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Abstract Details

Activity Number: 101
Type: Invited
Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract - #303781
Title: Hierarchical Models for Climate Model Predictions
Author(s): Bruno Sanso*+
Companies: University of California at Santa Cruz
Address: UC Santa Cruz, Santa Cruz, ,
Keywords: Bayesian Models ; Hierarchical Models ; Climate change
Abstract:

At present the standard practice for the study of future climate is the use of ensembles of climate projections. This framework presents the challenge of summarizing the information from the multiple models and providing a realistic quantification of the discrepancies between different model predictions. To this end we use Bayesian hierarchical models. Such models are coupled with descriptions of the space and time dynamics of the fields produced by the climate models. We then obtain summaries of the fields that allow for effective comparisons between model simulations, as well as with observational records. We consider examples of global climate model simulations, as well as of regional climate models.


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