Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements

Abstract

Abstract. Daily measurements of snow depth and snowfall can vary strongly over short distances. However, it is not clear if there is a seasonal dependence in these variations and how they impact common snow climate indicators based on mean values, as well as estimated return levels of extreme events based on maximum values. To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements from the Swiss Alps consisting of 30 station pairs with up to 77 years of parallel data. Station pairs are usually located in the same villages (or within 3 km horizontal and 150 m vertical distances). Investigated snow climate indicators include average snow depth, maximum snow depth, sum of new snow, days with snow on the ground, days with snowfall, and snow onset and disappearance dates, which are calculated for various seasons (December to February (DJF), November to April (NDJFMA), and March to April (MA)). We computed relative and absolute error metrics for all these indicators at each station pair to demonstrate the potential variability. We found the largest relative inter-pair differences for all indicators in spring (MA) and the smallest in DJF. Furthermore, there is hardly any difference between DJF and NDJFMA, which show median variations of less than 5 % for all indicators. Local-scale variability ranges between less than 24 % (DJF) and less than 43 % (MA) for all indicators and 75 % of all station pairs. The highest percentage (90 %) of station pairs with variability of less than 15 % is observed for days with snow on the ground. The lowest percentage (30 %) of station pairs with variability of less than 15 % is observed for average snow depth. Median differences of snow disappearance dates are rather small (3 d) and similar to the ones found for snow onset dates (2 d). An analysis of potential sunshine duration could not explain the higher variabilities in spring. To analyse the impact of local-scale variations on the estimation of extreme events, 50-year return levels were quantified for maximum snow depth and maximum 3 d new snow sum, which are often used for avalanche prevention measures. The found return levels are within each other's 95 % confidence intervals for all (but three) station pairs, revealing no striking differences. The findings serve as an important basis for our understanding of variabilities of commonly used snow indicators and extremal indices. Knowledge about such variabilities in combination with break-detection methods is the groundwork in view of any homogenization efforts regarding snow time series.

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

“…In contrast to the larger thresholds of the snow day analysis, dHS5 shows almost no differences between the original and homogenised series, confirming the stability of this metric as described by Buchmann et al (2021b). The elevation-dependent pattern with the strongest adjustment effects for dHS30 and dHS50 between 1000 and 1700 m can be explained by the fact that, firstly, at stations below 1000 m a.s.l.…”

“…To investigate the effects of homogenisation on extreme snow depths, return levels for the seasonal maximum snow depth (HSmax) (Marty and Blanchet, 2012) are calculated for a fixed return period of 50 years (Buchmann et al, 2021a;Marty and Blanchet, 2012) based on original and homogenised data (R50HSmax). This approach was chosen because the international standards for maximum snow load on buildings are based on R50HSmax (see e.g.…”

“…Snow days are relevant for ecology (Stone et al, 2002;Jonas et al, 2008), climatology (Scherrer et al, 2004; or the ski tourism industry (Abegg et al, 2020), whereas the average and maximum snow depths are particularly applicable to climatology and engineering applications. Trend and extreme value analyses of snow indices (Scherrer et al, 2013;Matiu et al, 2021) are common methods in climate monitoring (Bocchiola et al, 2008;Marty and Blanchet, 2012;Buchmann et al, 2021a) and model verification (Brown et al, 2003;Essery et al, 2013), while extreme value analyses are important for defining snow loads and limits for building codes (Croce et al, 2021;Schellander et al, 2021;Al-Rubaye et al, 2022).…”

Diverging snowfall trends across months and elevation in the northeastern Italian Alps

“…The assessment of homogeneity of climate series represents a crucial aspect when long‐term trends are analysed. While several homogeneity tests have been developed and are a common practice for temperature and precipitation series, only a few studies discussed and addressed the homogenization of snow series (Marcolini et al ., 2017; Marcolini et al ., 2019; Schöner et al ., 2019; Buchmann et al ., 2021; Buchmann et al ., 2022). However, due to the relative short temporal extent of the HN series analysed here, and the lack of homogeneous series to be used as reference, no systematic test for homogeneity assessment and series homogenization was applied to the HN database.…”

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

“…Trends and extreme value analyses of snow variables (e.g. Scherrer et al, 2013;Matiu et al, 2021) are commonly used methods in climate monitoring (Bocchiola et al, 2008;Marty and Blanchet, 2012;Buchmann et al, 2021a), model verification (e.g. Brown et al, 2003;Essery et al, 2013), whereas extreme value analysis is important for the definition of snow loads and limits for building-codes (e.g.…”