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

Checked 5 Nov 2026. Open license (CC-BY, CC-BY-SA, CC0, public domain): full text indexed and used in synthesis.

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.

• Luo et al. 2026 carry the agentic-science paradigm into atmosphere–ocean science: their EarthLink copilot interprets climate researchers' intent, generates experimental plans, and writes analysis code, and they envision autonomous, goal-oriented Earth-science agents that design experiments and test hypotheses from multisource data. Trace →
• The paradigm-shift rhetoric meets a ground-level dissent: where Luo et al. 2026 call AI not merely a tool but a paradigm shift, Nelson et al. 2026's 32 interviews with AI-experienced manufacturing and materials researchers find AI mainly reducing the experiment-intensity of searching design spaces—likely facilitating sustaining innovation while possibly undercutting disruptive innovation—more another tool in the research and development toolkit than a radical transformation. Trace →

• AI for science and agentic research