Multi-Correlation Matrix (M-CM) for the screening complexity in the Statistical Downscaling Model (SDSM)

Abstract

The statistical downscaling model (SDSM) has been applied for the projection of future climate pattern in Kedah, Malaysia. But it is quite difficult to make a correct decision on the potential correlation between multi-site predict ands and multi-predictors during the screening process based on the SDSM tool, because of its limited ability. In this regard, the M-CM analysis has been used to determine the correlation between 26 predictors and 20 predictand (rainfall station) in a single running. The concept of M-CM is sufficient to show the capability and reliability of the predictors based on the correlation value that can be explained in the dependent variable using the independent variable. The potential of predictor selection based on this method has been tested using MAE, MSE, and StD results. Results revealed the simulated value produced by these predictors set was closer to the observed value except at stn.IBT, KT, SL, SIK, SG and Kg.LS. It was consistent to the discrepancies (MAE and MSE) and StD results that showed bigger error compared to others rainfall stations. However, the error is still can be acceptable because produced less than 10% of discrepancies. Therefore, the future climate trend at this region was generated using constant predictors provided by HadCM3 under A2 scenarios