Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise.
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 asin situdata 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 whichin situdata are hard to obtain. © 2015 The Authors.Environmetricspublished by John Wiley & Sons Ltd.
School of Geographical Sciences, University of Bristol Bristol, BS8 1SS, U.K. ; Department of Mathematics, University of Bristol Bristol, BS8 1TW, U.K.
Andrew
A
Zammit-Mangion
Department of Mathematics, University of Bristol Bristol, BS8 1TW, U.K.
Jonathan
J
Rougier
School of Geographical Sciences, University of Bristol Bristol, BS8 1SS, U.K.
Nana
N
Schön
Department of Mathematical Sciences, University of Bath Bath, BA2 7AQ, U.K.
Finn
F
Lindgren
School of Geographical Sciences, University of Bristol Bristol, BS8 1SS, U.K.
Jonathan
J
Bamber
Multivariate modelling; parallel MCMC; sea-level rise; spatio-temporal statistics; stochastic partial differential equations