Abstract

We combine the seasonal mean precipitation and temperature from different datasets into a Bayesian framework using Multi-variable Bayesian Merging (MBaM) algorithm to draw a unified conclusion about their trend over the Indo-Gangetic Basin (IGB). The time series produced by the Bayesian method is combined into a Multi-variable Trend Principal Component (MTPC) setup to derive the equivalent climate change signals. These signals are then used to estimate the importance of different regional and global drivers influencing the climate change over the IGB. We show that the climate change over the IGB during pre-monsoon and monsoon seasons is very significant, during post-monsoon is less significant, and during winter season there is no indication of significant climate change. Global teleconnections are shown to have very little correlation to the climate change signal (e.g. RNino3=0.06∼0.21). On the other hand, the concentrations of greenhouse gases are found to exhibit very strong correlation (e.g RCO2 =0.94∼0.99) to the climate change signal, indicating their importance as drivers of the climate change over the IGB.

Keywords: Geographical variations; Bayesian framework; Global teleconnections