Depth?duration?frequency of extreme precipitation events under internal climate variability: Indian summer monsoon

Abstract

Uncertainty quantification and characterization in changing climate scenarios have a direct impact on the efforts toward mitigation and adaptation. The chaotic and nonlinear 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. Specifically, we quantify the relative contribution of uncertainty due to internal variability and model uncertainty in the depth and volatility of Indian Summer Monsoon rainfall extremes of different duration and frequencies. We establish that the role of internal variability in extreme precipitation indices such as 100?years and 30?years return levels are comparable to the uncertainty arising from structural differences in the model captured through bias?corrected ensembles of multimodel outputs. From the regional analysis, we find that internal variability is not only comparable, but it also shows higher mean and uncertainty in estimating extreme precipitation indices in central India. The intensifying precipitation extremes have quantifiable impacts on Depth Duration Frequency (DDF) curves, which directly implicate hydraulic design and water resources planning and management. We show that combining outputs from multiple initial condition ensembles generated to span the range of internal climate variability can help us reduce uncertainty and provide the distinguished uncertainty bounds of DDF curves.