The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming
Researchers have evaluated the climate response of several machine learning models, including ACE2-ERA5, NeuralGCM, and cBottle, to uniform sea surface temperature warming. These models were compared against NOAA's Geophysical Fluid Dynamics Laboratory AM4, a physics-based general circulation model. While the ML models showed promise in replicating aspects of the physical model's response, particularly in precipitation patterns, they also exhibited significant deviations in areas like radiative responses and land warming, indicating a need for further development in out-of-sample generalization for climate change applications. AI
IMPACT Highlights limitations in current ML climate models, suggesting further research is needed for reliable climate change prediction.