Application of transform software for downscaling global climate model EdGCM results in north-eastern Bangladesh

Abstract

Downscaling is a state-of-the-art technique to generate fine-resolution climate change prediction and an obvious tool for forecasting future climate scenarios for many data-scarce areas like Bangladesh. The Educational Global Climate Model (EdGCM) predicts numerically and its performance was not evaluated for Bangladesh earlier. Due to this reason, an attempt has been made to apply a new geostatistical approach with the help of transform software to downscale EdGCM for identifying the trend of surface air temperature at the Sylhet district. Both Doubled_CO<sup>2</sup> and Global_Warming_01 are simulated from EdGCM and maps are generated to depict global temperature variations. Downscaling is applied to the outputs from Doubled_CO<sup>2</sup> scenario. Percent of bias (PBIAS), Nash-Sutcliffe efficiency (NSE) and the ratio of root mean square error to the standard deviation of measured data (RSR) values are satisfactory and acceptable. The trend analysis was performed using the Mann-Kendall Trend test and Sen’s slope estimator. Temperature changes are significant for both downscaled and observed results of p-value which is less than alpha = 0.05. Mann-Kendall Z tests for annual downscaled and IPCC during (2006-2020) show a positive trend. Downscaled predicted annual average temperature (simulations by Doubled_CO<sup>2</sup>) for 2020 is 21.67˚C for the Sylhet district.