[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126828-en":3,"doc-seo-126828-105":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},126828,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","A Machine Learning-Based Approach to Quantify ENSO Sources of Predictability","A machine learning method identifies sources of long-term ENSO predictability in both ocean and atmosphere. The analysis shows tropical sea-surface temperature (SST) is the primary contributor to predictability skill. Near-surface zonal wind (U10) alone displays predictive skill comparable to SST between 11 and 21 months ahead, varying by season. The long-lead signal arises from coupled wind–SST interactions over the Indian Ocean and propagates across the Pacific through an atmospheric bridge mechanism.","RESEARCH LETTER  \n10.1029/2023GL105194  \nKey Points:  \n• A deep learning‐based approach suggests near‐surface 10 m wind to play a significant role in providing skills for long‐term ENSO predictability  \n• The skill of the 10‐m zonal wind is large from 12 months up to 23 months in advance (depending on the season)  \n• The signal is generated by coupled wind‐SST interactions in the Indian Ocean and later propagates across the Pacific  \nSupporting Information:  \nSupporting Information may be found in the online version of this article.  \nCorrespondence to:  \nI. Colfescu,  \n[ioana.colfescu@ncas.ac.uk](ioana.colfescu@ncas.ac.uk)  \n[Citation:](Citation:)  \nColfescu, I., Christensen, H., & Gagne, D.  \nJ. (2024) . A machine learning‐based approach to quantify ENSO sources of predictability. Geophysical Research Letters, 51, e2023GL105194. [https://doi](https://doi). org/10.1029/2023GL105194  \nReceived 24 JULY 2023  \nAccepted 13 APR 2024  \nAuthor Contributions:  \nConceptualization: Hannah Christensen  \nData curation: Ioana Colfescu  \nFormal analysis: Ioana Colfescu  \nFunding acquisition: Hannah Christensen  \nInvestigation: Ioana Colfescu, Hannah Christensen, David John Gagne Methodology: Ioana Colfescu, Hannah Christensen  \nProject administration:  \nHannah Christensen, David John Gagne Resources: Hannah Christensen  \nSoftware: Ioana Colfescu, David John Gagne  \nSupervision: Hannah Christensen, David John Gagne  \nValidation: Ioana Colfescu  \n© 2024. The Authors. Geophysical Research Letters 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.  \nA Machine Learning‐Based Approach to Quantify ENSO Sources of Predictability  \nIoana Colfescu1,2 , Hannah Christensen2 , and David John Gagne3   \n1National Centre for Atmospheric Science (NCAS), University of Leeds, Leeds, UK, 2Department of Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK, 3National Center for Atmospheric Research, Boulder, CO, USA  \nAbstract A machine learning method is used to identify sources of long‐term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near‐surface zonal wind (U10)) . Tropical SST represents the primary source of predictability skill. While U10 does not increase the skill when associated with SST, our analysis suggests U10 alone has apredictive skill comparable to that of SST between 11 and 21 months in advance, from late fall up to late spring. The long‐lead signal originates from coupled wind‐ SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond 1 year ahead and identify the key role of U10 over the IO.  \nPlain Language Summary Many extreme events, such as floods or droughts, can be attributed to the El Niño Southern Oscillation, a mode of large‐scale ocean‐atmosphere coupled variability in the tropical Pacific Ocean occurring with a period of approximately 4 years. In this analysis, we use a machine learning methodology to disentangle the key atmospheric and oceanic ENSO components’ relative contribution to its predictability, particularly the role of near‐surface 10‐m zonal wind. We quantify the potential for improved ENSO predictions for up to 2 years in advance and present a mechanistic understanding of the location of the sources of predictability. While equatorial sea surface temperature represents the primary source of ENSO predictability, the equatorial U10 plays a vital role from late spring to fall, from 1 to 2 years in advance. The enhanced predictability skill is s","cbCairNB2lvO4KhW","https://ap.wps.com/l/cbCairNB2lvO4KhW","pdf",2804841,1,9,"English","en",105,"# Key Findings\n## Long-term skill at 10 m zonal wind\n## Coupled wind–SST mechanism and propagation\n# Abstract\n## Ocean and atmosphere predictability sources\n## Lead-time range and seasonal dependence\n# Plain Language Summary\n## ENSO definition and climate impacts\n## Role of U10 and Indian Ocean SST anomalies\n# Introduction\n## ENSO signature and frequency\n## Competing physical mechanism views\n## Prediction challenges and current dynamical models","[{\"question\":\"What does the machine learning approach aim to determine in this study?\",\"answer\":\"It separates the relative contributions of oceanic and atmospheric ENSO components to long-term predictability, with emphasis on the near-surface 10-m zonal wind.\"},{\"question\":\"Which factors are identified as the main sources of ENSO predictability skill?\",\"answer\":\"Tropical SST is identified as the primary source. U10 alone also provides substantial predictive skill between 11 and 21 months in advance, depending on the season.\"},{\"question\":\"How does the study explain the origin and propagation of the long-lead predictability signal?\",\"answer\":\"The signal is generated by coupled wind–SST interactions over the Indian Ocean and then propagates across the Pacific via an atmospheric bridge mechanism.\"}]","A Machine Learning-Based Approach to Quantify ENSO Sources of Predictability | PDF",1785935064,23,{"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},"a-machine-learning-based-approach-to-quantify-enso-sources-of-predictability","",{"@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/a-machine-learning-based-approach-to-quantify-enso-sources-of-predictability/126828/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the machine learning approach aim to determine in this study?","Question",{"text":75,"@type":76},"It separates the relative contributions of oceanic and atmospheric ENSO components to long-term predictability, with emphasis on the near-surface 10-m zonal wind.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which factors are identified as the main sources of ENSO predictability skill?",{"text":80,"@type":76},"Tropical SST is identified as the primary source. U10 alone also provides substantial predictive skill between 11 and 21 months in advance, depending on the season.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study explain the origin and propagation of the long-lead predictability signal?",{"text":84,"@type":76},"The signal is generated by coupled wind–SST interactions over the Indian Ocean and then propagates across the Pacific via an atmospheric bridge mechanism.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]