Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products

Yang, Ruqi; Wang, Jun; Zeng, Ning; Sitch, Stephen; Tang, Wenhan; McGrath, Matthew Joseph; Cai, Qixiang; Liu, Di; Lombardozzi, Danica; Tian, Hanqin; Jain, Atul K.; Han, Pengfei

doi:https://doi.org/10.5194/esd-13-833-2022

Articles | Volume 13, issue 2

https://doi.org/10.5194/esd-13-833-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esd-13-833-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 2

Research article

|

19 Apr 2022

Research article |

| 19 Apr 2022

Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products

Ruqi Yang, Jun Wang, Ning Zeng, Stephen Sitch, Wenhan Tang, Matthew Joseph McGrath, Qixiang Cai, Di Liu, Danica Lombardozzi, Hanqin Tian, Atul K. Jain, and Pengfei Han

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Final revised paper (published on 19 Apr 2022)
Supplement to the final revised paper
Preprint (discussion started on 23 Sep 2021)
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Interactive discussion

Status: closed

AC1: 'Correction figure 4 for "Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products"', Ruqi Yang, 30 Sep 2021

The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2021-69/esd-2021-69-AC1-supplement.pdf

Citation: https://doi.org/10.5194/esd-2021-69-AC1
RC1:
'Comment on esd-2021-69', Anonymous Referee #1, 27 Oct 2021

Ruqi Yang et al. shows spatial patterns of GPP trends based on multiple datasets including NIRv, satellite-based, and DGVM and found out differences between datasets. I believe this information is useful to study global GPP changes and the uncertainty of each dataset. I have only a few minor comments on this manuscript.

L79 While NIRv is introduced in the abstract, it is needed to describe here again

"Long-term satellite-based near-infrared radiance of vegetation (NIRv)"

L90 Instead of mentioning TRHENDYv6 here, how about simulations from process-based models?

L96 I think the explanation of “NIRv” is missing in section 2 (Datasets and methods). I believe the authors need to explain details about NIRv in section 2 (should be 2.1). For example, how this dataset was gained and evaluated in the previous literature.

L248 Can you provide 95% confidence intervals for the trend of global GPP such as 0.37 ± ??? (DGVM ensemble mean)

L403 And -> Also,

Citation: https://doi.org/10.5194/esd-2021-69-RC1
- AC2: 'Reply on RC1', Ruqi Yang, 22 Feb 2022
  
  The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2021-69/esd-2021-69-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/esd-2021-69-AC2
RC2:
'Comment on esd-2021-69', Anonymous Referee #2, 29 Jan 2022

The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2021-69/esd-2021-69-RC2-supplement.pdf

Citation: https://doi.org/10.5194/esd-2021-69-RC2
- AC3: 'Reply on RC2', Ruqi Yang, 22 Feb 2022
  
  The comment was uploaded in the form of a supplement: https://esd.copernicus.org/preprints/esd-2021-69/esd-2021-69-AC3-supplement.pdf
  
  Citation: https://doi.org/10.5194/esd-2021-69-AC3

Peer review completion

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

ED: Reconsider after major revisions (25 Feb 2022) by Somnath Baidya Roy

AR by Ruqi Yang on behalf of the Authors (28 Feb 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (04 Mar 2022) by Somnath Baidya Roy

RR by Anonymous Referee #2 (14 Mar 2022)

ED: Publish as is (14 Mar 2022) by Somnath Baidya Roy

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Short summary

We comprehensively investigate historical GPP trends based on five kinds of GPP datasets and analyze the causes for any discrepancies among them. Results show contrasting behaviors between modeled and satellite-based GPP trends, and their inconsistencies are likely caused by the contrasting performance between satellite-derived and modeled leaf area index (LAI). Thus, the uncertainty in satellite-based GPP induced by LAI undermines its role in assessing the performance of DGVM simulations.