Pitfalls of Climate Network Construction—A Statistical Perspective

Abstract

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. Significance Statement Network-based approaches have gained renewed attention regarding the prediction of climate phenomena such as El Niño events, extreme regional precipitation patterns, anomalous polar vortex dynamics, and regarding understanding the Earth system. Even though climate networks are constructed from a limited amount of noisy data, they typically are not studied from a statistical perspective. However, such an approach is crucial: due to sampling uncertainty, climate networks unavoidably contain false and missing edges. We analyze how sampling artifacts impact the conclusions drawn from the networks and present both pitfalls and statistically robust procedures of network construction and evaluation. We aim to contribute to understanding the limitations and fully leveraging the potentials of network methods in climate and Earth system science.