Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods

Abstract. Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed temperature and precipitation exist but have rarely been applied to snow-cover-related time series. We undertook homogeneity assessment Swiss monthly snow depth series by running comparing the results from three well-established semi-automatic break point detection (ACMANT – Adapted Caussinus-Mestre Algorithm Networks Temperature series, Climatol Climate Tools, HOMER HOMogenizaton softwarE R). The multi-method approach allowed us compare different establish more robust using consensus at least two change points close proximity each other. investigated 184 various lengths between 1930 2021 ranging 200 2500 m a.s.l. found 45 valid 41 investigated, which 71 % could be attributed relocations or observer changes. Metadata are helpful not sufficient verification as than 90 recorded events (relocation change) did lead points. Using combined (two out methods) highly beneficial it increases confidence identified contrast single method, with without metadata.