Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation
This work addresses the problem of making climate model emulation more accessible and trustworthy for climate scientists, though it appears incremental in bridging existing gaps.
The paper tackles the challenge of using machine learning emulators for climate models due to computational and trust issues, proposing a framework that integrates climate science and ML perspectives to design reliable and easy-to-adopt emulators.
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.