DiffESM: Conditional Emulation of Earth System Models with Diffusion Models

Understand

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.