Development of regional extreme climate scenarios for Canada

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Both scientific and socioeconomic global climate change research has focused mostly on scenarios of gradual shifts as suggested by Atmosphere-Ocean General Circulation Models simulations. Due to their coarse resolution, such scenarios cannot be applied directly at the regional/local scale. Hence, regionalization techniques, i.e. Regional Climate Model (RCM) and statistical downscaling (SD) methods, are needed to develop high resolution climate scenarios at appropriate temporal and spatial scales relevant for impact studies. As the vulnerability of a system faced to climate change is strongly related to climate variability and, in particular, to the frequency and severity of extreme events, rather than to gradual changes in the average climate, assessing risks and uncertainties associated with climate extremes is needed. In this context, the main aim of the proposed research is therefore to support Canada's intensifying effort to understand how climate extremes will affect Canada's social, economic, and natural systems. The overall objective of the proposed research is to advance scientific knowledge on climate extremes and to improve society's ability to manage risks associated with potential changes of climate extremes at the regional/local scale, by creating and developing improved tools and methods for producing high-quality impact assessments. The proposed integrated approach is organized around three inter-related specific objectives on (1) the development of appropriate atmospheric variables (i.e. predictors) to improve the capacity of SD methods to simulate extremes and to link with the occurrence of extremes at the regional/local scale, (2) the incorporation into appropriate SD tools relevant predictors to allow the translation of large/regional scale climate variables into simulated extremes, and (3) the construction of reliable and consistent extreme climate scenarios for Canada from both RCM and SD tools. The suggested research will contribute to the reduction of uncertainties in climate scenarios developed at the regional/local scale, to the dissemination of high quality climate information in different research areas of environmental sciences, and to improve climate risk assessment studies.