Detection and Attribution of Anthropogenic Changes in Climate Extremes and Variability

极端气候和变率的人为变化的检测和归因

• 批准号: 0634654
• 负责人: Gabriele Hegerl
• 金额: $ 41.08万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-12-15 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0634654&HistoricalAwards=false
• 关键词: Detection; Attribution; Anthropogenic; Changes; Climate

Climate models are widely used to predict future climate changes associated with increasing greenhouse gas emissions and other anthropogenic influences on climate. Detection and attribution of climate change provides a rigorous evaluation of the ability of climate models to realistically simulate past climate change associated with natural and anthropogenic influences. Detection methods also provide a means for estimating the probability distribution of future changes based on observed changes, and to estimate the contribution from greenhouse gas forcing to changes in the risk of extreme events. Scientific Objectives and Intellectual Merit: The research aims at detecting and attributing large-scale changes in the statistics of climate that describe extremes. The first thrust of the work is to apply fingerprint detection and attribution approaches to changes in hot days and nights, and projected global-scale increases in heavy precipitation, particularly over several days. Results will be used to estimate anthropogenic changes in the risk of heat waves and rare temperature extremes. A second thrust of work is to explore changes in climate extremes and variability that have been determined as potentially important for impacts on ecosystems and society. Examples are heat waves, false springs, or droughts resulting from decreases in growing season rainfall or combined rainfall and temperature changes. The work is now feasible because of recent improvements in the quality and quantity of observational data sets and climate model simulations. A large set of the latest climate model simulations has recently become available from simulations performed for the 4th IPCC assessment report. These can provide estimates of climate change signals and model uncertainties in these signals. Furthermore, indices of daily temperature and rainfall extremes are being collected that cover an increasing area of the global land mass. Also, long station data are becoming available that allow to test internal climate variability estimates from climate models. Broader Impacts: Climatic extremes have large impacts on society, agriculture, and ecosystems, and many impacts of climate change may be directly related to changes in climate extremes. Therefore, it is of great importance to society to understand the variations in climate extremes encountered over the 20th century, and to evaluate the ability of climate models to simulate such changes. Detection of anthropogenic changes in climatic extremes is essential for understanding emerging risks in the current climate, and realistically predicting further changes and their uncertainties. The activity also has impacts on teaching and education. The budget contains funding for a graduate student, and thus for training a student in important problems of climate research, societal impacts associated with it, and applying up-to-date statistical techniques.

气候模式被广泛用于预测与温室气体排放增加和其他人类活动对气候的影响有关的未来气候变化。 气候变化的探测和归因提供了对气候模式真实模拟与自然和人为影响相关的过去气候变化的能力的严格评估。 探测方法还提供了一种手段，根据观测到的变化估计未来变化的概率分布，并估计温室气体强迫对极端事件风险变化的贡献。科学目标和知识价值：研究旨在检测和归因于描述极端气候统计数据的大规模变化。 这项工作的第一个重点是将指纹检测和归因方法应用于炎热昼夜的变化，以及预测全球范围内强降水的增加，特别是在几天内。 结果将用于估计热浪和罕见极端温度风险的人为变化。 工作的第二个重点是探索已被确定为对生态系统和社会具有潜在重要影响的气候极端和变异性的变化。 例如热浪、假泉或生长季节降雨量减少或降雨量和温度变化共同导致的干旱。 这项工作现在是可行的，因为最近观测数据集和气候模型模拟的质量和数量都有所改善。 最近，为IPCC第四次评估报告进行的模拟提供了大量最新的气候模式模拟。 这些可以提供对气候变化信号的估计以及这些信号中的模型不确定性。 此外，正在收集日温度和降雨量极端值指数，这些指数覆盖全球陆地面积越来越大的地区。 此外，长期台站数据也逐渐可用，从而可以检验气候模型得出的内部气候变率估计值。更广泛的影响：极端气候事件对社会、农业和生态系统都有很大的影响，气候变化的许多影响可能与极端气候事件的变化直接相关。 因此，了解20世纪世纪以来极端气候的变化，并评估气候模式模拟这种变化的能力，对社会具有重要意义。 检测极端气候的人为变化对于了解当前气候中新出现的风险和现实地预测进一步的变化及其不确定性至关重要。 这一活动也对教学和教育产生了影响。 预算包括一名研究生的经费，因此也包括在气候研究的重要问题、与之相关的社会影响和应用最新统计技术方面对学生进行培训的经费。