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AI-empowered next-generation multiscale climate modelling for mitigation and adaptation

Nature Geoscience volume 17, pages 963–971 (2024)Cite this article

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Abstract

Earth system models have been continously improved over the past decades, but systematic errors compared with observations and uncertainties in climate projections remain. This is due mainly to the imperfect representation of subgrid-scale or unknown processes. Here we propose a next-generation Earth system modelling approach with artificial intelligence that calls for accelerated models, machine-learning integration, systematic use of Earth observations and modernized infrastructures. The synergistic approach will allow faster and more accurate policy-relevant climate information delivery. We argue a multiscale approach is needed, making use of kilometre-scale climate models and improved coarser-resolution hybrid Earth system models that include essential Earth system processes and feedbacks yet are still fast enough to deliver large ensembles for better quantification of internal variability and extremes. Together, these can form a step change in the accuracy and utility of climate projections, meeting urgent mitigation and adaptation needs of society and ecosystems in a rapidly changing world.

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Fig. 1: Feedback mechanisms across the Earth’s system introduce uncertainties in climate projections, affecting carbon cycles and climate change responses.
Fig. 2: Schematic of the proposed AI-empowered multiscale climate modelling approach for urgent mitigation and adaptation needs.

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Acknowledgements

We thank K. Hafner (University of Bremen, DLR) for her help with Fig. 1, A. Paçal (DLR) for his help with Fig. 2 and M. Rapp (DLR) for his comments on a draft manuscript. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modeling, coordinated and promoted CMIP. V.E., P.G., G.C.-V. and M.R.’s research for this study was funded by the European Research Council (ERC) Synergy Grant ‘Understanding and Modeling the Earth System with Machine Learning’ (USMILE) under the Horizon 2020 Research and Innovation programme (grant agreement no. 855187). V.E. was additionally supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the Gottfried Wilhelm Leibniz Prize awarded to V.E. (reference no. EY 22/2-1). Additional funding for P.G. and D.M.L. by the National Science Foundation Science and Technology Center, Learning the Earth with Artificial Intelligence and Physics, LEAP (grant no. 2019625), and for P.G. from Schmidt Futures, M2LInES, is also acknowledged.

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Authors and Affiliations

  1. Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany

    Veronika Eyring

  2. University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany

    Veronika Eyring

  3. Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA

    Pierre Gentine

  4. Image Processing Laboratory, Universitat de València, València, Spain

    Gustau Camps-Valls

  5. National Center for Atmospheric Research, Boulder, CO, USA

    David M. Lawrence

  6. Max Planck Institute for Biogeochemistry, Jena, Germany

    Markus Reichstein

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Contributions

V.E. led the writing and developed the multiscale climate modelling approach with AI for urgent mitigation and adaptation needs jointly with P.G. and all co-authors. All authors contributed to the writing of the manuscript and the development of the proposed approach.

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Correspondence to Veronika Eyring.

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Eyring, V., Gentine, P., Camps-Valls, G. et al. AI-empowered next-generation multiscale climate modelling for mitigation and adaptation. Nat. Geosci. 17, 963–971 (2024). https://doi.org/10.1038/s41561-024-01527-w

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