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A cutting-edge global model of the atmosphere combines machine learning with a numerical model based on the laws of physics. This ‘hybrid’ system accurately predicts the weather — and even shows promise for climate simulations.
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- Oliver Watt-MeyerORCID: http://orcid.org/0000-0001-8419-15260
- Oliver Watt-Meyer
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Oliver Watt-Meyer is at the Allen Institute for Artificial Intelligence, Seattle, Washington 98103, USA.
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Numerical models of Earth’s climate system, based on the laws of physics, are essential tools for predicting changes in weather and climate in our warming world. However, persistent uncertainties in the representation of small-scale processes, such as cloud formation, have resulted in only slow improvements in the fidelity of these models. Interest in using machine learning to provide a data-driven representation of these uncertain processes has therefore skyrocketed. Yet challenges have persisted in coupling these representations to conventional atmospheric models. Writing in Nature, Kochkov et al.1 report a hybrid global atmospheric model that overcomes these challenges, combining physics-based approaches with machine learning. This model not only predicts the weather up to 15 days ahead with state-of-the-art accuracy, but also simulates historical temperature trends over a 40-year period, demonstrating its ability to assess climate.
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doi: https://doi.org/10.1038/d41586-024-02558-4
References
Kochkov, D. et al. Nature https://doi.org/10.1038/s41586-024-07744-y (2024).
Rasp, S., Pritchard, M. S. & Gentine, P. Proc. Natl Acad. Sci. USA 115, 9684–9689 (2018).
Brenowitz, N. D. & Bretherton, C. S. Geophys. Res. Lett. 45, 6289–6298 (2018).
Bi, K. et al. Nature 619, 533–538 (2023).
Lam, R. et al. Science 382, 1416–1421 (2023).
Frostig, R., Johnson, M. J. & Leary, C. In Proc. Syst. Mach. Learn. abstr. 146 (2018).
Banerjee, A., Fyfe, J. C., Polvani, L. M., Waugh, D. & Chang, K.-L. Nature 579, 544–548 (2020).
IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2023).
Beucler, T. et al. Sci. Adv. 10, eadj7250 (2024).
Clark, S. K. et al. J. Adv. Model. Earth Syst. 14, e2022MS003219 (2022).
Eyring, V. et al. Geosci. Model Dev. 9, 1937–1958 (2016).
Competing Interests
The author declares no competing interests.
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