Data-mining analysis of the global distribution of soil carbon in observational databases and Earth system models

Hashimoto, Shoji; Nanko, Kazuki; Ťupek, Boris; Lehtonen, Aleksi

doi:https://doi.org/10.5194/gmd-10-1321-2017

Articles | Volume 10, issue 3

https://doi.org/10.5194/gmd-10-1321-2017

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

https://doi.org/10.5194/gmd-10-1321-2017

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

Articles | Volume 10, issue 3

Methods for assessment of models

|

28 Mar 2017

Methods for assessment of models |

| 28 Mar 2017

Data-mining analysis of the global distribution of soil carbon in observational databases and Earth system models

Shoji Hashimoto, Kazuki Nanko, Boris Ťupek, and Aleksi Lehtonen

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Final revised paper (published on 28 Mar 2017)
Supplement to the final revised paper
Preprint (discussion started on 30 Jun 2016)
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: 'Promising analysis marred by poor presentation and no reproducibility', Anonymous Referee #1, 03 Aug 2016
- AC1: 'Reply to the comments from Referee 1', S. Hashimoto, 07 Oct 2016
RC2: 'More context and analysis please', Katherine Todd-Brown, 17 Aug 2016
- AC2: 'Reply to the comments from K. Todd-Brown', S. Hashimoto, 07 Oct 2016

Peer-review completion

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

AR by S. Hashimoto on behalf of the Authors (07 Oct 2016) Author's response Manuscript

ED: Referee Nomination & Report Request started (08 Dec 2016) by David Ham

RR by Ben Bond-Lamberty (10 Dec 2016)

RR by Katherine Todd-Brown (05 Jan 2017)

Suggestions for revision or reasons for rejection

First I would like to thank the authors for their revisions to the previous version. The manuscript is much improved and, in particular, the addition of the scripts lends critical reproducibility to the study. I would like to see more context for the CMIP5 analysis and soil carbon models in general, the description of soil carbon models simulating input/output fluxes as being the reason the ESMs fall out so similarly is particularly troubling (see below). I’m also very concerned with the treatment of the data product as observational truth instead of model output in and of itself (see below). Finally there are some additional readability issues that need to be smoothed out. Other issues brought up are relatively minor or mere suggestions.

At the risk of bringing up a new topic in a re-review, I would urge the authors to consider reframing their study results: both the data products and ESM simulations are, in fact, model results. In particular data products extrapolate empirical relationships observed between environmental variables and SOC using a paint-by-numbers or machine learning scheme, where as ESMs extrapolate relationships between input/output fluxes and SOC plus environmental variables. Both products are the result of modelling work but with separate assumptions. The value in comparing the two is that the assumptions are independent and if they were to agree we would have an increased confidence in our overall understanding of the system However, it is debatable whether one or the other is closer to reality. The current framing holds up the data products as ‘TRUTH” without acknowledging that there are significant uncertainties that go into creating these products.

I would like to suggest, but not require, a more active title that alludes to the key conclusion of the manuscript.

There are significant readability issues with the current version of this manuscript (introduction and discussion are rough, methods and results sections are ok). I would suggest that the authors consider reworking the phrasing and logical structure of the introduction and discussion sections. Example: p1 l 57-40 it is unclear what ‘basis of inter-extrapolation of model outputs’ means in this context.

P2 L13-14 Microbial explicit models tend to produce unrealistic temporal oscillations (Wang et al., 2014) it’s not clear they are more realistic at the global scale. However there are a number of general reviews that have suggested various mechanisms and processes that could be included in the next generation of soil decomposition models which could be reviewed in this paragraph including not limited to (Luo et al., 2015; Ostle et al., 2009; Wieder et al., 2015)

There have been several assessments made of the soil carbon dynamics in CMIP5 models beyond the studies by Todd-Brown et al and Exbrayat et al, including but not limited to (Anav et al., 2013; Arora et al., 2013; Friedlingstein et al., 2014; Koven et al., 2015; Shao et al., 2013). The authors need to include an overview of the general findings of this body of work.

The methods section and scripts are much improved. Thank you!

P5 Why did the ‘data’ drivers differ so much? How were the data products constructed differently that might lead to this pattern?

P6 L7-9 While this is entirely true, the ESM soil organic carbon is simulated by modelling the input/output fluxes, this does not account for the behavioural similarity seen in this study. One could model input and output fluxes as constants or as a chaotic dynamical system and this would generate completely divergent patterns. Their similarities likely lay in the fact they are all first order linear ordinary differential equations, all be it non-autonomous ODEs.

P6 L14-18 I like the discussion points about the lack of structure driven differences between the ESMs.

P7 L6 These Q10 values from Todd-Brown 2013, 2014 were inferred Q10 values, not documented Q10. They were fitted during the post-hoc however the sentence currently reads as if they are documented Q10. Please clarify.

P7 L9-11 This seems a bit disconnected, not sure what you are trying to say here.

P8 L5-7 This is a contradictory sentence.

P8 L18-20 I believe that the authors mean to say something like. “The R code, with tutorial, for the BRT algorithm is …” that being said this is more appropriate as a cited reference placed in the methods section then part of the code availability statement.

Table 2: According to CMIP5 data use this needs a very specific format for the model group names and other material. Currently it does not conform.

The index id’s don’t really add anything to the model names, consider removing this from Table 2 and Figure 5.

-----References-------

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Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A., Liddicoat, S. K. and Knutti, R.: Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks, J. Clim., 27, 511–526, doi:10.1175/JCLI-D-12-00579.1, 2014.
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Ostle, N. J., Smith, P., Fisher, R., Ian Woodward, F., Fisher, J. B., Smith, J. U., Galbraith, D., Levy, P., Meir, P., McNamara, N. P. and Bardgett, R. D.: Integrating plant–soil interactions into global carbon cycle models, J. Ecol., 97(5), 851–863, doi:10.1111/j.1365-2745.2009.01547.x, 2009.
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ED: Reconsider after major revisions (06 Jan 2017) by David Ham

AR by S. Hashimoto on behalf of the Authors (08 Feb 2017) Author's response Manuscript

ED: Referee Nomination & Report Request started (08 Feb 2017) by David Ham

RR by Katherine Todd-Brown (25 Feb 2017)

ED: Publish subject to technical corrections (28 Feb 2017) by David Ham

AR by S. Hashimoto on behalf of the Authors (06 Mar 2017) Author's response Manuscript

Short summary

Soil organic carbon (SOC) stock simulated by Earth system models (ESMs) and those of observational databases are not well correlated when the two are compared at fine grid scales. To identify the key factors that govern global SOC distribution, we applied a data-mining scheme to observational databases and outputs from ESMs. This study not only identifies key factors but it also presents a new approach that compares the observational databases with ESM outputs.