Polar Climate Change Figures


Near real-time visualizations


Animation of changes in average September sea ice extent from 1979 through 2021 – with substantial natural variability and a long-term decline. Data is freely available from the National Snow and Ice Data Center (NSIDC) at https://nsidc.org/data/seaice_index/. Updated on 10/12/2021.
Annual means composited by decade for near-surface air temperatures (top) and Arctic sea-ice thickness (bottom). Temperature anomalies are from ERA5 reanalysis (and the preliminary ERA5-BE) using a baseline climatology of 1951-1980. Sea-ice thickness data is simulated from PIOMAS.
Changes in annual mean surface air temperature anomalies (Berkeley Earth Surface Temperature; BEST), annual mean Arctic sea ice extent (NSIDC, Sea Ice Index v3), and annual mean sea surface temperature anomalies (NOAA Optimum Interpolation Sea Surface Temperature V2; OISSTv2) over the satellite era and within the Arctic (67N+ latitude). BEST is available from 1850 to 2021 at http://berkeleyearth.org/data/. OISSTv2 is available from 1982 to 2021 at https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html. Updated 2/18/2022.
Changes in annual mean Arctic sea ice extent (NSIDC, Sea Ice Index v3) and air temperature anomalies (Berkeley Earth Surface Temperature; BEST) over the satellite era. BEST is available from 1850 to 2021 at http://berkeleyearth.org/data/. Updated 2/18/2022.
Monthly temperature anomalies and rankings (1 = warmest, 43 = coldest) from ERA5 reanalysis of 2-m air temperatures in the Arctic (70N+). Anomalies are computed relative to a climatological baseline of 1981-2010. Figure is updated through 2021.
Monthly temperature anomalies and rankings (1 = warmest, 72 = coldest) from ERA5 reanalysis (back extension) of 2-m air temperatures in the Arctic (64N+) from January 1950 to December 2021. Anomalies for each month/year are computed relative to a climatological baseline of 1951-1980. Figure is updated through 2021.
Changes in annual mean Arctic sea ice extent (NSIDC, Sea Ice Index v3) and volume (PIOMAS v2.1, Zhang and Rothrock, 2003) over the satellite era. Updated 1/26/2021.
Change in land ice mass since 2002 (Right: Greenland, Left: Antarctica). Data is measured by NASA’s Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) satellites. Additional information can be found at https://climate.nasa.gov/vital-signs/land-ice/. Updated 9/3/2022.
Changes in annual mean Arctic sea ice extent (NSIDC; https://doi.org/10.7265/N5K072F8; 1979-2021) over the satellite era compared to a reconstruction of annual mean Arctic sea ice extent from Brennan et al. (2020; https://doi.org/10.1029/2019GL086843; 1850-2018). Anomalies are computed relative to a 1979-2013 baseline. The blue shading denotes the 2.5-97.5th percentile range of the MPI-HadCRUT4 reconstructed ensemble members. Additional data can be found from Brennan et al. (2020) at https://zenodo.org/record/3717240. Updated 9/23/2022.
Changes in annual mean Arctic sea ice extent (NSIDC; https://doi.org/10.7265/N5K072F8) and volume (PIOMAS v2.1; http://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/data/) over the satellite era compared to reconstructions of Arctic sea ice extent (SIBT1850; https://doi.org/10.7265/jj4s-tq79) and volume (PIOMAS-20C v1; https://psc.apl.uw.edu/research/projects/piomas-20c/). Updated 8/21/2022.
Reconstructed late-summer (August) Arctic sea ice extent during the last 1450 years. Sea ice extent data have been smoothed using a 40-year running mean (light blue). The shading shows the 95% confidence interval (dark blue). Smoothed observational data are compared using a dashed line (red). This figure is reproduced from Figure 3a in Kinnard et al. 2011 (Nature: https://www.nature.com/articles/nature10581).
A look at September Arctic sea ice concentration over the last 100 years (through 2017) using the latest NSIDC SIBT gridded 1850- reconstruction from Walsh et al. [2016]. The discontinuity between 1978-1979 is the transition to the passive microwave satellite era.
Daily Arctic sea ice extents (NSIDC, DMSP SSM/I-SSMIS) from 1979 through 2021. Missing data shown in black.
Daily Arctic sea ice extent anomalies (NSIDC, DMSP SSM/I-SSMIS) from 1979 through 2021. Missing data shown in black. Anomalies are calculated from an averaged 1981-2010 baseline.
Daily Arctic sea ice extent anomalies (NSIDC, DMSP SSM/I-SSMIS) from 1979 through 2021. Missing data shown in black. Z scores are calculated from an averaged 1979-2021 baseline.
Daily Arctic sea ice thickness (PIOMASv2.1; Zhang and Rothrock, 2003) from 1 January 1979 through 31 December 2021.
Daily Arctic sea ice thickness anomalies (PIOMASv2.1; Zhang and Rothrock, 2003) from 1 January 1979 through 31 December 2021. Anomalies are calculated from an averaged 1981-2010 baseline.
Daily Arctic sea ice thickness anomalies (PIOMASv2.1; Zhang and Rothrock, 2003) from 1 January 1979 through 31 December 2021. Data are standardized using a 1979-2021 baseline.
Monthly record high (blue – #43) and record low (red – #1) Arctic sea ice extents over the satellite era (1979-2021). Data is from the NSIDC Sea Ice Index v3.
Trends in sea ice thickness/volume are another important indicator of Arctic climate change. While sea ice thickness observations are sparse, here we utilize the ocean and sea ice model, PIOMAS (Zhang and Rothrock, 2003), to visualize October sea ice thickness from 1979 to 2021. Sea ice less than 1.5 meters is masked out (black) to emphasize the loss of thicker, older ice. Updated through October 2022.
Latest PIOMAS (model; Zhang and Rothrock, 2003) sea ice volume (SIV) across the Arctic (updated for October 2022).
Animation of annual mean near-surface temperature anomalies in the Arctic for each year from 1950 to 2021. Data is from ERA5’s preliminary back extension. Anomalies are calculated relative to a 1951 to 1980 climate baseline.
Trends in sea ice thickness are another important indicator of Arctic climate change. While sea ice thickness observations are sparse, here we utilize the ocean and sea ice model, PIOMAS (Zhang and Rothrock, 2003), to visualize mean sea ice thickness from 1979 to 2021. Updated through October 2022.
Changes in the annual maximum and minimum of daily Arctic sea-ice volume simulated from PIOMAS (Zhang and Rothrock, 2003). Trends are calculated using a linear least squares fit for the white dashed lines between 1979 and 2022. Graphic updated 10/4/2022.

My related research

[12] Timmermans, M.-L. and Z.M. Labe (2022). [The Arctic] Sea surface temperature [in “State of the Climate in 2021”]. Bull. Amer. Meteor. Soc., DOI:10.1175/BAMS-D-22-0082.1
[HTML][BibTeX][Code]
[Press Release]

[11] Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348
[HTML][BibTeX][Code]
[Plain Language Summary]

[10] Timmermans, M.-L. and Z.M. Labe (2021). Sea surface temperature [in “Arctic Report Card 2021”], NOAA, DOI:10.25923/2y8r-0e49
[HTML][BibTeX][Code]
[Press Release]

[9] Timmermans, M.-L. and Z.M. Labe (2021). [The Arctic] Sea surface temperature [in “State of the Climate in 2020”]. Bull. Amer. Meteor. Soc., DOI:10.1175/BAMS-D-21-0086.1
[HTML][BibTeX][Code]
[Press Release]

[8] Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss? Journal of Climate, DOI:10.1175/JCLI-D-20-0613.1
[HTML][BibTeX][Code]
[Plain Language Summary][CLIVAR Research Highlight]

[7] Timmermans, M.-L. and Z.M. Labe (2020). Sea surface temperature [in “Arctic Report Card 2020”], NOAA, DOI:10.25923/v0fs-m920
[HTML][BibTeX][Code]
[Press Release]

[6] Timmermans, M.-L., Z.M. Labe, and C. Ladd (2020). [The Arctic] Sea surface temperature [in “State of the Climate in 2019”], Bull. Amer. Meteor. Soc., DOI:10.1175/BAMS-D-20-0086.1
[HTML][BibTeX][Code]
[Press Release]

[5] Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583
[HTML][BibTeX]
[Plain Language Summary][CBS News][Science Magazine][The Washington Post]

[4] Thoman, R.L., U. Bhatt, P. Bieniek, B. Brettschneider, M. Brubaker, S. Danielson, Z.M. Labe, R. Lader, W. Meier, G. Sheffield, and J. Walsh (2019): The record low Bering Sea ice extent in 2018: Context, impacts and an assessment of the role of anthropogenic climate change [in “Explaining Extreme Events of 2018 from a Climate Perspective”]. Bull. Amer. Meteor. Soc, DOI:10.1175/BAMS-D-19-0175.1
[HTML][BibTeX][Code]
[Press Release]

[3] Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI:10.1029/2019GL083095
[HTML][BibTeX]
[Plain Language Summary]

[2] Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss, Geophysical Research Letters, DOI:10.1029/2018GL078158
[HTML][BibTeX]
[Plain Language Summary][Arctic Today]

[1] Labe, Z.M., G. Magnusdottir, and H.S. Stern (2018), Variability of Arctic sea ice thickness using PIOMAS and the CESM Large Ensemble, Journal of Climate, DOI:10.1175/JCLI-D-17-0436.1
[HTML][BibTeX]
[Plain Language Summary]


For more visualizations, follow me on Twitter (or Mastodon). All of the Python code used to generate these figures are available from my GitHub account. Most scripts use data sets that are generated via ftp retrieval.

*These figures may be freely distributed (with credit). Information about the data can be found on my references page and methods page.