DiffESM: Conditional Emulation of Earth System Models with Diffusion
Models
- URL: http://arxiv.org/abs/2304.11699v1
- Date: Sun, 23 Apr 2023 17:12:33 GMT
- Title: DiffESM: Conditional Emulation of Earth System Models with Diffusion
Models
- Authors: Seth Bassetti, Brian Hutchinson, Claudia Tebaldi, Ben Kravitz
- Abstract summary: A key application of Earth System Models (ESMs) is studying extreme weather events, such as heat waves or dry spells.
We show that diffusion models can effectively emulate the trends of ESMs under previously unseen climate scenarios.
- Score: 2.1989764549743476
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Earth System Models (ESMs) are essential tools for understanding the impact
of human actions on Earth's climate. One key application of these models is
studying extreme weather events, such as heat waves or dry spells, which have
significant socioeconomic and environmental consequences. However, the
computational demands of running a sufficient number of simulations to analyze
the risks are often prohibitive. In this paper we demonstrate that diffusion
models -- a class of generative deep learning models -- can effectively emulate
the spatio-temporal trends of ESMs under previously unseen climate scenarios,
while only requiring a small fraction of the computational resources. We
present a diffusion model that is conditioned on monthly averages of
temperature or precipitation on a $96 \times 96$ global grid, and produces
daily values that are both realistic and consistent with those averages. Our
results show that the output from our diffusion model closely matches the
spatio-temporal behavior of the ESM it emulates in terms of the frequency of
phenomena such as heat waves, dry spells, or rainfall intensity.
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