Articles | Volume 17, issue 5
https://doi.org/10.5194/tc-17-1989-2023
https://doi.org/10.5194/tc-17-1989-2023
Brief communication
 | 
12 May 2023
Brief communication |  | 12 May 2023

Brief communication: Non-linear sensitivity of glacier mass balance to climate attested by temperature-index models

Christian Vincent and Emmanuel Thibert

Related authors

Creep enhancement and sliding in a temperate, hard-bedded alpine glacier
Juan-Pedro Roldán-Blasco, Adrien Gilbert, Luc Piard, Florent Gimbert, Christian Vincent, Olivier Gagliardini, Anuar Togaibekov, Andrea Walpersdorf, and Nathan Maier
EGUsphere, https://doi.org/10.5194/egusphere-2024-1600,https://doi.org/10.5194/egusphere-2024-1600, 2024
Short summary
Reanalysis of the longest mass balance series in Himalaya using nonlinear model: Chhota Shigri Glacier (India)
Mohd Farooq Azam, Christian Vincent, Smriti Srivastava, Etienne Berthier, Patrick Wagnon, Himanshu Kaushik, Arif Hussain, Manoj Kumar Munda, Arindan Mandal, and Alagappan Ramanathan
EGUsphere, https://doi.org/10.5194/egusphere-2024-644,https://doi.org/10.5194/egusphere-2024-644, 2024
Short summary
New insights into the decadal variability in glacier volume of a tropical ice cap, Antisana (0°29′ S, 78°09′ W), explained by the morpho-topographic and climatic context
Rubén Basantes-Serrano, Antoine Rabatel, Bernard Francou, Christian Vincent, Alvaro Soruco, Thomas Condom, and Jean Carlo Ruíz
The Cryosphere, 16, 4659–4677, https://doi.org/10.5194/tc-16-4659-2022,https://doi.org/10.5194/tc-16-4659-2022, 2022
Short summary
Geodetic point surface mass balances: a new approach to determine point surface mass balances on glaciers from remote sensing measurements
Christian Vincent, Diego Cusicanqui, Bruno Jourdain, Olivier Laarman, Delphine Six, Adrien Gilbert, Andrea Walpersdorf, Antoine Rabatel, Luc Piard, Florent Gimbert, Olivier Gagliardini, Vincent Peyaud, Laurent Arnaud, Emmanuel Thibert, Fanny Brun, and Ugo Nanni
The Cryosphere, 15, 1259–1276, https://doi.org/10.5194/tc-15-1259-2021,https://doi.org/10.5194/tc-15-1259-2021, 2021
Short summary
Numerical modeling of the dynamics of the Mer de Glace glacier, French Alps: comparison with past observations and forecasting of near-future evolution
Vincent Peyaud, Coline Bouchayer, Olivier Gagliardini, Christian Vincent, Fabien Gillet-Chaulet, Delphine Six, and Olivier Laarman
The Cryosphere, 14, 3979–3994, https://doi.org/10.5194/tc-14-3979-2020,https://doi.org/10.5194/tc-14-3979-2020, 2020
Short summary

Related subject area

Discipline: Glaciers | Subject: Alpine Glaciers
Unprecedented 21st century glacier loss on Mt. Hood, Oregon, USA
Nicolas Bakken-French, Stephen J. Boyer, B. Clay Southworth, Megan Thayne, Dylan H. Rood, and Anders E. Carlson
The Cryosphere, 18, 4517–4530, https://doi.org/10.5194/tc-18-4517-2024,https://doi.org/10.5194/tc-18-4517-2024, 2024
Short summary
Distributed surface mass balance of an avalanche-fed glacier
Marin Kneib, Amaury Dehecq, Adrien Gilbert, Auguste Basset, Evan S. Miles, Guillaume Jouvet, Bruno Jourdain, Etienne Ducasse, Luc Beraud, Antoine Rabatel, Jérémie Mouginot, Guillem Carcanade, Olivier Laarman, Fanny Brun, and Delphine Six
EGUsphere, https://doi.org/10.5194/egusphere-2024-1733,https://doi.org/10.5194/egusphere-2024-1733, 2024
Short summary
Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery
Marin Kneib, Amaury Dehecq, Fanny Brun, Fatima Karbou, Laurane Charrier, Silvan Leinss, Patrick Wagnon, and Fabien Maussion
The Cryosphere, 18, 2809–2830, https://doi.org/10.5194/tc-18-2809-2024,https://doi.org/10.5194/tc-18-2809-2024, 2024
Short summary
Brief communication: Recent estimates of glacier mass loss for western North America from laser altimetry
Brian Menounos, Alex Gardner, Caitlyn Florentine, and Andrew Fountain
The Cryosphere, 18, 889–894, https://doi.org/10.5194/tc-18-889-2024,https://doi.org/10.5194/tc-18-889-2024, 2024
Short summary
The Aneto glacier's (Central Pyrenees) evolution from 1981 to 2022: ice loss observed from historic aerial image photogrammetry and remote sensing techniques
Ixeia Vidaller, Eñaut Izagirre, Luis Mariano del Rio, Esteban Alonso-González, Francisco Rojas-Heredia, Enrique Serrano, Ana Moreno, Juan Ignacio López-Moreno, and Jesús Revuelto
The Cryosphere, 17, 3177–3192, https://doi.org/10.5194/tc-17-3177-2023,https://doi.org/10.5194/tc-17-3177-2023, 2023
Short summary

Cited articles

Agatonovic-Kustrin, S. and Beresford, R.: Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research, J. Pharmaceut. Biomed., 22, 5, https://doi.org/10.1016/s0731-7085(99)00272-1, 2000. 
Bolibar, J., Rabatel, A., Gouttevin, I., Galiez, C., Condom, T., and Sauquet, E.: Deep learning applied to glacier evolution modelling, The Cryosphere, 14, 565–584, https://doi.org/10.5194/tc-14-565-2020, 2020. 
Bolibar, J., Rabatel, A., Gouttevin, I., Zekollari, H., and Galiez, C.: Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning, Nat. Commun., 13, 409, https://doi.org/10.1038/s41467-022-28033-0, 2022. 
Durand, Y., Laternser, M., Giraud, G., Etchevers, P., Lesaffre, B., and Mérindol, L.: Reanalysis of 44 yr of climate in the French Alps (1958–2002): Methodology, model validation, climatology, and trends for air temperature and precipitation, J. Appl. Meteorol. Clim., 48, 429–449, https://doi.org/10.1175/2008JAMC1808.1, 2009. 
Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Kopp, R. E., Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I. S., Ruiz, L., Sallée, J.-B., Slangen, A. B. A., and Yu, Y.: Ocean, Cryosphere and Sea Level Change, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1211–1362, https://doi.org/10.1017/9781009157896.011, 2021. 
Download
Short summary
Temperature-index models have been widely used for glacier mass projections in the future. The ability of these models to capture non-linear 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 non-linear responses of glacier MB to temperature and precipitation changes.