Collaborative Research: CAS-Climate: Risk Analysis for Extreme Climate Events by Combining Numerical and Statistical Extreme Value Models

合作研究：CAS-Climate：结合数值和统计极值模型进行极端气候事件风险分析

• 批准号: 2308679
• 负责人: Likun Zhang
• 金额: $ 17.5万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308679&HistoricalAwards=false
• 关键词: Collaborative; Research; CAS; Climate; Risk

Climate profoundly influences the severity and frequency of extreme phenomena like large wildfires, heatwaves, floods, and droughts. Resilience to the most dramatic effects of climate change requires an understanding of extreme events under future climate conditions. Climate models are invaluable tools for interrogating the dynamics of the Earth system, but they have shortcomings with respect to extreme event analysis. First, the behavior of extremes of many meteorological variables shows a profound mismatch compared to real-life observations. Second, they live in gridded spaces that must be reconciled with the continuous world in which observations are made and for which risk analysis is performed. To resolve these two difficulties, we will model weather phenomena in their native real-world domain, leveraging information from representations of large-scale patterns from climate models, in a way that preserves realistic properties of extreme events. The project also provides research training opportunities for graduate students. PI will focus on two main research aims, which develop and apply analytical tools that turn climate model output into a realistic analysis of extreme events. First, this project will develop models, and associated model-fitting software, that leverage dynamically-derived large-scale features from climate model output to inform stochastic process descriptions of local extreme meteorological phenomena in continuous space. This will require two interconnected modeling components: 1) a stochastic analogue model to link climate model output in gridded space to extreme spatial events in continuous space, and 2) a stochastic process model, conditional on the analogue model, that realistically represents spatial tail dependence. Second, this project will generate model-based projections of extreme events for use in impact analysis. Random draws from the model developed in the first research aim, conditional on climate model projections of large-scale features, functionally constitute an extreme weather generator. PI will use these random draws as inputs to one of the two impact models: precipitation draws feed into a hydrology model to project pluvial flood risks, and wind, temperature, and precipitation draw feed into a fire spread model to project wildfire risk. The software implementation of their model will produce stochastic draws of potential future climate variables that have realistic tail behavior, which can be used downstream as inputs to other numerical models that directly aid in risk assessment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

气候深刻地影响着极端现象的严重性和频率，如大型野火，热浪，洪水和干旱。要抵御气候变化的最严重影响，就需要了解未来气候条件下的极端事件。气候模型是研究地球系统动态的宝贵工具，但在极端事件分析方面存在缺陷。首先，许多气象变量的极端行为与现实生活中的观测结果存在严重的不匹配。其次，他们生活在网格化的空间中，必须与连续的世界相协调，在这个世界中进行观察，并进行风险分析。为了解决这两个难题，我们将在其本地现实世界领域中对天气现象进行建模，利用气候模型中大规模模式表示的信息，以保留极端事件的现实属性的方式。该项目还为研究生提供研究培训机会。PI将专注于两个主要研究目标，开发和应用分析工具，将气候模型输出转化为极端事件的现实分析。首先，该项目将开发模型和相关的模型拟合软件，利用气候模型输出的动态衍生大尺度特征，为连续空间中局部极端气象现象的随机过程描述提供信息。 这将需要两个相互关联的建模组件：1）随机模拟模型，将网格空间中的气候模型输出与连续空间中的极端空间事件联系起来，以及2）随机过程模型，以模拟模型为条件，真实地表示空间尾部依赖性。第二，该项目将生成基于模型的极端事件预测，用于影响分析。 随机抽取从模型开发的第一个研究目标，有条件的气候模式预测的大尺度功能，功能上构成了极端天气发生器。 PI将使用这些随机抽取作为两个影响模型之一的输入：降水将输入水文模型以预测洪水风险，风，温度和降水将输入火灾蔓延模型以预测野火风险。他们的模型的软件实现将产生潜在的未来气候变量的随机绘制，这些变量具有现实的尾部行为，可以在下游用作其他数值模型的输入，直接帮助风险评估。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。