Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19
Assessing the impact of uncertainties in ice-sheet models is a major and challenging issue that needs to be faced by the ice-sheet community to provide more robust and reliable model-based projections of ice-sheet mass balance. In recent years, uncertainty quantification (UQ) has been increasingly u...
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ftdoajarticles:oai:doaj.org/article:d344a7f026a94b56a072fe9fbf6f0772 2023-05-15T16:39:38+02:00 Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 K. Bulthuis E. Larour 2022-02-01T00:00:00Z https://doi.org/10.5194/gmd-15-1195-2022 https://doaj.org/article/d344a7f026a94b56a072fe9fbf6f0772 EN eng Copernicus Publications https://gmd.copernicus.org/articles/15/1195/2022/gmd-15-1195-2022.pdf https://doaj.org/toc/1991-959X https://doaj.org/toc/1991-9603 doi:10.5194/gmd-15-1195-2022 1991-959X 1991-9603 https://doaj.org/article/d344a7f026a94b56a072fe9fbf6f0772 Geoscientific Model Development, Vol 15, Pp 1195-1217 (2022) Geology QE1-996.5 article 2022 ftdoajarticles https://doi.org/10.5194/gmd-15-1195-2022 2022-12-31T03:35:47Z Assessing the impact of uncertainties in ice-sheet models is a major and challenging issue that needs to be faced by the ice-sheet community to provide more robust and reliable model-based projections of ice-sheet mass balance. In recent years, uncertainty quantification (UQ) has been increasingly used to characterize and explore uncertainty in ice-sheet models and improve the robustness of their projections. A typical UQ analysis first involves the (probabilistic) characterization of the sources of uncertainty, followed by the propagation and sensitivity analysis of these sources of uncertainty. Previous studies concerned with UQ in ice-sheet models have generally focused on the last two steps but have paid relatively little attention to the preliminary and critical step of the characterization of uncertainty. Sources of uncertainty in ice-sheet models, like uncertainties in ice-sheet geometry or surface mass balance, typically vary in space and potentially in time. For that reason, they are more adequately described as spatio-(temporal) random fields, which account naturally for spatial (and temporal) correlation. As a means of improving the characterization of the sources of uncertainties for forward UQ analysis within the Ice-sheet and Sea-level System Model (ISSM), we present in this paper a stochastic sampler for Gaussian random fields with Matérn covariance function. The class of Matérn covariance functions provides a flexible model able to capture statistical dependence between locations with different degrees of spatial correlation or smoothness properties. The implementation of this stochastic sampler is based on a notable explicit link between Gaussian random fields with Matérn covariance function and a certain stochastic partial differential equation. Discretization of this stochastic partial differential equation by the finite-element method results in a sparse, scalable and computationally efficient representation known as a Gaussian Markov random field. In addition, spatio-temporal samples can be ... Article in Journal/Newspaper Ice Sheet Directory of Open Access Journals: DOAJ Articles Geoscientific Model Development 15 3 1195 1217 |
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topic |
Geology QE1-996.5 |
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Geology QE1-996.5 K. Bulthuis E. Larour Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 |
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Geology QE1-996.5 |
description |
Assessing the impact of uncertainties in ice-sheet models is a major and challenging issue that needs to be faced by the ice-sheet community to provide more robust and reliable model-based projections of ice-sheet mass balance. In recent years, uncertainty quantification (UQ) has been increasingly used to characterize and explore uncertainty in ice-sheet models and improve the robustness of their projections. A typical UQ analysis first involves the (probabilistic) characterization of the sources of uncertainty, followed by the propagation and sensitivity analysis of these sources of uncertainty. Previous studies concerned with UQ in ice-sheet models have generally focused on the last two steps but have paid relatively little attention to the preliminary and critical step of the characterization of uncertainty. Sources of uncertainty in ice-sheet models, like uncertainties in ice-sheet geometry or surface mass balance, typically vary in space and potentially in time. For that reason, they are more adequately described as spatio-(temporal) random fields, which account naturally for spatial (and temporal) correlation. As a means of improving the characterization of the sources of uncertainties for forward UQ analysis within the Ice-sheet and Sea-level System Model (ISSM), we present in this paper a stochastic sampler for Gaussian random fields with Matérn covariance function. The class of Matérn covariance functions provides a flexible model able to capture statistical dependence between locations with different degrees of spatial correlation or smoothness properties. The implementation of this stochastic sampler is based on a notable explicit link between Gaussian random fields with Matérn covariance function and a certain stochastic partial differential equation. Discretization of this stochastic partial differential equation by the finite-element method results in a sparse, scalable and computationally efficient representation known as a Gaussian Markov random field. In addition, spatio-temporal samples can be ... |
format |
Article in Journal/Newspaper |
author |
K. Bulthuis E. Larour |
author_facet |
K. Bulthuis E. Larour |
author_sort |
K. Bulthuis |
title |
Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 |
title_short |
Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 |
title_full |
Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 |
title_fullStr |
Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 |
title_full_unstemmed |
Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 |
title_sort |
implementation of a gaussian markov random field sampler for forward uncertainty quantification in the ice-sheet and sea-level system model v4.19 |
publisher |
Copernicus Publications |
publishDate |
2022 |
url |
https://doi.org/10.5194/gmd-15-1195-2022 https://doaj.org/article/d344a7f026a94b56a072fe9fbf6f0772 |
genre |
Ice Sheet |
genre_facet |
Ice Sheet |
op_source |
Geoscientific Model Development, Vol 15, Pp 1195-1217 (2022) |
op_relation |
https://gmd.copernicus.org/articles/15/1195/2022/gmd-15-1195-2022.pdf https://doaj.org/toc/1991-959X https://doaj.org/toc/1991-9603 doi:10.5194/gmd-15-1195-2022 1991-959X 1991-9603 https://doaj.org/article/d344a7f026a94b56a072fe9fbf6f0772 |
op_doi |
https://doi.org/10.5194/gmd-15-1195-2022 |
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Geoscientific Model Development |
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15 |
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3 |
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1195 |
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1217 |
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