Inferring SST-Forced Seasonal Atmospheric Responses: An Ensemble Empirical Tool

Inferring SST-Forced Seasonal Atmospheric Responses: An Ensemble Empirical Tool | 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 Research Article Inferring SST-Forced Seasonal Atmospheric Responses: An Ensemble Empirical Tool Mingyue Chen, Arun Kumar, Tao Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8107512/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In regions with marginal seasonal forecast skill, such as the extratropical latitudes, attributing the origins of a predictive signal is a cornerstone of effective forecast communication. Traditional methods rely on historical observations to establish statistical relationships between predictors, such as tropical sea surface temperatures (SSTs), and atmospheric predictands. However, these observations contain a mixture of the SST-forced signal and unrelated intrinsic variability (noise), which can obscure the analysis. This study introduces Ensemble Principal Component Regression (EPCR), a method that addresses this challenge by using the noise-free, ensemble mean atmospheric response from model simulations. Using DJF 200-hPa geopotential height (z200) anomalies from a 100-member AMIP ensemble, we demonstrate its utility as a versatile diagnostic tool for: (1) attributing forecast anomalies to specific SST modes, (2) providing a "sanity check" for small-ensemble forecasts by filtering residual noise, and (3) establishing a statistical forecast baseline. Results show the predictable z200 signal is overwhelmingly low-dimensional, driven primarily by the atmospheric responses to ENSO and the global warming trend. The EPCR framework successfully attributes the predictable signal to these modes, as demonstrated for the DJF 2024 forecast. As a diagnostic, it effectively serves as a "sanity check" by identifying and filtering potential noise contamination in small ensembles. While not intended to replace the full dynamical model in terms of forecast skill, EPCR’s primary value lies in its ability to provide a clear, physically-grounded baseline of the linear, SST-forced signal, thereby establishing a robust diagnostic for interpreting and adding confidence to seasonal forecasts. Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 31 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers invited by journal 30 Nov, 2025 Editor assigned by journal 20 Nov, 2025 First submitted to journal 17 Nov, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8107512","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553071286,"identity":"45168876-d5b1-409a-bb52-3ec253a2e069","order_by":0,"name":"Mingyue Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACg8NQBv8MIPGBGC2WMC0SNxgYGGcQo8X+AMy6CAYGZh5itJgd5z38mrfNhsFAuvnYY9sdNvL8DewXH+PTa3aYL82aty2NwUDmWLpx7pk0wxkHeIqN8WvhMTPmbTvMYCCRYyad23aYseEAT5okPk8ZQLT8B2rJ/yZt2XbYfj4RWowf87YdAHo/h02ase1w4oYD7Mck8IU2yBbGOeeSeSRupJlJ9ralJW88zMNsgFfL+TPGH96U2cnxz0h+JvGzzcZ23vH2hw8S8GgBAjYpYPggBREzjwF+DUAlH3+gCrA/IKRlFIyCUTAKRhYAAL1eTAgkUgQxAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0001-7850-014X","institution":"NOAA NWS: National Weather Service","correspondingAuthor":true,"prefix":"","firstName":"Mingyue","middleName":"","lastName":"Chen","suffix":""},{"id":553071287,"identity":"830d76bf-2cf3-4023-88c2-7211b0697dfd","order_by":1,"name":"Arun Kumar","email":"","orcid":"","institution":"NOAA NWS: National Weather Service","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"","lastName":"Kumar","suffix":""},{"id":553071288,"identity":"e1c0d013-3f93-44bc-89f4-32d6ffbeb026","order_by":2,"name":"Tao Zhang","email":"","orcid":"","institution":"NOAA NWS: National Weather Service","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-11-13 15:58:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8107512/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8107512/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97288704,"identity":"89bf566a-c051-4cab-a413-b1b05ee42d59","added_by":"auto","created_at":"2025-12-02 19:04:56","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8248,"visible":true,"origin":"","legend":"","description":"","filename":"cldyCLDYD2501246.xml","url":"https://assets-eu.researchsquare.com/files/rs-8107512/v1/644e782b9b3702c1a15d249b.xml"},{"id":97288705,"identity":"13adceeb-0575-4234-8189-c6129d31879a","added_by":"auto","created_at":"2025-12-02 19:04:56","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":974,"visible":true,"origin":"","legend":"","description":"","filename":"CLDYD250124622702.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-8107512/v1/db273419e2779cc2eef5ce8e.xml"},{"id":97288706,"identity":"f810bd0d-f9d8-4863-924a-8acdb7099f7a","added_by":"auto","created_at":"2025-12-02 19:04:56","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":849,"visible":true,"origin":"","legend":"","description":"","filename":"CLDYD2501246Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-8107512/v1/42c0f971c39085c03dbde569.xml"},{"id":97368392,"identity":"c00fa8b4-dbe9-4539-8bb9-9d8b81e97772","added_by":"auto","created_at":"2025-12-03 16:22:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1161243,"visible":true,"origin":"","legend":"","description":"","filename":"attributionempiricaltoolmanuscirptCDsubmission20251117.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8107512/v1_covered_8fee1878-b374-484e-8e6e-842f1efe7eb3.pdf"}],"financialInterests":"","formattedTitle":"Inferring SST-Forced Seasonal Atmospheric Responses: An Ensemble Empirical Tool","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8107512/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8107512/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In regions with marginal seasonal forecast skill, such as the extratropical latitudes, attributing the origins of a predictive signal is a cornerstone of effective forecast communication. Traditional methods rely on historical observations to establish statistical relationships between predictors, such as tropical sea surface temperatures (SSTs), and atmospheric predictands. However, these observations contain a mixture of the SST-forced signal and unrelated intrinsic variability (noise), which can obscure the analysis.\nThis study introduces Ensemble Principal Component Regression (EPCR), a method that addresses this challenge by using the noise-free, ensemble mean atmospheric response from model simulations. Using DJF 200-hPa geopotential height (z200) anomalies from a 100-member AMIP ensemble, we demonstrate its utility as a versatile diagnostic tool for: (1) attributing forecast anomalies to specific SST modes, (2) providing a \"sanity check\" for small-ensemble forecasts by filtering residual noise, and (3) establishing a statistical forecast baseline.\nResults show the predictable z200 signal is overwhelmingly low-dimensional, driven primarily by the atmospheric responses to ENSO and the global warming trend. The EPCR framework successfully attributes the predictable signal to these modes, as demonstrated for the DJF 2024 forecast. As a diagnostic, it effectively serves as a \"sanity check\" by identifying and filtering potential noise contamination in small ensembles. While not intended to replace the full dynamical model in terms of forecast skill, EPCR’s primary value lies in its ability to provide a clear, physically-grounded baseline of the linear, SST-forced signal, thereby establishing a robust diagnostic for interpreting and adding confidence to seasonal forecasts.","manuscriptTitle":"Inferring SST-Forced Seasonal Atmospheric Responses: An Ensemble Empirical Tool","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 19:04:51","doi":"10.21203/rs.3.rs-8107512/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2025-12-31T08:56:22+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-12-01T15:48:26+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T01:22:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T17:15:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climate Dynamics","date":"2025-11-17T08:02:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b7c573e8-c36f-4244-b981-dd245d924d0d","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-16T12:43:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 19:04:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8107512","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8107512","identity":"rs-8107512","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}