Detection of Anthropogenic Changes in Climate Extremes

检测极端气候的人为变化

• 批准号: 0002206
• 负责人: Gabriele Hegerl
• 金额: $ 26.49万
• 依托单位: Texas A&M Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2001-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0002206&HistoricalAwards=false
• 关键词: Detection; Anthropogenic; Changes; Climate; Extremes

The proposed work will use statistical optimal detection methods to improve the ratio of anthropogenic signal to internal climate variability and allow for an earlier detection of changes. 'Fingerprints' for the expected spatial and temporal evolution of changes in extremes will be derived from climate model simulations. A detection study first applying model data only, will be performed to explore which extreme value indices should show the earliest detectable changes. The PI will also examine which procedure of detection is most promising for an early detection of changes in extremes, as well as which variable or combination of variables can and should be applied. The most promising procedure will be applied to observations. The focus is on temperature extremes, length of wet and dry periods and precipitation extremes, particularly over several days. Daily observations from land stations, mainly from the US, Canada, Europe, Russia, China and Japan will be used. The work is important because it will lead to increased understanding on climate change detection techniques.

拟议的工作将使用统计最佳检测方法来提高人为信号与内部气候变化的比率，并允许更早地检测变化。极端变化的预期空间和时间演变的“指纹”将从气候模型模拟中得出。将首先仅应用模型数据进行检测研究，以探索哪些极值指数应显示最早的可检测变化。 PI 还将检查哪种检测程序最有希望尽早检测极端变化，以及可以并且应该应用哪些变量或变量组合。最有希望的程序将应用于观察。重点是极端温度、潮湿和干燥期的长度以及极端降水量，特别是几天内的极端降水量。将使用主要来自美国、加拿大、欧洲、俄罗斯、中国和日本的陆地站的每日观测数据。这项工作很重要，因为它将加深人们对气候变化检测技术的了解。