Neural general circulation models for weather and climate.

ABSTRACT: General circulation models (GCMs) are the foundation of weather and climate prediction^1,2. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting^3,4. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate o

SUBMITTER: Kochkov D 

PROVIDER: S-EPMC11357988 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature