Title: Uncertainties in future projections of extreme rainfall: The role of climate model, emission
scenario and randomness
Contributors: Ali Nazemi, Md Sahabul Alam, Amin Elshorbagy
Abstract:
Climate change has resulted in substantial variations in annual extreme rainfall quantiles in
different durations and return periods. Predicting the future changes in extreme rainfall quantiles
is essential for various water resources design, assessment, and decision making purposes.
Current Predictions of future rainfall extremes, however, exhibit large uncertainties. According
to extreme value theory, rainfall extremes are rather random variables, with changing
distributions around different return periods; therefore there are uncertainties even under current
climate conditions. Regarding future condition, our large-scale knowledge is obtained using
global climate models, forced with certain emission scenarios. There are widely known
deficiencies with climate models, particularly with respect to precipitation projections. There is
also recognition of the limitations of emission scenarios in representing the future global change.
Apart from these large-scale uncertainties, the downscaling methods also add uncertainty into
estimates of future extreme rainfall when they convert the larger-scale projections into local
scale. The aim of this research is to address these uncertainties in future projections of extreme
rainfall of different durations and return periods. We plugged 3 emission scenarios with 2 global
climate models and used LARS-WG, a well-known weather generator, to stochastically
downscale daily climate models’ projections for the city of Saskatoon, Canada, by 2100. The
downscaled projections were further disaggregated into hourly resolution using our new
stochastic and non-parametric rainfall disaggregator. The extreme rainfall quantiles can be
consequently identified for different durations (1-hour, 2-hour, 4-hour, 6-hour, 12-hour, 18-hour
and 24-hour) and return periods (2-year, 10-year, 25-year, 50-year, 100-year) using Generalized
Extreme Value (GEV) distribution. By providing multiple realizations of future rainfall, we
attempt to measure the extent of total predictive uncertainty, which is contributed by climate
models, emission scenarios, and downscaling/disaggregation procedures. The results show
different proportions of these contributors in different durations and return periods.