Leger Tancrède P M, Jouvet Guillaume, Kamleitner Sarah, Mey Jürgen, Herman Frédéric, Finley Brandon D, Ivy-Ochs Susan, Vieli Andreas, Henz Andreas, Nussbaumer Samuel U
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland.
Department of Geography, University of Sheffield, Sheffield, UK.
Nat Commun. 2025 Jan 20;16(1):848. doi: 10.1038/s41467-025-56168-3.
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.
2.5万年前，欧洲阿尔卑斯山被千米厚的阿尔卑斯冰原覆盖。这种冰川作用的数值模拟一直受到模型与数据不一致的挑战，包括对冰厚度的高估。我们通过应用“指导冰川模型”来解决这个问题，这是一个通过物理信息机器学习增强的三维模型。这种方法使我们能够以300米的分辨率进行100次全阿尔卑斯范围、长达1.7万年的模拟。由于计算成本，以前这是不可行的，我们的实验既提高了模型与数据在冰范围上的一致性，又将冰厚度的偏差相对于以前的研究减少了200%至450%。我们的结果对于更好地估计阿尔卑斯山以前的冰流速、冰温度、底部条件、侵蚀过程和古气候具有重要意义。这项研究表明，物理信息机器学习有助于克服高分辨率冰川建模的瓶颈，并更好地测试参数化，而这两者都是准确描述复杂地形和冰动力学所必需的。
