Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction ...
Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. Whilst these boundary conditions are typically fixed using availab...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2111.12283 https://arxiv.org/abs/2111.12283 |
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ftdatacite:10.48550/arxiv.2111.12283 2024-02-04T10:04:26+01:00 Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction ... Astfalck, Lachlan Williamson, Daniel Gandy, Niall Gregoire, Lauren Ivanovic, Ruza 2021 https://dx.doi.org/10.48550/arxiv.2111.12283 https://arxiv.org/abs/2111.12283 unknown arXiv Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Applications stat.AP FOS Computer and information sciences Article Preprint CreativeWork article 2021 ftdatacite https://doi.org/10.48550/arxiv.2111.12283 2024-01-05T04:08:47Z Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. Whilst these boundary conditions are typically fixed using available reconstructions in climate modelling studies, they are highly uncertain, that uncertainty is unquantified, and the effect on the output of the experiment can be considerable. We develop efficient quantification of these uncertainties that combines relevant data from multiple models and observations. Starting from the coexchangeability model, we develop a coexchangable process model to capture multiple correlated spatio-temporal fields of variables. We demonstrate that further exchangeability judgements over the parameters within this representation lead to a Bayes linear analogy of a hierarchical model. We use the framework to provide a joint reconstruction of sea-surface temperature and sea-ice concentration ... : Accepted with major revisions in the Journal of the American Statistical Association, resubmission is in review ... Article in Journal/Newspaper Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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unknown |
topic |
Applications stat.AP FOS Computer and information sciences |
spellingShingle |
Applications stat.AP FOS Computer and information sciences Astfalck, Lachlan Williamson, Daniel Gandy, Niall Gregoire, Lauren Ivanovic, Ruza Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction ... |
topic_facet |
Applications stat.AP FOS Computer and information sciences |
description |
Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. Whilst these boundary conditions are typically fixed using available reconstructions in climate modelling studies, they are highly uncertain, that uncertainty is unquantified, and the effect on the output of the experiment can be considerable. We develop efficient quantification of these uncertainties that combines relevant data from multiple models and observations. Starting from the coexchangeability model, we develop a coexchangable process model to capture multiple correlated spatio-temporal fields of variables. We demonstrate that further exchangeability judgements over the parameters within this representation lead to a Bayes linear analogy of a hierarchical model. We use the framework to provide a joint reconstruction of sea-surface temperature and sea-ice concentration ... : Accepted with major revisions in the Journal of the American Statistical Association, resubmission is in review ... |
format |
Article in Journal/Newspaper |
author |
Astfalck, Lachlan Williamson, Daniel Gandy, Niall Gregoire, Lauren Ivanovic, Ruza |
author_facet |
Astfalck, Lachlan Williamson, Daniel Gandy, Niall Gregoire, Lauren Ivanovic, Ruza |
author_sort |
Astfalck, Lachlan |
title |
Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction ... |
title_short |
Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction ... |
title_full |
Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction ... |
title_fullStr |
Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction ... |
title_full_unstemmed |
Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction ... |
title_sort |
coexchangeable process modelling for uncertainty quantification in joint climate reconstruction ... |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2111.12283 https://arxiv.org/abs/2111.12283 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.48550/arxiv.2111.12283 |
_version_ |
1789972911230025728 |