AI for atmosphere–ocean sciences: advancements, challenges and ways forward

Jing‐Jia Luo, Jiangjiang Xia, Baoxiang Pan, Yoo-Geun Ham, Xiaofeng Li, Wei Shangguan, Wei Xue, Yaqiang Wang, Yaqiang Wang, Bin Mu, Youngjoon Hong, Hao Li et al.

National Science Review · 28 January 2026 · Allen Institute for AI (Ai2, ACE), China Meteorological Administration (CMA), Columbia University (LEAP, Lamont-Doherty), Fudan University (FuXi), Institute of Atmospheric Physics, Chinese Academy of Sciences, Nanjing University of Information Science & Technology (NUIST), Potsdam Institute for Climate Impact Research (PIK), Shanghai Artificial Intelligence Laboratory, Tsinghua University (NowcastNet), Technical University of Munich (TUM)

Paper

In one sentence. Review of AI for atmosphere–ocean science covering forecasting, data assimilation, downscaling, explainable AI, hybrid physics-AI models, and society-facing applications.

Abstract

Artificial intelligence (AI) is rapidly transforming Earth science, offering unprecedented capabilities to tackle the most pressing challenges in the field. This work explores significant advances and emerging challenges across the AI for atmosphere-ocean sciences, while outlining critical ways forward. We review deep-learning methods and their application in weather and climate forecasting, which outperforms dynamical models in accuracy and computational efficiency. The role of AI in detecting complex phenomena, enhancing data assimilation and reconstruction, bias correction and downscaling coarse model outputs is also examined. However, the 'black-box' nature of complex AI models necessitates a focus on explainable AI to build trust and extract mechanistic insight. The most promising path forward is identified as the development of hybrid physics-AI modeling, which integrates the data-driven power of AI with the foundational constraints of physical laws to ensure generalizability and causal consistency. A new framework for AI-based model intercomparison is essential for rigorous benchmark performance. Finally, we contextualize these technical developments by discussing the usefulness and applicability of AI to society, including the improvement of multi-hazard early-warning systems and green energy production. We conclude by envisioning the future of AI agents for Earth science-autonomous, goal-oriented systems capable of designing and running experiments, generating and testing hypotheses, and learning dynamics from multisource data. This synthesis underscores that AI is not merely a tool, but a paradigm shift, which will significantly improve how we understand and adapt to a changing climate.

Labels

Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.