R. Krishnan, P. Swapna, Ayantika Dey Choudhury, Sandeep Narayansetti, A. G. Prajeesh, Manmeet Singh, Aditi Modi, Roxy Mathew, Ramesh Vellore, J. Jyoti, T. P. Sa… 展开作者
R. Krishnan, P. Swapna, Ayantika Dey Choudhury, Sandeep Narayansetti, A. G. Prajeesh, Manmeet Singh, Aditi Modi, Roxy Mathew, Ramesh Vellore, J. Jyoti, T. P. Sabin, J. Sanjay, Sandip Ingle
Earth System Models (ESM) are important tools that allow us to understand and quantify the physical, chemical & biological mechanisms governing the rates of change of elements of the Earth System, comprising of the atmosphere, ocean, land, cryosphere and biosphere (terrestrial and marine) and related components. ESMs are essentially coupled numerical models which incorporate processes within and across the different Earth system components and are expressed as set of mathematical equations. ESMs are useful for enhancing our fundamental understanding of the climate system, its multi-scale variability, global and regional climatic phenomena and making projections of future climate change. In this chapter, we briefly describe the salient aspects of the Indian Institute of Tropical Meteorology ESM (IITM ESM), that has been developed recently at the IITM, Pune, India, for investigating long-term climate variability and change with focus on the South Asian monsoon.
Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models
Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Technical University Munich（慕尼黑工业大学）；Eberhard Karls Universität Tübingen（蒂宾根大学）；University of Exeter（埃克塞特大学）
Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models
Western Washington University（西华盛顿大学）；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（太平洋西北国家实验室）；AI and Data Analytics Division, Pacific Northwest National Laboratory（太平洋西北国家实验室数据分析与人工智能分部）
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）
Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Machine Learning
Technical University Munich（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；University of Exeter（埃克塞特大学）
Recommendations for Comprehensive and Independent Evaluation of Machine Learning-Based Earth System Models
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；University of California Davis（加州大学戴维斯分校）；Colorado State University（科罗拉多州立大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；National Center for Atmospheric Research（国家大气研究中心）；Indiana University Bloomington（印第安纳大学伯明顿分校）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of Maryland（马里兰大学）
Climate network characterization of the AMOC edge state
University of Geneva（日内瓦大学）；Centre pour la Vie dans l’Univers (CVU)（宇宙生命中心）；Institute for Marine and Atmospheric research Utrecht（乌得勒支海洋与大气研究所）；Utrecht University（乌得勒支大学）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）；Schools of Sciences, Great Bay University（大湾区大学理学院）
Dependence of Inferred Climate Sensitivity on the Discrepancy Model
我们考虑的影响，不同的时间误差结构的推断平衡气候敏感性\footnote{ECS is defined as the realized equilibrium surface warming---globally-averaged surface air temperature---for a doubling of CO$_2$}（ECS），在能量平衡模式（EBM）的背景下，通常采用在分析地球系统模式（ESM）和观测。我们考虑了从不相关（IID正常）到AR（1）到高斯相关（高斯过程GP）的误差结构，以分析21个不同ESM中的突变4xCO $_2$ CMIP 5实验。对于七个ESM，ECS的后验分布被认为依赖于相当弱的差异模型，这表明差异在很大程度上是不相关的。然而，四个ESM的差异较大，其余ESM的差异适中，这使我们认为AR（1）是一个合适的差异相关结构，可以用于本文所考虑的情况。其他重要调查结果包括：（a）当ECS（众数）的估计值不同时，使用IID的估计值较高（b）对于四种ESM，IID差异结构的ECS推断的不确定性高于其他相关结构，（c）GP参数估计的不确定性远高于IID或AR（1）参数估计的不确定性，可能是由于可识别性问题。他们需要进一步调查。
Accelerating exploration of Marine Cloud Brightening impacts on tipping points Using an AI Implementation of Fluctuation-Dissipation Theorem
University of Victoria（维多利亚大学）；Palo Alto Research Center（帕洛阿尔托研究中心）；Excarta；University of California San Diego（加利福尼亚大学圣迭戈分校）；University of Washington（华盛顿大学）
Towards Causal Representations of Climate Model Data
Mila –Quebec AI Institute（米拉-魁北克人工智能研究所）；McGill University（麦吉尔大学）；Osnabrück University（奥斯纳布吕克大学）；Université de Montréal（蒙特利尔大学）；Intel Labs（英特尔实验室）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Columbia University（哥伦比亚大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
