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

AR by Christopher Krich on behalf of the Authors (20 Jan 2020)

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.