# Pitfalls of Climate Network Construction: A Statistical Perspective

arXiv: https://arxiv.org/abs/2211.02888 · HTML: https://mlsanity.com/paper/2211.02888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 119 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [High-recall causal discovery for autocorrelated time series with latent confounders](https://mlsanity.com/paper/2007.01884/) (2020)
- [Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets](https://mlsanity.com/paper/1406.7343/) (2014)
- [Estimating Mutual Information for Discrete-Continuous Mixtures](https://mlsanity.com/paper/1709.06212/) (2017)
- [Data Analysis with Bayesian Networks: A Bootstrap Approach](https://mlsanity.com/paper/1301.6695/) (2013)
- [An Overview on the Estimation of Large Covariance and Precision Matrices](https://mlsanity.com/paper/1504.02995/) (2015)
- [A Kernel Independence Test for Random Processes](https://mlsanity.com/paper/1402.4501/) (2014)
- [Demystifying Fixed k-Nearest Neighbor Information Estimators](https://mlsanity.com/paper/1604.03006/) (2016)
- [Bootstrapping Networks with Latent Space Structure](https://mlsanity.com/paper/1907.10821/) (2019)
- [Fast robust correlation for high-dimensional data](https://mlsanity.com/paper/1712.05151/) (2017)
- [Teleconnection patterns of different El Ni~no types revealed by climate network curvature](https://mlsanity.com/paper/2203.07035/) (2022)
- [Snowboot: Bootstrap Methods for Network Inference](https://mlsanity.com/paper/1902.09029/) (2019)
- [Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes](https://mlsanity.com/paper/1509.09293/) (2015)
- [Weighted directed clustering: interpretations and requirements for heterogeneous, inferred, and measured networks](https://mlsanity.com/paper/2105.06318/) (2021)

## Abstract (arXiv)

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.