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

Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed for temperature and precipitation exist but have rarely been applied to snow-cover-related time series. We undertook...

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Bibliographic Details
Published in:The Cryosphere
Main Authors: Buchmann, Moritz, Coll, John, Aschauer, Johannes, Begert, Michael, Brönnimann, Stefan, Chimani, Barbara, Resch, Gernot, Schöner, Wolfgang, Marty, Christoph
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2022
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Online Access:https://doi.org/10.5194/tc-16-2147-2022
https://noa.gwlb.de/receive/cop_mods_00061385
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00060859/tc-16-2147-2022.pdf
https://tc.copernicus.org/articles/16/2147/2022/tc-16-2147-2022.pdf
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Summary:Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed for temperature and precipitation exist but have rarely been applied to snow-cover-related time series. We undertook a homogeneity assessment of Swiss monthly snow depth series by running and comparing the results from three well-established semi-automatic break point detection methods (ACMANT – Adapted Caussinus-Mestre Algorithm for Networks of Temperature series, Climatol – Climate Tools, and HOMER – HOMogenizaton softwarE in R). The multi-method approach allowed us to compare the different methods and to establish more robust results using a consensus of at least two change points in close proximity to each other. We investigated 184 series of various lengths between 1930 and 2021 and ranging from 200 to 2500 m a.s.l. and found 45 valid break points in 41 of the 184 series investigated, of which 71 % could be attributed to relocations or observer changes. Metadata are helpful but not sufficient for break point verification as more than 90 % of recorded events (relocation or observer change) did not lead to valid break points. Using a combined approach (two out of three methods) is highly beneficial as it increases the confidence in identified break points in contrast to any single method, with or without metadata.