ADMIRE: Improving Antarctic mass balance projections by coupling a Deep Learning basal melt emulator with the Kori-ULB ice sheet model

The future contribution of the Antarctic Ice Sheet to global sea-level rise remains the largest source of uncertainty in climate projections. This uncertainty is primarily driven by the complex interaction between the ocean and the ice shelf cavities. Most ice sheet models still rely on simplified melt parameterizations that fail to capture the complex oceanographic processes within sub-ice-shelf cavities, while fully coupled ice-ocean models remain too computationally expensive for large-scale sensitivity studies. In this study, we present ADMIRE (Antarctic Deep MELT and Ice REpresentation), a new ongoing-work intermediate-complexity framework. ADMIRE couples the ice sheet model Kori-ULB with DeepMELT, a deep learning emulator trained on high-resolution NEMO-SI3 simulations. This coupling allows for a more physically consistent representation of the ice-ocean interface at a fraction of the computational cost of a coupled ice-sheet-ocean model. We compare the sensitivity of the Antarctic grounding line migration and overall mass balance when using the DeepMELT emulator versus traditional melt parameterizations. Furthermore, we investigate the impact of temporal coupling steps and interpolation methods on the projections. Our preliminary results highlight the potential of machine learning-based emulators to bridge the gap between simple parameterizations and complex coupled models, providing more robust projections of Antarctica’s future but at a low computational cost, allowing for comprehensive and multi-century studies.