A foundation model for the Earth system.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 40399684.
- Also identified by DOI 10.1038/s41586-025-09005-y and PMC identifier 12119322.
- Licence recorded as CC BY-NC-ND.
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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 expensive<sup>1</sup>. Recent advances in artificial intelligence (AI) have shown promise in improving both predictive performance and efficiency<sup>2,3</sup>, 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.