Articles | Volume 17, issue 1
https://doi.org/10.5194/essd-17-221-2025
https://doi.org/10.5194/essd-17-221-2025
Data description paper
 | 
27 Jan 2025
Data description paper |  | 27 Jan 2025

Imputation of missing land carbon sequestration data in the AR6 Scenarios Database

Ruben Prütz, Sabine Fuss, and Joeri Rogelj

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-68', Thomas Bossy, 08 Apr 2024
    • AC1: 'Reply on RC1', Ruben Prütz, 22 Apr 2024
  • RC2: 'Comment on essd-2024-68', Anonymous Referee #2, 24 Jun 2024
    • AC2: 'Reply on RC2', Ruben Prütz, 27 Jun 2024
  • RC3: 'Comment on essd-2024-68', Anonymous Referee #3, 24 Jun 2024
    • AC3: 'Reply on RC3', Ruben Prütz, 28 Jun 2024
  • RC4: 'Comment on essd-2024-68', Anonymous Referee #4, 08 Jul 2024
    • AC4: 'Reply on RC4', Ruben Prütz, 09 Jul 2024
  • EC1: 'Comment on essd-2024-68', Martina Stockhause, 30 Jul 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Ruben Prütz on behalf of the Authors (26 Aug 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (02 Sep 2024) by Martina Stockhause
RR by Anonymous Referee #3 (19 Sep 2024)
RR by Anonymous Referee #2 (07 Oct 2024)
RR by Anonymous Referee #4 (01 Nov 2024)
ED: Publish subject to minor revisions (review by editor) (04 Nov 2024) by Martina Stockhause
AR by Ruben Prütz on behalf of the Authors (14 Nov 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (16 Nov 2024) by Martina Stockhause
AR by Ruben Prütz on behalf of the Authors (18 Nov 2024)  Manuscript 

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Ruben Prütz on behalf of the Authors (22 Jan 2025)   Author's adjustment   Manuscript
EA: Adjustments approved (22 Jan 2025) by Martina Stockhause
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Short summary
The AR6 Scenarios Database lacks data on carbon dioxide removal (CDR) via land sinks for many pathways, hindering secondary scenario analyses. We tested and compared regression models, identifying k-nearest neighbors regression as most effective to predict missing CDR data. We provide an imputation dataset for incomplete global scenarios (n = 404) and for regional scenario variants (n = 2358) and discuss the caveats of our study, its use cases, and how our dataset compares to other approaches.
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