Comparison of Bayesian calibration methodologies for climate system models
Thesis (M.Sc.)--Memorial University of Newfoundland, 2009. Physical Oceanography Includes bibliographical references (leaves 78-81) Earth Systems models that attempt to forecast equilibrium states or make long term predictions are sensitive to the unavoidable approximations they employ. It is theref...
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ftmemorialunivdc:oai:collections.mun.ca:theses4/30930 2023-05-15T17:23:33+02:00 Comparison of Bayesian calibration methodologies for climate system models Hauser, Tristan, 1981- Memorial University of Newfoundland. Dept. of Physical Oceanography 2009. x, 88 leaves : ill., maps. (chiefly col.) Image/jpeg; Application/pdf http://collections.mun.ca/cdm/ref/collection/theses4/id/30930 Eng eng Electronic Theses and Dissertations (10.66 MB) -- http://collections.mun.ca/PDFs/theses/Hauser_Tristan.pdf a3242495 http://collections.mun.ca/cdm/ref/collection/theses4/id/30930 The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries Bayesian statistical decision theory Climatic changes--Mathematical models Dynamic climatology--Mathematical models Text Electronic thesis or dissertation 2009 ftmemorialunivdc 2015-08-06T19:21:53Z Thesis (M.Sc.)--Memorial University of Newfoundland, 2009. Physical Oceanography Includes bibliographical references (leaves 78-81) Earth Systems models that attempt to forecast equilibrium states or make long term predictions are sensitive to the unavoidable approximations they employ. It is therefore important for such models to be parameterized through objective and repeatable methods that quantify the uncertainties associated with the inexactness of these approximations. In this study Ensemble Kalman Filters and Neural Network Bayesian Models are used to investigate parameter sets for the Budyko Energy Balance Model and the more computationally demanding Planet Simulator of the University of Hamburg Meteorological Institute. These calibration methods employ observational data to generate posterior probability distributions for model parameter sets, allowing the determination of high-probability parameter sets and their confidence intervals. Being fully Bayesian, such approaches accurately propagate uncertainties in observational data into the posterior distributions. Comparing calibrated model results permits the two approaches to be assessed under varying levels of model complexity. Thesis Newfoundland studies University of Newfoundland Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
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Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
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ftmemorialunivdc |
language |
English |
topic |
Bayesian statistical decision theory Climatic changes--Mathematical models Dynamic climatology--Mathematical models |
spellingShingle |
Bayesian statistical decision theory Climatic changes--Mathematical models Dynamic climatology--Mathematical models Hauser, Tristan, 1981- Comparison of Bayesian calibration methodologies for climate system models |
topic_facet |
Bayesian statistical decision theory Climatic changes--Mathematical models Dynamic climatology--Mathematical models |
description |
Thesis (M.Sc.)--Memorial University of Newfoundland, 2009. Physical Oceanography Includes bibliographical references (leaves 78-81) Earth Systems models that attempt to forecast equilibrium states or make long term predictions are sensitive to the unavoidable approximations they employ. It is therefore important for such models to be parameterized through objective and repeatable methods that quantify the uncertainties associated with the inexactness of these approximations. In this study Ensemble Kalman Filters and Neural Network Bayesian Models are used to investigate parameter sets for the Budyko Energy Balance Model and the more computationally demanding Planet Simulator of the University of Hamburg Meteorological Institute. These calibration methods employ observational data to generate posterior probability distributions for model parameter sets, allowing the determination of high-probability parameter sets and their confidence intervals. Being fully Bayesian, such approaches accurately propagate uncertainties in observational data into the posterior distributions. Comparing calibrated model results permits the two approaches to be assessed under varying levels of model complexity. |
author2 |
Memorial University of Newfoundland. Dept. of Physical Oceanography |
format |
Thesis |
author |
Hauser, Tristan, 1981- |
author_facet |
Hauser, Tristan, 1981- |
author_sort |
Hauser, Tristan, 1981- |
title |
Comparison of Bayesian calibration methodologies for climate system models |
title_short |
Comparison of Bayesian calibration methodologies for climate system models |
title_full |
Comparison of Bayesian calibration methodologies for climate system models |
title_fullStr |
Comparison of Bayesian calibration methodologies for climate system models |
title_full_unstemmed |
Comparison of Bayesian calibration methodologies for climate system models |
title_sort |
comparison of bayesian calibration methodologies for climate system models |
publishDate |
2009 |
url |
http://collections.mun.ca/cdm/ref/collection/theses4/id/30930 |
genre |
Newfoundland studies University of Newfoundland |
genre_facet |
Newfoundland studies University of Newfoundland |
op_source |
Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries |
op_relation |
Electronic Theses and Dissertations (10.66 MB) -- http://collections.mun.ca/PDFs/theses/Hauser_Tristan.pdf a3242495 http://collections.mun.ca/cdm/ref/collection/theses4/id/30930 |
op_rights |
The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. |
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