A stepwise cluster analysis approach for downscaled climate projection – A Canadian case study

Abstract

Downscaling techniques are used to obtain high-resolution climate projections for assessing the impacts of climate change at a regional scale. This study presents a statistical downscaling tool, SCADS, based on stepwise cluster analysis method. The SCADS uses a cluster tree to represent the complex relationship between large-scale atmospheric variables (namely predictors) and local surface variables (namely predictands). It can effectively deal with continuous and discrete variables, as well as nonlinear relations between predictors and predictands. By integrating ancillary functional modules of missing data detecting, correlation analysis, model calibration and graphing of cluster trees, the SCADS is capable of performing rapid development of downscaling scenarios for local weather variables under current and future climate forcing. An application of SCADS is demonstrated to obtain 10 km daily mean temperature and monthly precipitation projections for Toronto, Canada in 2070–2099. The contemporary reanalysis data derived from NARR is used for model calibration (1981–1990) and validation (1991–2000). The validated cluster trees are then applied for generating future climate projections.