Articles | Volume 16, issue 2
https://doi.org/10.5194/esd-16-475-2025
https://doi.org/10.5194/esd-16-475-2025
Perspective
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28 Mar 2025
Perspective | Highlight paper |  | 28 Mar 2025

Potential for equation discovery with AI in the climate sciences

Chris Huntingford, Andrew J. Nicoll, Cornelia Klein, and Jawairia A. Ahmad

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Cited articles

Abrams, J. F., Huntingford, C., Williamson, M. S., McKay, D. I. A., Boulton, C. A., Buxton, J. E., Sakschewski, B., Loriani, S., Zimm, C., Winkelmann, R., and Lenton, T. M.: Committed Global Warming Risks Triggering Multiple Climate Tipping Points, Earth's Future, 11, e2022EF003250, https://doi.org/10.1029/2022EF003250, 2023. a
Aldeia, G. and De França, F.: Measuring Feature Importance of Symbolic Regression Models Using Partial Effects, in: 2021 Genetic and Evolutionary Computation Conference (GECCO '21), 10–14 July 2021, Lille, France, ACM, New York, NY, USA, 9 pp., https://doi.org/10.1145/3449639.3459302, 2021. a
Baldocchi, D., Falge, E., Gu, L. H., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X. H., Malhi, Y., Meyers, T., Munger, W., Oechel, W., U, K. T. P., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities, B. Am. Meteorol. Soc., 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001. a, b
Bao, J., Stevens, B., Kluft, L., and Muller, C.: Intensification of daily tropical precipitation extremes from more organized convection, Science Adv., 10, 1–11, https://doi.org/10.1126/sciadv.adj6801, 2024. a
Berliner, L. M., Levine, R. A., and Shea, D. J.: Bayesian climate change assessment, J. Climate, 13, 3805–3820, https://doi.org/10.1175/1520-0442(2000)013<3805:BCCA>2.0.CO;2, 2000. a
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Artificial Intelligence (AI) is often criticised for being a "black-box" approach that provides no physical insights into the data being analysed. Very recently, a new branch of AI has emerged, called “AI-led equation discovery”. As the name suggests, it aims to reveal process equations underlying the data. This Perspective Article offers a path to align AI methods with climate research, with a focus on the use of AI-led equation discovery in support of Earth System Models.
Short summary
AI is impacting science, providing key data insights, but most algorithms are statistical requiring cautious "out-of-sample" extrapolation. Yet climate research concerns predicting future climatic states. We consider a new method of AI-led equation discovery. Equations offer process interpretation and more robust predictions. We recommend this method for climate analysis, suggesting illustrative application to atmospheric convection, land–atmosphere CO2 flux, and global ocean circulation models.
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