Nonlinear spectral analysis extracts harmonics from land-atmosphere fluxes

TL;DR

This paper demonstrates that nonlinear spectral analysis, specifically NLSA, effectively extracts harmonics from land-atmosphere CO2 flux data, outperforming linear methods and aiding in understanding complex climate interactions.

Contribution

The study shows that NLSA can better identify periodic signals and harmonics in land-atmosphere flux data compared to traditional linear methods, especially under ideal measurement conditions.

Findings

NLSA outperforms linear methods in harmonic detection.

NLSA accurately extracts seasonal cycles from flux data.

Measurement irregularities hinder harmonic detection.

Abstract

Understanding the dynamics of the land-atmosphere exchange of CO$_2$ is key to advance our predictive capacities of the coupled climate-carbon feedback system. In essence, the net vegetation flux is the difference of the uptake of CO$_2$ via photosynthesis and the release of CO$_2$ via respiration, while the system is driven by periodic processes at different time-scales. The complexity of the underlying dynamics poses challenges to classical decomposition methods focused on maximizing data variance, such as singular spectrum analysis. Here, we explore whether nonlinear data-driven methods can better separate periodic patterns and their harmonics from noise and stochastic variability. We find that Nonlinear Laplacian Spectral Analysis (NLSA) outperforms the linear method and detects multiple relevant harmonics. However, these harmonics are not detected in the presence of substantial measurement irregularities. In summary, the NLSA approach can be used to both extract the seasonal cycle more accurately than linear methods, but likewise detect irregular signals resulting from irregular land-atmosphere interactions or measurement failures. Improving the detection capabilities of time-series decomposition is essential for improving land-atmosphere interactions models that should operate accurately on any time scale.