Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth and predicting how it might change in the future under ongoing anthropogenic forcing. In recent years, however, artificial intelligence (AI) methods have been increasingly used to augment or even replace classical ESM tasks, raising hopes that AI could solve some of the grand challenges of climate science. In this Perspective we survey the recent achievements and limitations of both process-based models and AI in Earth system and climate research, and propose a methodological transformation in which deep neural networks and ESMs are dismantled as individual approaches and reassembled as learning, self-validating and interpretable ESM–network hybrids. Following this path, we coin the term neural Earth system modelling. We examine the concurrent potential and pitfalls of neural Earth system modelling and discuss the open question of whether AI can bolster ESMs or even ultimately render them obsolete.
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This study was funded by the Helmholtz Association and by the Initiative and Networking Fund of the Helmholtz Association through the project Advanced Earth System Modelling Capacity (ESM). N.B. acknowledges funding by the Volskwagen foundation and the European Union’s Horizon 2020 research and innovation program under grant agreement number 820970 (TiPES, contribution #121). E.A.B. was supported, in part, by the US National Science Foundation under grant number AGS-1749261. M.S. acknowledges funding from the Cooperative Institute for Modeling the Earth System, Princeton University, under award number NA18OAR4320123 and from the National Oceanic and Atmospheric Administration, US Department of Commerce. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of Princeton University, the National Oceanic and Atmospheric Administration or the US Department of Commerce.
The authors declare no competing interests.
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Irrgang, C., Boers, N., Sonnewald, M. et al. Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nat Mach Intell 3, 667–674 (2021). https://doi.org/10.1038/s42256-021-00374-3