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Abstract

For reconstructing paleoclimate or studying glacial isostatic effects on the Earth’s lithosphere, increasingly more studies focus on modeling the large-scale ice cover in mountainous regions over long time scales. However, balancing model complexity and the spatial extent with computational costs is challenging. Previous studies of large-scale ice cover simulation in mountain areas such as the European Alps, New Zealand, and the Tibetan Plateau, typically used 1-2 km spatial resolution. However, mountains are characterized by high peaks and steep slopes - topographic features that are crucial for glacier mass balance and dynamics, but poorly resolved in coarse resolution topography. The Instructed Glacier Model (IGM) is a novel 3D ice model equipped with a Convolutional Neural Network which is trained from high order ice models to simulate ice flow. This results in a significant acceleration of run times, and thereby opening the possibility of higher spatial resolution runs. We use IGM to model the glaciation of the European Alps with different resolutions (2 km, 1 km and 200 m) over a time period of 160,000 years. We apply a linear cooling rate to today’s climate until 6 °C colder to mimic ice age conditions. Preliminary results indicate systematic, resolution-related differences: At the beginning of cooling the 2 km resolution yields slightly more ice volume. However, this trend reverses after the large valleys are filled with thick ice. When the Alps are fully ice covered, we find up to 15% more ice volume in the higher resolution models.