Regional climate risks from compound effects and their previsibility under climate change

复合效应带来的区域气候风险及其在气候变化下的可预测性

• 批准号: RGPIN-2022-05032
• 负责人: Gachon, Philippe
• 金额: $ 3.13万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746641
• 关键词: Regional; climate; risks; compound; effects

Our planet's climate system is on the margin of a major shift. According to the last Intergovernmental Panel on Climate Change assessment report, Canada will experience one of the strongest warmings in the world by the end of this century, in the context of a strong arctic amplification of warming. This is likely not only to modify the meteorological hazards and extremes, but also the duration, intensity and occurrence of precipitation (of all types), and the regime of winds and temperatures, especially in areas where warming will be most marked. Depending on future warming thresholds, it becomes increasingly crucial to assess the high-impact weather conditions that combine multivariate climate variables, because these are often associated with a higher risk, often with greater robustness or high probability, than those resulting from changes in the variables considered in isolation. In addition, assessments based on univariate statistics can significantly underestimate the risks associated with given extremes, especially if the impacts are the result of several dependent variables or moderate weather conditions that combine and generate extreme impacts at the regional or local scale. That's why, the previsibility of compound effects from concurrent extremes or meteorological hazards is needed, that include a better understanding and identification of key physical processes at fine scales, in order to reduce their negative impacts and to better prevent those events. The assessment of robust climate risk levels at the regional or local scale is currently lacking, mainly because the physical processes responsible for their occurrence are not well resolved in coarse scale climate models currently used in Canadian climate change research. This requires the use of very high-resolution weather and climate models (e.g., using "convection-permitting" models) to take into account the combination of local factors (e.g., topography, surface conditions, environmental features, etc.) and large-scale influences. In addition, the previsibility and assessment of hydrometeorological risks undoubtedly needs to be improved with oriented information based on their impacts. This Discovery Grant (DG) program will combine both regional climate model simulations at intermediate resolution (ex. 11 km) and very high resolution (e.g., convection-permitting < 3 km) to identify and evaluate the severity, duration and occurrence of atmospheric hazards and high impact weather conditions from compound effects. This will help to develop early warning systems with physically based and oriented impact approaches, and to be better prepare and to reduce consequences from a range of adverse weather and climate conditions under climate change. This DG research will also contribute to help stakeholders and governmental organizations to reinforce the resilience of organization, communities and individuals, considering the most plausible changes in atmospheric hazards.

我们星球的气候系统正处于重大转变的边缘。根据政府间气候变化专门委员会最近的一份评估报告，在北极地区升温幅度大幅扩大的情况下，加拿大将在本世纪末经历世界上最强烈的升温。这可能不仅会改变气象灾害和极端情况，而且还会改变（所有类型的）降水的持续时间、强度和发生情况，以及风和温度的状况，特别是在变暖最明显的地区。 取决于未来的变暖阈值，评估结合了联合收割机多变量气候变量的高影响天气条件变得越来越重要，因为这些条件往往与更高的风险相关联，往往具有更大的鲁棒性或更高的概率，而不是孤立地考虑变量的变化所造成的风险。此外，基于单变量统计数据的评估可能大大低估与特定极端事件相关的风险，特别是如果影响是几个因变量或温和天气条件的结果，这些因素联合收割机结合在一起，在区域或地方范围内产生极端影响。这就是为什么需要对同时发生的极端事件或气象灾害的复合影响进行预测，包括更好地了解和确定细微尺度上的关键物理过程，以减少其负面影响，更好地预防这些事件。目前缺乏对区域或地方尺度的强有力的气候风险水平的评估，主要是因为在加拿大气候变化研究目前使用的粗尺度气候模型中，导致其发生的物理过程没有得到很好的解决。这需要使用非常高分辨率的天气和气候模型（例如，使用“对流允许”模型）来考虑局部因素的组合（例如，地形、表面状况、环境特征等）和大规模的影响。此外，水文气象风险的预测和评估无疑需要根据其影响提供有针对性的信息。这个发现补助金（DG）计划将联合收割机结合区域气候模式模拟在中间分辨率（例如。11公里）和非常高的分辨率（例如，（在对流允许的情况下，< 3公里），以确定和评估大气灾害的严重性、持续时间和发生情况以及复合效应造成的高影响天气条件。这将有助于发展预警系统，采用基于物理和面向影响的办法，并更好地做好准备，减少气候变化下一系列不利天气和气候条件的后果。这项危险品研究还将有助于帮助利益攸关方和政府组织加强组织、社区和个人的复原力，同时考虑到大气危害中最可能发生的变化。