Global mean temperature is reported as the mean of the five data sets listed below. Global mean temperature anomalies are expressed relative to the 1850–1900 average. However, only HadCRUT5 goes back to 1850. The averages for the NOAAGlobalTemp and GISTEMP data sets are set equal to that of HadCRUT4 over the period 1880–1900 and the averages for the two reanalyses are set to equal that of HadCRUT4 over the period 1981–2010. HadCRUT4 is used as a basis for aligning other data sets for continuity with previous reports.
HadCRUT.18.104.22.168 —Morice, C.P. et al., 2021. An Updated Assessment of Near-Surface Temperature Change From 1850: The HadCRUT5 Data Set. Journal of Geophysical Research: Atmospheres, 126(3): e2019JD032361. doi: https://doi.org/10.1029/2019JD032361. HadCRUT.22.214.171.124 data were obtained from http://www.metoffice.gov.uk/hadobs/hadcrut5 on 14 February 2021 and are © British Crown Copyright, Met Office 2021, provided under an Open Government License, http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/.
NOAAGlobalTemp v5 — Zhang, H.-M., et al., NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 5.0. NOAA National Centers for Environmental Information. doi:10.7289/V5FN144H, https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.no....
Huang, B. et al., 2020: Uncertainty Estimates for Sea Surface Temperature and Land Surface Air Temperature in NOAAGlobalTemp Version 5. Journal of Climate 33(4): 1351–1379, https://journals.ametsoc.org/view/journals/clim/33/4/jcli-d-19-0395.1.xml.
GISTEMP v4 — GISTEMP Team, 2019: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies, https://data.giss.nasa.gov/gistemp/.
Lenssen, N.J.L. et al., 2019: Improvements in the GISTEMP Uncertainty Model. Journal of Geophysical Research: Atmospheres 124(12): 6307–6326, doi: https://doi.org/10.1029/2018JD029522.
ERA5 — Hersbach, H. et al., 2020 : The ERA5 Global Reanalysis. Quarterly Journal of the Royal Meteorological Society 146(730): 1999–2049, doi: https://doi.org/10.1002/qj.3803.
JRA-55 — Kobayashi, S. et al., 2015: The JRA-55 Reanalysis: General Specifications and Basic Characteristics. Journal of the Meteorological Society of Japan. Ser. II 93(1): 5–48, doi: 10.2151/jmsj.2015-001, https://www.jstage.jst.go.jp/article/jmsj/93/1/93_2015-001/_article.
GREENHOUSE GAS DATA
Estimated concentrations from 1750 are used to represent pre-industrial conditions. Calculations assume a pre-industrial mole fraction of 278 ppm for CO2, 722 ppb for CH4 and 270 ppb for N2O.
World Meteorological Organization, 2020: WMO Greenhouse Gas Bulletin: The State of Greenhouse Gases in the Atmosphere Based on Global Observations through 2019, No. 16, https://library.wmo.int/index.php?lvl=notice_display&id=21795#.X7v7lM1KhPY.
World Data Centre for Greenhouse Gases operated by Japan Meteorological Agency, https://gaw.kishou.go.jp/.
OCEAN HEAT CONTENT DATA
Cheng, L. et al., 2017: Improved Estimates of Ocean Heat Content from 1960 to 2015. Science Advances 3(3): e1601545, doi: 10.1126/sciadv.1601545, https://advances.sciencemag.org/content/3/3/e1601545.
CMEMS (CORA, http://marine.copernicus.eu/science-learning/ocean-monitoring-indicators)
Desbruyères, D.G. et al., 2016: Deep and Abyssal Ocean Warming from 35 Years of Repeat Hydrography. Geophysical Research Letters 43(19): 10,356-10,365, doi: https://doi.org/10.1002/2016GL070413.
Domingues, C.M. et al., 2008: Improved Estimates of Upper-Ocean Warming and Multi-Decadal Sea-Level Rise. Nature 453(7198): 1090–1093, doi: 10.1038/nature07080, https://www.nature.com/articles/nature07080.
Gaillard, F. et al., 2016: In Situ–Based Reanalysis of the Global Ocean Temperature and Salinity with ISAS: Variability of the Heat Content and Steric Height. Journal of Climate 29(4): 1305–1323, doi: https://journals.ametsoc.org/view/journals/clim/29/4/jcli-d-15-0028.1.xml.
Good, S.A. et al., 2013: EN4: Quality Controlled Ocean Temperature and Salinity Profiles and Monthly Objective Analyses with Uncertainty Estimates. Journal of Geophysical Research: Oceans 118(12): 6704–6716, doi: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JC009067.
Hosoda, S. et al., 2008: A Monthly Mean Dataset of Global Oceanic Temperature and Salinity Derived from Argo Float Observations. JAMSTEC Report of Research and Development, 8: 47–59, doi: https://www.jstage.jst.go.jp/article/jamstecr/8/0/8_0_47/_article.
Ishii, M. et al., 2017: Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present Observational Data Sets. Sola, 13: 163–167, doi: https://www.jstage.jst.go.jp/article/sola/13/0/13_2017-030/_article.
Levitus, S. et al., 2012: World Ocean Heat Content and Thermosteric Sea Level Change (0–2000 m), 1955–2010. Geophysical Research Letters, 39(10), doi: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012GL051106.
Li, H. et al., 2017: Development of a Global Gridded Argo Data Set with Barnes Successive Corrections. Journal of Geophysical Research: Oceans, 122(2): 866–889, doi: https://doi.org/10.1002/2016JC012285.
Roemmich, D. and J. Gilson. 2009: The 2004–2008 Mean and Annual Cycle of Temperature, Salinity, and Steric Height in the Global Ocean from the Argo Program. Progress in Oceanography, 82(2): 81–100, doi: https://www.sciencedirect.com/science/article/abs/pii/S0079661109000160?....
Roemmich, D. et al., 2015: Unabated Planetary Warming and Its Ocean Structure since 2006. Nature Climate Change, 5(3): 240–245. doi: https://www.nature.com/articles/nclimate2513?page=1.
von Schuckmann, K. and P.-Y. Le Traon, 2011: How Well Can We Derive Global Ocean Indicators from Argo Data? Ocean Science, 7(6): 783–791, doi: https://os.copernicus.org/articles/7/783/2011/.
Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO): Legeais, J.-F. et al., 2018: An Improved and Homogeneous Altimeter Sea Level Record from the ESA Climate Change Initiative. Earth System Science Data, 10(1): 281–301, doi: https://essd.copernicus.org/articles/10/281/2018/.
Copernicus Marine Environment Monitoring Service (CMEMS): Pujol, M.-I. et al., 2016: DUACS DT2014: The New Multi-Mission Altimeter Data Set Reprocessed over 20 Years. Ocean Science, 12(5): 1067–1090. doi: https://os.copernicus.org/articles/12/1067/2016/.
Ablain, M. et al., 2017: Satellite Altimetry-Based Sea Level at Global and Regional Scales. Surveys in Geophysics, 38(1): 7–31. doi: https://link.springer.com/article/10.1007/s10712-016-9389-8.
Escudier, P. A. et al., 2017: Satellite radar altimetry: principle, accuracy and precision. In Satellite Altimetry Over Oceans and Land Surfaces (D. Stammer and A. Cazenave, eds).
MARINE HEATWAVE DATA
MHWs are categorized as moderate when the sea-surface temperature (SST) is above the 90th percentile of the climatological distribution for five days or longer; the subsequent categories are defined with respect to the difference between the SST and the climatological distribution average: strong, severe, or extreme, if that difference is, respectively, more than two, three or four times the difference between the 90th percentile and the climatological distribution average (Hobday et al., 2018).
The baseline used for MHWs is 1982–2011, which is shifted by one year from the standard normal period of 1981–2010 because the satellite SST series on which it is based starts in 1981.
Hobday, A.J. et al., 2018: Categorizing and Naming Marine Heatwaves. Oceanography, 31(2): 1–13. doi: https://eprints.utas.edu.au/27875/.
NOAA OISST v2: Optimum Interpolation Sea Surface Temperature (OISST):
Banzon, V. et al., 2016: A Long-Term Record of Blended Satellite and in Situ Sea-Surface Temperature for Climate Monitoring, Modeling and Environmental Studies. Earth System Science Data, 8(1): 165–176. doi: https://essd.copernicus.org/articles/8/165/2016/.
OCEAN ACIDIFICATION DATA
Data from sampling sites were extracted from the 14.3.1 data portal (http://oa.iode.org) for the time period from 1 January 2010 to 8 January 2020. Annual averages, maximums and minimums were calculated for each station for each year.
The global pH data set is based on a variety of oceanographic variables from Copernicus Marine Service (CMEMS):
Data set background:
The sea ice section uses data from the EUMETSAT OSI SAF Sea Ice Index v2.1 (OSI-SAF, based on Lavergne et al., 2019) and the NSIDC v3 Sea Ice Index (Fetterer et al., 2017). Sea-ice concentrations are estimated from microwave radiances measured from satellites. Sea-ice extent is calculated as the area of ocean grid cells where the sea-ice concentration exceeds 15%. Although there are relatively large differences in the absolute extent between data sets, the data sets have good agreement on the year-to-year changes and the trends. In this report, NSIDC data are reported for absolute extents (for example, “18.95 million km2”) for consistency with earlier reports, while rankings are reported for both data sets.
Fetterer, F., K. Knowles, W. N. Meier, M. Savoie and A. K. Windnagel. 2017, updated daily. Sea Ice Index, Version 3. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. doi: https://nsidc.org/data/G02135/versions/3.
EUMETSAT Ocean and Sea Ice Satellite Application Facility, Sea-ice index 1979-onwards (v2.1, 2020), OSI-420, Data extracted from OSI SAF FTP server: 1979–2020, northern and southern hemisphere.
Lavergne, T. et al., 2019: Version 2 of the EUMETSAT OSI SAF and ESA CCI Sea-Ice Concentration Climate Data Records. The Cryosphere, 13(1): 49–78, doi: https://tc.copernicus.org/articles/13/49/2019/.
GREENLAND ICE SHEET DATA
Data are from the Polar Portal http://polarportal.dk/en/home/.
The ice discharge series has been available since 1986 and is derived from satellite data which can be used to measure glacier flow speeds all around the edges. These data are used to estimate how much ice is being lost as icebergs.
Slightly different models have been used to calculate the SMB over time. These models, using different forcing data, may give slightly different results.
ANTARCTIC ICE SHEET DATA
Data shown are from:
Velicogna, I. et al., 2020: Continuity of Ice Sheet Mass Loss in Greenland and Antarctica From the GRACE and GRACE Follow-On Missions. Geophysical Research Letters, 47(8): e2020GL087291, doi: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL087291.
Information on glaciers is from the World Glacier Monitoring Service https://wgms.ch/.
WGMS (2020, updated, and earlier reports). Global Glacier Change Bulletin No. 3 (2016–2017). Zemp, M., Gärtner-Roer, I., Nussbaumer, S. U., Bannwart, J., Rastner, P., Paul, F., and Hoelzle, M. (eds.), ISC(WDS)/IUGG(IACS)/UNEP/UNESCO/WMO, World Glacier Monitoring Service, Zurich, Switzerland, 274 pp., publication based on database version: doi: https://wgms.ch/data_databaseversions/.
The following GPCC data sets were used in the analysis:
• First Guess Monthly, doi: 10.5676/DWD_GPCC/FG_M_100, https://opendata.dwd.de/climate_environment/GPCC/html/gpcc_firstguess_do...
• Monitoring Product (Version 6), doi: 10.5676/DWD_GPCC/MP_M_V6_100 https://opendata.dwd.de/climate_environment/GPCC/html/gpcc_monitoring_v6...
• Full Data Monthly (Version 2018), doi: 10.5676/DWD_GPCC/FD_M_V2018_100 https://opendata.dwd.de/climate_environment/GPCC/html/fulldata-monthly_v...
• First Guess Daily, doi: 10.5676/DWD_GPCC/FG_D_100 https://opendata.dwd.de/climate_environment/GPCC/html/gpcc_firstguess_da...
• Full Data Daily (Version 2018), doi: 10.5676/DWD_GPCC/FD_D_V2018_100 https://opendata.dwd.de/climate_environment/GPCC/html/fulldata-daily_v20...
The normal period used in the precipitation maps is 1951–2010. A longer reference period is used because precipitation is more variable than temperature, so a longer averaging period is needed in order to obtain a reliable average. This is particularly important in arid regions, where precipitation is intermittent.
For extreme precipitation indices, the period 1982–2016 is used as this is the period covered by the full GPCC daily data set (version 2018). As for assessing changes in annual precipitation, a longer baseline allows for a more reliable estimate of the thresholds and averages on which the extremes indices are based.
ARCTIC SIDEBAR DATA
The Arctic sidebar data is based on data from other sections within this report, on information from regional and country reports, as well as on the following data sets:
Copernicus Climate Change Service (C3S) ERA5 temperature dataset. Described in Hersbach, H, Bell, B, Berrisford, P, et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146: 1999– 2049. https://doi.org/10.1002/qj.3803, available at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecv-for-climate-chang....
CAMS wildfire emission data set and interpretation thereof https://atmosphere.copernicus.eu/copernicus-reveals-summer-2020s-arctic- wildfires-set-new-emission-records#
Copernicus Atmosphere Monitoring Service Global Fire Assimilation System (GFAS) https://confluence.ecmwf.int/display/CKB/CAMS%3A+Global+Fire+Assimilatio...
CAMS wildfire emission data set and interpretation thereof: https://atmosphere.copernicus.eu/copernicus-reveals-summer-2020s-arctic-...