Articles | Volume 12, issue 4
Earth Syst. Sci. Data, 12, 2725–2746, 2020
https://doi.org/10.5194/essd-12-2725-2020
Earth Syst. Sci. Data, 12, 2725–2746, 2020
https://doi.org/10.5194/essd-12-2725-2020

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 et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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)
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
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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.