A Review of Bayesian Modelling in Glaciology

Abstract

Bayesian methods for modelling and inference are being increasingly used in the cryospheric sciences, and glaciology in particular. Here, we present a review of recent works in glaciology that adopt a Bayesian approach when conducting an analysis. We organise the chapter into three categories: i) Gaussian-Gaussian models, ii) Bayesian hierarchical models, and iii) Bayesian calibration approaches. In addition, we present two detailed case studies that involve the application of Bayesian hierarchical models in glaciology. The first case study is on the spatial prediction of surface mass balance across the Icelandic mountain glacier Langjökull, and the second is on the prediction of sea-level rise contributions from the Antactcic ice sheet. This chapter is presented in such a way that it is accessible to both statisticians as well as earth scientists. This work will appear as a chapter of the forthcoming book titled Statistical Modelling Using Latent Gaussian Models -with Applications in Geophysics and Environmental Sciences, expected to be published by Springer in 2022.

Bayesian estimation of glacier surface elevation changes from DEMs

“…Bayesian methods have recently gained significant momentum in the glaciological community as they allow the combination of various sources of information within a single statistically coherent framework (see, for example, Zammit-Mangion et al, 2015;Brinkerhoff et al, 2016;Guillet et al, 2020;Werder et al, 2020;Zhang and Cressie, 2020;Gopalan et al, 2021). This paper presents a method to derive probabilistic estimates of glacier surface elevation changes by conditioning glacier volume variation observations on previously available knowledge of glacier surface elevation changes (i.e., previously published data and/or surface mass balance model outputs).…”