Development of regional extreme climate scenarios for Canada

无论是科学研究还是社会经济研究，全球气候变化研究都主要集中在大气-海洋大气环流模型模拟所提出的逐渐变化的情景上。由于分辨率较低，这类方案不能直接适用于区域/地方一级。因此，需要区域化技术，即区域气候模式(RCM)和统计降尺度(SD)方法，以在适当的时间和空间尺度上制定与影响研究相关的高分辨率气候情景。由于面临气候变化的系统的脆弱性与气候变异性密切相关，特别是与极端事件的频率和严重程度密切相关，而不是与平均气候的逐渐变化有关，因此需要评估与极端气候事件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，通过创建和开发改进的工具和方法以产生高质量的影响评估，促进关于极端气候的科学知识，并提高社会在区域/地方范围内管理与气候极端的潜在变化有关的风险的能力。拟议的综合方法围绕三个相互关联的具体目标进行组织：(1)发展适当的大气变量(即预报器)，以改进可持续发展方法模拟极端事件的能力，并将其与区域/地方尺度上极端事件的发生联系起来；(2)将相关预报器纳入适当的可持续发展方法工具，以便能够将大/区域尺度气候变量转换为模拟的极端气候；以及(3)利用RCM和SD工具，为加拿大构建可靠和一致的极端气候情景。建议的研究将有助于减少区域/地方范围内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，并有助于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.