Using UNSEEN approach to attribute regional UK winter rainfall extremes

IF 3.5 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES International Journal of Climatology Pub Date : 2024-04-10 DOI:10.1002/joc.8460
Daniel F. Cotterill, Dann Mitchell, Peter A. Stott, Paul Bates
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Abstract

Three out of the five highest daily winter rainfall totals on record over Northern England have occurred from 2015 onwards. Heavy rainfall events in the winters of 2013–2014, 2015–2016 and 2019–2020 led to more than 2.8-billion-pounds of insurance losses from flooding in the UK. Has the frequency of these events been influenced by human-induced climate change? Winter rainfall in the UK is extremely variable year-to-year, which makes the attribution of rainfall extremes particularly challenging. To tackle this problem, we introduce an UNprecedented Simulated Extreme Ensemble (UNSEEN) approach for the attribution of such extremes, thereby increasing the data available, and apply this approach to five recent flooding events on a regional scale. Using this method, for all five events we found a significant climate signal in the extreme regional rainfall totals immediately preceding the flooding. Results were fairly similar for each—with the events being found to become from 1.4 to 2.6 times more likely. An alternative attribution method that uses a different model with substantially less data did not find significant increases, reinforcing the need for very large amounts of data to detect significant changes in extreme rainfall against a noisy background of natural variability. We also examine how extreme rainfall is changing more broadly across English regions in winter, finding that 1-in-10 to 1-in-90-year winter rainfall totals have changed significantly in Northern England. The high volume of data using UNSEEN has enabled us to examine the dynamics of these events, showing that daily extremes in winter are likely to have increased across all the circulation patterns responsible for high rainfall in English regions.

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使用 UNSEEN 方法归因英国区域冬季极端降雨量
在英格兰北部有记录以来日降雨量最高的五个冬季中,有三个发生在 2015 年以后。2013-2014年、2015-2016年和2019-2020年冬季的强降雨事件导致英国洪水保险损失超过28亿英镑。这些事件的发生频率是否受到人为气候变化的影响?英国的冬季降雨量每年变化极大,这使得极端降雨的归因变得尤为困难。为了解决这个问题,我们引入了一种 "前所未有的极端降雨模拟集合"(UNSEEN)方法来归因于此类极端降雨,从而增加了可用数据,并将这种方法应用于近期发生的五次区域性洪水事件。利用这种方法,我们在所有五次洪水事件中都发现了洪水发生前区域极端降雨总量中的显著气候信号。每个事件的结果都相当相似--事件发生的可能性增加了 1.4 到 2.6 倍。另一种归因方法使用了不同的模型,数据量大大减少,但没有发现显著的增加,这说明需要大量的数据才能在自然变异的嘈杂背景下发现极端降雨量的显著变化。我们还研究了英国各地区冬季极端降雨量的变化情况,发现英格兰北部 10 年一遇到 90 年一遇的冬季降雨总量发生了显著变化。使用 UNSEEN 的大量数据使我们能够研究这些事件的动态变化,显示冬季的日极端降雨量很可能在所有造成英格兰地区高降雨量的环流模式中都有所增加。
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来源期刊
International Journal of Climatology
International Journal of Climatology 地学-气象与大气科学
CiteScore
7.50
自引率
7.70%
发文量
417
审稿时长
4 months
期刊介绍: The International Journal of Climatology aims to span the well established but rapidly growing field of climatology, through the publication of research papers, short communications, major reviews of progress and reviews of new books and reports in the area of climate science. The Journal’s main role is to stimulate and report research in climatology, from the expansive fields of the atmospheric, biophysical, engineering and social sciences. Coverage includes: Climate system science; Local to global scale climate observations and modelling; Seasonal to interannual climate prediction; Climatic variability and climate change; Synoptic, dynamic and urban climatology, hydroclimatology, human bioclimatology, ecoclimatology, dendroclimatology, palaeoclimatology, marine climatology and atmosphere-ocean interactions; Application of climatological knowledge to environmental assessment and management and economic production; Climate and society interactions
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