Snow depth sensitivity to mean temperature, precipitation, and elevation in the Austrian and Swiss Alps

Abstract. Snow depth plays an important role in the seasonal climatic and hydrological cycles of alpine regions. Previous studies have shown predominantly decreasing trends in average seasonal snow depth across the European Alps. Additionally, prior work has shown bivariate statistical relationships between average seasonal snow depth and mean air temperature or precipitation. Building upon existing research, our study uses observational records of in situ station data across Austria and Switzerland to better quantify the sensitivity of historical changes in seasonal snow depth through a multivariate framework that depends on elevation, mean temperature, and precipitation. These historical sensitivities, which are obtained over the 1901–1902 to 1970–1971 period, are then used to estimate snow depths over the more recent period of 1971–1972 to 2020–2021. We find that the year-to-year estimates of snow depths, which are derived from an empirical–statistical model (SnowSens), that rely solely on the historical sensitivities are nearly as skillful as the operational SNOWGRID-CL model used by the weather service at GeoSphere Austria. Furthermore, observed long-term changes over the last 50 years are in better agreement with SnowSens than with SNOWGRID-CL. These results indicate that historical sensitivities between snow depth, temperature, and precipitation are quite robust over decadal-length scales of time, and they can be used effectively to translate expected long-term changes in temperature and precipitation into changes in seasonal snow depth.