Abstract:
Uncertainty quantification and characterization in changing climate scenarios can have a direct impact on the efforts to mitigate and adapt. Chaotic and non-linear nature of atmospheric processes results in high sensitivity to initial conditions resulting in considerable variability. Multiple model ensembles of Earth System Models are often used to visualize the role of parametric uncertainties in mean and extreme attributes of precipitation trends in various time horizons. However, studies quantifying the role of internal variability in controlling extreme precipitation statistics in decadal and interdecadal scales are limited. In this study, we use a thirty one-member ensemble of Community Earth System Model Large ensemble project and thirty-one ensembles from Coupled Model Intercomparison Project 5 (CMIP5) to quantify the relative contribution of uncertainty due to internal variability in the depth and volatility of Indian Summer Monsoon Rainfall extremes of different durations and frequencies. We find that in the short-term and long-term, the role of internal variability in extreme precipitation indices is comparable to the uncertainty arising from structural differences in the model captured through multiple model ensembles. Further, we show that combining outputs from multiple initial condition runs generated to span the range of internal climate variability can help us reduce uncertainty in infrastructure design relevant Depth Duration and Frequency (DDF) curves.