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

Tancrède P. M. Leger (tancrede.leger@unil.ch), Guillaume Jouvet, Sarah Kamleitner, Jürgen Mey, Frédéric Herman, Brandon D. Finley, Susan Ivy-Ochs, Andreas Vieli, Andreas Henz and Samuel U. Nussbaumer
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Tancrède P. M. Leger: University of Lausanne
Guillaume Jouvet: University of Lausanne
Sarah Kamleitner: University of Lausanne
Jürgen Mey: University of Potsdam
Frédéric Herman: University of Lausanne
Brandon D. Finley: University of Lausanne
Susan Ivy-Ochs: ETH Zurich
Andreas Vieli: University of Zurich
Andreas Henz: University of Zurich
Samuel U. Nussbaumer: University of Zurich

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract 25 thousand years ago, the European Alps were covered by the kilometre-thick Alpine Ice Field. Numerical modelling of this glaciation has been challenged by model-data disagreements, including overestimations of ice thickness. We tackle this issue by applying the Instructed Glacier Model, a three-dimensional model enhanced with physics-informed machine learning. This approach allows us to produce 100 Alps-wide and 17 thousand-year-long simulations at 300 m resolution. Previously unfeasible due to computational costs, our experiment both increases model-data agreement in ice extent and reduces the offset in ice thickness by between 200% and 450% relative to previous studies. Our results have implications for better estimating former ice velocities, ice temperature, basal conditions, erosion processes, and paleoclimate in the Alps. This study demonstrates that physics-informed machine learning can help overcome the bottleneck of high-resolution glacier modelling and better test parameterisations, both of which are required to accurately describe complex topographies and ice dynamics.

Date: 2025
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DOI: 10.1038/s41467-025-56168-3

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