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Abstract. 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.

Austrian observations of snow depth date back to 1895 and are thus among the longest available quantitative snow information from hydrometeorological networks worldwide. It is well known that such long-term observations are prone to inhomogeneities, which may not only affect climatologies and trends, but derived products used in research or practice. While the reliability of available methods for detecting breaks in snow time series has been shown before and could also be confirmed by our work, we focused on improving the adjustment method. Conventional methods often refer to the median of difference or quotient series (INTERP), whereas our proposed method also uses a quantilewise adjustment (InterpQM), which is useful to minimize a bias on the tails of the frequency distribution. We demonstrated the success of the new method by using Swiss parallel snow depth observations. Errors of the analysed indicators could be reduced in 68% of the cases when compared with INTERP. The results were best for large snow depths, being up to 19% better. Overall, Inter-pQM was better in 75% of validation cases for the daily large, 72% of all observations and 56% of mean seasonal snow depth cases. We describe the performed homogenization procedure in detail, including quality control, gap filling, homogeneity testing, break detection, calculation of and improvements to the adjustment method. Our results show that snow depth time series generally have a lower number of breaks compared with station data of other climate variables. This underlines their high quality, even if measuring snow presents challenges. Using Austrian snow depth series as an example, the effects of the new adjustment method on trends were analysed using the Mann-Kendall and Sen's Slope. Homogenization may have a significant effect on derived trends: Two of the six adjusted series were changed from nonsignificant to significant and one vice versa.

We investigate relationships between synoptic-scale atmospheric variability and the mass-balance of 13 Andean glaciers (located 16–55° S) using Pearson correlation coefficients (PCCs) and multiple regressions. We then train empirical glacier mass-balance models (EGMs) in a cross-validated multiple regression procedure for each glacier. We find four distinct glaciological zones with regard to their climatic controls: (1) The mass-balance of the Outer Tropics glaciers is linked to temperature and the El Niño-Southern Oscillation (PCC ⩽ 0.6), (2) glaciers of the Desert Andes are mainly controlled by zonal wind intensity (PCC ⩽ 0.9) and the Antarctic Oscillation (PCC ⩽0.6), (3) the mass-balance of the Central Andes glaciers is primarily correlated with precipitation anomalies (PCC ⩽ 0.8), and (4) the glacier of the Fuegian Andes is controlled by winter precipitation (PCC ≈ 0.7) and summer temperature (PCC ≈ −0.9). Mass-balance data in the Lakes District and Patagonian Andes zones, where most glaciers are located, are too sparse for a robust detection of synoptic-scale climatic controls. The EGMs yield R2 values of ~ 0.45 on average and ⩽ 0.74 for the glaciers of the Desert Andes. The EGMs presented here do not consider glacier dynamics or geometry and are therefore only suitable for short-term predictions.

Abstract. 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 with only little experience for snow. We undertook a homogeneity assessment of Swiss snow depth series by running and comparing the results from three well-established semi-automatic break point detection methods (ACMANT, Climatol, and HOMER). Break points identified by each method allowed us to compare the results of the different methods, and by only treating break points as valid if detected in reasonably close proximity by at least two methods, we increased the robustness of the results. We investigated 184 series, of various length between 1930 and 2021 and ranging from 200 to 2500 m a.s.l. and found 45 valid break points. Of those 45, 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 did not lead to valid break points. Using such a combined approach (2 out of 3 methods) is highly beneficial, as it increases the confidence in identified break points in contrast to any single method, with or without metadata.