Climate Change Data Portal
| DOI | 10.1002/joc.8460 |
| Using UNSEEN approach to attribute regional UK winter rainfall extremes | |
| 发表日期 | 2024 |
| ISSN | 0899-8418 |
| EISSN | 1097-0088 |
| 起始页码 | 44 |
| 结束页码 | 7 |
| 卷号 | 44期号:7 |
| 英文摘要 | 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. |
| 英文关键词 | climate change attribution; extreme rainfall; trend detection; UNSEEN |
| 语种 | 英语 |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| WOS类目 | Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001199296000001 |
| 来源期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/289056 |
| 作者单位 | Met Office - UK; Hadley Centre; University of Bristol; Met Office - UK |
| 推荐引用方式 GB/T 7714 | . Using UNSEEN approach to attribute regional UK winter rainfall extremes[J],2024,44(7). |
| APA | (2024).Using UNSEEN approach to attribute regional UK winter rainfall extremes.INTERNATIONAL JOURNAL OF CLIMATOLOGY,44(7). |
| MLA | "Using UNSEEN approach to attribute regional UK winter rainfall extremes".INTERNATIONAL JOURNAL OF CLIMATOLOGY 44.7(2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
