Jump to ContentJump to Main Navigation
Bayesian Statistics 9$
Users without a subscription are not able to see the full content.

José M. Bernardo, M. J. Bayarri, James O. Berger, A. P. Dawid, David Heckerman, Adrian F. M. Smith, and Mike West

Print publication date: 2011

Print ISBN-13: 9780199694587

Published to Oxford Scholarship Online: January 2012

DOI: 10.1093/acprof:oso/9780199694587.001.0001

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2019. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in OSO for personal use (for details see www.oxfordscholarship.com/page/privacy-policy).date: 26 June 2019

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

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

Chapter:
(p.639) Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models*
Source:
Bayesian Statistics 9
Author(s):

Claudia Tebaldi

Bruno Sansó

Richard L. Smith

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199694587.003.0021

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

Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .