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

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

Research output: Contribution to journalArticle

13 Citations (Scopus)
130 Downloads (Pure)

Abstract

Antarctica is the world's largest fresh-water reservoir, with the potential to raise sea levels by about 60m. 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.

Original languageEnglish
Pages (from-to)159-177
JournalEnvironmetrics
Volume26
Issue number3
Early online date16 Jan 2015
DOIs
Publication statusPublished - May 2015

Keywords

  • Multivariate modelling
  • Parallel MCMC
  • Sea-level rise
  • Spatio-temporal statistics
  • Stochastic partial differential equations

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