Wavelet-based predictor screening for statistical downscaling of precipitation and temperature using the artificial neural network method

Abstract

One of the challenging issues in statistical downscaling of climate models is to select dominant large-scale climate variables (predictors). Correlation-based methods have been revealed to be efficacious to select the predictors; however, traditional correlation analysis has shown limited ability due to the nonstationary and nonlinear nature of climatic time series. Hence, in this study, Wavelet Coherence Transform (WTC) was employed to assess the high common powers and the multi-scale correlation between two time series (i.e., predictand and predictor) as a function of time and frequency. To this end, a coefficient correlation (CC) and a wavelet-based method were used for predictor screening and the results were compared in statistical downscaling. To apply the wavelet-based method, Continuous Wavelet Transform (CWT) was utilized to identify the potent periodicity in the time series of predictands. WTC was applied to determine the coherence between predictors and predictands in the potent periodicities, and Scale Average (SA) wavelet coherency was applied to rank them. In order to implement statistical downscaling, the ANN model was developed. In this study, three climate models including BNU-ESM Can-ESM5, and INM-CM5 have been used. The projection of the future climate based on the ANN downscaling revealed that precipitation will undergo a 7.1-28.92% downward trend, while the temperature will experience a 2.25-4.21 °C increase.