Pitfalls of Climate Network Construction: A Statistical Perspective

TL;DR

This paper examines the statistical challenges in climate network construction, revealing how estimation uncertainties can lead to spurious links and biases, and proposes improved ensemble methods for more reliable analysis.

Contribution

It highlights the impact of estimation uncertainty on climate network features and introduces a statistically sound ensemble framework for significance testing.

Findings

Estimation variance causes spurious network features.

Locally coherent structures lead to false teleconnections.

Standard resampling methods are inadequate for significance assessment.

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.