Observed snow depth trends in the European Alps: 1971 to 2019

Abstract

Abstract. 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.

Evaluating methods for reconstructing large gaps in historic snow depth time series

“…Six different methods are employed to interpolate a missing winter of snow depth data at a certain station with help of neighboring stations or by using measured meteorological data at the gap station. In case neighboring stations are used as predictors for reconstructing the missing data, these stations have to be within a radius of 200 km and show an absolute elevation difference of less than 500 m. We choose these limits based on a correlation analysis of Matiu et al (2021). For all methods which use HS data from neighboring stations, the best n correlated neighboring stations are chosen as predictor stations.…”

“…The normal ratio method was first introduced by Paulhus and Kohler (1952) and assumes a constant ratio of the average state of two neighboring stations (Young, 1992;Yozgatligil et al, 2013). In the version of Matiu et al (2021), missing values are filled bŷ…”

“…where n is the number of neighboring reference stations, y i is the snow depth at neighboring station i,ȳ andȳ i are the mean snow depth at the target station and reference station i in the training period respectively and w i is the weight of station i based on the vertical distance Z − Z i calculated as w i = e − ln 2(Z−Zi) 2 /250 2 which is a Gaussian weight function with a full width at half maximum of 500 m. Reconstructed values are rounded to the nearest cm integer. In order to have equal conditions within our method comparison, the selected neighboring stations do not need to have a correlation coefficient larger than 0.7 with the target contrary to the original version in Matiu et al (2021).…”

“…(e.g. Matiu et al, 2021), to verify modeling studies (e.g. Olefs et al, 2020) or to calculate return levels of extreme events for constructional guidelines (e.g.…”

“…Some of the studies focus on shorter gaps in hourly automatic measured snow data (Avanzi et al, 2020) while other studies focus on monthly means and employ very simple statistical models based on temperature only (Hughes and Robinson, 1993;Brown et al, 1995). For daily data, weighted averages of HS data from neighboring stations are employed (Matiu et al, 2021). Schöner and Koch (2016) use spatial averages and a temperature-index model to reconstruct missing daily HS data in a project of the Austrian meteorological service.…”