Comparative Analysis of Internal Climate Variability and Model
Uncertainty on Indian Summer Monsoon Extreme Precipitation
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.