Application of the AI2 Climate Emulator to E3SMv2's global atmosphere
model, with a focus on precipitation fidelity
Abstract
Can the current successes of global machine learning-based weather
simulators be generalized beyond two-week forecasts to stable and
accurate multiyear runs? The recently developed AI2 Climate Emulator
(ACE) suggests this is feasible, based upon 10-year simulations trained
on a realistic global atmosphere model using a grid spacing of
approximately 110~km and forced by a repeating annual
cycle of sea-surface temperature. Here we show that ACE, without
modification, can be trained to emulate another major atmospheric model,
EAMv2, run at a comparable grid spacing for at least ten years with
similarly small climate biases. ACE accurately reproduces EAMv2’s
frequency distribution of daily-mean precipitation, its time-mean
spatial pattern of precipitation, and its space-time structure of
tropical precipitation, including the Madden-Julian Oscillation.
Moreover, ACE’s climate biases with respect to EAMv2 are substantially
smaller than EAMv2’s own biases compared to the observed historical
average surface precipitation rate and top-of-atmosphere radiative
fluxes.