Patterns and controls of inter-annual variability in the terrestrial carbon budget

Marcolla, Barbara; Rödenbeck, Christian; Cescatti, Alessandro

doi:https://doi.org/10.5194/bg-14-3815-2017

Articles | Volume 14, issue 16

https://doi.org/10.5194/bg-14-3815-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/bg-14-3815-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 14, issue 16

Research article

|

23 Aug 2017

Research article |

| 23 Aug 2017

Patterns and controls of inter-annual variability in the terrestrial carbon budget

Barbara Marcolla, Christian Rödenbeck, and Alessandro Cescatti

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Final revised paper (published on 23 Aug 2017)
Preprint (discussion started on 18 Jan 2017)

Interactive discussion

Status: closed

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

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RC1: 'Review of Marcolla et al. ``Patterns and controls of inter-annual variability in the terrestrial carbon budget''', Nir Krakauer, 26 Jan 2017
RC2: 'Interesting study with some weaknesses in presentation', Anonymous Referee #2, 26 Feb 2017
RC3: 'Marcolla IAV review', Anonymous Referee #3, 09 Mar 2017
AC1: 'Responses to reviewers', Alessandro Cescatti, 10 Apr 2017

Peer-review completion

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

ED: Reconsider after major revisions (10 Apr 2017) by Alexey V. Eliseev

AR by Anna Wenzel on behalf of the Authors (13 Jun 2017) Author's response

ED: Referee Nomination & Report Request started (13 Jun 2017) by Alexey V. Eliseev

RR by Nir Krakauer (16 Jun 2017)

RR by Anonymous Referee #3 (30 Jun 2017)

Suggestions for revision or reasons for rejection

Marcolla and co-authors have done a nice job revising the manuscript. In particular, I appreciate the efforts that went into interpreting the results in section 3 of the revised text.

Broad comments
Sorry to keep harping on this, but if IAV is the SD of NEE calculated using a sliding, 12-month window (line 130) doesn’t this effectively remove potential trends in the results? This isn’t a field I work in, but results in Fig 5 showing that the ‘residuals’ explain nearly all of the variance in NEE seems like a completely expected result of how the analysis was done. Perhaps this assumption on my part is misconstrued, but I’d recommend removing this figure from the text, as it does little to inform broader message of the manuscript. More, the regions where trends (line 238) seem more important in the Jena product seem particularly void of observational constraints (Fig 1c).

Minor comments & Technical corrections

Again, standard deviation and IAV are used interchangeably throughout the manuscript, since they mean the same thing please just use one term and stick with it throughout the manuscript.

Line 17. I might reorganize this sentence for clarity: “Results show that the global average of IAV, quantified as the standard deviation of annual NEE, at FLUXNET sites is ~120 g C m-2 y-1 and peaks in arid ecosystems. This variability is almost six times larger than…”

Line 20. It’s unclear what the “two data-driven global products” are referring to (I’m assuming this is MPI and Jena)? If so maybe this sentence can be reorganized, “Most of the temporal variability observed in the last three decades of the MPI-MTE and Jena Inversion products is due to yearly anomalies, whereas the temporal trends explain only about 15% and 20% of the variability, respectively.

Why is tropics often capitalized?

Line 92, can a reference be supplied for this statement, or is all in the 2011 Jung paper?

I can see investigating the lag correlations of climate drivers and NEE variability is perhaps outside the scope of the paper, but still feel the topic still should be mentioned, as ground observations clearly demonstrate a temporal mismatch between the timing of weather anomalies and their ultimate influence on different components of the C cycle (e.g. Doughty et al. 2015). The lack of potential lags is mentioned for the MTE product (line 95, but not the present study)

Line 136, maybe it’s explained elsewhere, but how were IAV[NEE] normalized?

Line 161 “resumed” is a funny word choice here. Maybe replace with ‘shown’

Line 204. What is shown in the right panels? The text says “it’s the ratio of the mean IAV and GPP (right column)” while the caption claims “IAV[NEE] (CV[NEE], right panels). Please be clear and consistent with language throughout the text.

Line 239. Please provide references to support this claim.

Lines 250 to 265. The eruption of undefined pft abbreviations in this paragraph make the text nearly unintelligible.

Please refer to particular panels in the figure captions.

Fig 1 Would marking the observations used in the MTE product be useful (as in panels A & B)? Also, the figure caption should call out the different scale bars used for FLUXNET and gridded results.

Fig. 2. Please clarify in the caption what color bar is being used for MPI IAV. I’m assuming it’s the same as the Jena panels below, but it’s not really clear.

Fig. 3. I don’t see any black dots on the figure, I’m assuming MPI is in grey?

Fig. 4. It’s confusing to have the dot colors switch between data products in Figs 3 & 4. Can these display items use a common color scheme (e.g. fluxnet = red, Jena = green). Selecting colors appropriate for color blind readers would also be nice.

Fig 8 referring to panels in the caption would be helpful. Also, avoiding abbreviations in the caption will help reader understanding.

Figure 9 is still unclear. Is this the grid cell correlation between IAV[NEE] and (a) MPI GPP-TER (b) MIP CUP – CRP and (c) Jena CUP – CRP? Again, the caption is not clear.

References:
Doughty, C. E., et al. (2015), Drought impact on forest carbon dynamics and fluxes in Amazonia, Nature, 519(7541), 78-82, doi:10.1038/nature14213.

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ED: Publish subject to minor revisions (Editor review) (30 Jun 2017) by Alexey V. Eliseev

AR by Alessandro Cescatti on behalf of the Authors (10 Jul 2017) Author's response

ED: Publish as is (10 Jul 2017) by Alexey V. Eliseev

Short summary

Patterns and controls of the inter-annual variability of carbon net ecosystem exchange were analysed using three different data streams: ecosystem-level observations (FLUXNET database), a global upscaling of site-level fluxes (MPI-MTE), and a top–down estimate of fluxes (Jena CarboScope Inversion). Consistencies and discrepancies in the temporal and spatial patterns and in the climatic and physiological controls of the inter-annual variability were investigated for the three data sources.