DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models

TL;DR

DiffESM employs diffusion models to downscale Earth System Model outputs from monthly to daily data, enabling detailed climate analysis with reduced computational costs.

Contribution

This paper introduces a novel use of diffusion models for downscaling climate data, providing high-resolution daily outputs from low-cost monthly emulators.

Findings

DiffESM accurately reproduces ESM daily temperature and precipitation patterns.

The model captures extreme weather event characteristics effectively.

It significantly reduces computational resources needed for climate simulations.

Abstract

Earth System Models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analysis of risks associated with extreme weather events. While low-cost climate emulators have emerged as an alternative to emulate ESMs and enable rapid analysis of future climate, many of these emulators only provide output on at most a monthly frequency. This temporal resolution is insufficient for analyzing events that require daily characterization, such as heat waves or heavy precipitation. We propose using diffusion models, a class of generative deep learning models, to effectively downscale ESM output from a monthly to a daily frequency. Trained on a handful of ESM realizations, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input, and is capable of producing daily values with statistical characteristics close to ESM output. Combined with a low-cost emulator providing monthly means, this approach requires only a small fraction of the computational resources needed to run a large ensemble. We evaluate model behavior using a number of extreme metrics, showing that DiffESM closely matches the spatio-temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity.