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

TL;DR

This paper develops a Bayesian framework using surrogate neural networks to infer subglacial process parameters from surface velocity data, improving understanding of ice dynamics in Greenland.

Contribution

It introduces a surrogate-based Bayesian inference method coupling ice dynamics and hydrology models, enabling efficient parameter estimation from surface velocity observations.

Findings

Surface velocity data strongly constrain model parameters.

The model explains 60% of observed variance.

Multiple configurations fit the data, indicating model degeneracy.

Abstract

Basal motion is the primary mechanism for ice flux outside Antarctica, yet a widely applicable model for predicting it in the absence of retrospective observations 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, classical MCMC 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.