Deep learning advances weather forecasts and aids climate predictions
Artificial intelligence pathways from weather to climate
Artificial IntelligenceMachine Learning
Summary
Forecasting the weather has improved a lot thanks to deep learning, which is a type of artificial intelligence (AI). This paper looks at how these AI methods can be stretched to predict climate changes, which is harder because it requires understanding how the climate reacts to things like greenhouse gases over a long time. The authors suggest key ways to build climate AI models that can handle new, extreme situations and different environmental factors clearly. They also explain how AI can speed up climate models by focusing on important features and running faster than traditional physics-based models while still giving detailed local predictions.
What this means in practice
- •For weather forecasters: Generate faster and well-calibrated weather forecasts by integrating AI models that run more efficiently than conventional dynamical systems.
- •For climate model developers: Incorporate explicit external forcing factors into AI climate models to predict climate responses under scenarios not seen in historical data.
A survey. It maps existing work.
Authors
Tom Beucler, J. David Neelin, Hui Su, Shivanshi Asthana, Chris Bretherton, Will Chapman, Costa Christopoulos, Spencer K. Clark, Aditya Grover, Ignacio Lopez-Gomez, Tapio Schneider, Adam Subel, Oliver Watt-Meyer
Abstract
Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models across nowcasting, medium-range, and subseasonal-to-seasonal lead times, producing well-calibrated ensemble forecasts at reduced cost. We review these advances and consider their extension to climate horizons, where the challenge shifts from initial-condition skill to producing reliable statistical responses under altered forcings. AI-powered climate prediction systems must produce credible forced responses to drivers (e.g., greenhouse gases, land-use change) typically outside the observed record. We propose two minimum requirements for AI in climate modeling: (i) external forcing agents must enter explicitly enough to support interventions in which they vary independently; and (ii) robustness must be stress-tested in out-of-distribution regimes, including extremes and counterfactual trajectories. Using leading AI autoregressive emulators and hybrid physics-AI models, we identify development and coupling challenges. Comparing the reported throughput of these models with that of GPU-ported dynamical models highlights how AI can reduce time-to-solution by advancing only the target variables at the required resolution and using longer time steps, rather than integrating a full high-frequency, multivariate state. Diverse AI downscaling strategies can partially substitute for explicit fine-scale resolution, paving the way toward inexpensive local hazard assessment across prediction horizons.