EMS Annual Meeting Abstracts
Vol. 18, EMS2021-282, 2021, updated on 09 Jan 2024
https://doi.org/10.5194/ems2021-282
EMS Annual Meeting 2021
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

The Swiss snow climatology as seen by reanalyses and further gridded datasets

Monika Goeldi1,2, Stefanie Gubler1, Christian Steger2, Simon C. Scherrer1, and Sven Kotlarski1
Monika Goeldi et al.
  • 1MeteoSwiss, Zürich-Flughafen, Switzerland (stefanie.gubler@meteoswiss.ch)
  • 2Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.

In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.

The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations – e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.

How to cite: Goeldi, M., Gubler, S., Steger, C., Scherrer, S. C., and Kotlarski, S.: The Swiss snow climatology as seen by reanalyses and further gridded datasets, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-282, https://doi.org/10.5194/ems2021-282, 2021.

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