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Activity Number:
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92
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Type:
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Invited
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Date/Time:
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Monday, August 3, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #302941 |
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Title:
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Combining Ensembles of Regional Climate Model Output Using Markov Random Field Models
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Author(s):
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Reinhard Furrer*+
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Companies:
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Colorado School of Mines
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Address:
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Mathematical and Computer Sciences, Golden, CO, 80403,
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Keywords:
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Bayesian hierarchical models ; Gaussian Fields ; Laplace approximation
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Abstract:
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We present probabilistic projections for fields of future climate change using a multivariate Bayesian analysis. The statistical technique is based on the assumption that spatial patterns of climate change can be separated into a large scale signal related to the true forced climate change and a small scale signal stemming from model bias and internal variability. The different scales are represented via a dimension reduction technique in a hierarchical Bayes model. Posterior probabilities are obtained using a Markov chain Monte Carlo simulation technique. In contrast to other techniques, the method presented here takes into account uncertainty due to the use of structurally different climate models and provides PDFs of localized climate change that are nevertheless coherent with the distribution of climate change in neighboring locations.
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