The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland

Abstract

Abstract. Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are prone to inhomogeneities that can influence and even change trends if not taken into account. In order to assess the relevance of homogenisation for time-series analysis of daily snow depths, we investigated the effects of adjusting inhomogeneities in the extensive network of Swiss snow depth observations for trends and changes in extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depths in the period 1961–2021. Three homogenisation methods were compared for this task: Climatol and HOMER, which apply median-based adjustments, and the quantile-based interpQM. All three were run using the same input data with identical break points. We found that they agree well on trends of seasonal average snow depth, while differences are detectable for seasonal maxima and the corresponding extreme values. Differences between homogenised and non-homogenised series result mainly from the approach for generating reference series. The comparison of homogenised and original values for the 50-year return level of seasonal maximum snow depth showed that the quantile-based method had the smallest number of stations outside the 95 % confidence interval. Using a multiple-criteria approach, e.g. thresholds for series correlation (>0.7) as well as for vertical (<300 m) and horizontal (<100 km) distances, proved to be better suited than using correlation or distances alone. Overall, the homogenisation of snow depth series changed all positive trends for derived series of snow days to either no trend or negative trends and amplifying the negative mean trend, especially for stations >1500 m. The number of stations with a significant negative trend increased between 7 % and 21 % depending on the method, with the strongest changes occurring at high snow depths. The reduction in the 95 % confidence intervals of the absolute maximum snow depth of each station indicates a decrease in variation and an increase in confidence in the results.

Comment on egusphere-2024-1056

“…Following Buchmann et al (2022), in this study we have adjusted the scale parameter of the vertical coordinate (wz) so that the elevation counts 100 Climatol has been employed in this study also to check for homogeneity of the investigated time series. The use of this toolbox for the homogenisation of snowfall data has been explored, with encouraging results, in some recent works (Buchmann et al, 2022;Buchmann et al, 2023). As described in detail by Guijarro (2018) and by Kuya et al (2022), the Climatol homogenization method is based on the Standard Normal Homogeneity Test (SNHT; Alexandersson, 1986) for the identification of the breaks and on a linear regression approach for the adjustments (Easterling and Peterson, 1995).…”

“…The break magnitude corrections are computed as the variation of the mean before and after homogenisation procedure. Additional details about the calculation of the adjustment factor can be found in Guijarro (2018), in Kuya et al (2022) and in Buchmann et al (2023). The last step of Climatol processing consists in the filling of all missing values using the weighted ratios of neighbouring series and in the production of the final high quality, homogeneous and complete time series.…”