Episodically occurring internal (climatic) and external (non-climatic) disruptions of normal climate variability are known to both affect spatio-temporal patterns of global surface air temperatures (SAT) at time-scales between multiple weeks and several years. The magnitude and spatial manifestation of the corresponding effects depend strongly on the specific type of perturbation and may range from weak spatially coherent yet regionally confined trends to a global reorganization of co-variability due to the excitation or inhibition of certain large-scale teleconnectivity patterns. Here, we employ functional climate network analysis to distinguish qualitatively the global climate responses to different phases of the El Niño–Southern Oscillation (ENSO) from those to the three largest volcanic eruptions since the mid-20th century as the two most prominent types of recurrent climate disruptions. Our results confirm that strong ENSO episodes can cause a temporary breakdown of the normal hierarchical organization of the global SAT field, which is characterized by the simultaneous emergence of consistent regional temperature trends and strong teleconnections. By contrast, the most recent strong volcanic eruptions exhibited primarily regional effects rather than triggering additional long-range teleconnections that would not have been present otherwise. By relying on several complementary network characteristics, our results contribute to a better understanding of climate network properties by differentiating between climate variability reorganization mechanisms associated with internal variability versus such triggered by non-climatic abrupt and localized perturbations.

众所周知，正常气候变率的偶然发生的内部（气候）和外部（非气候）破坏都会影响全球地表气温 (SAT) 在数周到数年之间的时间尺度上的时空模式。相应影响的幅度和空间表现在很大程度上取决于特定类型的扰动，范围可能从弱空间相干但区域受限的趋势到由于某些大规模遥相关模式的激发或抑制而导致的全球共变重组。这里，我们采用功能性气候网络分析来定性区分对厄尔尼诺 - 南方涛动 (ENSO) 不同阶段的全球气候响应，以及自 20 世纪中叶以来最大的三个火山爆发作为两种最突出的周期性气候破坏类型. 我们的结果证实，强烈的 ENSO 事件会导致全球 SAT 场的正常等级组织暂时崩溃，其特征​​是同时出现一致的区域温度趋势和强遥相关。相比之下，最近的强烈火山喷发主要表现出区域效应，而不是触发其他情况下不会出现的额外远程遥相关。依靠几个互补的网络特性，