[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122464-en":3,"doc-seo-122464-105":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":20,"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},122464,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine Learning for the Physics of Climate - Review and Perspectives","Exponential growth in computing power and rapidly increasing climate observations are transforming climate science, enabling big-data, machine-learning approaches to study the climate system with unprecedented detail. ML inference supports causal investigations and improves prediction beyond classical limits. Coupled with modeling experiments and robust parameterization research, ML accelerates computation, increases accuracy, and enables very large ensembles at reduced cost. This review surveys key achievements, major remaining challenges, and the outlook for applying ML to complex climate physics problems.","Machine Learning for the Physics of Climate  \nAnnalisa Bracco1, Julien Brajart2, Henk A. Dijkstra3, Pedram Hassanzadeh4, Christian Lessing5 and Claire Monteleoni6,7  \n1School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA 2 Nansen Environmental and Remote Sensing Center (NERSC), Bergen, Norway  \n3 Institute for Marine and Atmospheric research, Utrecht University, Utrecht, the Netherlands  \n4 Department of Geophysical Sciences and Commitee on Computational and Applied Mathematics, University of Chicago, IL, USA  \n5 European Centre for Medium Range Weather Forecasts (ECMWF), Reading, UK 6 INRIA Paris, France  \n7Computer Science Department, University of Colorado Boulder, Boulder, CO, USA  \n*[e-mail:abracco@gatech.edu](e-mail:abracco@gatech.edu)  \nABSTRACT  \nAn exponential growth in computing power, which has brought more sophisticated and higher resolution simulations of the climate system, and an exponential increase in observations since the first weather satellite was put in orbit, are revolutionizing climate science. Big data and associated algorithms, coalesced under the field of Machine Learning (ML), offer the opportunity to study the physics of the climate system in ways, and with an amount of detail, infeasible few years ago. The inference provided by ML has allowed to ask causal questions and improve prediction skills beyond classical barriers. Furthermore, when paired with modeling experiments or robust research in model parameterizations, ML is accelerating computations, increasing accuracy and allowing for generating very large ensembles at a fraction of the cost.  \nIn light of the urgency imposed by climate change and the rapidly growing role of ML, we review its broader accomplishments in climate physics. Decades long standing problems in observational data reconstruction, representation of sub-grid scale phenomena and climate (and weather) prediction are being tackled with new and justified optimism. Ultimately, this review aims at providing a perspective on the benefits and major challenges of exploiting ML in studying complex systems.  \nKey points:  \n• The use of Machine Learning is poised to transform the climate physics field.  \n• Major advances sofar have occurred in extending observational data records in time, space and observables.  \n• Innovative approaches in sub-grid scale parameterizations may soon contribute to new (hybrid) climate models.  \n• Classical predictability barriers have been broken.  \n• Weather forecasting skills have improved, at a fraction of computing resources.  \nWebsite summary: With the availability of big data and increasing computational power, methods from artificial intelligence, specifically machine learning, are being massively applied to climate physics. We focus here on novel results obtained so far in reconstruction, sub-grid scale parameterization and weather/climate prediction, and remaining challenges.  \nPlain Language Summary  \nThis review article covers the broader accomplishments of Machine Learning (ML) in the climate physics realm, and provides a perspective on the benefits and major challenges of exploiting ML advances. The intent is for both the limitations and opportunities highlighted to be relevant to other areas of physics broadly, and fluid dynamics more specifically.  \n1 Introduction  \nThe climate of our planet, usually defined as the average weather over a period of years, constrains the weather we get. Accurate predictions of the climate system trajectory are a crucial science priority of the coming decades. Society needs detailed regional  \nprojections of future weather and climate extremes to better inform mitigation and adaptation strategies, and constrained estimates of the likelihood of reaching climate tipping points; assessments of impact and feedbacks of natural and engineered solutions to the climate challenge; and estimates of the uncertainties, risks, and economic and social impacts associated to deep cuts in","cbCaipJtX5kYNGR9","https://ap.wps.com/l/cbCaipJtX5kYNGR9","pdf",13334246,1,25,"English","en",105,"# Introduction\n## ML in climate science: three themes\n## Advances linked to ML applications","[{\"question\":\"How does machine learning help climate science according to the review?\",\"answer\":\"Machine learning enables better use of observations, improved sub-grid parameterizations for small-scale processes, and faster or more accurate multi-scale predictions.\"},{\"question\":\"What major computational advantages does ML provide for climate modeling?\",\"answer\":\"When paired with modeling and parameterizations, ML can accelerate computations, improve accuracy, and generate large ensembles at a fraction of the usual cost.\"},{\"question\":\"Which climate research challenges does the review focus on tackling with ML?\",\"answer\":\"The review emphasizes challenges in observational data reconstruction, representation of sub-grid scale phenomena, and climate and weather prediction, along with the benefits and remaining obstacles of exploiting ML.\"}]","Machine Learning for the Physics of Climate - Review and Perspectives | PDF",1785810787,63,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-the-physics-of-climate-review-and-perspectives","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-the-physics-of-climate-review-and-perspectives/122464/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does machine learning help climate science according to the review?","Question",{"text":75,"@type":76},"Machine learning enables better use of observations, improved sub-grid parameterizations for small-scale processes, and faster or more accurate multi-scale predictions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What major computational advantages does ML provide for climate modeling?",{"text":80,"@type":76},"When paired with modeling and parameterizations, ML can accelerate computations, improve accuracy, and generate large ensembles at a fraction of the usual cost.",{"name":82,"@type":73,"acceptedAnswer":83},"Which climate research challenges does the review focus on tackling with ML?",{"text":84,"@type":76},"The review emphasizes challenges in observational data reconstruction, representation of sub-grid scale phenomena, and climate and weather prediction, along with the benefits and remaining obstacles of exploiting ML.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]