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Spring snow conditions on Arctic sea ice north of Svalbard, during the Norwegian Young Sea ICE (N‐ICE2015) expedition
Author(s) -
Gallet JeanCharles,
Merkouriadi Ioanna,
Liston Glen E.,
Polashenski Chris,
Hudson Stephen,
Rösel Anja,
Gerland Sebastian
Publication year - 2017
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2016jd026035
Subject(s) - snow , snowpack , snow field , sea ice , snow line , arctic ice pack , arctic , environmental science , geology , climatology , spring (device) , sea ice thickness , antarctic sea ice , atmospheric sciences , oceanography , snow cover , geomorphology , mechanical engineering , engineering
Snow is crucial over sea ice due to its conflicting role in reflecting the incoming solar energy and reducing the heat transfer so that its temporal and spatial variability are important to estimate. During the Norwegian Young Sea ICE (N‐ICE2015) campaign, snow physical properties and variability were examined, and results from April until mid‐June 2015 are presented here. Overall, the snow thickness was about 20 cm higher than the climatology for second‐year ice, with an average of 55 ± 27 cm and 32 ± 20 cm on first‐year ice. The average density was 350–400 kg m −3 in spring, with higher values in June due to melting. Due to flooding in March, larger variability in snow water equivalent was observed. However, the snow structure was quite homogeneous in spring due to warmer weather and lower amount of storms passing over the field camp. The snow was mostly consisted of wind slab, faceted, and depth hoar type crystals with occasional fresh snow. These observations highlight the more dynamic character of evolution of snow properties over sea ice compared to previous observations, due to more variable sea ice and weather conditions in this area. The snowpack was isothermal as early as 10 June with the first onset of melt clearly identified in early June. Based on our observations, we estimate than snow could be accurately represented by a three to four layers modeling approach, in order to better consider the high variability of snow thickness and density together with the rapid metamorphose of the snow in springtime.