Spatiotemporal variability of air-sea CO₂ fluxes in the Northern Indian Ocean: Insights from ocean color remote sensing

Spatiotemporal variability of air-sea CO₂ fluxes in the Northern Indian Ocean: Insights from ocean color remote sensing | 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 Spatiotemporal variability of air-sea CO₂ fluxes in the Northern Indian Ocean: Insights from ocean color remote sensing Nandha Kumar M., Mukeem Thoufiq Ahmed, Nazeera Begum Pathan, P. Brahmaji Rao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8054322/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Quantifying sea surface pCO 2 (pCO₂ sw ), atmospheric pCO 2 (pCO 2air ), and the CO₂ flux between these two media is essential for understanding the global carbon cycle and its interactions with climate change. Satellite based quantification offers a significant advantage in monitoring oceanic CO₂ absorption by providing continuous, widespread, and high-resolution data, thereby overcoming the limitations posed by sparse in-situ measurements particularly in the North Indian Ocean (NIO). This study integrates satellite derived datasets, regression equations, and models to estimate pCO₂ fields and compute air-sea CO 2 fluxes across the NIO region, including the Andaman Sea, Arabian Sea (AS), Lakshadweep Sea, and Bay of Bengal (BoB). Key input variables include sea surface temperature (SST), sea surface salinity (SSS), chlorophyll-a (Chla), wind speed (WS), atmospheric CO₂ concentration, and sea level pressure (SLP). A multi-parameter regression approach was used to estimate pCO₂ sw using SST, SSS, and Chla, while pCO₂ air was derived using atmospheric CO₂ and sea level pressure. CO 2 flux was then calculated based on ΔpCO₂, solubility coefficients, seawater density, WS, and gas transfer velocity. The results reveal a spatially diverse pattern across the NIO. The AS and Persian Gulf act as CO 2 sources, whereas the BoB and Andaman Sea serve as sinks. The Lakshadweep Sea exhibits dual characteristics, functioning both as a source and a sink depending on temporal and spatial factors. SST and Chla play significant roles in regulating sea surface pCO₂, while SSS has a minor but notable influence on the direction and magnitude of flux. These insights contribute to improving the accuracy of air-sea CO 2 flux estimation, understanding regional carbon dynamics, and projecting future trends. They also provide valuable input for shaping effective climate policies and mitigation strategies. Air-sea CO2 flux NIO region surface ocean pCO2 atmospheric pCO2 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 21 Dec, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 19 Nov, 2025 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 07 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-8054322","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550413913,"identity":"919526d3-5a1c-4ea2-847d-f742602abcdc","order_by":0,"name":"Nandha Kumar M.","email":"","orcid":"","institution":"Central University of Karnataka","correspondingAuthor":false,"prefix":"","firstName":"Nandha","middleName":"Kumar","lastName":"M.","suffix":""},{"id":550413914,"identity":"4834faa0-fe03-407c-ab2a-b441f8080f28","order_by":1,"name":"Mukeem Thoufiq Ahmed","email":"","orcid":"","institution":"Andhra University","correspondingAuthor":false,"prefix":"","firstName":"Mukeem","middleName":"Thoufiq","lastName":"Ahmed","suffix":""},{"id":550413915,"identity":"f33c5677-eaa3-4e9b-ae29-824c8827292e","order_by":2,"name":"Nazeera Begum Pathan","email":"","orcid":"","institution":"Acharya Nagarjuna University (ANU)","correspondingAuthor":false,"prefix":"","firstName":"Nazeera","middleName":"Begum","lastName":"Pathan","suffix":""},{"id":550413916,"identity":"fd2bd5ed-0403-4c45-8e11-a0b6e6dd0a0e","order_by":3,"name":"P. 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sensing","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":"ocean-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"odyn","sideBox":"Learn more about [Ocean Dynamics](https://link.springer.com/journal/10236)","snPcode":"10236","submissionUrl":"https://submission.springernature.com/new-submission/10236/3","title":"Ocean Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Air-sea CO2 flux, NIO region, surface ocean pCO2, atmospheric pCO2","lastPublishedDoi":"10.21203/rs.3.rs-8054322/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8054322/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eQuantifying sea surface pCO\u003csub\u003e2\u003c/sub\u003e (pCO₂\u003csub\u003esw\u003c/sub\u003e), atmospheric pCO\u003csub\u003e2\u003c/sub\u003e (pCO\u003csub\u003e2air\u003c/sub\u003e), and the CO₂ flux between these two media is essential for understanding the global carbon cycle and its interactions with climate change. Satellite based quantification offers a significant advantage in monitoring oceanic CO₂ absorption by providing continuous, widespread, and high-resolution data, thereby overcoming the limitations posed by sparse in-situ measurements particularly in the North Indian Ocean (NIO). This study integrates satellite derived datasets, regression equations, and models to estimate pCO₂ fields and compute air-sea CO\u003csub\u003e2\u003c/sub\u003e fluxes across the NIO region, including the Andaman Sea, Arabian Sea (AS), Lakshadweep Sea, and Bay of Bengal (BoB). Key input variables include sea surface temperature (SST), sea surface salinity (SSS), chlorophyll-a (Chla), wind speed (WS), atmospheric CO₂ concentration, and sea level pressure (SLP). A multi-parameter regression approach was used to estimate pCO₂\u003csub\u003esw\u003c/sub\u003e using SST, SSS, and Chla, while pCO₂\u003csub\u003eair\u003c/sub\u003e was derived using atmospheric CO₂ and sea level pressure. CO\u003csub\u003e2\u003c/sub\u003e flux was then calculated based on ΔpCO₂, solubility coefficients, seawater density, WS, and gas transfer velocity. The results reveal a spatially diverse pattern across the NIO. The AS and Persian Gulf act as CO\u003csub\u003e2\u003c/sub\u003e sources, whereas the BoB and Andaman Sea serve as sinks. The Lakshadweep Sea exhibits dual characteristics, functioning both as a source and a sink depending on temporal and spatial factors. SST and Chla play significant roles in regulating sea surface pCO₂, while SSS has a minor but notable influence on the direction and magnitude of flux. These insights contribute to improving the accuracy of air-sea CO\u003csub\u003e2\u003c/sub\u003e flux estimation, understanding regional carbon dynamics, and projecting future trends. They also provide valuable input for shaping effective climate policies and mitigation strategies.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal variability of air-sea CO₂ fluxes in the Northern Indian Ocean: Insights from ocean color remote sensing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 06:22:05","doi":"10.21203/rs.3.rs-8054322/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-21T09:58:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259608306850877633959967250294434706920","date":"2025-11-25T08:18:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-19T14:26:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-11T09:59:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-11T09:56:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Ocean Dynamics","date":"2025-11-07T07:32:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ocean-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"odyn","sideBox":"Learn more about [Ocean Dynamics](https://link.springer.com/journal/10236)","snPcode":"10236","submissionUrl":"https://submission.springernature.com/new-submission/10236/3","title":"Ocean Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d2fc5d6c-19f8-46a1-9af1-0099d2b94f79","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-27T06:22:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-27 06:22:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8054322","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8054322","identity":"rs-8054322","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}