Preprints
https://doi.org/10.5194/tc-2021-260
https://doi.org/10.5194/tc-2021-260
25 Aug 2021
 | 25 Aug 2021
Status: this preprint has been withdrawn by the authors.

The statistics of blowing snow occurrences from multi-year autonomous snow flux measurements in the French Alps

Zhipeng Xie, Yaoming Ma, Weiqiang Ma, Zeyong Hu, and Genhou Sun

Abstract. Wind-driven snow transport has important implications for the spatial-temporal heterogeneity of snow distribution and snowpack evolution in mountainous areas, such as the European Alps. The climatological and hydrological significance of this region have been extensively investigated using satellite and numerical models. However, knowledge of the spatiotemporal variability of blowing snow is in its infancy because of inaccuracies in satellite-based blowing snow algorithms and the absence of quantitative assessments. Here, we present the spatiotemporal variability and magnitude of blowing snow events, and explore the potential links with ambient meteorological conditions using near surface blowing snow observations from the ISAW outdoor environmental monitoring network. Results show frequent occurrence of blowing snow, and contrasting seasonal variability in the French Alps. On average, monthly blowing snow days range from 5.0 to 14.3 days when using the snow flux threshold of 0.1 g m−2 s−1. The minimum and maximum frequencies of blowing snow days are observed in September and January, respectively, accounting for between 16.7 % and 46.1 % of the month. However, the frequency of monthly blowing snow days varies widely between stations, and this variability is more pronounced at lower threshold levels. Blowing snow events with relatively high magnitudes of snow mass flux (1.0 g m−2 s−1) occur more frequently than low-intensity events (snow mass flux ranges from 0.1 to 0.5 g m−2 s−1). By imposing a minimum duration of 4 h, the monthly cumulative hours with blowing snow occurrences can be up to 255 hours, but show significant seasonal and spatial variability. The considerable variability observed across this region can be explained by contrasting local climate (particularly wind speed and air temperature), snowpack properties, topography and vegetation. The snow-mass transported during relatively high magnitude blowing snow events accounts for about 90 % of all the transported snow mass, highlighting the importance of major events. Blowing snow events that occur with concurrent snowfall are generally associated with intense snow transport. Transport of wet snow and dry snow is mostly concentrated in the range of 0.1 to 0.5 g m−2 s−1 and 0.5 to 1.0 g m−2 s−1, respectively. Understanding the spatiotemporal variability of blowing snow occurrences and the potential links with ambient meteorological conditions is critical for constructing effective avalanche disaster warning systems, and for promoting quantitative evaluation and development of satellite retrieval algorithms and blowing snow models.

This preprint has been withdrawn.

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Zhipeng Xie, Yaoming Ma, Weiqiang Ma, Zeyong Hu, and Genhou Sun

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-260', Anonymous Referee #1, 06 Sep 2021
  • RC2: 'Comment on tc-2021-260', Anonymous Referee #2, 02 Nov 2021

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-260', Anonymous Referee #1, 06 Sep 2021
  • RC2: 'Comment on tc-2021-260', Anonymous Referee #2, 02 Nov 2021
Zhipeng Xie, Yaoming Ma, Weiqiang Ma, Zeyong Hu, and Genhou Sun
Zhipeng Xie, Yaoming Ma, Weiqiang Ma, Zeyong Hu, and Genhou Sun

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Latest update: 03 Nov 2024
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This preprint has been withdrawn.

Short summary
Wind-driven snow transport greatly influences spatial-temporal distribution of snow in mountainous areas. Knowledge of the spatiotemporal variability of blowing snow is in its infancy because of inaccuracies in satellite-based blowing snow algorithms and the absence of quantitative assessments. Here, we present the spatiotemporal variability and magnitude of blowing snow events, and explore the potential links with ambient meteorological conditions using near surface blowing snow observations.