Pushing the Frontiers in Climate Modeling and Analysis with Machine Learning
Abstract
Climate and Earth system models are important tools to understand and project climate change. Due to their complexity, they are limited in their horizontal resolutions, and some processes remain uncertain. Machine Learning (ML) together with short km-scale simulations and Earth observations provide new opportunities to reduce long-standing systematic errors and to improve projection capability. In this Perspective, we argue that ML should be fully exploited to (a) develop hybrid ML/physics Earth system models with greater fidelity, (b) to improve detection, attribution, and forecasting of extremes, and (c) to advance climate model analysis and understanding of the Earth system. We further discuss how tackling key ML challenges such as generalization, physical constraints, uncertainty quantification, explainable artificial intelligence, causal inference, and benchmarks can help achieve these goals. This effort will require bringing together ML and climate scientists, while also leveraging the private sector, to accelerate progress towards desperately-needed actionable climate science.
