Chapter

Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models*

Claudia Tebaldi, Bruno Sansó and Richard L. Smith

in Bayesian Statistics 9

Published in print October 2011 | ISBN: 9780199694587
Published online January 2012 | e-ISBN: 9780191731921 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780199694587.003.0021
Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models*

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The use of projections from ensembles of climate models to characterize fu ture climate change at regional scales has become the most widely adopted framework, as opposed to what was standard practice until just a few years ago when a single model's projections constituted the basis for arguing about future changes and their impacts. It is believed that comparing and synthe sizing simulations of multiple models is key to quantifying a best estimate of the future changes and its uncertainty. In the last few years there has been an explosion of literature in climate change science where mostly heuristic meth ods of synthesizing the output of multiple models have been proposed, and the statistical literature is showing more involvement by our community as well, of late. In this paper we give a brief overview of the mainstreams of research in this area and then focus on our recent work, through which we have proposed the framework of hierarchical Bayesian models to combine information from model simulations and observations, in order to derive posterior probabilities of temperature and precipitation change at regional scales.

Keywords: Climate change; Climate models; Ensembles; Bayesian hierarchical models; Forecast validation

Chapter.  10034 words.  Illustrated.

Subjects: Probability and Statistics

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