Journal Article

Accelerating Subglacial Hydrology for Ice Sheet Models With Deep Learning Methods

Robel, Alexander, Verjans, Vincent

2024

Overview

Subglacial drainage networks regulate the response of ice sheet flow to surface meltwater input to the subglacial environment. Simulating subglacial hydrology evolution is critical to projecting ice sheet sensitivity to climate, and contribution to sea‐level change. However, current numerical subglacial hydrology models are computationally expensive, and, consequently, evolving subglacial hydrology is neglected in large‐scale ice sheet simulations. We present a deep learning emulator of a state‐of‐the‐art subglacial hydrology model, trained at multiple Greenland glaciers. Our emulator performs strongly in both temporal (R2 > 0.99) and spatial (R2 > 0.95) generalization, offers high computational savings, and can be used to force numerical ice sheet models. This will enable century‐ and large‐scale ice sheet model simulations, including interactions between ice flow and increased meltwater input to the subglacial environment. Generally, our work demonstrates that machine learning can further improve ice sheet models, reduce computational bottlenecks, and exploit information from high‐fidelity models and novel observational platforms. Plain Language Summary Meltwater at the surface of ice sheets can drain to the subglacial environment, lubricate the bed, and influence ice sheet flow. Complex numerical subglacial hydrology models represent the subglacial drainage system, but are too computationally expensive to be included in large‐scale and long‐term ice sheet simulations. Consequently, model predictions of future ice sheet contribution to sea‐level rise ignore ice flow modulation by evolving subglacial hydrology. Here, we use deep learning to emulate a state‐of‐the‐art subglacial hydrology model. The emulator can directly force large‐scale ice sheet models to capture ice flow sensitivity to subglacial hydrology. The computational speed and accuracy of our emulator show the potential to use machine learning to efficiently incorporate previously neglected processes into ice sheet models. Key Points We develop a deep learning emulator to simulate evolving subglacial hydrology in response to meltwater input for ice sheet simulations The emulator shows generalization capabilities, large computational savings, and can be used to force numerical ice sheet models We demonstrate that machine learning has substantial potential in improving ice sheet models, through using information‐rich data sets

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Computer applications / Deep learning / Drainage patterns / Drainage systems / Emulators / Evolution / Glaciation / Glaciers / Glaciohydrology / Hydrologic models / Hydrology / Ice / ice sheet modeling / Ice sheet models / Ice sheets / Laminar flow / Learning algorithms / Machine learning / Meltwater / Sea level changes / Sea level rise / Sensitivity / Sheet modelling / Simulation / subglacial hydrology