Improved Statistical Approach for Climate Projection over Bangladesh using Downscaling of Global Climate Model Outputs

Abstract

Climate of Bangladesh is expected to be changed under the influence of Global Warming conditions. However, it is essential to quantify it irrespective of time and space. Empirical Statistical Downscaling (ESD) is an approach through which the magnitudes of the projected variable at the local level with time can be estimated based on the projected output of Global Climate Models (GCMs). In this study, two main climate variables of mean temperature and rainfall are considered for projection as well as to calculate their projected magnitudes at different future lengths. In this way, 10 most suitable GCMs are considered for temperature projection. But for rainfall projection 5 most appropriate GCMs are selected for the pre-monsoon season (March-May); 3 most appropriate GCMs for monsoon (June-September) and post-monsoon seasons (October-November) separately. The mean of the selected models for each group is also calculated to determine the ensemble prediction. To understand the current climate and its behaviour and to justify the model performance on historical records, daily mean temperature (average of the maximum and minimum temperature records) and rainfall of 34 stations (which are widely distributed over Bangladesh) are collected from the archive of Bangladesh Meteorological Department (BMD) during the period of 1981-2010. There is a missing value within daily records, which is essential to extract, and the missing value of temperature and rainfall are calculated using the R-package of multiple regression, and consequently, an updated climate record has been prepared for this study. To proceed for the study three emission scenarios of RCP2.6, RCP4.5 and RCP8.5 under CMIP5 are selected and the relevant GCM model outputs are commonly selected from all of these scenarios. Then the GCM outputs are downscaled through ESD Package (an R-package specially developed for statistical downscaling), adopting the Principal Component Analysis (PCA) method to produce the projected magnitudes of mean temperature and rainfall at each of the selected locations on month basis for the period of 2021-2100. The simulated results are evaluated with observation at station location basis as well as at the national average (considering the average of all BMD stations location). The evaluation of the projection result has been conducted using the technique of the five-fold crossvalidation method. To evaluate how well the downscaled GCM ensembles represent the past trends

and interannual variability for each station, the observed seasonal data in the period 1981–2010 is

compared with the statistical characteristics of the downscaled ensembles. The trend in the period

of

1981–2010 is calculated for the observed value and each downscaled ensemble member at all stations. Then the result has been analyzed. Analysis reveals that the seasonal and annual mean temperatures are projected to increase in the near future and far future for each emission scenario. The increment rates during pre-monsoon season are +0.62, +0.5 and +0.54°C in the near future and 0.78, 1.19 and 2.04°C in the far future, respectively for RCP2.6, RCP4.5 and RCP8.5. In the monsoon season, the projected rates are +0.25, +0.13 and +0.37°C in the near future and +0.33, +0.45 and +1.27°C in the far future, respectively. The projection of post-monsoon season is higher than other seasons; in the near future, the magnitudes are +0.6, +0.72 and +0.94°C and are +0.76, +1.42 and +2.81°C in the far future. Winter season is also projected to be warming at high rate, and the projection rates are +0.45, +0.44 and +0.86°C for the near future and are +0.65, +1.06 and +2.23°C in the far future. The annual mean temperature is likely to be higher with the projection rate of +0.48, +0.45 and +0.68°C in the near future and it is of +0.71, +1.16 and +2.83°C in far future, respectively for RCP2.6, RCP4.5 and RCP8.5. Analysis also depicts that the frequency of the wet-day (with rainfall ≥ 1mm/day) is projected to increase in all seasons in the near and far future for RCP2.6. It is projected to increase in pre-monsoon and post-monsoon seasons as well as annually both in the near and far future, but it is likely to decrease in both of the time slabs of monsoon season for RCP4.5. For the case of a very high emission scenario of RCP8.5, wet-day frequency is projected to increase in pre-monsoon but decrease in monsoon season both in the near and far future. As a whole, the annual wet day frequency indicates an increasing trend.