Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Temperature-index models have been widely used for glacier-mass projections over the 21<sup>st</sup> 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.

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