AI-Enhanced Climate Modeling: Improving Sustainability Forecasting and Economic Decision-Making Under Climate Uncertainty

AI-Enhanced Climate Modeling: Improving Sustainability Forecasting and Economic Decision-Making Under Climate Uncertainty | 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 AI-Enhanced Climate Modeling: Improving Sustainability Forecasting and Economic Decision-Making Under Climate Uncertainty Lemuel Kenneth David This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9460993/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change modeling is essential for sustainability planning and economic decision-making, yet traditional General Circulation Models (GCMs) are often constrained by coarse spatial resolution and high computational demands. This study develops an AI-enhanced climate modeling framework that integrates ensemble machine learning and deep learning techniques to improve the accuracy and efficiency of climate prediction. Using global climate data from 1970 to 2023 sourced from NOAA and NASA, the study models key variables including temperature, precipitation, humidity, CO₂ levels, sea surface temperature, and sea ice extent. The methodological framework applies Gradient Boosting Machines, Random Forest, Support Vector Machines, and Long Short-Term Memory networks, alongside weighted approaches to enhance predictive robustness. The results show that the proposed models outperform traditional GCMs by reducing prediction errors and increasing explanatory power across major climate indicators. The findings also demonstrate the practical relevance of improved forecasting for climate-sensitive sectors such as agriculture, extreme weather preparedness, sea-level risk management, and air quality planning. Beyond methodological improvement, the study highlights how AI-driven climate forecasting can reduce uncertainty in resource allocation, strengthen sustainability planning, and support more informed economic and policy decisions under climate uncertainty. Climate change modeling machine learning sustainability economic decision-making GCM LSTM ensemble models Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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-9460993","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633946563,"identity":"cac72eb5-e59b-44ab-bfd8-db68c40ffb57","order_by":0,"name":"Lemuel Kenneth David","email":"data:image/png;base64,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","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Lemuel","middleName":"Kenneth","lastName":"David","suffix":""}],"badges":[],"createdAt":"2026-04-19 09:40:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9460993/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9460993/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109249588,"identity":"14394e18-a389-4b55-8c7a-1a31d90ded80","added_by":"auto","created_at":"2026-05-14 08:57:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1520172,"visible":true,"origin":"","legend":"","description":"","filename":"ForecastingandEconomic.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9460993/v1_covered_98715e8f-c94b-46d7-89a4-d63cd3676f92.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Enhanced Climate Modeling: Improving Sustainability Forecasting and Economic Decision-Making Under Climate Uncertainty","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate change modeling, machine learning, sustainability, economic decision-making, GCM, LSTM, ensemble models ","lastPublishedDoi":"10.21203/rs.3.rs-9460993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9460993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change modeling is essential for sustainability planning and economic decision-making, yet traditional General Circulation Models (GCMs) are often constrained by coarse spatial resolution and high computational demands. 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