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  • Perspective
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Towards neural Earth system modelling by integrating artificial intelligence in Earth system science

Nature Machine Intelligence volume 3, pages 667–674 (2021)Cite this article

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Abstract

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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Fig. 1: Symbolic representation of Earth system components in terms of knowledge clusters.
Fig. 2: Successive stages of the fusion process of ESMs and AI towards NESYM.

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Acknowledgements

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.

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Authors and Affiliations

  1. Helmholtz Centre Potsdam, German Research Centre for Geosciences GFZ, Potsdam, Germany

    Christopher Irrgang & Jan Saynisch-Wagner

  2. Department of Mathematics and Computer Science, Free University of Berlin, Berlin, Germany

    Niklas Boers

  3. Potsdam Institute for Climate Impact Research, Potsdam, Germany

    Niklas Boers

  4. Department of Mathematics and Global Systems Institute, University of Exeter, Exeter, UK

    Niklas Boers

  5. Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA

    Maike Sonnewald

  6. Ocean and Cryosphere Division, Geophysical Fluid, Dynamics Laboratory, NOAA/OAR, Princeton, NJ, USA

    Maike Sonnewald

  7. School of Oceanography, University of Washington, Seattle, WA, USA

    Maike Sonnewald

  8. Colorado State University, Fort Collins, CO, USA

    Elizabeth A. Barnes

  9. German Climate Computing Center DKRZ, Hamburg, Germany

    Christopher Kadow

  10. Institute of Coastal Systems, Helmholtz-Zentrum Hereon, Geesthacht, Germany

    Joanna Staneva

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Contributions

C.I. conceived the study and organized the collaboration. All authors contributed to writing and revising all sections of this manuscript. In particular, N.B. and C.I. drafted the ESM overview, J.S.-W. and J.S. drafted the ESO and data assimilation overview, C.I. and C.K. drafted the ‘From ML-based data exploration towards learning physics’ section, C.I. and J.S.-W. and N.B. drafted the ‘Fusion of process-based models and AI’ section and M.S. and E.A.B. and CI drafted the ‘Peering into the black box’ section.

Corresponding author

Correspondence to Christopher Irrgang.

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The authors declare no competing interests.

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Peer review information Nature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work.

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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

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