Climate change and uncertainty in high-resolution rainfall extremes

Abstract. A methodology to analyze the impact of climate change on rainfall extremes with a high temporal resolution is presented. It is based on a rainfall stochastic simulator which consists of a point process model for the daily scale (Neymann-Scott Rectangular Pulse Model) and a nested disaggregation scheme for the 10-min rainfall scale (Multiplicative Random Cascade Model). Climate change signals are included as Factors of Change applied to key statistics (first–third order moments, correlation and intermittency) as simulated with ten GCM-RCM model chains of the ENSEMBLES Project. The stochastic simulator was calibrated with data from 22 meteorological stations in Switzerland, and used to analyze rainfall extremes from 30-yr long realizations for the current climate and two future climate periods (mid-Century and end-of-Century). The stochastic simulator reproduces first and higher-order statistics of precipitation very well for temporal scales from 10-min to 24-hr, including annual maxima for a range of return periods relevant for urban hydrology. The internal climate variability (stochasticity) in rainfall extremes can be directly quantified using simulations, and is very high. Despite the imposed climate change signals, the distributions of annual maxima for the current and future climates largely overlap in most climate model chains. There are relatively few cases where the mean future extremes lie outside of the 10–90 % uncertainty bounds of the current climate, and in fact both increases and decreases are found in simulations. The rainfall stochastic simulator can be improved in the future by including the relation between extreme rainfall intensity and air temperature in the parametrisation. We conclude that the climate change signal generated by climate models has a low signal-to-noise ratio in rainfall extremes at high resolutions at the station level and predictions of change are therefore highly uncertain. At the same time, accounting for current climate variability in rainfall extremes for urban hydrologic design is vital because it may already cover a large part of the uncertainty connected with expected climate change in the future.