[Paper Review] 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 models0 citations

TL;DR

The paper compares two common boundary-correction approaches (subtraction and division) for climate networks of extreme precipitation and temperature, finding statistically different results and highlighting seasonally distinct network patterns.

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.

Motivation & Objective

• Motivate the need to address spatial boundary effects in climate networks.
• Construct complex networks of extreme precipitation and temperature events (EPEs and ETEs) across the CONUS for two seasons (summer and winter).
• Compute network metrics (degree centrality, clustering coefficient, mean geographic distance, betweenness centrality) under both correction methods.
• Statistically compare the two correction methods to determine if they yield different results.
• Interpret seasonal differences in network structure and connectivity in light of atmospheric drivers.

Proposed method

• Build climate networks of EPEs and ETEs across the CONUS for JJA (summer) and DJF (winter).
• Compute network measures: degree centrality (DC), clustering coefficient (CC), mean geographic distance (MGD), and betweenness centrality (BC).
• Apply two surrogate-based boundary correction methods: subtraction (adjust by subtracting the surrogate mean) and division (normalize by dividing by the surrogate mean).
• Perform statistical analyses to compare networks obtained with subtraction versus division at the 95% confidence level.
• Analyze spatial patterns and seasonal shifts in network hubs and teleconnections in relation to known atmospheric drivers.

Experimental results

Research questions

• RQ1 Do subtraction and division surrogate corrections yield statistically different network measures for EPE and ETE networks?
• RQ2 How do the two correction methods affect spatial patterns of hubs and connectivity in the CONUS?
• RQ3 What are the seasonal differences in EPE and ETE networks, and how do they relate to atmospheric drivers such as Pacific-North American and North Atlantic Oscillation modes?

Key findings

• Subtraction- and division-corrected network measures are significantly different at the 95% confidence level.
• EPE networks show seasonal shifts in hubs: northwest US in summer and eastward shift in winter.
• ETE networks exhibit strong spatial coherence and pronounced regional teleconnections in both seasons, with higher connectivity and longer synchronization distances in winter.
• CC and MGD are more sensitive to the correction method than DC and BC, especially for EPE networks.
• Results align with known large-scale circulation patterns influencing extreme precipitation and temperature.