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How AI is improving climate forecasts

Researchers are using various machine-learning strategies to speed up climate modelling, reduce its energy costs and hopefully improve accuracy.
  1. Carissa Wong
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Conceptual collage illustration combining themes of AI and climate.

Illustration: Jasiek Krzysztofiak. Hurricane: Rawpixel; Globe/computer code: Pexels; Weather map: US Dept of Commerce, Weather Bureau (CC BY 2.0)

Climate scientist Tapio Schneider is delighted that machine learning has taken the drudgery out of his day. When he first started modelling how clouds form, more than a decade ago, this mostly involved painstakingly tweaking equations that describe how water droplets, air flow and temperature interact. But since 2017, machine learning and artificial intelligence (AI) have transformed the way he works.

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Nature 628, 710-712 (2024)

doi: https://doi.org/10.1038/d41586-024-00780-8

References

  1. Kitsios, V., O’Kane, T. J. & Newth, D. Commun. Earth Environ. 4, 355 (2023).

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  2. Watt-Meyer, O. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2310.02074 (2023).

  3. Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J. K. & Grover, A. Preprint at arXiv https://doi.org/10.48550/arXiv.2301.10343 (2023).

  4. Watson-Parris, D. et al. J. Adv. Model. Earth Syst. 14, e2021MS002954 (2022).

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  5. Charbonneau, A., Deck, K. & Schneider, T. A Physics-Constrained Neural Differential Equation for Data-Driven Seasonal Snowpack Forecasting (Caltech, 2023).

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