The Southern Annular Mode (SAM) is the leading mode of atmospheric variability in the mid–high latitudes of the Southern Hemisphere, representing large-scale variations in pressure and the polar front jet (PFJ). In SAM events, the combination of the SAM and other modes may result in different atmospheric patterns. In this study, a neural-network-based cluster technique, the self-organizing map, was applied to extract the distinct patterns of SAM events on the monthly time scale based on geopotential height anomalies at 500 hPa. Four pairs of distinguishable patterns of positive and negative SAM events were identified, representing the diversity in spatial distribution, especially the zonal symmetry of the center of action at high latitudes—that is, symmetric patterns, split-center patterns, West Antarctica patterns, and a tripole pattern. Although the SAM is well known to be belt-shaped, within the selected SAM events, the occurrence frequency of symmetric patterns is only 23.8%—less than that of West Antarctica patterns. Diverse PFJ variations were found in the symmetric and asymmetric patterns of SAM events. The more asymmetric the spatial distribution of the pressure anomaly, the more localized the adjusted zonal wind anomaly. The adjusted PFJ varied in meridional displacement and strength in different patterns of SAM events. In addition, the entrance and exit of the jet changed in most of the patterns, especially in the asymmetric patterns, which might result in different climate impacts of the SAM.摘要南半球环状模 (SAM) 是南半球中–高纬度地区大气变化的主导模态, 表现为气压和极锋急流 (PFJ) 的大尺度变动, 形成强烈的气候影响. 当SAM事件发生时, 气压场异常可呈现出不同的空间结构. 本文利用自组织映射网络方法对月尺度的SAM事件进行分类, 可识别出四对具有显著差异的正, 负SAM事件类型, 包括对称型, 中心分裂型, 西南极洲型和一种三极型分布. 气压异常的空间分布越不对称, 调整后的纬向风异常越局地化. PFJ的经向位移和强度变化入口和出口的变化, 可能导致了SAM的不同气候影响.
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