Using Artificial Intelligence to identify CMIP6 models from daily SLP maps

Using Artificial Intelligence to identify CMIP6 models from daily SLP maps | 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 Using Artificial Intelligence to identify CMIP6 models from daily SLP maps Pascal Yiou, Soulivanh Thao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6254473/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted 9 You are reading this latest preprint version Abstract Large databases of climate model simulations are essential to sample climate variability and estimate how it can evolve in any future. The chaotic nature of climate has motivated the simulation of large ensembles of simulations, which sample the uncertainty due to internal climate variability in single models. Exploiting large ensembles (for impact or attribution studies) implicitly relies on the hypothesis that simulations are interchangeable. This is not the case for variables like temperature, due to biases (which can be corrected). Some synoptic fields, like SLP, do not yield obvious biases, which might justify their use to enrich reanalysis data. In this paper, we examine this hypothesis through a neural network classification approach. The goal is to determine whether it is possible to recognize a climate model (among 16 models and a reanalysis) from one single sea-level pressure (SLP) map over the North Atlantic. We find that models are highly identifiable in the summer (and less in other seasons), while SLP average structures are very similar. From this classification, we identify sororities of climate models, and investigate how climate change can affect SLP daily patterns toward the end of the 21st century. This study allows identifying which climate models could be used as input for artificial intelligence model forecasts. Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics Physical sciences/Mathematics and computing/Statistics Neural Network classification Climate models SLP Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Nov, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted Editorial decision: Revision requested 14 Apr, 2025 Reviews received at journal 14 Apr, 2025 Reviews received at journal 02 Apr, 2025 Reviewers agreed at journal 21 Mar, 2025 Reviewers agreed at journal 20 Mar, 2025 Reviewers invited by journal 20 Mar, 2025 Editor assigned by journal 20 Mar, 2025 Submission checks completed at journal 20 Mar, 2025 First submitted to journal 18 Mar, 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. 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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-6254473","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":432462377,"identity":"d78c8800-223c-409a-99fa-bdabacd187a5","order_by":0,"name":"Pascal Yiou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYNCCAgYGNgYGxgcPQBxmIH7AIEdAiwFYC7NBAkxLAoMxYS1AwCaRABPAp8Wc/fjDDz8MGBL72HvMKhL+2MkxsPMe/pDAYJCPS4tlT46xZA9QSxvPGbMbiW3JxgzMfGlA6wwsG3A56UAOGwMPSItEDlBLw4HEBmYeM6DD/hjg9MX5588Y/0C1FCT8OVAP1GIMchhuLTcSzJhhtjAksB1IYAByJfBpsZzxxlhaxkDCuI3nWLEE0C+GbWC/GODWYs6f/vDjmwob2fntzRs/fPhjJ8/Pf/bwhw8VeBwGoSQcG2AiwNBAiOPRwmCPJMaDW/koGAWjYBSMSAAAGMJKfKQHKm0AAAAASUVORK5CYII=","orcid":"","institution":"Laboratoire des Sciences du Climat et de l'Environnement, UMR CEA-CNRS-UVSQ","correspondingAuthor":true,"prefix":"","firstName":"Pascal","middleName":"","lastName":"Yiou","suffix":""},{"id":432462378,"identity":"0c0a4060-c8c0-4ad7-a948-87be2e949d01","order_by":1,"name":"Soulivanh Thao","email":"","orcid":"","institution":"Laboratoire des Sciences du Climat et de l'Environnement, UMR CEA-CNRS-UVSQ","correspondingAuthor":false,"prefix":"","firstName":"Soulivanh","middleName":"","lastName":"Thao","suffix":""}],"badges":[],"createdAt":"2025-03-18 15:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6254473/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6254473/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41612-025-01246-y","type":"published","date":"2025-11-17T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96650117,"identity":"77bac312-7730-4e0a-b1ca-bb2ab083120b","added_by":"auto","created_at":"2025-11-24 16:08:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":283564,"visible":true,"origin":"","legend":"","description":"","filename":"CMIP6SLPClassification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6254473/v1_covered_8fe4b316-8f9d-4e43-933a-99a251a1d969.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using Artificial Intelligence to identify CMIP6 models from daily SLP maps","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":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":"Neural Network, classification, Climate models, SLP","lastPublishedDoi":"10.21203/rs.3.rs-6254473/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6254473/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Large databases of climate model simulations are essential to sample climate variability and estimate how it can evolve in any future. \r\nThe chaotic nature of climate has motivated the simulation of large ensembles of simulations, which sample the uncertainty due to internal climate variability in single models. 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