Climate Change 2001:
Working Group I: The Scientific Basis
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10.6 Empirical/Statistical and Statistical/Dynamical Methods

10.6.1 Introduction

As with the dynamical downscaling of RCMs, the methods described in this section rely on the concept that regional climates are largely a function of the large-scale atmospheric state. In empirical downscaling the cross-scale relationship is expressed as a stochastic and/or deterministic function between a set of large-scale atmospheric variables (predictors) and local/regional climate variables (predictands). Predictor and predictand can be the same variables on different spatial scales (e.g., Bürger, 1997; Wilks, 1999b; Widmann and Bretherton, 2000), but more commonly are different.

When using downscaling for assessing regional climate change, three implicit assumptions are made:

A diverse range of downscaling methods has been developed, but, in principle, these models are based on three techniques:

Each of these approaches has relative strengths and weaknesses in representing the range of temporal variance of the local climate predictand. Consequently, the above approaches are often used in conjunction with one another in order to compensate for the relative deficiencies in one method.

Most downscaling applications have dealt with temperature and precipitation. However, a diverse array of studies exists in which other variables have been investigated. Appendix 10.4 provides a non-exhaustive list of past studies indicating predictands, geographical domain, and technique category. In light of the diversity in the literature, we concentrate on references to applications since 1995 and based on recent global climate change projections.

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