[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126406-en":3,"doc-seo-126406-105":30,"detail-sidebar-cat-0-en-105":92},{"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":11,"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},126406,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","AeroGP - machine learning如何研究气溶胶影响区域气候","Aerosol particles from natural and anthropogenic sources strongly influence Earth’s climate through interactions with solar radiation and clouds. Historically, human-emitted aerosols and precursors produced a global cooling effect that partially offset greenhouse-gas warming, but changing emissions can alter the magnitude and location of these impacts. The study introduces AeroGP, a Gaussian-process machine-learning climate emulator trained on NorESM ensemble data, preserving spatial correlation while reducing computational cost, enabling rapid assessment of policy-relevant mitigation scenarios.","AeroGP: machine learning how aerosols impact regional climate  \nArticle  \nPublished Version  \nCreative Commons: Attribution 4.0 (CC-BY)  \nOpen Access  \nDewey, M. , Hansson, H.-C. , Watson-Parris, D. , Samset, B. H. , Wilcox, L. J. ORCID: [https://orcid.org/0000-0001-5691-1493](https://orcid.org/0000-0001-5691-1493) , Lewinschal, A. , Sand, M. , Seland, Ø . , Krishnan, S. and Ekman, A. M. L. (2025) AeroGP: machine learning how aerosols impact regional climate. Journal of Geophysical Research: Machine learning and computation, 2 (4) . e2025JH000741 . ISSN 2993-5210 doi:  \n10. 1029/2025JH000741 Available at [https://centaur. reading.ac. uk/127462/](https://centaur. reading.ac. uk/127462/)  \nIt is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing.  \nTo link to this article DOI: [http://dx.doi.org/10.1029/2025JH000741](http://dx.doi.org/10.1029/2025JH000741)  \nPublisher: AGU  \nAll outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders . Terms and conditions for use of this material are defined in the End User Agreement  .  \n[www. reading.ac. uk/centaur](www. reading.ac. uk/centaur)  \nCentAUR  \nCentral Archive at the University of Reading  \nReading’s research outputs online  \nRESEARCH ARTICLE  \n10.1029/2025JH000741  \nKey Points:  \n• Anthropogenic aerosols play a key role in global and regional climate change  \n• There is a need for more aerosol‐ focused climate modeling, as aerosols represent a major source of uncertainty in future climate change  \n• We use Gaussian processes (GPs) to accurately predict global spatial patterns of the temperature response to aerosol emission perturbations  \nSupporting Information:  \nSupporting Information may be found in the online version of this article.  \nCorrespondence to:  \nM. Dewey,  \n[maura.dewey@misu.su.se](maura.dewey@misu.su.se)  \nCitation:  \nDewey, M., Hansson, H.‐C., Watson‐Parris, D., Samset, B. H., Wilcox, L. J., Lewinschal, A., et al. (2025) . AeroGP: Machine learning how aerosols impact regional climate. Journal of Geophysical Research: Machine Learning and Computation, 2, e2025JH000741 .  \n[https://doi.org/10.1029/2025JH000741](https://doi.org/10.1029/2025JH000741)  \nReceived 16 APR 2025  \nAccepted 4 OCT 2025  \nAuthor Contributions:  \nConceptualization: Maura Dewey, Hans‐ Christen Hansson, Duncan Watson‐Parris, Bjørn H. Samset, Laura J. Wilcox, Annica M. L. Ekman  \nFormal analysis: Maura Dewey, Annica M. L. Ekman  \nFunding acquisition: Annica  \nM. L. Ekman  \nMethodology: Maura Dewey, Duncan Watson‐Parris, Annica  \nM. L. Ekman  \nProject administration: Annica  \nM. L. Ekman  \n© 2025 The Author(s) . Journal of Geophysical Research: Machine Learning and Computation published by Wiley Periodicals LLC on behalf of American Geophysical Union.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nAeroGP: Machine Learning How Aerosols Impact Regional Climate  \nMaura Dewey1,2 , Hans‐Christen Hansson2,3, Duncan Watson‐Parris4 , Bjørn H. Samset5 , Laura J. Wilcox6 , Anna Lewinschal1,2, Maria Sand5 , Øyvind Seland7 , Srinath Krishnan5, and Annica M. L. Ekman1,2  \n1Department of Meteorology (MISU), Stockholm University, Stockholm, Sweden, 2Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden, 3Department of Environmental Science (ACES), Stockholm University, Stockholm, Sweden, 4Scripps Institution of Oceanography and Halıcıoğlu Data Science Institute, UC San Diego, San Diego, CA, USA, 5Center for International Climate Research (CICERO), Oslo, Norway, 6Department of Meteorology, National Centre for Atmospheric Science, University of Reading, Reading, UK, 7Norwegian Meteorological Institute, Oslo, Norway  \nAbstract Aerosol particles from both natural and anthropo","cbCairRqCTDFtsp2","https://ap.wps.com/l/cbCairRqCTDFtsp2","pdf",4086966,1,28,"English","en",105,"# Key Points\n## Gaussian-process climate emulator (AeroGP)\n## Dataset and training approach\n## Capturing spatial correlation with coregionalization\n## Assessment of future aerosol emission scenarios","[{\"question\":\"为什么气溶胶对区域气候影响重要？\",\"answer\":\"气溶胶会与太阳辐射和云相互作用，从而显著影响气候。人类活动排放的气溶胶在过去对全球降温有贡献，但排放减少与空间格局变化可能带来区域差异的气候效应。\"},{\"question\":\"AeroGP是什么方法？\",\"answer\":\"AeroGP是一种基于高斯过程的机器学习气候“模拟器（emulator）”。它用来快速评估不同政策决策对未来气候减缓策略的影响。\"},{\"question\":\"AeroGP如何处理气候数据的空间相关性？\",\"answer\":\"AeroGP通过coregionalization来刻画输出变量的联合空间协方差，从而保留气候数据的空间复杂性，并在计算成本显著降低的情况下实现对表面温度响应的预测。\"}]","AeroGP - machine learning如何研究气溶胶影响区域气候 | PDF",1785904896,71,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"aerogp-machine-learning-how-aerosols-impact-regional-climate","",{"@graph":36,"@context":86},[37,54,69],{"@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/aerogp-machine-learning-how-aerosols-impact-regional-climate/126406/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"为什么气溶胶对区域气候影响重要？","Question",{"text":76,"@type":77},"气溶胶会与太阳辐射和云相互作用，从而显著影响气候。人类活动排放的气溶胶在过去对全球降温有贡献，但排放减少与空间格局变化可能带来区域差异的气候效应。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"AeroGP是什么方法？",{"text":81,"@type":77},"AeroGP是一种基于高斯过程的机器学习气候“模拟器（emulator）”。它用来快速评估不同政策决策对未来气候减缓策略的影响。",{"name":83,"@type":74,"acceptedAnswer":84},"AeroGP如何处理气候数据的空间相关性？",{"text":85,"@type":77},"AeroGP通过coregionalization来刻画输出变量的联合空间协方差，从而保留气候数据的空间复杂性，并在计算成本显著降低的情况下实现对表面温度响应的预测。","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]