Collaborative Research: EaSM 2 Advancing extreme value analysis of high impact climate and weather events

合作研究：EaSM 2 推进高影响气候和天气事件的极值分析

• 批准号: 1243102
• 负责人: Daniel Cooley
• 金额: $ 85.73万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1243102&HistoricalAwards=false
• 关键词: Collaborative; Research; EaSM; Advancing; extreme

1243102Dan Cooley, Colorado State University1243333J. Keith Gilless, University of California-Berkeley1242957Richard Smith, University of North Carolina at Chapel Hill One of the most important questions in climate science is how extreme weather changes as a result of natural and anthropogenic forcings. At this project's core is a goal to address three areas that are critical to understanding extreme events, but whose methods are not yet developed enough to answer impact-relevant questions. First, the investigators advance and develop multivariate statistical methods that can describe and model extreme events that arise from a combination of meteorological factors that may or may not individually be extreme. Second, the investigators advance spatial downscaling methods to be applicable to studying extreme phenomena that occur at spatial scales not resolved by climate models. Third, the investigators put the study of detection of changes in extremes and attribution of extreme events on a solid statistical foundation, and apply the spatial and multivariate techniques to this area. The participants collaborate with social scientists in incorporating the improved methodology into models that analyze the impact of extreme weather events on agricultural production and the forestry sector for specific regions of the US. They also develop risk assessment measures that take into account possible increases in the frequency of extreme weather events. Extreme weather can have profound consequences for the well-being of individuals, societies, and natural systems. This project studies several aspects of weather extremes and how the nature of extreme events is likely to change under an altered climate. The project develops probability-based measures of risk that take account of the fact that impacts of extreme weather events cannot be predicted with certainty. Another feature of the project is that high-impact events often arise from a combination of extreme weather conditions. For example, wildfires may arise from a combination of heat, wind, and lack of moisture; we therefore need methods of analysis that take into account multiple meteorological variables. Most of the methods being developed rely on large computer programs known as climate models to project future climatic events. However, climate models often work on time and space scales that are much larger than the high-impact events we are interested in; therefore, data from climate models must be translated to the scales of interest. The specific focus on extreme events is different from most current research using climate models for the detection and attribution of climate change. Detecting changes in extreme behavior is essential for risk assessment, as risk-related quantities such as the 100-year flood, fire, burning, and heat indexes are estimated based on data that may no longer be relevant if extreme behavior has changed. The determination of the impact of such changes at a broad range of socio-economic levels is needed to determine appropriate societal responses to extreme weather events associated with climate change. In addition to university-based investigators, the project includes Michael Wehner and collaborators at Lawrence Berkeley National Laboratory. who are an intrinsic part of the team. University-based investigators are funded by NSF while the Department of Energy supports LBNL investigators.

1243102丹库利，科罗拉多州立大学基思吉勒斯，加州大学伯克利分校1242957理查德史密斯，北卡罗来纳州大学查佩尔山 气候科学中最重要的问题之一是极端天气如何由于自然和人为强迫而发生变化。 该项目的核心目标是解决对理解极端事件至关重要的三个领域，但其方法尚未发展到足以回答影响相关问题。 首先，研究人员提出并开发了多变量统计方法，可以描述和模拟极端事件，这些极端事件是由一系列可能极端或不极端的气象因素组合而成的。 其次，研究人员推进了空间降尺度方法，以适用于研究气候模型无法解决的空间尺度上发生的极端现象。 第三，研究人员将极端变化的检测和极端事件的归因研究置于坚实的统计基础之上，并将空间和多变量技术应用于这一领域。 参与者与社会科学家合作，将改进的方法纳入模型，分析极端天气事件对美国特定地区农业生产和林业部门的影响。 它们还制定风险评估措施，考虑到极端天气事件发生频率可能增加的情况。 极端天气可能对个人、社会和自然系统的福祉产生深远的影响。 该项目研究极端天气的几个方面，以及极端事件的性质在气候变化下可能如何变化。 该项目制定了基于概率的风险措施，其中考虑到极端天气事件的影响无法确定地预测。 该项目的另一个特点是，高影响事件往往是由极端天气条件的组合引起的。 例如，野火可能是由热、风和缺乏水分的组合引起的;因此，我们需要考虑多种气象变量的分析方法。 大多数正在开发的方法依赖于被称为气候模型的大型计算机程序来预测未来的气候事件。 然而，气候模型的时间和空间尺度往往比我们感兴趣的高影响事件大得多;因此，气候模型的数据必须转换到感兴趣的尺度。 对极端事件的具体关注不同于目前大多数利用气候模型检测和归因气候变化的研究。 检测极端行为的变化对于风险评估至关重要，因为与风险相关的数量，如百年一遇的洪水，火灾，燃烧和热指数，是根据极端行为发生变化时可能不再相关的数据进行估计的。 需要确定这种变化在广泛的社会经济层面的影响，以确定社会对与气候变化有关的极端天气事件的适当反应。 除了大学的研究人员外，该项目还包括Michael Wehner和劳伦斯伯克利国家实验室的合作者。他们是团队的一部分 基于大学的研究人员由NSF资助，而能源部支持LBNL研究人员。