Abstract
A comprehensive analysis of sea ice and its snow cover during the summer in the Arctic Pacific sector was conducted using the observations recorded during the 7th Chinese National Arctic Research Expedition (CHIANRE-2016) and the satellite-derived parameters of the melt pond fraction (MPF) and snow grain size (SGS) from MODIS data. The results show that there were many low-concentration ice areas in the south of 78°N, while the ice concentration and thickness increased significantly with the latitude above the north of 78°N during CHIANRE-2016. The average MPF presented a trend of increasing in June and then decreasing in early September for 2016. The average snow depth on sea ice increased with latitude in the Arctic Pacific sector. We found a widely developed depth hoar layer in the snow stratigraphic profiles. The average SGS generally increased from June to early August and then decreased from August to September in 2016, and two valley values appeared during this period due to snowfall incidents.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Carlsen T, Birnbaum G, Ehrlich A, et al. 2017. Comparison of different methods to retrieve optical-equivalent snow grain size in central Antarctica. The Cryosphere, 11(6): 2727–2741, doi: https://doi.org/10.5194/tc-11-2727-2017
Comiso J C. 2012. Large decadal decline of the Arctic multiyear ice cover. Journal of Climate, 25(4): 1176–1193, doi: https://doi.org/10.1175/JCLI-D-11-00113.1
Dou Tingfeng, Xiao Cunde, Guo Rui, et al. 2012. Analysis on features of snow cover on the Arctic sea ice in summer of 2008. Journal of Glaciology and Geocryology (in Chinese), 34(1): 43–48
Fetterer F, Untersteiner N. 1998. Observations of melt ponds on Arctic sea ice. Journal of Geophysical Research: Oceans, 103(C11): 24821–24835, doi: https://doi.org/10.1029/98JC02034
Frey K E, Maslanik J A, Kinney J C, et al. 2014. Recent variability in sea ice cover, age, and thickness in the Pacific Arctic region. In: Grebmeier I M, Maslowski W, eds. The Pacific Arctic Region: Ecosystem Status and Trends in a Rapidly Changing Environment. Dordrecht: Springer, 31–63
Grenfell T C, Maykut G A. 1977. The optical properties of ice and snow in the Arctic Basin. Journal of Glaciology, 18(80): 445–463, doi: https://doi.org/10.1017/S0022143000021122
Intrieri I M, Shupe M D, Uttal T, et al. 2002. An annual cycle of Arctic cloud characteristics observed by radar and LIDAR at SHEBA. Journal of Geophysical Research: Oceans, 107(C10): SHE 5–1–SHE5–15
Istomina L, Heygster G, Huntemann M, et al. 2015. Melt pond fraction and spectral sea ice albedo retrieval from MERIS data-Part 2: Case studies and trends of sea ice albedo and melt ponds in the Arctic for years 2002–2011. The Cryosphere, 9(4): 1567–1578, doi: https://doi.org/10.5194/tc-9-1567-2015
Kwok R, Rothrock D A. 2009. Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008. Geophysical Research Letters, 36(15): L15501
Laxon S W, Giles KA, Ridout A L, et al. 2013. CryoSat-2 estimates of Arctic sea ice thickness and volume. Geophysical Research Letters, 40(4): 732–737, doi: https://doi.org/10.1002/grl.50193
Lyapustin A, Tedesco M, Wang Y, et al. 2009. Retrieval of snow grain size over Greenland from MODIS. Remote Sensing of Environment, 113:1976–1987, doi: https://doi.org/10.1016/j.rse.2009.05.008
Maslanik I, Stroeve I, Fowler C, et al. 2011. Distribution and trends in Arctic sea ice age through spring 2011. Geophysical Research Letters, 38(13): L13502
Nghiem S V, Rigor I G, Perovich D K, et al. 2007. Rapid reduction of Arctic perennial sea ice, Geophysical Research Letters, 34(19): L19504, doi: https://doi.org/10.1029/2007GL031138
Nolin AW, Dozier I. 1993. Estimating snow grain size using AVIRIS data. Remote Sensing of Environment, 44(2–3): 231–238, doi: https://doi.org/10.1016/0034-4257(93)90018-S
Rösel A, Itkin P, King I, et al. 2018. Thin sea ice, thick snow, and widespread negative freeboard observed during N-ICE2015 north of Svalbard. Journal of Geophysical Research: Oceans, 123(2): 1156–1176, doi: https://doi.org/10.1002/2017IC012865
Rösel A, Kaleschke L, Birnbaum G. 2012. Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network. The Cryosphere, 6(2): 431–446, doi: https://doi.org/10.5194/tc-6-431-2012
Shokr M, Sinha N. 2015. Sea Ice: Physics and Remote Sensing. Washington, DC: American Geophysical Union
Sturm M, Morris K, Massom R. 2013. The winter snow cover of the West Antarctic pack ice: Its spatial and temporal variability. In: Jeffries M O, ed. Antarctic Sea Ice: Physical Processes, Interactions and Variability. Washington, DC: American Geophysical Union, 1–18
Suttles I T, Green R N, Minis P, et al. 1988. Angular radiation models for earth-atmosphere system. Hampton, Virginia, USA: NASA Langley Research Center
Sun Xiaoyu, Shen Hui, Li Chunhua, et al. 2017. Arctic sea ice observation and characteristic analysis based on the seventh National Arctic Research expedition in summer 2016. Marine Forecasts (in Chinese), 34(4): 11–19
Teleti P R, Luis A I. 2013. Sea ice observations in Polar regions: Evolution of technologies in remote sensing. International Journal of Geosciences, 4(7): 1031–1050, doi: https://doi.org/10.4236/ijg.2013.47097
Tschudi M A, Maslanik I A, Perovich D K. 2008. Derivation of melt pond coverage on Arctic sea ice using MODIS observations. Remote Sensing of Environment, 112(5): 2605–2614, doi: https://doi.org/10.1016/j.rse.2007.12.009
Warren S G, Rigor IG, Untersteiner N, et al. 1999. Snow depth on Arctic sea ice. Journal of Climate, 12(6): 1814–1829, doi: https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2
Webster M A, Rigor I G, Nghiem S V, et al. 2014. Interdecadal changes in snow depth on Arctic Sea Ice. Journal of Geophysical Research: Oceans, 119(8): 5395–5406, doi: https://doi.org/10.1002/2014JC009985
Wiebe H, Heygster G, Zege E, et al. 2013. Snow grain size retrieval SGSP from optical satellite data: validation with ground measurements and detection of snow fall events, Remote Sensing of Environment, 128: 11–20, doi: https://doi.org/10.1016/j.rse.2012.09.007
Wiscombe W J, Warren S G. 1980. A model for the spectral albedo of snow. I: Pure snow. Journal of the Atmospheric Sciences, 37(12): 2712–2733, doi: https://doi.org/10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2
Xia Wentao, Xie Hongjie, Ke Changqing. 2014. Assessing trend and variation of Arctic sea-ice extent during 1979–2012 from a latitude perspective of ice edge, Polar Research, 33(1): 21249, doi: https://doi.org/10.3402/polar.v33.21249
Xiao Cunde, Qin Dahe, Ren Jiawen. 1997. The feature of sea ice cover, snow distribution and its densification in the central Arctica. Scientia Geographica Sinica (in Chinese), 17(4): 289–296
Yackel J J, Nandan V, Mahmud M, et al. 2018. A spectral mixture analysis approach to quantify Arctic first-year sea ice melt pond fraction using QuickBird and MODIS reflectance data, Remote Sensing of Environment, 204: 704–716, doi: https://doi.org/10.1016/j.rse.2017.09.030
Zege E, Katsev I, Malinka A, et al. 2008. New algorithm to retrieve the effective snow grain size and pollution amount from satellite data, Annals of Glaciology, 49: 139–144, doi: https://doi.org/10.3189/172756408787815004
Acknowledgements
We thank the members of the 7th Chinese National Arctic Research Expedition and the crews of R/V Xuehngior their assistance during the ship-based and station-based ice and snow observations.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: The National Key Research and Development Program of China under contract No. 2016YFC1402704; the National Natural Science Foundation of China under contract No. 42076235; the Special Fund for High Resolution Images Surveying and Mapping Application System under contract No. 42-Y30B04-9001-19/21.
Rights and permissions
About this article
Cite this article
Ji, Q., Liu, Y., Pang, X. et al. Characterization of sea ice and its snow cover in the Arctic Pacific sector during the summer of 2016. Acta Oceanol. Sin. 40, 33–42 (2021). https://doi.org/10.1007/s13131-021-1716-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13131-021-1716-3