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\begin{document}

\title{Application of the AI2 Climate Emulator to E3SMv2's global atmosphere
model, with a focus on precipitation fidelity}



\author[1]{James PC Duncan}%
\author[2]{Elynn Wu}%
\author[3]{Jean-Christophe Golaz}%
\author[3]{Peter Martin Caldwell}%
\author[2]{Oliver Watt-Meyer}%
\author[4]{Spencer Koncius Clark}%
\author[2]{Jeremy McGibbon}%
\author[2]{Gideon Dresdner}%
\author[5]{Karthik Kashinath}%
\author[5]{Boris Bonev}%
\author[6]{Michael S Pritchard}%
\author[2]{Christopher S. Bretherton}%
\affil[1]{University of California, Berkeley}%
\affil[2]{Allen Institute for Artificial Intelligence}%
\affil[3]{Lawrence Livermore National Laboratory (DOE)}%
\affil[4]{Allen Institute for Artificial Intelligence / NOAA-GFDL}%
\affil[5]{NVIDIA}%
\affil[6]{University of California, Irvine and NVIDIA}%


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  \date{May 24, 2024}


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\begin{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 with a
network trained on output from a physics-based 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 -- a necessary prerequisite to wider
applicability. With a comprehensive analysis from multiple temporal,
spatial, and frequency domain perspectives, we show that ACE faithfully
represents the spatiotemporal structure of EAMv2 precipitation and
related variables. Finally, we show that a pretrained ACE network is
able to adapt to a new global climate model simulation dataset with 10x
fewer training steps than when starting from random initialization, all
while still maintaining low levels of climate bias. Further analysis of
these fine-tuning experiments reveal ACE's attractive ability to
interpolate between distinct global climate models.%
\end{abstract}%



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