# State of the climate in 2020: Global Climate

Robert Dunn, F. Aldred, Nadine Gobron, John Miller, Kate Willett, M. Ades, Robert Adler, Richard, Allan, Rob Allan, J. Anderson, et al.

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Robert Dunn, F. Aldred, Nadine Gobron, John Miller, Kate Willett, et al.. State of the climate in 2020: Global Climate. Bulletin of the American Meteorological Society, 2021, 102 (8), pp.S11-S142. 10.1175/BAMS-D-21-0098.1 . hal-04846883

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<figure><img src="image_1.png" /></figure>
# STATE OF THE CLIMATE IN 2020

## GLOBAL CLIMATE

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

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

Special Online Supplement to the *Bulletin of the American Meteorological Society* Vol.102, No. 8, August, 2021

doi:10.1175/BAMS-D-21-0098.1

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

©2021 American Meteorological Society

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.
# STATE OF THE CLIMATE IN 2020
## Global Climate

**Editors**

Jessica Blunden
Tim Boyer

**Chapter Editors**

Freya Aldred
Peter Bissolli
Howard J. Diamond
Matthew L. Druckenmiller
Robert J. H. Dunn
Catherine Ganter
Nadine Gobron
Gregory C. Johnson
Tim Li
Rick Lumpkin
Ademe Mekonnen
John B. Miller
Twila A. Moon
Ahira Sánchez-Lugo
Ted A. Scambos
Carl J. Schreck III
Sharon Stammerjohn
Richard L. Thoman
Kate M. Willett

**Technical Editor**

Andrea Andersen

**BAMS Special Editor for Climate**

Michael A. Alexander

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

Caption: Spring leaf emergence at Harvard Forest, Petersham, MA, USA, exemplified by the Witness Tree (Mapes 2017), a century-old red oak (*Quercus rubra*). Image courtesy of the PhenoCam Network.

Mapes, L.M. *Witness Tree: Seasons of Change with a Century-Old Oak*. Bloomsbury USA, New York. 240 pp.

Global Climate is one chapter from the *State of the Climate in 2020* annual report and is available from https://doi.org/10.1175/BAMS-D-21-0098.1 Compiled by NOAA's National Centers for Environmental Information, *State of the Climate in 2020* 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/10.1175/2021BAMSStateoftheClimate.1.

**How to cite this document:**

**Citing the complete report:**

Blunden, J. and T. Boyer, Eds., 2021: "State of the Climate in 2020". *Bull. Amer. Meteor. Soc.*, **102** (8), S1–S475, https://doi.org/10.1175/2021BAMSStateoftheClimate.1.

**Citing this chapter:**

Dunn, R. J. H., F. Aldred, N. Gobron, J. B Miller, and K. M. Willett, Eds., 2021: Global Climate [in "State of the Climate in 2020"). *Bull. Amer. Meteor. Soc.*, **102** (8), S11–S141, https://doi.org/10.1175/BAMS-D-21-0098.1.

**Citing a section (example):**

Tye, M. R., S. Blenkinsop, M. G. Bosilovich, M. G. Donat, I. Durre, A. J. Simmons, and M. Ziese, 2021: Land-based precipitation extremes [in "State of the Climate in 2020"). *Bull. Amer. Meteor.*, **102** (8), S57–S60, https://doi.org/10.1175/BAMS-D-21-0098.1.
**Ades, M.**, 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, F.**, Met Office Hadley Centre, Exeter, United Kingdom

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

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

**Anderson, J.**, Department of Atmospheric and Planetary Science, Hampton University, Hampton, Virginia

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

**Arosio, C.**, University of Bremen, Bremen, Germany

**Augustine, John A.**, NOAA/OAR Earth System Research Laboratory, Boulder, Colorado

**Azorin-Molina, C.**, Centro de Investigaciones sobre Desertificación - Spanish National Research Council, Moncada (Valencia), Spain; and Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

**Barichivich, J.**, Instituto de Geografia, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

**Beck, H. E.**, GloH2O, Almere, the Netherlands

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

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

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

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

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

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

**Bodin, X.**, 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

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

**Calmettes, B.**, Collecte Localisation Satellites (CLS), Ramonville Saint-Agne, France

**Carrea, Laura**, Department of Meteorology, University of Reading, Reading, United Kingdom

**Castia, Laura**, NOAA Climate Prediction Center, College Park, Maryland

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

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

**Chung, E.-S.**, Korea Polar Research Institute, Incheon, South Korea

**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; NOAA Chemical Sciences Laboratory, Boulder, Colorado

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

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

**Cretaux, J.-F.**, CNES/LEGOS, UMR5566, Université de Toulouse, Toulouse, France

**Crotwell, M.**, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado; NOAA/OAR Global Monitoring Laboratory, Boulder, Colorado

**Davis, Sean M.**, NOAA Chemical Sciences Laboratory, Boulder, Colorado

**de Jeu, Richard A. M.**, VanderSat B.V., Haarlem, The Netherlands

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

**Delaloye, R.**, Department of Geosciences, University of Fribourg, Fribourg, Switzerland

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

**Donat, Markus G.**, Barcelona Supercomputing Centre, Barcelona, Spain

**Dorigo, Wouter A.**, TU Wien - Vienna University of Technology, Vienna, Austria

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

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

**Dutton, Geoff S.**, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado; NOAA/OAR Global Monitoring Laboratory, Boulder, Colorado

**Duveiller, Gregory**, European Commission, Joint Research Centre, Ispra, Italy; currently at the Max Planck Institute for Biogeochemistry, Jena, Germany

**Elkins, James W.**, NOAA/OAR Global Monitoring Laboratory, Boulder, Colorado

**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; NASA Goddard Space Flight Center, Greenbelt, Maryland

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

**Garforth, J.**, Woodland Trust, Grantham, United Kingdom

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

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

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

**Hahn, S.**, TU Wien - Vienna University of Technology, Vienna, Austria

**Haimberger, Leopold**, Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria

**Hall, Brad D.**, NOAA/OAR Global Monitoring Laboratory, Boulder, Colorado

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

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

**Hirschi, M.**, ETH Zürich, Zürich, Switzerland

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

**Hrbacek, F.**, Department of Geography, Masaryk University, Brno, Czechia

**Hubert, Daan**, Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Hurst, Dale F., Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado; NOAA/OAR Global Monitoring Laboratory, Boulder, Colorado

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

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

John, Jiju 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

Kaiser, J. W., Satellite-based Climate Monitoring Unit, Deutscher Wetterdienst, Offenbach am Main, Germany

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

Kellerer-Pirklbauer, A., 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, R., Earth Observation Data Centre (EODC), Vienna, Austria

Kim, Hyungjun, Moon Soul Graduate School of Future Strategy, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

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

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

Kraemer, B. M., IGB Leibniz Institute for Freshwater Ecology and Inland Fisheries, Berlin, Germany

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

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

Lantz, Kathleen O., NOAA/OAR Earth System Research Laboratory, Boulder, Colorado

Lavers, D., 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, R., CESBIO (Université Toulouse 3, CNES, CNRS, INRAE, IRD), Toulouse, France

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

McCabe, M. 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; Australian Research Council Centre of Excellence for Climate Extremes, Sydney, New South Wales, Australia

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

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

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

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

Moesinger, L., TU Wien - University of Technology, Vienna, Austria

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

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

Mösinger, L., TU Wien - University of Technology, 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

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

O'Keefe, J., 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, T., NASA Ames Research Center, Moffett Field, California; Bay Area Environmental Research Institute, Moffett Field, California

Pasik, A. J., TU Wien - Vienna University of Technology, Vienna, Austria

Pellet, C., Department of Geosciences, University of Fribourg, Fribourg, Switzerland

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

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

Petron, G., Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado; NOAA/OAR Global Monitoring Laboratory, Boulder, Colorado

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

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

Polvani, L., Columbia University, New York, New York

Preimesberger, W., TU Wien - Vienna University of Technology, Vienna, Austria

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

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

Rayner, Nick A., Met Office Hadley Centre, Exeter, United Kingdom

Rémy, Samuel, HYGEOS, Lille, France

Ricciardulli, L., Remote Sensing Systems, Santa Rosa, California

Richardson, A. 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, Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

Rodríguez-Fernández, N. J., CESBIO (Université Toulouse 3, CNES, CNRS, INRAE, IRD), Toulouse, France

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

Roth, C., University of Saskatchewan, Saskatoon, Canada

Rozanov, A., University of Bremen, Bremen, Germany

Rutishäuser, T., Institute of Geography and Oeschger Center, University of Berne, Berne, Switzerland

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

Sawaengphokhai, P., Science Systems and Applications, Inc. (SSAI), Hampton, Virginia
Scanlon, T., TU Wien - University of Technology, Vienna, Austria

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

Schlegel, R. W., Sorbonne Université, CNRS, Villefranche-sur-mer, France

Sharma, S., York University, Toronto, Ontario, Canada

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

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, Colorado, USA, and NOAA/OAR Global Monitoring Laboratory, Boulder, Colorado

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

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

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

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

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

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

Stengel, Martin, Deutscher Wetterdienst, Offenbach, Germany

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

Sun-Mack, Sunny, Science Systems and Applications, Inc. (SSAI), Hampton, Virginia

Tans, P., NOAA/OAR Global Monitoring Laboratory, Boulder, Colorado

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

Thibert, E., Université Grenoble Alpes, INRAE, UR ETGR, Grenoble, France

Tokuda, D., 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, VanderSat B.V., Haarlem, The Netherlands

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

van der Vliet, M., VanderSat B.V., Haarlem, The Netherlands

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

Vance, A., Met Office Hadley Centre, Exeter, United Kingdom

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/OAR Global Monitoring Laboratory, Boulder, Colorado

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

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

Wang, Ray, Georgia Institute of Technology, Atlanta, Georgia

Weber, Markus, University of Bremen, Bremen, Germany

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

Wilber, Anne C., Science Systems and Applications, Inc. (SSAI), Hampton, Virginia

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

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

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

Woolway, R. Iestyn, European Space Agency Climate Office, ECSAT, Harwell Campus, Didcot, United Kingdom

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

Zhao, Guangyu, University of Illinois at Urbana-Champaign, Illinois

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

Zhou, Xinjia, Global Science and Technology, Inc., Greenbelt, Maryland

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

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

Zotta, R. M., TU Wien - University of Technology, Vienna, Austria

Editorial and Production Team

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

Andersen, Andrea, Technical Editor, Innovative Consulting 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 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. Overview</td><td>S19</td></tr><tr><td>b. Temperature</td><td>S26</td></tr><tr><td>1. Global surface temperature</td><td>S26</td></tr><tr><td>2. Lake surface water temperature</td><td>S28</td></tr><tr><td>3. Land and marine temperature extremes</td><td>S31</td></tr><tr><td>4. Tropospheric temperature</td><td>S34</td></tr><tr><td>5. Stratospheric temperature and winds</td><td>S37</td></tr><tr><td>Sidebar 2.1: Night marine air temperature</td><td>S39</td></tr><tr><td>c. Cryosphere</td><td>S42</td></tr><tr><td>1. Permafrost thermal state</td><td>S42</td></tr><tr><td>Sidebar 2.2: Rock glacier kinematics</td><td>S44</td></tr><tr><td>2. Northern Hemisphere continental snow cover extent</td><td>S46</td></tr><tr><td>3. Alpine glaciers</td><td>S47</td></tr><tr><td>4. Lake ice</td><td>S48</td></tr><tr><td>d. Hydrological cycle</td><td>S51</td></tr><tr><td>1. Surface humidity</td><td>S51</td></tr><tr><td>2. Total column water vapor</td><td>S53</td></tr><tr><td>3. Upper tropospheric humidity</td><td>S55</td></tr><tr><td>4. Precipitation</td><td>S56</td></tr><tr><td>5. Land-based precipitation extremes</td><td>S57</td></tr><tr><td>6. Lake water levels</td><td>S60</td></tr><tr><td>7. Cloudiness</td><td>S61</td></tr><tr><td>8. River discharge and runoff</td><td>S63</td></tr><tr><td>9. Groundwater and terrestrial water storage</td><td>S65</td></tr><tr><td>10. Soil moisture</td><td>S67</td></tr><tr><td>11. Monitoring global drought using the self-calibrating Palmer Drought Severity Index</td><td>S68</td></tr><tr><td>12. Land evaporation</td><td>S70</td></tr><tr><td>e. Atmospheric circulation</td><td>S71</td></tr><tr><td>1. Mean sea level pressure and related modes of variability</td><td>S71</td></tr><tr><td>2. Land and ocean surface winds</td><td>S73</td></tr><tr><td>3. Upper air winds</td><td>S77</td></tr></table>
## 2. Table of Contents

<table><tr><td>f. Earth radiation budget</td><td>S79</td></tr><tr><td>1. Earth radiation budget at top-of-atmosphere</td><td>S79</td></tr><tr><td>2. Mauna Loa apparent transmission</td><td>S82</td></tr><tr><td>g. Atmospheric composition</td><td>S83</td></tr><tr><td>1. Long-lived greenhouse gases</td><td>S83</td></tr><tr><td>2. Ozone-depleting substances</td><td>S87</td></tr><tr><td>3. Aerosols</td><td>S89</td></tr><tr><td>4. Stratospheric ozone</td><td>S92</td></tr><tr><td>5. Stratospheric water vapor</td><td>S95</td></tr><tr><td>6. Tropospheric ozone</td><td>S98</td></tr><tr><td>7. Carbon monoxide</td><td>S101</td></tr><tr><td>h. Land surface properties</td><td>S103</td></tr><tr><td>1. Land surface albedo dynamics</td><td>S103</td></tr><tr><td>2. Terrestrial vegetation dynamics</td><td>S104</td></tr><tr><td>3. Biomass burning</td><td>S106</td></tr><tr><td>4. Phenology of primary producers</td><td>S108</td></tr><tr><td>Sidbar 2.3: Long-term monitoring of vegetation state through passive microwave satellites</td><td>S110</td></tr><tr><td>Acknowledgments</td><td>S113</td></tr><tr><td>Appendix 1: Chapter 2 – Acronyms</td><td>S116</td></tr><tr><td>Appendix 2: Supplemental Materials</td><td>S119</td></tr><tr><td>References</td><td>S128</td></tr></table>

<footnote>*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.</footnote>
## 2. GLOBAL CLIMATE

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

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

For reasons other than the climate, 2020 was an extraordinary year. The COVID-19 pandemic has affected almost all of us, changing the lives of many people around the globe. While the economic disruption associated with COVID-19 led to modest estimated reductions of 6–7% (e.g., le Quere et al. 2020; Friedlingstein et al. 2020; BP Statistical Review of the World Energy 2021) in global anthropogenic carbon dioxide ($CO_2$) emissions, atmospheric $CO_2$ levels continued to grow rapidly—a reminder of its very long residence time in the atmosphere and the challenge of reducing atmospheric $CO_2$. As we show in this chapter, the climate has continued to respond to the resulting warming from these increases in $CO_2$ and other greenhouse gases such as methane and nitrous oxide, which also experienced record increases in 2020.

The year 2020 was one of the three warmest since records began in the mid-to-late 1800s, with global surface temperatures around 0.6°C above the 1981–2010 average, despite the El Niño–Southern Oscillation progressing from neutral to La Niña conditions by August (see section 4b). Lower tropospheric temperatures matched those from 2016, the previous warmest year. Meanwhile, stratospheric temperatures continued to cool as a result of anthropogenic $CO_2$ increases. Along with the above-average surface temperatures, an unprecedented (since instrumental records began) geographic spread of heat waves and warm spells occurred. Antarctica observed its highest temperature on record (18.3°C) at Esperanza in February. In August, Death Valley, California, reported the highest temperature observed anywhere on Earth since 1931 (preliminary value of 54.4°C).

Consequently, many permafrost measurement sites experienced their highest temperatures on record; Northern Hemisphere (NH) snow cover was below the 51-year average and the fourth-least extensive on record. Glaciers in alpine regions experienced their 33rd consecutive year of negative mass balance and 12th year of average losses of more than 500 mm depth. On average, NH lakes froze over 3 days later and thawed 5.5 days earlier than the 1981–2010 average during the 2019/20 winter, which was the third-shortest ice cover season since 1979/80.

The atmosphere responded to higher temperatures accordingly by holding more water. Total column water vapor was high relative to the 1981–2010 average, ranging from 0.75 to 1.06 mm over ocean and 0.58 to 0.94 mm over land, but did not reach the record values of 2016. At the surface, specific humidity over oceans was at record high levels (0.23 to 0.41 g kg⁻¹) and was well above average over land (0.14 to 0.36 g kg⁻¹). Conversely, relative humidity was well below average over land (–1.28 to –0.68 %rh), continuing the long-term declining trend. Precipitation increased compared to 2019, driven largely by land values, but there were few exceptional extreme precipitation events, coupled with below-average cloudiness over most of the land. More lakes showed positive water level anomalies than 2019, and in East Africa, Lake Victoria's level rose by over a meter due to a wet long-rains season. Soil moisture and terrestrial water storage showed stronger regional variations than in previous years, with East Africa and India being especially moist. Global drought area continued to increase for most of the year, reaching a peak in October, with the third-highest global land area experiencing extreme drought according to the Palmer Drought
Severity Index (6.8%). Despite progression to a neutral Indian Ocean dipole (IOD) this year, from a strongly positive IOD in 2019 (see section 4h), the western Indian Ocean and East Africa showed above-average hydrological cycle anomalies generally, including upper tropospheric humidity.

Greenhouse gas levels continued to rise, with the three primary greenhouse gases, CO₂, CH₄, and N₂O, all reaching their highest levels in at least 800,000 years. Radiative forcing from the long-lived greenhouse gases also reached a new record level of 3.2 W m⁻², with CO₂ being responsible for a large majority of the total. Tropospheric ozone, another greenhouse gas (although shorter lived and not well-mixed throughout the atmosphere), continued a modest upward trend, which was dominated by trends over and downwind of Asia. Stratospheric ozone, on the other hand, exhibited unusually large negative anomalies, especially in the Arctic and Antarctic. These large ozone depletions resulted mainly from stable polar vortices despite continued reduction in equivalent effective stratospheric chlorine, as calculated from tropospheric values of ozone-depleting substances.

COVID-19 impacts on the troposphere, at least at large spatial scales, were not readily apparent in 2020 anomalies. Modest anthropogenic CO₂ emissions reductions of perhaps 6–7% were too small to be identified on a background of large interannual CO₂ variability driven by the terrestrial biosphere. Carbon monoxide (CO) and aerosol optical depth anomalies could also not be clearly tied to COVID-19-related emissions reductions. However, significant CO and aerosol anomalies related to large fires in southeastern Australia, the western United States, and Siberia were evident.

The warmer temperatures were also felt in the biosphere, with an earlier start of season, later end of season, and hence, longer growing season as measured by the normalized difference vegetation index, for example, the United Kingdom had the earliest “first leaf” of the Pedunculate oak in a 20-year series. Anomalies of vegetation productivity reached a record positive peak in the NH. Overall, 2020 saw one of the lowest fire years in the record but regionally some locations experienced extreme fire activity, notably southeastern Australia, the Siberian Arctic, and western United States, as noted above.

Three new measurements are included as sidebars in this year's report. Night marine air temperature (NMAT) provides a useful independent comparison against sea surface temperature datasets to explore ongoing warming over oceans. Available NMAT observations (and marine humidity) have declined from around 7000 Voluntary Observing Ships in the 1980s to around 1000 at present, severely endangering our monitoring ability. Extending our cryosphere coverage, rock glacier kinematics, which is linked to the state of the permafrost, shows speeds in 2020 in the European Alps close to the maximum recorded. Increasing our monitoring of the biosphere, the final sidebar outlines the use of passive microwave satellite measurements for determining the vegetation properties via the amount of attenuation (the vegetation optical depth).

Time series and anomaly maps for many of the variables in this chapter are shown in Plates 1.1 and 2.1, respectively.
<figure><img src="image_3.png" /><figcaption>(c) Warm Days</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(e) Lower Tropospheric Temperature</figcaption></figure>

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

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

<figure><img src="image_7.png" /><figcaption>(d) Cool Nights</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(f) Night Marine Air Temperature</figcaption></figure>

<figure><img src="image_9.png" /></figure>

Plate 2.1. (a) NOAA NCEI Global land and ocean surface annual temperature anomalies (°C); (b) Satellite-derived lake surface water temperature anomalies (°C); (c) GHCNDEX warm day threshold exceedance (TX90p); (d) GHCNDEX cool night threshold exceedance (TN10p); (e) Average of RSS and UAH lower tropospheric temperature anomalies (°C). Hatching (stipling) denotes regions in which 2020 was the warmest year on record for UAH (RSS); (f) CLASSnmat night marine air temperature anomalies (°C); (g) HadISDH surface specific humidity anomalies (g kg⁻¹);
<figure><img src="image_10.png" /><figcaption>(j) Upper Tropospheric Humidity</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(l) Rx1day Anomalies</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(n) Cloudiness</figcaption></figure>

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

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

<figure><img src="image_15.png" /><figcaption>(m) Lake Water Level</figcaption></figure>

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

Plate 2.1. (cont.) (h) HadISDH surface relative humidity anomalies (%rh); (i) ERA5 TCWV anomalies (mm). 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) Lake water level anomalies (m); (n) PATMOS-x/AVHRR+HIRS global cloudiness anomalies (%);
<figure><img src="image_17.png" /><figcaption>(q) Terrestrial Water Storage</figcaption></figure>

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

<figure><img src="image_19.png" /><figcaption>(p) River Discharge</figcaption></figure>

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

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

<figure><img src="image_22.png" /></figure>

Plate 2.1. (cont.) (o) ELSE (Ensemble Land State Estimator) global distribution of runoff anomalies (mm yr⁻¹); (p) ELSE global distribution of river discharge anomalies (m³ s⁻¹); (q) GRACE and GRACE-FO difference in annual-mean terrestrial water storage between 2019 and 2020 (cm); (r) ESA CCI average surface anomalies (m³ m⁻³); (s) Mean scPDSI for 2020. Droughts are indicated by negative values (brown), wet episodes by positive values (green); (t) GLEAM land evaporation anomalies (mm yr⁻¹);
<figure><img src="image_23.png" /><figcaption>(w) Upper Air (850-hPa) Northward Winds (SOND)</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>(y) AOD Ratio</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>(aa) Stratospheric (Total Column) Ozone</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>(v) Surface Winds</figcaption></figure>

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

<figure><img src="image_28.png" /><figcaption>(z) Extreme Aerosol Days</figcaption></figure>

<figure><img src="image_29.png" /></figure>

Plate 2.1. (cont.) (u) ERA5 surface pressure anomalies (hPa); (v) Surface wind speed anomalies (m s⁻¹) from the observational HadISD3 dataset (land, circles), the MERRA-2 reanalysis output (land, shaded areas), and RSS satellite observations (ocean, shaded areas); (w) ERA5 Sep–Dec average 850-hPa northward wind speed anomalies (m s⁻¹); (x) Total aerosol optical depth (AOD) anomalies at 550 nm; (y) Ratio of total AOD at 550 nm in 2020 relative to 2003–19; (z) Number of days with AOD above the 99.9th percentile; (aa) GOME2 total column ozone anomalies (DU; using GOME, SCIAMACHY, GOME-2A and -2B [GSG]);
<figure><img src="image_30.png" /><figcaption>(ad) Land Surface Albedo in the Visible</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>(af) Fraction of Absorbed Photosynthetically Active Radiation</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>(ah) Vegetation Optical Depth</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>(ac) Carbon Monoxide</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>(ae) Land Surface Albedo in the Near-Infrared</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>(ag) Carbon Emissions from Biomass Burning</figcaption></figure>

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

Plate 2.1. (cont.) (ab) OMI/MLS tropospheric ozone column anomalies for 60°S-60°N (DU); (ac) CAMS reanalysis total column CO anomalies (%); (ad) Land surface visible broad-band albedo anomalies (%); (ae) Land surface near-infrared albedo anomalies (%); (af) FAPAR anomalies; (ag) GFAS1.4 carbonaceous emission anomalies (g C m⁻² yr⁻¹) from biomass burning; (ah) VODCA Ku-band VOD anomalies.
## b. Temperature

### 1) Global surface temperature— A. Sánchez-Lugo, C. Morice, J. P. Nicolas, and A. Argüez

The year 2020 was one of the three warmest years since global records began in the mid-to-late 1800s, with a global land and ocean surface temperature 0.54°–0.62°C above the 1981–2010 average, according to five global temperature datasets (Table 2.1). These datasets consist of three independent global in situ surface temperature analyses (NASA-GISS, Lenssen et al. 2019; HadCRUT5, Morice et al. 2021; NOAAGlobalTemp, Zhang et al. 2019) and two global atmospheric reanalyses (ERA5, Hersbach et al. 2020; JRA-55, Kobayashi et al. 2015). Depending on the dataset, 2020 was either the warmest year on record, 2020 was tied with 2016 as the warmest on record, 2020 was the second-warmest year on record, or 2020 was the third-warmest (Fig. 2.1).

The year began in El Niño–Southern Oscillation (ENSO)-neutral conditions, transitioning to La Niña by August (see section 4b for details). The global monthly temperature anomalies were high throughout 2020, with each month from January through November ranking among the five warmest for each respective month across all datasets. December had the smallest temperature anomaly of the year. Despite the slightly cooler end to the year, this was the warmest non-El Niño year on record, surpassing 2017 by 0.07°–0.11°C. Furthermore, the five datasets agree that the last 7 years (2014–20) were the seven warmest years on record.

Even though each dataset might differ slightly on the yearly rankings and anomalies, it is worth noting that these differences are small and that, overall, temperature anomalies for each dataset are in close agreement. The three global in situ surface temperature analyses assessed here are derived from air temperatures observed at weather stations over land and sea surface temperatures (SSTs) observed from ships and buoys. The differences between each analysis are mainly due to how each methodology treats areas with little to no data and how each analysis accounts for changes in measurement methods (for more details see Kennedy et al. 2010; Hansen et al. 2010; Huang et al. 2015; Sánchez-Lugo et al. 2017). The global average surface temperature has increased at an average rate of 0.08°C decade⁻¹ since 1880 with a rate more than twice as high since 1981 (0.19°–0.20°C decade⁻¹, depending on the dataset).

Unlike the global in situ surface temperature analyses, 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. These characteristics give reanalyses a unique ability to produce globally-complete temperature fields in a physically consistent manner; however, these datasets can also suffer from regional model biases and the effects of changes in the observation network over time (Simmons et al. 2017, 2021). Nonetheless, surface temperatures from reanalyses should be consistent with in situ analyses in regions of good observational coverage. One of the reanalyses used here, ERA5, provides data from 1950 onward, but because of lower confidence in its surface temperature data prior to 1967 (Simmons et al. 2021), only data from 1967 onward are shown. In addition, temperatures

<table><caption>Table 2.1. Temperature anomalies (°C) and uncertainties (where available) for 2020 with respect to the 1981–2010 base period. Where uncertainty ranges are provided, temperature anomalies correspond to the central values of a range of possible estimates. Uncertainty ranges represent a 95% confidence interval. Note that for HadCRUT5, land values were computed using the CRUTEM 5.0.1.0 dataset (Osborn et al. 2021), ocean values were computed using the HadSST4.0.0.0 dataset (Kennedy et al. 2019), and global land and ocean values used the HadCRUT5.0.1.0 dataset (Morice et al. 2021).</caption><thead><tr><th>Global</th><th>NASA-GISS<br>(°C)</th><th>HadCRUT5<br>(°C)</th><th>NOAA<br>GlobalTemp<br>(°C)</th><th>ERA5<br>(°C)</th><th>JRA-55<br>(°C)</th></tr></thead><tbody><tr><td>Land</td><td>+0.97</td><td>+0.85 ± 0.13</td><td>+0.95 ± 0.14</td><td>+0.99</td><td>+0.88</td></tr><tr><td>Ocean</td><td>+0.37</td><td>+0.42 ± 0.07</td><td>+0.39 ± 0.16</td><td>+0.47</td><td>+0.41</td></tr><tr><td>Land and Ocean</td><td>+0.60<br>±0.05</td><td>+0.57 ± 0.08</td><td>+0.54 ± 0.15</td><td>+0.62</td><td>+0.54</td></tr></tbody></table>
<figure><img src="image_37.png" /></figure>

Fig. 2.1. Global average surface air temperature anomalies (°C; 1981–2010 base period). In situ estimates are shown from NOAA/NCEI (Zhang et al. 2019), NASA-GISS (Lenssen et al. 2019), HadCRUT5 (Morice et al. 2021), CRUTEM5 (Osborn et al. 2021), and HadSST4 (Kennedy et al. 2019). Reanalyses estimates are shown from ERA5 (Hersbach et al. 2020) and JRA-55 (Kobayashi et al. 2015). Please note change in x-axis scale pre/post 2000.
over the Great Lakes are adjusted as described in Simmons et al. (2021) to correct for a produc-
tion error. This correction has a negligible impact on the global average temperature. The other
reanalysis, JRA-55, provides data from 1958 onward. The JRA-55 global average temperature is
computed as in Simmons et al. (2017, 2021), that is by using JRA-55 analysis temperature over land
and its background temperature over ocean and other water bodies. For both reanalyses, the 2-m
air temperature is used over both land and ocean whereas the global in situ analyses use SST
over ocean. This difference is expected to have only a very small impact on the global averages
assessed here (see Fig. 1 of Simmons et al. 2017).

While annual temperature rankings provide an intuitive measure of the state of global tem-
peratures, a recently introduced global annual temperature score (Arguez et al. 2020) comple-
ments the annual temperature ranking by providing a basic characterization of the impacts of
interannual variability on global temperature relative to the sustained upward trend since the
mid-1970s. Scores range from 1 to 10, with a score of 1 (10) indicating the coldest (warmest) 10%
of anomalies relative to the trend. In an era of seemingly perpetual near-record warm rankings,
the annual temperature scores can help characterize whether the annual temperature ranking at-
tained in a given year was due primarily to the secular trend, interannual variability, or both. For
example, 2016 was not only the warmest year on record, but it also exhibited a temperature score
of 10, whereas 2014 previously attained a ranking of warmest yet exhibited a temperature score
of 4 (on the colder half of the scale). This indicates that, on top of the secular trend, interannual
variability had a prominent contribution to the record temperature in 2016, whereas interannual
variability did not synergistically contribute to 2014's previous record temperature. Using global
annual time series from 1975 through 2020, the year 2020 registers a global annual temperature
score of 9 (corresponding to the 80th–90th percentile) in the NASA-GISS and NOAAGlobalTemp
datasets and a score of 8 (70th–80th percentile) in the HadCRUT5 dataset. This indicates that
2020, much like 2019, was moderately-to-considerably warmer than would be expected due to
the secular trend alone, suggesting that its ranking of warmest or second warmest for the three
in situ datasets was enhanced by the effects of the interannual variability.

Separately, the global land surface temperature for 2020 was the highest in four of the five
datasets, surpassing the previous record set in 2016 by 0.05°–0.11°C. The fifth dataset (JRA-55)
has the global land surface temperature tying with 2016 as the highest. The globally averaged
SST was either third or fourth highest on record, depending on the dataset.

The year was characterized by higher-than-average temperatures across much of the globe (Plate 2.1a; Appendix Figs. A2.1–A2.4). The most notable feature of 2020 is the very large positive temperature anomalies (+4.0°C or higher above the 1981–2019 base period) over Arctic Siberia and the adjacent sector of the Arctic Ocean (Appendix Fig. A2.2). Large positive anomalies (+2.0°C or higher) are also found across northern Europe, northern Asia, and the North Pacific Ocean. In contrast, average to below-average conditions were limited to the central and eastern tropical Pacific Ocean and across parts of northern North America, subpolar North Atlantic, and the southern Indian Ocean.

2) Lake surface water temperature—L. Carrea, C. Merchant, B. Calmettes, and J.-F. Cretaux

In 2020, the worldwide averaged satellite-derived lake surface water temperature (LSWT)
warm-season anomaly was +0.11°C with respect to the 1996–2016 baseline. The mean warming
trend during 1996–2020 was 0.22 ± 0.01°C decade⁻¹, broadly consistent with previous analyses
(Woolway et al. 2017; Woolway et al. 2018; Carrea et al. 2019, 2020). On average, anomalies in
2020 were only 0.01°C higher than in 2019. The warm-season anomalies for each lake are shown
in Plate 2.1b. Lake mean temperature anomalies were positive for 55% of lakes and negative for
45%. Some lakes in eastern Africa recorded notable positive anomalies for both LSWT and lake
water level (LWL; section 2d6). The LWL is defined as the height, in meters above the geoid (the
shape that the surface would take under the influence of the gravity and rotation of Earth), of
the reflecting surface. Changes in lake water levels can be critical, as they affect water quantity and quality, food stocks, recreational opportunities, and transportation.

Globally, distinct regions of coherent warm and cool LSWT anomalies can be identified in 2020. Lakes in subtropical eastern China were markedly warm, with the three largest warm anomalies (+2.54°C, +2.39°C, +2.38°C) in this region. In northern Europe, Canada, the southeastern United States, and southeastern Australia, negative anomalies were observed for 70% or more of the water bodies, while southern Europe, Alaska, the Middle East, northern Russia, and eastern Africa had positive anomalies.

Four regions are considered here in more detail: Canada (number of lakes, n = 246, Fig. 2.2); Europe (n = 127, Fig. 2.2); Tibet (n = 104, Fig. 2.2); and Africa (n = 70). The boreal warm season (July–September) LSWT calculated from the satellite data shows a warming tendency of +0.39 ± 0.01°C decade⁻¹ in Europe (Fig. 2.2a) and +0.18 ± 0.01 °C decade⁻¹ in Canada (Fig. 2.2d). In Africa and Tibet, the tendency is closer to neutral (Figs. 2.2b,c). In Canada, 166 lakes had negative anomalies and 80 had positive in 2020, with an overall average of −0.22°C. In Tibet, 72% of the lakes had moderate-positive anomalies and 28% had negative anomalies, with an average of +0.20°C. In Europe, cool anomalies in northern Europe (67 lakes) balanced warmer anomalies in the south (60 lakes), producing +0.03°C on average. In Africa, positive anomalies were recorded for 80% of the 70 lakes over the considered period. Several of the warmest anomalies occurred in eastern Africa, where the LWL was also consistently higher than the 1996–2016 average. Therefore, for some of the eastern African lakes, LSWT was compared with their LWL anomalies, calculated using a time series of LWL changes obtained from satellite altimetry.

<figure><img src="image_38.png" /><figcaption>Fig. 2.2. Satellite-derived warm-season lake surface water temperature anomalies (°C; 1996–2016 base period) per year from 1995 to 2020 for (a) Europe, (b) Africa (c) Tibet, and (d) Canada and per-lake temperature anomalies in 2020 (colored dots) in (e) Europe, (f) Canada, and (g) Tibet. These values were calculated for the warm season (Jul–Sep in the NH; Jan–Mar in the SH; Jan–Dec in the tropics).</figcaption></figure>
Figure 2.3 presents a selection of African lakes (Victoria, Tanganyika, Malawi, Turkana, Rukwa, Albert, Kyoga, Edward, Mweru, Tana, and Bangweulu) for which the LSWT and the LWL normalized anomalies from 1996 to 2020 are reported for each of the lakes, together with the spatial distribution of the 2020 LSWT anomalies. All the lakes exhibit positive LWL anomalies in 2020, while Lakes Turkana, Edward, and Rukwa have notably high LSWT positive anomalies. For these lakes, the LSWT 2020 anomalies were consistently positive across their full spatial extent, while there was a mix of positive and negative anomalies spatially across other lakes. Most of the lakes exhibited an upward long-term trend for both the LSWT and LWL.

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

The 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, and the Sea and Land Surface Temperature Radiometers (SLSTRs) on Sentinel3A and 3B. 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 LWL observations for 11 African lakes were analyzed where long time series are available from radar altimetry (Cretaux et al. 2011). The LWL were validated using a set of in situ data over lakes in South America, North America, Russia, and Europe (Ričko et al. 2012). For lakes with sizes comparable to those in East Africa, the accuracy is generally within 0.1 m (Cretaux et al. 2018; Quartly et al. 2020).

The satellite-derived LSWT data are spatial averages for each of 947 lakes, for which high-quality temperature records were available in 2020. The satellite-derived LSWT data were validated with in situ measurements with an average satellite minus in situ temperature difference less than

<figure><img src="image_39.png" /><figcaption>Fig. 2.3. Satellite-derived lake surface water temperature (LSWT) and lake water level (LWL) normalized anomalies relative to the 1996–2016 period from 1995 to 2020 for 11 lakes in East Africa, together with the spatial distribution of the 2020 LSWT anomalies (in °C) for the same lakes.</figcaption></figure>
0.5°C and, consequently, a good agreement was found. Lake-wide average surface temperatures have been shown to provide a more representative picture of LSWT responses to climate change than single-point measurements (Woolway and Merchant 2018).

3) Land and marine temperature extremes—S. E. Perkins-Kirkpatrick, R. J. H. Dunn, R. W. Schlegel, M. G. Donat, and Michael G. Bosilovich.

Averaged over global land regions using the Global Historical Climatology Network-Daily dataset (GHCNDEX; Donat et al. 2013), 2020 recorded the highest number of days where the maximum temperature was above the climatological 90th percentile (TX90p, “warm days”; Fig. 2.4). There were over 70 days, which is almost double the average of 36.5 days during 1961–90, and 10 days more than 2019. The number of cool nights (TN10p, where the minimum temperature was below the 10th percentile) was lower than the 1961–90 average, at just over 20 nights throughout the year. This was below average compared to the last 70 years but comparable to the recent decade.

<figure><img src="image_40.png" /><figcaption>Fig. 2.4. Time series of (a) TX90p (warm days) and (b) TN10p (cool nights) from GHCNDEX relative to 1961–90. The red dashed line shows a binomial smoothed variation and red shading the coverage uncertainties estimated using ERA5 following Brohan et al. (2006). The dotted black line shows the percentage of land grid boxes with valid data in each year. Time series of (c) TX90p (warm days) and (d) TN10p (cool nights) from ERA5 relative to 1981–2010.</figcaption></figure>
The behavior of the GHCNDEX time series is comparable to the more spatially complete ERA5 dataset (Fig. 2.4; Hersbach et al. 2019) for the last 40 years.

More detail on regional extreme temperature events is available in Chapter 7. There was a high number of warm days during 2020 in Europe, China, and northeast Australia compared to average (Plate 2.1c), and the number of warm days was larger than the number of cool nights almost everywhere (Plate 2.1d). Many extreme maximum temperatures were recorded (Table 2.2), several of which are described in the following text, and others discussed in Chapter 7 and the World Meteorological Organization (WMO) *State of the Global Climate* (2021).

<table><caption>Table 2.2. Examples of extreme maximum temperatures in 2020 described in this section.</caption><thead><tr><th>Country</th><th>Location</th><th>Date</th><th>Value (°C)</th><th>Notes</th></tr></thead><tbody><tr><td>UK</td><td>Heathrow</td><td>31 Jul</td><td>37.8</td><td>Third-hottest UK day</td></tr><tr><td>Spain</td><td>Around Seville</td><td>5 Mar</td><td>36</td><td>—</td></tr><tr><td>Russia</td><td>Verkhoyansk (Siberia)</td><td>20 Jun</td><td>38</td><td>Hottest regional day (Provisional Arctic Circle record)</td></tr><tr><td>United States</td><td>Furnace Creek, Death Valley, California</td><td>16 Aug</td><td>54.4</td><td>Globally third-hottest day on record</td></tr><tr><td>United States</td><td>Woodland Hills, Los Angeles</td><td>6 Sep</td><td>49.4</td><td>—</td></tr><tr><td>Canada</td><td>Montreal</td><td>27 May</td><td>36.6</td><td>Hottest May</td></tr><tr><td>Canada</td><td>Miramichi</td><td>19 Jun</td><td>37.2</td><td>New annual record</td></tr><tr><td>Iraq</td><td>Basra</td><td>27 and 28 Jul</td><td>53</td><td>—</td></tr><tr><td>Iraq</td><td>Baghdad</td><td>28 Jul</td><td>51.8</td><td>New record</td></tr><tr><td>Lebanon</td><td>Houch al-Oumara?</td><td>28 Jul</td><td>45.6</td><td>New record</td></tr><tr><td>Syria</td><td>Damascus</td><td>29 Jul</td><td>46</td><td>New record</td></tr><tr><td>Japan</td><td>Hamamatsu</td><td>18 Aug</td><td>41.4</td><td>Equal record</td></tr><tr><td>Australia</td><td>Sydney</td><td>29 Nov</td><td>25.4</td><td>Hottest November night</td></tr><tr><td>Antarctica</td><td>Casey</td><td>24 Jan</td><td>9.2</td><td>New record</td></tr><tr><td>Antarctica</td><td>Esperanza Base</td><td>6 Feb</td><td>18.3</td><td>New Antarctic record</td></tr></tbody></table>

In the United States, Furnace Creek in Death Valley (California) recorded a yet-to-be certified temperature of 54.4°C—the hottest temperature measured on Earth since 1931—on 16 August during a heat wave that affected the western and midwestern states. Another heat wave hit the southwest in early September where the extreme heat fueled wildfires (see sections 2h3, 7b2) and set new records. In Canada, Montreal and Burlington experienced six consecutive days at 32°C during a June heat wave. This heat wave lasted into July, enhancing conditions for wildfires in the Quebec province and seeing numerous daily maximum temperature records broken. Many locations in South America experienced extreme temperatures during September and October, with multiple records broken. Concepción, Paraguay, reached 42.6°C on 2 October; Sao José de Chiquitos, Bolivia, reached 43.4°C on 8 October, and Sao Paulo, Brazil, recorded four of its five highest daily maximum temperatures on record during this time (see sections 7d2, 7d3). Extreme heat also occurred over the Caribbean and Mexico during April. Daily maximum temperatures reached 39.7°C and 48.8°C at Veguitas, Cuba, and Gallinas, Mexico, respectively, on 12 April.

A protracted extreme temperature event occurred over Siberia during the first half of 2020. Heat wave frequency (HWF) and magnitude (HWM) indices for April–June over Siberia were the largest in the MERRA2 record (Collow et al. 2020; Fig. 2.5). The long-term and widespread heat helped
<figure><img src="image_41.png" /><figcaption>Fig. 2.5. MERRA-2 Apr-Jun seasonal heatwave frequency (HWF: count) and heatwave magnitude (HWM; °C; Callow et al. 2020) area averaged for the Siberian region affected by anomalous heat wave conditions (60°–160°E, 50°–80°N, land only).</figcaption></figure>

fuel large wildfires in the region (see sections 2h3, 7g2, Sidebar 5.1). Verkhoyansk recorded 38.2°C on 22 June, provisionally the highest temperature ever measured within the Arctic Circle.

Antarctica experienced a period of record-breaking temperatures during 23–26 January 2020 at Casey, where minimum temperatures did not fall below 0°C and the highest ever daily maximum and minimum temperatures were recorded (9.2°C and 2.5°C, respectively). On 6 February, the highest Antarctic temperature on record was measured at Esperanza Base (18.3°C; see section 6b, Sidebar 6.1). This was part of a warm spell lasting from 5–13 February, causing widespread glacial melting.

During early August, record warm nights were widespread across the United Kingdom, with parts of the southeast experiencing five consecutive tropical nights (temperatures greater than 20°C) and 6 days with peak temperatures over 34°C (see section 7f2). A heat wave (defined as a period of three or more consecutive TX90p days [Perkins and Alexander 2013]) affected Spain and Portugal in May (see section 7f5).

An intense heat wave occurred over the Middle East during July, with daily maximum temperatures reaching over 53°C in Basra (Iraq) on both the 27th and 28th and widespread maximum temperatures over 45°C. During a heat wave in August, Tokyo experienced three consecutive days of maximum temperature above 35°C and multiple locations in central southwest Japan recorded temperatures above 39°C on 17 August. Numerous large-scale heat waves occurred over Vietnam, with maximum daily temperatures greater than 35°C over large parts of the country during June.

Australia had a warm start to 2020, with its second-warmest summer (December 2019–February 2020) on record for maximum and minimum temperature (2.11°C and 1.64°C above the 1961–90 average, respectively). On 4 January the Sydney suburb of Penrith recorded 48.9°C, the hottest temperature ever recorded across all Australian metropolitan areas. The country also experienced an anomalously warm spring, with records for nationally averaged minimum spring (September–November) and November temperatures (1.91°C and 2.9°C above average, respectively). Numerous local maximum temperature records across the southeast were also broken during November (see section 7h4 for details).

Marine heatwaves (MHW) are defined as SST above the climatological 90th percentile for five or more days (Hobday et al. 2016). Categories of MHW are defined in Hobday et al. (2018). Using NOAA OISST v2 (Banzon et al. 2020), 84% of the surface of the ocean experienced at least one MHW in 2020 (Fig. 2.6). Category 2 – Strong events were the most common (45%), vastly exceeding Category 1 – Moderate events (28%), marking the seventh consecutive year that Category 2 – Strong MHWs have been the dominant category. The ocean experienced a global average of 77 MHW days, exceeding the 2019 average of 74 days, but fewer than the 2016 record of 83 days (Fig. 2.6). On average, 21% of the surface of the ocean in 2020 was experiencing a MHW on any given day (Fig. 2.6). This is slightly higher than the 2019 average of 20%, but lower than the 2016 record of 23%. Roughly the entire surface of the ocean experienced at least one MHW in 2020, with the exception of the equatorial Pacific Ocean. This is likely because heat anomalies in the equatorial Pacific Ocean are tightly linked with the ENSO, which was in a neutral or moderately
<figure><img src="image_42.png" /></figure>

MHW category summaries: 1982–2020
NOAA OISST; Climatology period: 1982–2011

**Fig. 2.6.** Annual global marine heatwave (MHW) occurrence from NOAA OISST using a climatology base period of 1982–2011. (a) Daily average percent of the ocean that experienced an MHW. (b) Total percent of the ocean that experienced an MHW at some point during the year. The values shown are for the highest category of MHW experienced by each ocean grid cell during 2020. (c) Total average of daily MHW occurrence throughout the entire ocean.

negative phase in 2020 (see section 4b). The subpolar North Atlantic, southeast of Greenland, was another area that did not experience MHWs in 2020, a pattern persistent from 2014–18.

To show temperature extremes over land, we use a subset of the moderate extremes indices developed by the WMO Expert Team in Climate Change Detection and Indices (ETCCDI; Zhang et al. 2011). In the GHCNDEX dataset (Donat et al. 2013), daily temperature values from the GHCND (Menne et al. 2012) are interpolated onto a regular 2.5° grid. As in previous years, the spatial coverage is sparse (Plates 2.1c,d) and restricted to North America, parts of Eurasia, and Australia. To fill the gaps, we use the ERA5 reanalysis (Hersbach et al. 2019), though we have not included the preliminary release of the extension from 1950 to 1978. With the shorter temporal coverage, the reference period for the extremes indices is 1981–2010 (compared to 1961–90 for GHCNDEX), which can lead to differences when comparing recent trends (Dunn et al. 2020a; Yosef et al. 2021). Siberian heat waves were calculated from the MERRA-2 dataset (Gelaro et al. 2017; Collow et al. 2020). HWF frequency is the count of days satisfying heat wave conditions, where heat waves are defined as the MERRA-2 daily mean 2-m temperature exceeding the calendar day 90th percentile for at least three consecutive days. HWM magnitude is the average daily mean 2-m temperature anomaly over all heat wave days.

### 4) Tropospheric temperature—S. Po-Chedley, J. R. Christy, L. Haimberger, and C. A. Mears

The 2020 annual global lower tropospheric temperature (LTT) tied with 2016 as the highest on record. The annual average LTT was 0.49°–0.72°C above the 1981–2010 average, depending on dataset, and 10%–16% of Earth's surface experienced record high temperatures (Plate 2.1e). Such expansive and record warmth is notable because it occurred even though the ENSO exhibited neutral or La Niña conditions throughout the year (Fig. 2.7a) and is thus consistent with the background upward trend since 1958. In the past, record warm and cold tropospheric temperatures have typically followed El Niño and La Niña events, respectively (Figs. 2.7a,b). A La Niña pattern was established in August, which will likely depress the LTT in 2021 because tropospheric temperature lags ENSO by several months.

More than 90% of Earth's lower troposphere experienced above-average temperatures (Plate 2.1e). Regions experiencing record warmth included much of Europe and Russia, the Indian Ocean, the northeast and South Pacific, and a region off the coast of East Antarctica. Limited areas of below-average LTT included Canada, Greenland, and parts of Antarctica and the Southern Ocean.

Above-average lower tropospheric temperatures are consistent with long-term greenhouse gas warming and less-pronounced volcanic cooling over the past 3 decades (relative to significant cooling from the eruptions of Agung, El Chichón, and Pinatubo in 1963, 1982, and 1991, respectively;
<figure><img src="image_43.png" /><figcaption>Fig. 2.7. (a) Sea surface temperature anomaly (°C) in the Niño 3.4 region in the central equatorial Pacific. (b) Fraction of Earth (%) with record warm (red) and cold (blue) monthly LTT values. The width of the line represents the difference between the UAH and RSS datasets.</figcaption></figure>

e.g., Santer et al. 2014; Christy and McNider 2017). Recent warmth is recorded by in-situ radiosonde (balloon-borne), microwave (satellite), and reanalysis datasets (Fig. 2.8). Global and tropical tropospheric temperature (TTT) trends since 1958 and 1979 are approximately +0.18°C decade⁻¹ (Table 2.3). While the individual tropospheric temperature datasets are broadly consistent, the temperature time series and trends vary across datasets, by altitude (TTT samples temperature at higher altitudes than LTT), and by region. As noted above, 2016 and 2020 are statistically tied for the warmest year on record; the average annual near-global LTT across all eight datasets was 0.68°C and 0.65°C above the climatological normal, respectively. In individual datasets, 2016 was the warmest year in the RATPACvA2, RICHv1.7, and RAOBCORE v1.7 radiosonde datasets (Free et al. 2004; Haimberger et al. 2012), the UAH v6.0 satellite product (Spencer et al. 2017), and JRA-55 reanalysis (Kobayashi et al. 2015). 2020 was the warmest year in the RSS v4.0 satellite product (Mears and Wentz 2016) and the ERA5 and MERRA-2 reanalyses (Hersbach et al. 2020; Gelaro et al. 2017). Structural uncertainty in satellite dataset construction can also affect the spatial pattern of record warm temperatures. RSS has a larger global surface area of record warm LTT values in 2020 compared to the UAH dataset (16% versus 10%, respectively; Plate 2.1e).

The tropical troposphere is expected to experience substantial warming in response to the increasing concentration of atmospheric carbon dioxide (CO₂; Flato et al. 2013). Simulations of satellite era tropical and global tropospheric warming in the most recent generation of climate models generally exhibit substantially greater warming than observations (McKittrick and Christy 2018, 2020). Over 1979–2014, the multimodel average TTT trend is +0.30°C decade⁻¹, while satellite-derived trends range from 0.09° to 0.20°C decade⁻¹ (Po-Chedley et al. 2021). The difference in the rate of warming is partially attributable to Pacific decadal climate variability, which has reduced warming in the observed record (Po-Chedley et al. 2021). Such internal climate variability is random and is only captured by chance in climate model simulations. A number of individual model realizations simulate similar Pacific decadal climate variability and approximately 13% (24%) have tropical (global) tropospheric temperature trends that are within the range of satellite observations (Po-Chedley et al. 2021). Other possible drivers of this model-observational
<figure><img src="image_44.png" /></figure>

Fig. 2.8. Monthly average lower tropospheric temperature (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.

<table><caption>Table 2.3. Temperature trends (°C decade⁻¹) for near-global lower tropospheric temperature (LTT) and tropical tropospheric temperature (TTT) over 1958–2020 and 1979–2020.</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>0.19</td><td>0.21</td><td>0.16</td><td>0.17</td></tr><tr><td>RAOBCOREv1.7</td><td>0.18</td><td>0.19</td><td>0.15</td><td>0.15</td></tr><tr><td>RICHv1.7</td><td>0.20</td><td>0.21</td><td>0.17</td><td>0.19</td></tr><tr><td rowspan="5">Radiosonde</td><td>UAHv6.0</td><td>—</td><td>0.14*</td><td>—</td><td>0.13</td></tr><tr><td>RSS v4.0</td><td>—</td><td>0.22</td><td>—</td><td>0.18</td></tr><tr><td>UWv1.0</td><td>—</td><td>—</td><td>—</td><td>0.18</td></tr><tr><td>NOAA STAR v4.1</td><td>—</td><td>—</td><td>—</td><td>0.23</td></tr><tr><td>ERA5</td><td>—</td><td>0.18</td><td>—</td><td>0.16</td></tr><tr><td rowspan="2">Satellite</td><td>JRA-55</td><td>0.17</td><td>0.19</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>0.19</td></tr><tr><td colspan="2">Median</td><td>0.19</td><td>0.19</td><td>0.16</td><td>0.18</td></tr></tbody></table>

<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>
discrepancy include model biases in their response to greenhouse gas forcing, deficiencies in the external forcing applied to models, and observational biases.

The 2σ trend error estimate for individual satellite datasets is approximately 0.04°C decade⁻¹ (Mears et al. 2011; Po-Chedley et al. 2015; Spencer et al. 2017). Uncertainty in satellite datasets arise from instrument calibration and the removal of non-climatic artifacts, particularly between 2000 and 2005 (Christy et al. 2018). The conversion of level temperatures in radiosonde and re-analysis data to synthetic satellite brightness temperatures and incomplete spatial sampling in radiosonde data contribute to the trend error (Mears et al. 2011). The 2σ tropospheric trend error associated with these effects is approximately 0.01°C decade⁻¹ and 0.02°C decade⁻¹, respectively, in both the tropical and global domain.

### 5) Stratospheric temperature and winds—W. J. Randel, C. Covey, and L. Polvani

Temperatures in the middle and upper stratosphere continued to exhibit strong decadal-scale cooling as a result of anthropogenic CO₂ increases. Lower stratospheric temperatures have been relatively constant since ~1998 as the Montreal Protocol stabilized ozone levels, but a transient temperature increase occurred in early 2020, likely related to enhanced stratospheric aerosols from extreme Australian bushfires. The Arctic and Antarctic stratospheric polar vortices were remarkably strong and undisturbed in 2020, with accompanying large polar ozone depletion in both hemispheres. Additionally, a new anomalous disruption of the stratospheric quasi-biennial oscillation (QBO) occurred in 2020, following a similar event in 2016.

Time series of global monthly temperature anomalies from the lower to upper stratosphere based on satellite measurements are shown in Fig. 2.9. The middle and upper stratosphere data (Stratospheric Sounding Unit [SSU] 1, 2, 3) represent ~20-km thick layers from infrared (SSU) data merged with more recent measurements (Zou and Qian 2016; Randel et al. 2016), while the lower stratospheric temperatures (TLS) represent the layer over ~13–22 km from microwave data. As shown in previous reports (Randel et al. 2020), the satellite TLS measurements agree well with radiosonde and reanalysis datasets. Middle- and upper-stratosphere temperatures show strong cooling since 1979 with larger negative trends at higher altitudes, a long-predicted response to increases in atmospheric CO₂ (Manabe and Wetherald 1967). The upper stratospheric cooling is modulated by stratospheric ozone changes, with weaker cooling after 1998 tied to observed increases in upper-stratospheric ozone (Maycock et al. 2018). The ozone is evolving as a response to changes in ozone-depleting substances linked to the Montreal Protocol (WMO 2018). In addition to long-term cooling, the upper-stratosphere time series show modulation by the 11-year solar cycle and transient warming from large volcanic eruptions in 1982 and 1991.

TLS have been relatively constant since the later 1990s with small year-to-year variability. Over most of the globe, the TLS layer spans the cross-over between tropospheric warming and stratospheric cooling associated with CO₂ increases; hence ozone variations strongly influence temperatures in this layer. TLS cooling prior to ~1998 is tied to ozone decreases in the lower stratosphere, while there are small ozone changes thereafter (WMO 2018). The TLS in Fig. 2.9 show an unusually large short-term warm anomaly in early 2020 that is probably related to enhanced stratospheric aerosols in the SH caused by extreme Australian bushfires and resulting smoke injection into the stratosphere (Kablick et al. 2020; Khaykin et al. 2020; Schwartz et al. 2020; Hirsch and Koren 2021; Yu et al. 2021).

The stratospheric winter polar vortices were uniquely strong and undisturbed in both hemispheres in 2020. The Arctic polar vortex was the strongest since the beginning of the satellite era and coincided with record-low stratospheric ozone levels in the Arctic that lasted into spring, together with a record-breaking positive Arctic Oscillation index in the troposphere during January–March (Lawrence et al. 2020). The Antarctic polar vortex in 2020 was also anomalously strong and persistent, with polar temperatures at record cold levels throughout spring (November–December). This strong vortex was linked to a large and persistent ozone hole over the Antarctic,
<figure><img src="image_45.png" /></figure>

Fig. 2.9. Monthly global stratospheric temperature anomalies from the lower 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. Each time series has been normalized to zero for the period 1995–2005, and curves are offset for clarity.

which lasted to the end of December. While the polar vortices were anomalously cold, they have
minimal influence on global average temperatures.

Another notable feature of stratospheric circulation in 2020 was a new disruption of the QBO, which is a repeating reversal of equatorial zonal winds, characterized by downward-propagating easterly and westerly wind regimes with a mean periodicity of ~28 months. While regular downward propagation from the upper to lower stratosphere had been observed continuously since its discovery in the early 1960s, a disruption occurred in 2016, when anomalous easterlies appeared in the lower stratosphere disconnected from upper levels, and a similar disruption occurred in 2020 (see Fig. 2.46). This behavior has been attributed to strong wave forcing from extratropical latitudes (e.g., Osprey et al. 2016; Coy et al. 2017; Anstey et al. 2021). With two disruptions over the last 5 years, there is now substantial uncertainty regarding QBO predictability and future evolution.
## Sidebar 2.1: Night marine air temperature—R. C. CORNES, D. I. BERRY, R. JUNOD, E. C. KENT, AND N. A. RAYNER

Sea surface temperature (SST) is the principal variable for monitoring surface temperature change across ocean regions. Ship-based SST measurements provide the mainstay of the record, which extends back to the mid-nineteenth century, and these data have been supplemented with observations from moored and drifting buoys since the 1990s. In addition to recording SST, many ships also take measurements of air temperature but in general these marine air temperature (MAT) data are sparser than SST and the values are prone to daytime heating biases (Berry et al. 2004). To mitigate these biases and improve the accuracy of gridded anomaly datasets, only the nighttime values are selected. These night marine air temperature (NMAT) datasets provide a useful independent comparison against SST datasets.

NMAT readings require adjustment to account for variations in ship observation height, including an increase over time in the mean height of the bridge where the observations are typically taken (Kent et al. 2013). If this adjustment is not applied, a reduced trend would be apparent in the data series since temperature generally decreases with height. The temperature values are typically adjusted to the standard reference height of 10-m although the CLASSnmat dataset (Cornes et al. 2020) also provides values adjusted to 2 m and 20 m for comparison against other air temperature datasets.

Over long time periods and over sufficiently large spatial regions it has been assumed that anomalies of SST and NMAT anomalies show similar variability and trends (Kennedy et al. 2019). Climate model simulations indeed depict this relationship. Huang et al. (2015) demonstrate using the GFDL-coupled model that NMAT and SST display a consistent trend over the 1875–2000 period, and this evidence was used to justify the use of NMAT to bias-correct the SST data in the ERSST dataset. However, in situ NMAT and SST datasets indicate a divergent trend at the global scale, with NMAT anomalies increasing at a slower rate than SST (Cornes et al. 2020; Folland and Karl 2001; Kennedy et al. 2019). Initial analyses into this subject concluded that while it is difficult to ascertain the cause, the magnitude of the difference is small relative to the global warming trend (Folland and Karl 2001). However, the more up-to-date CLASSnmat and UAHNMAT (Junod and Christy 2020) datasets indicate an increase in this differential (Fig. SB2.1)—particularly in recent years— when compared against modern SST datasets, in this case HadSST4 (Kennedy et al. 2019).

The NMAT-SST discontinuity may appear as a step-like change in the early 1990s (Kennedy et al. 2019), although this may be a manifestation of a long-term divergence between SST and NMAT coupled with the use of a common 1961–90 base period for the calculation of the anomalies. In the evaluation of the long-term trends in NMAT and SST, differences in spatial coverage may have a large influence on the results (Jones 2020). In general, SST is more spatially complete than NMAT. This results from both the increase in drifting buoy observations and the considerable decline in voluntary observing ships (VOS; https://www.vos.noaa.gov/vos_schema.shtml) from >7000 in the 1980s to ~1000 at present. Hence, only co-located grid-cell values across the three datasets are averaged in Fig. SB2.1. Note that differences in actual temperatures cannot be inferred from this figure because the time series are expressed as anomalies from climatological averages.

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

Fig. SB2.1. Large-scale average annual anomalies in the CLASSnmat (Cornes et al. 2020), UAHNMAT (Junod and Christy 2020), and HadSST4 (Kennedy et al. 2019) datasets relative to a 1961–90 base period over the period 1900–2020 (UAHNMAT to 2018).
The trend difference in NMAT and SST is strongest in the tropics (Fig. SB2.1), which may be attributable to changes in the atmospheric circulation across the region (Christy et al. 2001). Recent analyses by Rubino et al. (2020) examined the difference in MAT (day and night) and SST data recorded by moored tropical ocean atmosphere buoys in the tropical Pacific Ocean. The buoy data series are generally short in length (covering at most the period 1985–2010) relative to the century-long ship data, which inhibits definitive conclusions regarding long-term trends in MAT data relative to SST. However, the authors observed marked differences in these variables, particularly on sub-decadal timescales, which highlight the difficulty in assuming that MAT and SST are interchangeable.

<figure><img src="image_47.png" /><figcaption>Fig. SB2.2. Large-scale average anomalies in 2-m air temperature across ocean regions and sea surface temperature from the ERA5 reanalysis dataset (Hersbach et al. 2020) from 1950 to 2020. Note that in contrast to Fig. SB2.1, the anomalies in this figure are expressed relative to 1981–2010 averages. The data prior to 1978 are currently considered experimental. Areas with sea ice are masked from the averaging.</figcaption></figure>

<table><caption>Table SB2.1. Decadal trends (°C decade⁻¹) in large-scale average anomalies from 1979 to 2020 in the sea surface temperature (SST) and marine air temperature (MAT) data from ERA5 and in CLASSnmat and HadSST4. Note that ERA5 has complete coverage over ocean regions whereas CLASSnmat and HadSST4 are not complete. CLASSnmat and HadSST4 are masked to have the same spatial coverage.</caption><thead><tr><th rowspan="2">Region</th><th colspan="4">ERA5</th></tr><tr><th>SST</th><th>Air Temperature</th><th>CLASSnmat</th><th>HadSST4</th></tr></thead><tbody><tr><td>Global</td><td>0.120</td><td>0.127</td><td>0.093</td><td>0.125</td></tr><tr><td>Northern extratropics</td><td>0.171</td><td>0.183</td><td>0.114</td><td>0.151</td></tr><tr><td>Tropics</td><td>0.116</td><td>0.123</td><td>0.039</td><td>0.101</td></tr><tr><td>Southern extratropics</td><td>0.081</td><td>0.084</td><td>0.069</td><td>0.098</td></tr></tbody></table>

Reanalysis datasets provide additional information about the (N)MAT-SST trend difference. Figure SB2.2 shows global and hemispheric annual average 2-m air temperature and SST anomalies (relative to 1981–2010) calculated from ERA5 over the period 1950–2020. This figure indicates a comparable trend in SST and co-located air temperature across all regions including the tropics (see also Table SB2.1), which is in contrast to the results in Figure SB2.1 using the gridded NMAT and SST datasets. Note that ERA5 is spatially complete (apart from the masking of sea ice regions) in Fig. SB2.2 whereas the in situ datasets have missing grid cells. Conversely, ERA5 data across the Arctic region, which are excluded in the Figure SB2.2 averages, show a much greater warming trend in air temperature relative to SST (Fig. SB2.3); however, SST is derived indirectly in these regions using sea ice concentration data (Hirahara et al. 2016).

It remains unclear if the trend difference seen in SST and NMAT datasets is due to physical processes or if it results from biases in either the SST or NMAT data or both. Understanding this feature is particularly important because global mean surface temperature (GMST) data products (Lenssen et al. 2019; Morice et al. 2021; Vose et al. 2012) combine anomalies of near-surface temperature over land with anomalies of SST rather than MAT. Resolving this question would also inform the debate about the suitability of comparing these merged GMST datasets against global climate model simulations of air temperature (Cowtan et al. 2015; Jones 2020), especially since simulated values using MAT for the marine component of global air temperature have been shown to warm at a slightly faster rate than a comparable dataset that used SST as the marine component (Richardson et al. 2018)
<figure><img src="image_48.png" /><figcaption>Fig. SB2.3. Linear trends in the difference between 2-m air temperature and sea surface temperature (SST) anomalies (°C decade⁻¹; relative to 1981–2010 averages) in the ERA5 dataset over the period 1980–2020. Red colors indicate where air temperature is warming faster than SST and blue colors where the reverse is true.</figcaption></figure>

These analyses of NMAT and MAT illustrate the importance of exploring many different variables, using as many different methods as possible, and that some questions are yet unanswered. Despite the various issues discussed and differences in long-term trend, NMAT and MAT show similar year-to-year variability to spatially-matched SST in terms of the global

average time series (Figs. SB2.1, SB2.2) and spatially for the annual average anomalies (Plate 2.1f). However, while 2020 was marginally the warmest year in globally average SST and reanalysis-derived MAT data (see section 2.b.1), this is not the case with CLASSnmat, which was cooler than 2016 and thus ranked 2020 as the second-warmest year in the record.
### c. Cryosphere

1) *Permafrost thermal state*—J. Noetzli, H. H. Christiansen, F. Hrbacek, K. Isaksen, S. L. Smith, L. Zhao, and D. A. Streletskiy

Ongoing increases in global permafrost temperatures have occurred over the past several decades, with regional variability in magnitude. There have been short breaks in the warming trend due to shorter-term meteorological fluctuations, such as summer heat waves or snow-poor winters (e.g., Biskaborn et al. 2019; Romanovsky et al. 2007; Harris et al. 2009; Wu and Zhang 2008; PERMOS 2019; Etzelmueller et al. 2020). The largest increases were observed for sites with low permafrost temperatures, i.e., several degrees below 0°C, and low ground ice contents. Warmer and ice-rich permafrost warms up at a lower rate due to latent heat uptake during ice melt. This global picture continued in 2020. Record values were observed at many sites in polar and mountain regions. However, data could not be collected from all permafrost observation sites in 2020 (particularly in North America) due to pandemic-related travel restrictions.

Permafrost temperatures reported in 2020 for the Arctic regions were the highest on record at a majority of the observation sites. Warming rates for colder permafrost were as high as 0.8°C decade⁻¹, compared to less than 0.3°C decade⁻¹ for permafrost at temperatures close to

0°C. Details on Arctic permafrost are given in section 5h. Increasing permafrost temperatures were reported from the Antarctic Peninsula and Victoria Land for the past decade up to 2018 (cf. Noetzli et al. 2019); however, deep boreholes and complete time series were scarce and the trend lacks statistical significance.

Mountain permafrost accounts for approximately 30% of the global area underlain by permafrost (Hock et al. 2019). Data are primarily available from the European Alps, the Nordic countries, and central Asia (Qinghai-Tibetan Plateau; QTP), but they are sparse for other mountain regions. A mean permafrost temperature increase of 0.19°C decade⁻¹ was observed for 2007–16 (Biskaborn et al. 2019). Warming rates are heterogeneous due to the high spatial variability in thermal conditions resulting from complex topography, snow regime, and ground ice content. Highest rates are observed for bedrock with a low ice content and permafrost temperatures several degrees below 0°C and without a thicker winter snow cover. Permafrost temperatures recorded in 2020 in the European Alps were higher than in 2019 and close to or above the previous maximum observed in 2015 at the majority of sites (Fig. 2.10; Noetzli et al. 2020; updated from Pogliotti et al. 2015; PERMOS 2019) due to an early onset of the snow cover in autumn 2019 and the warmest year

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

**Fig. 2.10.** Permafrost temperature measured in boreholes in the European Alps and the Nordic countries at a depth of approximately 10 m (monthly means, upper panel) and 20 m (annual means, lower panel). (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.)
recorded in Europe (Copernicus Climate Change Service 2021). Permafrost temperatures are thus higher or similar as before the temporary cooling in 2016 and 2017, which persisted in 2018 and only started to reverse in 2019. Temperatures at Murtèl-Corvatsch in the Engadin (Switzerland) increased by ~0.6°C at 20-m depth and by more than 1°C at 10-m depth over the past 3 decades. On Stockhorn above Zermatt (Switzerland), temperatures at 23-m depth increased by ~0.4°C over the past 2 decades. Surface velocities of rock glaciers generally follow the evolution of the permafrost temperatures. In the European Alps, rock glacier surface velocities for the year 2020 are at or above the previous maximum observed in 2015 (see Sidebar 2.1).

In the Nordic countries, permafrost temperatures measured in 2020 were the highest or second highest on record, continuing the reported warming trend (Fig. 2.10; Noetzli et al. 2020; Etzelmüller et al. 2020). In the cold mountain permafrost at Juvvasshøe in southern Norway, permafrost temperatures at 20-m depth increased by 0.5°C from 1999 to 2020. Permafrost temperatures decreased in Svalbard at 10-m depth compared to the previous extremely warm years due to the relatively cold winters in 2019 and 2020 (Christiansen et al. 2021). However, they are still above the long-term average; for example, at Kapp Linne they were 0.7°C higher in 2020 than at the start of the record in 2009.

Permafrost temperatures measured in the hinterland of the QTP in Central Asia continued to increase at all sites, with remarkable warming trends but variable rates: at 10-m depth they range between 0.45°C decade⁻¹ (QTB15, Fig. 2.11) and 0.04°C decade⁻¹ (QTB06), and at 20-m depth between 0.24 and 0.02 °C decade⁻¹ (Zhao et al. 2020, 2021).

The active layer thickness (ALT) is the ground layer that freezes and thaws annually and lies above the permafrost. Changes in ALT are a key indicator for changing permafrost conditions. ALT was not or only partly reported for some sites in Canada and Alaska due to COVID-19 travel restrictions. The ALT in northern Alaska was 6 cm thinner in 2020 than the

decadal average (2008–17) and 8 cm thinner than in 2019. In the Alaska Interior, ALT was thicker than average, but 5 cm thinner than in 2019. ALT at the majority of sites in the Nordic region was similar to the previous year, at or close to record values. In Russia, ALT was thicker than average and thicker than in 2019 in all regions, except for Chukotka, where ALT was thinner than in 2019. The Siberian heat wave (see section 7g2, Sidebar 5.1) caused particularly thick ALT, with more than 10 cm larger values than in 2019 in West Siberia and neighboring sites in northwestern Russia. ALT in the regions of central and eastern Siberia was only 3 cm above previous regional averages. More details on ALT in Arctic regions are given in section 5h.

In the Scandinavian and European Alps, ALT values for 2020 were at or close to the previous maximum at most of the sites. In the Swiss Alps, record values were observed in 2020 for most sites, with values up to 10 m in extreme cases. Along the Qinghai-Tibet Highway (Kunlun mountain pass to Liangdaohe), an ALT increase was observed with a mean of 19.5 cm decade⁻¹ from 1981 to 2019 (Fig. 2.12). In Antarctica, the February 2020 heat wave in the northwest Weddell Sea sector (section 2b3) accelerated active layer thickening. Thaw depth on James Ross Island reached 80 cm. This is comparable to observations in 2016/17 (Hrbáček et al. 2021), one of the warmest years so far measured in this sector (J. Turner et al. 2020).

<figure><img src="image_50.png" /><figcaption>Fig. 2.11. Temperature measured in permafrost boreholes along the Qinghai-Xizang Highway on the Tibetan Plateau at 10-m depth from 2005 to 2019. (Source: Cryosphere Research Station on Qinghai-Xizang Plateau, CAS.)</figcaption></figure>
<figure><img src="image_51.png" /><figcaption>Fig. 2.12. The active layer thickness (cm) and air temperature anomaly (°C) in the permafrost zone along the Qinghai-Tibet Highway during the period 1981–2019. The air temperature anomaly is estimated relative to the climate baseline 1981–2010.</figcaption></figure>

Long-term observation of permafrost relies on field observations of ALT and permafrost temperatures measured in boreholes. 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 manually or continuously using multi-sensor cables in boreholes reaching at least the depth of the zero annual amplitude. An assessment of the measurement accuracy of permafrost temperatures worldwide varied from 0.01° to 0.25°C, with an assumed overall accuracy of about 0.1°C (Biskaborn et al. 2019; Romanovsky et al. 2010). ALT is determined by mechani-

cal probing where possible and has an accuracy of ~1 cm. Probing is not possible in bedrock or debris material, particularly in mountain regions. Here, ALT is interpolated from temperature sensors in boreholes. The current global coverage of permafrost monitoring sites is sparse; it is particularly limited in regions such as Siberia, central Canada, Antarctica, and the Himalayan and Andes Mountains.

### Sidebar 2.2: **Rock glacier kinematics**—C. PELLET, X. BODIN, R. DELALOYE, V. KAUFMANN, J. NOETZLI, E. THIBERT, AND A. KELLERER-PIRKLBAUER

Rock glaciers are geomorphological indicators of permafrost occurrence in mountain areas and develop in most mountain ranges worldwide. Their kinematics derived from surface displacement measurements typically range from several centimeters up to several meters per year (Kääb and Vollmer 2000). Long-term studies from the European Alps have shown that the velocity of rock glaciers in a specific region responds sensitively and synchronously to interannual and decennial changes in ground temperature (e.g. Bodin et al. 2009; Delaloye et al. 2008, 2010; Kääb et al. 2007; Kellerer-Pirklbauer and Kaufmann 2012, 2018; Staub et al. 2016; Thibert et al. 2018; PERMOS 2019). Measurements of the surface velocity of rock glaciers based on aerial images and geodetic surveys first started in the 1960s in the European Alps (Haeberli 1985). Today, the majority of monitored rock glaciers are in the European Alps, and surface velocity measurements based on repeated terrestrial geodetic surveys have become part of operational permafrost monitoring in several European countries (Austria, France, Switzerland; see PERMOS 2019). In addition to their importance as climate indicators, rock glaciers are highly relevant for natural hazards risk management in mountain regions as well as for land use planning. Active rock glaciers are sediment conveyers and their

increasing velocity can lead to a higher frequency of rock fall or debris flows from their frontal parts (e.g., Kummert et al. 2018).

The surface velocity of the majority of the observed rock glaciers in the European Alps behaved similarly during the past decades, despite variable size, morphology, and velocity range (Fig. SB2.4). The surface velocity increased by a factor of 2 to 10 from 1980s to 2015, and a maximum was reached in 2015. The acceleration was temporarily interrupted (i.e., velocity decrease was observed) for most of the landforms between 2004 and 2006, as well as between 2016 and 2018, coinciding with a decrease in ground temperatures (Noetzli et al. 2018; PERMOS 2019). The acceleration resumed in 2018. In 2020, the surface velocity of rock glaciers was close to or even higher than the maximum observed in 2015, which corresponds to the high ground temperatures observed (see section 2c1). Compared to the values of 2019, the surface velocity increase spans from +17% (Dösen [Austria] and Gemmi/Furggentälti [Switzerland]) to +45% (Grosses Gufer [Switzerland] and Hinteres Langtalkar [Austria]), which is in the same range as the acceleration observed between 2014 and 2015.

Long-term in situ measurements of rock glacier kinematics are scarcely available from other regions of the world. However,
the increasing emergence of open-access and high-resolution satellite data (e.g., optical and Synthetic Aperture Radar [SAR]) facilitates the setup of regional surveys worldwide (e.g., Strozzi et al. 2020). Recent studies in northern Norway (Eriksen et al. 2018) and in the Tien Shan Mountains (Kääb et al. 2020) found an overall increase of the rock glaciers' surface velocity from the 1950s on. These observations are consistent with the results obtained in the European Alps.

According to in situ measurement (e.g., Arenson et al. 2002; Buchli et al. 2018) and modeling approaches (e.g., Kannan and Rajagopal 2013), the displacement at the surface of rock glaciers mainly results from shearing within a layer of several decimeters to a few meters thickness, which typically lies between 15- and 30-m depth. The changes in rock glacier kinematics are mostly

related to the evolution of ground temperature and liquid water content between the permafrost table and the main shearing horizon at depth: the closer to 0°C the temperature is, the faster the rock glacier is moving (Cicoira et al. 2019; Frauenfelder et al. 2003; Staub et al. 2016). A time lag of around 1 to 2 years has been observed between high air temperatures and the resulting acceleration (Kellerer-Pirklbauer and Kaufmann 2012; Staub et al. 2016).

The consistent regional evolution of rock glacier velocity and its sensitivity to changes in ground temperature, together with their global presence, make rock glaciers ideal climate indicators. An Action Group of the International Permafrost Association (IPA; see Delaloye et al. 2018) aims to internationally harmonize and coordinate measurements of rock glacier kinematics (RGK).

Based on their recommendation, the Global Terrestrial Network for Permafrost (GTN-P) is proposing to include RGK as a new product of the GCOS essential climate variable (ECV) permafrost, in addition to the thermal state of permafrost and active layer thickness. RGK measurements are based on repeated terrestrial geodetic surveys or determined photogrammetrically using aerial images. Geodetic surveys are performed annually at the same time of the season (usually at the end of the summer). The coordinates and elevation are measured for a number of selected boulders (10–100 per landform) with an average accuracy in the range of millimeters to centimeters (Delaloye et al. 2008; PERMOS 2019). Multi-temporal aerial images are compared with each other to obtain rock glacier-wide movement information. Typically, horizontal displacement metrics are computed based on 2D ortho-image matching algorithms or digital elevation model matching. The accuracy of the photogrammetrically derived displacements strongly depends on the spatial resolution of the aerial images and on the image quality (e.g., sharpness, contrast, and so forth).

<figure><img src="image_52.png" /><figcaption>Fig. SB2.4. (a) Long-term in situ permafrost temperature measured at 20-m depth [blue lines]) and air temperature measurements (composite anomaly to the 1981–2010 norm [red and blue bars]) and composite 20-year running mean (solid line) at five selected sites in the European Alps (Switzerland, France, Austria): Besse France, Grand Saint-Bernard Switzerland, Sonnblick Austria and Zugspitze Germany. (b) Rock glacier surface velocities (m yr⁻¹) measured using in situ geodetic surveys and photogrammetrics. (Sources: Météo France, Deutscher Wetterdienst DWD, MeteoSwiss, Zentralanstalt für Meteorologie und Geodynamik ZAMG, Swiss Permafrost Monitoring Network, University of Fribourg, University of Graz, Graz University of Technology, Université Grenoble Alpes [INRAE].)</figcaption></figure>
## 2) Northern Hemisphere continental snow cover extent—D. A. Robinson

Snow cover extent (SCE) displays considerable intra- and interannual variability. As such, variations in SCE impact surface albedo and thus, the overall surface energy balance. SCE also plays a role in atmospheric circulation and surface hydrology. Annual SCE over Northern Hemisphere (NH) lands averaged 24.1 million km² in 2020. This is 1.0 million km² less than the 51-year average (mapping extends back to late 1966, although three early years in the record are incomplete) and ranks 2020 as having the fourth-least extensive cover on record (Fig. 2.13; Table 2.4) and 0.7 million km² less than the 2019 mean extent. SCE over both NH continents, including the Greenland ice sheet, was considered in this analysis. Monthly SCE in 2020 ranged from 46.4 million km² in January to 2.3 million km² in August. The only years in the satellite record with less NH SCE than in 2020 were, from lowest upward, 1990, 1988, and 2007.

During the first half of 2020, SCE was well below average across the NH. Monthly rankings ranged from below average for the 54-year record in January to third-least extensive in February. NH spring (March–May) SCE ranked fourth lowest on record, consistent with a generally persistent earlier snow melt in recent decades. Rankings of second- to fourth-least extensive cover occurred across Eurasia from February to June. North American snow cover was below average in five of the first six months of 2020, the exception being April, where a delayed melt resulted in above-average cover.

The NH SCE was above average in October and November, ranking 10th- and 12th-most extensive, respectively. The past nine autumns (September–November) have had average SCE exceeding 20 million km², while only eight of the prior 41 years exceeded that mark.

December SCE was close to average. The excessive cover was primarily driven by conditions in North America, where October cover was the largest on record and November cover was 13th largest. December SCE was close to average in Eurasia, while a major turnaround occurred across North

<figure><img src="image_53.png" /><figcaption>Fig. 2.13. Twelve-month running anomalies of monthly snow cover extent (million km² over NH lands as a whole, and Eurasia and North America separately, plotted on the seventh month using values from Nov 1966 to Dec 2020. Anomalies are calculated from NOAA snow maps. Mean hemispheric snow extent is 25.1 million km² for the full period of record. Monthly means for the period of record are used for nine missing months during 1968, 1969, and 1971 to create a continuous series of running means. Missing months fall between Jun and Oct, no winter months are missing.</figcaption></figure>

<table><caption>Table 2.4. Monthly and annual climatological information on NH and continental snow extent between Nov 1966 and Dec 2020. Included are the numbers of years with data used in the calculations, NH means, standard deviations, 2020 values, and rankings. Areas are in million km². The years 1968, 1969, and 1971 have 1, 5, and 3 missing months, respectively, thus are not included in the annual calculations. N. Am. includes Greenland. Ranks are from most extensive (1) to least (ranges from 51 to 55 depending on the month).</caption><thead><tr><th></th><th>Years of data</th><th>Mean</th><th>Std. Dev.</th><th>2020</th><th>2020 NH rank</th><th>2020 Eurasia rank</th><th>2020 N Am. rank</th></tr></thead><tbody><tr><td>Jan</td><td>54</td><td>47.2</td><td>1.5</td><td>46.4</td><td>37</td><td>39</td><td>33</td></tr><tr><td>Feb</td><td>54</td><td>46.0</td><td>1.8</td><td>43.5</td><td>52</td><td>52</td><td>39</td></tr><tr><td>Mar</td><td>54</td><td>40.4</td><td>1.9</td><td>37.7</td><td>50</td><td>51</td><td>32</td></tr><tr><td>Apr</td><td>54</td><td>30.5</td><td>1.7</td><td>29.1</td><td>42</td><td>52</td><td>14</td></tr><tr><td>May</td><td>54</td><td>19.2</td><td>2.0</td><td>16.7</td><td>49</td><td>52</td><td>28</td></tr><tr><td>Jun</td><td>53</td><td>9.4</td><td>2.5</td><td>6.0</td><td>49</td><td>52</td><td>43</td></tr><tr><td>Jul</td><td>51</td><td>3.9</td><td>1.2</td><td>2.4</td><td>50</td><td>51</td><td>50</td></tr><tr><td>Aug</td><td>52</td><td>3.0</td><td>0.7</td><td>2.3</td><td>50</td><td>52</td><td>42</td></tr><tr><td>Sep</td><td>52</td><td>5.4</td><td>0.9</td><td>4.5</td><td>42</td><td>50</td><td>22</td></tr><tr><td>Oct</td><td>53</td><td>18.6</td><td>2.7</td><td>21.2</td><td>10</td><td>19</td><td>1</td></tr><tr><td>Nov</td><td>55</td><td>34.3</td><td>2.1</td><td>36.0</td><td>12</td><td>17</td><td>13</td></tr><tr><td>Dec</td><td>55</td><td>43.7</td><td>1.8</td><td>43.7</td><td>32</td><td>26</td><td>37</td></tr><tr><td>Ann</td><td>51</td><td>25.1</td><td>0.8</td><td>24.1</td><td>48</td><td>49</td><td>26</td></tr></tbody></table>
America, with SCE below average. Research by Allchin and Dery (2020) supports the autumn increases, attributing this to atmospheric circulation patterns that have increased moisture fluxes into areas that are cold enough to sustain an autumn snow cover but previously were somewhat moisture deficient.

SCE over the contiguous United States during the first half of 2020 saw monthly rankings of third- to 20th-least extensive, with the exception of April which had above-average cover for the 54-year record. October 2020 was its most extensive SCE on record, with November above average and December below average.

SCE is calculated at the Rutgers Global Snow Lab from daily SCE maps produced by meteorologists at the National Ice Center (a United States joint NOAA, Navy, and Coast Guard facility), who rely primarily on visible satellite imagery to construct the maps (Estilow et al. 2015). Maps depicting daily, weekly, and monthly conditions, daily and monthly anomalies, and monthly climatologies for the entire period of record may be viewed at the Rutgers Global Snow Lab website (https://snowcover.org). Monthly SCE for the NH, Eurasia, North America, contiguous United States, Alaska, and Canada are also posted, along with information on how to acquire weekly areas and the weekly and monthly gridded products.

### 3) Alpine glaciers—M. Pelto

For 2019/20, the overall mean annual mass balance of 33 reporting reference glaciers, from 12 nations on four continents, was –621 mm and was –590 mm for all 79 reporting glaciers to date (World Glacier Monitoring Service [WGMS] 2020). This makes 2020 the 33rd consecutive year with a global alpine mass balance loss and the 12th consecutive year with a mean global mass balance below –500 mm, but it was less negative than the previous 2 years, which were the most negative of the entire 1950–2020 record. In the hydrological year 2017/18, reference glaciers experienced a mass balance loss of –1184 mm and in 2018/19 of –1177 mm.

Figure 2.14 illustrates glacier mass balance for the WGMS global reference glaciers with more than 30 continued observation years for the period 1950–2019. Global values were calculated using a single value (averaged) for each of 19 mountain regions in order to avoid a bias to well-observed regions. Zemp et al. (2019) indicated that the collective loss of alpine glaciers from 2006 to 2016 provided a global sea level contribution of 0.92 ± 0.39 mm yr⁻¹ (see section 3f).

The decadal averaged annual mass balance was –214 mm in the 1980s, –499 mm in the 1990s, –527 mm in the 2000s, and –896 mm for the 2010s. The average mass loss reported by Slater et al (2021) identified a similar rise with a loss of 62 Gt yr⁻¹ in the 1980s, 206 Gt yr⁻¹ in the 1990s, 252 Gt yr⁻¹ in the 2000s, and 327 Gt yr⁻¹ in the 2010s. The increasing rate of glacier mass loss, with eight out of the 10 most negative mass balance years recorded after 2010 during a period of retreat, indicates that alpine glaciers are not approaching equilibrium and retreat will continue to be the dominant terminus response (WGMS 2020).

All 19 reporting glaciers in the Alps had a negative mass balance averaging –873 mm in 2020. In Austria in 2019, of the 92 glaciers with annual terminus observations, 86 (93.4%) withdrew, five remained stationary, and one advanced (Lieb and Kellerer-Pirklbauer 2020). This retreat trend continued in 2020.

<figure><img src="image_54.png" /><figcaption>Fig. 2.14. Mass balance of alpine glaciers reporting to the World Glacier Monitoring Service in mm of water equivalent (mm w.e.). The values from 1980 to 2020 are based on average annual value determined for 19 different Alpine regions.</figcaption></figure>
In Sweden, all three glaciers reporting had a negative balance averaging -320 mm. In Norway, the eight reporting glaciers had a positive average mass balance of +365 mm in 2020. All 36 Norway glaciers surveyed in 2019 were retreating (Andreasson 2020). On Svalbard, the mean loss of three glaciers in 2020 was -1485 mm. Iceland completed surveys of nine glaciers, of which eight had negative balances with a mean mass balance of -442 mm.

In Alaska and Washington, all 14 glaciers observed in 2020 had a negative mass balance averaging -722 mm. This was significantly larger than the long-term average of four United States Geological Survey benchmark glaciers, which had a cumulative mass loss since the mid-twentieth century that averaged from -580 to -300 mm yr⁻¹ (O'Neel et. al. 2019).

In South America, 2020 mass balance data were reported from two glaciers in Chile, one in Ecuador, and one in Argentina; all were negative with a mean of -1056 mm. This was greater than the 2000-18 average loss observed in the Andes of -720 ± 220 mm yr⁻¹ (Dussaillant et. al. 2019).

In Kyrgyzstan and Kazakhstan, nine glaciers in the Tien Shan Range had near

equilibrium balances. In the Himalayas, the two reporting reference glaciers had negative balances averaging -487 mm. King et al. (2019) identified that in the Mount Everest region mass loss has increased each of the last 6 decades. In 2020, the post-monsoon season and early winter were warm and dry in the Himalayas, leading to the ablation season extending into January with the snow line retreating over 100 m from October into January (Fig. 2.15; Patel 2021). This raises the question, when does the ablation season end in the region in our warmer climate?

The WGMS record of mass balance and terminus behavior (WGMS 2017, 2018) provides a global index for alpine glacier behavior. Glacier mass balance is the difference between accumulation and ablation, reported here in mm of water equivalent (mm w.e.).

### 4) Lake ice—S. Sharma and R. I. Woolway

In the 2019/20 winter, lake ice phenology (the timing of ice-on and ice-off) across the NH (calculated from Copernicus Climate Change Service [C3S] ERA5 [Hersbach et al. 2020]) continued to experience later ice-on dates, earlier ice-off dates, and shorter seasonal ice continuing the pattern seen over 1980-2020 (Magnuson et al. 2000; Benson et al. 2012; Woolway et al. 2020). The hemispheric average for ice-on was 1.5 days later decade⁻¹ and ice-off was 1.5 days earlier per decade⁻¹. In line with these calculated changes in ice phenology, the data suggest that the duration of lake ice cover was shortening at an average rate of 3 days decade⁻¹, albeit with considerable inter-annual variability (R² = 0.44). Relative to the 1981-2010 base period, NH lakes froze,

<figure><img src="image_55.png" /><figcaption>Fig. 2.15. LandSat imagery of Nangpa La (NPL-5806 m) and Nup La (NL-5850 m) 25–50 km west of Mount Everest, indicating the rise of the snow line from 13 Oct 2020 to 17 Jan 2021, leaving Nangpa La at the crest of the Gyabarg (G) and Bhote Koshi Glacier (BK) snow free. Nup La at the crest of Rongbuk (R) and Ngozumpa Glacier (NG) is also snow free on 17 Jan 2021.</figcaption></figure>
on average, 3 days later and thawed 5.5 days earlier during the 2019/20 winter season (Fig. 2.16). By ranking these ice phenology metrics according to the earliest and latest days in which they occurred since 1979/80 (the years in which these records began) we calculated that, in 2019/20, the hemispheric average ice-on was the eighth latest on record and ice-off was the third earliest. Relative to the 1981–2010 average, lake ice duration in 2019/20 was 8.5 days shorter across the NH. This was the third-shortest ice cover season since 1979/80. The regional variations in ice duration were consistent with the NH cold season (November–April) average surface air temperature anomalies (relative to 1981–2010) in 2019/20, similar to previous studies (Sharma and Woolway 2020). Most notably, some regions in North America, such as Canada, experienced below-average air temperatures, which resulted in longer-than-average ice duration. Conversely, many regions in Eurasia experienced warmer-than-average conditions that resulted in shorter-than-average ice duration (Figs. 2.16c,d).

<figure><img src="image_56.png" /><figcaption>Fig. 2.16. Anomalies (days) in 2020 in (a) ice on, (b) ice off, and (c) ice duration for lakes across the NH, and (d) surface air temperature anomalies (°C) for the NH cold-season (Nov–Apr average), the time of year in which lakes typically freeze. The base period is 1981–2010. (Sources: ERA5, GISTEMP.)</figcaption></figure>
In situ ice phenological records from 20 monitored lakes, situated mostly in Finland, the United States, Russia, and Canada, reveal that ice-on was 15 days later, ice-off was 11 days earlier, and there were 27 fewer days of ice cover over the winter season in 2020, on average, relative to 1981–2010 (Fig. 2.17). Lakes in Finland experienced remarkably warm conditions such that ice-on was 29 days later, ice-off was 13 days earlier, and ice duration was 42 days shorter. Typically, these Finnish lakes freeze in early December. However, during the 2020 winter, some of these same lakes froze as late as February (e.g., Lakes Nasijarvi and Visuvesi). Lakes in North America also experienced a warmer winter in 2020, with 16 fewer days of ice cover on average. Ice cover was especially anomalously low in the Finger Lakes region of New York state. For example, ice-on was 26 days later, ice-off was 16 days earlier, and ice duration was 43 days shorter for Cazenovia Lake. The winter of 2020 generally followed the long-term warming trend of 11 fewer days of ice cover for the 20 in situ lakes, on average.

In 2020, the Laurentian Great Lakes had substantially less ice cover, consistent with a warmer winter in the region. On average, the Laurentian Great Lakes had 33.9% less maximal ice coverage relative to 1981–2010. The smallest and most southern lake, Lake Erie, had the highest anomaly with a 65.4% reduction in ice coverage. Maximal ice coverage decreased by 38.1% in Lake Superior and 30.8% in Lake Huron, the two largest and most northern Great Lakes (Fig. 2.18).

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 ECMWFs ERA5 reanalysis product (Hersbach et al. 2020) were analyzed. Here, ice cover metrics were only calculated for pixels where lakes occupied greater than 1% of the land surface area. Lake ice conditions in 2020 were given as anomalies, calculated relative to the 1981–2010 average.

<figure><img src="image_57.png" /><figcaption>Fig. 2.17. (a) Lake ice on, (b) ice off, and (c) ice duration anomalies from 1980 to 2020 derived from in situ observations and ERA5. Base period is 1981–2010. In situ observations of ice on, ice off, and ice duration are derived from nine lakes monitored in Finland, one lake in Russia, nine lakes in the United States, and one lake in Canada.</figcaption></figure>

<figure><img src="image_58.png" /><figcaption>Fig. 2.18. Anomalies in Great Lakes maximum ice cover extent (%) for 1973–2020 (base period is 1981–2010). The black line shows the average anomaly for all of the Great Lakes, whereas the other lines show individual lakes (Erie, Michigan, Superior, Ontario, Huron).</figcaption></figure>
Long-term in situ observations of ice-on, ice-off, and ice duration data were obtained for nine lakes in Finland, one lake in Russia, nine lakes in the United States, and one lake in Canada (Benson et al. 2000). Further, annual maximum ice cover (%) data for each of the Laurentian Great Lakes from 1973–2020 was obtained from the Great Lakes Environmental Research Laboratory. A combination of composite ice charts and observations from satellites, ships, and aircraft were used to quantify the maximum amount of ice coverage observed over the winter season in the Great Lakes (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).

## d. Hydrological cycle

### 1) Surface humidity—K. M. Willett, A. Vance, A. Simmons, M. Bosilovich, D. I. Berry, and D. Lavers

During 2020, the land surface specific humidity ($q_{land}$)—a measure of atmospheric water vapor—remained well above average (0.14 to 0.36 g kg⁻¹), while relative humidity ($RH_{land}$)—a measure of saturation—remained well below average (−1.28 to −0.68 %rh). Over oceans, $q_{ocean}$ was a record high (0.23 to 0.41 g kg⁻¹) but $RH_{ocean}$ was close to the 1981–2010 average (−0.14 to 0.13 %rh). Although the various estimates broadly agree there are differences in magnitudes and rankings (Fig. 2.19). In situ-based HadISDH and reanalyses MERRA-2 and JRA-55 show 2020 $q_{land}$ as moister than 2019, ranking third, first, and fourth, respectively, within their records. ERA5 reanalysis shows 2020 tied with 2019, as sixth moistest on record. JRA-55 and HadISDH $RH_{land}$ were also more saturated in 2020 but still low (third and fifth, respectively). ERA5 $RH_{land}$ was slightly more arid than 2019, making it a record low for the second consecutive year. Over ocean, $q_{ocean}$ was a record moist year by a large margin for HadISDH and ERA5. MERRA-2 and JRA-55 ranked $q_{ocean}$ second and close to 2019. $RH_{ocean}$ was more saturated in HadISDH and JRA-55 while marginally more arid in ERA5.

Taking HadISDH uncertainty into account, these rankings are less clear, but the 2020 $q_{ocean}$ record lies outside the uncertainty range for all other years. HadISDH and ERA5 differ in input data, coverage, and processing, especially over ocean where no ship humidity data are assimilated (Simmons et al. 2021). The 2-sigma uncertainty for HadISDH broadly encompasses the ERA5 ocean values but not ERA5 land.

Surface humidity is driven by temperature and circulation patterns. The high $q_{land}$ and record high $q_{ocean}$ concur with the record/near-record high temperatures (section 2b1). Despite relatively neutral El Niño–Southern Oscillation (ENSO) conditions evolving to moderate La Niña conditions (section 4b), the $q_{ocean}$ peak surpasses those of strong El Niño events (e.g., 1998, 2010, 2015–16); and the $q_{land}$ peak is comparable for all datasets apart from ERA5.

Despite 2020 rankings differences, there is good agreement across estimates in long-term trends of increased $q$ and decreased $RH$ (Table 2.5). On average, the warmer air contains more water vapor, but not as much as it could, given its temperature. So, the air has become less saturated, even over oceans; ERA5, JRA-55, and HadISDH show small $RH_{ocean}$ decreases. This is surprising given that several climate model studies show negligible or small increases in future $RH_{ocean}$ (Held and Soden 2006; Schneider et al. 2010; Byrne and O’Gorman 2013, 2016, 2018).

HadISDH is affected by instrument and recording errors and biases along with changes in observation density, frequency, and precision (Willett et al. 2013, 2014, 2020). Reanalyses contain model and data biases and temporally changing data assimilation streams (Gelaro et al. 2017; Hersbach et al. 2020; Simmons et al. 2021). Unlike reanalyses, HadISDH is spatially incomplete, especially over the Southern Hemisphere oceans and many dry regions (where fewer people live and hence fewer weather stations). Spatially matching ERA5 to HadISDH slightly improves agreement (Fig. 2.19; Table 2.5). Over land, HadISDH reflects the well-observed regions and, hence, regions that are generally well constrained by observations in the reanalyses. Over oceans, ERA5 does not assimilate ship humidity or air temperature observations and thus poorer observational coverage has no effect, but various changes in satellite contributions do. Comparing trends over just the
<table><caption>Table 2.5. Global average decadal trends for specific humidity (q, g kg⁻¹) and relative humidity (RH; %rh) over 1979–2020 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 that the confidence intervals do not cross the zero line.</caption><thead><tr><th>Variable</th><th>HadISDH</th><th>ERA5</th><th>MERRA-2<br>(1980–2020)</th><th>JRA-55</th><th>HadISDH (continuous<br>gridboxes)</th><th>ERA5 (continuous<br>HadISDH gridboxes)</th><th>ERA5 (no HadISDH<br>gridboxes)</th></tr></thead><tbody><tr><td>Land q</td><td>0.09<br>(0.02)</td><td>0.06<br>(0.01)</td><td>0.09<br>(0.02)</td><td>0.07<br>(0.01)</td><td>0.10<br>(0.02)</td><td>0.10<br>(0.02)</td><td>0.05<br>(0.01)</td></tr><tr><td>Land RH</td><td>-0.22<br>(0.07)</td><td>-0.44<br>(0.06)</td><td>NA</td><td>-0.32<br>(0.04)</td><td>-0.33<br>(0.07)</td><td>-0.46<br>(0.07)</td><td>-0.24<br>(0.03)</td></tr><tr><td>Ocean q</td><td>0.08<br>(0.01)</td><td>0.05<br>(0.02)</td><td>0.10<br>(0.02)</td><td>0.04<br>(0.01)</td><td>0.10<br>(0.02)</td><td>0.14<br>(0.03)</td><td>0.05<br>(0.02)</td></tr><tr><td>Ocean RH</td><td>-0.05<br>(0.05)</td><td>-0.18<br>(0.09)</td><td>NA</td><td>-0.05<br>(0.01)</td><td>-0.08<br>(0.05)</td><td>-0.17<br>(0.04)</td><td>-0.18<br>(0.02)</td></tr></tbody></table>

<figure><img src="image_59.png" /><figcaption>Fig. 2.19. (a)–(d) Global average land and ocean surface humidity annual anomalies of specific humidity (q; g kg⁻¹) and (e)–(h) relative humidity (RH; %rh) from in situ and reanalyses datasets relative to the 1981–2010 base period. For the in situ datasets 2-m surface humidity is used over land and ~10 m over the oceans. For the reanalysis, 2-m humidity is used over the whole globe. For ERA5, ocean series-only points over open sea are selected. 2-sigma uncertainty is shown for HadISDH capturing the observation, gridbox sampling, and spatial coverage uncertainty. (Sources: HadISDH [Willett et al. 2013, 2014, 2020]; ERA5 [C3S 2017, Hersbach et al. 2020]; JRA-55 [Kobayashi et al. 2015]; MERRA-2 [Gelaro et al. 2017].)</figcaption></figure>

temporally complete HadISDH grid boxes with matched ERA5 grid boxes (Table 2.5) shows closer agreement. Compared to full coverage ERA5, HadISDH matched ERA5 shows stronger increasing *q* but near-identical decreasing RH, and HadISDH shows marginally stronger increasing *q* and slightly stronger decreasing RH. Trends over ERA5 where there are no HadISDH data are generally
weaker apart from RH<sub>ocean</sub>. This suggests that HadISDH is biased towards regions with stronger moistening (*q*), especially over oceans. Possibly, ERA5 *q* is showing weaker moistening/stronger drying where it is unconstrained by surface observations. Further investigation is needed. Regardless, compared to the 1960–70s, when humidity monitoring records begin, in 2020, Earth contained more water vapor at the surface, while being less saturated.

## 2) Total column water vapor—C. A. Mears, S. P. Ho, O. Bock, X. Zhou, and J. P. Nicolas

In 2020, global land and ocean averages of total column water vapor (TCWV), the total amount of water vapor in the atmosphere, were well above the 1981–2010 climatology, ranging from 0.75 to 1.06 mm over ocean and 0.58 to 0.94 mm over land, yet did not approach the record levels observed in 2016 (Fig. 2.20). This is surprising at first because global temperatures in 2020 were

essentially tied with those from 2016 in most surface and lower-tropospheric datasets (sections 2b1, 2b4). This discrepancy is likely explained by the highest temperature anomalies having occurred well away from the tropics (Plates 2.1a,e) where the sensitivity of TCWV to temperature changes is largest because of the Clausius–Clapeyron relationship.

Water vapor is an important part of the transport of energy in the atmosphere, and influences patterns of precipitation and evaporation, and thus drought and floods. Large-scale averages of TCWV are strongly correlated with atmospheric and surface temperature. Thus, as the planet warms, TCWV will also increase. TCWV estimates are derived from satellite-borne microwave radiometers over the ocean (RSS Satellite; Mears et al. 2018), from Global Positioning System–Radio Occultation (GPS-RO) observations from the COSMIC, Metop-A, -B, and -C and COSMIC-2 satellite missions over land and ocean (satellite RO; Ho et al. 2020a,b, 2010a,b; Teng et al. 2013; Huang et al. 2013), and from ground-based Global Navigation Satellite System (GNSS) stations over land (Bock 2020). In addition, three reanalysis products are also used here: ERA5 (Hersbach et al. 2020),

MERRA-2 (Gelaro et al. 2017), and JRA-55 (Kobayashi et al. 2015). All three reanalyses assimilate satellite microwave radiometer and GPS-RO data and are therefore not independent from these two datasets. Ground-based GNSS measurements are not assimilated and are thus independent.

The most prominent TCWV anomaly features for 2020 (Plate 2.1i) were the strong north–south asymmetry over the tropical Pacific Ocean and the excess vapor anomaly over most of the tropical and subtropical Indian Ocean. Other regions showed a mix of smaller anomalies, with more regions showing positive anomalies than negative. The ocean patterns in TCWV from ERA5 (Plate 2.1i) are confirmed by the RSS satellite data (Appendix 2, Fig. A2.8), satellite RO ocean

<figure><img src="image_60.png" /><figcaption>**Fig. 2.20.** Global mean total column water vapor annual anomalies (mm) over the oceans from (a) observations and (b) reanalyses, and over land from (c) observations and (d) reanalyses averaged over 60°S–60°N. The shorter time series from the observations have been adjusted so that there is zero mean difference relative to the ERA5 results during their respective periods of record.</figcaption></figure>
measurements, and by the other two reanalyses. Over land, the patterns agree less well. There is good agreement in the dry anomalies over central South America and central Africa and in the wet anomalies over the Gulf of Mexico, Caribbean, North Africa, East Africa, and India. However, over the United States, satellite RO shows dry anomalies to the west and wet anomalies to the east, which is the opposite of ERA5 and ground-based GNSS. There are also opposing anomalies in western Europe, the southwest tip of Africa, eastern Asia, and central Australia. In ERA5, the Pacific Ocean wet-dry dipole and wetter-than-average Indian Ocean, in addition to many of the more regional features (e.g., dry western United States, wet East Africa), are consistent with those shown for surface specific humidity (section 2d1). Specific humidity should be broadly similar to TCWV in that the vast majority of water vapor lies close to the surface. The poorer agreement between ERA5 and RO over land may be partly due to different and incomplete temporal and spatial sampling from the satellite RO data. In 2020, only COSMIC-2, Metop-A, -B, and -C are used because COSMIC data are only available from January to April. COSMIC-2 mainly covers from 45°S to 45°N with about 4000 daily occultations and a relatively uniform local time coverage. The other RO missions (i.e., Metop-A, -B, and -C) cover both tropical and midlatitudes (60°S to 60°N) but with only about 1000 daily occultations and non-uniform local time coverage.

Ocean TCWV global average anomaly time series (Figs. 2.20a,b) from reanalyses and RSS satellite data show maxima in 1983–84, 1987–88, 1997–98, 2009–10, and 2015–16 associated with El Niño events. Both 2019 and 2020 approach, but do not exceed, the 2015–16 record levels due to the overall increasing trend. The RSS satellite data show a discernible increasing trend. On the other hand, the reanalysis products show different long-term trends up until the 1990s but agree well with each other and with the radiometer data after 2000. The satellite RO data are in good agreement with both the radiometer and reanalysis data but show a lower overall trend. TCWV is strongly driven by surface temperature and thus El Niño–Southern Oscillation conditions. After the 2015–16 El Niño peak, all datasets show a return to lower TCWV due to a generally neutral/weak La Niña in 2017–18, followed by larger TCWV anomalies associated with the weak El Niño in boreal winter-spring 2018–19. The positive anomaly continued into 2020. Although 2020 began with weak El Niño conditions, there was a shift by August, ending in moderate La Niña conditions in December (sections 2e1 and 4b).

Over land, the reanalyses, satellite RO missions, and ground-based GNSS agree well in terms of the global average anomaly time series (Figs. 2.20c,d). The small differences between ground-

based GNSS and the other datasets are due to asymmetry in the spatial sampling, with more stations located in the Northern Hemisphere, but the general trend and interannual variability are consistent among all datasets. An ERA5 latitude-time Hövmuller plot of TCWV anomalies over land and ocean (Fig. 2.21) indicates that the long-term increase in TCWV occurs at all latitudes, with less variability outside the tropics. Following the most recent strong El Niño in 2015–16, elevated moisture has persisted in the tropics, mainly north of the equator.

<figure><img src="image_61.png" /><figcaption>Fig. 2.21. Hövmuller plot of total column water vapor anomalies (mm; base period 1981–2010) derived from the ERA5 reanalysis.</figcaption></figure>
### 3) *Upper tropospheric humidity*—V. O. John, L. Shi, E.-S. Chung, R. P. Allan, S. A. Buehler, and B. J. Soden

The 2020 near-global-average (60°S–60°N) upper tropospheric (relative) humidity (UTH) remained close to the 2001–10 average (0.04, –0.16, and –0.35 %rh for the three datasets shown in Fig. 2.22a). This implies a continued moistening of the upper troposphere with warming. A near-zero decadal trend (less than 0.01 %rh per decade for all datasets in Fig. 2.22) in the UTH indicates an increase in absolute (specific) humidity in line with the warming mid- and upper troposphere (about 0.2 K per decade as shown, for example, in Santer et al. 2017 and Christy et al. 2020), and hence is consistent with a positive (amplifying) water vapor feedback (Chung et al. 2016).

This moistening of the free troposphere is demonstrated in Fig. 2.22b, which shows the difference between the Microwave Sounding Unit/Advanced Microwave Sounding Unit (MSU/AMSU) channel 2 brightness temperature (T2; sensitive to upper-tropospheric temperature emissions from oxygen molecules) and High Resolution Infra Red Radiation Sounder (HIRS) upper tropospheric channel brightness temperature (T12). As shown in Chung et al. (2014), the difference, T2 minus T12, measures the divergence in emission levels between upper-tropospheric water vapor and oxygen. This divergence provides a direct measure of the extent of upper-tropospheric moistening as the emission level of T12 elevates with increasing concentrations of water vapor, while the T2 emission level

remains the same because the oxygen concentration does not change over time. The positive trend in the T2 minus T12 time series thus indicates the moistening of the upper troposphere. The water vapor feedback is determined mainly by the mid- to upper troposphere though the concentration of water vapor is small there. This is because the radiative effect of absorption by water vapor is roughly proportional to the logarithm of its concentration, so it is the fractional change in water vapor concentration, not the absolute change, that governs its strength as a feedback mechanism (Allan et al. 1999; Held and Soden 2000; John and Soden 2007).

The microwave satellite data (Chung et al. 2013) and the ERA5 reanalysis (Hersbach et al. 2020) data show below-average UTH values throughout the year. However, the HIRS infrared satellite data (Shi and Bates 2011) show above-normal UTH values since summer 2020; the reason for this discrepancy is not yet understood. Despite this, there is broad agreement among the three datasets in interannual variability. During their common period, there is a correlation of 0.6 between the two satellite datasets and 0.5 between ERA5 and either of the satellite datasets. The mean and standard deviation of the anomaly time series are –0.01 ± 0.54, 0.08 ± 0.65, and –0.01 ± 0.33 %rh for the ERA5, HIRS, and microwave datasets, respectively, during their common period. HIRS and ERA5 show larger interannual variability compared to the microwave data, which can be

<figure><img src="image_62.png" /><figcaption>**Fig. 2.22.** (a) Global (60°S–60°N) average time series of upper tropospheric humidity anomalies (%rh) using HIRS (black), microwave (blue), and ERA5 (purple) datasets. (b) Anomalies of MSU/AMSU channel 2 brightness temperature (T2) minus HIRS channel 12 brightness temperature (T12) with increasing values indicative of higher absolute or specific humidity. The anomalies are computed with respect to the 2001–10 average, and the time series are smoothed to remove variability on time scales shorter than 3 months.</figcaption></figure>
attributed in part to the clear-sky sampling of the infrared HIRS observations (e.g., Fig. 10 of John et al. 2011) and to the use of a single level (400 hPa) RH for the ERA5. Negative (dry) anomalies in all datasets can be observed during strong El Niño events (e.g., 2015–16).

Annual anomalies of UTH for 2020 are shown in Plate 2.1j and Appendix Fig. A2.9 for the mi-crowave and HIRS datasets, respectively. Preconditioned in 2018–19 by a strong positive Indian Ocean dipole (IOD), the neutral to slightly positive phase of the IOD during 2020 led to widespread flooding in eastern Africa (Wainwright et al. 2020) and droughts in southeast Asia (Wang and Cai 2020). This is clearly reflected in the UTH data by positive anomalies over eastern Africa and surrounding oceans and negative anomalies over southeast Asia and eastern Australia. During the positive phase of IOD, sea surface temperature (SST) in the Indian Ocean near Africa’s east coast is warmer than usual, while SST in the waters northwest of Australia is comparatively cooler. These conditions lead to above-average precipitation in the western equatorial Indian Ocean and surrounding areas and the opposite in southeast Asia (section 2d4). Severe drought conditions in Madagascar, South America, and the western United States are also reflected in the anomalies. Above-normal monsoon rainfall in central and southern India is indicated by the positive anomalies in UTH over those regions. Dry anomalies over Europe are associated with the high geopotential height associated with a blocking pattern that led to dry, sunny conditions, especially in spring 2020 (van Heerwaarden et al. 2021; section 7f). This close connection of UTH to convection makes it suitable for monitoring large-scale dynamics of the troposphere.

The inter-satellite calibrated and bias-corrected infrared and microwave satellite measurements sample a broad upper tropospheric region (roughly between 500 and 200 hPa, but this layer varies slightly depending upon the atmospheric humidity profile) twice per day, and infrared observations only sample clear-sky scenes (John et al. 2011). The ERA5 reanalysis is based on model runs constrained with in situ and satellite data including the HIRS and microwave radiances. ERA5 samples all regions every hour but here are only displayed at 400 hPa.

### 4) Precipitation—R. S. Vose, R. Adler, A. Becker, and X. Yin

Precipitation over global land areas in 2020, as estimated from three different monitoring products, was near to or above the 1981–2000 long-term average (Fig. 2.23a). All three products indicate that global average precipitation in 2020 was higher than 2019. The observational datasets with the most complete global coverage, that is, the gauge-based product from the Global Precipitation Climatology Centre (GPCC; Becker et al. 2013) and the blended gauge–satellite product from the Global Precipitation Climatology Project (GPCP; Adler et al. 2018), had near-normal precipitation for 2020 (area-average anomalies of +0.46 mm and +0.63 mm, respectively). In contrast, the gauge-based Global Historical Climatology Network (GHCN; Peterson and Vose 1997) dataset was well above the long-term normal, with an area-average anomaly of +49.38 mm. GHCN has had consistently higher precipitation estimates than the other products for the past 4 years. According to the GPCP dataset, the precipitation anomaly over the global ocean (Fig. 2.23b) was +3.93 mm, and the global (land and ocean) anomaly (Figure 2.23c) was +3.22 mm, the latter being a slight increase from the previous year.

Examining the geographic distribution of precipitation anomalies, there was substantial variability across the planet in 2020 (Plate 2.1k). Over global land areas, the largest positive anomalies were over eastern China, with much of central Asia and sub-Saharan Africa also well above average. The strongest positive anomaly over land was in eastern China, the scene of devastating and long-lasting floods primarily during the summer months (see Sidebar 7.3). The largest negative anomalies were over the continent of South America, with much of temperate North America also below average (see sections 7d and 7b, respectively). Over the global oceans, the largest positive anomalies were over the Maritime Continent and the Indian Ocean, as well as along the Intertropical Convergence Zone (ITCZ) and the South Pacific Convergence Zone (SPCZ) in the Pacific Ocean. Much of the rest of the Pacific Ocean, however, had negative anomalies, as did much of the North
Atlantic and part of the southern Indian Ocean. Rainfall excesses associated with tropical cyclones were evident in several areas, including the southeastern United States and in the Caribbean and South China Seas.

Precipitation patterns in 2020 reflect the transition from weak El Niño-like conditions early in the year to a moderate La Niña late in the year (see sections 2e1, 4b). During the first 3 months of 2020, there was excess rainfall in the central Pacific along the equator and generally below-average precipitation over the Maritime Continent associated with the El Niño-like conditions. During the last 3 months of the year, influenced by La Niña, the central Pacific reversed from a positive anomaly to an intense negative anomaly, and the Maritime Continent became positive. Outside the ENSO-affected regions, the large-scale patterns, e.g., over the subtropics of the Pacific and the Atlantic, stayed roughly the same during the year.

<figure><img src="image_63.png" /><figcaption>Fig. 2.23. Globally averaged precipitation anomalies (mm yr⁻¹) relative to the 1981–2000 base period over (a) land, (b) ocean, and (c) the globe. Land and ocean time series were created using a proportional land/sea mask at the 1°×1° scale.</figcaption></figure>

5) Land-based precipitation extremes—M. R. Tye, S. Blenkinsop, M. G. Bosilovich, M. G. Donat, I. Durre, A. J. Simmons, and M. Ziese

Overall, extreme events during 2020 were less intense than normal across most of Eurasia and North America and more intense than normal over the tropics. The patterns of global mean and extreme anomalies illustrate the uneven spatial and temporal distribution of precipitation, whereby the heaviest events contribute disproportionately to the annual total volumes (PRCPTOT [see Table 2.6 for extremes index descriptions]; Pendergrass and Knutti 2018; see section 2d4, Plate 2.1k). Parts of Central America, Europe, and Asia reported very high PRCPTOT; and as illustrated by Plate 2.1l, Fig. 2.24 and Appendix Fig. A2.10, eastern China was influenced by very intense extremes (R95p, Rx1day, Rx5day) rather than frequent heavy rain days (R10mm, R20mm). Conversely, dryness in South America for instance, arose from the combined absence of heavy rain days and anomalous low intensity of extremes. In contrast with 2019, there was a far clearer pattern of exceptionally wet or dry regions (Blenkinsop et al. 2020), with global insured losses

<table><caption>Table 2.6. Precipitation indices used in this section and their definitions, as developed by the WMO ETCCDI (Zhang et al. 2011).</caption><thead><tr><td>Index</td><td>Definition</td><td>Figure/Plate</td></tr></thead><tbody><tr><td>Rx1day</td><td>Highest 1-day precipitation amount (mm)</td><td>Plate 2.1l; Figs. 2.24c,d; 2.25</td></tr><tr><td>Rx5day</td><td>Highest 5-day precipitation amount (mm)</td><td>Appendix Figs. A2.10c,d</td></tr><tr><td>R10mm</td><td>Heavy precipitation days >10 mm (days)</td><td>Appendix Figs. A2.10c,d</td></tr><tr><td>R20mm</td><td>Very heavy precipitation days >20 mm (days)</td><td>Fig. 2.24</td></tr><tr><td>R95pTOT</td><td>Total precipitation on days exceeding the 95th percentile of wet days (mm)</td><td>Not shown</td></tr><tr><td>PRCPTOT</td><td>Annual total precipitation falling on wet (>1 mm) days (mm)</td><td>Not shown</td></tr></tbody></table>
<figure><img src="image_64.png" /><figcaption>Fig. 2.24. Anomalies of 2020 indices relative to a 1981–2010 baseline for: R20mm (days) derived from (a) MERRA-2 (Gelaro et al. 2017) and (b) ERA5 (Hersbach et al. 2020) reanalyses, (c) Rx1day (mm), and (d) R95p (mm) derived from the in situ-based GHCNDEX relative to a 1961–90 baseline (Donat et al. 2013).</figcaption></figure>

of $76 billion U.S. from natural hazards largely driven by floods from severe convective storms and drought-influenced fires (Swiss Re 2020).

Several notable events stand out in relation to the long-term mean of extreme precipitation indices (Table 2.6). Reanalysis products (MERRA-2, Gelaro et al. 2017; ERA5, Hersbach et al. 2020) and gridded observations (GPCC, Schamm et al. 2013; GHCNDEX, Donat et al. 2013) generally show similar patterns, with the exception of the tropics, parts of central and southern Africa, and South America for extreme indices (e.g., R10mm, Rx1day) as noted in recent publications (Alexander et al. 2020; Hersbach et al. 2020). Rx1day and Rx5day highlight storm tracks and anomalous events over the Middle East, southeast Asia, southeastern United States, and northern and eastern Australia (Fig. 2.24, Appendix Fig. A2.10b, Table 2.7).

The “Dragon Storm” over the Middle East/North Africa on 12 March was an unusually powerful midlatitude cyclone for early boreal spring, bringing ~70 mm of precipitation in 24 hours to the north coast and Nile Delta in Egypt (NESDIS 2020; The Watchers 2020). Above-average SSTs in the Indian Ocean (see section 3b), coupled with favorable atmospheric conditions over East Africa, resulted in a particularly wet long-rains season (March–June), with several prolonged duration events contributing to floods (see section 7e4). These events follow a pattern of increased duration and total volume in persistent extremes (Du et al. 2019). Exceptionally wet monsoon conditions led to the highest Rx1day on record in Karachi-Faisal, Pakistan (see section 7g4; WMO 2021; Table 2.7), compounding the effects of Super Cyclonic Storm Amphan over India and Bangladesh (Floodlist 2020a). The Indian Ocean conditions also contributed to the first known landfalling tropical cyclone in Somalia, with accompanying precipitation extremes (Floodlist 2020b).

Few records were broken over Australia during 2020, the exception being the Northern Territory during January where the highest Rx1day totals on record accompanied Cyclone Claudia (see section 4g7; BoM 2020; Table 2.7). Eastern Australia was wetter than average, reversing drought conditions in the southeast. New Zealand experienced a drier-than-normal year over the northern and eastern North Island, and near-normal conditions in most other locations.
<table><caption>Table 2.7. Notable events and new precipitation maxima.</caption><thead><tr><th>Location</th><th>Duration</th><th>Amount</th><th>Comment</th><th>Reference</th></tr></thead><tbody><tr><td>Wagait Beach and Dum in Mirrie, Australia</td><td>24 hours</td><td>515.2 mm<br>562 mm</td><td>10 Jan<br>pre-cyclone Claudia<br>(Wagait Beach—new January record,<br>Dum in Mirrie—new record for Northern Territory)</td><td>BoM 2021</td></tr><tr><td>Belo Horizonte, Brazil</td><td>24 hours</td><td>172 mm</td><td>24 Jan (new record)</td><td>WMO 2021</td></tr><tr><td>Kolkata, India<br>Ishwardi, Bangladesh</td><td>24 hours</td><td>250 mm<br>155 mm</td><td>20 May<br>Super Cyclonic Storm Amphan</td><td>Floodlist 2020a</td></tr><tr><td>Karachi-Faisal, Pakistan</td><td>24 hours</td><td>231 mm</td><td>28 Aug (new record for Karachi area)</td><td>WMO 2021</td></tr><tr><td>Khombole, Senegal</td><td>24 hours</td><td>225.8 mm</td><td>5 Sep (new record)</td><td>WMO 2021</td></tr><tr><td>Jeju Island, South Korea</td><td>48 hours</td><td>963.5 mm</td><td>7 Sep<br>Tropical Cyclone Julian (Mayask)</td><td>WMO 2021</td></tr><tr><td>Hué, Vietnam</td><td>7 days</td><td>1500 mm</td><td>7–13 Oct</td><td>WMO 2021</td></tr><tr><td>Napier, New Zealand</td><td>1 hour<br>24 hours</td><td>54 mm<br>242.4 mm</td><td>9 Nov (new hourly record)</td><td>Floodlist 2020d</td></tr><tr><td>Ciro Marina, Calabria Italy</td><td>48 hours</td><td>456.8 mm</td><td>20–22 Nov</td><td>Floodlist 2020c</td></tr><tr><td>Bosaso Somalia</td><td>24 hours</td><td>128 mm</td><td>22 Nov<br>Tropical Cyclone Gati</td><td>Floodlist 2020b</td></tr></tbody></table>

A longer-term perspective using near-complete series of extreme indices (≥50 years) from ~6000 gauges over Europe (Klein Tank et al. 2002) indicates new Rx1day (Rx5day) records at 70 (40) gauges. These are fewer than normally expected and suggestive of an inactive year for European rainfall extremes. Many record-breakers were in Germany (Fig. 2.25), which accounted for 43 Rx1day records, but also included a total of 200.2 mm in Dimitrovgrad, Serbia, exceeding the previous record of 123.3 mm. In total, 11.6% (7.5%) of the Rx1day (Rx5day) values ranked in the top 10% of annual values for an individual gauge in 2020. Comparing these data on upper decile events for each year in the period 1980–2020 shows that 2020 ranked 15th (34th) out of the 41 years for which at least ~4500 gauges contribute. The R10mm (R20mm) indices for 2020 were around average with 8.8% (10.6%) of gauges in the top decile for the location, ranking 24th (19th) with new records at 82 (84) gauges, particularly across Scandinavia.

<figure><img src="image_65.png" /><figcaption>Fig. 2.25. Comparison of absolute Rx1day values (mm) and their ratio to the previous record from GHCN-Daily (Menne et al. 2012) over Germany.</figcaption></figure>
Component 4 of the U.S. NOAA Climate Extremes Index (CEI4, area of the United States that experienced 1-day precipitation totals exceeding the 90th percentile; Gleason et al. 2008; NOAA 2021) was low for 2020, ranking 75th in the 111-year record. Of the seasonal indices, only autumn ranked in the top tercile at 34th. From a regional perspective, the western United States registered CEI4 = 0% in all seasons, or no areas of heavy precipitation. CEI4 registered in the top decile during spring and summer in the upper midwest and autumn in the southeast, also illustrated by the location of Rx1day events (Plate 2.11; Fig. 2.24c). Despite the very active Atlantic hurricane season (see section 4g2), CEI4 values along the eastern seaboard were at or below average.

### 6) Lake water levels—B. M. Kraemer

In 2020, the average water level anomaly across 249 of Earth's largest lakes was +1.05 m (range: -9.16 m to +44.45 m) compared to their mean lake water levels from 1993–2001 (Fig. 2.26). Water level anomalies were positive in 73% of the lakes (183 out of the 249) compared to 68% in the previous year. Measurements of lake water level variation provide an important indicator of global hydrological change, water availability, drought, and human hydrological influence. Publicly available satellite altimetry data are used to assess changes in surface water storage. This year there are an additional 51 monitored lakes compared to 2019.

The 2020 water level anomalies differed widely both between and within regions. Lakes in southern Brazil and the Caucasus region had consistent negative water level anomalies while

<figure><img src="image_66.png" /><figcaption>Fig. 2.26. (a) Lake water level time series for 249 globally distributed lakes ranked by their 2020 anomaly (m) relative to the 1993–2001 mean. Ninety-five of the 249 water level time series had substantial data gaps from 2002 to 2008. The subset of lakes that are named on the y-axis of (a) and plotted in (b) are those with the five largest positive anomalies and five largest negative anomalies when water levels anomalies were weighted by the surface area of each lake.</figcaption></figure>
Canada, equatorial Africa, and western China had consistent positive water level anomalies (Plate 2.1m). The Tibetan-Qinghai Plateau experienced positive water level anomalies in most lakes (Plate 2.1m), consistent with the expected effects of climate change on regional rainfall and glacier melt (Woolway et al. 2020). Aside from broadscale and consistent regional variability, lake water level anomalies in nearby lakes within regions also varied. For example, Cahora Bassa and Kariba, two large, nearby reservoirs on the Zambezi River in southern Africa (within 185 km), had strongly diverging water levels (+4.38 m and -2.28 m, respectively), potentially due to differences in their water level management as reservoirs. The Middle East, Australia, and northern Asia all included lakes with both strong positive water level anomalies and strong negative anomalies, often in close proximity.

When lake level anomalies were multiplied by the lake surface area, the resulting approximate volumetric anomalies were most negative for the Caspian Sea (-433 km³), Aral Sea (-47 km³), Kara-Bogaz-Gol (-32 km³), Urmia (-22 km³), and Kariba (-19 km³). The largest positive volumetric anomalies were found in Tanganyika (+26 km³), Volta (+28 km³), Superior (+45 km³), Victoria (+76 km³), and Huron/Michigan (+105 km³). These lakes are highlighted in the time series shown in Fig. 2.26. The largest volumetric water level anomalies matched global patterns in terrestrial water storage assessed using data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission (see Fig. 2.31).

Water level data were acquired from the NASA/CNES Topex/Poseidon and Jason satellite missions via the Global Reservoir and Lake Monitoring (G-REALM) project version 2.4 and via Theia's Hydroweb database. The 2020 water level anomalies in 80 lakes that had data from both sources were averaged across the two sources. One hundred and sixty lakes were unique to the G-REALM dataset and nine lakes were unique to the Hydroweb dataset. Satellite altimeters were originally developed to map ocean surface height. A small subset of the world's lakes are monitored in this way because the space-borne sensors must pass directly over the lake with sufficient regularity to produce accurate and complete time series. The lakes in this study comprise the 249 lakes with the longest (>29 years) and highest temporal resolution time series which are updated in near real time. Comparing the satellite altimeter measurements to in situ measurements, the root mean squared error of elevation variations is ~5 cm for large lakes such as those analyzed here. Water levels are typically measured every 10 days, but the exact dates on which water levels are measured vary from lake to lake. To make water level data temporally consistent, we have linearly interpolated each lake's time series to the daily scale so that all lakes had time series of the same interval. Of the 249 water level time series, 95 had substantial data gaps from 2002 to 2008, so we used a period prior to these gaps (1993–2001) as the baseline for calculating 2020 water level anomalies.

In situ monitoring of lake water levels is vital for cross-validating and calibrating altimeter-based estimates of long-term water level variation in lakes. However, our capacity to monitor changes in a global population of lakes with in situ data alone is currently limited due to the scarcity of publicly available near-real time data from key regions. Impediments to data sharing need to be overcome, and data delivery needs to be more timely in order to monitor water cycle variation with in situ data. Landsat-based surface water extent datasets, such as those produced by the Copernicus Programme (Pekel et al. 2016), could be used for near real-time monitoring of water storage in many thousands of lakes but to date, no near real-time data products exist to support such efforts.

### 7) Cloudiness
—M. J. Foster, L. Di Girolamo, C. Phillips, M. Stengel, S. Sun-Mack, and G. Zhao

Global cloudiness in 2020 increased by 0.31% (±0.14%) relative to 2019, based on several satellite records (Fig. 2.27). Mean global cloudiness tends to stay reasonably stable, but regional distribution of cloud changes year-to-year. Some of this is due to normal variation in synoptic-scale weather events, but cloudiness can also be driven by modes of variability, the best-known being the El Niño–Southern Oscillation (ENSO). ENSO is characterized by a shifting gradient of SST and
low-level winds between the eastern and central equatorial Pacific and Indonesia. This gradient can in turn enhance or suppress convection, which drives the formation of clouds. This means that different phases of ENSO (and other modes) are frequently accompanied by characteristic patterns of cloudiness, which typically have a seasonal dependence. These large-scale patterns are important for several reasons. Clouds cool Earth by reflecting incoming solar radiation and warm it by trapping outgoing terrestrial radiation. Whether the overall effect is one of cooling or warming depends on many factors including the geographic distribution, height, and opacity of clouds (Bony et al. 2015). Clouds are also a gauge for moisture, and their presence may increase or decrease the risk of hydrological hazards like droughts, flooding, and wildfires.

<figure><img src="image_67.png" /><figcaption>Fig. 2.27. Annual global cloudiness (a) anomalies and (b) actual (%) for 1980–2020. The anomaly is defined as the annual value minus the mean, derived between 2003 and 2015, a period common to the satellite records excluding CALIPSO, where the entire record was used instead. The datasets include (1) PATMOS-x/AVHRR (Pathfinder Atmospheres Extended; Heidinger et al. 2013), (2) HIRS High Cloud (Wylie et al. 2005, Menzel et al. 2016), (3) MISR (Multi-angle Imaging SpectroRadiometer; Di Girolamo et al. 2010), (4) Aqua + Terra MODIS C6 (Moderate Resolution Imaging Spectroradiometer Collection 6; Platnick et al. 2015), (5) CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation; Winker et al. 2007), (6) CERES Aqua MODIS (Clouds and the Earth’s Radiant Energy System; Trepte et al. 2010; Minnis et al. 2008), (7) SatCORPS (satellite cloud and radiative property retrieval system; Minnis et al. 2016), (8) CLARA-A2 (cloud, albedo, and radiation dataset; Karlsson et al. 2017), (9) PATMOS-x/AQUA MODIS (created specifically for this report), (10) CLOUD_CCI (Cloud Climate Change Initiative AVHRR-PM v3.0; Stengel et al. 2017), and (11) PATMOS-x/AVHRR+HIRS (Foster et al. 2018).</figcaption></figure>

The Indian Ocean dipole (IOD) can also be characterized by a shifting gradient in SST but affects the Indian Ocean, and a positive phase typically means fewer clouds and drier-than-normal conditions over much of Australia (BoM 2021). After being strongly positive and the most notable mode of variability present in 2019, the IOD returned to neutral conditions in 2020 (see section 4f). Meanwhile, ENSO shifted from neutral conditions to a moderate La Niña phase, which began in the boreal summer and persisted through the rest of the year (see section 4b). These features are apparent in the cloudiness anomalies across the four typical seasons of 2020 (Fig. 2.28). Positive cloudiness anomalies and wet conditions in the tropical central and eastern Pacific are indicative of weak El Niño-like conditions during December 2019–February 2020, while negative anomalies over the western tropical Pacific paired with positive anomalies over Indonesia are characteristic of La Niña during June–August 2020.

Plate 2.1n shows global cloudiness anomalies for 2020. Large and/or persistent changes over a specific region can result in a statistically significant cloudiness anomaly. This is an anomaly that, when averaged over the entire year, falls more than 2 standard deviations outside the mean as determined from the PATMOS-x/AVHRR climatology base period (1981–2010). In 2020 there were several such anomalies, and, in particular, there were large numbers of negative continental anomalies. Significant maritime anomalies include increased cloudiness in the eastern tropical Pacific and northern Indian ocean, particularly the Arabian Sea and Gulf of Aden, and decreased cloudiness in the northern Pacific. The increased cloudiness in the tropical Pacific is on either side of the ITCZ, the equatorial belt where northern and southern trade winds converge to produce precipitating convective clouds, and suggests a poleward shift of convective activity. Continental
<figure><img src="image_68.png" /><figcaption>Fig. 2.28. Global seasonal cloudiness anomaly (%; 1981–2010 base period) for (a) Dec–Feb (DJF), (b) Mar–May (MAM), (c) Jun–Aug (JJA), and Sep–Nov (SON) generated from the 30-year PATMOS-x/AVHRR+HIRS cloud climatology.</figcaption></figure>

anomalies include decreased cloudiness in the Americas over the western United States and portions of Brazil, Paraguay, Uruguay, and Argentina. Africa experienced decreased cloudiness in the south over Namibia and South Africa and in the north over Libya, Tunisia, and Algeria. Europe experienced negative anomalies driven by decreased cloudiness during March–May (Fig. 2.28). Decreased cloudiness also occurred in Asia over Turkey, southern Russia, Kazakhstan, Mongolia, and parts of western China. The continental anomalies frequently coincided with regions that experienced below-average precipitation and/or severe wildfires (Plates 2.1k,x,z,ag).

8) *River discharge and runoff*—H. Kim and D. Tokuda

Since 2017, global land has been overall slightly wetter than average, and the anomalies of runoff (Plate 2.1o) and discharge (Plate 2.1p) make clear that the distribution of wet and dry regions were similar to those of 2019. In many regions, the deviations intensified during 2020. A wet state of runoff prevailed in a large area of the United States (except the west), Canada (except the southeast), and the Eurasian continent. In particular, a strong wet signal has been discerned in East Asia (i.e., China, Japan, and Korea) in contrast to the strong dry spell of the previous year. During summer, anomalous runoff was generated by a disastrous amount of rainfall fostered by the enhanced Changma (also known as Meiyu in China and Baiu in Japan; see Sidebar 7.3 for details). According to recent studies, the East Asia Summer Monsoon lifecycle has intensified (Park et al. 2020; Wang et al. 2019). The United Kingdom also observed an anomalous wet year due to an exceptionally wet 2019/20 winter, and all the seasons of 2020 except for the record-breaking sunny spring (see section 7f2; Met Office 2020). Most regions of the European continent suffered anomalous dry spells, while the climate state of Scandinavian countries shifted to become wetter. Also, a large area of South America experienced a drier hydroclimate. Such anomalous states of climate were reflected in the global distributions of runoff and river discharge.
It has been well-known that ENSO and Pacific Decadal Oscillation (PDO; Zhang et al. 1997) are key drivers modulating global freshwater discharge (e.g., Kim 2020). The long-term variability of total runoff and those climate modes are shown in Fig. 2.29. This indicates that a positive (negative) phase inherent to ENSO and PDO tends to be associated with a drier (wetter) state of the global freshwater discharge by which about 46% of total variance is explained by the combined contribution of ENSO and PDO. Globally, a continuous wet state since 2017 was prolonged and then further intensified during 2020 due to the emergence of La Niña in the second half of the year. 2020 saw the third-highest (~95th percentile) runoff of the 63-year period (1958–2020).

Figure 2.30 displays the inter-annual variability and climatology of freshwater discharge into the Atlantic, Pacific, Indian, and Arctic basins, which comprise approximately 75% and 85% of the entire terrestrial land area and river discharge, respectively. For the Atlantic Ocean, it was nearly neutral during 2020, while river runoff was above normal for much of the past decade. The seasonality was relatively weaker due to the excess and deficit of discharge during the dry and wet season, respectively. The Pacific Ocean received significantly greater volumes of water from rivers during the wet season (May–July) and over the rest of the year. In terms of long-term variability, there is a strong upward trend since the 1990s. Therefore, it was significantly wet during 2020. A similar upward trend is seen in the Indian Ocean as well, following a long-term decline since the mid-twentieth century. During the entire analysis period, the freshwater discharge into the Arctic Ocean has been increasing, and the wet season of 2020 was significantly anomalous (see section 5g for details).

The 63-year series of runoff and freshwater discharge were provided from off-line hydrologic simulations of the Ensemble Land State Estimator (ELSE; Kim et al. 2009) and a global-scale river routing model, Catchment-based Macro-scale Floodplain (CaMa-Flood; Yamazaki et al. 2011) over 1° and 0.5° global grids, respectively. To keep uniformity with the other estimates, river networks information was prepared in a regular grid system, 30-min drainage direction map (DDM30; Döll and Lehner 2002), and sub-grid-scale parameters (e.g., river length and floodplain shape) were derived accordingly. To distinguish the freshwater discharge to each oceanic basin, the World Ocean Atlas 2018 (Garcia et al. 2019) was referenced. The Japanese global atmospheric reanalysis (JRA-55; Kobayashi et al. 2015) and the GPCC Monitoring Product version 2020 (Schneider et al. 2020) were combined to produce the atmospheric boundary conditions.

<figure><img src="image_69.png" /><figcaption>Fig. 2.29. Interannual variability of Ocean Nino Index (ONI; lower), Pacific Decadal Oscillation (PDO) index (upper), and global runoff (middle; mm; thick black line is 12-month moving average). ONI and PDO index are shaded red (positive phase) or blue (negative phase). Shading above and below the zero-line of global runoff is proportional to PDO and ONI, respectively.</figcaption></figure>
<figure><img src="image_70.png" /><figcaption>Fig. 2.30. Interannual variability of freshwater discharge to global ocean basins (km³ yr⁻¹). Line and shades indicate annual mean and monthly anomaly, respectively. (Left) Seasonality of freshwater discharge to global ocean basins (km³ yr⁻¹). Thick black line, thin blue line, and gray shade indicate long-term climatology, seasonal variation during 2020, and 1σ of long-term variability. J, A, J, O for the tick labels of the shared-ordinate between left and right panels indicate Jan, Apr, Jul, and Oct, respectively (right).</figcaption></figure>

9) *Groundwater and terrestrial water storage*—M. Rodell and D. Wiese

Terrestrial water storage (TWS) comprises all the water on and below the land surface: ground-water, soil moisture, surface water, snow, and ice. In general, snow and ice dominate interannual TWS variability at high latitudes and in alpine regions, surface water dominates in the wet tropics, and groundwater dominates elsewhere (Getirana et al. 2017).

Changes in mean annual TWS between 2019 and 2020 as measured by the GRACE (Tapley et al. 2004) and GRACE-FO (Landerer et al. 2020) satellite missions, shown in Plate 2.1q as equivalent heights of water in centimeters (cm), integrate the effects of multiple hydroclimatic variables (see Plates 2.1k,p,t). Of note in 2020, heavy rains raised TWS in a large region of south-central and eastern Africa by 12–25 cm equivalent height of water. Exacerbated by increased runoff due to environmental degradation and urbanization (Mafaranga 2020), Lake Victoria consequently gained more than a meter of water (see sections 2d6, 7e4), with flooding in the surrounding region. Groundwater and TWS remained depressed over most of Europe, excluding Scandinavia, following losses in 2019. Much of India experienced large TWS gains, but, directly to the east, drought worsened in the Indochina Peninsula. Above-normal precipitation increased TWS in
much of the rest of far eastern Asia as well as north-central Asia. TWS changes in Australia were of mixed sign and generally mild. In North America, the most notable changes were considerable increases in TWS in central Canada and the southeastern United States and drying that stretched from the coast of California to eastern Texas, all of which were driven by precipitation anomalies (see section 7b2). Consistent with past years (Tapley et al. 2004), some of the most massive TWS changes occurred in South America, including large increases in eastern Brazil and a north-south swath of large decreases down the center of the continent (see section 7d).

Figures 2.31 and 2.32 depict time series of zonal mean and global mean monthly TWS anomalies after removing the seasonal cycle. Ice sheet and glacier ablation continued to produce large TWS declines in Antarctica, Greenland, the Gulf Coast of Alaska, and polar islands, hence we excluded these regions from the data used in Figs. 2.31 and 2.32, but additional ice mass loss is still apparent at high latitudes in Fig. 2.31. The large TWS increase in south-central and eastern Africa was apparent near the equator in Fig. 2.31.

TWS decreases near 40°N can be attributed to four factors (Plate 2.1q): droughts in California, the central United States, and the land adjacent to the Aegean Sea and Black Seas; glacier ablation in the Alay Mountains of Tajikistan; persistent water level decline in the Caspian Sea; and groundwater depletion to support irrigated agriculture in the North China Plain. At the global scale (Fig. 2.32), mean TWS decreased by about 7 mm equivalent height of water from the start of 2020 through June and then quickly gained nearly 12 mm, much of that in central Africa, causing a temporary 4 mm decline in sea level (see section 3f).

In situ measurement records of the TWS components are rarely available outside of the United States and parts of Europe and Australia; however, GRACE and GRACE-FO have provided monthly, global maps of TWS anomalies based on precise measurements of Earth's time-varying gravity field. The GRACE and GRACE-FO data were the basis for this analysis. Uncertainty in the derived TWS anomalies varies depending on the latitude (higher near the equator), size of the region of interest (higher at small scales), TWS anomaly averaging period (higher for short periods), and orientation (higher for north-south oriented regions near substantially different gravity change signals). At scales greater than about 500,000 km², uncertainty in monthly TWS anomalies is typically around 1–2 cm equivalent height of water (Wiese et al. 2016).

<figure><img src="image_71.png" /><figcaption>Fig. 2.31. 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 base period of 2004–09.</figcaption></figure>

<figure><img src="image_72.png" /><figcaption>Fig. 2.32. Global average terrestrial water storage anomalies from GRACE (gray) and GRACE-FO (black), in cm equivalent height of water, relative to a 2004–09 mean baseline.</figcaption></figure>
10) Soil moisture—R. van der Schalie, T. Scanlon, W. Preimesberger, A. J. Pasik, 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

Monitoring global soil moisture conditions is key for our understanding of the climate system, as soil moisture has a defining role in the energy and water fluxes at the land-atmosphere boundary. The global surface soil moisture conditions in 2020, as measured by satellite, were on average close to the climatology derived from historical data of the 1991–2010 period (Fig. 2.33), being slightly wetter than normal at the start of the year. Although still present, the large discrepancy between the Northern Hemisphere (NH) and Southern Hemisphere (SH) observed at the end of 2019 became slightly less pronounced in 2020 (Fig. 2.34). Drier-than-usual conditions persisted in the SH throughout 2020, and while the record wet peak of the NH at the end of 2019 weakened, it remained historically high in 2020. Plate 2.1r and Appendix Fig. A2.11a show the yearly and monthly soil moisture anomalies for 2020, respectively. There are strong spatial anomalies, which are discussed per individual continent.

In North America the year started with mostly wet conditions, but from the spring onward a strong deviation started to develop between the eastern United States, northwest Canada, and Alaska, with wet anomalies, and the Great Plains, southwestern United States, and Mexico, with strong dry anomalies. In the United States, this was linked to the precipitation anomalies (NOAA 2021), which show a similar pattern, with Nevada and Utah posting record lows. The peak in dry anomalies occurred in autumn and coincided with the record-breaking 2020 western United States wildfire season (see section 7b2 for details about the U.S. drought and wildfires). Consistent with the La Niña forming in the second half of the year (see section 4b), these dry conditions remained in place until the end of 2020.

For Europe an overall wet anomaly was recorded, with the only exception being the region around the Black Sea, which experienced drier-than-normal conditions. April diverted from this, with high-pressure systems dominating the weather in Europe, causing extraordinary warm, sunny, and dry conditions, and consequently led to a sharp decline in soil moisture conditions. The eastern part

<figure><img src="image_73.png" /><figcaption>Fig. 2.33. Time series of global, NH, and SH surface soil moisture anomalies for 1991–2020 (upper, m³ m⁻³; 1991–2010 base period) and the percentage of valid retrievals over land (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_74.png" /><figcaption>Fig. 2.34. Time-latitude diagram of surface soil moisture anomalies (m³ m⁻³; 1991–2010 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.)</figcaption></figure>
of Europe was the most affected, and related early season wildfires were recorded in multiple countries, including Germany, Poland, and Ukraine. From the second half of May onward, the moisture deficiencies returned to wetter-than-usual conditions for most of Europe (see section 7f).

South America experienced a widespread drought (see section 7d), which is clearly visible in the strong dry anomalies found over central Brazil, Bolivia, Paraguay, Uruguay, and Argentina. This situation was amplified by the emergence of La Niña (section 4b) in the second half of the year (Penalba and Rivera 2016). One region that clearly stands out with a severe dry anomaly is Brazil's Pantanal, known as the world's largest wetland, which endured its worst drought in almost 50 years and saw more than a quarter of its area burned. Eastern Brazil is the only major exception to this, with an opposite strong wet anomaly caused by heavy rains early on in the year. Wetter-than-usual conditions in this region generally coincide with La Niña events (see section 7d for details).

In eastern Africa an intensification of the already above-average wet conditions was observed. In the second half of 2019 above-average rainfall was recorded in this region, caused by a strong positive Indian Ocean dipole (Preimesberger et al. 2020), leading to above-average conditions in early 2020. This wet anomaly remained intact due to exceptionally heavy seasonal rains. The dry conditions in southern Africa alleviated in 2020, while southern Madagascar became much drier. The Sahel saw strong wet anomalies developing from August onwards, caused by heavy rainfall (see section 7e for details).

Asia mainly experienced wetter-than-normal soil moisture conditions throughout 2020, especially in India, China, Mongolia, North Korea, and South Korea, where unusually long and strong monsoon rains were reported (see section 7g). The countries in the Lower Mekong Basin continued to experience dry conditions, according to the Mekong River Commission, with below-average annual rainfall in 2019 and a shorter-than-normal monsoon season in 2020. In eastern Siberia, a widespread dry anomaly was observed in the region that experienced massive wildfires and a record heat wave (Overland and Wang 2020; see section 7g2).

While most of Australia still recorded below-average soil moisture conditions in 2020, it shifted considerably toward the long-term mean following the extraordinary low values seen in 2019 (Preimesberger et al. 2020). The only exception to this was southeastern Australia, where soil moisture increased to above-average conditions, providing some much needed relief from the multi-year drought (see section 7h4 for details).

The soil moisture anomalies used in this analysis were derived from the COMBINED product of ESA's Climate Change Initiative for Soil Moisture v05.3 (ESA CCI SM; Dorigo et al., 2017), which is a product that merges satellite soil moisture retrievals from multiple active (Wagner et al. 2013) and passive microwave (Van der Schalie et al. 2017) sensors to achieve the most accurate and consistent climate data record of soil moisture (representing the top ~5 cm of the soil). Merging is done based on both the quality and the temporal and spatial availability of observations, in order to achieve both an improved coverage and quality as compared to any single sensor dataset (Gruber et al. 2017, 2019).

## 11) Monitoring global drought using the self-calibrating Palmer Drought Severity Index—

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

The sharp 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), continued in 2020, and reached a historical peak in October, with a small decrease afterward (Fig. 2.35). Around 6.8% of the global land area experienced extreme drought conditions in October, marking the third historical peak since 1950 after earlier peaks in October 1984 (7.7%) and October 1983 (7.3%). The extent of severe plus extreme drought conditions peaked at 15% of the global land area in October and November, matching the largest historical peaks of this drought severity in September 1983. Moderate or worse drought conditions peaked in August at 27.8% of the global
<figure><img src="image_75.png" /><figcaption>Fig. 2.35. Percentage of global land area (excluding ice sheets and deserts) with scPDSI indicating moderate (<-2), severe (<-3) and extreme (<-4) drought for each month of 1950–2020. Inset: Each month of 2020, denoted by first letter.</figcaption></figure>

<figure><img src="image_76.png" /><figcaption>Fig. 2.36. Change in drought from 2019 to 2020 (mean scPDSI for 2020 minus mean scPDSI for 2019). Increases in drought severity are indicated by negative values (brown), decreases by positive values (green). No calculation is made where a drought index is meaningless (gray areas: ice sheets or deserts with approximately zero mean precipitation).</figcaption></figure>

land area, marking the fifth historical peak after June 1987 (29.6%) and the largest peak since August 2002 (29%).

Extensive severe-to-extreme drought conditions during 2020 affected most of the SH, southern and central Europe, the Middle East, and Southeast Asia (Plate 2.1s). Compared to 2019 (Barichivich et al. 2020), drought severity worsened to extreme in central South America (Fig. 2.36). Worsening drought during the dry season (austral winter and spring) contributed to ravaging fires across the Chaco floodplains and Pantanal wetland in northern Argentina and southern Brazil (Rodríguez 2020; see section 7d). The decadal drought in north-central Chile (Garreaud et al. 2017; Alvarez-Garreton et al. 2021) continued through its 11th year in 2020, with extreme conditions in the central and most populated region of the country (Plate 2.1s). In North America, the east-west moisture contrast observed across the United States since 2017 (Osborn et al. 2018; Barichivich et al. 2020) also persisted during 2020 (Plate 2.1s). Extensive wet conditions extended over the whole eastern half of the country and moderate but protracted drought prevailed in the west. Under these persistent drought conditions, California saw another extreme season of wildfires (Goss et al. 2020).

Previous drought conditions in southern Africa eased slightly in general, but worsened in northern Mozambique (Fig. 2.36). South Africa declared a state

of disaster as many parts of the country had remained under extreme drought since 2018. Wet conditions from 2019 in most of Central and East Africa persisted in 2020 (Plate 2.1s), though moisture anomalies in these regions were uncertain due to sparse coverage of station data. Previous drought conditions also eased in Australia (Fig. 2.36) but most of the country remained under drought during 2020 (Plate 2.1s).

In Southeast Asia, extreme drought because of a weak monsoon season affected Malaysia, Myanmar, Thailand, Cambodia, Vietnam, and particularly Laos (Plate 2.1s), contributing to record low levels of the Mekong River. Extreme drought affected a vast region of northeastern Siberia. Dry conditions through the Sakha Republic, Russia, were associated with anomalously extensive wildfires that burned around 6 million ha. Most of the midlatitude belt from Mongolia in central Asia to western Europe and the Mediterranean saw moderate to extreme drought severity during 2020 (Plate 2.1s). Extreme drought in Europe was once again exacerbated by increasingly recurrent spring and summer heat waves combined with below-average spring precipitation and
antecedent soil moisture deficit. In the midst of two heat waves, France experienced its driest July on record. As in 2019, the most intense annual drought persisted across northern Germany and Poland, where a strong soil moisture deficit has developed since 2018 (Fig. 2.36). In contrast to central and southern Europe, wet conditions occurred across northern Europe from the British Isles to Fennoscandia and the Ural Mountains.

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 as measured by the scPDSI (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 Climatic Research Unit gridded Time Series (CRU TS 4.05 dataset; Harris et al. 2020). Moisture categories are calibrated over the complete 1901–2020 period to ensure that “extreme” droughts and pluvials (wet periods) relate to events that do not occur more frequently than in approximately 2% of the months. This calibration affects direct comparison with other hydrological cycle variables in Plate 2.1 that use a different baseline period.

## 12) Land evaporation—D. G. Miralles, A. Koppa, D. Rains, H. E. Beck, and M. F. McCabe

The geographical patterns of land evaporation anomalies for the year 2020 are illustrated in Plate 2.1t. Several regions experienced anomalously low evaporation, including most of the west and central United States and parts of Russia and central Africa. Moreover, a strong negative anomaly was recorded in central South America, comprising Bolivia, Paraguay, and large parts of Brazil and Argentina. This anomaly reflected severe meteorological drought conditions in autumn, which propagated as agricultural and hydrological drought as the year progressed, leading to the unprecedented dry-out of the Parana River (see sections 2d11, 7d3). Likewise, the drought conditions in the west and central United States led to lower-than-usual evaporation across vast areas of the country. On the other side of the spectrum, regions of positive anomalies included the eastern half of the United States, western Europe, the Amazon basin, the Greater Horn of Africa, and India. In the first three instances, these anomalies related to higher-than-usual surface net radiation and air temperature (see sections 2b1, 2b3). In the case of Amazonia, they occurred despite the widespread meteorological drought conditions in South America, highlighting the positive influence that mild droughts can have on rainforest productivity and transpiration due to associated anomalies of incoming solar radiation (see e.g., Liu et al. 2017). In India, the seasonal

monsoon was exceptionally wet in 2020 (see sections 4e, 7g4), which explains the large positive anomaly in evaporation over the region.

The global mean land evaporation in 2020 was the highest on record, exceeding the values of the 2010 La Niña year (Fig. 2.37). The trend of approximately 0.3 mm year⁻¹, according to the Global Land Evaporation Amsterdam Model (GLEAM) v3.5, falls within the range reported in recent literature (Zhang et al. 2016a; Brutsaert et al. 2017; Anabalón and Sharma 2017). This multi-decadal tendency to higher evaporation has been attributed to increasing global temperatures (Miralles et al. 2014) and greening (Cheng et al. 2017). The overall positive

<figure><img src="image_77.png" /><figcaption>Fig. 2.37. Land evaporation anomaly (mm yr⁻¹; 1981–2010 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_78.png" /><figcaption>Fig. 2.38. Zonal mean terrestrial evaporation anomalies (mm month⁻¹; 1981–2010 base period). (Source: GLEAM.)</figcaption></figure>

global evaporation anomaly in 2020 resulted from the mean positive anomaly in the NH (Figs. 2.37, 2.38), likely associated to the unusually high temperatures across Eurasia (see sections 2b1, 2b3, 7f). The characteristic negative anomalies in land evaporation in the SH during El Niño years (Miralles et al. 2014; Martens et al. 2018) dissipated as the atmosphere returned to a neutral ENSO state during early 2020 and then shifted toward La Niña later in the year (see Southern Oscillation Index; SOI in Fig. 2.37). Nonetheless, evaporation in the SH remained below average, particularly during the first half of the year, and especially over the latitudes where the South American drought occurred (Fig. 2.38).

Evaporation is the phase change of liquid water into vapor. On average, two-thirds of the precipitation over land is evaporated (Dorigo et al. 2021). The ability to monitor its spatial and temporal dynamics is critical for agriculture and water management, as well as to diagnose climate changes. Its crucial climatic role, combined with an increased monitoring ability, has led to the recent consideration of land evaporation as an essential climate variable (ECV) by the World Meteorological Organization (WMO). This consideration was enabled by the proliferation, over the past 2 decades, of approaches dedicated to quantifying this flux at regional to continental scales based on satellite data (McCabe et al. 2016; Miralles et al. 2016). A handful of such approaches regularly update their simulations, including the Land Surface Analysis Satellite Applications Facility (LSA-SAF; Ghilain et al. 2011), the Atmosphere–Land Exchange Inverse (ALEXI; Anderson et al. 2011), and GLEAM (Miralles et al. 2011). The latter provides global-scale estimates and serves as the basis for the results presented in this section.

The accuracy of GLEAM v3 has been reported to be on 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). Some climate zones are known to be challenging for models of terrestrial evaporation such as GLEAM (McCabe et al. 2017a; Fisher et al. 2017; Talsma et al. 2018): in semiarid regions, difficulties in capturing the response of evaporation to drought stress affect the accuracy, while for tropical forests, interception loss remains a key source of uncertainty. Moreover, reported global trends are affected by the poor representation of the effects of carbon dioxide (CO₂) and atmospheric aridity on stomatal conductance in current evaporation models (Zhang et al. 2016a). Further advances in the field of global terrestrial evaporation monitoring need to be realized in order to reduce these uncertainties. These may include developments in high-resolution optical platforms (McCabe et al. 2017b) and thermal missions such as ECOSTRESS (Fisher et al. 2020) or TRISHNA (Lagouarde et al. 2018).

## e. Atmospheric Circulation

### 1) Mean sea level pressure and related modes of variability—R. Allan and B. Noll

Global atmospheric circulation patterns are dominated by the El Niño–Southern Oscillation (ENSO), measured in the atmosphere by the Southern Oscillation Index (SOI), and in the Northern Hemisphere (NH) by the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO); ENSO is measured in the Southern Hemisphere (SH) by the Antarctic Oscillation (AAO), also known as
the Southern Annular Mode (SAM; see Fig. 2.39). A detailed summary of all the above modes, their definitions, and so forth, are given in Kaplan (2011).

In section 4b, 2019 to mid-2020 conditions are denoted as being ENSO-neutral, with evidence for the development of a La Niña by August–September 2020. However, from March–April 2018 until July–August 2020, monthly Niño-4 sea surface temperature (SST) anomalies (https://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices) remained positive and thus passed one criterion for this period being indicative of a protracted El Niño episode (Allan et al. 2019). Different “flavors” of ENSO have been addressed in the literature (Capotondi et al. 2015; L’Heureux et al. 2017; Wang et al. 2017; Timmermann et al. 2018; Santoso et al. 2019), including protracted El Niño and La Niña episodes (Allan and D’Arrigo 1999; Allan et al. 2019). The latter are distinguished by periods of both sustained above-average SST anomalies in the Niño-4 region in the western equatorial Pacific (5°S–5°N, 160°E–150°W) and a persistent negative SOI. This pattern is similar to El Niño and La Niña episodes known as Modoki (Ashok et al. 2007; Weng et al. 2007; Ashok and Yamagata 2009) and they could be argued to be the same phenomenon (Allan et al. 2019; see also section 4b).

The second criteria, for the SOI to have acted similarly by being consistently negative (allowing for at most two consecutive months to have gone positive), occurred from June 2018 to August 2020 (Fig. 2.39). This period of continuously warm Niño-4 SST anomalies led to enhanced atmospheric convection over that region and the generation of a teleconnection that caused large-scale subsidence across eastern Australia in the early 2018 to mid-to-late 2020 period (Allan et al. 2021). Together with the continued impact of a positive Indian Ocean dipole (IOD) from late 2019, the warm Niño-4 SST teleconnection suppressed rainfall across southern and eastern Australia and New Zealand (Zhang et al. 2021; see also sections 2d5, 7h).

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

Fig. 2.39. Time series for modes of variability described using sea level pressure for the (left) complete period of record and (right) 2006–20. (a),(b) Southern Oscillation Index (SOI; provided by the Australian Bureau of Meteorology); (c),(d) Arctic Oscillation (AO; NOAA NCEP Climate Prediction Center); (e),(f) Antarctic Oscillation (AAO; NOAA NCEP Climate Prediction Center); (g),(h) winter (Dec–Feb) North Atlantic Oscillation (NAO) average (NCAR; presented for winter at the beginning of each year so winter 2020/21 is not shown).
In the NH, the last several boreal winters have displayed a variety of AO and NAO conditions (Figs. 2.39, 2.40). The 2019/20 boreal winter (Figs. 2.40b,e) was characterized by a persistent, mainly positive NAO, which led to mild conditions across the entire European region (see section 7f). This NAO phase contributed to heavy rainfall leading to flooding and a series of deep Atlantic cyclones, culminating in large storms in February 2020 (e.g., Storms Ciara and Dennis that impacted the United Kingdom; see section 7f2 for details). A strong stratospheric polar vortex dominated the winter, extending down through the troposphere and leading to abnormally cold air temperatures extending eastward from Alaska to Greenland and Svalbard (see section 5b).

During the 2020/21 boreal winter (Figs. 2.40c,f), the NAO was near average in December but shifted to negative throughout January, becoming the most negative observed in 11 years, and this extended through the first half of February 2021. In December 2020, Europe experienced its fourth-warmest December on record, with such conditions most pronounced over Scandinavia. Above-average precipitation occurred over most of western, northern, and southern Europe, with localized damage and flooding. This extended into January and February for western and central Europe, while temperatures were generally close to average across the continent, though colder in the west and north.

In the SH, the AAO was positive over 60% of the time during 2020, associated with a wide swath of higher-than-normal air pressure in the southwest Pacific (Figs. 2.40b,e), both early and again late in the year. This, along with the continued impact of a positive IOD event from late 2019, was associated with one of the worst droughts on record for northern New Zealand (NIWA 2020; see section 7h5). Slightly higher-than-normal air pressure in the Great Australian Bight also occurred as Australia recorded its fourth-warmest year on record (BoM 2020a; see section 7h4). Pressures were also above normal across South America, leading to Argentina's second-warmest year on record (see section 7d3). Influenced by the aforementioned IOD and the Pacific Niño-4 teleconnection, tropical cyclone activity was near or slightly above normal in the South Indian Ocean and southwest Pacific but below normal in the Australian region (see section 4g).

The frequently positive AAO also meant that pressures were below normal across Antarctica, which in September 2020 experienced an above-average maximum sea ice extent of $19.06 \times 10^6$ km² (NOAA 2021; see section 6f). Frequent patterns of lower-than-normal pressure near South Africa caused a wetter-than-normal winter during 2020 (see section 7e5), leading to the full recharge of Cape Town's dams for the first time in about 6 years (City of Cape Town 2021), following the severe drought of 2015–18 (Otto et al. 2018; section 2d9; SOTC 2018).

The AAO remained mostly positive during the 2020/21 austral summer (Figs. 2.40c,f), contributing to a drier-than-normal summer across New Zealand for the second consecutive year (NIWA 2021; see section 7h5). During December, Severe Tropical Cyclone Yasa formed as a pulse of enhanced convection crossed the tropical Pacific, becoming the South Pacific's strongest tropical cyclone since Winston in 2016 and making landfall in Fiji. Above-average sea surface temperatures to the north of Australia, as is typical during La Niña, led to above-normal rainfall totals in the tropical north and southeast, a marked change compared to the previous year (BoM 2021).

## 2) Land and ocean surface winds—C. Azorin-Molina, R. J. H. Dunn, L. Ricciardulli, C. A. Mears, T. R. McVicar, and J. P. Nicolas

The strengthening in global average surface wind speed over land (i.e., ~10 m above the ground) persisted in 2020, consistent with the reversal in global terrestrial winds observed since around 2010 (e.g., Azorin-Molina et al. 2020; Zeng et al. 2019; Fig. 2.41a). Prior to ~2010, a slowdown of terrestrial surface winds (termed stilling; Roderick et al. 2007) had dominated globally and regionally since the 1960s (e.g., Kim and Paik 2015; Azorin-Molina 2018a; Zeng et al. 2019). The global land average wind speed anomaly in 2020 with respect to the 1981–2010 climatology (Table 2.8) showed a positive value (+0.052 m s⁻¹), which was the highest over the last 2 decades. Europe (+0.082 m s⁻¹), Central Asia (+0.178 m s⁻¹), and East Asia (+0.051 m s⁻¹) continued with the recovery of
<figure><img src="image_80.png" /></figure>

Fig. 2.40. Boreal winter sea level pressure anomalies (hPa; 1981–2010 base period) around the NH averaged over Dec–Feb for (a) 2018/19, (b) 2019/20, and (c) 2020/21 (constructed using ERA5, Hersbach et al. 2019). North Atlantic Oscillation (NAO) daily time series (hPa) for boreal winter (d) 2018/19, (e) 2019/20, and (f) 2020/21. The 5-day running mean is shown by the solid black line (constructed using Met Office MIDAS data). Austral summer sea level pressure anomalies (hPa; 1981–2010 base period) around the Southern Hemisphere (hPa; 1981–2010 base period) averaged over Dec–Feb for (g) 2018/19, (h) 2019/20, and (i) 2020/21 (constructed using ERA5). Antarctic Oscillation (AAO) daily time series (hPa) for austral summer (j) 2018/19, (k) 2019/20, and (l) 2020/21 (NOAA NCEP Climate Prediction Center).
<figure><img src="image_81.png" /><figcaption>Fig. 2.41. Global (excluding Australia in panels [a], [c] and [d]) and regional annual time series of land surface wind speed anomaly (m s⁻¹; 1981–2010 base period) using (a) HadISD3 (1973–2020) and (b) ERA5 (1979–2020), MERRA-2 (1980–2020), and 20CRv3 (1836–2015, only 1970–2015 shown here). HadISD3 occurrence frequencies (in %) for wind speeds (c) >3 m s⁻¹ and (d) >10 m s⁻¹.</figcaption></figure>

winds, while North America showed less negative anomalies (−0.084 m s⁻¹) compared to the lowest value (−0.253 m s⁻¹), which occurred in 2012. Wind speed frequencies above a moderate threshold (>3 m s⁻¹; Fig. 2.41c) exhibited a weak recovery in the last decade, with no trend for stronger wind speeds over land (>10 m s⁻¹; Fig. 2.41d).

The assessment of wind speed changes across land and ocean surfaces for the 1979–2020 period is based on two types of products. First, global in situ anemometer observations were obtained from the quality-controlled HadISD3 dataset (v3.1.1.2020f; 1973–2020; Dunn et al. 2012, 2016, 2019) for 2554 stations that had sufficient coverage during the 1981–2010 climatology period. Second, wind speed estimates from three grid-ded reanalysis products were used to cover Earth’s surface evenly: (i) ERA5 (1979–2020; Hersbach et al. 2020); (ii) MERRA-2 (1980–2020; Gelaro et al. 2017); and (iii) 20CRv3 (1836–2015; Slivinski et al. 2019). A major shortcoming of these products is their inability to capture the stilling and reversal phenomena shown by observations (Fig. 2.41b; Torralba et al. 2017; Ramon et al. 2019; Wohland et al. 2019); therefore, trends should be interpreted with caution.

Despite the rebound of surface winds observed since 2012, the sign of the long-term 1979–2020 trends of terrestrial wind speed remained negative. Globally, land surface winds weakened at a rate of −0.056 m s⁻¹ decade⁻¹ (Table 2.8). This slowdown is of lesser magnitude com-

pared to previous reports (Azorin-Molina et al. 2020) and especially when compared to the global average trend in observed terrestrial near-surface wind speeds of −0.140 m s⁻¹ decade⁻¹ reviewed by McVicar et al. (2012) Regions also exhibited a weakening of negative trends and in the magnitudes of the 5th to 95th percentile confidence ranges because of the reversal of winds in the 2010s, with Central Asia and North America showing the strongest changes, and Europe and East Asia the weakest. As shown in Fig. 2.42, negative trends mostly occur across midlatitude regions of the NH, where most land-based observations exist. In the SH, ERA5 shows a greater dominance of weak positive trends over continents, particularly for Antarctica. In fact, the percentage of positive trends for stations and grid-points increased from 37% in 2019 to 42% because of the recovery of terrestrial winds, especially in Asia.