Preprints
https://doi.org/10.5194/egusphere-2022-715
https://doi.org/10.5194/egusphere-2022-715
 
09 Aug 2022
09 Aug 2022
Status: this preprint is open for discussion.

Homogenizing Swiss snow depth series – Impact on decadal trends and extremes

Moritz Buchmann1,2,, Gernot Resch3,, Michael Begert4, Stefan Brönnimann2,5, Barbara Chimani6, Wolfgang Schöner3, and Christoph Marty1 Moritz Buchmann et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 2Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • 3Department of Geography and Regional Science, University of Graz, Graz, Austria
  • 4Federal Office of Meteorology and Climatology (MeteoSwiss), Zurich Airport, Switzerland
  • 5Institute of Geography, University of Bern, Bern, Switzerland
  • 6Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Vienna, Austria
  • These authors contributed equally to this work.

Abstract. Our current knowledge on snow depth trends is based almost exclusively on these non-homogenized data.Long-term observations of deposited snow are well suited as indicator of climate change. However, like all other long-term observations, they are prone to inhomogeneities that can influence and change trends if not taken into account. We investigated the effects of removing inhomogeneities in the large network of Swiss snow depth observations on trends and extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depth in the period 1961–2021. For this task, three homogenization methods were applied: Climatol and HOMER, which use a median based adjustment method, and interpQM, which applies quantile based adjustments. All three were run using the same break points and input data. This allowed us to investigate and quantify the effects of these methods on the homogenization results. We found that all three methods agree well on trends in seasonal average snow depth, while differences are visible for seasonal maximum snow depth and the corresponding extreme values. Here, the quantile-based method performed slightly better than the two median-based methods, as it had the smallest number of stations outside the 95 % confidence interval for 50-year return periods of maximum snow depth. These differences are mainly caused by the way the reference series are selected. The combination of a high minimum correlation (>0.7) and restrictions in vertical (<300 m) and horizontal (<100 km) distances proved to be better suited than only using correlations or distances respectively as criteria. The adjustments removed all positive trends for snow days in the original data and strengthened the negative mean trend, especially for stations >1500 m. In addition, the number of significant negative stations was increased between 7–21 %, with the strongest changes at higher snow depths.

Moritz Buchmann et al.

Status: open (until 09 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-715', Anonymous Referee #1, 13 Sep 2022 reply
  • RC2: 'Comment on egusphere-2022-715', Anonymous Referee #2, 20 Sep 2022 reply

Moritz Buchmann et al.

Moritz Buchmann et al.

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Short summary
Long-term observations of snow are well suited as indicators of climate change, but our current knowledge of snow depth trends is based almost entirely on non-homogenised data, which shows only a veiled picture of the real trends due to inhomogeneities. We compare the impact of homogenization methods on snow indices using Swiss time series between 1961–2021 and present ways to improve the results. E.g., the adjustments removed all positive trends for snow days, especially for stations >1500 m.