Toward a Learnable Climate Model in the Artificial Intelligence Era

Abstract

Artificial intelligence (AI) models have significantly impacted various areas of the atmospheric sciences, reshaping our approach to climate-related challenges. Amid this AI-driven transformation, the foundational role of physics in climate science has occasionally been overlooked. Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics, rather than an “either/or” scenario. Scrutinizing controversies around current physical inconsistencies in large AI models, we stress the critical need for detailed dynamic diagnostics and physical constraints. Furthermore, we provide illustrative examples to guide future assessments and constraints for AI models. Regarding AI integration with numerical models, we argue that offline AI parameterization schemes may fall short of achieving global optimality, emphasizing the importance of constructing online schemes. Additionally, we highlight the significance of fostering a community culture and propose the OCR (Open, Comparable, Reproducible) principles. Through a better community culture and a deep integration of physics and AI, we contend that developing a learnable climate model, balancing AI and physics, is an achievable goal.