Causal inference for stratospheric chemistry: insights into tropical middle stratospheric ozone variability

Causal inference for stratospheric chemistry: insights into tropical middle stratospheric ozone variability | 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 Causal inference for stratospheric chemistry: insights into tropical middle stratospheric ozone variability Evgenia Galytska, Birgit Hassler, Carlo Arosio, Martyn P. Chipperfield, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6426983/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract This study investigates the coupling between chemical and dynamical processes driving tropical middle stratospheric ozone (O 3 ) variability using a causal inference framework that combines causal discovery with causal effect estimation. This approach integrates qualitative physical knowledge through a causal graph applied to satellite observations and a chemistry-transport model (CTM) simulation. The analysis is split into two subperiods of monthly data: 2004–2011, characterized by an O 3 decrease, and 2012–2018, when O 3 increased in the tropical middle stratosphere. Causal inference identifies distinct processes governing O 3 behaviour. During 2004–2011, a robust negative contemporaneous connection from N 2 O to NO 2 emerged, while in 2012–2018 this shifted to a one-month lag. This slower response reduced NO 2 production from N 2 O oxidation, limiting O 3 loss via the NO x catalytic cycle. Further analysis across Quasi-Biennial Oscillation (QBO) regimes reveals regime-dependent differences in the causal links. The N 2 O to NO 2 connection is weaker during westerly shear, associated with reduced upwelling, and stronger during easterly shear, reflecting enhanced upwelling. Our study highlights the pivotal role that causal inference can play in disentangling complex chemical-dynamical influences on O 3 , complementing traditional statistical methods. This approach lays the foundation for broader applications in stratospheric chemistry, where many relations remain uncertain. By discovering and quantifying causal links, this methodology addresses open questions with environmental and societal relevance. Therefore, integrating causal reasoning into data-driven science enhances process understanding and strengthens the synergy between machine learning and statistical methods in Earth and environmental sciences. Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric chemistry Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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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This approach integrates qualitative physical knowledge through a causal graph applied to satellite observations and a chemistry-transport model (CTM) simulation. The analysis is split into two subperiods of monthly data: 2004–2011, characterized by an O\u003csub\u003e3\u003c/sub\u003e decrease, and 2012–2018, when O\u003csub\u003e3\u003c/sub\u003e increased in the tropical middle stratosphere. Causal inference identifies distinct processes governing O\u003csub\u003e3\u003c/sub\u003e behaviour. During 2004–2011, a robust negative contemporaneous connection from N\u003csub\u003e2\u003c/sub\u003eO to NO\u003csub\u003e2\u003c/sub\u003e emerged, while in 2012–2018 this shifted to a one-month lag. This slower response reduced NO\u003csub\u003e2\u003c/sub\u003e production from N\u003csub\u003e2\u003c/sub\u003eO oxidation, limiting O\u003csub\u003e3\u003c/sub\u003e loss via the NO\u003csub\u003ex\u003c/sub\u003e catalytic cycle. Further analysis across Quasi-Biennial Oscillation (QBO) regimes reveals regime-dependent differences in the causal links. The N\u003csub\u003e2\u003c/sub\u003eO to NO\u003csub\u003e2\u003c/sub\u003e connection is weaker during westerly shear, associated with reduced upwelling, and stronger during easterly shear, reflecting enhanced upwelling.\u003c/p\u003e\n\u003cp\u003eOur study highlights the pivotal role that causal inference can play in disentangling complex chemical-dynamical influences on O\u003csub\u003e3\u003c/sub\u003e, complementing traditional statistical methods. This approach lays the foundation for broader applications in stratospheric chemistry, where many relations remain uncertain. By discovering and quantifying causal links, this methodology addresses open questions with environmental and societal relevance. Therefore, integrating causal reasoning into data-driven science enhances process understanding and strengthens the synergy between machine learning and statistical methods in Earth and environmental sciences.\u003c/p\u003e","manuscriptTitle":"Causal inference for stratospheric chemistry: insights into tropical middle stratospheric ozone variability","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-09-24 15:08:24","doi":"10.21203/rs.3.rs-6426983/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}},{"code":1,"date":"2025-04-28 11:38:59","doi":"10.21203/rs.3.rs-6426983/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"d863f8b1-0ac4-4572-996e-10838d7f1dfc","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47785574,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric chemistry"},{"id":47785575,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics"}],"tags":[],"updatedAt":"2025-06-20T03:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 15:08:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-6426983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6426983","identity":"rs-6426983","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}