A Review of Bayesian Modelling in Glaciology

TL;DR

This paper reviews recent applications of Bayesian modeling in glaciology, highlighting different model types and case studies on glacier surface mass balance and sea-level rise contributions.

Contribution

It provides a comprehensive overview of Bayesian approaches in glaciology, including Gaussian models, hierarchical models, and calibration methods, with detailed case studies.

Findings

Bayesian methods effectively model spatial glacier data.

Hierarchical models improve prediction accuracy.

Bayesian calibration enhances model reliability.

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 Langj\"okull, 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.