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Stringent mitigation substantially reduces risk of unprecedented near-term warming rates

Nature Climate Change volume 11, pages 126–131 (2021)Cite this article

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Abstract

Following the Paris Agreement, many countries are enacting targets to achieve net-zero GHG emissions. Stringent mitigation will have clear societal benefits in the second half of this century by limiting peak warming and stabilizing climate. However, the near-term benefits of mitigation are generally thought to be less clear because forced surface temperature trends can be masked by internal variability. Here we use observationally constrained projections from the latest comprehensive climate models and a simple climate model emulator to show that pursuing stringent mitigation consistent with holding long-term warming below 1.5 °C reduces the risk of unprecedented warming rates in the next 20 years by a factor of 13 compared with a no mitigation scenario, even after accounting for internal variability. Therefore, in addition to long-term benefits, stringent mitigation offers substantial near-term benefits by offering societies and ecosystems a greater chance to adapt to and avoid the worst climate change impacts.

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Fig. 1: Near-term (2021–2040) GSAT trends and anomalies relative to the near-present-day (1995–2014) baseline.
Fig. 2: The effect of mitigation versus no mitigation on near-term (2021–2040) GSAT trend distributions from FaIR.
Fig. 3: GSAT trends from FaIR starting in 2021 for different end years or trend lengths.

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Data availability

The data that support the findings of this study are available at https://github.com/Priestley-Centre/Near_term_warming with the identifier https://doi.org/10.5281/zenodo.4252506 (ref. 86). This repository includes the FaIR simulation data, the constrained CMIP6 projections, the observation-based data and the observation-based estimates of internal variability (in fully processed form only). The SSP emissions datasets used in the FaIR simulations were downloaded from https://www.rcmip.org/, and the NDCs emissions dataset was provided by J. Rogelj. The constrained CMIP6 projections are based on ref. 17 and used surface air temperature data downloaded from ESGF (4 December 2019). The raw data used to calculate the observation-based estimates of internal variability are based on ref. 19 and were provided by K. Haustein. The surface air temperature data for the CMIP6 pre-industrial control simulations were obtained from the JASMIN/CEDA archive (29 July 2020). Further details of any CMIP6 data used are given in Supplementary Table 3 (refs. 87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155).

Code availability

The FaIR model is available at https://doi.org/10.5281/zenodo.3588880 (ref. 156). FaIR version 1.5 is used for all simulations in this paper. The code used to set up the FaIR simulations, analyse the data and produce the figures is available at https://github.com/Priestley-Centre/Near_term_warming with the identifier https://doi.org/10.5281/zenodo.4252506 (ref. 86). Python/Matplotlib was used for all coding and data visualization; for some figures, the vector graphics editor Inkscape (available at https://inkscape.org/) was used to combine different figure parts into one file.

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Acknowledgements

We thank J. Rogelj for providing the NDC scenario data and K. Haustein for providing the data used to calculate the observation-based estimates of internal variability. C.M.M., A.C.M., P.M.F., C.J.S. and K.B.T. were supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 820829 (CONSTRAIN project). A.C.M. was supported by the Natural Environment Research Council (grant no. NE/M018199/1) and Leverhulme Trust. C.J.S. was supported by a NERC/IIASA Collaborative Research Fellowship (no. NE/T009381/1). We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.

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Authors and Affiliations

  1. School of Earth and Environment, University of Leeds, Leeds, UK

    Christine M. McKenna, Amanda C. Maycock, Piers M. Forster & Christopher J. Smith

  2. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

    Christopher J. Smith

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

    Katarzyna B. Tokarska

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  1. Christine M. McKenna
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  2. Amanda C. Maycock
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Contributions

P.M.F. and A.C.M. designed the study. C.M.M. performed the analysis and produced the figures. C.J.S. performed the FaIR simulations. K.B.T. provided the constrained CMIP6 projections. All authors contributed to writing the manuscript.

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Correspondence to Christine M. McKenna.

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Peer review information Nature Climate Change thanks Daniel Huppmann, Giacomo Marangoni and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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McKenna, C.M., Maycock, A.C., Forster, P.M. et al. Stringent mitigation substantially reduces risk of unprecedented near-term warming rates. Nat. Clim. Chang. 11, 126–131 (2021). https://doi.org/10.1038/s41558-020-00957-9

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