Minisymposium Presentation

The AI2 Climate Emulator (ACE) marks a significant leap in climate modeling, employing a deep learning framework to replicate the comprehensive dynamics of the FV3GFS atmospheric model efficiently. ACE incorporates a Spherical Fourier Neural Operator (SFNO) with approximately 200M parameters. Using the previous weather state and externally prescribed forcings, this model forecasts the atmospheric state 6 hours ahead, alongside diagnostics such as surface precipitation rate, and turbulent and radiative fluxes. This variable set facilitates a robust assessment of the moisture and dry air mass budgets, and allows us to incorporate constraints to conserve dry air mass and ensure a closed moisture budget. Trained on a dataset with 100 years of atmospheric states simulated by a physics-based global atmosphere model, coarsened to a resolution of 1° and eight vertical levels, ACE demonstrates the ability to conduct stable multi-decadal simulations that maintain accurate weather dynamics and seasonal cycles, closely mirroring the reference model's precipitation and temperature patterns. Notably, ACE uses 60 times less energy than the FV3GFS model, leveraging modern GPU technology for efficient inference. This study underscores the potential of machine learning in climate prediction, offering a path towards fast and accessible climate models.