General circulation models (GCMs) are the foundation of weather and climate prediction,.
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,. However, these models have
not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term …

Atmospheric modeling is crucial for predicting weather and understanding the climate
system. Recent deep learning models such as Pangu and GraphCast have shown that pure
machine learning (ML) can be competitive with standard numerical weather prediction
models for global weather forecasting. However, pure ML models are hard to interpret, may
produce non-physical and blurry forecasts, and cannot be straightforwardly combined with
existing models for other parts of the Earth system.