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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 s...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Blackwell Publishing Ltd
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4413369/ https://www.ncbi.nlm.nih.gov/pubmed/25937792 http://dx.doi.org/10.1002/env.2323 |
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author | Zammit-Mangion, Andrew Rougier, Jonathan Schön, Nana Lindgren, Finn Bamber, Jonathan |
author_facet | Zammit-Mangion, Andrew Rougier, Jonathan Schön, Nana Lindgren, Finn Bamber, Jonathan |
author_sort | Zammit-Mangion, Andrew |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4413369 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Blackwell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-44133692015-04-29 Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise Zammit-Mangion, Andrew Rougier, Jonathan Schön, Nana Lindgren, Finn Bamber, Jonathan Environmetrics Research Articles 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. Blackwell Publishing Ltd 2015-05 2015-01-16 /pmc/articles/PMC4413369/ /pubmed/25937792 http://dx.doi.org/10.1002/env.2323 Text en © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Zammit-Mangion, Andrew Rougier, Jonathan Schön, Nana Lindgren, Finn Bamber, Jonathan Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise |
title | Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise |
title_full | Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise |
title_fullStr | Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise |
title_full_unstemmed | Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise |
title_short | Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise |
title_sort | multivariate spatio-temporal modelling for assessing antarctica's present-day contribution to sea-level rise |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4413369/ https://www.ncbi.nlm.nih.gov/pubmed/25937792 http://dx.doi.org/10.1002/env.2323 |
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