Bayesian Inference of Ice Softness and Basal Sliding Parameters at Langjökull

Giri Gopalan,Finnur Pálsson,Guðfinna Aðalgeirsdóttir,Birgir Hrafnkelsson

doi:10.3389/feart.2021.610069

Giri Gopalan, Finnur Pálsson + Show 2 more

Open Access

https://doi.org/10.3389/feart.2021.610069

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Abstract

We develop Bayesian statistical models that are designed for the inference of ice softness and basal sliding parameters, important glaciological quantities. These models are applied to Langjökull, the second largest temperate ice cap in Iceland at about 900 squared kilometers in area. The models make use of a relationship between physical parameters and ice velocity as stipulated by a shallow ice approximation that is generally applicable to Langjökull. The posterior distribution for ice softness concentrates around 18.2 × 10−25s−1Pa−3; moreover, spatially varying basal sliding parameters are inferred allowing for the decomposition of velocity into a deformation component and a sliding component, with spatial variation consistent with previous studies. Bayesian computation is conducted with a Gibbs sampling approach. The paper serves as an example of statistical inference for ice softness and basal sliding parameters at temperate, shallow glaciers using surface velocity data.

Highlights

The dynamics of glaciers have become of greater scientific interest in recent times due to global climate change and its impact on the size and flow of glaciers; perhaps most crucially, melting glaciers have an effect on sea levels (Björnsson et al, 2006; Zammit-Mangion et al, 2014; Hock et al, 2019)
The posterior mean of ice softness using the t distribution is 18.2 with a posterior standard deviation of 0.847, and the posterior mean for ice softness using the normal distribution is 17.8 with a posterior standard deviation of 1.11
The objective of this paper is to develop Bayesian models for glacier surface velocity, based on a shallow ice approximation, that allow for the statistical inference of important glacial parameters: ice softness and basal sliding

Summary

Introduction

The dynamics of glaciers have become of greater scientific interest in recent times due to global climate change and its impact on the size and flow of glaciers; perhaps most crucially, melting glaciers have an effect on sea levels (Björnsson et al, 2006; Zammit-Mangion et al, 2014; Hock et al, 2019). Glacial dynamics are dependent on two main physical parameters: ice softness, related to the deformation of ice, and basal sliding, related to basal velocity (Cuffey and Paterson, 2010). Both ice softness and basal sliding parameters cannot be measured directly and must be estimated in order to properly understand glacial dynamics and, globally relevant phenomena such as sea level rise. The purpose of this paper is to use surface velocity data in combination with Bayesian statistical inference in order to infer ice softness and basal sliding parameters, with particular application to Langjökull, a prominent Icelandic glacier (Björnsson, 2017). The basal sliding parameter can vary both spatially and temporally, especially during glacial surge events (Björnsson et al, 2003)

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