Articles | Volume 20, issue 3
https://doi.org/10.5194/tc-20-1841-2026
https://doi.org/10.5194/tc-20-1841-2026
Research article
 | 
30 Mar 2026
Research article |  | 30 Mar 2026

Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature

Nils Bochow, Philipp Hess, and Alexander Robinson

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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 egusphere-2025-3927', Anonymous Referee #1, 08 Oct 2025
  • RC2: 'Comment on egusphere-2025-3927', Anonymous Referee #2, 17 Oct 2025

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) (01 Dec 2025) by Ruth Mottram
AR by Nils Bochow on behalf of the Authors (14 Jan 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to revisions (further review by editor and referees) (10 Feb 2026) by Ruth Mottram
AR by Nils Bochow on behalf of the Authors (11 Feb 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (03 Mar 2026) by Ruth Mottram
AR by Nils Bochow on behalf of the Authors (12 Mar 2026)  Manuscript 
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Short summary
This study presents a fast, physics-guided machine-learning method that downscales coarse climate fields to fine resolution while enforcing conservation of large-scale totals. Trained on regional climate simulations and driven by Earth system model output, it handles extremes and outperforms linear interpolation, providing realistic, high-resolution forcing for ice-sheet models and improving projections of Greenland’s sea-level contribution.
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