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

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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. 
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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 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.