Assessing regional climate simulations of the last 30 years (1982–2012) over Ganges–Brahmaputra–Meghna River Basin

Abstract

The Ganges–Brahmaputra–Meghna (GBM) River Basin presents a spatially diverse hydrological regime due to it’s complex topography and escalating demand for freshwater resources. This presents a big challenge in applying the current state-of-the-art regional climate models (RCMs) for climate change impact studies in the GBM River Basin. In this study, several RCM simulations generated by RegCM4.4 and PRECIS are assessed for their seasonal and interannual variations, onset/withdrawal of the Indian monsoon, and long-term trends in precipitation and temperature from 1982 to 2012. The results indicate that in general, RegCM4.4 and PRECIS simulations appear to reasonably reproduce the mean seasonal distribution of precipitation and temperature across the GBM River Basin, although the two RCMs are integrated over a different domain size. On average, the RegCM4.4 simulations overestimate monsoon precipitation by $${\sim }26$$ and $${\sim }5\%$$ in the Ganges and Brahmaputra–Meghna River Basin, respectively, while PRECIS simulations underestimate (overestimate) the same by $${\sim }7\%$$ ( $${\sim }16\%$$ ). Both RegCM4.4 and PRECIS simulations indicate an intense cold bias (up to $$10\,^\circ \hbox {C}$$ ) in the Himalayas, and are generally stronger in the RegCM4.4 simulations. Additionally, they tend to produce high precipitation between April and May in the Ganges (RegCM4.4 simulations) and Brahmaputra–Meghna (PRECIS simulations) River Basins, resulting in early onset of the Indian monsoon in the Ganges River Basin. PRECIS simulations exhibit a delayed monsoon withdrawal in the Brahmaputra–Meghna River Basin. Despite large spatial variations in onset and withdrawal periods across the GBM River Basin, the basin-averaged results agree reasonably well with the observed periods. Although global climate model (GCM) driven simulations are generally poor in representing the interannual variability of precipitation and winter temperature variations, they tend to agree well with observed precipitation anomalies when driven by perfect boundary conditions. It is also seen that all GCM driven simulations feature significant positive surface temperature trends consistent with the observed datasets.