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

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

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VIEWED BY OTHERS PUSHING THE FRONTIERS IN CLIMATE MODELLING AND ANALYSIS WITH MACHINE LEARNING Article 23 August 2024 AI FOR CLIMATE IMPACTS: APPLICATIONS IN FLOOD RISK Article Open access

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Scholar  Download references 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. AUTHOR INFORMATION AUTHORS AND

AFFILIATIONS * Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany Veronika Eyring * University of Bremen, Institute of

Environmental Physics (IUP), Bremen, Germany Veronika Eyring * Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA Pierre Gentine * Image Processing

Laboratory, Universitat de València, València, Spain Gustau Camps-Valls * National Center for Atmospheric Research, Boulder, CO, USA David M. Lawrence * Max Planck Institute for

Biogeochemistry, Jena, Germany Markus Reichstein Authors * Veronika Eyring View author publications You can also search for this author inPubMed Google Scholar * Pierre Gentine View author

publications You can also search for this author inPubMed Google Scholar * Gustau Camps-Valls View author publications You can also search for this author inPubMed Google Scholar * David M.

Lawrence View author publications You can also search for this author inPubMed Google Scholar * Markus Reichstein View author publications You can also search for this author inPubMed Google

Scholar 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. CORRESPONDING AUTHOR Correspondence to Veronika Eyring. ETHICS DECLARATIONS COMPETING

INTERESTS The authors declare no competing interests. ETHICS This study has been conducted in full conformity with _Nature’s_ research ethic policies and principles of scholarly freedom and

responsibility. CONSENT TO PARTICIPATE All the authors have actively accepted an invitation from the corresponding author to participate in this study. CONSENT FOR PUBLICATION All the

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