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Abstract: How Interpretable Machine Learning Advances Our Understanding of Climate Extremes and Their Impacts on Ecosystems and Infrastructure (AGU25)

How Interpretable Machine Learning Advances Our Understanding of Climate Extremes and Their Impacts on Ecosystems and Infrastructure

Wenli Zhao1, Biqing Zhu2, Kevin W Bowman3, Jianing Fang1, Guo Yu Qiu4 and Pierre Gentine1, (1)Columbia University, Department of Earth and Environmental Engineering, New York, United States, (2)ETH Swiss Federal Institute of Technology Zurich, Institute of Biogeochemistry and Pollutant Dynamics, Zurich, Switzerland, (3)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (4)Peking University Shenzhen Graduate School, School of Environment and Energy, Shenzhen, China
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