Abstract
AbstractBasal motion is the primary mechanism for ice flux in Greenland, yet a widely applicable model for predicting it remains elusive. This is due to the difficulty in both observing small-scale bed properties and predicting a time-varying water pressure on which basal motion putatively depends. We take a Bayesian approach to these problems by coupling models of ice dynamics and subglacial hydrology and conditioning on observations of surface velocity in southwestern Greenland to infer the posterior probability distributions for eight spatially and temporally constant parameters governing the behavior of both the sliding law and hydrologic model. Because the model is computationally expensive, characterization of these distributions using classical Markov Chain Monte Carlo sampling is intractable. We skirt this issue by training a neural network as a surrogate that approximates the model at a sliver of the computational cost. We find that surface velocity observations establish strong constraints on model parameters relative to a prior distribution and also elucidate correlations, while the model explains 60% of observed variance. However, we also find that several distinct configurations of the hydrologic system and stress regime are consistent with observations, underscoring the need for continued data collection and model development.
Highlights
Glaciers and ice sheets convert potential energy in the form of accumulated ice at high elevations into heat, either by viscous dissipation within the ice itself or by frictional dissipation at the interface between the ice and the underlying bedrock or sediment
The difficulty results from a discrepancy in spatial and temporal scales between the physics driving sliding and water flux versus the scale of glaciers and ice sheets: physics at the bed occur on the order of a few meters with characteristic timescales of minutes, while relevant timescales for ice-sheet evolution occur over kilometers and years
We developed a coupled model of subglacial hydrology and glacier flow, and used it to infer the posterior probability distribution of eight key model parameters
Summary
Glaciers and ice sheets convert potential energy in the form of accumulated ice at high elevations into heat, either by viscous dissipation within the ice itself or by frictional dissipation at the interface between the ice and the underlying bedrock or sediment This latter process, hereafter referred to as ‘sliding’, is responsible for .90% of observed surface velocity over much of Greenland, even in regions that are not fast flowing (Maier and others, 2019). Validating the models of sliding and hydrology remains elusive, partly due to potential model misspecification, and due to a lack of sufficient observational constraints on model parameters such as hydraulic conductivity of different components of the subglacial system, characteristic length scales of bedrock asperities and the scaling between effective pressure and basal shear stress
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