Global warming projections still suffer from a limited representation of the permafrost-carbon feedback. Predicting the response of the permafrost temperature to climate changes requires accurate simulations of the Arctic snow and soil properties. This study assesses the capacity of the coupled models ISBA-Crocus and ISBA-ES to simulate snow and soil properties at Bylot Island, a high Arctic site. Field measurements complemented with ERA-interim reanalysis were used to drive the models and to

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... aluate simulation outputs. Snow height, density, temperature, thermal conductivity and thermal resistance are examined to determine the critical variables involved in the soil thermal regime. Simulated soil properties are compared with measurements of thermal conductivity, temperature and water content. The simulated snow density profiles are erroneous, because Crocus and ES do not represent the upward water vapour fluxes generated by the strong temperature gradients within the snowpack. The resulting vertical profiles of thermal conductivity are inverted compared to observations, with high simulated values at the bottom of the snowpack. Still, ISBA-Crocus manages to successfully simulate the soil temperature in winter. Results are satisfactory in summer, but the temperature of the top soil could be better reproduced by representing adequately surface organic layers, i.e. mosses and litter, and in particular their water retention capacity. Transition periods (soil freezing and thawing) are the least well reproduced because the high basal snow thermal conductivity induces too rapid heat transfers between the soil and the snow in simulations. Hence, global climate models should carefully consider Arctic snow thermal properties, and especially the thermal conductivity of the basal snow layer, to perform accurate predictions of the permafrost evolution under climate changes.