AI and the World's Hard Problems ─ Earth & climate science
Science and scholarship advanced by AI, by discipline
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Forecasting Tipping Points with AI
Tipping points, after which a system cannot return to its original state, are harder to predict than short-term weather changes. A Caltech-led team showed that a recurrent neural operator (RNO), a type of neural network, can predict when tipping points occur in simple models. These include a climate-change system model and simulations of turbulent airflow over a plane's wing. The paper was published in PNAS.
Advancing subseasonal forecasting with machine learning
Forecast skill drops sharply at subseasonal timescales of 2 to 6 weeks ahead. The authors introduce probabilistic bias correction, a machine learning framework that learns to correct historical probabilistic forecasts, and apply it to ECMWF's leading dynamical and AI models. It doubled the modest subseasonal skill of the AI Forecasting System and improved the dynamical model for most pressure, temperature and precipitation targets, and in ECMWF's 2025 real-time competition its global forecasts were the best.
Forecasting space weather risks on power grids
Extreme space-weather events can induce currents in transmission networks that damage equipment, and grid operators need to know when and where the effects will be most severe with enough warning. Microsoft Research built a machine learning pipeline that combines solar-wind data, AE and Dst forecasts, local latitude, geology, and ground conductivity to produce risk estimates for 66,935 substations in the continental United States. During the evaluation period it detected nearly 80% of major space-weather events and can warn operators 30 to 60 minutes before a specific risk appears.
AI tool for mapping smallholder crops proves itself in Senegal
Senegal's small, rain-fed farms leave much of the population exposed to climate shocks, yet satellite crop-mapping tools are largely out of reach. University of Cambridge researchers used Tessera, an open-source AI model trained on satellite images, to map crops in Senegal's groundnut basin. In tests it gave the correct result 84% of the time, more accurate than current methods, while using only a fraction of the computing resources and labelled data. In one scenario it performed 28% better than the next-best model.
Regional climate risk assessment from climate models using probabilistic machine learning
Climate models are too coarse for regional risk decisions, and existing downscaling methods are too costly for large ensembles, miss multivariate dependencies, or need time-aligned training pairs that free-running projections lack. GenFocal is a generative AI framework that produces statistically accurate fine-scale weather from coarse projections without paired training data. It synthesises long-lived hazards such as heatwaves and tropical cyclones, samples rare high-impact events more accurately than leading methods, and improves regional estimates of compound extremes.