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

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

doi:https://doi.org/10.5194/bg-17-1033-2020

Articles | Volume 17, issue 4

https://doi.org/10.5194/bg-17-1033-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-17-1033-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

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Final revised paper (published on 26 Feb 2020)
Preprint (discussion started on 13 Aug 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review', Anonymous Referee #1, 04 Sep 2019
- AC3: 'Response to review 1', Christopher Krich, 16 Oct 2019
RC2: 'Review 2', Anonymous Referee #2, 12 Sep 2019
- AC2: 'Response to reviewer 2', Christopher Krich, 16 Oct 2019
RC3: 'Review of Krich et al.', Benjamin L. Ruddell, 14 Sep 2019
- AC1: 'Response to Ben Ruddell', Christopher Krich, 16 Oct 2019
AC2: 'Response to reviewer 2', Christopher Krich, 16 Oct 2019

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.