Abstract. In alpine regions, wind-induced snow transport strongly influences the spatio-temporal evolution of the snow cover throughout the winter season. To gain understanding on the complex processes that drive the redistribution of snow, a new numerical model is developed. It directly couples the detailed snowpack model Crocus with the atmospheric model Meso-NH. Meso-NH/Crocus simulates snow transport in saltation and in turbulent suspension and includes the sublimation of suspended snow particles. The coupled model is evaluated against data collected around the experimental site of Col du Lac Blanc (2720 m a.s.l., French Alps). First, 1-D simulations show that a detailed representation of the first metres of the atmosphere is required to reproduce strong gradients of blowing snow concentration and compute mass exchange between the snowpack and the atmosphere. Secondly, 3-D simulations of a blowing snow event without concurrent snowfall have been carried out. Results show that the model captures the main structures of atmospheric flow in alpine terrain. However, at 50 m grid spacing, the model reproduces only the patterns of snow erosion and deposition at the ridge scale and misses smaller scale patterns observed by terrestrial laser scanning. When activated, the sublimation of suspended snow particles causes a reduction of deposited snow mass of 5.3 % over the calculation domain. Total sublimation (surface + blowing snow) is three times higher than surface sublimation in a simulation neglecting blowing snow sublimation.

The French critical zone initiative, called OZCAR (Observatoires de la Zone Critique-Application et Recherche or Critical Zone Observatories-Application and Research) is a National Research Infrastructure (RI). OZCAR-RI is a network of instrumented sites, bringing together 21 pre-existing research observatories monitoring different compartments of the zone situated between "the rock and the sky," the Earth's skin or critical zone (CZ), over the long term. These observatories are regionally based and have specific initial scientific questions, monitoring strategies, databases, and modeling activities. The diversity of OZCAR-RI observatories and sites is well representative of the heterogeneity of the CZ and of the scientific communities studying it. Despite this diversity, all OZCAR-RI sites share a main overarching mandate, which is to monitor, understand, and predict ("earthcast") the fluxes of water and matter of the Earth's near surface and how they will change in response to the "new climatic regime." The vision for OZCAR strategic development aims at designing an open infrastructure, building a national CZ community able to share a systemic representation of the CZ , and educating a new generation of scientists more apt to tackle the wicked problem of the Anthropocene. OZCAR articulates around: (i) a set of common scientific questions and cross-cutting scientific activities using the wealth of OZCAR-RI observatories, (ii) an ambitious instrumental development program, and (iii) a better interaction between data and models to integrate the different time and spatial scales. Internationally, OZCAR-RI aims at strengthening the CZ community by providing a model of organization for pre-existing observatories and by offering CZ instrumented sites. OZCAR is one of two French mirrors of the European Strategy Forum on Research Infrastructure (eLTER-ESFRI) project.

For the first time a simulation of blowing snow events was validated in detail using one-month long observations (January 2010) made in Adélie Land, Antarctica. A regional climate model featuring a coupled atmosphere/blowing snow/snowpack model is forced laterally by meteorological re-analyses. The vertical grid spacing was 2 m from 2 to 20 m above the surface and the horizontal grid spacing was 5 km. The simulation was validated by comparing the occurrence of blowing snow events and other meteorological parameters at two automatic weather stations. The Nash test allowed us to compute efficiencies of the simulation. The regional climate model simulated the observed wind speed with a positive efficiency (0.69). Wind speeds higher than 12 m s −1 were underestimated. Positive efficiency of the simulated wind speed was a prerequisite for validating the blowing snow model. Temperatures were simulated with a slightly negative efficiency (−0.16) due to overestimation of the amplitude of the diurnal cycle during one week, probably because the cloud cover was underestimated at that location during the period concerned. Snowfall events were correctly simulated by our model, as confirmed by field reports. Because observations suggested that our instrument (an acoustic sounder) tends to overestimate the blowing snow flux, data were not sufficiently accurate to allow the complete validation of snow drift values. However, the simulation of blowing snow occurrence was in good agreement with the observations made during the first 20 days of January 2010, despite the fact that the blowing snow flux may be underestimated by the regional climate model during pure blowing snow events. We found that blowing snow occurs in Adélie Land only when the 30-min wind speed value at 2 m a.g.l. is >10 m s −1 . The validation for the last 10 days of January 2010 was less satisfactory because of complications introduced by surface melting and refreezing.

Abstract. Using the original setup described in Gallée et al. (2013), the MAR regional climate model including a coupled snowpack/aeolian snow transport parameterization, was run at a fine spatial (5 km horizontal and 2 m vertical) resolution over 1 summer month in coastal Adélie Land. Different types of feedback were taken into account in MAR including drag partitioning caused by surface roughness elements. Model outputs are compared with observations made at two coastal locations, D17 and D47, situated respectively 10 and 100 km inland. Wind speed was correctly simulated with positive values of the Nash test (0.60 for D17 and 0.37 for D47) but wind velocities above 10 m s −1 were underestimated at both D17 and D47; at D47, the model consistently underestimated wind velocity by 2 m s −1 . Aeolian snow transport events were correctly reproduced with the right timing and a good temporal resolution at both locations except when the maximum particle height was less than 1 m. The threshold friction velocity, evaluated only at D17 for a 7-day period without snowfall, was overestimated. The simulated aeolian snow mass fluxes between 0 and 2 m at D47 displayed the same variations but were underestimated compared to the second-generation FlowCapt ™ values, as was the simulated relative humidity at 2 m above the surface. As a result, MAR underestimated the total aeolian horizontal snow transport for the first 2 m above the ground by a factor of 10 compared to estimations by the second-generation FlowCapt ™ . The simulation was significantly improved at D47 if a 1-order decrease in the magnitude of z 0 was accounted for, but agreement with observations was reduced at D17. Our results suggest that z 0 may vary regionally depending on snowpack properties, which are involved in different types of feedback between aeolian transport of snow and z 0 .

Knowledge of snow particle speeds is necessary for deepening our understanding of the internal structures of drifting snow. In this study, we utilized a snow particle counter (SPC) developed to observe snow particle size distributions and snow mass flux. Using high-frequency signals from the SPC transducer, we obtained the sizes of individual particles and their durations in the sampling area. Measurements were first conducted in the field, with more precise measurements being obtained in a boundary layer established in a cold wind tunnel. The obtained results were compared with the results of a numerical analysis. Data on snow particle speeds, vertical velocity profiles, and their dependence on wind speed obtained in the field and in the wind tunnel experiments were in good agreement: both snow particle speed and wind speed increased with height, and the former was always 1 to 2 m s À1 less than the latter below a height of 1 m. Thus, we succeeded in obtaining snow particle speeds in drifting snow, as well as revealing the dependence of particle speed on both grain size and wind speed. The results were verified by similar trends observed using random flight simulations. However, the difference between the particle speed and the wind speed in the simulations was much greater than that observed under real conditions. Snow transport by wind is an aeolian process. Thus, the findings presented here should be also applicable to other geophysical processes relating to the aeolian transport of particles, such as blown sand and soil.

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E Di ssipati on of the tu rbul ent kin etic energy (m 2 s :1)

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