Preprints
https://doi.org/10.5194/egusphere-2026-2612
https://doi.org/10.5194/egusphere-2026-2612
26 May 2026
 | 26 May 2026
Status: this preprint is open for discussion and under review for The Cryosphere (TC).

Temperature sensitivity of snow viscoplasticity: evidence from controlled creep experiments

Louis Védrine, Marius Brun, Mathis Bozon, Benoît Laurent, and Pascal Hagenmuller

Abstract. Snow is a warm, porous material whose densification under its own weight is highly temperature-dependent. Despite decades of research, reported activation energies for snow viscoplasticity remain highly scattered, ranging from 40 to 600 kJ mol-1. We quantified the temperature dependence of snow viscoplasticity using in-tomograph creep experiments with a newly developed thermo-mechanical cell. This design allows accurate load and temperature control, micrometric displacement measurement, and microstructural evolution monitoring via X-ray tomography. To disentangle microstructural evolution and temperature effects on the compression rate, we used a state-of-the-art viscoplastic model. We conducted five experiments on centimetre-scale samples of decomposing and fragmented precipitation particles with initial densities ranging from 278 to 320 kg m-3 under an applied stress of 1.25 kPa. Each experiment comprised five temperature steps from -6 to -18°C, each lasting one day, and resulted in a mean final vertical strain of 4%. We show that the viscoplastic response follows a two-regime Arrhenius law, with activation energies Qh = 126 ± 6 kJ mol-1 for temperatures above -13 ± 1°C and Ql = 51 ± 18 kJ mol-1 below. This temperature sensitivity matches that reported for polycrystalline ice but is greater than that used in detailed snowpack models.

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Louis Védrine, Marius Brun, Mathis Bozon, Benoît Laurent, and Pascal Hagenmuller

Status: open (until 07 Jul 2026)

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Louis Védrine, Marius Brun, Mathis Bozon, Benoît Laurent, and Pascal Hagenmuller
Louis Védrine, Marius Brun, Mathis Bozon, Benoît Laurent, and Pascal Hagenmuller
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Short summary
Snow slowly compacts under its own weight, and this process depends strongly on temperature, but past estimates vary widely. We developed a device to precisely control temperature and tested the same snow sample under different conditions. We found two distinct temperature regimes and a stronger sensitivity than assumed in existing snowpack models, improving how snow compaction is represented.
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