Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation

TL;DR

This paper proposes a framework that combines climate science and machine learning to develop reliable, easy-to-use emulators, addressing barriers like accessibility and trust, to improve climate model efficiency.

Contribution

It introduces a novel integrated framework that facilitates the development of trustworthy ML-based climate model emulators by bridging scientific and technical gaps.

Findings

Designing task-specific, reliable emulators is feasible.

Addressing accessibility and trust enhances ML adoption in climate science.

Bridging disciplines accelerates climate model development.

Abstract

While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine learning (ML) emulators offer a way to bypass the high computational costs, their effective use remains challenging. The hurdles are diverse, ranging from limited accessibility and a lack of specialized knowledge to a general mistrust of ML methods that are perceived as insufficiently physical. Here, we introduce a framework to overcome these barriers by integrating both climate science and machine learning perspectives. We find that designing easy-to-adopt emulators that address a clearly defined task and demonstrating their reliability offers a promising path for bridging the gap between our two fields.