Ecosystem physio-phenology revealed using circular statistics

Pabon-Moreno, Daniel E.; Musavi, Talie; Migliavacca, Mirco; Reichstein, Markus; Römermann, Christine; Mahecha, Miguel D.

doi:https://doi.org/10.5194/bg-17-3991-2020

Articles | Volume 17, issue 15

https://doi.org/10.5194/bg-17-3991-2020

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

https://doi.org/10.5194/bg-17-3991-2020

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

Articles | Volume 17, issue 15

Research article

|

06 Aug 2020

Research article |

| 06 Aug 2020

Ecosystem physio-phenology revealed using circular statistics

Daniel E. Pabon-Moreno, Talie Musavi, Mirco Migliavacca, Markus Reichstein, Christine Römermann, and Miguel D. Mahecha

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Final revised paper (published on 06 Aug 2020)
Supplement to the final revised paper
Preprint (discussion started on 15 Oct 2019)
Supplement to the preprint

Interactive discussion

Status: closed

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

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RC1: 'Reviewer's comment', Anonymous Referee #1, 12 Nov 2019
- AC1: 'Response to reviewers', Daniel Ernesto Pabon Moreno, 15 Feb 2020
RC2: 'review on "Ecosystem physio-phenology revealed using circular statistics"', Anonymous Referee #2, 26 Nov 2019
- AC2: 'Response to reviewers', Daniel Ernesto Pabon Moreno, 15 Feb 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (24 Feb 2020) by David Bowling

AR by Daniel Ernesto Pabon Moreno on behalf of the Authors (06 Apr 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (20 Apr 2020) by David Bowling

RR by Anonymous Referee #1 (29 Apr 2020)

Suggestions for revision or reasons for rejection

In the manuscript entitled "Ecosystem physio-phenology revealed using circular statistics" submitted to Biogeosciences by Pabon-Moreno et al, the authors have performed an analysis of the timing of GPPmax across 52 flux sites using a circular regression method. The authors have also compared circular regression and linear regressing using simulated data and concluded the circular regression may be more suitable for these kinds of analysis.

Although the work is not of its first kind on the topic of circular stats for phenological modeling -as the authors have also pointed this out-, but the manuscript is still interesting. I believe the presentation of the work, however, can be much improved. There are also important questions and ambiguity about some of the technical parts of the manuscript that are not currently clear. In the manuscript, there are assumptions without proper justifications or performing sensitivity analysis for those assumptions. The presentations of the figures could also be improved. Overall, there is a lot of room for improvement.

- L18-19: the last sentence of the abstract is suggested to rephrased, it kind of awkward.

- L22: remove "e.g."

- L 45-48: "Bauerle et al. (2012) studied how photoperiod and temperature influence plants photosynthetic capacity, reporting that the photoperiod explains the variability of photosynthetic capacity better than temperature." Should mention study area, this is not a general fact, as it is a debated topic.

-L74-78: What about the issue regarding southern vs northern hemisphere? earlier in the Introduction you discussed about it.

- L93: define i and tau, right after the equation.

- L96: Is w0=1? If so, you need to tell the readers.

- L98: Not clear why you chose a decaying weight relationship, there should be a sensitivity analysis on how the results are affected by different weights. Your assumption is simply not justified.

- L109: Based on equation (2), y is the phase (angle) not DOY as it is introduced in the next line. You need a coefficient to transform.

- L127: replace "artificial" with "simulated"

- L128: Again, your choices of β1 = 0.3 and β2 = 0.1 are arbitrary, you need to do a sensitivity analysis and test the results for ranges of β1 and β2.

- Figure 2: not a great figure. very difficult to follow, what is 6.697? I would redraw with a better idea.

- Figure 4: in (a) the circles are smashed to together so the trends are not very clear.

- L202: What is "the power of the prediction"? vague term.

- Figure 7: Another figure to be improved. The smoothed curves do not add anything. It is not even clear how they are obtained or if there is any quantitative metric to see the fit. See EBF for instance. I would get rid of them. Also, the plots must have square aspect ratio.

- L214: The sentence referred to a paper from 2013, and "concluded that phenology models need to be improved". In fact, there has been much improvements on phenology models since then.

- L217-218: "Indeed we considered our approach as a first step to implement more complex statistical techniques like decision trees, Gaussian process, or artificial neural networks, targeting a circular response variable." But the authors have not showed us how. From the manuscript, the connection between the circular regression and other methods mentioned here is not clear.

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ED: Publish subject to minor revisions (review by editor) (14 May 2020) by David Bowling

AR by Daniel Ernesto Pabon Moreno on behalf of the Authors (12 Jun 2020) Author's response Manuscript

ED: Publish as is (26 Jun 2020) by David Bowling

AR by Daniel Ernesto Pabon Moreno on behalf of the Authors (30 Jun 2020) Manuscript

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Short summary

Ecosystem CO₂ uptake changes in time depending on climate conditions. In this study, we analyze how different climate variables affect the timing when CO₂ uptake is at a maximum (DOY_GPPmax). We found that the joint effects of radiation, temperature, and vapor pressure deficit are the most relevant controlling factors of DOY_GPPmax and that if they increase, DOY_GPPmax will happen earlier. These results help us to better understand how CO₂ uptake could be affected by climate change.