /><figcaption>Fig. 2.30. Interannual variability of the Oceanic Niño Index (ONI; lower, °C), the Pacific Decadal Oscillation (PDO; upper, °C), and global runoff (middle; mm; thick line is 12-month moving average). The ONI and PDO are shaded red (positive phase) or blue (negative phase). Shading above and below the zero-line of global runoff is proportional to the PDO and ONI, respectively.</figcaption></figure>
Runoff is a local residue of the water budget to be integrated along pathways turning into discharge, which is the most important freshwater resource to humanity. The El-Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO; Zhang et al. 1997) are well-known factors that influence global freshwater discharge (e.g., Kim and Tokuda 2020, 2021). Figure 2.30 depicts the long-term fluctuation of the total runoff and those climate modes. Positive and negative phases of ENSO and PDO correspond to drier and wetter conditions of global freshwater discharge, with ENSO and PDO accounting for around 47% of the total variance. The interannual variability and climatology of freshwater discharge into the major oceans are shown in Fig. 2.31 for the Pacific, Atlantic, Arctic, and Indian basins, where they receive 85% of global freshwater discharge, of which the drainage area reaches 75% of the total land surface area. The Pacific Ocean has received an increasing amount of freshwater discharge during the last two decades, reaching a record maximum in 2021, with above-average discharge throughout the entire year. Freshwater discharge into the Atlantic Ocean remained in a positive phase due to an anomalously large discharge during boreal summer (June–September). The Arctic Ocean received less discharge in 2021, and its month-to-month variability was near-average; however, there is an increasing trend present over the entire analysis period beginning in 1958. An increasing trend is also found in the

<figure><img src="image_65.png" /><figcaption>Fig. 2.31. (a–d, left panels) Interannual variability of freshwater discharge (km³ yr⁻¹) to global ocean basins. The black line and vertical shaded boxes indicate the annual mean and monthly anomaly, respectively. (a–d, right panels) Seasonality of freshwater discharge (km³ yr⁻¹) to global ocean basins. The thick black line, thin blue line, and gray shade indicate long-term climatology, seasonal variation during 2021, and 1 std. dev. of long-term variability, respectively.</figcaption></figure>
Indian Ocean, but it is weaker and shorter, beginning around 1990. The Indian Ocean received slightly less discharge on average in 2021, with a less-than-normal amount occurring during the first wet season of the year.

The time series of natural variability-only estimates of global runoff and discharge were generated using global off-line hydrologic simulations by the Ensemble Land State Estimator (ELSE; Kim et al. 2009) on a 1° grid and a large-scale river routing and flood inundation model Catchment-based Macro-scale Floodplain (CaMa-Flood; Yamazaki et al. 2011) on a 30-minute drainage direction map (DDM30; Döll and Lehner 2002). The World Ocean Atlas 2018 (Garcia et al. 2019) was used to separate the freshwater discharge into each oceanic basin. The atmospheric boundary conditions were created based on the Japanese global atmospheric reanalysis (JRA-55; Kobayashi et al. 2015), and the Global Precipitation Climatology Centre (GPCC) Monitoring Product version 2020 (Schneider et al. 2020) was used to adjust bias in the reanalysis precipitation field.

## 8. GROUNDWATER AND TERRESTRIAL WATER STORAGE—M. Rodell and D. Wiese

Changes in terrestrial water storage (TWS), which is the sum of groundwater, soil moisture, surface water, snow, and ice, have been measured on regional to global scales by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) satellite missions on a monthly basis since 2002 (Tapley et al. 2004; Landerer et al. 2020). Interannual changes tend to be dominated by snow and ice at high latitudes and in alpine regions, by surface water in the wet tropics, and by groundwater elsewhere (Getirana et al. 2017).

Plate 2.1p displays observed changes in mean annual TWS between 2021 and 2020 as equivalent heights of water in cm. Changes in TWS were mixed across Eurasia. Most of western Europe continued to recover from the 2019 drought, while Scandinavia in the north dried (lost TWS) after a wet 2020. Copious rain increased TWS in southern India, where it was already elevated at the start of the year. To the east, the Indochina peninsula recovered from drought, receiving abundant rainfall from Typhoon Surigae (see section 4g5). Northeastern China also gained water, while drought afflicted areas south and west of the Caspian Sea. In Australia, TWS increased along the northern and southeastern coasts. Droughts in Africa caused TWS decreases in Angola and Madagascar, while TWS increased rapidly in Zimbabwe and adjacent areas at the start of

2021, including a 2-m increase in the level of Lake Kariba. In North America, TWS declines extended from southern California into southern Canada, regions where drought conditions prevailed, and across to the Great Lakes region, where declines exceeded 12 cm equivalent height of water in some parts. However, TWS in the center of the continent (including the Great Lakes region) had been elevated since 2019, when record flooding occurred, and the decrease in 2021 was largely a return to more typical conditions. Northern South America gained a massive amount of water, with increases exceeding 12 cm over a large area, but TWS declined in southern Brazil. Time series of zonal mean and global mean monthly TWS anomalies, after removing the seasonal cycle, are plotted in Figs. 2.32 and 2.33, respectively. TWS declines

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

Fig. 2.32. Zonal means of terrestrial water storage anomalies, excluding those in Antarctica, Greenland, the gulf coast of Alaska, and polar islands, in cm equivalent height of water, based on gravity observations from GRACE and GRACE-FO. The anomalies are relative to a 2003–20 base period.
<figure><img src="image_67.png" /><figcaption>Fig. 2.33. Global average terrestrial water storage anomalies from GRACE (gray) and GRACE-FO (black), in cm equivalent height of water, relative to a 2003–20 base period.</figcaption></figure>

TWS decline near 40°N mainly reflects drought across the western and central United States, drought stretching from Turkey to the Caspian Sea (including water level declines in that sea), and long-term groundwater depletion associated with agricultural irrigation in the North China Plain (Rodell et al. 2018). Droughts in southern Brazil and Angola caused the TWS decline near 15°S. Overall, global-scale TWS variability in 2021 was muted compared with past years (Plate 2.1p). Owing to the scarcity of in situ TWS measurement records, we relied on TWS data derived from GRACE and GRACE-FO satellite observations of Earth's time-varying gravity field. Uncertainty in the derived TWS anomalies is heterogeneous, but as a point of reference, uncertainty in monthly TWS anomalies is typically around 1–2 cm equivalent height of water at 500,000 km² and larger scales (Wiese et al. 2016).

in Antarctica, Greenland, the Gulf Coast of Alaska, and polar islands are attributed to ice sheet and glacier ablation as opposed to meteorological variations, so those regions were excluded from the calculations for Figs. 2.32 and 2.33. However, the effects of additional high latitude ice mass losses remain. The large TWS increase, seen straddling the equator in Fig. 2.32, corresponds to the massive wet anomaly in northern South America. The TWS decline just south of 30°N is due almost entirely to long-term groundwater depletion in northern India (Rodell et al. 2018), exacerbated by a drought just east of that area in 2021 (section 2d10). The

9. **SOIL MOISTURE**—R. van der Schalie, W. Preimesberger, P. Stradiotti, M. van der Vliet, L. Mösinger, N. J. Rodríguez-Fernández, R. Madelon, S. Hahn, M. Hirschi, R. Kidd, R. A. M. de Jeu, and W. A. Dorigo

Soil moisture plays a key role in land–atmosphere interaction due to its defining effect on boundary conditions for energy and water fluxes (Seneviratne et al. 2010). In 2021, global satellite-observed soil moisture recorded wetter-than-usual conditions (Fig. 2.34). Note that wetter/drier within this section refers to higher/lower soil moisture volumes in the first centimeters of the soil as compared to the 1991–2020 reference period. The discrepancy between NH and SH, as recorded in 2020 (van der Schalie et al. 2021), was strongly reduced, with NH continuing to be wetter-than-usual and SH observing near-neutral conditions. Given that North America and central Asia experienced dry conditions, the wet conditions in Europe, eastern Asia, and the Indian subcontinent more than compensated for that in the NH average. For the SH, widespread dry

<figure><img src="image_68.png" /><figcaption>Fig. 2.34. Time series of global (black), NH (blue), and SH (red) monthly surface soil moisture anomalies for the period 1991–2021 (upper, m³ m⁻³; 1991–2020 base period) and the valid retrievals 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 due to dense vegetation, frozen soil, radio frequency interference, and so forth. (Source: ESA CCI Soil Moisture.)</figcaption></figure>
<figure><img src="image_69.png" /></figure>

Fig. 2.35. Time-latitude diagram of monthly surface soil moisture anomalies (m³ m⁻³; 1991–2020 base period). Data are masked where no retrieval is possible or where the quality is not assured and flagged due to dense vegetation, frozen soil, radio frequency interference, and so forth. (Source: ESA CCI Soil Moisture.)

conditions in central to southern South America are balanced out by wet anomalies seen in other regions.

These findings are clearly shown in Fig. 2.35, where NH and equatorial latitudes show positive anomalies since early 2020. South of 15°S in the SH, consistent negative anomalies have been present since at least 2018, moving closer to neutral conditions in 2021. More detailed maps of the spatial distribution are shown in Plate 2.10 (average for 2021) and Appendix Fig. A2.12 (monthly).

Linked to the La Niña conditions in winter, part of North America (Mexico) started the year with mostly negative anomalies (Appendix Fig. A2.12). These negative anomalies expanded northward to the western and central United States and Canada during the first four months of the year. The dry conditions in the

Canadian Prairies and Montana intensified during an extreme heatwave that occurred in June and July (Philip et al. 2021; section 2b4). The wetter-than-normal autumn that followed eased the anomalies, resulting in close-to-normal conditions at the end of the year. The southeastern United States recorded a widespread positive anomaly over 2021, caused by above-normal precipitation (NOAA 2022e; section 2d4).

In 2021, the southern half of South America mostly saw a continuation (van der Schalie et al. 2021) of drier-than-normal soil moisture conditions. These conditions were linked to the drought in the La Plata basin covering Uruguay, Bolivia, Paraguay, northeastern Argentina, and southern Brazil (Naumann et al. 2021). Southern regions in Argentina, e.g., Patagonia and surrounding areas, also experienced drier-than-normal soil moisture conditions. In northern South America, Venezuela, and Colombia experienced wetter-than-normal conditions throughout 2021, while in northeastern Brazil and northern Chile the positive anomalies were linked to heavy rain in the last three months of the year (sections 2d4, 7d).

Overall, Europe had wet soil moisture conditions, which were most intense in the western coastal and central-north areas and around the Black Sea region (contrasting with negative anomalies along the southern coast of the Black Sea and in the Middle East in April/May). The positive anomalies align with heavy rainfall and flooding throughout Europe during the summer (sections 2d5, 7f). Scandinavia and southern Spain were the only regions that recorded an overall drier-than-normal status for 2021.

The drought in the northern coastal regions of Africa, e.g., Morocco and Tunisia, were reflected in the widespread drier-than-normal soil moisture conditions. Western Africa and the Sahel started 2021 with above-average conditions, which later became drier-than-normal due to drought. Southern Chad and Sudan experienced wet conditions, caused by heavy rains during the summer. Eastern Africa had both positive anomalies (linked to widespread flooding in inland regions like South Sudan) and negative anomalies (regions experiencing droughts like Somalia and Kenya). Southern Africa had a clear split between below-average soil moisture conditions in Angola, Namibia, south Madagascar and southwest South Africa and above-average conditions in Botswana and northeast South Africa. This seems to align better with the precipitation extremes (section 2d5) and terrestrial water storage (section 2d8) than general precipitation anomalies
(section 2d4). In late 2021, drier-than-usual conditions were present in Madagascar and Tanzania, following wetter-than-usual conditions at the beginning of the year.

In Russia, Siberia and surrounding regions experienced low rainfall and high temperatures that continued below-average soil moisture conditions overall. These negative anomalies were widespread throughout much of central Asia, with countries like Kazakhstan, Kyrgyzstan, Turkmenistan, and Uzbekistan experiencing drought events. For Siberia, this was a continuation of 2020 conditions. India, Thailand, Mongolia, and large parts of China observed positive anomalies, caused by above-average precipitation (section 2d4). Northeastern China and southern India are particularly noteworthy, as they recorded the highest wet anomalies globally of 2021, linked to the second highest rainfall totals since 1961 in parts of China (Li et al. 2022) and highest recorded November rainfall since 1901 in southern India (IMD 2022).

The wetting trend in Australia continued in 2021, partly related to La Niña, with most regions recording average or above-average soil moisture levels. The most intense positive anomaly was detected over New South Wales, which coincides with the above-average rainfall in that region (section 2d4). New Zealand generally saw average soil moisture conditions.

The soil moisture anomalies (representing the top ~5 cm of the soil) used in this analysis were derived from the COMBINED product of ESA's Climate Change Initiative for Soil Moisture v06.2 (https://climate.esa.int/en/projects/soil-moisture/; Dorigo et al. 2017), a merged product based on multiple active (Wagner et al. 2013) and passive microwave (van der Schalie et al. 2017) sensors. Merging is done based on both the quality and the temporal and spatial availability of observations (Gruber et al. 2017, 2019).

# 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 ongoing increase in global drought area, based on different severities of the self-calibrating Palmer Drought Severity Index (scPDSI), that began in mid-2019 (Barichivich et al. 2020) reached a new historical peak around August 2021 (Fig. 2.36), surpassing the previous high peak in October 2020 (Barichivich et al. 2021).

Around 5.9% of the global land area experienced extreme drought conditions in September 2021, matching the earlier historical peak in October 1984. The extent of severe plus extreme drought conditions in 2021 peaked at 17% of the global land area in July and August, surpassing the earlier historical peak of this drought severity in December 2002 (16.6%). Similarly, moderate or worse drought conditions peaked in August at 32% of the global land area, surpassing the earlier historical peak in November 2002 (31.6%).

The most extensive severe-to-extreme drought conditions in 2021 occurred across most of South America and western

North America (Plate 2.1r), whereas the most extensive severe-to-extreme drought conditions in 2020 had been in Europe (Barichivich et al. 2021). Widespread drought in South America was mostly due to an intensification of previous drought (Fig. 2.37), leading to the expansion and intensification of earlier drought hotspots in central Chile and the Chaco region in northern Argentina (Barichivich et al. 2021). The ongoing protracted drought in central Chile reached its 12th consecutive year in 2021, becoming the longest drought in the historical record in the region. In

<figure><img src="image_70.png" /><figcaption>Fig. 2.36. Percentage of global land area (excluding ice sheets and deserts) with scPDSI levels of less than -2, -3, and -4, indicating moderate, severe, and extreme drought, respectively, for each month of 1950–2021.</figcaption></figure>
<figure><img src="image_71.png" /><figcaption>Fig. 2.37. Change in drought categories from 2020 to 2021 (mean scPDSI for 2021 minus mean scPDSI for 2020). Increases in drought severity are indicated by negative values (brown), decreases by positive values (blue). No calculation is made where a drought index is meaningless (gray areas: ice sheets or deserts with approximately zero mean precipitation).</figcaption></figure>

North America, the intensification and expansion of drought through most of the western and formerly wetter central United States in 2021 (Fig. 2.37) weakened the east-west moisture contrast observed across the country since 2017 (Plate 2.1r). Under these persistent drought conditions, California saw another extreme season of wildfires (section 2h3), which was intensified by La Niña conditions.

Previous extensive severe-to-extreme drought across central Europe eased to moderate drought in 2021 (Fig. 2.37). Wet conditions in northern Europe from the British Isles to Scandinavia and western Russia continued through 2021 (Plate 2.1r). In northern Africa, previous drought conditions intensified along the Mediterranean coast. Most of tropical Africa saw

a continuation of wet conditions from 2019 (Plate 2.1r), though changes in moisture anomalies in this region are uncertain due to the sparse coverage of meteorological station data. Persistent severe-to-extreme drought conditions in southern Africa since 2018 continued through 2021 but eased slightly compared to 2020 (Fig. 2.37).

In Australia, previous drought eased, but most of the country continued under drought conditions during 2021 (Plate 2.1r). In contrast, the Maritime Continent (Southeast Asia) experienced wet conditions, and previous drought in region eased, particularly across Vietnam. A large stretch along northeastern Siberia and the Far East region of Russia saw an intensification and expansion of severe drought along with extreme heat (Plate 2.1r), which led to unprecedented wildfires. Most of the Middle East from Turkey to Pakistan saw an intensification of drought to moderate-to-severe conditions.

Hydrological drought results from a period of abnormally low precipitation, sometimes exacerbated by a concurrent increase in evapotranspiration (ET). Its occurrence can be apparent in reduced river discharge, soil moisture, and/or groundwater storage, depending on season and duration of the event. Here, a simple estimate of drought called the self-calibrating Palmer Drought Severity Index (Wells et al. 2004; van der Schrier et al. 2013) is presented, using global precipitation and Penman-Monteith Potential ET from an early update of the CRU TS 4.06 dataset (Harris et al. 2020). Moisture categories are calibrated over the complete 1901–2021 period to ensure that “extreme” drought and pluvial (wet periods) relate to events that do not occur more frequently than in approximately 2% of the months. This calibration affects direct comparison with other hydrological cycle variables in Plate 2.1r that use a different baseline period.

This year's update is based on an extensively revised dataset that incorporates new estimates of some variables in CRU TS4.06 compared with CRU TS4.05 used in the report on 2020 (Barichivich et al. 2021). The revisions affect both precipitation (via an improved baseline climatology) and potential ET (all input variables are affected by the improved baseline climatology, and cloud cover is further modified by a new method for estimating it from diurnal temperature range). These revisions modify the scPDSI drought index values throughout, including during the mid-1980s period of extensive drought, which has a reduction in the extent of drought compared to that previously estimated.
In 2021, most regions experienced positive anomalies (more evaporation than normal), relative to the 30-year reference period (1991–2020), as illustrated in Plate 2.1s. Unusually high values were experienced in southeast Asia and India, eastern Australia, Amazonia, and the Congo rainforest. Some of these anomalies related to higher-than-normal radiation and temperatures (section 2b1). In Amazonia, the high evaporation also reflects above-average rainfall in 2021 (Espinoza et al. 2022; section 2d4); precipitation in rainforests can enhance evaporation via interception loss, i.e., the intense vaporization of water from wet vegetation canopies (van Dijk et al. 2015). In eastern Australia, positive evaporation anomalies mostly were in response to the exceptionally wet autumn in 2021, which caused floods during the month of March (Reid et al. 2021; section 2d4). On the other side of the spectrum, anomalously low evaporation was recorded in Angola and across the Horn of Africa, in the Mato Grosso and Caatinga regions in Brazil, in western North America, and in the Middle East. These negative anomalies in water-limited regions are mostly related to below-normal rainfall (Orimoloye et al. 2022; section 2d4). In the Horn of Africa, the negative evaporation anomalies relate to a multi-year drought (sections 2d10, 7e4).

The globally averaged land evapora-
tion in 2021 was above the 1991–2020
mean, but slightly below the positive
1980–2021 trend (Fig. 2.38). This long-
term trend of +0.76 mm yr⁻¹ falls within
the high-end of the values previously
published in the literature (Y.-Q. Zhang
et al. 2016; Brutsaert et al. 2017; Anabalón
and Sharma 2017), although this value
is still substantially lower than the lat-
est estimates of +2.30 mm yr⁻¹ based
on gravimetric remote sensing for the
period 2003–19 (Pascolini-Campbell
et al. 2021). The overall positive global
evaporation anomaly in 2021 resulted
from mean positive anomalies in both
hemispheres (Fig. 2.38), which persisted
at most latitudes throughout the entire
year (Fig. 2.39). The positive anomalies
in Southern Hemisphere mean evapora-
tion—that were also reflected in the glob-
al means—are a characteristic signature
of La Niña conditions (Miralles et al. 2014;
Martens et al. 2018), such as those that
prevailed during 2020 and 2021 (see SOI
in Fig. 2.38). This ENSO-induced variabil-
ity in evaporation is superimposed upon
the long-term trend, which is attributed
to increasing global temperatures (Mi-
ralles et al. 2014) and terrestrial greening
(Cheng et al. 2017).

<figure><img src="image_72.png" /><figcaption>Fig. 2.38. Land evaporation anomaly (mm yr⁻¹; 1991–2020 base period) for the NH, SH, 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. (Source: GLEAM.)</figcaption></figure>

<figure><img src="image_73.png" /><figcaption>Fig. 2.39. Zonal mean terrestrial evaporation anomalies (mm month⁻¹; relative to 1991–2020 base period). (Source: GLEAM.)</figcaption></figure>
The evaporation data that serve as the basis for these results come from version 3.6 of the Global Land Evaporation Amsterdam Model (GLEAM; Miralles et al. 2011). GLEAM v3.6 is based on microwave observations (of surface soil moisture, vegetation optical depth, and snow water equivalent), a precipitation dataset produced by blending gauge, satellite, and reanalysis sources (Beck et al. 2019), and a new bias-corrected, reanalysis-based product as atmospheric forcing (Beck et al. 2022). The accuracy of GLEAM v3 has been reported to be in the order of 0.7 mm day⁻¹ (unbiased root mean square error), and its correlation against in situ eddy-covariance measurements is around 0.8 on average (Martens et al. 2017). While the ability to routinely monitor evaporation dynamics is critical for agriculture and water management, as well as to diagnose climate changes, global evaporation datasets (such as GLEAM v3.6) are still subject to model assumptions and uncertainties derived from parameterizations. In particular, the accuracy of the reported long-term trends is affected by the indirect representation of the influence of CO₂ and atmospheric aridity on stomatal conductance (Martens et al. 2017).

### e. Atmospheric circulation

#### 1. MEAN SEA LEVEL PRESSURE AND RELATED MODES OF VARIABILITY—B. Noll,

D. Fereday, and N. Fedaeff

Global climate variability is influenced by the El Niño-Southern Oscillation (ENSO; indicated in the atmosphere by the Southern Oscillation Index [SOI]), as well as hemispheric-specific modes such as the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO) and the Pacific/North America (PNA) pattern in the Northern Hemisphere (NH), and the Southern Annular Mode (SAM), also known as the Antarctic Oscillation (AAO), in the Southern Hemisphere (SH; see Kaplan 2011 for more details).

In 2021, La Niña conditions were present from January to May and then again from August through the end of the year, according to monthly Niño-3.4 SST anomalies (see also section 4b). The atmospheric response to La Niña was characterized by SOI values that were predominantly positive through the year. During July, a negative Indian Ocean dipole (IOD) event became established, in association with above-normal ocean temperatures in the tropical eastern Indian Ocean and cooler conditions relative to normal in the west (see section 4f). The development of an IOD event is correlated with ENSO by way of variations in the Walker Circulation (Behera et al. 2006). The overlying atmospheric pattern was dominated by the sinking branch of the Walker Circulation over the central and eastern Pacific and stronger-than-normal rising branch over Australia, the Maritime Continent, and the eastern Indian Ocean. These large-scale patterns influenced global atmospheric circulation patterns during the year.

In the NH, the PNA was on average negative in 2021, consistent with La Niña conditions (Yeh et al. 2018; Plate 2.1r). The principal large-scale circulation features occurred in the 2020/21 and 2021/22 winters. January and February 2021 saw a negative NAO, following a January sudden stratospheric warming (SSW; Figs. 2.40a,c). In SSW events, which occur in around 60% of boreal winters (Rao et al. 2019), the stratospheric polar vortex is disrupted by planetary-scale tropospheric waves. These waves may have been amplified in winter 2020/21 by reduced Arctic sea ice and a deepened Aleutian Low (Lu et al. 2021). In the weeks following SSW, the signal descends, often leading to the negative AO/NAO phase (Baldwin and Dunkerton 2001). The 2021 SSW followed this pattern (Figs. 2.40a,c), with the negative NAO in February possibly driven by the Madden-Julian Oscillation (MJO; Cassou 2008; Lockwood et al. 2022). Cold February conditions in northern Eurasia and eastern North America were a typical response to the negative AO/NAO.

By contrast, the winter 2021/22 stratospheric polar vortex was stronger than average (Fig. 2.40b). A strong vortex favors a positive NAO (Baldwin and Dunkerton 2001); although the December NAO was overall negative (Fig. 2.40d), the late-winter NAO was positive, consistent with the strong vortex. These contrasting winters (Figs. 2.40e,f) highlight some of the NAO drivers that vary interannually, reinforcing or counteracting each other (Scaife et al. 2014).
<figure><img src="image_74.png" /><figcaption>Fig. 2.40. Circulation differences between 2020/21 and 2021/22 boreal winters. (a),(b) Time-height cross section of zonal mean zonal wind anomalies (m s⁻¹) at 60°N (Met Office analysis). (c),(d) Daily North Atlantic Oscillation (NAO) index (Azores minus Iceland MSLP, hPa) anomaly, 1981–2010 base period (NCEP reanalysis). (e),(f) DJF mean MSLP anomaly (hPa), 1981–2010 base period (ERA5 reanalysis).</figcaption></figure>

The SH featured a positive SAM during 73% of the year in 2021 (Fig. 2.41e). This was the fourth-highest percentage (and highest SAM index, section 2e3) since 1979. Accordingly, a broad area of higher-than-normal air pressure extended from east of New Zealand, across the South Pacific, and into southern Chile and Argentina (Figs. 2.41a–d). This was associated with New Zealand’s warmest year on record, during which northerly quarter winds (315°W to 45°E) frequently affected the country, drawing down warmth from the tropics and contributing to a marine heatwave event in its coastal waters (NIWA 2021). Conversely, and as is consistent with the positive phase of the SAM, air pressure was lower than normal around much of Antarctica and the Southern Ocean (Figs. 2.41a–d). This was also linked to a stronger-than-normal stratospheric polar vortex above the South Pole, which contributed to very cold temperatures and the fifth-largest Antarctic sea ice extent for the month of August on record (NOAA 2021; see section 6e). Pressures were slightly lower than normal over Australia, as influenced by the combined effect of the IOD and La Niña teleconnection (Figs. 2.41a–d). 2021 was Australia’s coolest year since 2012 and wettest since 2016, which resulted in a recharge of water storage following drought events from 2017 to 2019 (Bureau of Meteorology 2022; see also section 7h4).
<figure><img src="image_75.png" /><figcaption>Fig. 2.41. Southern Hemisphere circulation in 2021. Seasonal mean MSLP anomalies (hPa) for (a) DJF 2020/21, (b) MAM 2021, (c) JJA 2021, and (d) SON 2021, 1981–2010 base period (ERA5 reanalysis). (e) Daily AAO index time series (NOAA CPC).</figcaption></figure>

## 2. LAND AND OCEAN SURFACE WINDS—C. Azorin-Molina, R. J. H. Dunn, L. Ricciardulli, C. A. Mears, J. P. Nicolas, and T. R. McVicar

After about eight years of weak positive (near zero) anomalies in observed land surface winds (i.e., ~10 m above the ground), negative anomalies (with respect to the 1991–2020 climatology; Table 2.8) were recorded in the Northern Hemisphere (–0.028 m s⁻¹) in 2021, primarily in eastern North America (–0.063 m s⁻¹) and western Europe (–0.095 m s⁻¹). These exceptionally low winds, for example, in areas of the United Kingdom (Plate 2.1u), have not occurred in East Asia (+0.045 m s⁻¹), central Asia (+0.080 m s⁻¹), or South America (+0.080 m s⁻¹). Overall, the stilling (Roderick et al. 2007) observed before the 2010s (McVicar et al. 2012) ceased in the last decade, with a weak reversal or stabilization of surface winds globally (Zeng et al. 2019) and regionally (Minola et al. 2022; Fig. 2.42a). Two thresholds of wind intensities (i.e., > 3 m s⁻¹ and > 10 m s⁻¹) show an opposite trend behavior with no trends (or even positive ones) for moderate winds (> 3 m s⁻¹; Fig. 2.42c) and moderate slowdowns for strong winds speeds (> 10 m s⁻¹; Fig. 2.42d), e.g., in South
<table><caption>Table 2.8. Northern Hemisphere (20°–70°N) and regional statistics for land surface wind speed (m s⁻¹) using the observational HadISD3 dataset for the period 1979–2021.</caption><thead><tr><th>Region</th><th>Mean 1991–2020 (m s<sup>-1</sup>)</th><th>Anomaly 2021 (m s<sup>-1</sup>)</th><th>Trend 1979–2021 (m s<sup>-1</sup> decade<sup>-1</sup>) and 5th–95th percentile confidence range</th><th>Number of stations</th></tr></thead><tbody><tr><td>Northern Hemisphere</td><td>3.309</td><td>-0.028</td><td>-0.059 (-0.073 to 0.044)</td><td>2886</td></tr><tr><td>North America</td><td>3.639</td><td>-0.063</td><td>-0.073 (-0.089 to 0.055)</td><td>853</td></tr><tr><td>Europe</td><td>3.652</td><td>-0.095</td><td>-0.052 (-0.073 to 0.034)</td><td>933</td></tr><tr><td>Central Asia</td><td>2.737</td><td>+0.080</td><td>-0.077 (-0.123 to 0.048)</td><td>304</td></tr><tr><td>East Asia</td><td>2.717</td><td>+0.045</td><td>-0.031 (-0.046 to 0.017)</td><td>534</td></tr><tr><td>South America</td><td>3.451</td><td>+0.080</td><td>+0.051 (-0.032 to 0.069)</td><td>101</td></tr></tbody></table>

<figure><img src="image_76.png" /><figcaption>Fig. 2.42. Northern Hemisphere (20°–70°N) and regional annual time series of land surface wind speed anomaly (m s⁻¹; 1991–2020 base period) using (a) HadISD3 (1973–2021) observational dataset and (b) ERA5 (1979–2021) and MERRA-2 (1980–2021) reanalyses. HadISD3 occurrence frequencies (% yr⁻¹) for wind speeds (c) > 3 m s⁻¹ and (d) > 10 m s⁻¹.</figcaption></figure>

America (Azorin-Molina et al. 2021; Zhang et al. 2021).

This annual land surface wind report for 1979–2021 is based on 1) global in situ anemometer observations from the HadISD3 dataset (1973–2021; Dunn et al. 2012, 2016, 2019) and 2) two gridded reanalysis products: ERA5 (1979–2021; Hersbach et al. 2020) and MERRA-2 (1980–2021; Galaro et al. 2017). Compared to in situ observations, the magnitudes of the anomalies from the reanalyses are smaller and do not reproduce their multidecadal variability (Fig. 2.42b; Torralba et al. 2017; Ramon et al. 2019; Wohland et al. 2019); observations are the basis for quantifying wind changes.

Over most land regions, the last 43 years (1979–2021) have seen a declining long-term trend (Northern Hemisphere: -0.059 m s⁻¹ decade⁻¹; Table 2.8). This stilling has ceased or even reversed since the ~2010s, but winds are not yet back up to 1979 levels, so on average a long-term decline in global land wind speeds is still observed. South America, however, has experienced a positive trend (though with many fewer stations). Despite the lack of land-based observations in the Southern Hemisphere, both in situ and ERA5 data in Fig. 2.43 support this interhemispheric asymmetry of wind speed changes, also

projected for the twenty-first century (Deng et al. 2022; Yu et al. 2022), such as a dominance of decreases across midlatitude regions of the Northern Hemisphere and weak increases in the Southern Hemisphere (e.g., parts of Antarctica and the Southern Ocean).
<figure><img src="image_77.png" /><figcaption>Fig. 2.43. Wind speed trends (m s⁻¹ decade⁻¹) from (a) ERA5 reanalysis output over land/ice and Remote Sensing Systems (RSS) satellite radiometers (SSM/I, SSMIS, TMI, GMI, AMSR2, ASMR-E, and WindSat) over ocean (shaded areas) for the period 1988–2021 and (b) the observational HadISD3 dataset over land (circles) for the period 1979–2021.</figcaption></figure>

<figure><img src="image_78.png" /><figcaption>Fig. 2.44. Annual anomalies of global mean wind speed (m s⁻¹; 1991–2020 base period) over the ocean from satellite radiometers and scatterometers, MERRA-2, and ERA5.</figcaption></figure>

The assessment of ocean surface winds for 1988–2021 was conducted using ERA5 reanalysis and satellite-based products including the Special Sensor Microwave/Imager (SSM/I), the Special Sensor Microwave Imager/Sounder (SSMIS), the Advanced Microwave Scanning Radiometer (AMSRE and AMSR2), TMI, GMI, WindSat, QuikSCAT, and ASCAT (Wentz 1997; Wentz et al. 2007, 2015; Ricciardulli and Wentz 2015; Ricciardulli and Manaster 2021). In 2021, positive anomalies were recorded by satellite radiometers (RSS: +0.078 m s⁻¹), slightly larger than satellite scatterometers (ASCAT: +0.041 m s⁻¹) and reanalysis (ERA5: +0.030 m s⁻¹) (Fig. 2.44). The most

prominent positive anomalies were recorded over the Southern Ocean, specifically in the South Pacific (> +1.2 m s⁻¹) associated with a positive SAM phase, along with the equatorial Pacific Ocean, Gulf of Alaska, and Bering Sea (Plate 2.1u). This agrees with previous studies (e.g., Deng et al. 2022), which report a strengthening and shifting poleward of Southern Hemisphere westernlies. In contrast, large negative anomalies occurred in the North Atlantic, equatorial Atlantic, and Indian Oceans. Changes in ocean surface winds show a dominance of negative trends for 1988–2021, especially in the Indian and West Pacific Oceans (Fig. 2.43). Positive trends prevail in the Southern Ocean, the Pacific trade winds, the Bering Sea, and near the coastlines (e.g., North America; Young and Ribal 2019).

Studies investigating the trends and variability of surface winds demonstrated that the primary driver is exerted by internal decadal ocean–atmosphere oscillations (Zeng et al. 2019; Zhou et al. 2021), as modulated by changes in the temperature gradient (Zhang et al. 2021) induced by anthropogenic greenhouse gas forcings (Deng et al. 2021, 2022). Regionally and locally, land-use changes (Minola et al. 2022) and/or instrumentation issues (Azorin-Molina et al. 2018; Safaei Pirooz et al. 2020) have an impact on wind changes. Recent studies predict an interhemispheric asymmetry of future wind speed changes due to the reduction in Hadley, Ferrel, and Polar cells over the Northern Hemisphere and the strengthening of the Hadley cell over the Southern Hemisphere (Zha et al. 2021).
The 2021 global mean wind speed anomaly at 850 hPa was about 0.1 m s⁻¹ above the 1991–2020 climatology (Fig. 2.45a). The neutral or positive long-term wind trends at 850 hPa in the NH (40°–60°N) extratropics and in the global mean are now more consistent with surface winds (section 2e2), where the wind stilling reported in past annual reports has ceased or even reversed (section 2e2). As 2021 was the year with the highest Southern Annular Mode (SAM) index (average 0.8, estimated from NOAA monthly data) since 1979, we show the 70°–50°S average zonal wind time series in (Fig. 2.45b). The correlation coefficient between the AAO index and the 70°–50°S zonal mean 850-hPa wind speed on monthly timescales, calculated for 1979–2021, is 0.9. Thus, it is not surprising that the annual mean wind speed anomaly in this latitude belt was clearly positive in 2021, with maxima in autumn (not shown). Consistent with

climate model predictions (Lee et al. 2019) there has been a notable increase in wind speed in this latitude belt, with highly significant trends 1991–2021 between 0.16 and 0.22 m s⁻¹ decade⁻¹. Large positive zonal wind speed anomalies occur at 850 hPa over nearly all longitudes of the Southern Ocean (Plate 2.1v).

As in previous years, we consider large-scale anomalies in upper air winds through the velocity potential (χ) and divergent winds at 200 hPa. This diagnostic is particularly sensitive to changes in tropical convective activity and associated changes to the Walker Circulation. The most prominent seasonal anomalies of the velocity potential in 2021 were present during boreal autumn (SON), with negative χ anomalies centered over the Indo-Pacific Warm Pool and positive χ anomalies extending from the central tropical Pacific into the northern subtropical Atlantic. The negative anomaly is consistent with strengthened convective activity arising from increased SSTs in the eastern Indian Ocean (associated with weakly negative Indian Ocean dipole conditions during August–November; see section 4f) and in the western tropical Pacific (associated with moderate La Niña conditions; see sections 2e1, 4b). Moreover, a few weak Madden-Julian Oscillation (Madden and Julian 1971) events were observed over the Maritime Continent during SON 2021 (see section 4c), which contributed additionally to convective activity in that region. The positive χ anomalies over the eastern tropical Pacific as well as westerly anomalies of 200-hPa divergent wind in the western equatorial Pacific are consistent with below-average SSTs in this region (La Niña). Interestingly, the center of the positive χ anomaly, as well as the convergence of 200-hPa winds, extended well into the western subtropical Atlantic. Anomalies in the χ field have been used to link tropical cyclone activity with large-scale tropical climate anomalies (e.g., Wood et al. 2020; Bell and Chelliah 2006). However, despite the strong χ anomaly pattern, tropical cyclone activity during the 2021 season was close to climatology in all Northern Hemisphere basins in terms of accumulated cyclone energy (ACE; see section 4g); only named storms in the Atlantic basin were 50% above average.

<figure><img src="image_79.png" /><figcaption>Fig. 2.45. Annual anomalies of (a) global mean, (b) 50°–70°S belt zonal mean 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. ERA-Interim time series ends in 2019.</figcaption></figure>
<figure><img src="image_80.png" /><figcaption>Fig. 2.46. Anomalous velocity potential (× 10⁶ m² s⁻¹) and divergence winds (arrows, m s⁻¹) in SON 2021 from ERA5 (1991–2020 climatology) at 200 hPa. Stippling denotes anomalies exceeding 1.65σ of the temporal standard deviation of seasonally averaged velocity potential anomalies.</figcaption></figure>

The availability of wind observations from the Aeolus instrument has been shown to constrain the uncertainties of notoriously difficult upper level divergence patterns and tropical weather forecast errors on short timescales (Rennie et al. 2021; Martin et al. 2021). Since those data will be used in future reanalyses (ERA5 does not yet assimilate Aeolus data), we looked at systematic impacts on seasonal means of winds and divergence to assess the potential for temporal discontinuities in these datasets. The root-mean square (rms) difference between velocity potentials from ECMWF assimilation experiments with/without Aeolus reaches only 2% of the rms value of the anomaly diagnostic in Fig. 2.46. Similar

analysis of upper tropospheric zonal mean cross-equatorial velocity and divergence showed only negligible systematic differences, although on shorter time scales (sub-monthly) differences can become considerable. Overall these results suggest that Aeolus data are not expected to introduce major spurious shifts at their advent on seasonal and longer time scales, at least not in the quantities considered here.

After its latest disruption in 2020, the behavior of the quasi-biennial oscillation (QBO) in 2021 can be characterized as typical, comparable, for example, with 1958, 1981, 2009, and 2014. The western phase was at its strongest at 50 hPa in April, with a speed of 12.8 m s⁻¹. The following easterly phase, that has persisted above 15 hPa since April 2019, reached –36.8 m s⁻¹ in October at the 20 hPa level, which is stronger than average, but not exceptional. This phase descended at a rate of about 0.61 km month⁻¹, which is close to the long term (60-year) average (–0.66 ± 0.75 km month⁻¹).

## f. Earth radiation budget

### 1. EARTH RADIATION BUDGET AT TOP-OF-ATMOSPHERE—T. Wong, P. W. Stackhouse Jr.,

P. Sawaengphokhai, J. Garg, and N. G. Loeb

The top-of-atmosphere (TOA) Earth radiation budget (ERB) is defined as the difference between incoming total solar irradiance (TSI) and outgoing radiation from Earth, given by the sum of reflected shortwave (RSW) and outgoing longwave radiation (OLR). Regional imbalances in TOA ERB drive atmospheric and oceanic circulations.

An analysis of CERES TOA ERB measurements (Table 2.9) shows that the global annual mean OLR and RSW decreased by 0.65 W m⁻² and 0.10 W m⁻² in 2021, relative to 2020 (rounded to nearest 0.05 W m⁻²). In contrast, the global annual mean TSI and net radiation increased by 0.05 W m⁻² and 0.80 W m⁻², over the same period. Figure 2.47 shows regional annual mean maps of the difference between 2021 and 2020 in TOA OLR and TOA RSW. The largest reductions in OLR and increases in RSW are seen over the tropical western Pacific Ocean, Philippines, Indonesia, and New Guinea. Negative OLR and positive RSW differences also occur over the eastern Pacific Ocean, off the coast of Peru. The largest increases in OLR and decreases in RSW are observed in the tropical western Indian Ocean. These regional changes are associated with ENSO oscillation in the tropics as La Niña conditions persisted throughout much of 2021. Relative to the multiyear average from 2001 to 2020, the 2021 global annual mean TOA flux anomalies are +0.05, 0.0, –0.75, and +0.70 W m⁻² for OLR, TSI, RSW, and total net flux (Table 2.9). These anomalies are within their respective 2-sigma interannual variability (Table 2.9) for this period.
<table><caption>Table 2.9. Global annual mean TOA radiative flux changes between 2020 and 2021, the 2021 global annual mean radiative flux anomalies relative to their corresponding 2001–20 mean climatological values, and the 2-sigma interannual variabilities of the 2001–20 global annual mean fluxes (all units in W m⁻²) for the outgoing longwave radiation (OLR), total solar irradiance (TSI), reflected shortwave (RSW), absorbed solar radiation (ASR, determined from TSI-RSW) and total net fluxes. All flux values have been rounded to the nearest 0.05 W m⁻² and only balance to that level of significance.</caption><thead><tr><th></th><th>One-Year Change<br>(2021 minus 2020)<br>(W m<sup>-2</sup>)</th><th>2021 Anomaly<br>(Relative to Climatology)<br>(W m<sup>-2</sup>)</th><th>Climatological Mean<br>(2001–20)<br>(W m<sup>-2</sup>)</th><th>Interannual Variability<br>(2001–20)<br>(W m<sup>-2</sup>)</th></tr></thead><tbody><tr><td>OLR</td><td>-0.65</td><td>+0.05</td><td>240.25</td><td>±0.65</td></tr><tr><td>TSI</td><td>+0.05</td><td>0.00</td><td>340.00</td><td>±0.15</td></tr><tr><td>RSW</td><td>-0.10</td><td>-0.75</td><td>99.00</td><td>±0.95</td></tr><tr><td>ASR</td><td>+0.15</td><td>+0.75</td><td>241.00</td><td>±0.90</td></tr><tr><td>Net</td><td>+0.80</td><td>+0.70</td><td>0.80</td><td>±0.80</td></tr></tbody></table>

Throughout 2021, the global monthly mean TOA OLR anomaly varied between positive and negative values (Fig. 2.48). OLR anomalies peaked at +0.7 W m⁻² in February, but otherwise fluctuated between ±0.5 W m⁻² the rest of the year. These results are consistent with NOAA HIRS (Lee and NOAA CDR Program 2011) and NASA AIRS (Susskind et al. 2012) OLR datasets (not shown). For the year as a whole, the 2021 global annual mean TOA OLR anomaly was +0.05 W m⁻². The global monthly mean TOA absorbed solar radiation (ASR, determined from TSI minus RSW) anomaly remained positive throughout 2021, peaking at +1.4 W m⁻² in June. For the year as a whole, the 2021 global annual mean TOA ASR anomaly was +0.75 W m⁻². The global monthly mean TOA total net anomaly, which is calculated from ASR anomaly minus OLR anomaly, also stayed positive throughout 2021, topping out at +1.8 W m⁻² in June. For the year as a whole, the 2021 global annual mean TOA total net anomaly was +0.70 W m⁻². The Earth energy imbalance (EEI) that had been observed prior to 2020 grew even larger during 2021, so that the doubling in observed EEI between 2005 and 2019 from both CERES and in

situ observations (Loeb et al. 2021) continued to substantially increase, primarily because of an increase in ASR radiation. It should be noted that a 21-year trend is not necessarily indicative of a longer-term trend. At this timescale, internal variability in the climate system can offset or augment any forced, longer-term trend in net radiation.

The TSI data were obtained from the Total Irradiance Monitor aboard the Solar Radiation and Climate Experiment (SORCE) mission (Kopp and Lean 2011), the Royal Meteorological Institute

<figure><img src="image_81.png" /><figcaption>Fig. 2.47. Annual average TOA flux differences between 2021 and 2020 for (a) outgoing longwave radiation (OLR) and (b) reflected shortwave (RSW) radiation (W m⁻²). The annual mean maps for 2021 were derived after adjusting Dec 2021 FLASHFlux v4A using the difference between EBAF and FF v4A in 2020.</figcaption></figure>
<figure><img src="image_82.png" /></figure>

Fig. 2.48. Time series of global monthly mean deseasonalized anomalies (W m⁻²) of TOA Earth radiation budget for out-going longwave radiation (OLR; upper), absorbed solar radiation (ASR, determined from TSI-RSW; middle), and total net (TSI-RSW-OLR; lower) from Mar 2000 to Dec 2021. Anomalies are relative to their calendar month climatology (2001–20). Time series shows the CERES EBAF Ed4.1 1° data (Mar 2000–Nov 2021) in red and the CERES FLASHFlux version 4A data (Dec 2021) in blue; see text for merging procedure (Sources: https://ceres-tool.larc.nasa.gov/ord-tool/jsp/EBAF41Selection.jsp and https://ceres-tool.larc.nasa.gov/ord-tool/jsp/FLASH_TISASelection.jsp.)

of Belgium composite dataset (Dewitte et al. 2004), and the Total Solar and Spectral Irradiance Sensor-1 (TSIS-1, Coddington, 2017) mission, all renormalized to the SORCE Version 15. The TOA RSW and TOA OLR data, which are constructed with measurements from the CERES instruments (Wielicki et al. 1996, 1998) aboard *Terra* and *Aqua* spacecraft, are based on the CERES EBAF (Energy Balanced and Filled) Ed4.1 product (Loeb et al. 2009, 2012, 2018a) for March 2000–November 2021 and the CERES Fast Longwave and Shortwave Radiative Fluxes (FLASHFlux) version 4A product (Kratz et al. 2014) for December 2021. The FLASHFlux to EBAF data normalization procedure (Stackhouse et al. 2016) results in 2-sigma monthly uncertainties of ±0.30, ±0.04, ±0.18, and ±0.48 W m⁻² for the OLR, TSI, RSW, and total net radiation, respectively.

## 2. MAUNA LOA APPARENT TRANSMISSION—J. A. Augustine, K. O. Lantz, H. Telg, J.-P. Vernier, and M. Todt

One of the longest records in the atmospheric sciences is the apparent atmospheric transmission at the Mauna Loa Observatory on Hawaii, which dates to 1958. Atmospheric transmission describes the fraction of solar radiation impinging at the top-of-atmosphere that is transmitted vertically to the surface. Because of its high altitude (3397 m a.s.l.), transmission at Mauna Loa is primarily affected by stratospheric aerosols, although tropospheric effects occur when dust from springtime storms in Asia pass over the island (Bodhaine et al. 1981). Since Mauna Loa is located near 20°N latitude, its transmission primarily reflects events in the Northern Hemisphere and is relatively uninfluenced by those in the Southern Hemisphere.

The updated time series of apparent transmission at Mauna Loa through 2021 is presented in Fig. 2.49. Recent trends show a gradual reduction in late 2017 and early 2018, caused by pyro-cumulonimbus in Canada, and the June 2019 eruption of Raikoke in the Kuril Islands, ~800 km northeast of Japan. In 2020, transmission stabilized at a low level that was maintained by the eruption of Taal in the Philippines in January, remnants of Raikoke, and California wildfires (Augustine et al. 2020, 2021).

The inset of Fig. 2.49 shows the new data for 2021. The annual average of 0.925 is 0.009 lower than the background. The transmission held steady in early 2021 from the relatively low levels of
<figure><img src="image_83.png" /></figure>

Fig. 2.49. Apparent transmission at Mauna Loa, Hawaii, from 1958 through 2021. Red dots are monthly averages of morning apparent transmission, the gray curve in the full time series plot is a LOWESS fit with a six-month smoother applied, and the horizontal dashed gray line is the average transmission for the clean period before the volcanic eruption of Mount Agung. Insert is an enlargement of the newest data for 2021.

the previous year. Significant aerosol activity began on 15 March with the largest sandstorm in the Gobi Desert since 2010, lofting dust up to 8 km (Gui et al. 2021). HYSPLIT trajectory analysis shows that dust plumes reached Mauna Loa in seven days. Volcanoes La Soufriere (Caribbean Island of St. Vincent) and Fukutoku-Okanoba (25 m below sea level, ~60 km south of Iwo Jima) erupted explosively during 9–12 April and 13 August, respectively. Fukutoku-Okanoba’s plume extended only to the lower boundary of the stratosphere, and trajectory analysis revealed its plume never reached Mauna Loa.

La Soufriere erupted at least 20 times on four consecutive days. Its plume was observed as high as 20 km by MISR, well into the lower stratosphere (LS), and HYSPLIT trajectory analysis showed that it reached Mauna Loa one month later. CALIPSO detected a strong signal between 21 and 23 km in mid-August between the equator and 35°N. Lidar observations on Mauna Loa detected a large signal just above 21 km on 20 August, and SAGE tracked that same signal in the stratosphere through October. Balloon-borne aerosol concentration measurements in Kansas (United States) in August and later in France detected high aerosol concentrations at 20–21 km. Similar observations in Boulder, Colorado, show near-normal concentrations in the LS through mid-April, followed by elevated values between 15 and 19 km through the end of the year, albeit decreasing a bit in December. It also shows a small peak near 21 km in early October. Together, these observations strongly suggest that La Soufriere’s plume circled the globe in the LS for the remainder of 2021.

Extensive pyro cumulonimbus activity occurred in the western United States and Canada in July and August. These events were associated with the drought and heat wave over the affected regions at that time (sections 2b4, Sidebar 7.1); however, CALIPSO and HYSPLIT analyses show the smoke plumes initially traveled northward and remained north of 30°N, well displaced from Mauna Loa.

Apparent transmission is calculated from the ratio of direct-normal pyrheliometer measurements at two solar elevations, following Ellis and Pueschel (1971). An average of three successive ratios, from morning pyrheliometer measurements made at 2, 3, 4, and 5 atmospheric pathlengths, is considered representative for the day. While any of these ratios are mathematically equivalent to a vertical transmission, variability at the longer pathlengths render the calculation an “apparent” transmission. Only morning data are used because upslope afternoon winds often carry sea level aerosols to the summit.
## Sidebar 2.1: Lightning—M. FÜLLEKRUG, E. WILLIAMS, C. PRICE, S. GOODMAN, R. HOLZWORTH, K. VIRTS, AND D. BUECHLER

The World Meteorological Organization (WMO) recently declared lightning flashes to be an essential climate variable (ECV), based on a recommendation by the Task Team on Lightning Observation for Climate Applications (TT-LOCA) as part of the Atmospheric Observation Panel for Climate (AOPC) of the WMO and the Global Climate Observing System (GCOS; Aich et al. 2018; WMO 2019a). This endorsement reinforces the WMO Integrated Global Observing System (WIGOS) Vision 2040 (WMO 2019b) toward the operational observation of lightning by space agencies during the coming decades.

Lightning flashes are generated by thunderstorms, which develop when hot and humid air destabilizes the atmosphere and enables deep convection. As a result, the lightning ECV is grouped with other ECVs describing the atmosphere (Bojinski et al. 2014) which are closely related to thunderstorm development, such as the Earth radiation budget, upper-air temperature, water vapor, wind speed, and cloud properties (see sections 2f1, 2b1, 2b5, 2d2, 2e2, and 2d6). The lightning ECV is also related to ECVs that impact atmospheric composition, such as lightning NOx and cloud condensation nuclei (see sections 2g3 and 2g6).

Lightning is a natural hazard associated with the severe weather impacts of thunderstorms including high wind speeds with falling trees and branches, intense precipitation causing flooding, large hail affecting transport vehicles and crops, and cloud-to-ground light-

ning which can lead to casualties, ignite wildland fires, and cause significant damage to infrastructure, such as power lines (Cooper and Holle 2019; Holle 2016). Lightning has significant societal implications for public safety (Holle et al. 1999),

power distribution (Piantini 2020), aviation (Ryley et al. 2020), and wildfires (Holzworth et al. 2021). Wildfires can increase convective instability for pyrocumulus to develop (Rudlosky et al. 2020; Liu et al. 2021; Augustine et al. 2021). Lightning is

<figure><img src="image_84.png" /><figcaption>Fig. SB2.1. Lightning observations from space. (a) Global distribution of lightning flash rate density (fl km⁻² yr⁻¹) for the period of record 1995–2021 from NASA’s low earth orbit lightning imagers OTD (Optical Transient Detector, May 1995–Apr 2000), TRMM LIS (Lightning Imaging Sensor, Jan 1998–Dec 2014) and ISS LIS (Feb 2017–Dec 2021). Global lightning is dominant over the continental tropical belt. (b) Monthly (solid black) and annual (blue) mean lightning flash rates (fl s⁻¹) observed by the TRMM and ISS LIS instruments within the ±38° latitudinal coverage of the TRMM orbit. The black dotted line is the combined mean monthly global flash rate (41.2 fl s⁻¹). The mean monthly flash rate varies from ~24 to 57 fl s⁻¹. The seasonal variations are due to the annual cycle of lightning activity linked to the larger land area of the Northern Hemisphere. (Source: Courtesy of the NASA Lightning Imaging Sensor Science Team.)</figcaption></figure>