The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

Pollutant dispersion processes over complex terrain are much more complicated than over flat areas, as they are affected by atmospheric interactions with the orography at different spatial scales. This paper reviews recent findings and progress in this field, focusing on both experimental and modeling perspectives. It highlights open questions and challenges to our capability for better understanding and representing atmospheric processes controlling the fate of pollutants over mountainous areas. In particular, attention is focused on new measurement techniques for the retrieval of spatially distributed turbulence information and air quality parameters, and on challenges for meteorological and dispersion models to reproduce fine-scale processes influenced by the orography. Finally, specific needs in this field are discussed, along with possible directions for future research efforts.

This paper describes and evaluates physical parameterizations accounting for the effect of rooftop mitigation strategies (RMSs) on the urban environment, in the context of the mesoscale model Weather Research and Forecasting (WRF). Through the new implementation, the sensitivity of near‐surface air temperature and building energy consumption to different RMSs is evaluated by means of numerical simulations in idealized urban areas, for typical summer and winter conditions. Rooftop mitigation strategies considered include cool roofs, green roofs, and rooftop photovoltaic panels. The reference case simulations are performed assuming buildings made by bricks, with roof composed of clay tiles. Results indicate that near‐surface air temperature is reduced by cool and green roofs during summer: cool roofs are the most efficient in decreasing air temperature, followed by irrigated green roofs. Photovoltaic panels, instead, induce a temperature increase during daytime and a small decrease during nighttime. Cool roofs reveal to be the most efficient strategy in reducing the energy consumption by air conditioning systems. During wintertime, green roofs maintain a higher near‐surface air temperature than clay tile roofs and largely decrease energy consumption. Even PVPs increase air temperature, as in the summer case. On the other hand, cool roofs reduce near‐surface air temperature during daytime, inducing an increase in energy consumption. The results presented here show that the parameterization schemes implemented in the WRF model can be a valuable tool to evaluate the effects of mitigation strategies in the urban environment.

High‐resolution simulations are performed with the Weather Research and Forecasting (WRF) model, coupled with an advanced urban parameterization scheme, to evaluate the modifications induced by the urban area of Trento on boundary‐layer processes in the Alpine Adige Valley in a typical summer sunny day. Specific gridded datasets of urban morphology parameters and anthropogenic heat releases were created to provide high‐resolution input information for the urban scheme. Comparison of model results against measurements from surface weather stations shows that the model simulates reasonably well the development of valley winds, as well as the complex interaction occurring north of Trento between the up‐valley wind of the Adige Valley and a lake breeze flowing from a tributary valley. The urban heat island of the city is also well captured by the model, with stronger intensities at night and lower values during daytime. Comparisons with an idealized simulation, where all the urban land use grid points are replaced by cropland, suggest that the city inhibits the development of the ground‐based thermal inversion at night and also affects valley winds, modifying both the typical down‐valley wind, and the interaction between the up‐valley wind of the Adige Valley and the lake breeze. Further sensitivity tests are performed to evaluate the impacts of the gridded datasets of urban morphology and anthropogenic heat releases, and to assess what are the benefits of using the advanced urban scheme against a simple bulk parameterization. Results highlight that even the moderate anthropogenic heat releases present in Trento appreciably affect nocturnal temperatures in the city, while gridded information on urban parameters is not essential in the present case, because urban morphology does not display a high spatial variability. However, it is shown that the choice of the urban scheme may have significant impacts on both temperature and wind fields.

The paper presents the results of high-resolution simulations performed with the WRF Model, coupled with two different land surface schemes, Noah and Noah_MP, with the aim of accurately reproducing winter season meteorological conditions in a typical Alpine valley. Accordingly, model results are compared against data collected during an intensive field campaign performed in the Adige Valley, in the eastern Italian Alps. In particular, the ability of the model in reproducing the time evolution of 2-m temperature and of incoming and outgoing shortwave and longwave radiation is examined. The validation of model results highlights that, in this context, WRF reproduces rather poorly near-surface temperature over snow-covered terrain, with an evident underestimation, during both daytime and nighttime. Furthermore it fails to capture specific atmospheric processes, such as the temporal evolution of the ground-based thermal inversion. The main cause of these errors lies in the miscalculation of the mean gridcell albedo, resulting in an inaccurate estimate of the reflected solar radiation calculated by both Noah and Noah_MP. Therefore, modifications to the initialization, to the land-use classification, and to both land surface models are performed to improve model results, by intervening in the calculation of the albedo, of the snow cover, and of the surface temperature. Qualitative and quantitative analyses show that, after these changes, a significant improvement in the comparability between model results and observations is achieved. In particular, outgoing shortwave radiation is lowered, 2-m temperature maxima increased accordingly, and ground-based thermal inversions are better captured.