Climate change indicators

All data are referenced at https://zacklabe.com/resources-and-data-references/.

Global average surface temperature anomalies for centered running 60-month periods. Monthly anomalies are calculated relative to pre-industrial levels (1850-1900 – outlined in the IPCC Special Report on Global Warming of 1.5°C). Data is from Berkeley Earth Surface Temperatures (BEST; http://berkeleyearth.org/data/). Graphic updated using data through October 2022.
Decadal trends in annual mean surface air temperatures over land areas from 1900 to 2021, 1940 to 2021, 1980 to 2021, and 2000 to 2021. Data are from NASA GISS Surface Temperature Analysis (GISTEMPv4; https://data.giss.nasa.gov/gistemp/).
Global average surface temperature anomalies for centered running 60-month periods (white line) compared to global average surface temperature anomalies over only land areas for centered running 60-month periods (red line). Monthly anomalies are calculated relative to pre-industrial levels (1850-1900 – outlined in the IPCC Special Report on Global Warming of 1.5°C). Data is from Berkeley Earth Surface Temperatures (BEST; http://berkeleyearth.org/data/). Graphic updated using data through October 2022.
Global average surface temperature anomalies for centered running 60-month periods (white line) compared to global average surface temperature anomalies over only land areas for centered running 60-month periods (red line) and global average surface temperature anomalies over only ocean areas for centered running 60-month periods (blue line). Monthly anomalies are calculated relative to pre-industrial levels (1850-1900 – outlined in the IPCC Special Report on Global Warming of 1.5°C). Data is from Berkeley Earth Surface Temperatures (BEST; http://berkeleyearth.org/data/). Graphic updated using data through October 2022.
Average surface temperature anomalies for extratropical land areas (67°S to 67°N) centered running 60-month periods (white line) compared to average surface temperature anomalies over only Northern Hemisphere land areas for centered running 60-month periods (orange line) and average surface temperature anomalies over only Southern Hemisphere land areas for centered running 60-month periods (purple line). Monthly anomalies are calculated relative to pre-industrial levels (1850-1900 – outlined in the IPCC Special Report on Global Warming of 1.5°C). Data is from Berkeley Earth Surface Temperatures (BEST; http://berkeleyearth.org/data/). Graphic updated using data through October 2022.
This graphic shows monthly data from January 1984 through July/October 2022. The first graph is a 12-month running mean of global mean surface temperature anomalies using ERA5 data. Anomalies are computed relative to a 1991-2020. The other three graphs show carbon dioxide abundance, global methane abundance, and global nitrous oxide abundance (https://gml.noaa.gov/ccgg/trends/).
Annual carbon dioxide levels from a merged ice-core record (Scripps CO2 program; https://scrippsco2.ucsd.edu/data/atmospheric_co2/icecore_merged_products.html) over the period of years 1600 to 2021.
Globally averaged near-surface (2-m) air temperature anomalies for each month from January 1979 to October 2022. Data is from ERA5 reanalysis using a 1981-2010 reference period and smoothed with a 12-month running mean. Updated 11/8/2022.
Change in annual mean global ocean heat content (vertical integral between 0-2000 m) since 1955. Graphic is updated through 2021. The current rate of change is approximately 5.98 × 10²² joules per decade. Data are from https://www.data.jma.go.jp/gmd/kaiyou/english/ohc/ohc_global_en.html.
Global mean sea level anomalies from 1993 to 2022 using satellite altimetry data from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6. See more information from https://climate.nasa.gov/vital-signs/sea-level/. The baseline is 1996-2016. The current rate of change is approximately 3.45 mm per year. Graphic updated through 9 August 2022.
Change in annual mean upper global ocean heat content (vertical integral between 0-700 m) since 1955. Graphic is updated through 2021. The current rate of change is approximately 5.98 × 10²² joules per decade in the 0-2000 m layer. Data are from https://www.data.jma.go.jp/gmd/kaiyou/english/ohc/ohc_global_en.html.
Decadal trends in annual mean surface air temperatures over land areas from 1990 to 2021. Data are from NASA GISS Surface Temperature Analysis (GISTEMPv4; https://data.giss.nasa.gov/gistemp/).
Cumulative change in the mass balance of reference glaciers around the world. Change is measured relative to 1970 levels. More information on the details and methods can be found at https://wgms.ch/global-glacier-state/. The units are equivalent to tonnes per square meter (1,000 kg/m²).
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.
Zonal-mean vertical cross-section (latitude vs. height) of decadal trends in annual mean temperature (90°S-90°N). Trends are calculated using ERA5 reanalysis over the 1979 to 2021 period.
Annual mean surface air temperature anomalies for the Arctic (67-90°N; white line), the global average over land areas (90°S-90°N; red line), and the global average over ocean areas (90°S-90°N; blue line) from 1900 to 2021. Linear trend lines (dashed) are also shown over the 1990 to 2021 period. GISS Surface Temperature Analysis (GISTEMPv4) is available from 1880 to 2021 at https://data.giss.nasa.gov/gistemp/. Tools including the NOAA/ESRL Physical Sciences Division Web-based Reanalysis Intercomparison Tool: Monthly/Seasonal Time Series (WRIT) have been used for the construction of this plot. Analysis will updated as annual data becomes available.
Monthly zonal mean (averaged over longitude) surface air temperature anomalies from 1880 to 2021. The y-axis is latitude, and the x-axis is time. The data are smoothed using a 12-month running mean from NASA GISS Surface Temperature Analysis (GISTEMPv4; https://data.giss.nasa.gov/gistemp/) with a reference period of 1951-1980.
Zonal-mean (averaged over longitude) temperature anomalies for each year from 1900 to 2021. The x-axis is latitude (not scaled by distance), and the y-axis is the temperature anomaly. Data is from Berkeley Earth Surface Temperatures (BEST; http://berkeleyearth.org/data/) using a reference period of 1951-1980.
Animation of surface air temperature anomalies over only land areas for each year from 1922 to 2021. Data are from NASA GISS Surface Temperature Analysis (GISTEMPv4; https://data.giss.nasa.gov/gistemp/) with a reference period of 1951-1980.
Annual mean surface air temperature anomalies for the entire globe from 1880 through 2021. A 5-year lowess smoothing line is also shown for this time series. See more on this temperature variability in Labe and Barnes (2022) (https://doi.org/10.1029/2022GL098173). GISS Surface Temperature Analysis (GISTEMPv4) is available from 1880 to 2021 at https://data.giss.nasa.gov/gistemp/. Analysis will updated with each year.
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).
Zonal-mean vertical cross-section (latitude vs. height) of decadal trends in annual mean geopotential heights (90°S-90°N). Trends are calculated using ERA5 reanalysis over the 1979 to 2021 period.
Zonal-mean vertical cross-section (latitude vs. height) of decadal trends in annual zonal wind (90°S-90°N). Trends are calculated using ERA5 reanalysis over the 1979 to 2021 period. The climatological zonal-mean zonal wind is shown with gray contours.

My visualizations:

  • Arctic Climate Seasonality and Variability
  • Arctic Sea Ice Extent and Concentration
  • Arctic Sea Ice Volume and Thickness
  • Arctic Temperatures
  • Antarctic Sea Ice Extent and Concentration
  • Climate Change Indicators
  • Climate model projections compared to observations in the Arctic
  • Global Sea Ice Extent and Concentration
  • Polar Climate Change Figures
  • Climate Viz of the Month

  • My related research

    [16] Po-Chedley, S., J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, and B.D. Santer (2022). Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming. Proceedings of the National Academy of Sciences, DOI:10.1073/pnas.2209431119
    [HTML][BibTeX][Code][Data]
    [Press Release]

    [15] 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
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    [Press Release]

    [14] 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
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    [Plain Language Summary]

    [13] Labe, Z.M. and E.A. Barnes (2022), Predicting slowdowns in decadal climate warming trends with explainable neural networks. Geophysical Research Letters, DOI:10.1029/2022GL098173
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    [Plain Language Summary][DOE Research Highlight]

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

    [11] 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
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    [Press Release]

    [10] Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464
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    [Plain Language Summary][Data Skeptic Podcast]

    [9] 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
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    [Plain Language Summary][CLIVAR Research Highlight]

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

    [7] 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]
    [Press Release]

    [6] 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
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    [Plain Language Summary][CBS News][Science Magazine][The Washington Post]

    [5] 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
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    [Press Release]

    [4] 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
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    [Plain Language Summary]

    [3] 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
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    [Plain Language Summary][Arctic Today]

    [2] 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
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    [1] Labe, Z.M., T.R. Ault, and R. Zurita-Milla (2016), Identifying Anomalously Early Spring Onsets in the CESM Large Ensemble Project, R. Clim Dyn, DOI:10.1007/s00382-016-3313-2
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    [Plain Language Summary][Cornell Press Release][The Cornell Daily Sun][Earther][National Phenology Network]


    The views presented here only reflect my own. These figures may be freely distributed (with credit). Information about the data can be found on my references page and methods page.