Evaluating two soil carbon models within the global land surface model JSBACH using surface and spaceborne observations of atmospheric CO<sub>2</sub>

Thum, Tea; Nabel, Julia E. M. S.; Tsuruta, Aki; Aalto, Tuula; Dlugokencky, Edward J.; Liski, Jari; Luijkx, Ingrid T.; Markkanen, Tiina; Pongratz, Julia; Yoshida, Yukio; Zaehle, Sönke

doi:https://doi.org/10.5194/bg-17-5721-2020

Articles | Volume 17, issue 22

https://doi.org/10.5194/bg-17-5721-2020

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

https://doi.org/10.5194/bg-17-5721-2020

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

Articles | Volume 17, issue 22

Research article

|

23 Nov 2020

Research article |

| 23 Nov 2020

Evaluating two soil carbon models within the global land surface model JSBACH using surface and spaceborne observations of atmospheric CO₂

Tea Thum, Julia E. M. S. Nabel, Aki Tsuruta, Tuula Aalto, Edward J. Dlugokencky, Jari Liski, Ingrid T. Luijkx, Tiina Markkanen, Julia Pongratz, Yukio Yoshida, and Sönke Zaehle

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Preprint (discussion started on 10 Feb 2020)
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Status: closed

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

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RC1: 'Thum soil respiration review', Anonymous Referee #1, 24 Mar 2020
- AC1: 'Reply to Reviewer #1', Tea Thum, 30 Apr 2020
RC2: 'Review', Anonymous Referee #2, 02 Apr 2020
- AC2: 'Reply to the Reviewer #2', Tea Thum, 30 Apr 2020

Peer-review completion

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

ED: Reconsider after major revisions (18 May 2020) by Kirsten Thonicke

AR by Tea Thum on behalf of the Authors (22 Jun 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (26 Jun 2020) by Kirsten Thonicke

RR by William Wieder (18 Jul 2020)

Suggestions for revision or reasons for rejection

I appreciate the efforts made to revise this paper. Two outstanding questions from the discussion remained and I have a number of suggestions re. display items. None of these are large enough to warrant another review of the paper once addressed.

Line 420-430 I don’t really understand why (or agree with) the assertion that potential biases in GPP (Fig S9) don’t influence in evaluation of the soil models and NEE fluxes that are simulated by the model? Yes, they’re consistently high between YAS and CBA, but if you’re ultimately trying to evaluate with observations based on the net fluxes (or atmospheric CO2 concentrations) then biases in the gross fluxes being simulated need to be considered? As opposed to brushing this concern aside, why not discuss this limitation in the approach?

This concern extends to the discussion of soil C pools and turnover times. With large positive biases in GPP and small soil C pools the mean turnover times in YAS are really small (14 years). To me this suggests the turnover times and fluxes simulated by this model kind of crazy, a sentiment that seems confirmed by results from Fig 7 & 8 where YAS misses the seasonal cycle in CO2 measurements at multiple scales. If the paper’s intent is “Evaluating two soil carbon models”, should conclusions about the models in question be more strongly worded? For example, although “The YAS model better captured the magnitude and spatial distribution of soil carbon stocks globally”, these stocks likely wouldn’t look so good if the model received lower inputs (NPP > 75 Pg C/y!). You've done a lot of work, can stronger conclusions about the strengths and weakness of each model be stated.

Minor and technical concerns:
Line 66, have the ‘two soil models’ been introduced outside of the abstract?

Line 228, how the models “show clear differences and similar behavior”? This statement is confusing.

Line 240-250 & Line 395 are these regional sensitivities predictable based on the functions shown in Fig. S1? If so maybe these results can be contextualized based on the assumptions of environmental sensitivities illustrates in S1?

Line 376 replace ‘abundance’ with ‘concentration’

Fig 3. I like the consistency of using of the same colors for YAS and CBA results in many of the revised figures, but then using the same colors to different latitude bands in Fig 3 (S2 & S9) is confusing.

Fig. 4. It’s not obvious why the authors report turnover time anomalies that are subtracted from different baselines? Moreover, why not use the same color bar if the results are supposed to be normalized somehow? When characterizing the models, does just showing the raw turnover times (with a common colorbar) tell us more? I guess this kind of plot nicely shows soil moisture vs. temperature gradient in turnover time, is that the intent? Maybe flip the order of Figs 4 & 5 so the broader differences in the models are first highlighted?

Fig. 5. I like this nod to the Koven et al. 2017 study, can the observationally derived turnover times (and their uncertainty) from that paper be included?

Fig 6, 7 & associated text. I’m not really sure what the ‘relative values of the seasonal cycle amplitudes’ are illustrating or how they help inform the story being told here. Given their sparing definition and interpretation maybe these sub-panels should be removed?

When comparing results from the two soil models please use the same axes (e.g. left y-axis Fig S8)

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RR by Anonymous Referee #2 (20 Jul 2020)

ED: Publish subject to minor revisions (review by editor) (26 Aug 2020) by Kirsten Thonicke

AR by Tea Thum on behalf of the Authors (09 Sep 2020) Author's response Manuscript

ED: Publish as is (28 Sep 2020) by Kirsten Thonicke

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Short summary

Global vegetation models are important tools in estimating the impacts of global climate change. The fate of soil carbon is of the upmost importance as its emissions will enhance the atmospheric carbon dioxide concentration. To evaluate the skill of global vegetation models to model the soil carbon and its responses to environmental factors, it is important to use different data sources. We evaluated two different soil carbon models by using atmospheric carbon dioxide concentrations.

Evaluating two soil carbon models within the global land surface model JSBACH using surface and spaceborne observations of atmospheric CO2

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Evaluating two soil carbon models within the global land surface model JSBACH using surface and spaceborne observations of atmospheric CO₂