How Can Machine Learning Emulators Best Support Climate Science?
Cluster of Excellence Machine Learning University of Tübingen（图宾根大学机器学习卓越中心）；Institute for Atmospheric and Climate Science ETH Zurich（大气与气候科学研究所，苏黎世联邦理工学院）
Luca Schmidt, Nina Effenberger, Vitus Benson, Philine L. Bommer, Robert Brunstein, Mikel N. Legasa, Maxim Samarin, Maybritt Schillinger
For decades, physics-based climate models have been used to provide insights for climate decision-making. Their application is, however, constrained by significant computational and technical demands. Machine learning (ML) emulators offer a way to reduce these high computational costs; yet, it remains challenging to use ML emulators effectively in climate research. In practice, climate scientists often bypass emulators altogether, and machine learning researchers frequently develop them as methodological showcases without proving their practical utility. The reasons are diverse, ranging from limited accessibility and a lack of specialized knowledge to broader concerns about the physical grounding of ML methods. Here, we discuss limitations and introduce a framework for guiding emulator development, considering both climate science and machine learning perspectives. We argue that designing easy-to-adopt emulators that address clearly defined tasks and demonstrate their reliability is essential. This offers a promising path towards making machine-learning approaches more relevant and usable for applied climate research.
MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards
University of Oxford（牛津大学）；Montreal Polytechnique（蒙特利尔理工学院）；Columbia Climate School（哥伦比亚气候学院）；International Institute for Applied Systems Analysis（国际应用系统分析研究所）；Universität Leipzig（莱比锡大学）；IRI THESys；Humboldt-Universität zu Berlin（柏林洪堡大学）；Institute Pierre-Simone-Laplace（皮埃尔-西蒙·拉普拉斯研究所）
High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator
Information Sciences and Technology, The Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术系）；Department of Applied Mathematics, University of California, Santa Cruz（加州大学圣克鲁兹分校应用数学系）；Allen Institute for Artificial Intelligence (AI2)（人工智能研究所）；School of Mechanical Engineering, Purdue University（普渡大学机械工程学院）
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（航空与航天系，麻省理工学院）；Center for Sustainability Science and Strategy, Massachusetts Institute of Technology（可持续科学与战略中心，麻省理工学院）；Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology（地球、大气与行星科学系，麻省理工学院）；Brahmal Vasudevan Institute for Sustainable Aviation, Department of Aeronautics, Imperial College London（可持续航空研究所，帝国理工学院伦敦校区）；Institute for Data, Systems, and Society, Massachusetts Institute of Technology（数据、系统与社会研究所，麻省理工学院）
Building Interpretable Climate Emulators for Economics
Institute of Computing, Università della Svizzera italiana（瑞士大学计算研究所）；Institute for Atmospheric and Climate Science, ETHZ（大气与气候科学研究所）；ifo Institute, Ludwig-Maximilian University of Munich（ifo研究所，慕尼黑莱布尼茨大学）；Department for Banking and Finance, University of Zürich（金融学院，苏黎世大学）；Department of Economics, University of Lausanne（经济学系，洛桑大学）；Grantham Research Institute, London School of Economics（格兰塔姆研究所，伦敦政治经济学院）
LUCIE-3D: A three-dimensional climate emulator for forced responses
Pennsylvania State University（宾夕法尼亚州立大学）；Allen Insitute of Artificial Intelligence（人工智能 Allen 机构）；Argonne National Laboratory（阿贡国家实验室）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts
Centre for Artificial Intelligence（人工智能中心）；Department of Computer Science（计算机科学系）；University College London（伦敦大学学院）；Division of Emerging Interdisciplinary Areas（新兴交叉学科领域）；Hong Kong University of Science and Technology（香港科技大学）；University of Oxford（牛津大学）
Copula-based synthetic data augmentation for machine-learning emulators
University of Reading（雷丁大学）；Imperial College London（帝国理工学院）；Leiden University（莱顿大学）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）
我们能用合成数据改进机器学习（ML）仿真器吗？如果数据稀缺或来源昂贵，并且物理模型可用，则统计生成的数据可以用于廉价地扩充训练集。在这里，我们探讨了使用基于Copula的模型生成综合增强数据集的天气和气候测试的玩具物理模型的下降流长波辐射和相应的神经网络仿真器的方法。结果表明，Copula增强的数据集，预测提高了62%的平均绝对误差（从1.17到0.44 W m $^{-2}$）。
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
Mila Quebec AI Institute（魁北克人工智能研究所）；McGill University（麦吉尔大学）；University of Osnabrück（奥斯纳布吕克大学）；University of Montreal（蒙特利尔大学）；Intel Labs（英特尔实验室）；German Aerospace Center（德国航空航天中心）；Technische Universität Berlin（柏林工业大学）
Samudra 2: Scaling Ocean Emulators across Resolutions
Courant Institute School of Mathematics, Computing, and Data Science, New York University（Courant学院数学、计算与数据科学系，纽约大学）；Open Athena AI Foundation, Inc.（开放Athena人工智能基金会）；Program in Atmospheric and Oceanic Sciences, Princeton University（大气与海洋科学项目，普林斯顿大学）
Sensitivity Analysis for Climate Science with Generative Flow Models
Dept. of Engineering University of Oxford（工程系大学学院 Oxford 大学）；Dept. of Statistics University of Oxford（统计系大学学院 Oxford 大学）；Dept. of Physics University of Oxford（物理系大学学院 Oxford 大学）；IBM（IBM 公司）
DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models
Utah State University（犹他州立大学）；Joint Global Change Research Institute（全球变化联合研究所）；Pacific Northwest National Laboratory（西北太平洋国家实验室）；Western Washington University（西华盛顿大学）；Indiana University（印第安纳大学）
AutoClimDS: Climate Data Science Agentic AI -- A Knowledge Graph is All You Need
NSF STC Learning the Earth with AI and Physics (LEAP), Columbia University（NSF STC 学习地球与人工智能和物理（LEAP），哥伦比亚大学）；AWS Generative AI Innovation Center（AWS 生成式人工智能创新中心）；Department of Statistics, Columbia University（哥伦比亚大学统计系）
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
Technische Universität Berlin（柏林工业大学）；ATB（莱布尼茨农业工程与生物经济研究所）；University of Leipzig（莱比锡大学）；University of Reading（雷丁大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所（BIFOLD））；UiT the Arctic University of Norway（挪威北极大学）；University of Potsdam（波茨坦大学）
A Self-Evolving AI Agent System for Climate Science
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Seoul National University（首尔大学）；Ocean University of China（中国海洋大学）；Laoshan Laboratory（崂山实验室）；Columbia University（哥伦比亚大学）；Japan Agency for Marine-Earth Science and Technology（日本海洋研究开发机构）；The Chinese University of Hong Kong（香港中文大学）；East China Normal University（华东师范大学）；Chinese Academy of Meteorological Sciences（中国气象科学研究院）；Tsinghua University（清华大学）
