The effect of snow density evolution on modelled snow depth in the Arctic

Yin Hao; Su Jie; Cheng Bin

doi:10.12284/hyxb2021143

Volume 43 Issue 7

Jul. 2021

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Yin Hao，Su Jie，Cheng Bin. 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

Citation:

Yin Hao，Su Jie，Cheng Bin. 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

Citation:

Yin Hao，Su Jie，Cheng Bin. 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

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The effect of snow density evolution on modelled snow depth in the Arctic

doi: 10.12284/hyxb2021143

Yin Hao^{1, 2
,},
Su Jie^{1, 2
,
,},
Cheng Bin³

1.
Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao 266100, China
2.
Joint Center for Polar Research of Chinese Universities, Beijing 100875, China
3.
Finnish Meteorological Institute, Helsinki FI-00101, Finland

Received Date: 2021-03-26
Rev Recd Date: 2021-06-03

Available Online: 2021-06-28

Publish Date: 2021-07-25

Abstract

Abstract

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

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References(42)

References

[1]	Kwok R, Rothrock D A. Decline in Arctic sea ice thickness from submarine and ICESat records: 1958−2008[J]. Geophysical Research Letters, 2009, 36(15): L15501.
[2]	Maslanik J, Stroeve J, Fowler C, et al. Distribution and trends in Arctic sea ice age through spring 2011[J]. Geophysical Research Letters, 2011, 38(13): L13502.
[3]	Markus T, Stroeve J C, Miller J. Recent changes in Arctic sea ice melt onset, freezeup, and melt season length[J]. Journal of Geophysical Research: Oceans, 2009, 114(C12): C12024. doi: 10.1029/2009JC005436
[4]	Bliss A C, Anderson M R. Arctic sea ice melt onset timing from passive microwave-based and surface air temperature-based methods[J]. Journal of Geophysical Research: Atmospheres, 2018, 123(17): 9063−9080. doi: 10.1029/2018JD028676
[5]	Webster M A, Rigor I G, Nghiem S V, et al. Interdecadal changes in snow depth on Arctic sea ice[J]. Journal of Geophysical Research: Oceans, 2015, 119(8): 5395−5406.
[6]	Simmonds I, Burke C, Keay K. Arctic climate change as manifest in cyclone behavior[J]. Journal of Climate, 2008, 21(22): 5777−5796. doi: 10.1175/2008JCLI2366.1
[7]	Boisvert L N, Webster M A, Petty A A, et al. Intercomparison of precipitation estimates over the Arctic Ocean and its peripheral seas from reanalyses[J]. Journal of Climate, 2018, 31(20): 8441−8462. doi: 10.1175/JCLI-D-18-0125.1
[8]	Webster M, Gerland S, Holland M, et al. Snow in the changing sea-ice systems[J]. Nature Climate Change, 2018, 8(11): 946−953. doi: 10.1038/s41558-018-0286-7
[9]	Merkouriadi I, Cheng B, Hudson S R, et al. Effect of frequent winter warming events (storms) and snow on sea-ice growth—a case from the Atlantic sector of the Arctic Ocean during the N-ICE2015 campaign[J]. Annals of Glaciology, 2020, 61(82): 164−170. doi: 10.1017/aog.2020.25
[10]	Leppäranta M. A growth model for black ice, snow ice and snow thickness in subarctic basins[J]. Hydrology Research, 1983, 14(2): 59−70. doi: 10.2166/nh.1983.0006
[11]	Cheng Bin, Launianen J, Vihma T. Modelling of superimposed ice formation and subsurface melting in the Baltic sea[J]. Geophysica, 2003, 39(1): 31−50.
[12]	Wang Caixin, Cheng Bin, Wang Keguang, et al. Modelling snow ice and superimposed ice on landfast sea ice in Kongsfjorden, Svalbard[J]. Polar Research, 2015, 34(1): 20828. doi: 10.3402/polar.v34.20828
[13]	Merkouriadi I, Cheng Bin, Graham R M, et al. Critical role of snow on sea ice growth in the Atlantic sector of the Arctic Ocean[J]. Geophysical Research Letters, 2017, 44(20): 10479−10485. doi: 10.1002/2017GL075494
[14]	Merkouriadi I, Liston G E, Graham R M, et al. Quantifying the potential for snow-ice formation in the Arctic Ocean[J]. Geophysical Research Letters, 2020, 47(4): e2019GL085020.
[15]	Granskog M A, Rösel A, Dodd P A, et al. Snow contribution to first-year and second-year Arctic sea ice mass balance north of Svalbard[J]. Journal of Geophysical Research: Oceans, 2017, 122(3): 2539−2549. doi: 10.1002/2016JC012398
[16]	Ledley T S. Snow on sea ice: Competing effects in shaping climate[J]. Journal of Geophysical Research: Atmospheres, 1991, 96(D9): 17195−17208. doi: 10.1029/91JD01439
[17]	Shapiro L H, Johnson J B, Sturm M, et al. Snow mechanics: Review of the state of knowledge and applications[R]. US Army Cold Regions: Research and Engineering Laboratory, 1997.
[18]	Anderson E A. A point energy and mass balance model of a snow cover[R]. Washington: US Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, Office of Hydrology, 1976.
[19]	Brun E, Martin Ε, Simon V, et al. An energy and mass model of snow cover suitable for operational avalanche forecasting[J]. Journal of Glaciology, 1989, 35(121): 333−342. doi: 10.1017/S0022143000009254
[20]	Lehning M, Bartelt P, Brown B, et al. Snowpack model calculations for avalanche warning based upon a new network of weather and snow stations[J]. Cold Regions Science and Technology, 1999, 30(1/3): 145−157.
[21]	Boone A. Description du schema de neige ISBA-ES (Explicit Snow)[Z/OL]. [2021−03−25]. http://www.umr-cnrm.fr/IMG/pdf/snowdoc_v2.pdf.
[22]	Huintjes E, Sauter T, Schröter B, et al. Evaluation of a coupled snow and energy balance model for Zhadang Glacier, Tibetan Plateau, using glaciological measurements and time-lapse photography[J]. Arctic Antarctic & Alpine Research, 2015, 47(3): 573−590.
[23]	Sauter T, Arndt A, Schneider C. COSIPY v1.3-an open-source coupled snowpack and ice surface energy and mass balance model[J]. Geoscientific Model Development, 2020, 13(11): 5645−5662. doi: 10.5194/gmd-13-5645-2020
[24]	Liston G E, Itkin P, Stroeve J, et al. A Lagrangian snow-evolution system for sea-ice applications (SnowModel-LG): Part I-model description[J]. Journal of Geophysical Research: Oceans, 2020, 125(10): e2019JC015913.
[25]	Launiainen J, Cheng Bin. Modelling of ice thermodynamics in natural water bodies[J]. Cold Regions Science and Technology, 1998, 27(3): 153−178. doi: 10.1016/S0165-232X(98)00009-3
[26]	Saloranta T M. Modeling the evolution of snow, snow ice and ice in the Baltic Sea[J]. Tellus A: Dynamic Meteorology and Oceanography, 2010, 52(1): 93−108.
[27]	Cheng Bin, Zhang Zhanhai, Vihma T, et al. Model experiments on snow and ice thermodynamics in the Arctic Ocean with CHINARE 2003 data[J]. Journal of Geophysical Research: Oceans, 2008, 113(C9): C09020.
[28]	Huwald H, Tremblay L B, Blatter H. Reconciling different observational data sets from Surface Heat Budget of the Arctic Ocean (SHEBA) for model validation purposes[J]. Journal of Geophysical Research: Oceans, 2005, 110(C5): C05009.
[29]	Zhao Jiechen, Cheng Bin, Vihma T, et al. Observation and thermodynamic modeling of the influence of snow cover on landfast sea ice thickness in Prydz Bay, East Antarctica[J]. Cold Regions Science and Technology, 2019, 168: 102869. doi: 10.1016/j.coldregions.2019.102869
[30]	Richter-Menge J A, Perovich D K, Elder B C, et al. Ice mass-balance buoys: A tool for measuring and attributing changes in the thickness of the Arctic sea-ice cover[J]. Annals of Glaciology, 2006, 44: 205−210. doi: 10.3189/172756406781811727
[31]	Polashenski C, Perovich D, Richter-Menge J, et al. Seasonal ice mass-balance buoys: Adapting tools to the changing Arctic[J]. Annals of Glaciology, 2011, 52(57): 18−26. doi: 10.3189/172756411795931516
[32]	Lei Ruibo, Li Na, Heil P, et al. Multiyear sea ice thermal regimes and oceanic heat flux derived from an ice mass balance buoy in the Arctic Ocean[J]. Journal of Geophysical Research: Oceans, 2014, 119(1): 537−547.
[33]	Nicolaus M, Hoppmann M, Arndt S, et al. Snow depth and air temperature seasonality on sea ice derived from snow buoy measurements[J]. Frontiers in Marine Science, 2021, 8: 655446. doi: 10.3389/fmars.2021.655446
[34]	Warren S G, Rigor I G, Untersteiner N, et al. Snow depth on Arctic sea ice[J]. Journal of Climate, 1999, 12(6): 1814−1829. doi: 10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2
[35]	Cheng Bin, Mäkynen M, Similä M, et al. Modelling snow and ice thickness in the coastal Kara Sea, Russian Arctic[J]. Annals of Glaciology, 2013, 54(62): 105−113. doi: 10.3189/2013AoG62A180
[36]	Li Shutong, Dou Tingfeng, Xiao Cunde. A preliminary investigation of Arctic sea ice negative freeboard from in-situ observations and radar altimetry[J]. Journal of Ocean University of China, 2021, 20(2): 307−314. doi: 10.1007/s11802-021-4380-5
[37]	Maksym T, Jeffries M O. A one-dimensional percolation model of flooding and snow ice formation on Antarctic sea ice[J]. Journal of Geophysical Research: Oceans, 2000, 105(C11): 26313−26331. doi: 10.1029/2000JC900130
[38]	Cheng Bin, Vihma T, Rontu L, et al. Evolution of snow and ice temperature, thickness and energy balance in Lake Orajärvi, northern Finland[J]. Tellus A: Dynamic Meteorology and Oceanography, 2014, 66(1): 21564. doi: 10.3402/tellusa.v66.21564
[39]	Liston G E, Sturm M. A snow-transport model for complex terrain[J]. Journal of Glaciology, 1998, 44(148): 498−516. doi: 10.1017/S0022143000002021
[40]	Liston G E, Haehnel R B, Sturm M, et al. Simulating complex snow distributions in windy environments using SnowTran-3D[J]. Journal of Glaciology, 2007, 53(181): 241−256. doi: 10.3189/172756507782202865
[41]	Aleksandrov Y I, Bryazgin N N, Førland E J, et al. Seasonal, interannual and long-term variability of precipitation and snow depth in the region of the Barents and Kara seas[J]. Polar Research, 2005, 24(1/2): 69−85.
[42]	Rösel A, Itkin P, King J, et al. Thin sea ice, thick snow, and widespread negative freeboard observed during N-ICE2015 north of svalbard[J]. Journal of Geophysical Research: Oceans, 2018, 123(2): 1156−1176. doi: 10.1002/2017JC012865