Skillful land and marine heatwave forecasting through hybrid statistical dynamical modelling

Skillful land and marine heatwave forecasting through hybrid statistical dynamical modelling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Skillful land and marine heatwave forecasting through hybrid statistical dynamical modelling Tongtiegang Zhao, Zeqing Huang, Hao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4605484/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Skillful forecasting of global heatwaves is crucial for mitigating their escalating impacts on human societies and ecosystems across various sectors. While global climate models generate invaluable dynamical temperature forecasts, the crucial role of model output statistics (MOS) in enhancing global heatwave forecasting has not been thoroughly investigated. In this study, we unravel the potential of hybrid statistical dynamical modelling in generating heatwave forecasts on a global scale. Specifically, a pioneering MOS toolkit is developed to iteratively take into consideration key attributes—bias, spread, trend, and association—within raw forecasts through a series of methodical one-factor-at-a-time experiments. A case study is devised for forecasts of 2-meter air temperature over land and sea surface temperature generated by the National Center for Environmental Prediction’s Climate Forecast System version 2. Our analysis exposes the detrimental impacts of biases and unreliable ensemble spreads within raw temperature forecasts, leading to an abundance of false positives and negatives, ultimately diminishing the skill of heatwave predictions, often plunging below − 100%. At the lead time of 0 months, integrating incremental considerations of bias, spread, trend, and association results in substantial skill enhancements across global land and marine grid cells. Notably, land heatwave forecast skill sees a remarkable ascent from a staggering − 171.63%±290.42% to a promising 5.61%±15.74%, while marine heatwave forecast skill improves from − 75.74%±206.68–23.96%±23.47%. Despite the anticipated degradation of skill with lead time, our results underscore MOS’s efficacy in leveraging raw forecast data to maintain positive forecasting outcomes. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction global climate model model output statistics 2-m temperature sea surface temperature heatwave forecasting forecast skill Full Text Additional Declarations There is NO Competing Interest. Supplementary Files FigureS1.png FIgure S1 Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4605484","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":319195884,"identity":"a3f69f62-ce4e-4609-9be2-cf01990899dd","order_by":0,"name":"Tongtiegang Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACCQglx8DA2HgATRCPlgMJDMZALQ2kaUlsANFEaZGf3fzw8ccftelr2w8Dbflz2N7gAPPB2zwMdnm4tDDOOWZscCDheO62M4kNBxjbDiduOMCWbM3DkFyMSwuzRIKZxIGEY7nbDoC0NBxOMDjAYybNw3AA7FRsgE0i/RtIS7rZ+Ycwh/F/w6uFRyIHZEtNgtkNoC0MbIcZNxzgYcOrRUIip9jgTNoBw203gLYktqUnzjzMZmw5xyAZpxb5GekbH1TY1MmbnU9/+ODDH2t7vuPND2+8qbDDqQUKDkOoBIZmYIiAWAb41QNBHQZjFIyCUTAKRgEcAADhwWE3v5lXDgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6943-258X","institution":"Sun Yat-Sen University","correspondingAuthor":true,"prefix":"","firstName":"Tongtiegang","middleName":"","lastName":"Zhao","suffix":""},{"id":319195885,"identity":"3b7879d7-00d5-4265-aa45-29a2b0ae6681","order_by":1,"name":"Zeqing Huang","email":"","orcid":"","institution":"Commonwealth Scientific and Industrial Research Organisation (CSIRO)","correspondingAuthor":false,"prefix":"","firstName":"Zeqing","middleName":"","lastName":"Huang","suffix":""},{"id":319195886,"identity":"b4a4e642-cb29-4011-86d9-cd03bcb4d75c","order_by":2,"name":"Hao Wang","email":"","orcid":"","institution":"China Institute of Water Resources and Hydropower Research","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-19 11:15:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4605484/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4605484/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63491644,"identity":"47e10c1e-d194-43e8-9e46-5897e37f526f","added_by":"auto","created_at":"2024-08-28 17:59:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1171676,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptv17.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4605484/v1_covered_569bb81c-921d-44ad-9670-760ae7dc2f4f.pdf"},{"id":63491379,"identity":"b5f5e18e-6abf-4bcc-9198-e16c4d946252","added_by":"auto","created_at":"2024-08-28 17:51:56","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":620331,"visible":true,"origin":"","legend":"FIgure S1","description":"","filename":"FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-4605484/v1/d8594af52713e44f9b781107.png"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Skillful land and marine heatwave forecasting through hybrid statistical dynamical modelling","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"global climate model, model output statistics, 2-m temperature, sea surface temperature, heatwave forecasting, forecast skill","lastPublishedDoi":"10.21203/rs.3.rs-4605484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4605484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSkillful forecasting of global heatwaves is crucial for mitigating their escalating impacts on human societies and ecosystems across various sectors. While global climate models generate invaluable dynamical temperature forecasts, the crucial role of model output statistics (MOS) in enhancing global heatwave forecasting has not been thoroughly investigated. In this study, we unravel the potential of hybrid statistical dynamical modelling in generating heatwave forecasts on a global scale. Specifically, a pioneering MOS toolkit is developed to iteratively take into consideration key attributes\u0026mdash;bias, spread, trend, and association\u0026mdash;within raw forecasts through a series of methodical one-factor-at-a-time experiments. A case study is devised for forecasts of 2-meter air temperature over land and sea surface temperature generated by the National Center for Environmental Prediction\u0026rsquo;s Climate Forecast System version 2. Our analysis exposes the detrimental impacts of biases and unreliable ensemble spreads within raw temperature forecasts, leading to an abundance of false positives and negatives, ultimately diminishing the skill of heatwave predictions, often plunging below \u0026minus;\u0026thinsp;100%. At the lead time of 0 months, integrating incremental considerations of bias, spread, trend, and association results in substantial skill enhancements across global land and marine grid cells. Notably, land heatwave forecast skill sees a remarkable ascent from a staggering \u0026minus;\u0026thinsp;171.63%\u0026plusmn;290.42% to a promising 5.61%\u0026plusmn;15.74%, while marine heatwave forecast skill improves from \u0026minus;\u0026thinsp;75.74%\u0026plusmn;206.68\u0026ndash;23.96%\u0026plusmn;23.47%. Despite the anticipated degradation of skill with lead time, our results underscore MOS\u0026rsquo;s efficacy in leveraging raw forecast data to maintain positive forecasting outcomes.\u003c/p\u003e","manuscriptTitle":"Skillful land and marine heatwave forecasting through hybrid statistical dynamical modelling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-28 17:51:51","doi":"10.21203/rs.3.rs-4605484/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41d0a513-bcdd-4d39-9edd-18425fe2c261","owner":[],"postedDate":"August 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":33749375,"name":"Earth and environmental sciences/Natural hazards"},{"id":33749376,"name":"Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction"}],"tags":[],"updatedAt":"2024-08-28T17:51:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-28 17:51:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4605484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4605484","identity":"rs-4605484","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}