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The effect of snow density evolution on modelled snow depth in the Arctic
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Hao Yin1, 2, Jie Su1, 2, *, Bin Cheng3
Haiyang Xuebao | 2021, 43(7) : 75 - 89
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Haiyang Xuebao | 2021, 43(7): 75-89
Polar sea ice and climate change
The effect of snow density evolution on modelled snow depth in the Arctic
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Hao Yin1, 2, Jie Su1, 2, *, Bin Cheng3
Affiliations
  • 1Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao 266100, China
  • 2Joint Center for Polar Research of Chinese Universities, Beijing 100875, China
  • 3Finnish Meteorological Institute, Helsinki FI-00101, Finland
Published: 2021-07-25 doi: 10.12284/hyxb2021143
Outline
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Due to its high surface albedo, snow plays an important role in the air-ice-ocean interaction in high-latitude regions. Accurate snow mass balance calculations are needed to understand the evolution of sea ice and interaction between snow-ice and atmosphere better. One of the factors affecting snow mass balance is snow density. Constant mean snow bulk density is used to convert snow water equivalent to snow depth in the present 1-D high-resolution thermodynamic snow-ice model (such as HIGHTSI). Simplified to 2 snow layers, being fresh and old, algorithm reference to Lagrangian snow-evolution model (SnowModel-LG) used to treat layered snow compaction is introduced into HIGHTSI to reproduce the physical process of compacting in both the fresh and old layer and affecting the snow depth following the principle of mass conservation. Forced by ERA-Interim reanalysis data, modified HIGHTSI was applied to investigate the impact of snow density on snow depth along drift trajectories of 15 sea ice mass balance buoys (IMB) during snow accumulation period and assess the model results against observation. In contrast to the previous bulk snow density setting, with a constant density of 330 kg/m3 (T1) or 200 kg/m3 (T2), our new algorithm calculates snow depth by considering both the fresh and old snow densifying over time (T3). The simulations indicate that the improved algorithm is more reasonable to deal with the density evolution, and can reproduced the snow depth well. The overaccumulation caused by heaping continuously at the lower density of new snowfall can be avoided by considering the response of both the fresh and old snow depth to compaction. The absolute error calculated by layered snow compaction is reduced by 5 cm by setting the observation as a reference of both the fresh and old snow depth to compaction. The absolute error calculated by layered snow compaction in T2 is reduced by 5 cm by setting the observation as a reference.

Arctic  /  snow depth  /  snow density  /  1-D high-resolution thermodynamic snow-ice model
Hao Yin, Jie Su, Bin Cheng. The effect of snow density evolution on modelled snow depth in the Arctic[J]. Haiyang Xuebao, 2021 , 43 (7) : 75 -89 . DOI: 10.12284/hyxb2021143
Year 2021 volume 43 Issue 7
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Article Info
doi: 10.12284/hyxb2021143
  • Receive Date:2021-03-26
  • Online Date:2026-02-26
  • Published:2021-07-25
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History
  • Received:2021-03-26
  • Revised:2021-06-03
Funding
Affiliations
    1Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao 266100, China
    2Joint Center for Polar Research of Chinese Universities, Beijing 100875, China
    3Finnish Meteorological Institute, Helsinki FI-00101, Finland
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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