Header

UZH-Logo

Maintenance Infos

Combining climate model output via model correlations


Sain, S R; Furrer, R (2010). Combining climate model output via model correlations. Stochastic Environmental Research and Risk Assessment, 24(6):821-829.

Abstract

In climate science, collections of climate model output, usually referred to as ensembles, are commonly used devices to study uncertainty in climate model experiments. The ensemble members may reflect variation in initial conditions, different physics implementations, or even entirely different climate models. However, there is a need to deliver a unified product based on the ensemble members that reflects the information contained in whole of the ensemble. We propose a technique for creating linear combinations of ensemble members where the weights are constructed from estimates of variation and correlation both within and between ensemble members. At the heart of this approach is a Bayesian hierarchical model that allows for estimation of the correlation between ensemble members as well as the study of the impact of uncertainty in the parameter estimates of the hierarchical model on the weights. The approach is demonstrated on an ensemble of regional climate model (RCM) output.

Abstract

In climate science, collections of climate model output, usually referred to as ensembles, are commonly used devices to study uncertainty in climate model experiments. The ensemble members may reflect variation in initial conditions, different physics implementations, or even entirely different climate models. However, there is a need to deliver a unified product based on the ensemble members that reflects the information contained in whole of the ensemble. We propose a technique for creating linear combinations of ensemble members where the weights are constructed from estimates of variation and correlation both within and between ensemble members. At the heart of this approach is a Bayesian hierarchical model that allows for estimation of the correlation between ensemble members as well as the study of the impact of uncertainty in the parameter estimates of the hierarchical model on the weights. The approach is demonstrated on an ensemble of regional climate model (RCM) output.

Statistics

Citations

10 citations in Web of Science®
14 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

77 downloads since deposited on 16 Aug 2010
15 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
Dewey Decimal Classification:510 Mathematics
Language:English
Date:August 2010
Deposited On:16 Aug 2010 14:05
Last Modified:05 Apr 2016 14:13
Publisher:Springer
ISSN:1436-3240
Additional Information:The final publication is available at www.springerlink.com
Publisher DOI:https://doi.org/10.1007/s00477-010-0380-5

Download

Preview Icon on Download
Filetype: PDF - Registered users only
Size: 545kB
View at publisher
Preview Icon on Download
Preview
Content: Accepted Version
Filetype: PDF
Size: 3MB