Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Zheng, Yi; Shen, Ruoque; Wang, Yawen; Li, Xiangqian; Liu, Shuguang; Liang, Shunlin; Chen, Jing M.; Ju, Weimin; Zhang, Li; Yuan, Wenping

doi:https://doi.org/10.5194/essd-12-2725-2020

Articles | Volume 12, issue 4

https://doi.org/10.5194/essd-12-2725-2020

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

https://doi.org/10.5194/essd-12-2725-2020

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

Articles | Volume 12, issue 4

Data description paper

|

12 Nov 2020

Data description paper |

| 12 Nov 2020

Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan

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Final revised paper (published on 12 Nov 2020)
Supplement to the final revised paper
Preprint (discussion started on 07 Aug 2019)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'More details to show the improvement of performance', Anonymous Referee #1, 30 Sep 2019
- AC1: 'Response to Reviewer #1', Yi Zheng, 18 Dec 2019
RC2: 'Thorough validation and uncertainty analysis are required.', Anonymous Referee #2, 10 Oct 2019
- AC2: 'Response to Reviewer #2', Yi Zheng, 18 Dec 2019

Peer-review completion

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

AR by Yi Zheng on behalf of the Authors (19 Dec 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (22 Jan 2020) by Yuyu Zhou

RR by Anonymous Referee #1 (14 Feb 2020)

RR by Anonymous Referee #2 (28 Feb 2020)

Suggestions for revision or reasons for rejection

The manuscript has been improved according to the review comments raised by reviewers. However, I am still much concerned about the use of the proposed approach and the resultant data. Most of all, it is not convincing that the proposed EC-LUE model outperforms or is comparable to the existing good models.

For this type of research requires a series validations to support the robustness of the proposed model. This study did some validations, but all of them were smoothed through spatio-temporal aggregation. For example, Figure 1 shows the annually aggregated data from the proposed model. Temporally detailed plots (density plots using monthly data) would be better to support the robustness of the model. In addition, a few other well-defined models can be used to generated such scatterplots for comparison. It is not clear whether the proposed model produces accurate GPP data or not. Considering the plot based GPP data as reference ones, it is crucial to compare the proposed model-derived GPP to other physical models' output in terms of the reference data, which is not shown in this paper.

In addition, it is necessary to closely look into a few specific sites (through selection of flux sites considering climate and other environmental factors) using time-series data. I guess the proposed model would work well for some sites, and not for others. Additional discussion should follow regarding that. When simply looking at Figure 8, the proposed model appears to somewhat underestimate GPP compared to the existing ones, which implies more validations are required on spatially and temporally detailed domains.

Regarding the uncertainty analysis, it would make the paper much robust if the authors provided uncertainty maps, not just narrative discussion based on potential factors that may result in uncertainty. More quantitative analysis should follow.

Overall, I agree that the authors put enormous effort on revising the model and comparing the proposed model-based GPP with other global models' output. However, I feel more (spatio-temporally) detailed validations and comparisons are necessary to evaluate the proposed model. ESSD is a data journal, which means if the accuracy of data is not well supported, they would not appeal the users.

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ED: Reconsider after major revisions (15 Mar 2020) by Yuyu Zhou

AR by Yi Zheng on behalf of the Authors (05 May 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (28 May 2020) by Yuyu Zhou

RR by Shanning Bao (13 Jun 2020)

ED: Reconsider after major revisions (17 Jun 2020) by Yuyu Zhou

AR by Yi Zheng on behalf of the Authors (30 Jul 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (19 Aug 2020) by Yuyu Zhou

RR by Anonymous Referee #3 (30 Aug 2020)

ED: Publish subject to minor revisions (review by editor) (02 Sep 2020) by Yuyu Zhou

AR by Yi Zheng on behalf of the Authors (12 Sep 2020) Author's response Manuscript

ED: Publish as is (23 Sep 2020) by Yuyu Zhou

AR by Yi Zheng on behalf of the Authors (25 Sep 2020) Manuscript

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Short summary

Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.