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Monte Carlo strategies for calibration in climate models


Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/9356

Monte Carlo strategies for calibration in climate models

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dc.contributor.author Villagran-Hernandez, Alejandro
dc.date.accessioned 2009-07-09T21:29:03Z
dc.date.available 2009-07-09T21:29:03Z
dc.date.issued 2009-07-09T21:29:03Z
dc.date.submitted May 2009
dc.identifier.uri http://hdl.handle.net/1928/9356
dc.description.abstract Intensive computational methods have been used by Earth scientists in a wide range of problems in data inversion and uncertainty quantification such as earthquake epicenter location and climate projections. To quantify the uncertainties resulting from a range of plausible model configurations it is necessary to estimate a multidimensional probability distribution. The computational cost of estimating these distributions for geoscience applications is impractical using traditional methods such as Metropolis/Gibbs algorithms as simulation costs limit the number of experiments that can be obtained reasonably. Several alternate sampling strategies have been proposed that could improve on the sampling efficiency including Multiple Very Fast Simulated Annealing (MVFSA) and Adaptive Metropolis algorithms. As a goal of this research, the performance of these proposed sampling strategies are evaluated with a surrogate climate model that is able to approximate the noise and response behavior of a realistic atmospheric general circulation model (AGCM). The surrogate model is fast enough that its evaluation can be embedded in these Monte Carlo algorithms. The goal of this thesis is to show that adaptive methods can be superior to MVFSA to approximate the known posterior distribution with fewer forward evaluations. However, the adaptive methods can also be limited by inadequate sample mixing. The Single Component and Delayed Rejection Adaptive Metropolis algorithms were found to resolve these limitations, although challenges remain to approximating multi-modal distributions. The results show that these advanced methods of statistical inference can provide practical solutions to the climate model calibration problem and challenges in quantifying climate projection uncertainties. The computational methods would also be useful to problems outside climate prediction, particularly those where sampling is limited by availability of computational resources. en_US
dc.description.sponsorship National Science Foundation, grant OCE-0415251. Consejo Nacional de Ciencia y Tecnologia, Mexico, grant 159764. en_US
dc.language.iso en_US en_US
dc.subject Monte Carlo en_US
dc.subject Climate Models en_US
dc.subject Adaptive Metropolis en_US
dc.subject Simulated Annealing en_US
dc.subject Model Calibration en_US
dc.subject Bayesian Inference en_US
dc.subject.lcsh Climatology--Statistical methods.
dc.subject.lcsh Global warming--Mathematical models.
dc.subject.lcsh Monte Carlo method.
dc.subject.lcsh Simulated annealing (Mathematics)
dc.title Monte Carlo strategies for calibration in climate models en_US
dc.type Dissertation en_US
dc.description.degree Doctor of Philosophy Statistics en_US
dc.description.level Doctoral en_US
dc.description.department University of New Mexico. Dept. of Mathematics and Statistics en_US
dc.description.advisor Huerta, Gabriel
dc.description.committee-member Bedrick, Edward
dc.description.committee-member Guindani, Michele
dc.description.committee-member Galewsky, Joseph

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