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    Computational Methods for Parameter Estimation in Climate Models

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    2008_Villagran.pdf (1.888Mb)
    Date
    2008
    Author
    Villagran, Ale
    Huerta, Gabriel
    Jackson, Charles S.
    Sen, Mrinal K.
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    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. 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. We 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.
    Department
    Institute for Geophysics
    Subject
    parametric uncertainties
    inverse problems
    simulated annealing
    adaptive metropolis
    climate models
    mathematics, interdisciplinary applications
    statistics & probability
    URI
    http://hdl.handle.net/2152/43146
    xmlui.dri2xhtml.METS-1.0.item-citation
    Villagran, Alejandro, Gabriel Huerta, Charles S. Jackson, and Mrinal K. Sen. "Computational methods for parameter estimation in climate models." Bayesian Analysis, Vol. 3, No. 4 (2008): 823-850.
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    • facebook
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    • CONTACT US
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    • Emergency Information
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    © The University of Texas at Austin