[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118155-en":3,"doc-seo-118155-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118155,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Pushing the Frontiers in Climate Modeling and Analysis with Machine Learning","Climate modeling and analysis face rising demands for more reliable projections and actionable climate information. The work argues for advancing machine learning beyond current best results by developing higher-fidelity ML-based Earth system models and enabling new capabilities. Key contributions include emulators for extreme-event projections using large ensembles, improved detection and attribution for extremes, and enhanced climate-model analysis and benchmarking. Achieving this requires addressing generalization, uncertainty quantification, explainable AI, and causality, coordinated across ML, climate science, and private-sector expertise.","1 Pushing the Frontiers in Climate Modeling and Analysis with  \n2 Machine Learning  \n3 Veronika Eyring 1,2*†, William D. Collins3,4*†, Pierre Gentine5, Elizabeth A. Barnes6, Marcelo Barreiro7, Tom  \n4 Beucler8, Marc Bocquet9, Christopher S. Bretherton 10, Hannah M. Christensen 11, Katherine Dagon 12, David John  \n5 Gagne 12, David Hall 13, Dorit Hammerling 14, Stephan Hoyer15, Fernando Iglesias-Suarez 1, Ignacio Lopez- 6 Gomez 15,16, Marie C. McGraw 17, Gerald A. Meehl 12, Maria J. Molina 18,12, Claire Monteleoni 19,20, Juliane  \n7 Mueller21, Michael S. Pritchard22,13, David Rolnick23,24, Jakob Runge25,26, Philip Stier 11, Oliver Watt-Meyer10, 8 Katja Weigel2,1, Rose Yu27 and Laure Zanna28  \n9  \n10 1 Deutsches Zentrum für Luft-und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany.  \n11 2 University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany.  \n12 3 Lawrence Berkeley National Laboratory, Berkeley, CA, USA.  \n13 4 University of California, Berkeley, Berkeley, CA, USA.  \n14 5 Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA.  \n15 6 Dept. of Atmospheric Science, Colorado State University, Fort Collins, CO, USA.  \n16 7 Institute of Physics, School of Sciences, University of the Republic, Montevideo, Uruguay.  \n17 8 University of Lausanne, Lausanne, Switzerland.  \n18 9 CEREA, École des Ponts and EdF R&D, Île-de-France, France.  \n19 10 Allen Institute for Artificial Intelligence (AI2), Seattle, WA, USA.  \n20 11 Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK.  \n21 12 NSF National Center for Atmospheric Research, Boulder, CO, USA.  \n22 13 NVIDIA Corporation, Santa Clara, CA, USA.  \n23 14 Colorado School of Mines, Golden, CO, USA.  \n24 15 Google Research, Mountain View, CA, USA.  \n25 16 Climate Modeling Alliance (CliMA), California Institute of Technology, Pasadena, CA, USA.  \n26 17 Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University, Fort Collins, CO, USA.  \n27 18 University of Maryland, College Park, MD, USA.  \n28 19 Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA.  \n29 20 INRIA Paris, Paris, France.  \n30 21 Computational Science Center, National Renewable Energy Laboratory, Golden, CO, USA.  \n31 22 University of California, Irvine, Irvine, CA, USA.  \n32 23 McGill University, Montreal, Canada.  \n33 24 Mila-Quebec AI Institute, Montreal, Canada.  \n34 25 Deutsches Zentrum für Luft-und Raumfahrt (DLR), Institut für Datenwissenschaften, Jena, Germany.  \n35 26 Technische Universität Berlin, Berlin, Germany.  \n36 27 University of California, San Diego, La Jolla, CA, USA.  \n37 28 Courant Institute, New York University, New York, NY, USA.  \n38  \n39 *Corresponding author(s) . E-mail(s): [veronika.eyring@dlr.de](veronika.eyring@dlr.de) ; [wdcollins@lbl.gov](wdcollins@lbl.gov) ;  \n40 †These authors contributed equally to this work.  \n41  \n42 Abstract.  \n43 Climate modeling and analysis are facing new demands to enhance projections and climate information.  \n44 We argue that now is the time to push the frontiers of Machine Learning (ML) beyond state-of-the-art  \n45 approaches, not only by developing ML-based Earth system models with greater fidelity, but also by  \n46 providing new capabilities through emulators for extreme event projections with large ensembles, 47 enhanced detection and attribution methods for extreme events, and advanced climate model analysis and  \n48 benchmarking. Utilizing this potential requires addressing key ML challenges, in particular  \n49 generalization, uncertainty quantification, explainable artificial intelligence, and causality. This  \n50 interdisciplinary effort requires bringing together ML and climate scientists, while also leveraging the  \n51 private sector, to accelerate progress towards actionable climate science.  \n52 Keywords: Climate modeling and analysis, machine learning, extreme events, ben","cbCailZnZePVbdna","https://ap.wps.com/l/cbCailZnZePVbdna","pdf",2270913,1,24,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What motivates pushing machine learning beyond state-of-the-art in climate science?\",\"answer\":\"Growing demands for improved projections and climate information, alongside limitations from model uncertainty and the increasing volume of climate data, motivate advancing ML capabilities beyond current approaches.\"},{\"question\":\"Which ML-enabled capabilities are highlighted for extreme events?\",\"answer\":\"The text highlights ML-based emulation for fast extreme-event projections with large ensembles, and ML-based detection and attribution methods to improve identification and explanation of extreme events.\"},{\"question\":\"What key ML challenges must be addressed to make these advances usable?\",\"answer\":\"Generalization, uncertainty quantification, explainable artificial intelligence, and causality are identified as key challenges required for actionable climate science.\"}]","Pushing the Frontiers in Climate Modeling and Analysis with Machine Learning | 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motivates pushing machine learning beyond state-of-the-art in climate science?","Question",{"text":75,"@type":76},"Growing demands for improved projections and climate information, alongside limitations from model uncertainty and the increasing volume of climate data, motivate advancing ML capabilities beyond current approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which ML-enabled capabilities are highlighted for extreme events?",{"text":80,"@type":76},"The text highlights ML-based emulation for fast extreme-event projections with large ensembles, and ML-based detection and attribution methods to improve identification and explanation of extreme events.",{"name":82,"@type":73,"acceptedAnswer":83},"What key ML challenges must be addressed to make these advances usable?",{"text":84,"@type":76},"Generalization, uncertainty quantification, explainable artificial intelligence, and causality are identified as key challenges required for actionable climate 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