Introduction

This page is a companion to the WMO State of the Global climate reports. It provides access to the latest versions of selected key global indicators used in the report.

Global climate indicators (for an overview see Trewin et al. 2021) provide a broad view of climate change at the largest scale, encompassing the composition of the atmosphere, energy changes, and the responses of the land, ocean, and ice. These indicators are closely related to one another. For example, the rise in CO₂ and other greenhouse gases in the atmosphere leads to an imbalance of energy and thus warming of the atmosphere and ocean. Warming of the ocean in turn leads to rising sea levels, to which is added the melting of ice on land in response to increasing atmospheric temperatures.

The global indicators draw on a wide range of data sets, which are listed at the bottom of the page. Differences between data sets for the same indicator indicate the degree of uncertainty in the indicator. Figures are updated at least annually, with some data sets being updated more frequently.

Under each of the figures, you will find links to the images in multiple file formats (png, pdf and svg), as well as a set of data as shown in the figure in a common comma-separated values (csv) format. The "Read more" link will take you to a wider range of linked indicators.

What the IPCC says

Regarding the large-scale changes in the climate, Working Group 1 from the sixth assessment report of the Intergovernmental Panel on Climate Change concluded that:

A.1 It is unequivocal that human influence has warmed the atmosphere, ocean and land. Widespread and rapid changes in the atmosphere, ocean, cryosphere and biosphere have occurred.

A.2 The scale of recent changes across the climate system as a whole - and the present state of many aspects of the climate system - are unprecedented over many centuries to many thousands of years.

A.3 Human-induced climate change is already affecting many weather and climate extremes in every region across the globe. Evidence of observed changes in extremes such as heatwaves, heavy precipitation, droughts, and tropical cyclones, and, in particular, their attribution to human influence, has strengthened since AR5.

Key messages

Global mean temperature

The year 2025 was ranked between the 2nd and 3rd warmest on record. The anomaly for 2025 was 1.45 [1.32 to 1.58]°C relative to the 1850-1900 average 6 data sets were used in this assessment: Berkeley Earth Hires (to Oct 2025), ERA5 (to Nov 2025), GISTEMP (to Nov 2025), HadCRUT5 (to Oct 2025), JRA-3Q (to Nov 2025), and NOAAGlobalTemp v6 (to Nov 2025).

Paragraph updated: 2025-12-16 09:53

Global mean sea level

The rate of change in the AVISO 2m data set is 3.61 mm/yr between 1993 and 2024. The rate of change in the past decade 2016-2025 is 4.28 mm/yr which is higher than the trend for the first decade of the satellite record 1993-2002 which was 3.25 mm/yr. The trend for 2015-2014 was 4.16 mm/yr. The rate of change in the AVISO CNES data set is 3.47 mm/yr between 1993 and 2025. The rate of change in the past decade 2016-2025 is 4.02 mm/yr which is higher than the trend for the first decade of the satellite record 1993-2002 which was 2.14 mm/yr. The trend for 2015-2014 was 4.12 mm/yr. The rate of change in the AVISO data set is 3.44 mm/yr between 1993 and 2024. The rate of change in the past decade 2016-2025 is 4.44 mm/yr which is higher than the trend for the first decade of the satellite record 1993-2002 which was 2.13 mm/yr. The trend for 2015-2014 was 4.28 mm/yr. The rate of change in the CMEMS data set is 3.35 mm/yr between 1993 and 2023. The rate of change in the past decade 2016-2025 is 4.10 mm/yr which is higher than the trend for the first decade of the satellite record 1993-2002 which was 2.15 mm/yr. The trend for 2015-2014 was 3.96 mm/yr. The rate of change in the Colorado data set is 3.45 mm/yr between 1992 and 2023. The rate of change in the past decade 2016-2025 is 4.01 mm/yr which is higher than the trend for the first decade of the satellite record 1993-2002 which was 2.66 mm/yr. The trend for 2015-2014 was 3.82 mm/yr. The rate of change in the CSIRO data set is 3.53 mm/yr between 1993 and 2020. The rate of change in the past decade 2016-2025 is 4.07 mm/yr which is higher than the trend for the first decade of the satellite record 1993-2002 which was 3.69 mm/yr. The trend for 2015-2014 was 4.02 mm/yr. The rate of change in the NASA data set is 3.31 mm/yr between 1993 and 2025. The rate of change in the past decade 2016-2025 is 3.85 mm/yr which is higher than the trend for the first decade of the satellite record 1993-2002 which was 2.79 mm/yr. The trend for 2015-2014 was 3.60 mm/yr. The rate of change in the NOAA data set is 3.22 mm/yr between 1992 and 2024. The rate of change in the past decade 2016-2025 is 3.82 mm/yr which is higher than the trend for the first decade of the satellite record 1993-2002 which was 2.72 mm/yr. The trend for 2015-2014 was 3.73 mm/yr.

Paragraph updated: 2025-12-16 09:53

Arctic sea-ice extent

Arctic sea ice extent in March 2025 was between 13.60 and 14.18million km². This was the 1st lowest extent on record. In September the extent was between 4.69 and 5.24million km². This was between the 11th and 13th lowest extent on record. Data sets used were: JAXA, NSIDC, NSIDC v4, OSI SAF v2.2, and OSI SAF v2.3

Paragraph updated: 2025-12-16 09:53

Dataset and processing details

Greenhouse gases

Carbon dioxide (CO₂) is one of the most important greenhouse gases. The concentration of CO₂ in the atmosphere is measured at stations around the world which are combined to provide a globally representative value.

The data in the above plot are available in a zip file containing a csv file for each data set.

Data file: Greenhouse_gases_data_files.zip
Checksum: 7f9df7952963dc8390c532d77177a200 Copy
Format: BADC CSV format

WDCGG

Original data file (external link)

Citation:

• WMO Greenhouse Gas Bulletin, No.18, 2022. 26 October 2022 ISSN 2078-0796.

To produce the plot, the following processing steps were performed:

• Data set created from file ['co2_annual_20251016.csv'] downloaded from ['https://gaw.kishou.go.jp/static/publications/global_mean_mole_fractions/2025/co2_annual_20251016.csv'] at ['2025-10-17 10:10:39']

Global temperature

Global mean temperature is based on measurements made at weather stations over land and by ships and buoys over the ocean. Temperatures are typically expressed as anomalies which are temperature differences from the average for a standard period. Here, 1850-1900 is used for the global mean. Instrumental temperature records are some of the longest climate records available, with some series extending back to the 17th century.

The data in the above plot are available in a zip file containing a csv file for each data set.

Data file: Global_temperature_data_files.zip
Checksum: 0edfd192fe23014d41e48bd8043695d9 Copy
Format: BADC CSV format

Berkeley Earth Hires

Original data file (external link)

Citation:

• Rohde, R. A. and Hausfather, Z.: The Berkeley Earth Land/Ocean Temperature Record, Earth Syst. Sci. Data, 12, 3469-3479, https://doi.org/10.5194/essd-12-3469-2020, 2020.

To produce the plot, the following processing steps were performed:

• Data set created from file ['Global_TAVG_monthly.txt'] downloaded from ['https://storage.googleapis.com/berkeley-earth-temperature-hr/global/Global_TAVG_monthly.txt'] at ['2025-12-16 09:05:52']
• Rebaselined to 1981-2010 for each month separately by calculating the arithmetic mean of the data over the baseline period and subtracting the mean from all data values. This is done for each month separately (Januarys, Februarys etc).
• Calculated annual average from monthly averages using arithmetic mean
• Selected years within the range 1850 to 2025.
• Added offset of 0.69 to all data values.
• Manually changed baseline to 1850-1900. Note that data values remain unchanged.

ERA5

Original data file (external link)

Citation:

• Hersbach H, Bell B, Berrisford P et al. 2020. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146: 1999-2049. https://doi.org/10.1002/qj.3803.

Data citation: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 monthly averaged data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.f17050d7 (Accessed on 2025-12-15 11:24:54)

Acknowledgement: Contains using Copernicus Climate Change Service information [2025]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

To produce the plot, the following processing steps were performed:

• Data set created from file ['C3S_Bulletin_temp_202511_Fig1b_timeseries_anomalies_ref1991-2020_global_allmonths_DATA.csv'] downloaded from ['https://climate.copernicus.eu/sites/default/files/2025-MMMM/C3S_Bulletin_temp_YLYLMLML_Fig1b_timeseries_anomalies_ref1991-2020_global_allmonths_DATA.csv'] at ['2025-12-15 11:24:54']
• Rebaselined to 1981-2010 for each month separately by calculating the arithmetic mean of the data over the baseline period and subtracting the mean from all data values. This is done for each month separately (Januarys, Februarys etc).
• Calculated annual average from monthly averages using arithmetic mean
• Selected years within the range 1850 to 2025.
• Added offset of 0.69 to all data values.
• Manually changed baseline to 1850-1900. Note that data values remain unchanged.

GISTEMP

Original data file (external link)

Citation:

• Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos., 124, no. 12, 6307-6326, doi:10.1029/2018JD029522.

Data citation: GISTEMP Team, 2022: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies. Dataset accessed 2025-12-11 11:34:21 at data.giss.nasa.gov/gistemp/.

To produce the plot, the following processing steps were performed:

• Data set created from file ['GLB.Ts+dSST.csv'] downloaded from ['https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv'] at ['2025-12-11 11:34:21']
• Rebaselined to 1981-2010 for each month separately by calculating the arithmetic mean of the data over the baseline period and subtracting the mean from all data values. This is done for each month separately (Januarys, Februarys etc).
• Calculated annual average from monthly averages using arithmetic mean
• Selected years within the range 1850 to 2025.
• Added offset of 0.69 to all data values.
• Manually changed baseline to 1850-1900. Note that data values remain unchanged.

HadCRUT5

Original data file (external link) Original data file (external link)

Citation:

• Morice, C. P., Kennedy, J. J., Rayner, N. A., Winn, J. P., Hogan, E., Killick, R. E., et al. (2021). An updated assessment of near-surface temperature change from 1850: the HadCRUT5 data set. Journal of Geophysical Research: Atmospheres, 126, e2019JD032361. https://doi.org/10.1029/2019JD032361.

Acknowledgement: HadCRUT.5.1.0.0 data were obtained from http://www.metoffice.gov.uk/hadobs/hadcrut5 on 2025-12-16 09:05:53 and are © British Crown Copyright, Met Office 2025, provided under an Open Government License, http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

To produce the plot, the following processing steps were performed:

• Data set created from file ['HadCRUT.5.1.0.0.analysis.summary_series.global.monthly.csv', 'HadCRUT.5.1.0.0.analysis.summary_series.global.annual.csv'] downloaded from ['https://www.metoffice.gov.uk/hadobs/hadcrut5/data/HadCRUT.5.0.2.0/analysis/diagnostics/HadCRUT.5.1.0.0.analysis.summary_series.global.monthly.csv', 'https://www.metoffice.gov.uk/hadobs/hadcrut5/data/HadCRUT.5.0.2.0/analysis/diagnostics/HadCRUT.5.1.0.0.analysis.summary_series.global.annual.csv'] at ['2025-12-16 09:05:53', '2025-12-16 09:05:53']
• Rebaselined to 1981-2010 for each month separately by calculating the arithmetic mean of the data over the baseline period and subtracting the mean from all data values. This is done for each month separately (Januarys, Februarys etc).
• Calculated annual average from monthly averages using arithmetic mean
• Selected years within the range 1850 to 2025.
• Added offset of 0.69 to all data values.
• Manually changed baseline to 1850-1900. Note that data values remain unchanged.

JRA-3Q

Original data file (external link)

Citation:

• Kosaka, Y.; Kobayashi, S.; Harada, Y.; Kobayashi, C.; Naoe, H.; Yoshimoto, K.; Harada, M.; Goto, N.; Chiba, J.; Miyaoka, K.; Sekiguchi, R.; Deushi, M.; Kamahori, H.; Nakaegawa, T.; Tanaka, T. Y.; Tokuhiro, T.; Sato, Y.; Matsushita, Y.; Onogi, K. The JRA-3Q Reanalysis. J. Meteorol. Soc. Jpn. Ser II 2024, 102 (1), 49–109. https://doi.org/10.2151/jmsj.2024-004.

Data citation: Japan Meteorological Agency. 2023, updated monthly. Japanese Reanalysis for Three Quarters of a Century (JRA-3Q). Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/AVTZ-1H78. Accessed 2025-12-16 08:39:00.

To produce the plot, the following processing steps were performed:

• Data set created from file ['JRA-3Q_tmp2m_global_ts_125_Clim9120.txt'] downloaded from [''] at ['2025-12-16 08:39:00']
• Rebaselined to 1981-2010 for each month separately by calculating the arithmetic mean of the data over the baseline period and subtracting the mean from all data values. This is done for each month separately (Januarys, Februarys etc).
• Calculated annual average from monthly averages using arithmetic mean
• Selected years within the range 1850 to 2025.
• Added offset of 0.69 to all data values.
• Manually changed baseline to 1850-1900. Note that data values remain unchanged.

NOAAGlobalTemp v6

Original data file (external link) Original data file (external link)

Citation:

• Yin, X., and Coauthors, 2024: NOAAGlobalTemp Version 6: An AI-Based Global Surface Temperature Dataset. Bull. Amer. Meteor. Soc., 105, E2184–E2193, https://doi.org/10.1175/BAMS-D-24-0012.1..

Data citation: Huang, Boyin; Yin, Xungang; Menne, Matthew J.; Vose, Russell S.; and Zhang, Huai-Min. 2024. NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 6.0.. NOAA National Centers for Environmental Information. https://doi.org/10.25921/rzxg-p717. Accessed 2025-12-16 09:05:57.

To produce the plot, the following processing steps were performed:

• Data set created from file ['aravg.mon.land_ocean.90S.90N.v6.0.0.202511.asc', 'aravg.ann.land_ocean.90S.90N.*.asc'] downloaded from ['https://www.ncei.noaa.gov/data/noaa-global-surface-temperature/v6/access/timeseries/aravg.mon.land_ocean.90S.90N.v6.0.0.202511.asc', 'https://www.ncei.noaa.gov/data/noaa-global-surface-temperature/v6/access/timeseries/aravg.ann.land_ocean.90S.90N.*.asc'] at ['2025-12-16 09:05:57']
• Rebaselined to 1981-2010 for each month separately by calculating the arithmetic mean of the data over the baseline period and subtracting the mean from all data values. This is done for each month separately (Januarys, Februarys etc).
• Calculated annual average from monthly averages using arithmetic mean
• Selected years within the range 1850 to 2025.
• Added offset of 0.69 to all data values.
• Manually changed baseline to 1850-1900. Note that data values remain unchanged.

Ocean Indicators

The data in the above plot are available in a zip file containing a csv file for each data set.

Data file: Ocean_Indicators_data_files.zip
Checksum: 43228848e6e2bcfb19daf812175872da Copy
Format: BADC CSV format

Cheng et al. 2024

Original data file (external link)

Citation:

• Cheng, Lijing, John Abraham, Zeke Hausfather, and Kevin E. Trenberth. 'How fast are the oceans warming?.' Science 363, no. 6423 (2019): 128-129. doi:10.1126/science.aav7619.

To produce the plot, the following processing steps were performed:

• Calculated annual average from monthly averages using arithmetic mean
• Data set created from file ['gohc_wmo_state_climate_22012025_Jm2.nc'] downloaded from [''] at ['2025-01-24 11:07:40']

Copernicus

Original data file (external link)

To produce the plot, the following processing steps were performed:

• Data set created from file ['gohc_wmo_state_climate_22012025_Jm2.nc'] downloaded from [''] at ['2025-01-24 11:07:40']

Miniere et al. 2023

Original data file (external link)

Citation:

• Minière, A., von Schuckmann, K., Sallée, JB. et al. Robust acceleration of Earth system heating observed over the past six decades. Sci Rep 13, 22975 (2023). https://doi.org/10.1038/s41598-023-49353-1.

To produce the plot, the following processing steps were performed:

• Calculated annual average from monthly averages using arithmetic mean
• Data set created from file ['gohc_wmo_state_climate_22012025_Jm2.nc'] downloaded from [''] at ['2025-01-24 11:07:40']

Sea level

Global mean sea level is a measured by satellites using radar altimeters that record the time taken for a radar signal to reach the sea-surface and return to the satellite. Longer records of sea level (not shown here) exist based on tide gauge measurements made along coastlines around the world since the late 19th century.

The data in the above plot are available in a zip file containing a csv file for each data set.

Data file: Sea_level_data_files.zip
Checksum: f0591f227ea06ee94723e466828225f3 Copy
Format: BADC CSV format

AVISO CNES

Original data file (external link)

Citation:

• Legeais, J.-F., Ablain, M., Zawadzki, L., Zuo, H., Johannessen, J. A., Scharffenberg, M. G., Fenoglio-Marc, L., Fernandes, M. J., Andersen, O. B., Rudenko, S., Cipollini, P., Quartly, G. D., Passaro, M., Cazenave, A., and Benveniste, J. (2018). An improved and homogeneous altimeter sea level record from the ESA Climate Change Initiative. Earth System Science Data, 10, 281-301. DOI: 10.5194/essd-10-281-2018.

Acknowledgement: Generated using AVISO+ Products

To produce the plot, the following processing steps were performed:

• Data set created from file ['MSL_Serie_MERGED_Global_AVISO_GIA_Adjust_Filter2m_NRT.nc'] downloaded from ['ftp://ftp.aviso.altimetry.fr/pub/oceano/AVISO/indicators/msl/MSL_Serie_MERGED_Global_AVISO_GIA_Adjust_Filter2m_NRT.nc'] at ['2025-12-15 11:26:41']
• Selected years within the range 1993 to 2025.

Sea ice

Sea-ice concentrations are estimated from microwave radiances measured from satellites (from 1979). 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, they agree well on the year-to-year changes and the trends.

The data in the above plot are available in a zip file containing a csv file for each data set.

Data file: Sea_ice_data_files.zip
Checksum: 8a343e7f21fc8f735bb7e41fbfe3124c Copy
Format: BADC CSV format

JAXA

Original data file (external link)

To produce the plot, the following processing steps were performed:

• Data set created from file ['JASMES_CLIMATE_SIE_5DAVG_PS_NHM.txt'] downloaded from ['ftp://apollo.eorc.jaxa.jp/pub/JASMES/Polar_XXkm/sie/'] at ['2025-12-16 08:46:46']
• Calculated monthly average from values using arithmetic mean of all dates that fall within each month
• Rebaselined to 1991-2020 for each month separately by calculating the arithmetic mean of the data over the baseline period and subtracting the mean from all data values. This is done for each month separately (Januarys, Februarys etc).
• Calculated annual series by extracting March from each year
• Calculated annual series by extracting September from each year

NSIDC v4

Original data file (external link) Original data file (external link) Original data file (external link) Original data file (external link) Original data file (external link) Original data file (external link) Original data file (external link) Original data file (external link) Original data file (external link) Original data file (external link) Original data file (external link) Original data file (external link)

Data citation: Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel. 2017, updated daily. Sea Ice Index, Version 3. 1979-present. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. doi: https://doi.org/10.7265/N5K072F8. [2025-12-15 11:26:54].

Acknowledgement: Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel. 2017, updated daily. Sea Ice Index, Version 3. 1979-present. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. doi: https://doi.org/10.7265/N5K072F8. [2025-12-15 11:26:54].

To produce the plot, the following processing steps were performed:

• Data set created from file ['N_01_extent_v4.0.csv', 'N_02_extent_v4.0.csv', 'N_03_extent_v4.0.csv', 'N_04_extent_v4.0.csv', 'N_05_extent_v4.0.csv', 'N_06_extent_v4.0.csv', 'N_07_extent_v4.0.csv', 'N_08_extent_v4.0.csv', 'N_09_extent_v4.0.csv', 'N_10_extent_v4.0.csv', 'N_11_extent_v4.0.csv', 'N_12_extent_v4.0.csv'] downloaded from ['ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_01_extent_v4.0.csv', 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_02_extent_v4.0.csv', 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_03_extent_v4.0.csv', 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_04_extent_v4.0.csv', 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_05_extent_v4.0.csv', 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_06_extent_v4.0.csv', 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_07_extent_v4.0.csv', 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_08_extent_v4.0.csv', 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_09_extent_v4.0.csv', 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_10_extent_v4.0.csv', 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_11_extent_v4.0.csv', 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/data/N_12_extent_v4.0.csv'] at ['2025-12-15 11:26:54', '2025-12-15 11:26:55', '2025-12-15 11:26:56', '2025-12-15 11:26:57', '2025-12-15 11:26:58', '2025-12-15 11:26:59', '2025-12-15 11:27:00', '2025-12-15 11:27:02', '2025-12-15 11:27:03', '2025-12-15 11:27:04', '2025-12-15 11:27:05', '2025-12-15 11:27:06']
• Rebaselined to 1991-2020 for each month separately by calculating the arithmetic mean of the data over the baseline period and subtracting the mean from all data values. This is done for each month separately (Januarys, Februarys etc).
• Calculated annual series by extracting March from each year
• Calculated annual series by extracting September from each year

OSI SAF v2.3

Original data file (external link)

Citation:

• Lavergne, T., Sorensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjar, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49-78, doi:10.5194/tc-13-49-2019, 2019..

Data citation: EUMETSAT Ocean and Sea Ice Satellite Application Facility, Sea ice index 1979-onwards (v2.2, 2023), OSI-420, Data extracted from OSI SAF FTP server: 1979-present, Northern Hemisphere, accessed 2025-12-15 11:27:07

Acknowledgement: The OSI SAF Sea Ice Index v2.2 is made available at https://osisaf-hl.met.no/v2p2-sea-ice-index. The OSI SAF Sea Ice Index v2p1 is prepared using EUMETSAT OSI SAF Sea Ice Concentration data, with R&D input from the ESA Climate Change Initiative (ESA CCI) (Lavergne et al. 2019)

To produce the plot, the following processing steps were performed:

• Data set created from file ['osisaf_nh_sie_monthly.txt'] downloaded from ['ftp://osisaf.met.no/prod_test/ice/index/v2p3/nh/osisaf_nh_sie_monthly.txt'] at ['2025-12-15 11:27:07']
• Rebaselined to 1991-2020 for each month separately by calculating the arithmetic mean of the data over the baseline period and subtracting the mean from all data values. This is done for each month separately (Januarys, Februarys etc).
• Calculated annual series by extracting March from each year
• Calculated annual series by extracting September from each year

Glaciers

Glaciers are measured using a variety of different techniques. Glacier mass balance data for the global network of reference glaciers are available from the World Glacier Monitoring Service (WGMS), https://www.wgms.ch.

The data in the above plot are available in a zip file containing a csv file for each data set.

Data file: Glaciers_data_files.zip
Checksum: f4a9e684f31a77d592d1a3db0c5b465f Copy
Format: BADC CSV format

WGMS

Original data file (external link)

Citation:

• WGMS (2017, updated, and earlier reports): Global Glacier Change Bulletin No. 2 (2014-2015). Zemp, M., Nussbaumer, S. U., Gärtner-Roer, I., Huber, J., Machguth, H., Paul, F., and Hoelzle, M. (eds.), ICSU(WDS)/IUGG(IACS)/UNEP/UNESCO/WMO, World Glacier Monitoring Service, Zurich, Switzerland, 244 pp., based on database version: doi:10.5904/wgms-fog-2018-11..

Data citation: WGMS (2017, updated, and earlier reports): Global Glacier Change Bulletin No. 2 (2014-2015). Zemp, M., Nussbaumer, S. U., Gärtner-Roer, I., Huber, J., Machguth, H., Paul, F., and Hoelzle, M. (eds.), ICSU(WDS)/IUGG(IACS)/UNEP/UNESCO/WMO, World Glacier Monitoring Service, Zurich, Switzerland, 244 pp., based on database version: doi:10.5904/wgms-fog-2018-11.

To produce the plot, the following processing steps were performed:

• Data set created from file ['mb_ref.csv'] downloaded from ['http://wgms.ch/data/faq/mb_ref.csv'] at ['2025-12-15 11:27:21']

Greenland ice sheet

The Greenland ice sheet mass balance measures the change in ice mass of the Greenland ice sheet. The change in mass is estimated in three principle ways: gravimetric measurements, altimetric measurements and the input-output method. Gravimetric measurements infer mass changes from variations in the Earth's gravitational field as measured by the GRACE and GRACE-FO (Gravity Recovery and Climate Experiment - Follow On) satellites. Altimetric measurements, measured the height of the ice sheet surface, using radar and laser altimeters. Input-output methods, use weather conditions from a numerical weather prediction model, to estimate changes in mass balance at the surface of the ice sheet. These are combined with estimates of mass loss from glaciers around the edge of Greenland and melting on the underside of the glaciers. The IMBIE data set combines over 25 different estimates of Greenland mass balance to get a comprehensive view of the long-term changes.

The data in the above plot are available in a zip file containing a csv file for each data set.

Data file: Greenland_ice_sheet_data_files.zip
Checksum: 45c4f61d6dfb6dbfb7fd1a1f8b71c61b Copy
Format: BADC CSV format

NASA GRACE

Original data file (external link)

Citation:

• Watkins, M. M., D. N. Wiese, D. -N. Yuan, C. Boening, and F. W. Landerer (2015), Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res. Solid Earth, 120, 2648_2671, doi: 10.1002/2014JB011547..

Data citation: Wiese, D. N., D.-N. Yuan, C. Boening, F. W. Landerer, and M. M. Watkins (2019) JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height RL06M CRI Filtered Version 2.0, Ver. 2.0, PO.DAAC, CA, USA. Dataset accessed [2025-12-16 09:49:48] at http://dx.doi.org/10.5067/TEMSC-3MJ62.

Notes: Data from the GRACE and GRACE-FO JPL RL06Mv2 Mascon Solution

To produce the plot, the following processing steps were performed:

• Data set created from file ['greenland_mass_200204_2025MMMM.txt'] downloaded from ['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4/greenland_mass_200204_2025MMMM.txt'] at ['2025-12-16 09:49:48']
• Zeroed series on July 2005 by subtracting the value for that month from all data values (see next entry)
• Added offset of 617.66 to all data values.
• Manually changed baseline to 2005-2005. Note that data values remain unchanged.

IMBIE 2021

Original data file (external link)

Citation:

• Shepherd, A., Ivins, E., Rignot, E., Smith, B., van den Broeke, M., Velicogna, I., Whitehouse, P., Briggs, K., Joughin, I., Krinner, G., Nowicki, S., Payne, A., Scambos, T., Schlegel, N., A, G., Agosta, C., Ahlstrøm, A., Babonis, G., Barletta, V., … Wuite, J. (2021). Antarctic and Greenland Ice Sheet mass balance 1992-2020 for IPCC AR6 (Version 1.0) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452.

Data citation: Shepherd, A., Ivins, E., Rignot, E., Smith, B., van den Broeke, M., Velicogna, I., Whitehouse, P., Briggs, K., Joughin, I., Krinner, G., Nowicki, S., Payne, A., Scambos, T., Schlegel, N., A, G., Agosta, C., Ahlstrøm, A., Babonis, G., Barletta, V., … Wuite, J. (2021). Antarctic and Greenland Ice Sheet mass balance 1992-2020 for IPCC AR6 (Version 1.0) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452

To produce the plot, the following processing steps were performed:

• Data set created from file ['imbie_greenland_2021_Gt.csv'] downloaded from ['https://ramadda.data.bas.ac.uk/repository/entry/get/imbie_greenland_2021_Gt.csv?entryid=synth%3A77b64c55-7166-4a06-9def-2e400398e452%3AL2ltYmllX2dyZWVubGFuZF8yMDIxX0d0LmNzdg%3D%3D'] at ['2023-08-07 14:40:20']
• Zeroed series on July 2005 by subtracting the value for that month from all data values (see next entry)
• Added offset of 1000.3866 to all data values.
• Manually changed baseline to 2005-2005. Note that data values remain unchanged.

Mankoff et al. 2021

Original data file (external link)

Citation:

• Mankoff, K. D., Fettweis, X., Langen, P. L., Stendel, M., Kjeldsen, K. K., Karlsson, N. B., Noël, B., van den Broeke, M. R., Solgaard, A., Colgan, W., Box, J. E., Simonsen, S. B., King, M. D., Ahlstrøm, A. P., Andersen, S. B., and Fausto, R. S.: Greenland ice sheet mass balance from 1840 through next week, Earth Syst. Sci. Data, 13, 5001–5025, https://doi.org/10.5194/essd-13-5001-2021, 2021. doi: 10.5194/essd-13-5001-2021.

Data citation: https://doi.org/10.22008/FK2/OHI23Z

Notes: Filename should be MB_SMB_D_BMB.csv

To produce the plot, the following processing steps were performed:

• Data set created from file ['MB_SMB_D_BMB.csv'] downloaded from ['https://thredds.geus.dk/thredds/fileServer/MassBalance/MB_SMB_D_BMB.csv'] at ['2025-12-15 11:27:22']
• Zeroed series on July 2005 by subtracting the value for that month from all data values (see next entry)
• Added offset of 1453.376009 to all data values.
• Manually changed baseline to 2005-2005. Note that data values remain unchanged.

Velicogna et al.

Original data file (external link)

Citations:

• Geruo, A., Wahr, J., & Zhong, S. (2013). Computations of the viscoelastic response of a 3-D compressible Earth to surface loading: An application to Glacial Isostatic Adjustment in Antarctica and Canada. Geophysical Journal International, 192(2), 557–572.
• Ivins, E. R., T. S. James, J. Wahr, E. J. O Schrama, F. W. Landerer, and K. M. Simon (2013), Antarctic contribution to sea level rise observed by GRACE with improved GIA correction, J. Geophys. Res. Solid Earth, 118, 3126–3141, doi:10.1002/jgrb.50208.
• Loomis, B. D., Rachlin, K. E., & Luthcke, S. B. (2019). Improved Earth oblateness rate reveals increased ice sheet losses and mass-driven sea level rise. Geophysical Research Letters, 46, 6910–6917. https://doi.org/10.1029/2019GL082929.
• Peltier, W., Argus, D., & Drummond, R. (2015). Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model. Journal of Geophysical Research: Solid Earth, 120, 450–487. https://doi.org/10.1002/2014JB011176.
• Simpson, M. J., Milne, G. A., Huybrechts, P., & Long, A. J. (2009). Calibrating a glaciological model of the Greenland ice sheet from the Last Glacial Maximum to present-day using field observations of relative sea level and ice extent. Quaternary Science Reviews, 28(17), 1631–1657.
• Sutterley, T. C., & Velicogna, I. (2019). Improved estimates of geocenter variability from time-variable gravity and ocean model outputs. Remote Sensing, 11(18), 2108.
• Velicogna I., Mohajerani Y., A G., Landerer F., Mouginot J., Noel B., Rignot E., Sutterley T., van den Broeke M., van Wessem J., Wiese D. (2020). Continuity of ice sheet mass loss in Greenland and Antarctica from the GRACE and GRACE Follow-On missions, Geophys. Res. Lett. 47, e2020GL087291.

Notes: Gravimetric (GRACE) ice mass time series for the Greenland and Antarctic Ice Sheets are calculated using spherical harmonics from JPL RL06v1, following Velicogna et al (2020). The degree-1 geocentre terms are calculated using Sutterley and Velicogna (2019), using Loomis et al (2020) C2.0 and C3.0 coefficients. The GRACE/GRACE-FO data are corrected for the long-term trend of glacial isostatic adjustment (GIA) from the solid earth using the regional IJ05 R2 GIA model (Ivins et al., 2013) over Antarctica and the regional Simpson et al. (2009) GIA model over Greenland. These regional GIA models do not include realistic GIA signal outside the ice sheets. For this reason, outside of Greenland and Antarctica, GIA corrections are based on Geruou et al. (2013) with the ICE6G ice history (Peltier et al., 2015).

To produce the plot, the following processing steps were performed:

• Data set created from file ['out_ts_filled_4_261_GIS.txt'] downloaded from [''] at ['2024-02-28 15:05:54']
• Zeroed series on July 2005 by subtracting the value for that month from all data values (see next entry)
• Added offset of -1982.552 to all data values.
• Manually changed baseline to 2005-2005. Note that data values remain unchanged.

Antarctic ice sheet

The Antarctic ice sheet mass balance measures the change in ice mass of the Antarctic ice sheet. The change in mass is estimated in three principle ways: gravimetric measurements, altimetric measurements and the input-output method. Gravimetric measurements infer mass changes from variations in the Earth's gravitational field as measured by the GRACE and GRACE-FO (Gravity Recovery and Climate Experiment - Follow On) satellites. Altimetric measurements, measured the height of the ice sheet surface, using radar and laser altimeters. Input-output methods, use weather conditions from a numerical weather prediction model, to estimate changes in mass balance at the surface of the ice sheet. These are combined with estimates of mass loss from glaciers around the edge of the continent and melting on the underside of the glaciers. The IMBIE data set combines many estimates of Antarctic mass balance to get a comprehensive view of the long-term changes.

The data in the above plot are available in a zip file containing a csv file for each data set.

Data file: Antarctic_ice_sheet_data_files.zip
Checksum: ed6fb720116d59a762efec5e6cd9c0d8 Copy
Format: BADC CSV format

NASA GRACE

Original data file (external link)

Citation:

• Watkins, M. M., D. N. Wiese, D. -N. Yuan, C. Boening, and F. W. Landerer (2015), Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res. Solid Earth, 120, 2648_2671, doi: 10.1002/2014JB011547..

Data citation: Wiese, D. N., D.-N. Yuan, C. Boening, F. W. Landerer, and M. M. Watkins (2019) JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height RL06M CRI Filtered Version 2.0, Ver. 2.0, PO.DAAC, CA, USA. Dataset accessed [2025-12-16 09:49:07] at http://dx.doi.org/10.5067/TEMSC-3MJ62.

Notes: Data from the GRACE and GRACE-FO JPL RL06Mv2 Mascon Solution

To produce the plot, the following processing steps were performed:

• Data set created from file ['antarctica_mass_200204_2025MMMM.txt'] downloaded from ['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ANTARCTICA_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4/antarctica_mass_200204_2025MMMM.txt'] at ['2025-12-16 09:49:07']
• Zeroed series on June 2005 by subtracting the value for that month from all data values (see next entry)
• Added offset of 161.59 to all data values.
• Manually changed baseline to 2005-2005. Note that data values remain unchanged.

IMBIE 2021

Original data file (external link)

Citation:

• Shepherd, A., Ivins, E., Rignot, E., Smith, B., van den Broeke, M., Velicogna, I., Whitehouse, P., Briggs, K., Joughin, I., Krinner, G., Nowicki, S., Payne, A., Scambos, T., Schlegel, N., A, G., Agosta, C., Ahlstrøm, A., Babonis, G., Barletta, V., … Wuite, J. (2021). Antarctic and Greenland Ice Sheet mass balance 1992-2020 for IPCC AR6 (Version 1.0) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452.

Data citation: Shepherd, A., Ivins, E., Rignot, E., Smith, B., van den Broeke, M., Velicogna, I., Whitehouse, P., Briggs, K., Joughin, I., Krinner, G., Nowicki, S., Payne, A., Scambos, T., Schlegel, N., A, G., Agosta, C., Ahlstrøm, A., Babonis, G., Barletta, V., … Wuite, J. (2021). Antarctic and Greenland Ice Sheet mass balance 1992-2020 for IPCC AR6 (Version 1.0) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452

To produce the plot, the following processing steps were performed:

• Data set created from file ['imbie_antarctica_2021_Gt.csv'] downloaded from ['https://ramadda.data.bas.ac.uk/repository/entry/get/imbie_antarctica_2021_Gt.csv?entryid=synth%3A77b64c55-7166-4a06-9def-2e400398e452%3AL2ltYmllX2FudGFyY3RpY2FfMjAyMV9HdC5jc3Y%3D'] at ['2023-08-07 14:40:19']
• Zeroed series on June 2005 by subtracting the value for that month from all data values (see next entry)
• Added offset of 646.1411 to all data values.
• Manually changed baseline to 2005-2005. Note that data values remain unchanged.

Velicogna et al.

Original data file (external link)

Citations:

• Geruo, A., Wahr, J., & Zhong, S. (2013). Computations of the viscoelastic response of a 3-D compressible Earth to surface loading: An application to Glacial Isostatic Adjustment in Antarctica and Canada. Geophysical Journal International, 192(2), 557–572.
• Ivins, E. R., T. S. James, J. Wahr, E. J. O Schrama, F. W. Landerer, and K. M. Simon (2013), Antarctic contribution to sea level rise observed by GRACE with improved GIA correction, J. Geophys. Res. Solid Earth, 118, 3126–3141, doi:10.1002/jgrb.50208.
• Loomis, B. D., Rachlin, K. E., & Luthcke, S. B. (2019). Improved Earth oblateness rate reveals increased ice sheet losses and mass-driven sea level rise. Geophysical Research Letters, 46, 6910–6917. https://doi.org/10.1029/2019GL082929.
• Peltier, W., Argus, D., & Drummond, R. (2015). Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model. Journal of Geophysical Research: Solid Earth, 120, 450–487. https://doi.org/10.1002/2014JB011176.
• Simpson, M. J., Milne, G. A., Huybrechts, P., & Long, A. J. (2009). Calibrating a glaciological model of the Greenland ice sheet from the Last Glacial Maximum to present-day using field observations of relative sea level and ice extent. Quaternary Science Reviews, 28(17), 1631–1657.
• Sutterley, T. C., & Velicogna, I. (2019). Improved estimates of geocenter variability from time-variable gravity and ocean model outputs. Remote Sensing, 11(18), 2108.
• Velicogna I., Mohajerani Y., A G., Landerer F., Mouginot J., Noel B., Rignot E., Sutterley T., van den Broeke M., van Wessem J., Wiese D. (2020). Continuity of ice sheet mass loss in Greenland and Antarctica from the GRACE and GRACE Follow-On missions, Geophys. Res. Lett. 47, e2020GL087291.

Notes: Gravimetric (GRACE) ice mass time series for the Greenland and Antarctic Ice Sheets are calculated using spherical harmonics from JPL RL06v1, following Velicogna et al (2020). The degree-1 geocentre terms are calculated using Sutterley and Velicogna (2019), using Loomis et al (2020) C2.0 and C3.0 coefficients. The GRACE/GRACE-FO data are corrected for the long-term trend of glacial isostatic adjustment (GIA) from the solid earth using the regional IJ05 R2 GIA model (Ivins et al., 2013) over Antarctica and the regional Simpson et al. (2009) GIA model over Greenland. These regional GIA models do not include realistic GIA signal outside the ice sheets. For this reason, outside of Greenland and Antarctica, GIA corrections are based on Geruou et al. (2013) with the ICE6G ice history (Peltier et al., 2015).

To produce the plot, the following processing steps were performed:

• Data set created from file ['out_ts_filled_4_261_AIS.txt'] downloaded from [''] at ['2024-02-28 15:05:42']
• Zeroed series on June 2005 by subtracting the value for that month from all data values (see next entry)
• Added offset of -761.983 to all data values.
• Manually changed baseline to 2005-2005. Note that data values remain unchanged.

Precipitation

Precipitation quantiles are based on the nine months aggregated GPCC Monitoring Product and First Guess Monthly product. The baseline period is 1991-2020, using Full Data Monthly in its latest version. Quality controlled rain gauge (in situ) data are used and the quality control protocol depends on the data set. The percentiles are not calculated for those grid cells, where the precipitation total aggregated in the reference period is below ten millimetres.

The data in the above plot are available in a zip file containing a csv file for each data set.

Data file: Precipitation_data_files.zip
Checksum: 76cdb2bad9582d23c1f6f4d868218d6c Copy
Format: BADC CSV format

GPCC

Original data file (external link)

To produce the plot, the following processing steps were performed:

• Gridded dataset created from file ['gpcc_quantile_9month_*-YYYYMMMM.nc.gz'] downloaded from ['https://opendata.dwd.de/climate_environment/GPCC/GPCC_Quantile/Last9Month/gpcc_quantile_9month_*-YYYYMMMM.nc.gz']
• Selected single month 09/2025

Short-term Climate Drivers

The data in the above plot are available in a zip file containing a csv file for each data set.

Data file: Short-term_Climate_Drivers_data_files.zip
Checksum: 8ba423334b1d2c09bf11c7d6ce1e19be Copy
Format: BADC CSV format

ERSSTv5

Original data file (external link)

To produce the plot, the following processing steps were performed:

• Data set created from file ['nina34.data'] downloaded from ['https://psl.noaa.gov/data/correlation/nina34.data'] at ['2025-12-15 11:27:27']
• Rebaselined to 1991-2020 for each month separately by calculating the arithmetic mean of the data over the baseline period and subtracting the mean from all data values. This is done for each month separately (Januarys, Februarys etc).
• Selected years within the range 1950 to 2025.