Our current knowledge on multi‐decadal to centennial changes of snow in different parts of the world is based largely on observations of snow depth and depth of snowfall from national weather and hydrographic services. Studies analysing these snow observations in the European Alps are predominantly based on national data and are therefore limited by their respective borders in the detection of robust, spatiotemporal snow trends. In order to overcome this limitation, data from Austria and Switzerland, which cover a substantial fraction of the Alps when taken together, are merged for this study (196 station‐records). Additionally, it is the first time that such an analysis is based on homogenized data. Our homogenization study shows that, although the detection of breaks in snow depth series works quite well with the existing methods, further research is needed to adequately correct snow depth series at a daily resolution. Roughly, 70% (139 station‐records) of the snow depth series could be homogenized and are used for further trend analysis. The findings concern seven climatologically different areas that are identified by a regionalization (using empirical orthogonal functions) using station records from 1961 to 2012. These regions share a high degree of inner similarity and outer separation, and the temporal trends detected are rather different across the Swiss‐Austrian domain. Regions in the south show a clear decrease in the snow depth of up to −12 cm/decade on average, while those in the northeast are characterized by almost no change. The declining trend in the southern regions intensifies as altitude increases. Comparisons of these variations in depth changes with concurrent changes in air temperature and precipitation totals reveal a clear dichotomy with respect to elevation. Snow depths in low elevated areas are highly sensitive to air temperature changes, whereas those at high elevations strongly depend on alterations in precipitation totals.

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.

The climate warming trend and city growth contribute to the generation of excessive heat in urban areas. This could be reduced by introducing vegetation and open water surfaces in urban design. This study evaluates the cooling efficiency of green and blue infrastructure to reduce urban heat load using a set of idealized case simulations and a real city model application for Vienna. The idealized case simulations show that the cooling effect of green and blue infrastructure is dependent on the building type, time of the day and in case of blue infrastructure, the water temperature. The temperature reduction and the size of the cooled surface are largest in densely built-up environments. The real case simulations for Vienna, which include the terrain, inhomogeneous land use distribution and observed climate data, show that urban planning measures should be applied extensively in order to gain substantial cooling on the city scale. The best efficiency can be reached by targeted implementation of minor but combined measures such as a decrease in building density of 10 %, a decrease in pavement by 20 % and an enlargement in green or water spaces by 20 %. Additionally, the modelling results show that equal heat load mitigation measures may have different efficiency dependent on location in the city due to the prevailing meteorological conditions and land use characteristics in the neighbouring environment.

Despite the importance of snow in alpine regions, little attention has been given to the homogenization of snow depth time series. Snow depth time series are generally characterized by high spatial heterogeneity and low correlation among the time series, and the homogenization thereof is therefore challenging. In this work, we present a comparison between two homogenization methods for mean seasonal snow depth time series available for Austria: the standard normal homogeneity test (SNHT) and HOMOP. The results of the two methods are generally in good agreement for high elevation sites. For low elevation sites, HOMOP often identifies suspicious breakpoints (that cannot be confirmed by metadata and only occur in relation to seasons with particularly low mean snow depth), while the SNHT classifies the time series as homogeneous. We therefore suggest applying both methods to verify the reliability of the detected breakpoints. The number of computed anomalies is more sensitive to inhomogeneities than trend analysis performed with the Mann–Kendall test. Nevertheless, the homogenized dataset shows an increased number of stations with negative snow depth trends and characterized by consecutive negative anomalies starting from the late 1980s and early 1990s, which was in agreement with the observations available for several stations in the Alps. In summary, homogenization of snow depth data is possible, relevant and should be carried out prior to performing climatological analysis.

Abstract. 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.

No abstract

Austrian observations of snow depth date back to 1895 and are thus among the longest available quantitative snow information from hydrometeorological networks worldwide. It is well known that such long-term observations are prone to inhomogeneities, which may not only affect climatologies and trends, but derived products used in research or practice. While the reliability of available methods for detecting breaks in snow time series has been shown before and could also be confirmed by our work, we focused on improving the adjustment method. Conventional methods often refer to the median of difference or quotient series (INTERP), whereas our proposed method also uses a quantilewise adjustment (InterpQM), which is useful to minimize a bias on the tails of the frequency distribution. We demonstrated the success of the new method by using Swiss parallel snow depth observations. Errors of the analysed indicators could be reduced in 68% of the cases when compared with INTERP. The results were best for large snow depths, being up to 19% better. Overall, Inter-pQM was better in 75% of validation cases for the daily large, 72% of all observations and 56% of mean seasonal snow depth cases. We describe the performed homogenization procedure in detail, including quality control, gap filling, homogeneity testing, break detection, calculation of and improvements to the adjustment method. Our results show that snow depth time series generally have a lower number of breaks compared with station data of other climate variables. This underlines their high quality, even if measuring snow presents challenges. Using Austrian snow depth series as an example, the effects of the new adjustment method on trends were analysed using the Mann-Kendall and Sen's Slope. Homogenization may have a significant effect on derived trends: Two of the six adjusted series were changed from nonsignificant to significant and one vice versa.

No abstract