Fingerprinting Methods for Detection and Attribution of Changes in Climate Extremes with Spatial Estimating Equations

利用空间估计方程检测和归因极端气候变化的指纹方法

• 批准号: 1521730
• 负责人: Jun Yan
• 金额: $ 10万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1521730&HistoricalAwards=false
• 关键词: Fingerprinting; Methods; Detection; Attribution; Changes

Changes in climate extremes often influence natural and human systems with more severe consequences than changes in climatic mean states. Detection of changes in climate extremes and attribution to possible causes, however, are much less studied than the counterpart in climatic mean states due to sparsity of data, low signal noise ratio, and the unique features of extremes. The optimal fingerprint method, which is standard in detection and attribution of changes in climatic mean states, has no satisfactory analog for changes in climate extremes. This project aims to close this gap by developing a close analog of the optimal fingerprint method for detection and attribution of changes in climate extremes with high power using spatial estimating equations. The project has cross-boundary impact in both statistics and climate research. The optimal fingerprinting method for extreme value analysis has wide applications and impact on climate research. Applications of the methods will increase the public awareness of the possible climate changes and their impact on environment and society. The open source software implementation under the strict quality control of the R system will not only make the methods widely accessible to practitioners in climate change, but also make them openly available for public scrutiny, both of which are important in understanding changes in climate extremes and attributing to possible causes.Specifically, the project aims to 1) develop inferences for spatial estimating equations as an analog of the fingerprint method for changes in climate extremes; 2) develop inferences for spatial estimating equations with measurement errors that are spatially and temporally dependent; 3) identify and attribute changes in extreme temperature at the regional scale for global lands and in extreme precipitation in North America; and 4) develop an open-source, high-quality, and user-friendly software package accompanying the proposed methodologies. The spatial estimating equations will be constructed by combining the score equations of the marginal generalized extreme value distributions at all sites, without specification of the spatial dependence. The combining weight that controls the efficiency will be based on the inverse of a working covariance matrix or multiple matrices each of which contrasts the score at a site with those from sites nearby. The spatially and temporally dependent measurement errors will be approached with the simulation extrapolation method, the simulation step of which will be handled by a random normalized contrasts approach to preserves the dependence structure. The methods will be applied to detection and attribution of changes in extreme temperature with multiple external forcings and in extreme precipitation with a single forcing. This project embraces the statistical challenges in detection and attribution of changes in climate extremes from the climate research community. The focus on extremes was made possible only recently by the large amount of observed data and climate model simulations. The proposed methods advance knowledge in statistics with the development of 1) efficient spatial estimating equations for inferences with primary focus on marginal regression coefficients, and 2) measurement error models with spatially and temporally dependent measurement error. These methods offer a close analog of the optimal fingerprint method for extreme value analysis. Applications in detection and attribution advance knowledge about the possible causes of changes in extreme temperature and extreme precipitation.

极端气候的变化通常会影响自然和人类系统，其后果比气候平均状态的变化更严重。然而，由于数据的稀疏性，低信号噪声比和极端的独特特征，检测到气候极端和可能原因的变化的研究要比气候平均状态的研究要小得多。最佳指纹方法是气候平均状态变化的检测和归因的标准，对于气候极端变化的变化没有令人满意的类似物。该项目的目的是通过使用空间估计方程来检测和归因于高功率的气候极端变化的最佳指纹方法，以缩小这一差距。该项目对统计和气候研究都有跨界的影响。极值分析的最佳指纹方法具有广泛的应用和对气候研究的影响。这些方法的应用将提高公众对可能的气候变化及其对环境和社会的影响的认识。在R系统的严格质量控制下实施的开源软件实施不仅将使从业者在气候变化中广泛访问的方法，而且还可以公开进行公开审查，这两个方法对于理解极端气候的变化和归因于可能原因的变化很重要。 2）为空间估计方程开发出具有空间和时间依赖性的测量误差的推论； 3）在区域尺度上确定和归因于全球土地和北美极端降水的极端温度变化； 4）伴随提出的方法论，开发一个开源，高质量和用户友好的软件包。空间估计方程将通过在所有位点组合边缘广义极值分布的得分方程来构建，而无需规格空间依赖性。控制效率的组合重量将基于工作协方差矩阵的倒数或多个矩阵，每个矩阵每个矩阵将分数与附近站点的分数与分数进行对比。将使用模拟外推法处理空间和时间依赖的测量误差，该方法将通过随机归一化对比度方法来处理其依赖性结构的仿真步骤。这些方法将用于通过多个外部强迫和极端降水的极端温度的检测和归因。该项目涵盖了气候研究界极端气候极端变化的检测和归因的统计挑战。直到最近，大量观察到的数据和气候模型模拟才使对极端的重点成为可能。提出的方法通过开发1）推理的有效空间估计方程，主要关注边缘回归系数，以及2）具有空间和时间依赖性测量误差的测量误差模型。这些方法为极值分析提供了最佳指纹方法的密切类似。在检测和归因方面的应用提前了解了极端温度和极端降水的可能原因。