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September 2013 Global space–time models for climate ensembles
Stefano Castruccio, Michael L. Stein
Ann. Appl. Stat. 7(3): 1593-1611 (September 2013). DOI: 10.1214/13-AOAS656

Abstract

Global climate models aim to reproduce physical processes on a global scale and predict quantities such as temperature given some forcing inputs. We consider climate ensembles made of collections of such runs with different initial conditions and forcing scenarios. The purpose of this work is to show how the simulated temperatures in the ensemble can be reproduced (emulated) with a global space/time statistical model that addresses the issue of capturing nonstationarities in latitude more effectively than current alternatives in the literature. The model we propose leads to a computationally efficient estimation procedure and, by exploiting the gridded geometry of the data, we can fit massive data sets with millions of simulated data within a few hours. Given a training set of runs, the model efficiently emulates temperature for very different scenarios and therefore is an appealing tool for impact assessment.

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Stefano Castruccio. Michael L. Stein. "Global space–time models for climate ensembles." Ann. Appl. Stat. 7 (3) 1593 - 1611, September 2013. https://doi.org/10.1214/13-AOAS656

Information

Published: September 2013
First available in Project Euclid: 3 October 2013

zbMATH: 06237189
MathSciNet: MR3127960
Digital Object Identifier: 10.1214/13-AOAS656

Keywords: climate ensembles , GCM , global space–time model , massive data set

Rights: Copyright © 2013 Institute of Mathematical Statistics

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Vol.7 • No. 3 • September 2013
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