Understanding spatiotemporal patterns of climate extremes has gained considerable relevance in the context of ongoing climate change. With enhanced computational capacity, data driven methods such as functional climate networks have been proposed and have already contributed to significant advances in understanding and predicting extreme events, as well as identifying interrelations between the occurrences of various climatic phenomena. While the (in its basic setting) parameter free event synchronization (ES) method has been widely applied to construct functional climate networks from extreme event series, its original definition has been realized to exhibit problems in handling events occurring at subsequent time steps, which need to be accounted for. Along with the study of this conceptual limitation of the original ES approach, event coincidence analysis (ECA) has been suggested as an alternative approach that incorporates an additional parameter for selecting certain time scales of event synchrony. In this work, we compare selected features of functional climate network representations of South American heavy precipitation events obtained using ES and ECA without and with the correction for temporal event clustering. We find that both measures exhibit different types of biases, which have profound impacts on the resulting network structures. By combining the complementary information captured by ES and ECA, we revisit the spatiotemporal organization of extreme events during the South American Monsoon season. While the corrected version of ES captures multiple time scales of heavy rainfall cascades at once, ECA allows disentangling those scales and thereby tracing the spatiotemporal propagation more explicitly.

中文翻译：

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功能性气候网络分析的事件同步措施：以南美降雨动态为例

在持续的气候变化背景下，了解极端气候的时空格局已具有重要意义。随着计算能力的提高，已经提出了诸如功能性气候网络之类的数据驱动方法，并且已经在理解和预测极端事件以及识别各种气候现象发生之间的相互关系方面做出了重大贡献。尽管（在其基本设置中）无参数事件同步（ES）方法已广泛应用于从极端事件序列构建功能气候网络，但其原始定义已实现，显示出在处理后续时间步长上发生的事件方面存在问题，这需要被考虑。除了研究原始ES方法的这一概念局限性之外，已建议将事件符合分析（ECA）作为一种替代方法，该方法结合了用于选择事件同步某些时标的附加参数。在这项工作中，我们比较了使用ES和ECA获得的南美强降水事件的功能气候网络表示的某些选定特征，这些特征不使用ESA和ECA，也没有使用时间事件聚类进行校正。我们发现这两种措施都表现出不同类型的偏差，这对由此产生的网络结构产生了深远的影响。通过结合ES和ECA收集的补充信息，我们重新审视了南美季风季节极端事件的时空组织。校正后的ES版本可以一次捕获多个时间尺度的强降雨级联，