Articles | Volume 17, issue 4
Research article
26 Feb 2020
Research article |  | 26 Feb 2020

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Christopher Krich, Jakob Runge, Diego G. Miralles, Mirco Migliavacca, Oscar Perez-Priego, Tarek El-Madany, Arnaud Carrara, and Miguel D. Mahecha

Data sets

TIGRAMITE-Causal discovery for time series datasets J. Runge

Climatic Research Unit (CRU): Time-series (TS) datasets of variations in climate with variations in other phenomena v3 University of East Anglia Climatic Research Unit, P. D. Jones, and I. C. Harris

A Non-Stationary 1981–2012 AVHRR NDVI3g Time Series ( J. E. Pinzon and C. J. Tucker

Short summary
Causal inference promises new insight into biosphere–atmosphere interactions using time series only. To understand the behaviour of a specific method on such data, we used artificial and observation-based data. The observed structures are very interpretable and reveal certain ecosystem-specific behaviour, as only a few relevant links remain, in contrast to pure correlation techniques. Thus, causal inference allows to us gain well-constrained insights into processes and interactions.
Final-revised paper