Small changes in the radiation budget at the earth's surface can lead to large climatological responses when persistent over time. With the increasing debate on anthropogenic influences on climatic processes during the 1980s the need for accurate radiometric measurements with higher temporal resolution was identified, and it was determined that the existing measurement networks did not have the resolution or accuracy required to meet this need. In 1988 the WMO therefore proposed the establishment of a new international Baseline Surface Radiation Network (BSRN), which should collect and centrally archive high-quality ground-based radiation measurements in 1 min resolution. BSRN began its work in 1992 with 9 stations; currently (status 2018-01-01), the network comprises 59 stations (delivering data to the archive) and 9 candidates (stations recently accepted into the network with data forthcoming to the archive) distributed over all continents and oceanic environments. The BSRN database is the World Radiation Monitoring Center (WRMC). It is hosted at the Alfred Wegener Institute (AWI) in Bremerhaven, Germany, and now offers more than 10 300 months of data from the years 1992 to 2017. All data are available at https://doi.org/10.1594/PANGAEA.880000 free of charge.

A comprehensive assessment of twenty-first century climate change in the European Alps is presented. The analysis is based on the EURO-CORDEX regional climate model ensemble available at two grid spacings (12.5 and 50 km) and for three different greenhouse gas emission scenarios (RCPs 2.6, 4.5 and 8.5). The core simulation ensemble has been subject to a dedicated evaluation exercise carried out in the frame of the CH2018 Climate Scenarios for Switzerland. Results reveal that the entire Alpine region will face a warmer climate in the course of the twenty-first century for all emission scenarios considered. Strongest warming is projected for the summer season, for regions south of the main Alpine ridge and for the high-end RCP 8.5 scenario. Depending on the season, medium to high elevations might experience an amplified warming. Model uncertainty can be considerable, but the major warming patterns are consistent across the ensemble. For precipitation, a seasonal shift of precipitation amounts from summer to winter over most parts of the domain is projected. However, model uncertainty is high and individual simulations can show change signals of opposite sign. Daily precipitation intensity is projected to increase in all seasons and all sub-domains, while the wet-day frequency will decrease in the summer season. The projected temperature change in summer is negatively correlated with the precipitation change, i.e. simulations and/or regions with a strong seasonal mean warming typically show a stronger precipitation decrease. By contrast, a positive correlation between temperature change and precipitation change is found for winter. Among other indicators, snow cover will be strongly affected by the projected climatic changes and will be subject to a widespread decrease except for very high elevation settings. In general and for all indicators, the magnitude of the change signals increases with the assumed greenhouse gas forcing, i.e., is smallest for RCP 2.6 and largest for RCP 8.5 with RCP 4.5 being located in between. These results largely agree with previous works based on older generations of RCM ensembles but, due to the comparatively large ensemble size and the high spatial resolution, allow for a more decent assessment of inherent projection uncertainties and of spatial details of future Alpine climate change.

We used the spatially distributed and physically based snow cover model SNOWGRID-CL to derive daily grids of natural snow conditions and snowmaking potential at a spatial resolution of 1 × 1 km for Austria for the period 1961–2020 validated against homogenized long-term snow observations. Meteorological driving data consists of recently created gridded observation-based datasets of air temperature, precipitation, and evapotranspiration at the same resolution that takes into account the high variability of these variables in complex terrain. Calculated changes reveal a decrease in the mean seasonal (November–April) snow depth (HS), snow cover duration (SCD), and potential snowmaking hours (SP) of 0.15 m, 42 days, and 85 h (26%), respectively, on average over Austria over the period 1961/62–2019/20. Results indicate a clear altitude dependence of the relative reductions (−75% to −5% (HS) and −55% to 0% (SCD)). Detected changes are induced by major shifts of HS in the 1970s and late 1980s. Due to heterogeneous snowmaking infrastructures, the results are not suitable for direct interpretation towards snow reliability of individual Austrian skiing resorts but highly relevant for all activities strongly dependent on natural snow as well as for projections of future snow conditions and climate impact research.

To assess how meteorological conditions favorable for the production of artificial snow vary in time and space, wet-bulb temperatures are calculated using temperature and humidity data of 14 Austrian stations between October and April for 1948–2007 (station altitudes 585–3105 m MSL). Technical specifications of snow guns are used to define a wet-bulb temperature threshold value of −2°C for snowmaking and a relationship between wet-bulb temperature and snowmaking capacity. The Mann–Kendall nonparametric-trend test is used to detect monotonic long-term changes in air temperature, relative humidity, wet-bulb temperature, and number of snowmaking days. It is applied multiple times to overlapping time periods to capture significant trends on different time scales. Results show a marked, common air- and wet-bulb seasonal mean (October–April) temperature increase between +1.5° and +3.1°C from 1980 to 1990 for a majority of stations with no trends thereafter. The number of snowmaking days per season decreased by −20 to −34 for half of the stations in the period around 1979–2003. No altitudes were especially affected by changes in the analyzed variables. The estimated volume of produced artificial snow shows high interannual variability and exhibits no trends at an hourly resolution over the last two decades.

The density of new snow is operationally monitored by meteorological or hydrological services at daily time intervals, or occasionally measured in local field studies. However, meteorological conditions and thus settling of the freshly deposited snow rapidly alter the new snow density until measurement. Physically based snow models and nowcasting applications make use of hourly weather data to determine the water equivalent of the snowfall and snow depth. In previous studies, a number of empirical parameterizations were developed to approximate the new snow density by meteorological parameters. These parameterizations are largely based on new snow measurements derived from local in situ measurements. In this study a data set of automated snow measurements at four stations located in the European Alps is analysed for several winter seasons. Hourly new snow densities are calculated from the height of new snow and the water equivalent of snowfall. Considering the settling of the new snow and the old snowpack, the average hourly new snow density is 68 kg m ⁻³ , with a standard deviation of 9 kg m ⁻³ . Seven existing parameterizations for estimating new snow densities were tested against these data, and most calculations overestimate the hourly automated measurements. Two of the tested parameterizations were capable of simulating low new snow densities observed at sheltered inner-alpine stations. The observed variability in new snow density from the automated measurements could not be described with satisfactory statistical significance by any of the investigated parameterizations. Applying simple linear regressions between new snow density and wet bulb temperature based on the measurements' data resulted in significant relationships ( r ² > 0.5 and p ≤ 0.05) for single periods at individual stations only. Higher new snow density was calculated for the highest elevated and most wind-exposed station location. Whereas snow measurements using ultrasonic devices and snow pillows are appropriate for calculating station mean new snow densities, we recommend instruments with higher accuracy e.g. optical devices for more reliable investigations of the variability of new snow densities at sub-daily intervals.