General circulation models (GCMs) form the backbone of weather and climate prediction, leveraging numerical solvers for large-scale dynamics and parameterizations for smaller-scale processes like cloud formation. Despite continuous improvements, GCMs face significant challenges, including persistent errors, biases, and uncertainties in long-term climate projections and extreme weather events. The recent machine-learning (ML) models have remarkably succeeded in short-term weather forecasts. Still, lack stability for long-term predictions and fail to provide calibrated uncertainty estimates, limiting their utility.
GoogleAI proposes NeuralGCM to address the limitations in weather and climate prediction using general circulation models (GCMs). Traditional GCMs, which rely on physics-based simulations, are computationally intensive and struggle with long-term stability and accurate ensemble forecasts. These GCMs combine numerical solvers for large-scale atmospheric dynamics with empirical parameterizations for smaller-scale processes like cloud formation. Machine-learning models, trained on historical data like ECMWF’s ERA5, have demonstrated impressive short-term weather prediction capabilities at lower computational costs but fail in long-term forecasting and ensemble accuracy.
GoogleAI’s NeuralGCM is a hybrid model combining a differentiable solver for atmospheric dynamics with machine-learning components for parameterizing physical processes. This model aims to leverage the strengths of both traditional GCMs and machine-learning approaches, offering stable and accurate forecasts over various timescales with significant computational efficiency.
NeuralGCM integrates a differentiable dynamical core with a learned physics module, which uses a neural network to predict the effects of unresolved atmospheric processes. The end-to-end training approach involves backpropagation through multiple simulation steps, gradually increasing the rollout length from 6 hours to 5 days. This method ensures that the model accounts for interactions between learned physics and large-scale dynamics, enhancing stability and accuracy.
Experiments were conducted to evaluate the performance of NeuralGCM against best-in-class models like ECMWF-HRES and ensemble prediction systems, as well as machine-learning models like GraphCast and Pangu. For 1- to 15-day weather forecasts, NeuralGCM achieves comparable accuracy, with the stochastic version showing lower error and better ensemble mean predictions. In climate simulations, NeuralGCM accurately tracks climate metrics over multiple decades and simulates emergent phenomena like tropical cyclones, with notable computational savings.
In conclusion, NeuralGCM successfully addresses the limitations of both traditional GCMs and pure machine-learning models, providing a stable and accurate hybrid approach for weather and climate prediction. By combining differentiable solvers with machine-learning parameterizations, NeuralGCM enhances the large-scale physical simulations essential for understanding and predicting the Earth’s system while offering significant computational efficiency.
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