Author(s): Abdullah Alodah; Ousmane Seidou
Linked Author(s):
Keywords: Climate change; Statistical downscaling; Quantile-Quantile transformation; Extreme events analysis
Abstract: Climate change studies are crucial as they assist decision-makers in understanding future risks and planning adequate adaptation measures. In general, outputs of Global Circulation Models (GCMs) and Regional Climate Models (RCMs) are the main sources of information on future states of the climate. However, the usefulness of these outputs is limited by their spatial resolution and significant biases that make post processing mandatory before they can be used for local and regional climate assessments. In this paper, the Quantile-Quantile transformation was used to make the statistical distribution of climate variables simulated by two RCMs (the Canadian Regional Climate Model version 3. 7. 1 and the ARPEGE model, SRES A1B emission scenario) as close as possible to the statistical distribution of the observed variables on the historical period. The transformation was applied to daily precipitation, maximum temperature and minimum temperature at seven stations in and around the South Nation watershed, Ontario, Canada. The two-sample Kolmogorov–Smirnov test showed that the Quantile-Quantile transformation was able to improve the shape of the PDF of RCM-simulated climate variables in calibration and validation periods. Results suggested that temperature would rise up significantly in the watershed under A1B SRES. They also suggested an increment in precipitation occurrence and intensity. Trends were performed on historical period (1961-2001) using the Mann-Kendall test and the Sen' s slope estimator. Discernible and often significant increases in terms of maximum and minimum temperatures were found. There was also a weak evidence of change in the time series of maximum and accumulated annual precipitation. Projections of both temperature variables were also found to exhibit stronger positive slopes. The study outlined how the frequency and intensity of some extreme weather events will evolve in the 2041-2081 period in response to the rise in atmospheric greenhouse gases (GHG) concentrations. Projected impacts were investigated by tracking future changes in four extreme temperature indices and three precipitation indices. It was found that hot spells were expected to occur more frequently and intensely, while cold events will be rarer and weaker.
Year: 2015