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Constraining human contributions to observed warming since the pre-industrial period

Nature Climate Change volume 11, pages 207–212 (2021)Cite this article

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Abstract

Parties to the Paris Agreement agreed to holding global average temperature increases “well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels”. Monitoring the contributions of human-induced climate forcings to warming so far is key to understanding progress towards these goals. Here we use climate model simulations from the Detection and Attribution Model Intercomparison Project, as well as regularized optimal fingerprinting, to show that anthropogenic forcings caused 0.9 to 1.3 °C of warming in global mean near-surface air temperature in 2010–2019 relative to 1850–1900, compared with an observed warming of 1.1 °C. Greenhouse gases and aerosols contributed changes of 1.2 to 1.9 °C and −0.7 to −0.1 °C, respectively, and natural forcings contributed negligibly. These results demonstrate the substantial human influence on climate so far and the urgency of action needed to meet the Paris Agreement goals.

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Fig. 1: Comparison of 1850–2019 global mean temperature evolution in observations and CMIP6 simulations.
Fig. 2: Results of a detection and attribution analysis applied to CMIP6 models.
Fig. 3: Imperfect model test of the multimodel attributable-warming calculation.

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Data availability

All figures in this manuscript use CMIP6 data available at https://esgf-node.llnl.gov/projects/cmip6/. The DOIs of the CMIP6 datasets (CMIP6 historical, DAMIP and ScenarioMIP) used from each model are: ACCESS-ESM1-5: https://doi.org/10.22033/ESGF/CMIP6.2288, https://doi.org/10.22033/ESGF/CMIP6.14362 and https://doi.org/10.22033/ESGF/CMIP6.2291; BCC-CSM2-MR: https://doi.org/10.22033/ESGF/CMIP6.1725, https://doi.org/10.22033/ESGF/CMIP6.1726 and https://doi.org/10.22033/ESGF/CMIP6.1732; CanESM5: https://doi.org/10.22033/ESGF/CMIP6.1303, https://doi.org/10.22033/ESGF/CMIP6.1305 and https://doi.org/10.22033/ESGF/CMIP6.1317; CESM2: https://doi.org/10.22033/ESGF/CMIP6.2185, https://doi.org/10.22033/ESGF/CMIP6.2187 and https://doi.org/10.22033/ESGF/CMIP6.2201; CNRM-CM6-1: https://doi.org/10.22033/ESGF/CMIP6.1375, https://doi.org/10.22033/ESGF/CMIP6.1376 and https://doi.org/10.22033/ESGF/CMIP6.1384; FGOALS-g3: https://doi.org/10.22033/ESGF/CMIP6.1783, https://doi.org/10.22033/ESGF/CMIP6.2048 and https://doi.org/10.22033/ESGF/CMIP6.2056; GFDL-ESM4: https://doi.org/10.22033/ESGF/CMIP6.1407, https://doi.org/10.22033/ESGF/CMIP6.1408 and https://doi.org/10.22033/ESGF/CMIP6.1414; GISS-E2-1-G: https://doi.org/10.22033/ESGF/CMIP6.1400, https://doi.org/10.22033/ESGF/CMIP6.2062 and https://doi.org/10.22033/ESGF/CMIP6.2074; HadGEM3-GC31-LL: https://doi.org/10.22033/ESGF/CMIP6.419, https://doi.org/10.22033/ESGF/CMIP6.471 and https://doi.org/10.22033/ESGF/CMIP6.10845; IPSL-CM6A-LR: https://doi.org/10.22033/ESGF/CMIP6.1534, https://doi.org/10.22033/ESGF/CMIP6.13801 and https://doi.org/10.22033/ESGF/CMIP6.1532; MIROC6: https://doi.org/10.22033/ESGF/CMIP6.881, https://doi.org/10.22033/ESGF/CMIP6.894 and https://doi.org/10.22033/ESGF/CMIP6.898; MRI-ESM2-0: https://doi.org/10.22033/ESGF/CMIP6.621, https://doi.org/10.22033/ESGF/CMIP6.634 and https://doi.org/10.22033/ESGF/CMIP6.638; NorESM2-LM: https://doi.org/10.22033/ESGF/CMIP6.502, https://doi.org/10.22033/ESGF/CMIP6.580 and https://doi.org/10.22033/ESGF/CMIP6.604. HadCRUT4 data (version 4.6.0.0, downloaded 24 March 2020) are available at https://www.metoffice.gov.uk/hadobs/hadcrut4/, GISTEMP data (version 4 with 1,200 km smoothing, downloaded 13 April 2020) are available at https://data.giss.nasa.gov/gistemp/ and NOAAGlobalTemp data (version 5.0.0, downloaded 13 April 2020) are available at https://www.ncdc.noaa.gov/noaa-merged-land-ocean-global-surface-temperature-analysis-noaaglobaltemp-v5, and HadCRUT.5.0.0.0 data are available at https://www.metoffice.gov.uk/hadobs/hadcrut5.

Code availability

The analysis code used in this study is based on ESMValTool and is available at https://github.com/ESMValGroup/ESMValTool/tree/gillett20.

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Acknowledgements

We thank D. Stone, N. Bellouin, S. Ying, G. Schmidt and M. Winton for helpful comments on the analysis and manuscript, L. Bock for assistance with ESMValTool, and C. Morice and N. Rayner for provision of HadCRUT5 data. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the modelling groups for producing and making available their model output and the Earth System Grid Federation for archiving the data and providing access. HS was supported by the Ministry of Education, Culture, Sports, Science and Techology, Japan (grant JPMXD0717935457).

Author information

Authors and Affiliations

  1. Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada

    Nathan P. Gillett

  2. Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada

    Megan Kirchmeier-Young

  3. CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

    Aurélien Ribes

  4. Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan

    Hideo Shiogama

  5. School of Geosciences, University of Edinburgh, Edinburgh, UK

    Gabriele C. Hegerl

  6. Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

    Reto Knutti

  7. LOCEAN, Sorbonne Université, Institut Pierre Simon Laplace, Paris, France

    Guillaume Gastineau

  8. NOAA/OAR Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA

    Jasmin G. John

  9. LASG, Institute of Atmospheric Physics, Beijing, China

    Lijuan Li

  10. NASA Goddard Institute for Space Studies, New York, NY, USA

    Larissa Nazarenko

  11. NCAR, Boulder, CO, USA

    Nan Rosenbloom

  12. Norwegian Meteorological Institute, Oslo, Norway

    Øyvind Seland

  13. Beijing Climate Center, China Meteorological Administration, Beijing, China

    Tongwen Wu

  14. Meteorological Research Institute, Tsukuba, Japan

    Seiji Yukimoto

  15. Oceans and Atmosphere, CSIRO, Aspendale, Victoria, Australia

    Tilo Ziehn

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Contributions

N.P.G. carried out the analysis and led writing the manuscript. M.K.-Y. developed the Python code used in the attribution analysis. A.R. developed the algorithm used in the analysis. All authors advised on the analysis and contributed to drafting the manuscript.

Corresponding author

Correspondence to Nathan P. Gillett.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Global mean surface temperature (GMST) anomalies in all DAMIP historical simulations.

The multi-model mean and 5–95% ensemble ranges, based on all available simulations with equal weight given to each model, are shown. HadCRUT4 GMST is shown in black on the top graph.

Extended Data Fig. 2 Results of a regression in which observed changes are decomposed into the response to natural forcings, well-mixed greenhouse gases, and other anthropogenic forcings.

As Fig. 2, except that the right panels show the results of a three-way regression of observations onto the simulated response to natural forcings (NAT), well-mixed greenhouse gases only (GHG), and other anthropogenic forcings (OTH), consisting of aerosols, ozone and land-use change. In this figure ozone and land-use change are grouped with aerosols, instead of with well-mixed greenhouse gases, as in Fig. 2.

Extended Data Fig. 3 Regression results based on GISTEMP.

As Fig. 2, except using GISTEMP in place of HadCRUT4.

Extended Data Fig. 4 Regression results based on NOAAGlobalTemp.

As Fig. 2, except using NOAAGlobalTemp in place of HadCRUT4.

Extended Data Fig. 5 Regression results based on hemispheric means.

As Fig. 2, except using 5-yr mean hemispheric means in place of 5-yr mean GMST in the regressions.

Extended Data Fig. 6 Regression coefficients derived using each of the 100 ensemble members of HadCRUT411.

Results are shown for two-way (a) and three-way (b) multi-model regression analyses, as shown in Fig. 2a,b, except using each of the 100 members of the HadCRUT4 ensemble dataset in turn.

Extended Data Fig. 7 The ratio of 2010–2019 warming relative to 1850–1900 in GSAT to HadCRUT4-masked GMST and globally-complete GMST.

The ratio of changes in GSAT to HadCRUT4-masked GMST is shown in (a), and the ratio of changes in GSAT to globally-complete GMST is shown in (b) for each individual historical-ssp245 simulation of each model.

Extended Data Fig. 8 Comparison of uncertainty calculation approaches.

As Fig. 2e,f, except that in each case uncertainties in attributable temperature change are calculated in two ways. Bars show confidence intervals calculated, as in the main analysis, accounting for uncertainty in the ensemble mean simulated 2010–2019 GSAT changes in the case of the individual model analyses, and accounting for uncertainties in the ratio of GSAT to GMST and observational uncertainty, in the case of the multi-model analysis. Horizontal ticks show confidence ranges neglecting these sources of uncertainty. The latter calculation corresponds to multiplying the 5–95% confidence range on the regression coefficient by the corresponding ensemble mean simulated 2010–2019 GSAT change.

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Gillett, N.P., Kirchmeier-Young, M., Ribes, A. et al. Constraining human contributions to observed warming since the pre-industrial period. Nat. Clim. Chang. 11, 207–212 (2021). https://doi.org/10.1038/s41558-020-00965-9

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