发表机构
Columbia University; École Normale Supérieure – PSL; University of Tübingen; Princeton University（哥伦比亚大学; 巴黎高等师范学院-PSL; 蒂宾根大学; 普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对地球系统模型的架构缺陷，提出由AI智能体构建的模块化可微分多尺度地球系统模型legoESM，其可跨尺度模拟、降低陆面温度偏差，为地球科学研究提供开放基础设施与模板。
AI 中文摘要
地球系统模型（ESMs）的真实度已大幅提升，但在温室气体强迫的气候响应方面仍存在关键不确定性，尤其源于云辐射反馈。此外，其软件架构并非为加速器硬件或现代人工智能（AI）设计。本文提出legoESM，这是一个用JAX编写的可组合、可微分、多尺度地球系统模型。它基于数十年社区开发的参数化方案和数值方法，在人类指定的科学约束下，通过AI编码智能体重构为统一框架，并经基准测试验证。动力核心、物理方案、网格、复杂度层级及组件均可像积木一样互换，可采用传统物理或机器学习代理模型。单一代码库覆盖米级大涡模拟至全球模拟、天气至气候场景。端到端可微分性支持基于梯度的校准、变分数据同化及在线训练。legoESM的模块化架构可系统评估多种模型变体，以探究结构不确定性并验证假设。legoESM能生成跨尺度的真实模拟，通过基于梯度的校准降低陆面温度偏差，并在GPU上高效扩展至公里级模拟。它为地球科学的假设检验、研究与教学提供开放的社区基础设施，也为多尺度物理系统提供模板。
英文摘要
Earth system models (ESMs) have grown tremendously in realism, yet key uncertainties persist in the climate response to greenhouse-gas forcing, particularly due to cloud radiative feedbacks. In addition, their software architecture was not designed for accelerator hardware or modern artificial intelligence (AI). Here we present legoESM, a composable, differentiable, multiscale ESM written in JAX. It builds on decades of community-developed parameterizations and numerical methods, recast in a unified framework by AI coding agents under a human-specified scientific contract and verified through benchmarking. Dynamical cores, physics schemes, grids, complexity levels and components are swappable like building blocks, and can use conventional physics or machine-learned emulators. A single code base spans metre-scale large-eddy simulation to global simulations and weather to climate. End-to-end differentiability enables gradient-based calibration, variational data assimilation and online training. legoESM modular architecture enables systematic evaluation of diverse model variants to explore structural uncertainty and test hypotheses. legoESM produces realistic simulations across scales, reduces land-surface temperature bias through gradient-based calibration, and scales efficiently on GPUs to kilometer-scale simulations. It offers an open, community infrastructure for hypothesis testing, research and teaching in Earth sciences and a template for multiscale physical systems.
