This article is published in Environmetrics as a special issue on Advances in Statistical Methods for Climate Analysis, edited by Peter Guttorp, University of Washington, Norwegian Computing Center, Stephan R. Sain, National Center for Atmospheric Research, Christopher K. Wikle, University of Missouri.
Special Issue Paper
Assessing variance components of general circulation model output fields†
Article first published online: 16 FEB 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Special Issue: Advances in Statistical Methods for Climate Analysis
Volume 23, Issue 5, pages 440–450, August 2012
How to Cite
Furrer, R., Geinitz, S. and Sain, S. R. (2012), Assessing variance components of general circulation model output fields. Environmetrics, 23: 440–450. doi: 10.1002/env.2139
- Issue published online: 25 JUL 2012
- Article first published online: 16 FEB 2012
- Manuscript Accepted: 13 JAN 2012
- Manuscript Revised: 12 JAN 2012
- Manuscript Received: 11 OCT 2011
- multivariate ANOVA;
- spatial data;
- mixed model;
- Bayesian inference
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. Copyright © 2012 John Wiley & Sons, Ltd.