Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise.

Andrew Zammit‐Mangion,Jonathan Bamber,Jonathan Rougier,Nana Schön,Finn Lindgren

doi:10.1002/env.2323

Andrew Zammit‐Mangion, Jonathan Bamber + Show 3 more

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https://doi.org/10.1002/env.2323

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Journal: Environmetrics	Publication Date: Jan 16, 2015
Citations: 47	License type: CC BY 4.0

Affiliation: University of Bristol, University of Bath

Abstract

Antarctica is the world's largest fresh-water reservoir, with the potential to raise sea levels by about 60 m. An ice sheet contributes to sea-level rise (SLR) when its rate of ice discharge and/or surface melting exceeds accumulation through snowfall. Constraining the contribution of the ice sheets to present-day SLR is vital both for coastal development and planning, and climate projections. Information on various ice sheet processes is available from several remote sensing data sets, as well as in situ data such as global positioning system data. These data have differing coverage, spatial support, temporal sampling and sensing characteristics, and thus, it is advantageous to combine them all in a single framework for estimation of the SLR contribution and the assessment of processes controlling mass exchange with the ocean.In this paper, we predict the rate of height change due to salient geophysical processes in Antarctica and use these to provide estimates of SLR contribution with associated uncertainties. We employ a multivariate spatio-temporal model, approximated as a Gaussian Markov random field, to take advantage of differing spatio-temporal properties of the processes to separate the causes of the observed change. The process parameters are estimated from geophysical models, while the remaining parameters are estimated using a Markov chain Monte Carlo scheme, designed to operate in a high-performance computing environment across multiple nodes. We validate our methods against a separate data set and compare the results to those from studies that invariably employ numerical model outputs directly. We conclude that it is possible, and insightful, to assess Antarctica's contribution without explicit use of numerical models. Further, the results obtained here can be used to test the geophysical numerical models for which in situ data are hard to obtain. © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd.

Highlights

Antarctica is the largest fresh-water resource on Earth and, potentially, the largest contributor to global sea-level rise (SLR)
We have shown that spatio-temporal modelling is a powerful tool in assessing Antarctica’s mass balance and contribution to SLR
We find that our results conform quite well with those making explicit use of numerical models, indicating that this approach may be used in the future to validate the models with remote sensing data

Summary

Introduction

Antarctica is the largest fresh-water resource on Earth and, potentially, the largest contributor to global sea-level rise (SLR). A review of the difficulties associated with estimating the SLR contribution from Antarctica and the assumptions of current approaches that tackle this problem is given in Zammit-Mangion et al (2014). Zammit-Mangion et al (2014) showed that under-determination, for this problem, can be tackled using a hierarchical modelling framework (Cressie and Wikle, 2011). Their approach, which as a proof of concept focused on only a part of the Antarctic ice sheet, took advantage of differing spectral characteristics to improve the separation of the individual processes b Department of Mathematics, University of Bristol, Bristol, BS8 1TW, U.K

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