Unusual past dry and wet rainy seasons over Southern Africa and South America from a climate perspective
Unusual past dry and wet rainy seasons over Southern Africa and South America from a climate perspective
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DOI：
10.1016/j.wace.2015.07.001
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发表时间：
2015-09-01
影响因子：
8
通讯作者：
Stott, Peter A.
中科院分区：
文献类型：
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作者：
Bellprat, Omar;Lott, Fraser C.;Stott, Peter A.
非洲南部和南美洲南部最近经历了旱季和雨季的极端天气，造成了严重的社会经济损失。这里研究了选定的过去极端事件，通过应用包括耦合模型比对项目 5 (CMIP5) 的全球气候模型的事件归因方法（Stott 等人，2004 年）来估计人类活动如何改变此类事件发生的风险。我们的评估表明，模型对南美洲南部平均降水变化的描述不足以对该地区的季节性降雨极端事件做出强有力的归因声明。在南部非洲，我们发现，由于人为气候变化，2002/2003 年期间出现异常干燥的南方夏季的可能性变得更大，而像 1999/2000 年那样异常潮湿的南方夏季的可能性已变得较小。有一些初步证据表明，该地区 5 天总降水量极高（如 1999/2000 年观察到）的风险有所增加。这些结果与 CMIP5 模型一致，CMIP5 模型预测 12 月至 1 月至 2 月 (DJF) 期间 SAF 总体干燥趋势，但当暴雨事件确实发生时，大气水分可用性也会增加，从而为强降雨事件提供支持。自举可归因风险部分的置信区间表明，如果事件非常罕见，则可归因风险的估计非常不确定。该研究强调了使用季节性降水和极端 5 天降水总量进行降水事件归因研究的一些挑战，并在海洋-大气耦合模型中考虑 ENSO 等自然驱动因素。 (C) 2015 年作者。由 Elsevier B.V. 出版
Southern Africa and Southern South America have experienced recent extremes in dry and wet rainy seasons which have caused severe socio-economic damages. Selected past extreme events are here studied, to estimate how human activity has changed the risk of the occurrence of such events, by applying an event attribution approach (Stott et al., 2004)comprising global climate models of Coupled Model Intercomparison Project 5 (CMIP5). Our assessment shows that models' representation of mean precipitation variability over Southern South America is not adequate to make a robust attribution statement about seasonal rainfall extremes in this region. Over Southern Africa, we show that unusually dry austral summers as occurred during 2002/2003 have become more likely, whereas unusually wet austral summers like that of 1999/2000 have become less likely due to anthropogenic climate change. There is some tentative evidence that the risk of extreme high 5-day precipitation totals (as observed in 1999/2000) have increased in the region. These results are consistent with CMIP5 models projecting a general drying trend over SAF during December-January-February (DJF) but also an increase in atmospheric moisture availability to feed heavy rainfall events when they do occur. Bootstrapping the confidence intervals of the fraction of attributable risk has demonstrated estimates of attributable risk are very uncertain, if the events are very rare. The study highlights some of the challenges in making an event attribution study for precipitation using seasonal precipitation and extreme 5-day precipitation totals and considering natural drivers such as ENSO in coupled ocean-atmosphere models. (C) 2015 The Authors. Published by Elsevier B.V.