Evaluating the robustness of snow climate indicators using a unique set of parallel snow measurement series. (9th October 2020)

• Record Type: Journal Article
• Authors: Buchmann, Moritz; Begert, Michael; Brönnimann, Stefan; Marty, Christoph
• Is Part Of: International journal of climatology. Volume 41(2021)Supplement 1
• Page Start: E2553
• Page End: E2563
• Publication Date: 2020-10-09
• Subjects: climate -- climate indicator -- parallel time series -- snow -- snow measurements; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.6863
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract: Snow on the ground is an important climate variable which is normally measured either as snow depth or height of new snow. Like any other meteorological variable, manually measured snow is prone to local influences, changes in the environment or procedure of the measurements. In order to investigate the robustness of snow measurement series towards such non‐climatic changes, a unique set of parallel manual snow measurements over 25 years from 23 station pairs between 490 and 1800 m a.s.l. was compiled. A sensitivity analysis based on typical snow climate indicators (e.g., mean snow depth, sum of new snow) from these parallel time series was carried out to find the most robust snow climate indicators for climatological analyses. Results show that there are only small differences in the sensitivity of the various snow climate indicators with regards to local changes. However, the indicators number of days with snow on the ground as well as the maximum snow depth are least affected by local influences and changes at station level. Median values of all station pairs reveal relative differences of about 7% for the number of days with snow cover and 11–16% for all other indicators. However, in extreme cases, the deviations within a single station pair can reach 25–40%. Abstract : Comparison of mean RPD for all station pairs and indicators. dHS1 and dHS5 show median RPD values <10%, HSmax the least variability over all station pairs