In November 2011, an Atlantic depression affected the Mediterranean basin, eventually evolving into a Tropical-Like Cyclone (TLC or Mediterranean Hurricane, usually designated as Medicane). In the region affected by the Medicane, mean sea level pressures down to 990 hPa, wind speeds of hurricane intensity close to the eye (around 115 km/h) and intense rainfall in the prefrontal zone were reported. The intensity of this event, together with its long permanence over the sea, suggested its suitability as a paradigmatic case for investigating the sensitivity of a numerical modeling system to different configurations, air-sea interface parameterizations and coupling approaches. Toward this aim, a set of numerical experiments with different parameterization schemes and levels of coupling complexity was carried out within the Coupled Ocean Atmosphere Wave Sediment Transport System (COAWST), which allows the description of air-sea dynamics by coupling an atmospheric model (WRF), an ocean circulation model (ROMS), and a wave model (SWAN). The sensitivity to different initialization times and Planetary Boundary Layer (PBL) parameterizations was firstly investigated by running a set of WRF standalone (atmospheric-only) simulations. In order to better understand the effect of coupling on the TLC formation, intensification and trajectory, different configurations of atmosphere-ocean coupling were subsequently tested, eventually including the full coupling among atmosphere, ocean and waves, also changing the PBL parameterization and the formulation of the surface roughness. Results show a strong sensitivity of both the trajectory and the intensity of this TLC to the initial conditions, while the tracks and intensities provided by the coupled modeling approaches explored in this study do not introduce drastic modifications with respect to those resulting from a fine-tuned standalone atmospheric run, though they provide by definition a better physical and energetic consistency. Nevertheless; the use of different schemes for the calculation of the surface roughness from wave motion, which reflects the description of air-sea interface processes, can significantly affect the results in the fully coupled runs.

We evaluate the exposure during nadir observations with JEM-EUSO, the Extreme Universe Space Observatory, on-board the Japanese Experiment Module of the International Space Station. Designed as a mission to explore the extreme energy Universe from space, JEM-EUSO will monitor the Earth's nighttime atmosphere to record the ultraviolet light from tracks generated by extensive air showers initiated by ultra-high energy cosmic rays. In the present work, we discuss the particularities of space-based observation and we compute the annual exposure in nadir observation. The results are based on studies of the expected trigger aperture and observational duty cycle, as well as, on the investigations of the effects of clouds and different types of background light. We show that the annual exposure is about one order of magnitude higher than those of the presently operating ground-based observatories

Water, being a primary element in the diet and a necessary resource for the agriculture, can be considered a basic need for humans. In addition, also industrial practices need a growing amount of water. Since human population is continuously growing at a rate that, in the last two centuries, approximates well the exponential, water demand is increasing. However, the water resources on the Earth are finite. For this reason, even disregarding the potential threats due to the climate change, this situation appears as one of the biggest challenges of the current era. Actually, several small-scale regions already face water sustainability problems, and the scarcity of water resources is expected to spread to wider areas in the near future, if the actual trends of development and population growth do not change. The situation is exacerbated as the climate is already changing, due to the anthropogenic emissions of greenhouse gases in the atmosphere, and its rate is expected to increase by the end of this century. The effects of these changes will increase the natural variability of the climate, exacerbating the extreme climatic phenomena (drought and flood events) and increasing the difficulty of managing water resources, especially in the most vulnerable regions.

Snow models are usually evaluated at sites providing high-quality meteorological data, so that the uncertainty in the meteorological input data can be neglected when assessing model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing used to drive snow models is typically derived from spatial interpolation of the available in situ data or from reanalyses, whose accuracy can be considerably lower. In order to fully characterize the performances of a snow model, the model sensitivity to errors in the input data should be quantified. In this study we test the ability of six snow models to reproduce snow water equivalent, snow density and snow depth when they are forced by meteorological input data with gradually lower accuracy. The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160 m a.s.l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolution, obtained by (i) sampling the original Torgnon 30 min time series at 3, 6, and 12 h, (ii) spatially interpolating neighbouring in situ station measurements and (iii) extracting information from GLDAS, ERA5 and ERA-Interim reanalyses at the grid point closest to the Torgnon site. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity. The results show that, when forced by accurate 30 min resolution weather station data, the single-layer, intermediate-complexity snow models HTESSEL and UTOPIA provide similar skills to the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower-complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills to the control run, while the use of 6- and 12-hourly temporal resolution forcings may lead to a reduction in model performances if the incoming shortwave radiation is not properly represented. The SMASH model generally shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighbouring stations and reanalyses are found to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. However, a simple bias-adjustment technique applied to ERA-Interim temperatures allowed all models to achieve similar performances to the control run. Regardless of their complexity, all models show weaknesses in the representation of the snow density.

Thunderstorms may cause large damages to infrastructures and population, therefore the possible identification of the areas with the highest occurrence of these events is especially relevant. Nevertheless, few extensive studies of these phenomena with high spatial and temporal resolution have been carried out in the Alps and none of them includes North-western Italy. To analyze thunderstorm events, the data of the meteorological radar network of the regional meteorological service of Piedmont region (ARPA Piemonte) have been used in this work. The database analyzed includes all thunderstorms occurred during the warm months (April to September) of a 6-year period (2005–2010). The tracks of each storm have been evaluated using a storm tracking algorithm. Several characteristics of the storms have been analyzed, such as the duration, the spatial and the temporaldistribution, the direction and the distance travelled. Obtained results revealed several important characteristics that may be useful for nowcasting purposes providing a first attempt of radar-based climatology in the considered region.

The atmospheric background CO₂ concentration is a key quantity for the analysis and evaluation of the ongoing climate change. Long-term CO₂ observations have been carried out at the high Plateau Rosa mountain station, in the north-western Alps since 1989. The complete time series covers thirty years, and it is suitable for climatological analysis. The continuous CO₂ measurements, collected since 1993, were selected, by means of a BaDS (Background Data Selection) filter, to obtain the hourly background data. The monthly background data series was analysed in order to individuate the parameters that characterise the seasonal cycle and the long-term trend. The growth rate was found to be 2.05 ± 0.03 ppm/year, which is in agreement with the global trend. The increased background CO₂ concentration at the Plateau Rosa site is the consequence of global anthropic emissions, whereas the natural variability of the climatic system taken from the SOI (South Oscillation Index) and MEI (Multivariate ENSO Index) signals was detected in the inter-annual changes of the Plateau Rosa growth rate.