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 behavior 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 a major impact on network characteristics. When the data have a 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 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.