A data-consistent model of the last glaciation in the Alps achieved with physics-driven AI

A data-consistent model of the last glaciation in the Alps achieved with physics-driven AI | 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 data-consistent model of the last glaciation in the Alps achieved with physics-driven AI Tancrède Leger, Guillaume Jouvet, Sarah Kamleitner, Jürgen Mey, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5043392/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract 25 thousand years ago, the European Alps were covered by a kilometre-thick body of ice, commonly described as the Alpine Ice Field. Numerical modelling of this glaciation has been challenged by persistent model-data disagreements, including large overestimations of its former thickness. Here, we tackle this issue by applying the Instructed Glacier Model, a three-dimensional, high-order, and thermo-mechanically coupled model enhanced with physics-informed machine learning. This new approach allows us to produce an ensemble of 100, Alps-wide and 17 thousand-year-long (35-18 ka) simulations at 300 m spatial resolution. Unfeasible with traditional models due to computational costs, our experiment substantially increases model-data agreement in both ice extent and thickness. The model-data offset in ice thickness, for instance, is here reduced by between 200% and 450% relative to previous studies. The results yield implications for more accurately reconstructing former ice velocities, ice temperature, basal conditions, glacial erosion processes, glacial isostatic adjustment, and climate evolution in the Alps during the last glaciation. Furthermore, this study demonstrates that physics-informed AI-driven glacier evolution models can overcome the bottleneck of high-resolution continental-scale modelling required to accurately describe complex topographies and ice dynamics. Earth and environmental sciences/Climate sciences/Cryospheric science Earth and environmental sciences/Planetary science/Cryospheric science Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryinformationLegeretal.pdf Supplementary information IcesoftnessLyonglacier.mp4 Video of depth-averaged ice softness for the Lyon outlet glacier SMBandELAAlpswide.mp4 Video of modelled SMB and ELA during the LGM for the entire European Alps IcethicknessAlpswide.mp4 Video of modelled ice thickness during the LGM for the entire European Alps SurfaceicevelocitiesAlpswide.mp4 Video of modelled surface ice velocities during the LGM for the entire European Alps BasalicetemperatureAlpswide.mp4 Video of modelled basal ice temperatures during the LGM for the entire European Alps Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Nature Communications → 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. 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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-5043392","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":351734535,"identity":"43c66c35-adf6-48a3-993d-003e27eaa91c","order_by":0,"name":"Tancrède 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