Pitfalls of Climate Network Construction—A Statistical Perspective

Abstract

Network-based analyses of dynamical systems have become increasingly popular in climate science. Here we address network construction from a statistical perspective and highlight the often ignored fact that the calculated correlation values are only empirical estimates. To measure spurious behaviour as deviation from a ground truth network, we simulate time-dependent isotropic random fields on the sphere and apply common network construction techniques. We find several ways in which the uncertainty stemming from the estimation procedure has major impact on network characteristics. When the data has locally coherent correlation structure, spurious link bundle teleconnections and spurious high-degree clusters have to be expected. Anisotropic estimation variance can also induce severe biases into empirical networks. We validate our findings with ERA5 reanalysis data. Moreover we explain why commonly applied resampling procedures are inappropriate for significance evaluation and propose a statistically more meaningful ensemble construction framework. By communicating which difficulties arise in estimation from scarce data and by presenting which design decisions increase robustness, we hope to contribute to more reliable climate network construction in the future.

Propagation pathways of Indo-Pacific rainfall extremes are modulated by Pacific sea surface temperatures

“…As the number of comparisons becomes very high even for moderately large datasets (in our case 10 8 comparisons), there is a non-negligible chance to consider singular pairs of time series as statistically significant, even though their significance is just by coincidence 84 . To avoid such spurious links, we assume that synchronous time series are supposed to be caused by physical mechanisms and thus show spatially coherent patterns 29 .…”

Comparison Link Function from Summer Rainfall Network in Amazon Basin

“…A functional climate network is composed of a set of nodes that represent a time series of a specific climatic variable (temperature, rainfall, pressure, wind component, etc.) and are embedded on the Earth's surface position (latitude, longitude) and the presence or absence of connection between nodes, called edges [49]. In this study, each node in the network is represented by a daily summer rainfall series (mm/day) associated with latitude and longitude coordinates corresponding to each point on the 0.5 • spatial IMERG grid.…”