A Review of Bayesian Modelling in Glaciology

Giri Gopalan (), Andrew Zammit-Mangion () and Felicity McCormack ()
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Giri Gopalan: Los Alamos National Laboratory, Statistical Sciences Group
Andrew Zammit-Mangion: University of Wollongong, School of Mathematics and Applied Statistics and Securing Antarctica’s Environmental Future
Felicity McCormack: Monash University, Securing Antarctica’s Environmental Future, School of Earth, Atmosphere & Environment

A chapter in Statistical Modeling Using Bayesian Latent Gaussian Models, 2023, pp 81-107 from Springer

Abstract: 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 Antarctic ice sheet. This chapter is presented in such a way that it is accessible to both statisticians and Earth scientists.

Date: 2023
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DOI: 10.1007/978-3-031-39791-2_2

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