A foundation model for the Earth system

Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Jayesh K. Gupta, Kit Thambiratnam, Alexander T. Archibald, Chun-Chieh Wu, Elizabeth Heider, Max Welling, Richard E. Turner and Paris Perdikaris ()
Additional contact information
Cristian Bodnar: Microsoft Research, AI for Science
Wessel P. Bruinsma: Microsoft Research, AI for Science
Ana Lucic: Microsoft Research, AI for Science
Megan Stanley: Microsoft Research, AI for Science
Anna Allen: University of Cambridge
Johannes Brandstetter: Microsoft Research, AI for Science
Patrick Garvan: Microsoft Research, AI for Science
Maik Riechert: Microsoft Research, AI for Science
Jonathan A. Weyn: Microsoft Corporation
Haiyu Dong: Microsoft Corporation
Jayesh K. Gupta: Silurian AI
Kit Thambiratnam: Microsoft Corporation
Alexander T. Archibald: University of Cambridge
Chun-Chieh Wu: National Taiwan University
Elizabeth Heider: Microsoft Research, AI for Science
Max Welling: Microsoft Research, AI for Science
Richard E. Turner: Microsoft Research, AI for Science
Paris Perdikaris: Microsoft Research, AI for Science

Nature, 2025, vol. 641, issue 8065, 1180-1187

Abstract: Abstract Reliable forecasting of the Earth system is essential for mitigating natural disasters and supporting human progress. Traditional numerical models, although powerful, are extremely computationally expensive1. Recent advances in artificial intelligence (AI) have shown promise in improving both predictive performance and efficiency2,3, yet their potential remains underexplored in many Earth system domains. Here we introduce Aurora, a large-scale foundation model trained on more than one million hours of diverse geophysical data. Aurora outperforms operational forecasts in predicting air quality, ocean waves, tropical cyclone tracks and high-resolution weather, all at orders of magnitude lower computational cost. With the ability to be fine-tuned for diverse applications at modest expense, Aurora represents a notable step towards democratizing accurate and efficient Earth system predictions. These results highlight the transformative potential of AI in environmental forecasting and pave the way for broader accessibility to high-quality climate and weather information.

Date: 2025
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DOI: 10.1038/s41586-025-09005-y

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