Boundary-Induced Biases in Climate Networks of Extreme Precipitation and Temperature

2026-01-31

AI Summary

This study systematically evaluates whether two commonly used spatial boundary correction methods—subtraction and division—yield statistically significant differences in climate network construction. Using extreme precipitation and temperature events across the contiguous United States, complex networks are constructed separately for summer and winter, and compared through surrogate data correction alongside key topological metrics including degree centrality, clustering coefficient, mean geographic distance, and betweenness centrality. Results show that, at the 95% confidence level, the two correction methods produce significantly distinct network structures, with the clustering coefficient and mean geographic distance exhibiting particular sensitivity to the choice of correction. The analysis further reveals seasonal hub migration in extreme precipitation networks and strong regional teleconnections in extreme temperature networks. This work provides the first quantitative comparison of these correction approaches, offering methodological guidance for constructing climate networks based on extreme events.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

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.

Problem

• climate networks
• boundary effects
• extreme precipitation
• extreme temperature
• surrogate correction

Innovation

• surrogate-based correction
• climate networks
• boundary effects
• extreme precipitation
• network metrics