• Abraham, J. P., and Coauthors, 2013: A review of global ocean temperature observations: Implications for ocean heat content estimates and climate change. Rev. Geophys., 51, 450483, https://doi.org/10.1002/rog.20022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Abram, N. J., E. W. Wolff, and M. A. J. Curran, 2013: A review of sea ice proxy information from polar ice cores. Quat. Sci. Rev., 79, 168183, https://doi.org/10.1016/j.quascirev.2013.01.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alexander, L. V., M. Bador, R. Roca, S. Contractor, M. G. Donat, and P. L. Nguyen, 2020: Intercomparison of annual precipitation indices and extremes over global land areas from in situ, space-based and reanalysis products. Environ. Res. Lett., 15, 055002, https://doi.org/10.1088/1748-9326/ab79e2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, M. R., and Coauthors, 2018: Framing and context. Global Warming of 1.5°C: An IPCC Special Report, V. Masson-Delmotte et al., Eds., IPCC, 49–91.

  • Blunden, J., D. S. Arndt, and G. Hartfield, Eds., 2018: State of the Climate in 2017. Bull. Amer. Meteor. Soc., 99 (8), SiS310, https://doi.org/10.1175/2018BAMSStateoftheClimate.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bojinski, S., M. Verstraete, T. C. Peterson, C. Richter, A. Simmons, and M. Zemp, 2014: The concept of essential climate variables in support of climate research, applications, and policy. Bull. Amer. Meteor. Soc., 95, 14311443, https://doi.org/10.1175/BAMS-D-13-00047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boyer, T., and Coauthors, 2016: Sensitivity of global upper-ocean heat content estimates to mapping methods, XBT bias corrections, and baseline climatologies. J. Climate, 29, 48174842, https://doi.org/10.1175/JCLI-D-15-0801.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Braithwaite, R. J., 1981: On glacier energy balance, ablation, and air temperature. J. Glaciol., 27, 381391, https://doi.org/10.1017/S0022143000011424.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Braun, M. H., and Coauthors, 2019: Constraining glacier elevation and mass changes in South America. Nat. Climate Change, 9, 130136, https://doi.org/10.1038/s41558-018-0375-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brun, F., E. Berthier, P. Wagnon, A. Kääb, and D. Treichler, 2017: A spatially resolved estimate of High Mountain Asia glacier mass balances from 2000 to 2016. Nat. Geosci., 10, 668673, https://doi.org/10.1038/ngeo2999.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cazenave, A., and Coauthors, 2019: Observational requirements for long-term monitoring of the global mean sea level and its components over the altimetry era. Front. Mar. Sci., 6, 582, https://doi.org/10.3389/fmars.2019.00582.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheng, L., and Coauthors, 2016: XBT science: Assessment of instrumental biases and errors. Bull. Amer. Meteor. Soc., 97, 924933, https://doi.org/10.1175/BAMS-D-15-00031.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheng, L., K. E. Trenberth, J. Fasullo, T. Boyer, J. Abraham, and J. Zhu, 2017: Improved estimates of ocean heat content from 1960 to 2015. Sci. Adv., 3, e1601545, https://doi.org/10.1126/sciadv.1601545.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., W. N. Meier, and R. Gersten, 2017: Variability and trends in the Arctic Sea ice cover: Results from different techniques. J. Geophys. Res. Oceans, 122, 68836900, https://doi.org/10.1002/2017JC012768.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cowtan, K., and R. G. Way, 2014: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Quart. J. Roy. Meteor. Soc., 140, 19351944, https://doi.org/10.1002/qj.2297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davy, R., L. Chen, and E. Hanna, 2018: Arctic amplification metrics. Int. J. Climatol., 38, 43844394, https://doi.org/10.1002/joc.5675.

  • Desbruyères, D. G., S. G. Purkey, E. L. McDonagh, G. C. Johnson, and B. A. King, 2016: Deep and abyssal ocean warming from 35 years of repeat hydrography. Geophys. Res. Lett., 43, 10 35610 365, https://doi.org/10.1002/2016GL070413.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Desbruyères, D. G., E. L. McDonagh, B. A. King, and V. Thierry, 2017: Global and full-depth ocean temperature trends during the early twenty-first century from Argo and repeat hydrography. J. Climate, 30, 1985–1997, https://doi.org/10.1175/JCLI-D-16-0396.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donat, M. G., L. V. Alexander, H. Yang, I. Durre, R. Vose, and J. Caesar, 2013: Global land-based datasets for monitoring climate extremes. Bull. Amer. Meteor. Soc., 94, 9971006, https://doi.org/10.1175/BAMS-D-12-00109.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eisenman, I., W. N. Meier, and J. R. Norris, 2014: A spurious jump in the satellite record: Has Antarctic sea ice expansion been overestimated? Cryosphere, 8, 12891296, https://doi.org/10.5194/tc-8-1289-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Etheridge, D. M., L. P. Steele, R. L. Langenfelds, R. J. Francey, J.-M. Barnola, and V. I. Morgan, 1996: Natural and anthropogenic changes in atmospheric CO2 over the last 1000 years from air in Antarctic ice and firn. J. Geophys. Res., 101, 41154128, https://doi.org/10.1029/95JD03410.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel, 2017: Sea ice index, version 3. National Snow and Ice Data Center, accessed 23 December 2019, https://doi.org/10.7265/N5K072F8.

    • Crossref
    • Export Citation
  • Freeman, E., and Coauthors, 2017: ICOADS release 3.0: A major update to the historical marine climate record. Int. J. Climatol., 37, 22112232, https://doi.org/10.1002/joc.4775.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gallaher, D. W., G. G. Campbell, and W. N. Meier, 2014: Anomalous variability in Antarctic sea ice extents during the 1960s with the use of Nimbus data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 881887, https://doi.org/10.1109/JSTARS.2013.2264391.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gardner, A. S., and Coauthors, 2013: A reconciled estimate of glacier contributions to Sea level rise: 2003 to 2009. Science, 340, 852857, https://doi.org/10.1126/science.1234532.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gehne, M., T. M. Hamill, G. N. Kiladis, and K. E. Trenberth, 2016: Comparison of global precipitation estimates across a range of temporal and spatial scales. J. Climate, 29, 77737795, https://doi.org/10.1175/JCLI-D-15-0618.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gill, A., 1982: Atmosphere-Ocean Dynamics. Academic Press, 662 pp.

  • Global Climate Observing System, 2017: Indicators of climate change: Outcome of a meeting held at WMO 3 February 2017. GCOS 206, World Meteorological Organization, 29 pp., https://library.wmo.int/doc_num.php?explnum_id=3418.

  • Good, S. A., M. J. Martin, and N. A. Rayner, 2013: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res. Oceans, 118, 67046716, https://doi.org/10.1002/2013JC009067.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grove, J. M., 2004: Little Ice Ages: Ancient and Modern. Vol. 1. Taylor and Francis, 402 pp.

  • Gu, G., and R. F. Adler, 2011: Precipitation and temperature variations on the interannual time scale: Assessing the impact of ENSO and volcanic eruptions. J. Climate, 24, 22582270, https://doi.org/10.1175/2010JCLI3727.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawkins, E., and Coauthors, 2017: Estimating changes in global temperature since the preindustrial period. Bull. Amer. Meteor. Soc., 98, 18411856, https://doi.org/10.1175/BAMS-D-16-0007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herold, N., L. V. Alexander, M. G. Donat, S. Contractor, and A. Becker, 2016: How much does it rain over land? Geophys. Res. Lett., 43, 341348, https://doi.org/10.1002/2015GL066615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hobbs, W. R., R. Massom, S. Stammerjohn, P. Reid, G. Williams, and W. Meier, 2016: A review of recent changes in Southern Ocean sea ice, their drivers and forcings. Global Planet. Change, 143, 228250, https://doi.org/10.1016/j.gloplacha.2016.06.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Horwath, M., and Coauthors, 2020: ESA Climate Change Initiative (CCI) Sea Level Budget Closure (SLBC_cci) final report D4.7, version 1.1. European Space Agency, 101 pp, https://climate.esa.int/media/documents/ESA_SLBC_cci_D4.7_v1.1.pdf.

  • Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the global precipitation record: GPCP version 2.1. Geophys. Res. Lett., 36, L17808, https://doi.org/10.1029/2009GL040000.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1585 pp.

  • IPCC, 2019a: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. H.-O. Pörtner et al., Eds., IPCC, 755 pp., https://www.ipcc.ch/srocc/download/.

  • IPCC, 2019b: Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems. P. R. Shukla et al., Eds., IPCC, 864 pp., https://www.ipcc.ch/site/assets/uploads/2019/11/SRCCL-Full-Report-Compiled-191128.pdf.

  • Ivanova, N., and Coauthors, 2015: Inter-comparison and evaluation of sea ice algorithms: Towards further identification of challenges and optimal approach using passive microwave observations. Cryosphere, 9, 17971817, https://doi.org/10.5194/tc-9-1797-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, G. C., and Coauthors, 2019: Ocean heat content [in “State of the Climate in 2018”]. Bull. Amer. Meteor. Soc., 100 (9), S74S77, https://doi.org/10.1175/2019BAMSStateoftheClimate.1.

    • Search Google Scholar
    • Export Citation
  • Kääb, A., E. Berthier, C. Nuth, J. Gardelle, and Y. Arnaud, 2012: Contrasting patterns of early twenty-first-century glacier mass change in the Himalayas. Nature, 488, 495498, https://doi.org/10.1038/nature11324.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kennedy, J. J., N. A. Rayner, C. P. Atkinson, and R. E. Killick, 2019: An ensemble data set of sea surface temperature change from 1850: The Met Office Hadley Centre HadSST.4.0.0.0 data set. J. Geophys. Res. Atmos., 124, 77197763, https://doi.org/10.1029/2018JD029867.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, A. D., M. G. Donat, and R. J. H. Dunn, 2019: Land surface temperature extremes [in “State of the Climate in 2018”]. Bull. Amer. Meteor. Soc., 100 (9), S14S16, https://doi.org/10.1175/2019BAMSStateoftheClimate.1.

    • Search Google Scholar
    • Export Citation
  • Kinnard, C., C. M. Zdanowicz, D. A. Fisher, E. Isaksson, A. de Vernal, and L. G. Thompson, 2011: Reconstructed changes in Arctic sea ice over the past 1,450 years. Nature, 479, 509512, https://doi.org/10.1038/nature10581.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavergne, T., and Coauthors, 2019: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records. Cryosphere, 13, 4978, https://doi.org/10.5194/tc-13-49-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leclercq, P. W., J. Oerlemans, H. J. Basagic, I. Bushueva, A. J. Cook, and R. Le Bris, 2014: A data set of worldwide glacier length fluctuations. Cryosphere, 8, 659672, https://doi.org/10.5194/tc-8-659-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Legeais, J. F., and Coauthors, 2018: An improved and homogeneous altimeter sea level record from the ESA Climate Change Initiative. Earth Syst. Sci. Data, 10, 281301, https://doi.org/10.5194/essd-10-281-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lyman, J. M., and G. C. Johnson, 2008: Estimating annual global upper-ocean heat content anomalies despite irregular in situ ocean sampling. J. Climate, 21, 56295641, https://doi.org/10.1175/2008JCLI2259.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marcott, S. A., and Coauthors, 2014: Centennial-scale changes in the global carbon cycle during the last deglaciation. Nature, 514, 616619, https://doi.org/10.1038/nature13799.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marzeion, B., J. G. Cogley, K. Richter, and D. Parkes, 2014: Attribution of global glacier mass loss to anthropogenic and natural causes. Science, 345, 919921, https://doi.org/10.1126/science.1254702.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meier, W. N., and J. J. Stewart, 2019: Assessing uncertainties in sea ice extent climate indicators. Environ. Res. Lett., 14, 035005, https://doi.org/10.1088/1748-9326/aaf52c.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meyssignac, B., and Coauthors, 2019: Measuring global ocean heat content to estimate the earth energy imbalance. Front. Mar. Sci., 6, 432, https://doi.org/10.3389/fmars.2019.00432.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J. Geophys. Res., 117, D08101, https://doi.org/10.1029/2011JD017187.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nerem, R. S., B. D. Beckley, J. Fasullo, B. D. Hamlington, D. Masters, and G. T. Mitchum, 2018: Climate change driven accelerated sea level rise detected in the altimeter era. Proc. Natl. Acad. Sci. USA, 115, 20222025, https://doi.org/10.1073/pnas.1717312115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ohmura, A., 2001: Physical basis for the temperature-based melt-index method. J. Appl. Meteor., 40, 753761, https://doi.org/10.1175/1520-0450(2001)040<0753:PBFTTB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, M. D., 2017: Reconciling estimates of ocean heating and Earth’s radiation budget. Curr. Climate Change Rep., 3, 7886, https://doi.org/10.1007/s40641-016-0053-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, M. D., and P. Brohan, 2011: Estimating sampling uncertainty in fixed-depth and fixed-isotherm estimates of ocean warming. Int. J. Climatol., 31, 980986, https://doi.org/10.1002/joc.2224.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, M. D., and D. J. McNeall, 2014: Internal variability of Earth’s energy budget simulated by CMIP5 climate models. Environ. Res. Lett., 9, 034016, https://doi.org/10.1088/1748-9326/9/3/034016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, M. D., K. Haines, S. F. B. Tett, and T. J. Ansell, 2007: Isolating the signal of ocean global warming. Geophys. Res. Lett., 34, L23610, https://doi.org/10.1029/2007GL031712.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, M. D., and Coauthors, 2017: Ocean heat content variability and change in an ensemble of ocean reanalyses. Climate Dyn., 49, 909930, https://doi.org/10.1007/s00382-015-2801-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parkinson, C. L., 2019: A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic. Proc. Natl. Acad. Sci. USA, 116, 14 41414 423, https://doi.org/10.1073/pnas.1906556116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perkins-Kirkpatrick, S. E., M. G. Donat, and R. J. H. Dunn, 2018: Land surface temperature extremes [in “State of the Climate in 2017”]. Bull. Amer. Meteor. Soc., 99 (8), S15S16, https://doi.org/10.1175/2018BAMSStateoftheClimate.1.

    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., and M. J. Manton, 2008: Monitoring changes in climate extremes: A tale of international cooperation. Bull. Amer. Meteor. Soc., 89, 12661271, https://doi.org/10.1175/2008BAMS2501.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pfeffer, W. T., and Coauthors, 2014: The Randolph glacier inventory: A globally complete inventory of glaciers. J. Glaciol., 60, 537552, https://doi.org/10.3189/2014JoG13J176.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purkey, S. G., and G. C. Johnson, 2010: Warming of global abyssal and deep southern ocean waters between the 1990s and 2000s: Contributions to global heat and sea level rise budgets. J. Climate, 23, 63366351, https://doi.org/10.1175/2010JCLI3682.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riser, S. C., and Coauthors, 2016: Fifteen years of ocean observations with the global Argo array. Nat. Climate Change, 6, 145153, https://doi.org/10.1038/nclimate2872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roemmich, D., W. J. Gould, and J. Gilson, 2012: 135 years of global ocean warming between the Challenger expedition and the Argo Programme. Nat. Climate Change, 2, 425428, https://doi.org/10.1038/nclimate1461.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., P. Berrisford, D. P. Dee, H. Hersbach, S. Hirahara, and J.-N. Thépaul, 2017: A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets. Quart. J. Roy. Meteor. Soc., 143, 101119, https://doi.org/10.1002/qj.2949.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tye, M. R., S. Blenkinsop, M. Donat, I. Durre, and M. Ziese, 2018: Land surface precipitation extremes [in “State of the Climate in 2017”]. Bull. Amer. Meteor. Soc., 99 (8), S29S31, https://doi.org/10.1175/2018BAMSStateoftheClimate.1.

    • Search Google Scholar
    • Export Citation
  • United Nations, 2015b: Transforming our world: The 2030 agenda for sustainable development. United Nations, 44 pp., https://sustainabledevelopment.un.org/post2015/transformingourworld/publication.

  • von Schuckmann, K., and Coauthors, 2016: An imperative to monitor Earth’s energy imbalance. Nat. Climate Change, 6, 138144, https://doi.org/10.1038/nclimate2876.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • von Schuckmann, K., and Coauthors, 2018: Copernicus marine service ocean state report. J. Oper. Oceanogr., 11 (Suppl. 1), S1S142, https://doi.org/10.1080/1755876X.2018.1489208.

    • Search Google Scholar
    • Export Citation
  • Vose, R. S., R. Adler, A. Becker, and X. Yin, 2018: Precipitation [in “State of the Climate in 2017”]. Bull. Amer. Meteor. Soc., 99 (8), S28S31, https://doi.org/10.1175/2018BAMSStateoftheClimate.1.

    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., F. Fetterer, J. S. Stewart, and W. L. Chapman, 2017: A database for depicting Arctic sea ice variations back to 1850. Geogr. Rev., 107, 89107, https://doi.org/10.1111/j.1931-0846.2016.12195.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wijffels, S. E., D. Roemmich, D. Monselesan, J. Church, and J. Gilson, 2016: Ocean temperatures chronicle the ongoing warming of Earth. Nat. Climate Change, 6, 116118, https://doi.org/10.1038/nclimate2924.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, M., and S. Eggleston, 2017: Using indicators to explain our changing climate to policymakers and the public. WMO Bull., 66, 3339.

    • Search Google Scholar
    • Export Citation
  • Witze, A., 2017: Ageing satellites put crucial sea-ice climate record at risk. Nature, 551, 1314, https://doi.org/10.1038/nature.2017.22907.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wong, A., R. Keeley, and T. Carval, 2018: Argo quality control manual for CTD and trajectory data. Ifremer, accessed 15 April 2020, https://doi.org/10.13155/33951.

    • Crossref
    • Export Citation
  • WCRP Global Sea Level Budget Group, 2018: Global sea-level budget 1993-present. Earth Syst. Sci. Data, 10, 15511590, https://doi.org/10.5194/essd-10-1551-2018.

    • Search Google Scholar
    • Export Citation
  • WMO, 2009: Technical Report of Global Analysis Method for Major Greenhouse Gases by the World Data Center for Greenhouse Gases. GAW Rep. 184, WMO/TD-1473, World Meteorological Organization, 31 pp., www.wmo.int/pages/prog/arep/gaw/documents/TD_1473_GAW184_web.pdf.

  • WMO, 2015: Status of the Global Observing System for Climate. World Meteorological Organization, 373 pp.

  • WMO, 2017a: Expert meeting on the WMO Statements on the Status of the Global Climate: Meeting report. WCDMP-84, World Meteorological Organization, 18 pp., www.wmo.int/pages/prog/wcp/wcdmp/documents/Report-Expert-meeting_final-WCDMP-84.pdf.

  • WMO, 2017b: WMO guidelines on the calculation of climate normals. WMO-1203, World Meteorological Organization, 29 pp., https://library.wmo.int/doc_num.php?explnum_id=4166.

  • WMO, 2018a: Commission for Climatology: A bridged final report of the Seventeenth Session. WMO-1216, World Meteorological Organization, 57 pp., https://library.wmo.int/doc_num.php?explnum_id=4611.

  • WMO, 2018b: 19th WMO/IAEA Meeting on Carbon Dioxide, Other Greenhouse Gases and Related Measurement Techniques (GGMT-2017). GAW Tech. Rep. 242, World Meteorological Organization, 134 pp., https://library.wmo.int/doc_num.php?explnum_id=5456.

  • WMO, 2018c: WMO statement on the state of the global climate in 2017. WMO-1212, World Meteorological Organization, 35 pp., https://library.wmo.int/doc_num.php?explnum_id=4453.

  • WMO, 2019: WMO statement on the state of the global climate in 2018. WMO-1233, World Meteorological Organization, 39 pp., https://library.wmo.int/doc_num.php?explnum_id=5789.

  • Wouters, B., A. S. Gardner, and G. Moholdt, 2019: Global glacier mass loss during the GRACE satellite mission (2002-2016). Front. Earth Sci., 7, 96, https://doi.org/10.3389/feart.2019.00096.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zanna, L., S. Khatiwala, J. M. Gregory, J. Ison, and P. Heimbach, 2019: Global reconstruction of historical ocean heat storage and transport. Proc. Natl. Acad. Sci. USA, 116, 11261131, https://doi.org/10.1073/pnas.1808838115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zemp, M., and Coauthors, 2015: Historically unprecedented global glacier decline in the early 21st century. J. Glaciol., 61, 745762, https://doi.org/10.3189/2015JoG15J017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zemp, M., and Coauthors, 2019: Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature, 568, 382386, https://doi.org/10.1038/s41586-019-1071-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Headline Indicators for Global Climate Monitoring

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  • 1 Australian Bureau of Meteorology, Melbourne, Victoria, Australia
  • 2 LEGOS, Observatoire Midi-Pyrenees, Toulouse, France
  • 3 Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada
  • 4 Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, and Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
  • 5 Intergovernmental Oceanographic Commission, UNESCO, Paris, France
  • 6 Met Office Hadley Centre, Exeter, United Kingdom
  • 7 World Meteorological Organization, Geneva, Switzerland
  • 8 Lund University, Lund, Sweden
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Abstract

The World Meteorological Organization has developed a set of headline indicators for global climate monitoring. These seven indicators are a subset of the existing set of essential climate variables (ECVs) established by the Global Climate Observing System and are intended to provide the most essential parameters representing the state of the climate system. These indicators include global mean surface temperature, global ocean heat content, state of ocean acidification, glacier mass balance, Arctic and Antarctic sea ice extent, global CO2 mole fraction, and global mean sea level. This paper describes how well each of these indicators are currently monitored, including the number and quality of the underlying datasets; the health of those datasets; observation systems used to estimate each indicator; the timeliness of information; and how well recent values can be linked to preindustrial conditions. These aspects vary widely between indicators. While global mean surface temperature is available in close to real time and changes from preindustrial levels can be determined with relatively low uncertainty, this is not the case for many other indicators. Some indicators (e.g., sea ice extent) are largely dependent on satellite data only available in the last 40 years, while some (e.g., ocean acidification) have limited underlying observational bases, and others (e.g., glacial mass balance) with data only available a year or more in arrears.

Supplemental material: https://doi.org/10.1175/BAMS-D-19-0196.2

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Blair Trewin, blair.trewin@bom.gov.au

Abstract

The World Meteorological Organization has developed a set of headline indicators for global climate monitoring. These seven indicators are a subset of the existing set of essential climate variables (ECVs) established by the Global Climate Observing System and are intended to provide the most essential parameters representing the state of the climate system. These indicators include global mean surface temperature, global ocean heat content, state of ocean acidification, glacier mass balance, Arctic and Antarctic sea ice extent, global CO2 mole fraction, and global mean sea level. This paper describes how well each of these indicators are currently monitored, including the number and quality of the underlying datasets; the health of those datasets; observation systems used to estimate each indicator; the timeliness of information; and how well recent values can be linked to preindustrial conditions. These aspects vary widely between indicators. While global mean surface temperature is available in close to real time and changes from preindustrial levels can be determined with relatively low uncertainty, this is not the case for many other indicators. Some indicators (e.g., sea ice extent) are largely dependent on satellite data only available in the last 40 years, while some (e.g., ocean acidification) have limited underlying observational bases, and others (e.g., glacial mass balance) with data only available a year or more in arrears.

Supplemental material: https://doi.org/10.1175/BAMS-D-19-0196.2

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Blair Trewin, blair.trewin@bom.gov.au

Supplementary Materials

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