The Analog Method as a Simple Statistical Downscaling Technique: Comparison with More Complicated Methods

TLDR

Statistical downscaling derives local climate information from coarse‑resolution GCMs using statistical models, with linear multivariate methods offering clearer physical interpretation while classification and neural networks are more complex and lack direct physical insight. The paper describes and applies a simple analog method for statistical downscaling. The analog method associates each GCM large‑scale circulation pattern with the local variables observed during the most similar historical pattern, using leading empirical orthogonal function coordinates to define similarity, and its performance is evaluated by reproducing local variable evolution in an independent period. The analog method performs comparably to more complex techniques for Iberian winter rainfall, accurately reproducing variability and spatial covariance for both normally and nonnormally distributed local variables.

Abstract

The derivation of local scale information from integrations of coarse-resolution general circulation models (GCM) with the help of statistical models fitted to present observations is generally referred to as statistical downscaling. In this paper a relatively simple analog method is described and applied for downscaling purposes. According to this method the large-scale circulation simulated by a GCM is associated with the local variables observed simultaneously with the most similar large-scale circulation pattern in a pool of historical observations. The similarity of the large-scale circulation patterns is defined in terms of their coordinates in the space spanned by the leading observed empirical orthogonal functions. The method can be checked by replicating the evolution of the local variables in an independent period. Its performance for monthly and daily winter rainfall in the Iberian Peninsula is compared to more complicated techniques, each belonging to one of the broad families of existing statistical downscaling techniques: a method based on canonical correlation analysis, as representative of linear methods; a method based on classification and regression trees, as representative of a weather generator based on classification methods; and a neural network, as an example of deterministic nonlinear methods. It is found in these applications that the analog method performs in general as well as the more complicated methods, and it can be applied to both normally and nonnormally distributed local variables. Furthermore, it produces the right level of variability of the local variable and preserves the spatial covariance between local variables. On the other hand linear multivariate methods offer a clearer physical interpretation that supports more strongly its validity in an altered climate. Classification and neural networks are generally more complicated methods and do not directly offer a physical interpretation.