Abstract
AbstractReliable projections of sea‐level rise depend on accurate representations of how fast‐flowing glaciers slip along their beds. The mechanics of slip are often parameterized as a constitutive relation (or “sliding law”) whose proper form remains uncertain. Here, we present a novel deep learning‐based framework for learning the time evolution of drag at glacier beds from time‐dependent ice velocity and elevation observations. We use a feedforward neural network, informed by the governing equations of ice flow, to infer spatially and temporally varying basal drag and associated uncertainties from data. We test the framework on 1D and 2D ice flow simulation outputs and demonstrate the recovery of the underlying basal mechanics under various levels of observational and modeling uncertainties. We apply this framework to time‐dependent velocity data for Rutford Ice Stream, Antarctica, and present evidence that ocean‐tide‐driven changes in subglacial water pressure drive changes in ice flow over the tidal cycle.
Highlights
Fast-flowing outlet glaciers that drain the Greenland and Antarctic Ice Sheets are major contributors to sea-level rise (SLR) (Church et al, 2013; Ritz et al, 2015)
We have presented a hybrid machine learning framework for learning the time-evolution of basal mechanics for glaciers and ice streams
This approach integrates into the learning procedure well-known ice flow momentum balance equations at various levels of complexity
Summary
Fast-flowing outlet glaciers that drain the Greenland and Antarctic Ice Sheets are major contributors to sea-level rise (SLR) (Church et al, 2013; Ritz et al, 2015). While widespread acceleration of these glaciers in response to changing climate conditions has magnified their importance in future projections of SLR, fundamental uncertainties about their long-term dynamical behavior and stability persist (Robel et al, 2019). One of the key sources of uncertainty is the unknown form of the parameterization used to describe how drag at the base of glaciers is related to basal sliding velocity, bed roughness, bed composition, and water pressure (Aschwanden et al, 2019; Ritz et al, 2015). The resistive force provided by basal drag plays a significant role in the evolution of glaciers in response to changes in atmospheric and oceanic conditions.
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