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Assessing variance components of general circulation model output fields


Furrer, R; Geinitz, S; Sain, S R (2012). Assessing variance components of general circulation model output fields. Environmetrics, 23(5):440-450.

Abstract

Recent internationally coordinated efforts have used deterministic climate models for a common set of experiments and have produced large datasets of future climate projections. These ensembles are subject to many sources of variability, and we propose an analysis of variance procedure to quantify the contribution from several sources to the overall variation. This procedure is based on a Bayesian linear model parameterization and is applicable for large spatial data. A key feature is that individual sources of variability are modeled through batches and assessed through the batches superpopulation variance, individual batch-level predictions, and finite population covariance. Further, for a large class of models, we show that the full posterior can be factored into conditionally independent distributions, consisting of a batch's superpopulation and batch levels. By doing so, we obviate the need for Markov chain Monte Carlo methods. Finally, this approach is applied to decadal summer temperatures for different climate models and various scenarios.

Abstract

Recent internationally coordinated efforts have used deterministic climate models for a common set of experiments and have produced large datasets of future climate projections. These ensembles are subject to many sources of variability, and we propose an analysis of variance procedure to quantify the contribution from several sources to the overall variation. This procedure is based on a Bayesian linear model parameterization and is applicable for large spatial data. A key feature is that individual sources of variability are modeled through batches and assessed through the batches superpopulation variance, individual batch-level predictions, and finite population covariance. Further, for a large class of models, we show that the full posterior can be factored into conditionally independent distributions, consisting of a batch's superpopulation and batch levels. By doing so, we obviate the need for Markov chain Monte Carlo methods. Finally, this approach is applied to decadal summer temperatures for different climate models and various scenarios.

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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 2012
Deposited On:22 Jan 2013 13:11
Last Modified:05 Apr 2016 16:18
Publisher:Wiley-Blackwell
ISSN:1099-095X
Publisher DOI:https://doi.org/10.1002/env.2139

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