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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (21 Oct 2019) by Ivonne Trebs
AR by Christopher Krich on behalf of the Authors (06 Dec 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (06 Jan 2020) by Ivonne Trebs
RR by Benjamin L. Ruddell (16 Jan 2020)
ED: Publish as is (20 Jan 2020) by Ivonne Trebs
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