Kavli Affiliate: Michael P. Brenner
| First 5 Authors: Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith
| Summary:
General circulation models (GCMs) are the foundation of weather and climate
prediction. GCMs are physics-based simulators which combine a numerical solver
for large-scale dynamics with tuned representations for small-scale processes
such as cloud formation. Recently, machine learning (ML) models trained on
reanalysis data 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 weather and
climate simulations. Here we present the first GCM that combines a
differentiable solver for atmospheric dynamics with ML components, and show
that it can generate forecasts of deterministic weather, ensemble weather and
climate on par with the best ML and physics-based methods. NeuralGCM is
competitive with ML models for 1-10 day forecasts, and with the European Centre
for Medium-Range Weather Forecasts ensemble prediction for 1-15 day forecasts.
With prescribed sea surface temperature, NeuralGCM can accurately track climate
metrics such as global mean temperature for multiple decades, and climate
forecasts with 140 km resolution exhibit emergent phenomena such as realistic
frequency and trajectories of tropical cyclones. For both weather and climate,
our approach offers orders of magnitude computational savings over conventional
GCMs. Our results show that end-to-end deep learning is compatible with tasks
performed by conventional GCMs, and can enhance the large-scale physical
simulations that are essential for understanding and predicting the Earth
system.
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