Articles | Volume 19, issue 2
https://doi.org/10.5194/tc-19-805-2025
https://doi.org/10.5194/tc-19-805-2025
Research article
 | 
21 Feb 2025
Research article |  | 21 Feb 2025

A minimal machine-learning glacier mass balance model

Marijn van der Meer, Harry Zekollari, Matthias Huss, Jordi Bolibar, Kamilla Hauknes Sjursen, and Daniel Farinotti

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2378', Signe Hillerup Larsen, 01 Oct 2024
  • RC2: 'Comment on egusphere-2024-2378', Anonymous Referee #2, 29 Oct 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (25 Nov 2024) by Brice Noël
AR by Marijn van der Meer on behalf of the Authors (26 Nov 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (27 Nov 2024) by Brice Noël
RR by Signe Hillerup Larsen (09 Dec 2024)
ED: Publish subject to minor revisions (review by editor) (18 Dec 2024) by Brice Noël
AR by Marijn van der Meer on behalf of the Authors (22 Dec 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (26 Dec 2024) by Brice Noël
AR by Marijn van der Meer on behalf of the Authors (02 Jan 2025)  Author's response   Manuscript 
Download
Short summary
Glacier retreat poses big challenges, making understanding how climate affects glaciers vital. But glacier measurements worldwide are limited. We created a simple machine-learning model called miniML-MB, which estimates annual changes in glacier mass in the Swiss Alps. As input, miniML-MB uses two climate variables: average temperature (May–Aug) and total precipitation (Oct–Feb). Our model can accurately predict glacier mass from 1961 to 2021 but struggles for extreme years (2022 and 2023).
Share