A Self-Evolving AI Agent System for Climate Science

• URL: http://arxiv.org/abs/2507.17311v3
• Date: Mon, 03 Nov 2025 09:17:56 GMT
• Title: A Self-Evolving AI Agent System for Climate Science
• Authors: Zijie Guo, Jiong Wang, Fenghua Ling, Wangxu Wei, Xiaoyu Yue, Zhe Jiang, Wanghan Xu, Jing-Jia Luo, Lijing Cheng, Yoo-Geun Ham, Fengfei Song, Pierre Gentine, Toshio Yamagata, Ben Fei, Wenlong Zhang, Xinyu Gu, Chao Li, Yaqiang Wang, Tao Chen, Wanli Ouyang, Bowen Zhou, Lei Bai,
• Abstract summary: We introduce EarthLink, the first self-evolving AI agent system designed as an interactive "copilot" for Earth scientists.<n>Through natural language interaction, EarthLink automates the entire research workflow by integrating planning, code execution, data analysis, and physical reasoning.<n>It exhibits human-like cross-disciplinary analytical ability and proficiency comparable to a junior researcher in expert evaluations on core large-scale climate tasks.
• Score: 59.08800209508371
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scientific progress in Earth science depends on integrating data across the planet's interconnected spheres. However, the accelerating volume and fragmentation of multi-sphere knowledge and data have surpassed human analytical capacity. This creates a major bottleneck for discovery, especially in climate science. To address this challenge, we introduce EarthLink, the first self-evolving AI agent system designed as an interactive "copilot" for Earth scientists. Through natural language interaction, EarthLink automates the entire research workflow by integrating planning, code execution, data analysis, and physical reasoning into a unified process that directly addresses this limitation. Beyond efficiency, it exhibits human-like cross-disciplinary analytical ability and achieves proficiency comparable to a junior researcher in expert evaluations on core large-scale climate tasks, including model-observation comparison and climate change understanding. When tasked with an open scientific problem, specifically the discovery of precursors of the Atlantic Ni\~no, EarthLink autonomously developed a research strategy, identified sources of predictability, verified its hypotheses with available data, and proposed a physically consistent mechanism. These emerging capabilities enable a new human-AI research paradigm. Scientists can focus on value and result judgments, while AI systems handle complex data analysis and knowledge integration. This accelerates the pace and breadth of discovery in Earth sciences. The system is accessible at our website https://earthlink.intern-ai.org.cn.

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