Toward a Learnable Climate Model in the Artificial Intelligence Era.

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. [ABSTRACT FROM AUTHOR]

摘 要: 当前, 人工智能(AI)迅速发展, 已经在大气科学的各个领域产生了深远影响, 并且不断改变和重塑着气候领域包括气候模拟和预测在内的核心问题的解决方法. 但是, 在这场AI驱动的变革中, 气候动力学的基础作用常被忽视. 通过对当前AI天气气候模型的争议以及AI和数值模式融合的分析, 本文认为, 未来处理气候模拟和预测等问题, 应该强调AI与基础动力学之间的协同关系, 而非将其视为非此即彼的选择. 结合对当前AI气象大模型物理不一致性争议的分析, 本文强调了更为全面、 细致的动力诊断和物理约束对于AI天气气候模型发展的重要性. 通过一些示例, 本文展示了如何对AI模型进行动力诊断和物理约束. 在AI与数值模式的融合方面, 目前离线的AI参数化方案训练目标是实现该参数的全局最优, 但可能无法得到相对于整个模式的全局最优解. 因此, 本文强调了构建双向耦合的在线AI参数化方案的重要性, 并结合当前正快速发展的可微分AI模型探讨构建在线AI参数化方案的思路. 此外, 我们认为加强气候社群的建设至关重要, 并提出了构建气候社群文化的开放、 可比较、 可重现(Open, Comparable, Reproducible; OCR)原则. 通过物理动力学和AI的深度融合, 以及开放共享的社区文化, 本文相信未来可以构建出一种可学习的气候模式, 既具有AI可学习可迭代的优点, 又保持数值模式的物理一致性和可解释性, 使得AI和物理的作用达到平衡. [ABSTRACT FROM AUTHOR]

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