Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference

Douglas Brinkerhoff, Andy Aschwanden, Mark Fahnestock

Research output: Contribution to journalArticlepeer-review

Abstract

Basal 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.

Original languageEnglish
Pages (from-to)385-403
Number of pages19
JournalJournal of Glaciology
Volume67
Issue number263
DOIs
StatePublished - Jun 2021

Keywords

  • Basal ice
  • glacier hydrology
  • ice velocity
  • ice-sheet modeling
  • subglacial processes

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