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...
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| Published in: | Environmetrics (London, Ont.) Vol. 26; no. 3; pp. 159 - 177 |
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| Main Authors: | , , , , |
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| Language: | English |
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Blackwell Publishing Ltd
01.05.2015
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| ISSN: | 1180-4009, 1099-095X |
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| 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. |
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| AbstractList | 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
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
data are hard to obtain. © 2015 The Authors.
published by John Wiley & Sons Ltd. 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. 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. copyright 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd. 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. |
| Author | Lindgren, Finn Bamber, Jonathan Rougier, Jonathan Zammit-Mangion, Andrew Schön, Nana |
| Author_xml | – sequence: 1 givenname: Andrew surname: Zammit-Mangion fullname: Zammit-Mangion, Andrew email: A.ZammitMangion@bristol.ac.uk organization: School of Geographical Sciences, University of Bristol, BS8 1SS, Bristol, U.K – sequence: 2 givenname: Jonathan surname: Rougier fullname: Rougier, Jonathan organization: Department of Mathematics, University of Bristol, BS8 1TW, Bristol, U.K – sequence: 3 givenname: Nana surname: Schön fullname: Schön, Nana organization: School of Geographical Sciences, University of Bristol, BS8 1SS, Bristol, U.K – sequence: 4 givenname: Finn surname: Lindgren fullname: Lindgren, Finn organization: Department of Mathematical Sciences, University of Bath, BA2 7AQ, Bath, U.K – sequence: 5 givenname: Jonathan surname: Bamber fullname: Bamber, Jonathan organization: School of Geographical Sciences, University of Bristol, BS8 1SS, Bristol, U.K |
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| Keywords | spatio-temporal statistics Multivariate modelling parallel MCMC sea-level rise stochastic partial differential equations |
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| Snippet | 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)... 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)... 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)... |
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| SubjectTerms | Antarctica Assessments Coastal Geophysics Ice sheets Markov models Mathematical models Monte Carlo methods Multivariate modelling parallel MCMC Sampling sea-level rise spatio-temporal statistics stochastic partial differential equations |
| Title | Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise |
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