Unfolding the Interdisciplinary Complexities of Climate Science: Fuxi-Climate Foundational Model

2026-08-23

AI Summary

为解决气候科学研究中的跨学科复杂性问题，本文提出Fuxi-Climate基础模型，通过增强的结构化推理能力来改善对气候风险和过渡路径的分析。

Abstract

Climate research and decision-making require integrating evidence across physical processes, socio-economic dynamics and policy responses. Large language models (LLMs) have been explored for accessing and synthesizing climate knowledge, but their ability to support structured interdisciplinary reasoning is still limited. Here we present the Fuxi-Climate Foundation Model (CFM), a climate-specialized LLM designed to support consistent reasoning across domains. CFM maintains more stable analytical behavior as interdisciplinary complexity increases, whereas performance in other models becomes more variable. On expert-designed climate transition tasks, CFM produces more structured analyses that explicitly address trade-offs and uncertainty, achieving 45% trade-off coverage and 47.27% uncertainty-aware reasoning. These results indicate that CFM can support more realistic analysis of climate risks and transition pathways, and provide a basis for agent-based systems to explore complex policy and decision scenarios. The model is openly available at https://huggingface.co/SII-yuning/cfm.

Problem

• Climate Science
• Interdisciplinary Reasoning
• Large Language Models
• Climate Knowledge
• Decision-making

Innovation

• climate-specialized LLM
• interdisciplinary reasoning
• trade-off coverage
• uncertainty-aware reasoning