Supplement Headline Indicators for Global Climate Monitoring Blair Trewin, Anny Cazenave, Stephen Howell, Matthias Huss, Kirsten Isensee, Matthew D. Palmer, Oksana Tarasova, and Alex Vermeulen

https://doi.org/10.1175/BAMS-D-19-0196.2 Corresponding author: Blair Trewin, [email protected] This document is a supplement to https://doi.org/10.1175/BAMS-D-19-0196.1 ©2020 American Meteorological Society For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.

AFFILIATIONS: Trewin—Australian Bureau of Meteorology, Melbourne, Victoria, Australia; Cazenave—LEGOS, Observatoire Midi-Pyrenees, Toulouse, France; Howell—Climate Research Division, Environment and Canada, Toronto, Ontario, Canada; Huss—Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, and Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland; Isensee—Intergovernmental Oceanographic Commission, UNESCO, Paris, France; Palmer—, Exeter, United Kingdom; Tarasova—World Meteorological Organization, Geneva, Switzerland; Vermeulen—Lund University, Lund, Sweden

AMERICAN METEOROLOGICAL SOCIETY JANUARY 2021 E6 Table ES1. Selected datasets currently used for monitoring the variables associated with the seven headline climate indica- tors. Datasets shown in boldface were used for monitoring key indicators reported in the 2018 WMO State of the Climate report.

Variable Dataset Reference Link HadCRUT4 (Met Office Hadley Centre/Univer- sity of East Anglia ) Morice et al. (2012) www.metoffice.gov.uk/hadobs/hadcrut4 (1850–present) www.ncdc.noaa.gov/data-access/marineocean- NOAA GlobalTemp (U.S. National Oceanic and Huang et al. (2020) data/noaa-global-surface-temperature-noaaglo- Atmospheric Administration) (1880–present) baltemp GISTEMP (U.S. National Aeronautics and Space Lenssen et al. (2019) data.giss..gov/gistemp Temperature Administration) (1880–present) BEST (Berkeley Earth) (1850–present) Rohde and Hausfather (2020) berkeleyearth.org/data www-users.york.ac.uk/~kdc3/papers/cover- Cowtan and Way (1850–present) Cowtan and Way ( 2014) age2013/series.html ERA5 (European Centre for Medium-Range www.ecmwf.int/en/forecasts/datasets/reanalysis- Hersbach et al. (2020) Weather Forecasts) (1979–present) datasets/era5 JRA-55 (Japan Meteorological Agency) Kobayashi et al. (2015) jra.kishou.go.jp/JRA-55/index_en.html (1955–present) www.data.jma.go.jp/gmd/kaiyou/english/ohc/ MRI-JMA (Japan Meteorological Agency) Ishii et al. (2017) ohc_global_en.html www.cmar.csiro.au/sealevel/thermal_expan- CSIRO/ACE CRC/IMAS-UTAS Domingues et al. (2008) sion_ocean_heat_timeseries.html oceans.pmel. noaa.gov/ Ocean heat PMEL/JPL/JIMAR Lyman and Johnson (2014) www.nodc.noaa.gov/OC5/3 M_HEAT_CONTENT/ content www.metoffice.gov.uk/hadobs/en4/download- NCEI Levitus et al. (2012) en4-0-2-l09.html climatedataguide.ucar.edu/climate-data/ocean- Met Office Hadley Centre Palmer et al. (2007) temperature-analysis-and-heat-content-estimate- institute-atmospheric- IAP/CAS Cheng and Zhu (2018) NOAA/NESDIS/ STAR Leuliette and Scharroo (2010) www.star.nesdis.noaa.gov/sod/lsa/SeaLevelRise/ Sea level Copernicus Ssalto/Duacs Multimission Altimeter LSA_SLR_timeseries.php marine.copernicus.eu/ Pujol and Mertz (2019) Products services-portfolio/access-to-products/ OSI-SAF version 2 (EUMETSAT Ocean and Sea osisaf.met.no/quicklooks/sie_graphs/figs_v2/ Lavergne et al. (2019) Sea ice extent Ice Satellite Application Facility) osisaf_nh_iceextent_seasonal.txt Sea Ice Index version 3 Fetterer et al. (2017) https://doi.org/10.7265/N5K072F8 Reference glaciers of the WGMS WGMS (2017) wgms.ch/data_databaseversions/ Glacier mass Composite data of WGMS in situ and geodetic Zemp et al. (2019) zenodo.org/record/1492141# balance mass balance GRACE Wouters et al. (2019) svs.gsfc.nasa.gov/3910 Ocean Global Observing Newton et al. (2015) www.goa-on.org acidification Network community.wmo.int/wmo-greenhouse-gas- WMO Greenhouse Gas Bulletin WMO (2019) concentrations bulletins

AMERICAN METEOROLOGICAL SOCIETY JANUARY 2021 E7 Table ES2. A summary of Essential Climate VariableECVs and the state of monitoring and data availability. Availability is based on reporting in the 2018 State of the Climate Report (Blunden and Arndt, 2019) and does not include variables presented only in map form. ECVs shown in boldface contribute to one of the seven global indicators.

Status of observation Global or near-global Variable (from Arndt et al. 2019) indicator available? Atmospheric: Surface Precipitation Fully monitored Yes Pressure Fully monitored Temperature Fully monitored Yes Surface radiation budget Wind speed and direction Fully monitored Yes (speed) Water vapor Fully monitored Yes Atmospheric: Upper atmosphere Earth radiation budget Fully monitored Yes Lightning Temperature Fully monitored Yes Water vapor Fully monitored Yes Wind speed and direction Fully monitored Yes (speed) Atmospheric: Composition Aerosols properties Partly monitored Yes (aerosol optical depth) Carbon dioxide, methane, and Fully monitored Yes other greenhouse gases Cloud properties Partly monitored Yes (amount) Ozone Fully monitored Yes Precursors (supporting the aerosols Partly monitored and ozone ECVs) Oceanic: Physical Ocean surface heat flux Yes Sea ice Fully monitored Yes Sea level Fully monitored Yes Sea state Sea surface salinity Fully monitored Sea surface temperature Fully monitored Yes Subsurface currents Partly monitored Subsurface salinity Fully monitored Subsurface temperature Fully monitored Yes Surface currents Fully monitored Surface stress Yes Oceanic: Biogeochemistry Carbon dioxide Partly monitored Yes Nitrous oxide Nutrients Ocean acidity Partly monitored Site data only Ocean color Fully monitored Oxygen Transient tracers

AMERICAN METEOROLOGICAL SOCIETY JANUARY 2021 E8 Table ES2. Continued.

Oceanic: Biology/ecosystems Marine habitat properties Plankton Fully monitored Yes Terrestrial Above-ground biomass Partly monitored Fully monitored Yes Anthropogenic greenhouse gas fluxes Anthropogenic water use Fire Partly monitored Yes Fraction of absorbed photosynthetically Partly monitored Yes active radiation (FAPAR) Glaciers Partly monitored Yes Groundwater Partly monitored Ice sheets and ice shelves Partly monitored Yes Lakes Partly monitored Yes Land cover Land surface temperature Latent and sensible heat fluxes Leaf area index (LAI) Permafrost Partly monitored Site data only River discharge Partly monitored Snow Fully monitored Yes Soil carbon Soil moisture Partly monitored Yes 1 www.goa-on.org 2 Results can be found on the WCC website at https://www.esrl.noaa.gov/gmd/ccgg/wmorr/wmorr_results.php 3 https://gaw.kishou.go.jp/publications/summary 4 https://climatedata-catalogue.wmo.int/about

AMERICAN METEOROLOGICAL SOCIETY JANUARY 2021 E9 References

Arndt, D. S., J. Blunden, and R. J. H. Dunn, 2019: Introduction [in “State of the Leuliette, E. W., and R. Scharroo, 2010: Integrating Jason-2 into a multiple- Climate in 2018”]. Bull. Amer. Meteor. Soc., 100 (9), S1–S4, https://doi.org/10 altimeter climate data record. Mar. Geod., 33, 504–517, https://doi.org/10.10 .1175/2019BAMSStateoftheClimate.1. 80/01490419.2010.487795. Blunden, J., and D. S. Arndt, Eds., 2019: State of the Climate in 2018. Bull. Amer. Levitus, S., and Coauthors, 2012: World ocean heat content and thermosteric sea Meteor. Soc., 100 (9), Si–S305, https://doi.org/10.1175/2019BAMSStateofth level change (0-2000 m), 1955-2010. Geophys. Res. Lett., 39, L10603, https:// eClimate.1. doi.org/10.1029/2012GL051106. Cheng, L., and L. Zhu, 2018: 2017 was the warmest year on record for the global Lyman, J. M., and G. C. Johnson, 2014: Estimating global ocean heat content ocean. Adv. Atmos. Sci., 35, 261–263, https://doi.org/10.1007/s00376-018- changes in the upper 1800 m since 1950 and the influence of 8011-z. choice. J. Climate, 27, 1945–1957, https://doi.org/10.1175/JCLI-D-12-00752.1. Cowtan, K., and R. G. Way, 2014: Coverage bias in the HadCRUT4 temperature Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying un- series and its impact on recent temperature trends. Quart. J. Roy. Meteor. Soc., certainties in global and regional temperature change using an ensemble 140, 1935–1944, https://doi.org/10.1002/qj.2297. of observational estimates: The HadCRUT4 data set. J. Geophys. Res., 117, Domingues, C. M., J. A. Church, N. J. White, P. J. Gleckler, S. E. Wijffels, P. M. Barker, D08101, https://doi.org/10.1029/2011JD017187. and J. R. Dunn, 2008: Improved estimates of upper-ocean warming and multi- Newton, J. A., R. A. Feely, E. B. Jewett, P. Williamson, and J. Mathis, 2015: Global decadal sea-level rise. Nature, 453, 1090–1093, https://doi.org/10.1038/ Ocean Acidification Observing Network: Requirements and governance nature07080. plan. 2nd ed. GOA-ON, 57 pp., www.goa-on.org/documents/general/GOA- Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel, 2017: Sea ice ON_2nd_edition_final.pdf. index, version 3. National Snow and Ice Data Center, accessed 23 December Palmer, M. D., K. Haines, S. F. B. Tett, and T. J. Ansell, 2007: Isolating the sig- 2019, https://doi.org/10.7265/N5K072F8. nal of ocean global warming. Geophys. Res. Lett., 34, L23610, https://doi. Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. org/10.1029/2007GL031712. Meteor. Soc., 146, 1999– 2049, https://doi.org/10.1002/qj.3803. Pujol, M.-I., and F. Mertz, 2019: Product user manual for sea level SLA products. Huang, B., and Coauthors, 2020: Uncertainty estimates for sea surface Copernicus Marine Environment Monitoring Service, 39 pp., http://resources. temperature and land surface air temperature in NOAAGlobalTemp marine.copernicus.eu/documents/PUM/CMEMS-SL-PUM-008-032-062.pdf. version 5. J. Climate, 33, 1351–1379, https://doi.org/10.1175/JCLI-D-19 Rohde, R. A., and Z. Hausfather, 2020: The Berkeley Earth Land/Ocean Tempera- -0395.1. ture Record. Earth Syst. Sci. Data, 12, 3469–3479, https://doi.org/10.5194/ Ishii, M., Y. Fukuda, S. Hirahara, S. Yasui, T. Suzuki, and K. Sato, 2017: Accuracy essd-12-3469-2020. of global upper ocean heat content estimation expected from present ob- WGMS, 2017: Global Glacier Change Bulletin No. 2 (2014–2015). World Glacier servational data sets. SOLA, 13, 163–167, https://doi.org/10.2151/SOLA.2017 Monitoring Service, 244 pp., https://wgms.ch/downloads/WGMS_GGCB_02.pdf. -030. WMO, 2019: WMO Greenhouse Gas Bulletin (GHG Bulletin) No. 15: The state of Kobayashi, S., and Coauthors, 2015: The JRA-55 reanalysis: General specifica- greenhouse gases in the atmosphere based on global observations through tions and basic specifications. J. Meteor. Soc. Japan, 93, 5–48, https://doi. 2018. World Meteorological Organization, 8 pp., https://library.wmo.int/doc_ org/10.2151/jmsj.2015-001. num.php?explnum_id=10100. Lavergne, T., and Coauthors, 2019: Version 2 of the EUMETSAT OSI SAF and Wouters, B., A. S. Gardner, and G. Moholdt, 2019: Global glacier mass loss during ESA CCI sea-ice concentration climate data records. Cryosphere, 13, 49–78, the GRACE satellite mission (2002-2016). Front. Earth Sci., 7, 96, https://doi. https://doi.org/10.5194/tc-13-49-2019. org/10.3389/feart.2019.00096. Lenssen, N. J. L., G. A. Schmidt, J. E. Hansen, M. J. Menne, A. Persin, R. Ruedy, and Zemp, M., and Coauthors, 2019: Global glacier mass changes and their contribu- D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. tions to sea-level rise from 1961 to 2016. Nature, 568, 382–386, https://doi. Res. Atmos., 124, 6307–6326, https://doi.org/10.1029/2018JD029522. org/10.1038/s41586-019-1071-0.

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