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Functional ANOVA and regional climate experiments: a statistical analysis of dynamic downscaling

Authors

  • Stephan R. Sain,

    Corresponding author
    1. Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO, USA
    • Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO, USA.
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  • Doug Nychka,

    1. Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO, USA
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  • Linda Mearns

    1. Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO, USA
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  • These articles are published in Environmetrics as a special issue on Handling complexity and uncertainty in environmental studies, arising from the TIES- GRASPA joint conference held in Bologna in 2009 and is edited by Daniela Cocchi, Department of Statistics University of Bologna, Italy and E. Marian Scott, School of Mathematics and Statistics, University of Glasgow, UK.

Abstract

Climate model experiments incorporating model runs conducted using different conditions have become popular for the study of uncertainty affecting model output and projections of climate change. Recently, such experiments have been used to also study of the uncertainties in producing high-resolution projections of climate change based on methods for dynamic downscaling of global models. In this paper, we discuss an initial analysis of a subset of the ensemble being produced by the North American Regional Climate Change Assessment Program. Using an approach based on a functional analysis of variance, we examine the differences between two different dynamic downscaling methods and demonstrate that there are significant differences between the two models and their projections of summer temperature and precipitation over North America. Copyright © 2010 John Wiley & Sons, Ltd.

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