Probabilistic uncertainty quantification and simulation for climate modelling
This thesis addresses probabilistic approaches to uncertainty quantification, within the context of climate science. For the results of climate studies to be appropriately understood and applied, it is necessary to quantify their relation to the observable world. Probability theory provides a formal...
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Memorial University of Newfoundland
2014
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ftmemorialuniv:oai:research.library.mun.ca:6342 2023-10-01T03:58:03+02:00 Probabilistic uncertainty quantification and simulation for climate modelling Hauser, Tristan Paul 2014-05 application/pdf https://research.library.mun.ca/6342/ https://research.library.mun.ca/6342/1/HAUSER_T.PDF https://research.library.mun.ca/6342/2/HAUSER_T.pdf en eng Memorial University of Newfoundland https://research.library.mun.ca/6342/1/HAUSER_T.PDF https://research.library.mun.ca/6342/2/HAUSER_T.pdf Hauser, Tristan Paul <https://research.library.mun.ca/view/creator_az/Hauser=3ATristan_Paul=3A=3A.html> (2014) Probabilistic uncertainty quantification and simulation for climate modelling. Doctoral (PhD) thesis, Memorial University of Newfoundland. thesis_license Thesis NonPeerReviewed 2014 ftmemorialuniv 2023-09-03T06:45:50Z This thesis addresses probabilistic approaches to uncertainty quantification, within the context of climate science. For the results of climate studies to be appropriately understood and applied, it is necessary to quantify their relation to the observable world. Probability theory provides a formal approach that can be applied commonly to the encountered uncertainties. Three studies are presented within. The first addresses the Bayesian calibration of climate simulators. This method quantifies simulation uncertainties by taking into account inherent model and observation uncertainties. Here an alternative method for the fast statistical emulators of model parameter relationships is tested, as well as a rigorous approach to quantifying model limitations. The second examines probabilistic methods for identifying regional climatological features and quantifying the related uncertainties. Such features serve as a basis of comparison for climate simulations, as well as defining, to some extent, how we view evolution of the modern climate. Here typical patterns are recreated using an approach that quantifies uncertainty in the data analysis. As well, temporal shifts in the distribution of these features and their relation to ocean variability is explored. The third study experiments with approaches to regional stochastic weather generation. There is an inherent residual between climate simulations and large scale features, and regional variability seen on daily timescales. Weather generators provide an error model to quantify this uncertainty, and define features and variability underrepresented in global simulations. A method is developed which allows for regional, rather than site specific, simulation for the North Atlantic, a region of very active and varied atmospheric activity. In total, the work presented within covers the range of uncertainty types that must be considered by climate studies. The individual articles addresses contemporary questions concerning appropriate methods and implementation for their ... Thesis North Atlantic Memorial University of Newfoundland: Research Repository |
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Memorial University of Newfoundland: Research Repository |
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This thesis addresses probabilistic approaches to uncertainty quantification, within the context of climate science. For the results of climate studies to be appropriately understood and applied, it is necessary to quantify their relation to the observable world. Probability theory provides a formal approach that can be applied commonly to the encountered uncertainties. Three studies are presented within. The first addresses the Bayesian calibration of climate simulators. This method quantifies simulation uncertainties by taking into account inherent model and observation uncertainties. Here an alternative method for the fast statistical emulators of model parameter relationships is tested, as well as a rigorous approach to quantifying model limitations. The second examines probabilistic methods for identifying regional climatological features and quantifying the related uncertainties. Such features serve as a basis of comparison for climate simulations, as well as defining, to some extent, how we view evolution of the modern climate. Here typical patterns are recreated using an approach that quantifies uncertainty in the data analysis. As well, temporal shifts in the distribution of these features and their relation to ocean variability is explored. The third study experiments with approaches to regional stochastic weather generation. There is an inherent residual between climate simulations and large scale features, and regional variability seen on daily timescales. Weather generators provide an error model to quantify this uncertainty, and define features and variability underrepresented in global simulations. A method is developed which allows for regional, rather than site specific, simulation for the North Atlantic, a region of very active and varied atmospheric activity. In total, the work presented within covers the range of uncertainty types that must be considered by climate studies. The individual articles addresses contemporary questions concerning appropriate methods and implementation for their ... |
format |
Thesis |
author |
Hauser, Tristan Paul |
spellingShingle |
Hauser, Tristan Paul Probabilistic uncertainty quantification and simulation for climate modelling |
author_facet |
Hauser, Tristan Paul |
author_sort |
Hauser, Tristan Paul |
title |
Probabilistic uncertainty quantification and simulation for climate modelling |
title_short |
Probabilistic uncertainty quantification and simulation for climate modelling |
title_full |
Probabilistic uncertainty quantification and simulation for climate modelling |
title_fullStr |
Probabilistic uncertainty quantification and simulation for climate modelling |
title_full_unstemmed |
Probabilistic uncertainty quantification and simulation for climate modelling |
title_sort |
probabilistic uncertainty quantification and simulation for climate modelling |
publisher |
Memorial University of Newfoundland |
publishDate |
2014 |
url |
https://research.library.mun.ca/6342/ https://research.library.mun.ca/6342/1/HAUSER_T.PDF https://research.library.mun.ca/6342/2/HAUSER_T.pdf |
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North Atlantic |
genre_facet |
North Atlantic |
op_relation |
https://research.library.mun.ca/6342/1/HAUSER_T.PDF https://research.library.mun.ca/6342/2/HAUSER_T.pdf Hauser, Tristan Paul <https://research.library.mun.ca/view/creator_az/Hauser=3ATristan_Paul=3A=3A.html> (2014) Probabilistic uncertainty quantification and simulation for climate modelling. Doctoral (PhD) thesis, Memorial University of Newfoundland. |
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thesis_license |
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1778530425484869632 |