El Ni\~no Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical Pacific, as Central Pacific (CP) events are typically weaker than Eastern Pacific (EP) events. SSTA pattern and intensity undergo low-frequency modulations, affecting ENSO prediction skill and remote impacts. Yet, how different ENSO types contribute to these decadal variations and long-term variance trends remain uncertain. Here, we decompose the low-frequency changes of ENSO variance into contributions from ENSO diversity categories. We propose a fuzzy clustering of monthly SSTA to allow for non-binary event category memberships. Our approach identifies two La Ni\~na and three El Ni\~no categories and shows that the shift of ENSO variance in the mid-1970s is associated with an increasing likelihood of strong La Ni\~na and extreme El Ni\~no events.

翻译：厄尔尼诺-南方涛动（ENSO）多样性基于热带太平洋最大海表温度异常（SSTA）的经度位置和振幅进行表征，其中中太平洋（CP）事件通常弱于东太平洋（EP）事件。SSTA的空间形态和强度受低频调制，影响ENSO预测技巧和远程影响。然而，不同ENSO类型如何贡献于这些年代际变化及长期方差趋势仍存在不确定性。本文通过分解ENSO方差的低频变化，将其归因于ENSO多样性类别。我们提出对月平均SSTA进行模糊聚类，以允许非二元事件类别隶属关系。该方法识别出两类拉尼娜和三厄尔尼诺类别，并表明20世纪70年代中期ENSO方差的转变与强拉尼娜和极端厄尔尼诺事件发生概率增加相关。