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Representing uncertainty in numerical climate models

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If you have a question about this talk, please contact Nikolaos Demiris .

It is recognised that projections of future climate can differ widely between climate models. It is therefore necessary to account for climate model uncertainty in any risk assessment exercise. Here we suggest that a hierarchical statistical model, implemented in a Bayesian framework, provides a logically coherent and interpretable way to think about climate model uncertainty in general. The ideas will be illustrated by considering the generation of future daily rainfall sequences at a single location in the UK, based on the outputs of four different climate models under the SRES A2 emissions scenario.

This talk is part of the MRC Biostatistics Unit Seminars series.

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