Global-scale modelling of mountain glacier evolution since the last interglacial

For most glaciated areas, detailed mountain glacier evolution since the last interglacial is largely unknown. Due to limitations of traditional numerical modelling, previous studies have typically operated at a coarse spatial resolution, limited study area size, or focused on major climate events. Here we address these limitations by applying the Instructed Glacier Model (IGM), a deep-learning ice-flow emulator enabling efficient GPU-accelerated transient simulations. We model mountain glacier evolution since 130 ka and up to the present-day at 500 m resolution across eight broad mountain ranges in North America, South America, Eurasia, and Africa. Paleoclimate variables are approximated and regionalised using a combination of global and regional climate proxy datasets. We perform 617 parameter-calibration experiments varying paleoclimate and ice-dynamic parameters, with an average runtime of 21 hours per experiment. Model performance is assessed using combined areal and volumetric validation at two known glacial states; the last glacial maximum and the present-day. We also introduce a reproducible probabilistic model-evaluation framework combining confusion matrix validation score and simulation rank to identify sets of acceptable model parameters rather than a single best-fit solution. Our results show that IGM can model realistic ice-flow patterns, glacier geometries, and transient evolution across full glacial-interglacial cycles, demonstrating that machine-learning models of ice dynamics generalise to new domains and conditions, although performance can decline at coarser spatial resolutions. Together, these results demonstrate the feasibility of global-scale, high-resolution, transient glacier modelling over orbital timescales using a deep learning instructed model, while providing a 100-year interval dataset including glacier extent, ice thickness, and ice flow patterns for the last glacial cycle.