Development of regional extreme climate scenarios for Canada

全球气候变化的科学和社会经济研究主要侧重于大气-海洋环流模式模拟所提出的逐渐变化的情景。由于分辨率较低，这些情景不能直接应用于区域/地方尺度。因此，需要区域化技术，即区域气候模式和统计降尺度方法，以在与影响研究相关的适当时间和空间尺度上制定高分辨率的气候情景。由于一个系统面对气候变化的脆弱性与气候多变性，特别是与极端事件的频率和严重性，而不是与平均气候的逐渐变化密切相关，因此需要评估与极端气候有关的风险和不确定性。因此，在这种情况下，拟议研究的主要目的是支持加拿大加紧努力，了解极端气候将如何影响加拿大的社会，经济和自然系统。拟议研究的总体目标是，通过创建和开发用于进行高质量影响评估的改进工具和方法，增进关于极端气候的科学知识，提高社会管理区域/地方一级极端气候潜在变化相关风险的能力。拟议的综合办法围绕三个相互关联的具体目标：（1）制定适当的大气变量（即预测因子），以提高可持续发展方法模拟极端情况的能力，并与区域/地方尺度的极端情况联系起来，（2）将相关的预报因子纳入适当的可持续发展工具，以便将大/区域尺度的气候变量转化为模拟的极端情况，以及（3）从RCM和SD工具中为加拿大构建可靠和一致的极端气候情景。所建议的研究将有助于减少在区域/地方一级制定的气候情景的不确定性，有助于在环境科学的不同研究领域传播高质量的气候信息，并改进气候风险评估研究。

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.