Articles | Volume 14, issue 9
https://doi.org/10.5194/tc-14-3235-2020
https://doi.org/10.5194/tc-14-3235-2020
Research article
 | 
29 Sep 2020
Research article |  | 29 Sep 2020

Possible biases in scaling-based estimates of glacier change: a case study in the Himalaya

Argha Banerjee, Disha Patil, and Ajinkya Jadhav

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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
ED: Reconsider after major revisions (further review by editor and referees) (30 Mar 2020) by Valentina Radic
AR by Argha Banerjee on behalf of the Authors (28 Apr 2020)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (13 May 2020) by Valentina Radic
RR by Anonymous Referee #2 (19 May 2020)
RR by Eviatar Bach (26 May 2020)
ED: Reconsider after major revisions (further review by editor and referees) (10 Jun 2020) by Valentina Radic
AR by Argha Banerjee on behalf of the Authors (25 Jun 2020)  Manuscript 
ED: Publish subject to revisions (further review by editor and referees) (19 Jul 2020) by Valentina Radic
AR by Argha Banerjee on behalf of the Authors (21 Jul 2020)  Author's response   Manuscript 
ED: Publish subject to minor revisions (review by editor) (05 Aug 2020) by Valentina Radic
AR by Argha Banerjee on behalf of the Authors (10 Aug 2020)  Author's response   Manuscript 
ED: Publish as is (27 Aug 2020) by Valentina Radic
AR by Argha Banerjee on behalf of the Authors (27 Aug 2020)  Manuscript 
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Short summary
Simple models of glacier dynamics based on volume–area scaling underestimate climate sensitivity and response time of glaciers. Consequently, they may predict a faster response and a smaller long-term glacier loss. These biases in scaling models are established theoretically and are analysed in detail by simulating the step response of a set of 703 Himalayan glaciers separately by three different models: a scaling model, a 2-D shallow-ice approximation model, and a linear-response model.