• canonical correlation analysis;
  • downscaling;
  • monthly temperature;
  • redundancy analysis;
  • Sampson correlation;
  • singular spectrum analysis;
  • Turkey


The problem of statistical linkages between large-scale and local-scale processes is investigated through noise reduction by singular spectrum analysis (SSA) and spatial principal component analysis in order to construct appropriate statistical models for estimating the local-scale climate variables from large-scale climate processes. This paper presents an approach for downscaling monthly temperature series over Turkey by upper air circulations derived from the National Centers for Environmental Prediction–National Center for Atmospheric Research Reanalysis data sets (500 hPa geopotential heights and 500–1000 hPa thicknesses). The proposed approach consists of three stages. First, the available data sets are separated into deterministic, statistical components and random components by SSA. Second, the deterministic components are saved and the random components are eliminated by spatial principal component analysis. Subsequently, the statistical components are combined with the deterministic components constituting a noise-free data set. Furthermore, so-called Sampson correlation patterns are determined between the noise-free large-scale and the local-scale variables for interpreting the large-scale process impacts on local-scale features. Third, the significant redundancy variates based on canonical correlation analysis are extracted in order to identify the statistical downscaling model for temperature series of 62 stations in Turkey. The results show that the interpretation of the local-scale processes with the noise-free data sets is more significant than with the raw data sets. Copyright © 2005 Royal Meteorological Society