Seminar: ACE: A fast, skillful learned global atmospheric model for climate prediction

Speaker: Oliver Watt-Meyer, Allen Institute for Artificial Intelligence

Abstract

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 detailed 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 layers, ACE demonstrates the ability to conduct stable multi-decadal simulations that maintain accurate weather dynamics and seasonal cycles, closely mirroring the reference model's time-averaged precipitation and temperature patterns. Notably, ACE uses over 100 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.

Biography

Oliver (Oli) Watt-Meyer is a Lead Research Scientist on the Climate Modeling team at the Allen Institute for Artificial Intelligence. His research expertise is in large-scale atmospheric dynamics, including stratosphere-troposphere coupling and the interactions between the tropics and extratropics. He obtained his PhD from the University of Toronto in 2016 and completed a postdoc at the University of Washington Department of Atmospheric Sciences. He joined the Allen Institute for AI Climate Modeling team in 2019.

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