Articles | Volume 14, issue 1
https://doi.org/10.5194/esd-14-173-2023
https://doi.org/10.5194/esd-14-173-2023
Research article
 | 
14 Feb 2023
Research article |  | 14 Feb 2023

Reliability of resilience estimation based on multi-instrument time series

Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers

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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 esd-2022-41', Anonymous Referee #1, 07 Oct 2022
  • RC2: 'Comment on esd-2022-41', Anonymous Referee #2, 20 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (13 Nov 2022) by Anping Chen
AR by Taylor Smith on behalf of the Authors (06 Dec 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (08 Dec 2022) by Anping Chen
RR by Anonymous Referee #1 (16 Dec 2022)
RR by Anonymous Referee #3 (20 Dec 2022)
ED: Reconsider after major revisions (03 Jan 2023) by Anping Chen
AR by Taylor Smith on behalf of the Authors (16 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (18 Jan 2023) by Anping Chen
RR by Anonymous Referee #3 (26 Jan 2023)
ED: Publish subject to minor revisions (review by editor) (26 Jan 2023) by Anping Chen
AR by Taylor Smith on behalf of the Authors (27 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (28 Jan 2023) by Anping Chen
AR by Taylor Smith on behalf of the Authors (02 Feb 2023)  Manuscript 
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Short summary
Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
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