[论文解读] Boundary-Induced Biases in Climate Networks of Extreme Precipitation and Temperature

Behzad Ghanbarian, Victor Oladoja+3 more|arXiv (Cornell University)|Jan 31, 2026

Climate variability and models被引用 0

一句话总结

论文比较两种常见边界纠正方法（减法与除法）在极端降水与温度气候网络中的应用，结果在统计上不同，并强调网络模式存在季节性差异。

ABSTRACT

To address spatial boundary effects in climate networks, two surrogate-based correction methods, (1) subtraction and (2) division, have been widely applied in the literature. In the subtraction method, an original network measure is adjusted by subtracting the expected value obtained from a surrogate ensemble, whereas in the division method, it is normalized by dividing by this expected value. However, to the best of our knowledge, no prior study has assessed whether these two correction approaches yield statistically different results. In this study, we constructed complex networks of extreme precipitation and temperature events (EPEs and ETEs) across the CONUS for both summer (June-August, JJA) and winter (December-February, DJF) seasons. We computed key network metrics degree centrality (DC), clustering coefficient (CC), mean geographic distance (MGD), and betweenness centrality (BC) and applied both correction methods. Although the corrected spatial patterns generally appeared visually similar, statistical analyses revealed that the network measures derived from the subtraction and division methods were significantly different at the 95 percent confidence level. Across the CONUS, network hubs of EPEs were primarily concentrated in the northwestern United States during summer and shifted toward the east during winter, reflecting seasonal differences in the dominant atmospheric drivers. In contrast, the ETE networks showed strong spatial coherence and pronounced regional teleconnections in both seasons, with higher connectivity and longer synchronization distances in winter, consistent with large-scale circulation patterns such as the Pacific-North American and North Atlantic Oscillation modes. Our results indicated that the network metrics CC and MGD were more sensitive to the correction methods than the DC and BC, particularly in the EPE networks.

研究动机与目标

• 解释在气候网络中需要解决空间边界效应的动机。
• 在CONUS范围内为两个季节（夏季和冬季）构建极端降水和温度事件（EPEs和ETEs）复杂网络。
• 在两种纠正方法下计算网络指标（度中心性、聚类系数、平均地理距离、介数中心性）。
• 在统计上比较两种纠正方法以确定它们是否产生不同的结果。
• 结合大气驱动因素，解释网络结构和连接性在不同季节的差异。

提出的方法

• 在CONUS范围内为JJA（夏季）和DJF（冬季）构建EPEs与ETEs的气候网络。
• 计算网络度量：度中心性（DC）、聚类系数（CC）、平均地理距离（MGD）和介数中心性（BC）。
• 应用两种基于替代的边界纠正方法：减法（通过减去替代均值来调整）和除法（通过除以替代均值来归一化）。
• 在95%置信水平上进行统计分析，以比较减法得到的网络与除法得到的网络。
• 分析网络枢纽和远程传输在空间模式上的差异及季节性转变，与已知大气驱动因素相关联。

实验结果

研究问题

• RQ1 减法和除法替代纠正是否在EPE与ETE网络的网络度量上产生统计差异？
• RQ2 两种纠正方法如何影响CONUS的枢纽和连接性的空间格局？
• RQ3 EPE与ETE网络的季节性差异如何，以及它们与太平洋-北美振荡和北大西洋振荡等大气驱动因素的关系？

主要发现

• 在95%置信水平上，减法与除法校正的网络度量显著不同。
• EPE网络在枢纽上呈季节性转移：夏季为美国西北部，冬季向东移位。
• ETEs网络在两个季节都展现出强烈的空间相干性和明显的区域远程传输，在冬季具有更高的连接性和更长的同步距离。
• CC和MGD比DC和BC对纠正方法更敏感，特别是在EPE网络中。
• 结果与影响极端降水和温度的已知大尺度环流模式相一致。