Diffenbaugh Noah S, Singh Deepti, Mankin Justin S, Horton Daniel E, Swain Daniel L, Touma Danielle, Charland Allison, Liu Yunjie, Haugen Matz, Tsiang Michael, Rajaratnam Bala
Department of Earth System Science, Stanford University, Stanford, CA 94305;
Woods Institute for the Environment, Stanford University, Stanford, CA 94305.
Proc Natl Acad Sci U S A. 2017 May 9;114(19):4881-4886. doi: 10.1073/pnas.1618082114. Epub 2017 Apr 24.
Efforts to understand the influence of historical global warming on individual extreme climate events have increased over the past decade. However, despite substantial progress, events that are unprecedented in the local observational record remain a persistent challenge. Leveraging observations and a large climate model ensemble, we quantify uncertainty in the influence of global warming on the severity and probability of the historically hottest month, hottest day, driest year, and wettest 5-d period for different areas of the globe. We find that historical warming has increased the severity and probability of the hottest month and hottest day of the year at >80% of the available observational area. Our framework also suggests that the historical climate forcing has increased the probability of the driest year and wettest 5-d period at 57% and 41% of the observed area, respectively, although we note important caveats. For the most protracted hot and dry events, the strongest and most widespread contributions of anthropogenic climate forcing occur in the tropics, including increases in probability of at least a factor of 4 for the hottest month and at least a factor of 2 for the driest year. We also demonstrate the ability of our framework to systematically evaluate the role of dynamic and thermodynamic factors such as atmospheric circulation patterns and atmospheric water vapor, and find extremely high statistical confidence that anthropogenic forcing increased the probability of record-low Arctic sea ice extent.
在过去十年中,人们为了解历史时期全球变暖对个别极端气候事件的影响所做的努力有所增加。然而,尽管取得了重大进展,但在当地观测记录中前所未有的事件仍然是一个持续存在的挑战。利用观测数据和一个大型气候模型集合,我们量化了全球变暖对全球不同地区历史上最热月份、最热日子、最干旱年份以及最潮湿5天时段的严重程度和概率影响的不确定性。我们发现,在超过80%的可用观测区域,历史变暖增加了一年中最热月份和最热日子的严重程度和概率。我们的框架还表明,历史气候强迫分别在57%和41%的观测区域增加了最干旱年份和最潮湿5天时段的概率,不过我们也指出了一些重要的注意事项。对于持续时间最长的炎热和干旱事件,人为气候强迫的最强和最广泛影响出现在热带地区,包括最热月份概率至少增加4倍,最干旱年份概率至少增加2倍。我们还展示了我们的框架系统评估大气环流模式和大气水汽等动力和热力因素作用的能力,并发现有极高的统计置信度表明人为强迫增加了北极海冰范围创历史新低的概率。