前兆分析集合传播信号,预示着平流层的突然变暖

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Akira Yamazaki, Shunsuke Noguchi
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引用次数: 0

摘要

摘要本研究对与平流层突然变暖(SSW)事件相关的分析集合扩散行为进行了深入的研究。本文使用平流层解析集合数据同化系统来记录导致一对变暖事件的分析传播的演变。在2018年12月和2019年8月至9月分别在北半球和南半球发生的两次SSW事件发生前几天,发现了总体扩散增加的前兆信号。这些信号出现在平流层的上层和中层,在较低的高度没有出现。当信号出现时,发现在预测-分析(数据同化)循环中,预测和分析增量的趋势同时变大。经验正交函数分析表明,前兆信号的主导结构为等效正压,与分析集合-平均场呈90°异相。在同一时期,上层和中层平流层变得比以前更容易受到正压不稳定的影响。我们得出的结论是,在SSW事件发生之前,整体成员之间不同的正压不稳定模式的增长可以放大传播。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Precursory analysis ensemble spread signals that foreshadow stratospheric sudden warmings
Abstract This study conducts a thorough investigation into the behaviors of analysis ensemble spreads linked to stratospheric sudden warming (SSW) events. A stratosphere-resolving ensemble data assimilation system is used here to document the evolution of analysis spread leading up to a pair of warming events. Precursory signals of the increased ensemble spreads were found a few days prior to two SSW events that occurred during December 2018 and August–September 2019 in the northern and southern hemispheres respectively. The signals appeared in the upper and middle stratosphere and did not appear at lower heights. When the signals appeared it was found that both tendency by forecast and analysis increment in a forecast-analysis (data assimilation) cycle simultaneously became large. An empirical orthogonal function analysis showed that the dominant structures of the precursory signals were equivalent barotropic and were 90° out-of-phase with the analysis ensemble-mean field. Over the same period the upper and middle stratosphere became more susceptible to barotropic instability than in their previous states. We conclude that the differing growth of barotropically unstable modes across ensemble members can amplify spread during the lead-up to SSW events.
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来源期刊
Monthly Weather Review
Monthly Weather Review 地学-气象与大气科学
CiteScore
6.40
自引率
12.50%
发文量
186
审稿时长
3-6 weeks
期刊介绍: Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.
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