An observational dataset from a wintertime field campaign in the Inn Valley, Austria, is analysed in order to study mechanisms of air pollution transport in an Alpine valley. The results illustrate three types of mechanisms: transport by a density current, backand-forth transport by valley winds, and transport by slope winds. The first type is associated with an air mass difference along the valley. Cooler air located in the lower part of the valley behaves like a density current and produces the advection of pollutants by upvalley winds. In the second type, strong horizontal gradients in pollution concentrations exist close to ground. Multiple wind reversals result in a back-and-forth transport of pollutants by weak valley winds. In the third type, upslope winds during daytime decrease low-level pollution concentrations and cause the formation of elevated pollution layers.

The mesoscale atmospheric model WRF is used over three Svalbard glaciers. The simulations are done with a setup of the model corresponding to the state-of-the-art model for polar conditions, Polar WRF, and it was validated using surface observations. The ERA-Interim reanalysis was used for boundary forcing and the model was used with three nested smaller domains, 24 and 8 km, and 2.7 km resolution. The model was used for a two-year period as well as for a more detailed study using 3 summer and winter months. In addition sensitivity tests using finer horizontal and vertical resolution in the boundary layer and using different physics schemes were performed. Temperature and incoming short- and long-wave radiation were skillfully simulated, with lower agreement between measured and modelled wind speed. Increased vertical resolution improved the frequency distributions of the wind speed and the temperature. The choice of different physics schemes only slightly changed the model results. The polar-optimized microphysics scheme outperformed a slightly simpler microphysics scheme, but the two alternative and more sophisticated PBL schemes improved the model score. A PBL scheme developed for very stable stratifications (QNSE) proved to be better in the winter.

[1] Multiyear glaciometeorological data have been collected at about the equilibrium line of Kongsvegen glacier in Svalbard. During the first summer of the investigation period, about 0.6 m of superimposed ice (SI) was built up because of effective meltwater refreeze upon the former glacier surface. Thus winter accumulation is completely retained from runoff, and latent heat is released equivalent to 27% of July net radiation. This ice layer, as well as part of the former year's ice, disappeared during the subsequent summer. Using a physically based snow model, the evolution of the snow pack properties as well as the observed amount of SI is successfully reproduced. However, the latter is only possible by modifying the model's water transport routine to also treat effects of ponding water, which have been observed. Two distinct phases of SI formation can be distinguished. A first one (15 cm) is initiated as soon as meltwater has penetrated to the underlying cold glacier ice. Toward the end of the melt period, however, the remaining body of SI is rapidly formed because of intense atmospheric cooling. The availability of ponding water plays an essential role in this context. During the following summer the initial refreezing phase was terminated by exposure of the newly built SI at the surface. The whole amount of the actual year and the major part of the former year's SI was melted then. A positive ice-albedo feedback process plays an additional role there. Sensitivity studies prove that the model results and their interpretation do not critically depend on possible uncertainties of input data and model parameters. Despite their local nature these studies are valuable steps toward understanding the regional distribution of SI on Arctic glaciers as well as their climate sensitivity. Remote sensing studies indicate that SI presently covers 35% of Kongsvegen's surface area.

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The flow and turbulence structure in the atmospheric boundary layer over complex mountainous terrain determines Earth–atmosphere interaction, that is, the exchange of energy, mass, and momentum between the surface over such terrain and the free atmosphere. Numerical models for weather and climate, even when operated at high or very high grid resolution, are known to be deficient, leading to inaccurate local forecasts (weather) or scenarios (climate). The nature and reasons for these deficiencies, however, are difficult to assess because systematic and long-term combined observational/modeling studies in mountainous terrain are missing. The Innsbruck Box (i-Box) project aims at filling in this gap through a network of long-term turbulence sites in truly complex terrain, complemented by similarly continuous (surface based) remote sensing and numerical modeling at high to highest [i.e., large-eddy simulation (LES)] resolution. This contribution details the i-Box approach, the experimental design, and available data, as well as the numerical modeling strategy. The first scientific highlights are presented to illustrate the potential of the i-Box data pool and possible future directions.