Preprints
https://doi.org/10.5194/tc-2022-210
https://doi.org/10.5194/tc-2022-210
28 Oct 2022
 | 28 Oct 2022
Status: a revised version of this preprint is currently under review for the journal TC.

Brief communication: Nonlinear sensitivity of glacier-mass balance attested by temperature-index models

Christian Vincent and Emmanuel Thibert

Abstract. Temperature-index models have been widely used for glacier-mass projections over the 21st century. The ability of temperature-index models to capture nonlinear responses of glacier mass balance (MB) to high deviations in air temperature and solid precipitation has recently been questioned by mass-balance simulations employing advanced machine-learning techniques. Here, we performed numerical experiments with a classic and simple temperature-index model and confirmed that such models are capable of detecting nonlinear responses of glacier MB to temperature and precipitation changes. Nonlinearities derive from the change of the degree-day factor over the ablation season and from the lengthening of the ablation season.

Christian Vincent and Emmanuel Thibert

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review of Vincent and Thibert', Jordi Bolibar, 24 Nov 2022
    • AC1: 'Reply on RC1', C. Vincent, 07 Feb 2023
  • RC2: 'Comment on tc-2022-210', Anonymous Referee #2, 05 Dec 2022
    • AC2: 'Reply on RC2', C. Vincent, 07 Feb 2023
  • RC3: 'Comment on tc-2022-210', Anonymous Referee #3, 13 Dec 2022
    • AC3: 'Reply on RC3', C. Vincent, 07 Feb 2023

Christian Vincent and Emmanuel Thibert

Christian Vincent and Emmanuel Thibert

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Short summary
Temperature-index models have been widely used for glacier-mass projections in the future. The ability of these models to capture nonlinear responses of glacier mass balance (MB) to high deviations in air temperature and solid precipitation has recently been questioned by mass-balance simulations employing advanced machine-learning techniques. Here, we confirmed that temperature-index models are capable of detecting nonlinear responses of glacier MB to temperature and precipitation changes.