A Novel Approach to Predict the Arctic Stratospheric Ozone from Stratospheric Polar Vortex Dynamics using Explainable Machine Learning

A Novel Approach to Predict the Arctic Stratospheric Ozone from Stratospheric Polar Vortex Dynamics using Explainable Machine Learning | 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 A Novel Approach to Predict the Arctic Stratospheric Ozone from Stratospheric Polar Vortex Dynamics using Explainable Machine Learning Anish Kumar, Joyjit Mandal, Sina Mehrdad, Christoph Jacobi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6170589/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract A significant decreasing trend of Arctic stratospheric ozone has been observed since 2019, with the first reported ozone holein the Arctic Stratospheric Polar Vortex (SPV) in 2020, raising concerns for humanity. This underlines that it is essential todevelop an algorithm capable of predicting Arctic ozone levels, preferably using minimal computing resources. This studypresents a novel approach to ozone prediction based on the morphological and dynamical properties of the SPV. We developedan algorithm using an explainable machine learning model, achieving an R2 score of 80% and a correlation of 0.91 withobservations. The algorithm accurately predicts the daily and seasonal patterns of ozone variations. It successfully capturesthe pattern of the lowest recorded ozone levels in 2020, though it overestimates ozone values by approximately 20 Dobsonunits. Moreover, in some years the predicted ozone values also show a strong alignment with the observations. Notably, thealgorithm relies solely on physics based features of the SPV to predict chemical ozone loss, demonstrating the potential ofdynamical parameters in predict the ozone variability. Earth and environmental sciences/Climate sciences/Atmospheric science Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.pdf Cite Share Download PDF Status: Published Journal Publication published 20 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Aug, 2025 Reviews received at journal 16 Aug, 2025 Reviews received at journal 21 May, 2025 Reviewers agreed at journal 27 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 21 Apr, 2025 Editor assigned by journal 11 Mar, 2025 Submission checks completed at journal 07 Mar, 2025 First submitted to journal 06 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. 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