Temporal neural networks for downscaling climate variability and extremes

Abstract

Global climate models (GCMs) are inherently unable to present local subgrid-scale features and dynamics and consequently, outputs from these models cannot be directly applied in many impact studies. This paper presents the issues of downscaling the outputs of GCMs using a temporal neural network (TNN) approach. The method is proposed for downscaling daily precipitation and temperature series for a region in northern Quebec, Canada. The performance of the temporal neural network downscaling model is compared to a regression-based statistical downscaling model with emphasis on their ability in reproducing the observed climate variability and extremes. The downscaling results for the base period (1961- 2000) suggest that the TNN is an efficient method for downscaling both daily precipitation as well as daily temperature series. Furthermore, the different model test results indicate that the TNN model significantly outperforms the statistical models for the downscaling of daily precipitation extremes and variability.