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COST Action 719 Final report 61
II.2.6.1 Downscaling methods
Further on are physically based Global Climate Models (GCMs) among the most seminal
tools for providing climate scenario information. However, for many impact applications the
horizontal resolution of a GCM is much too coarse (about 300 km). Therefore, downscaling
methods are required to provide climate information at a regional to local scale. The methods
can be distinguished into two types (1) statistical downscaling and (2) dynamical
downscaling.
II.2.6.2 Statistical Downscaling (SDS)
The concept of statistical downscaling (SDS) is based of the assumption that regional climate
is conditioned by two factors (a) the large scale climatic state and (b) regional/local
physiographic features such as topography, land-sea distribution and land use (von Storch,
1995, 1999). Therefore, a statistical model has to be found that relates large-scale climate
variables (‘predictors’) to regional and local variables (‘predictands’). By feeding the largescale
output of a GCM into the statistical model, the corresponding local and climate
characteristic is estimated.
The main advantages of SDS are:
- SDS is computationally inexpensive
- SDS can be used to provide site-specific information
- Rapid application to multiple GCMs
The main disadvantages/weakness of SDS are:
- the basic assumption is not verifiable, what means that possible future changes in the
statistical relationship are not taken into account
- requires long time series of surface and upper air observations
In the following, the main SDS techniques are briefly described. The categorization is similar
to that used by IPCC WG1 (Giorgi et al. 2001)
I) Weather classification schemes
Typically, weather states are defined by applying cluster analysis to atmospheric fields (Huth,
2000; Hewitson et al., 2002) or using subjective circulation classification schemes (Jones et
al. 1993). In both cases the weather pattern are grouped according to their similarity.
II) Regression Models
Here, a linear or nonlinear relationship between predictands and the large scale atmospheric
forcing is established. Commonly applied methods include multiple regression (Murphy,
1999), canonical correlation analysis (CCA) (von Storch et al., 1993) and artificial neural
networks which are akin to nonlinear regression (Crane et al. 1998).
III) Weather generators
Weather generators are statistical models of observed sequences of weather variables (Wilks
et al., 1999). They replicate the statistical attributes of a local climate variable like mean or
variance, but not observed sequences of events. Most of them focus on the daily time-scale, as
required by many impact models, but sub-daily models are also available (e.g. Katz et al.,
1995).