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

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Abstract. Snow depth is an incredibly important component of the climatic and hydrological cycles. Previous studies have shown predominantly decreasing trends of 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 1901/02–1970/01 period, are then used to forecast snow depths over the more recent period 1971/72–2020/21. We find that the year-to-year forecasts 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 physically based 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 with temperature and precipitation are quite robust over decadal-length scales of time, and they can be used to effectively translate expected long-term changes in temperature and precipitation to changes in seasonal snow depth.

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