Articles | Volume 16, issue 6
https://doi.org/10.5194/tc-16-2147-2022
https://doi.org/10.5194/tc-16-2147-2022
Research article
 | 
09 Jun 2022
Research article |  | 09 Jun 2022

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

Moritz Buchmann, John Coll, Johannes Aschauer, Michael Begert, Stefan Brönnimann, Barbara Chimani, Gernot Resch, Wolfgang Schöner, and Christoph Marty

Related authors

The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland
Moritz Buchmann, Gernot Resch, Michael Begert, Stefan Brönnimann, Barbara Chimani, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 17, 653–671, https://doi.org/10.5194/tc-17-653-2023,https://doi.org/10.5194/tc-17-653-2023, 2023
Short summary
Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements
Moritz Buchmann, Michael Begert, Stefan Brönnimann, and Christoph Marty
The Cryosphere, 15, 4625–4636, https://doi.org/10.5194/tc-15-4625-2021,https://doi.org/10.5194/tc-15-4625-2021, 2021
Short summary

Related subject area

Discipline: Snow | Subject: Seasonal Snow
Snow depth sensitivity to mean temperature, precipitation, and elevation in the Austrian and Swiss Alps
Matthew Switanek, Gernot Resch, Andreas Gobiet, Daniel Günther, Christoph Marty, and Wolfgang Schöner
The Cryosphere, 18, 6005–6026, https://doi.org/10.5194/tc-18-6005-2024,https://doi.org/10.5194/tc-18-6005-2024, 2024
Short summary
Use of multiple reference data sources to cross-validate gridded snow water equivalent products over North America
Colleen Mortimer, Lawrence Mudryk, Eunsang Cho, Chris Derksen, Mike Brady, and Carrie Vuyovich
The Cryosphere, 18, 5619–5639, https://doi.org/10.5194/tc-18-5619-2024,https://doi.org/10.5194/tc-18-5619-2024, 2024
Short summary
Characterization of non-Gaussianity in the snow distributions of various landscapes
Noriaki Ohara, Andrew D. Parsekian, Benjamin M. Jones, Rodrigo C. Rangel, Kenneth M. Hinkel, and Rui A. P. Perdigão
The Cryosphere, 18, 5139–5152, https://doi.org/10.5194/tc-18-5139-2024,https://doi.org/10.5194/tc-18-5139-2024, 2024
Short summary
A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products
Aleksandra Elias Chereque, Paul J. Kushner, Lawrence Mudryk, Chris Derksen, and Colleen Mortimer
The Cryosphere, 18, 4955–4969, https://doi.org/10.5194/tc-18-4955-2024,https://doi.org/10.5194/tc-18-4955-2024, 2024
Short summary
Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau?
Shirui Yan, Yang Chen, Yaliang Hou, Kexin Liu, Xuejing Li, Yuxuan Xing, Dongyou Wu, Jiecan Cui, Yue Zhou, Wei Pu, and Xin Wang
The Cryosphere, 18, 4089–4109, https://doi.org/10.5194/tc-18-4089-2024,https://doi.org/10.5194/tc-18-4089-2024, 2024
Short summary

Cited articles

Aguilar, E. and Llanso, P.: Guidelines on climate metadata and homogenization, World Meteorological Organization, WCDMP-No. 53, https://library.wmo.int/doc_num.php?explnum_id=10751 (last access: 8 June 2022), 2003. a
Alexandersson, H.: A homogeneity test applied to precipitation data, J. Climatol., 6, 661–675, https://doi.org/10.1002/joc.3370060607, 1986. a
Alexandersson, H. and Moberg, A.: Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends, Int. J. Climatol., 17, 25–34, https://doi.org/10.1002/(sici)1097-0088(199701)17:1<25::aid-joc103>3.0.co;2-j, 1997. a, b, c
Aschauer, J. and Marty, C.: Providing Data Provision for a Sensitivity Analysis of Snow Time Series, resreport, WSL Institute for Snow and Avalanche Research SLF, research Report for GCOS Switzerland, https://www.meteoschweiz.admin.ch/content/dam/meteoswiss/en/Forschung-und-Zusammenarbeit/Internationale-Zusammenarbeit/GCOS/doc/Final_report_Poviding_Data_Provision_for_a_Sensitivity_Analysis_of_Snow_Time_Series.pdf (last access: 8 June 2022), 2020. a
Begert, M., Schlegel, T., and Kirchhofer, W.: Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000, Int. J. Climatol., 25, 65–80, https://doi.org/10.1002/joc.1118, 2005. a
Download
Short summary
Knowledge about inhomogeneities in a data set is important for any subsequent climatological analysis. We ran three well-established homogenization methods and compared the identified break points. By only treating breaks as valid when detected by at least two out of three methods, we enhanced the robustness of our results. We found 45 breaks within 42 of 184 investigated series; of these 70 % could be explained by events recorded in the station history.