CR prepress abstract  -  doi: 10.3354/cr01456

Statistical downscaling of monthly mean temperature for Kazakhstan in Central Asia

Yafei Li, Xiaodong Yan*

*Email: yxd@bnu.edu.cn

ABSTRACT: Very few studies on the impact of climate change have been carried out in Kazakhstan, which is located in Central Asia. It is the largest landlocked country in the world and has a sensitive natural environment and a human society vulnerable to climate change. In this study, we evaluated a statistical model for downscaling the monthly mean temperature in the Kazakhstan area built from a linear regression model combined with a principal component analysis (PCA) as the preprocessing method for predictors. The air temperature, geopotential height and both components of the wind were selected as predictor variables. The result shows that the linear regression model was able to simulate a reasonable monthly mean temperature averaged over the Kazakhstan region as a whole, while a few disagreements with observations exist for some stations and in some months. A further analysis of the results of downscaling also reveals that the monthly mean temperature in summer is easier to be downscaled accurately by this model than that in winter, with the R2 metric of 0.8 for summer being significantly larger than that for winter of 0.7. Moreover, this statistical downscaling model shows poor performance in complex terrain areas compared to flat terrain areas, given that the R2 of the models applied in the southeastern mountain station and the station by the Caspian Sea are smaller than that of the other stations in Kazakhstan.