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Activity Number:
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235
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 8, 2006 : 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 - #307065 |
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Title:
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Spatial Patterns of Global Climate Change Fields
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Author(s):
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Reinhard Furrer*+ and Reto Knutti
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Companies:
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Colorado School of Mines and National Center for Atmospheric Research
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Address:
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1500 Illinois Street, Golden, CO, 80401,
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Keywords:
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climate change ; spatial processes ; hierarchical Bayes ; large data sets
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Abstract:
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We present probabilistic projections for spatial patterns of future climate change using a multivariate Bayesian analysis. The methodology is applied to the output from 21 global coupled climate models used for the 4th Assessment Report of the Intergovernmental Panel on Climate Change. 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 from model bias and variability. The scales are represented via dimension reduction techniques in a hierarchical Bayes model. Posterior probabilities are obtained with a MCMC simulation. The posterior fields can be analyzed as such or down-scaled or weighted arbitrarily. For example, we show that 74% of the land areas are likely to warm by more than 2K by the end of the century (SRES A1B).
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