Resolving Circumarctic Zero-Curtain Phenomena with AI-Integrated Earth Observations

Resolving Circumarctic Zero-Curtain Phenomena with AI-Integrated Earth Observations | 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 Resolving Circumarctic Zero-Curtain Phenomena with AI-Integrated Earth Observations Bradley Gay, Kimberley Miner, Nils Rietze, Benjamin Poulter, Neal Pastick, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8646037/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Across the circumarctic, permafrost landscapes store approximately 1700 billion metric tons of organic carbon - nearly twice atmospheric levels - yet reservoir stability depends on the zero-curtain, a subsurface thermal plateau that emerges when latent heat maintains soil temperatures near 0°C during freeze-thaw phase transitions. The zero-curtain sustains liquid water through cryosuction within the active layer, enabling microbial activity to persist well into the cold season and regulating permafrost thermal stability. However, zero-curtain intensity, duration, and spatial extent remain inadequately quantified during transitional seasons when isothermal buffering exhibits maximum variability, limiting our understanding of permafrost-climate feedbacks. Here, we show that zero-curtain phenomena exhibit pronounced seasonal asymmetry, with extended vernal intensification (1000-4000 hours) relative to compressed winter occurrence (100-500 hours), and significant longitudinal variations, with moderate intensity patterns across the North American Arctic, enhanced vernal amplification in Siberia, reduced winter suppression in Fennoscandia, and a delayed vernal response in the Canadian Archipelago, with systematic amplification under warming conditions (2015-2024). GeoCryoAI, a hybridized physics-informed transfer learning framework, integrates 62.71 million in situ measurements and 3.3 billion remote sensing observations to quantify these dynamics at 30 m resolution, achieving 96.4% detection accuracy and 39% improvement over conventional models. Ablation studies confirm that the full GeoCryoAI architecture achieves 11.8% improvement over baseline MLP architectures (93.4% vs 81.6% in component validation experiments), with physics-informed constraints providing essential regularization for thermodynamic consistency. Mechanistic analysis reveals soil moisture-latent heat coupling drives 60-90% of duration variability, amplified 20-40% under warming. This framework establishes a NISAR-ready circumarctic monitoring protocol, enabling 3-6-month forecasts and quantitative constraints for Earth system models simulating carbon-climate feedbacks in a warming Arctic. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences zero-curtain artificial intelligence permafrost circumarctic remote sensing data assimilation transfer learning Full Text Additional Declarations No competing interests reported. Supplementary Files srzerocurtaingaysiln.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 24 Jan, 2026 Editor invited by journal 23 Jan, 2026 Editor assigned by journal 21 Jan, 2026 Submission checks completed at journal 21 Jan, 2026 First submitted to journal 20 Jan, 2026 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-8646037","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":584889597,"identity":"6f30ec81-cff1-48a8-9887-ab5941fc91dd","order_by":0,"name":"Bradley 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GeoCryoAI, a hybridized physics-informed transfer learning framework, integrates 62.71 million in situ measurements and 3.3 billion remote sensing observations to quantify these dynamics at 30 m resolution, achieving 96.4% detection accuracy and 39% improvement over conventional models. Ablation studies confirm that the full GeoCryoAI architecture achieves 11.8% improvement over baseline MLP architectures (93.4% vs 81.6% in component validation experiments), with physics-informed constraints providing essential regularization for thermodynamic consistency. Mechanistic analysis reveals soil moisture-latent heat coupling drives 60-90% of duration variability, amplified 20-40% under warming. 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