Articles | Volume 17, issue 6
https://doi.org/10.5194/tc-17-2231-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-2231-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jun 2023
Research article |
| 02 Jun 2023
| 02 Jun 2023
Annual evolution of the ice–ocean interaction beneath landfast ice in Prydz Bay, East Antarctica
Haihan Hu
School of Geospatial Engineering and Science, Sun Yat-sen University,
and Southern Marine Science and Engineering Guangdong Laboratory,
Zhuhai 519082, China
Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
Qingdao Innovation and Development Base (Centre) of Harbin Engineering University, Qingdao 266500, China
College of Underwater Acoustic Engineering, Harbin Engineering
University, Harbin 150001, China
Petra Heil
Australia Antarctic Division & Australian Antarctic Programmer
Partnership, Private Bag 80, Hobart TAS 7001, Australia
Zhiliang Qin
Qingdao Innovation and Development Base (Centre) of Harbin Engineering University, Qingdao 266500, China
College of Underwater Acoustic Engineering, Harbin Engineering
University, Harbin 150001, China
Jingkai Ma
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Centre, Beijing 100081, China
Fengming Hui
CORRESPONDING AUTHOR
School of Geospatial Engineering and Science, Sun Yat-sen University,
and Southern Marine Science and Engineering Guangdong Laboratory,
Zhuhai 519082, China
Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
Xiao Cheng
School of Geospatial Engineering and Science, Sun Yat-sen University,
and Southern Marine Science and Engineering Guangdong Laboratory,
Zhuhai 519082, China
Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
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Preprint archived
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Arctic sea ice has rapidly declined due to global warming, leading to extreme weather events. Accurate ice monitoring is vital for understanding and forecasting these impacts. Combining SAR and AMSR2 data with machine learning is efficient but requires sufficient labels. We propose a framework integrating the U-Net model with the Multi-textRG algorithm to achieve ice-water classification at SAR-level resolution and to generate accurate labels for improved U-Net model training.
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The Cryosphere, 17, 279–308, https://doi.org/10.5194/tc-17-279-2023, https://doi.org/10.5194/tc-17-279-2023, 2023
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Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study gives a systematic inter-comparison and evaluation of eight SITY products. Main results include differences in SITY products being significant, with average Arctic multiyear ice extent up to 1.8×106 km2; Ku-band scatterometer SITY products generally performing better; and factors such as satellite inputs, classification methods, training datasets and post-processing highly impacting their performance.
Chong Liu, Xiaoqing Xu, Xuejie Feng, Xiao Cheng, Caixia Liu, and Huabing Huang
Earth Syst. Sci. Data, 15, 133–153, https://doi.org/10.5194/essd-15-133-2023, https://doi.org/10.5194/essd-15-133-2023, 2023
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Rapid Arctic changes are increasingly influencing human society, both locally and globally. Land cover offers a basis for characterizing the terrestrial world, yet spatially detailed information on Arctic land cover is lacking. We employ multi-source data to develop a new land cover map for the circumpolar Arctic. Our product reveals regionally contrasting biome distributions not fully documented in existing studies and thus enhances our understanding of the Arctic’s terrestrial system.
Minghu Ding, Xiaowei Zou, Qizhen Sun, Diyi Yang, Wenqian Zhang, Lingen Bian, Changgui Lu, Ian Allison, Petra Heil, and Cunde Xiao
Earth Syst. Sci. Data, 14, 5019–5035, https://doi.org/10.5194/essd-14-5019-2022, https://doi.org/10.5194/essd-14-5019-2022, 2022
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The PANDA automatic weather station (AWS) network consists of 11 stations deployed along a transect from the coast (Zhongshan Station) to the summit of the East Antarctic Ice Sheet (Dome A). It covers the different climatic and topographic units of East Antarctica. All stations record hourly air temperature, relative humidity, air pressure, wind speed and direction at two or three heights. The PANDA AWS dataset commences from 1989 and is planned to be publicly available into the future.
Qi Liang, Wanxin Xiao, Ian Howat, Xiao Cheng, Fengming Hui, Zhuoqi Chen, Mi Jiang, and Lei Zheng
The Cryosphere, 16, 2671–2681, https://doi.org/10.5194/tc-16-2671-2022, https://doi.org/10.5194/tc-16-2671-2022, 2022
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Using multi-temporal ArcticDEM and ICESat-2 altimetry data, we document changes in surface elevation of a subglacial lake basin from 2012 to 2021. The long-term measurements show that the subglacial lake was recharged by surface meltwater and that a rapid drainage event in late August 2019 induced an abrupt ice velocity change. Multiple factors regulate the episodic filling and drainage of the lake. Our study also reveals ~ 64 % of the surface meltwater successfully descended to the bed.
Fengguan Gu, Qinghua Yang, Frank Kauker, Changwei Liu, Guanghua Hao, Chao-Yuan Yang, Jiping Liu, Petra Heil, Xuewei Li, and Bo Han
The Cryosphere, 16, 1873–1887, https://doi.org/10.5194/tc-16-1873-2022, https://doi.org/10.5194/tc-16-1873-2022, 2022
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The sea ice thickness was simulated by a single-column model and compared with in situ observations obtained off Zhongshan Station in the Antarctic. It is shown that the unrealistic precipitation in the atmospheric forcing data leads to the largest bias in sea ice thickness and snow depth modeling. In addition, the increasing snow depth gradually inhibits the growth of sea ice associated with thermal blanketing by the snow.
Tian R. Tian, Alexander D. Fraser, Noriaki Kimura, Chen Zhao, and Petra Heil
The Cryosphere, 16, 1299–1314, https://doi.org/10.5194/tc-16-1299-2022, https://doi.org/10.5194/tc-16-1299-2022, 2022
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This study presents a comprehensive validation of a satellite observational sea ice motion product in Antarctica by using drifting buoys. Two problems existing in this sea ice motion product have been noticed. After rectifying problems, we use it to investigate the impacts of satellite observational configuration and timescale on Antarctic sea ice kinematics and suggest the future improvement of satellite missions specifically designed for retrieval of sea ice motion.
Yijing Lin, Yan Liu, Zhitong Yu, Xiao Cheng, Qiang Shen, and Liyun Zhao
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-325, https://doi.org/10.5194/tc-2021-325, 2021
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We introduce an uncertainty analysis framework for comprehensively and systematically quantifying the uncertainties of the Antarctic mass balance using the Input and Output Method. It is difficult to use the previous strategies employed in various methods and the available data to achieve the goal of estimation accuracy. The dominant cause of the future uncertainty is the ice thickness data gap. The interannual variability of ice discharge caused by velocity and thickness is also nonnegligible.
Joey J. Voermans, Qingxiang Liu, Aleksey Marchenko, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Takuji Waseda, Takehiko Nose, Tsubasa Kodaira, Jingkai Li, and Alexander V. Babanin
The Cryosphere, 15, 5557–5575, https://doi.org/10.5194/tc-15-5557-2021, https://doi.org/10.5194/tc-15-5557-2021, 2021
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We have shown through field experiments that the amount of wave energy dissipated in landfast ice, sea ice attached to land, is much larger than in broken ice. By comparing our measurements against predictions of contemporary wave–ice interaction models, we determined which models can explain our observations and which cannot. Our results will improve our understanding of how waves and ice interact and how we can model such interactions to better forecast waves and ice in the polar regions.
Mengzhen Qi, Yan Liu, Jiping Liu, Xiao Cheng, Yijing Lin, Qiyang Feng, Qiang Shen, and Zhitong Yu
Earth Syst. Sci. Data, 13, 4583–4601, https://doi.org/10.5194/essd-13-4583-2021, https://doi.org/10.5194/essd-13-4583-2021, 2021
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A total of 1975 annual calving events larger than 1 km2 were detected on the Antarctic ice shelves from August 2005 to August 2020. The average annual calved area was measured as 3549.1 km2, and the average calving rate was measured as 770.3 Gt yr-1. Iceberg calving is most prevalent in West Antarctica, followed by the Antarctic Peninsula and Wilkes Land in East Antarctica. This annual iceberg calving dataset provides consistent and precise calving observations with the longest time coverage.
Linlu Mei, Vladimir Rozanov, Evelyn Jäkel, Xiao Cheng, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2781–2802, https://doi.org/10.5194/tc-15-2781-2021, https://doi.org/10.5194/tc-15-2781-2021, 2021
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This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 2 of two companion papers and shows the results and validation. The paper performs the new retrieval algorithm on the Sea and Land
Surface Temperature Radiometer (SLSTR) instrument and compares the retrieved snow properties with ground-based measurements, aircraft measurements and other satellite products.
Diana Francis, Kyle S. Mattingly, Stef Lhermitte, Marouane Temimi, and Petra Heil
The Cryosphere, 15, 2147–2165, https://doi.org/10.5194/tc-15-2147-2021, https://doi.org/10.5194/tc-15-2147-2021, 2021
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The unexpected September 2019 calving event from the Amery Ice Shelf, the largest since 1963 and which occurred almost a decade earlier than expected, was triggered by atmospheric extremes. Explosive twin polar cyclones provided a deterministic role in this event by creating oceanward sea surface slope triggering the calving. The observed record-anomalous atmospheric conditions were promoted by blocking ridges and Antarctic-wide anomalous poleward transport of heat and moisture.
Yu Zhou, Jianlong Chen, and Xiao Cheng
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2021-21, https://doi.org/10.5194/esurf-2021-21, 2021
Preprint withdrawn
Cited articles
ALEC ACTD–DF, Japanese JFE Advantech Co., Ltd.:
https://www.analyticalsolns.com.au/product/conductivity_temperature_depth_logger_miniature_.html, last access: 24 February 2023.
Allison, I.: Antarctic sea ice growth and oceanic heat flux, in: Sea Level, Ice and
Climate Change, Proceedings of the IUGG Canberra Symposium, 1979, IAHS Publ., 131, 161–170, 1981.
Cheng, B., Vihma, T., Pirazzini, R., and Granskog, M. A.: Modelling of
superimposed ice formation during the spring snowmelt period in the Baltic
Sea, Ann. Glaciol., 44, 139–146,
https://doi.org/10.3189/172756406781811277, 2006.
Comiso, J. C., Parkinson, C. L., Gersten, R., and Stock, L.: Accelerated
decline in the Arctic sea ice cover, Geophys. Res. Lett., 35, L01703,
https://doi.org/10.1029/2007GL031972, 2008.
E, D., Huang, J., and Zhang, S.: Analysis of Tidal Features of Zhongshan
Station, East Antarctic, Geomatics and information science of WUHAN
UNIVERSITY, 379–382, https://doi.org/10.13203/j.whugis2013.04.025,
2013 (in Chinese).
Fedotov, V. I., Cherepanov, N. V., and Tyshko, K. P.: Some Features of the
Growth, Structure and Metamorphism of East Antarctic Landfast Sea Ice, in:
Antarctic Research Series, edited by: Jeffries, M. O., American Geophysical
Union, Washington, D. C., 343–354, https://doi.org/10.1029/AR074p0343,
2013.
Fraser, A. D., Massom, R. A., Handcock, M. S., Reid, P., Ohshima, K. I., Raphael, M. N., Cartwright, J., Klekociuk, A. R., Wang, Z., and Porter-Smith, R.: Eighteen-year record of circum-Antarctic landfast-sea-ice distribution allows detailed baseline characterisation and reveals trends and variability, The Cryosphere, 15, 5061–5077, https://doi.org/10.5194/tc-15-5061-2021, 2021.
Giles, K. A., Laxon, S. W., and Ridout, A. L.: Circumpolar thinning of
Arctic sea ice following the 2007 record ice extent minimum, Geophys. Res.
Lett., 35, L22502, https://doi.org/10.1029/2008GL035710, 2008.
Global Ocean Physics Analysis and Forecast, E.U. Copernicus Marine Service
Information: https://doi.org/10.48670/moi-00016, last access: 24 February 2023.
Guo, G., Shi, J., and Jiao, Y.: Temporal variability of vertical heat flux
in the Makarov Basin during the ice camp observation in summer 2010, Acta
Oceanol. Sin., 34, 118–125, https://doi.org/10.1007/s13131-015-0755-z,
2015.
Heil, P.: Atmospheric conditions and fast ice at Davis, East Antarctica: A
case study, J. Geophys. Res., 111, C05009,
https://doi.org/10.1029/2005JC002904, 2006.
Heil, P., Allison, I., and Lytle, V. I.: Seasonal and interannual variations
of the oceanic heat flux under a landfast Antarctic sea ice cover, J.
Geophys. Res., 101, 25741–25752, https://doi.org/10.1029/96JC01921, 1996.
Hou, S. and Shi, J.: Variability and Formation Mechanism of Polynyas in
Eastern Prydz Bay, Antarctica, Remote Sens.-Basel, 13, 5089,
https://doi.org/10.3390/rs13245089, 2021.
Kirillov, S., Dmitrenko, I., Babb, D., Rysgaard, S., and Barber, D.: The
effect of ocean heat flux on seasonal ice growth in Young Sound (Northeast
Greenland): The Ocean Heat Flux in young sound Fjord, J. Geophys. Res.
Oceans, 120, 4803–4824, https://doi.org/10.1002/2015JC010720, 2015.
Launiainen, J. and Cheng, B.: Modelling of ice thermodynamics in natural
water bodies, Cold Reg. Sci. Technol., 27, 153–178,
https://doi.org/10.1016/S0165-232X(98)00009-3, 1998.
Lei, R., Li, Z., Cheng, Y., Wang, X., and Chen, Y.: A New Apparatus for
Monitoring Sea Ice Thickness Based on the Magnetostrictive-Delay-Line
Principle, J. Atmos. Ocean. Tech., 26, 818–827,
https://doi.org/10.1175/2008JTECHO613.1, 2009.
Lei, R., Li, Z., Cheng, B., Zhang, Z., and Heil, P.: Annual cycle of
landfast sea ice in Prydz Bay, east Antarctica, J. Geophys. Res., 115,
C02006, https://doi.org/10.1029/2008JC005223, 2010.
Lei, R., Li, N., Heil, P., Cheng, B., Zhang, Z., and Sun, B.: Multiyear sea
ice thermal regimes and oceanic heat flux derived from an ice mass balance
buoy in the Arctic Ocean: Arctic Sea-Ice Thermal Regimes, J. Geophys. Res.-Oceans, 119, 537–547, https://doi.org/10.1002/2012JC008731, 2014.
Lei, R., Cheng, B., Hoppmann, M., Zhang, F., Zuo, G., Hutchings, J. K., Lin,
L., Lan, M., Wang, H., Regnery, J., Krumpen, T., Haapala, J., Rabe, B.,
Perovich, D. K., and Nicolaus, M.: Seasonality and timing of sea ice mass
balance and heat fluxes in the Arctic transpolar drift during 2019–2020,
Elementa: Science of the Anthropocene, 10, 000089,
https://doi.org/10.1525/elementa.2021.000089, 2022.
Li, N., Lei, R., Heil, P., Cheng, B., Ding, M., Tian, Z., and Li, B.: Seasonal and interannual variability of the landfast ice mass balance between 2009 and 2018 in Prydz Bay, East Antarctica, The Cryosphere, 17, 917–937, https://doi.org/10.5194/tc-17-917-2023, 2023.
Li, X., Shokr, M., Hui, F., Chi, Z., Heil, P., Chen, Z., Yu, Y., Zhai, M.,
and Cheng, X.: The spatio-temporal patterns of landfast ice in Antarctica
during 2006–2011 and 2016–2017 using high-resolution SAR imagery, Remote
Sens. Environ., 242, 111736,
https://doi.org/10.1016/j.rse.2020.111736, 2020.
Liu, J. and Curry, J. A.: Accelerated warming of the Southern Ocean and its
impacts on the hydrological cycle and sea ice, P. Natl.
Acad. Sci. USA, 107, 14987–14992,
https://doi.org/10.1073/pnas.1003336107, 2010.
Lytle, V. I., Massom, R., Bindoff, N., Worby, A., and Allison, I.:
Wintertime heat flux to the underside of East Antarctic pack ice, J.
Geophys. Res., 105, 28759–28769, https://doi.org/10.1029/2000JC900099,
2000.
Massom, R. A. and Stammerjohn, S. E.: Antarctic sea ice change and
variability – Physical and ecological implications, Polar Sci., 4,
149–186, https://doi.org/10.1016/j.polar.2010.05.001, 2010.
Massom, R. A., Hill, K. L., Lytle, V. I., Worby, A. P., Paget, M. J., and
Allison, I.: Effects of regional fast-ice and iceberg distributions on the
behaviour of the Mertz Glacier polynya, East Antarctica, Ann. Glaciol., 33,
391–398, https://doi.org/10.3189/172756401781818518, 2001.
Maykut, G. A. and McPhee, M. G.: Solar heating of the Arctic mixed layer, J.
Geophys. Res., 100, 24691, https://doi.org/10.1029/95JC02554, 1995.
Maykut, G. A. and Untersteiner, N.: Some results from a time-dependent
thermodynamic model of sea ice, J. Geophys. Res., 76, 1550–1575,
https://doi.org/10.1029/JC076i006p01550, 1971.
McPhee, M. G.: The Effect of the Oceanic Boundary Layer on the Mean Drift of
Pack Ice: Application of a Simple Model, J. Phys. Oceanogr., 9, 388–400,
https://doi.org/10.1175/1520-0485(1979)009<0388:TEOTOB>2.0.CO;2, 1979.
McPhee, M. G.: Turbulent heat flux in the upper ocean under sea ice, J.
Geophys. Res., 97, 5365, https://doi.org/10.1029/92JC00239, 1992.
McPhee, M. G.: Turbulent stress at the ice/ocean interface and bottom
surface hydraulic roughness during the SHEBA drift, J. Geophys. Res., 107,
8037, https://doi.org/10.1029/2000JC000633, 2002.
McPhee, M. G. and Untersteiner, N.: Using sea ice to measure vertical heat
flux in the ocean, J. Geophys. Res., 87, 2071,
https://doi.org/10.1029/JC087iC03p02071, 1982.
McPhee, M. G., Ackley, S. F., Guest, P., Stanton, T. P., Huber, B. A.,
Martinson, D. G., Morison, J. H., Muench, R. D., and Padman, L.: The
Antarctic Zone Flux Experiment, B. Am. Meteorol. Soc., 77, 1221–1232,
https://doi.org/10.1175/1520-0477(1996)077<1221:TAZFE>2.0.CO;2, 1996.
McPhee, M. G., Kottmeier, C., and Morison, J. H.: Ocean Heat Flux in the
Central Weddell Sea during Winter, J. Phys. Oceanogr., 29, 1166–1179,
https://doi.org/10.1175/1520-0485(1999)029<1166:OHFITC>2.0.CO;2, 1999.
McPhee, M. G., Morison, J. H., and Nilsen, F.: Revisiting heat and salt
exchange at the ice-ocean interface: Ocean flux and modeling considerations,
J. Geophys. Res., 113, C06014, https://doi.org/10.1029/2007JC004383, 2008.
Miles, B. W. J., Stokes, C. R., and Jamieson, S. S. R.: Simultaneous disintegration of outlet glaciers in Porpoise Bay (Wilkes Land), East Antarctica, driven by sea ice break-up, The Cryosphere, 11, 427–442, https://doi.org/10.5194/tc-11-427-2017, 2017.
Millero, F.: Freezing point of seawater, Eighth Report of the Joint Panel on Oceanographic Tables and Standards, UNESCO Tech. Paper Mar. Sci., 28, 29–31, 1978.
Millero, F. J. and Poisson, A.: International one-atmosphere equation of
state of seawater, Deep-Sea Res.,
28, 625–629, https://doi.org/10.1016/0198-0149(81)90122-9, 1981.
Nihashi, S. and Ohshima, K. I.: Circumpolar Mapping of Antarctic Coastal
Polynyas and Landfast Sea Ice: Relationship and Variability, J.
Clim., 28, 3650–3670, https://doi.org/10.1175/JCLI-D-14-00369.1, 2015.
Pan, H., Lv, X., Wang, Y., Matte, P., Chen, H., and Jin, G.: Exploration of
Tidal-Fluvial Interaction in the Columbia River Estuary Using
S_ TIDE, J. Geophys. Res.-Oceans, 123, 6598–6619,
https://doi.org/10.1029/2018JC014146, 2018.
Parkinson, C. L. and DiGirolamo, N. E.: Sea ice extents continue to set new
records: Arctic, Antarctic, and global results, Remote Sens.
Environ., 267, 112753, https://doi.org/10.1016/j.rse.2021.112753, 2021.
Parkinson, C. L. and Washington, W. M.: A large-scale numerical model of sea
ice, J. Geophys. Res., 84, 311, https://doi.org/10.1029/JC084iC01p00311,
1979.
Perovich, D. K. and Elder, B.: Estimates of ocean heat flux at Sheba:
estimates of ocean heat flux at Sheba, Geophys. Res. Lett., 29, 581–584,
https://doi.org/10.1029/2001GL014171, 2002.
Peterson, A. K., Fer, I., McPhee, M. G., and Randelhoff, A.: Turbulent heat
and momentum fluxes in the upper ocean under Arctic sea ice: turbulent
fluxes under arctic sea ice, J. Geophys. Res.-Oceans, 122, 1439–1456,
https://doi.org/10.1002/2016JC012283, 2017.
Purdie, C. R., Langhorne, P. J., Leonard, G. H., and Haskell, T. G.: Growth
of first-year landfast Antarctic sea ice determined from winter temperature
measurements, Ann. Glaciol., 44, 170–176,
https://doi.org/10.3189/172756406781811853, 2006.
Raphael, M. N. and Handcock, M. S.: A new record minimum for Antarctic sea
ice, Nat. Rev. Earth Environ., 3, 215–216,
https://doi.org/10.1038/s43017-022-00281-0, 2022.
Semtner, A. J.: A Model for the Thermodynamic Growth of Sea Ice in Numerical
Investigations of Climate, J. Phys. Oceanogr., 6, 379–389,
https://doi.org/10.1175/1520-0485(1976)006<0379:AMFTTG>2.0.CO;2, 1976.
Sirevaag, A.: Turbulent exchange coefficients for the ice/ocean interface in
case of rapid melting, Geophys. Res. Lett., 36, L04606,
https://doi.org/10.1029/2008GL036587, 2009.
Sirevaag, A. and Fer, I.: Early Spring Oceanic Heat Fluxes and Mixing
Observed from Drift Stations North of Svalbard, J. Phys.
Oceanogr., 39, 3049–3069, https://doi.org/10.1175/2009JPO4172.1, 2009.
SonTek Argonaut–ADV, the xylem company:
https://www.xylem.com/siteassets/brand/sontek/resources/specification/sontek-argonaut-adv-brochure-s11-02-1119.pdf, last access: 24 February 2023.
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using
AMSR-E 89-GHz channels, J. Geophys. Res.-Oceans, 113, C02S03,
https://doi.org/10.1029/2005JC003384, 2008.
SRSL SIMBA, SAMS Enterprise Ltd.:
https://www.sams-enterprise.com/services/autonomous-ice-measurement/, last
access: 24 February 2023.
Tian, Z., Cheng, B., Zhao, J., Vihma, T., Zhang, W., Li, Z., and Zhang, Z.:
Observed and modelled snow and ice thickness in the Arctic Ocean with
Chinare buoy data, Acta Oceanol. Sin., 36, 66–75,
https://doi.org/10.1007/s13131-017-1020-4, 2017.
Untersteiner, N.: On the mass and heat budget of arctic sea ice, Arch. Met.
Geoph. Biokl. A., 12, 151–182, https://doi.org/10.1007/BF02247491, 1961.
Vihma, T.: Surface heat budget over the Weddell Sea: Buoy results and model
comparisons, J. Geophys. Res., 107, 3013,
https://doi.org/10.1029/2000JC000372, 2002.
Wang, J., Luo, H., Yang, Q., Liu, J., Yu, L., Shi, Q., and Han, B.: An
Unprecedented Record Low Antarctic Sea-ice Extent during Austral Summer
2022, Adv. Atmos. Sci., 39, 15911597,
https://doi.org/10.1007/s00376-022-2087-1, 2022.
Welch, P.: The use of fast Fourier transform for the estimation of power
spectra: A method based on time averaging over short, modified periodograms,
IEEE Trans. Audio, 15, 70–73,
https://doi.org/10.1109/TAU.1967.1161901, 1967.
Williams, G. D., Herraiz-Borreguero, L., Roquet, F., Tamura, T., Ohshima, K.
I., Fukamachi, Y., Fraser, A. D., Gao, L., Chen, H., McMahon, C. R.,
Harcourt, R., and Hindell, M.: The suppression of Antarctic bottom water
formation by melting ice shelves in Prydz Bay, Nat. Commun., 7, 1–9,
https://doi.org/10.1038/ncomms12577, 2016.
Yang, Y., Zhijun, L., Leppäranta, M., Cheng, B., Shi, L., and Lei, R.:
Modelling the thickness of landfast sea ice in Prydz Bay, East Antarctica,
Antarct. Sci., 28, 59–70, https://doi.org/10.1017/S0954102015000449,
2016.
Zhao, J. and Hu, H.: Air-ice-ocean temperature profile recorded in the
landfast ice region in Prydz Bay, East Antarctica, derived from Sea Ice Mass
Balance Array (SIMBA) from April to November 2021 (V1), Science Data Bank [data set],
https://doi.org/10.57760/sciencedb.07684, 2023a.
Zhao, J. and Hu, H.: Seawater temperature and salinity 2-m beneath landfast
ice in Prydz Bay, East Antarctica, recorded from a cable-type CTD from April
to November 2021 (V1), Science Data Bank [data set], https://doi.org/10.57760/sciencedb.07693, 2023b.
Zhao, J. and Hu, H.: The 3-D current velocity 5-m beneath landfast ice in
Prydz Bay, East Antarctica, recorded from an Acoustic Doppler Velocimeter
(ADV) from April to November 2021 (V1), Science Data Bank [data set],
https://doi.org/10.57760/sciencedb.07692, 2023c.
Zhao, J., Yang, Q., Cheng, B., Wang N., Hui, F., Shen H., Han S., Zhang, L.,
and Vihma, T.: Snow and land-fast sea ice thickness derived from
thermistor chain buoy in the Prydz Bay, Antarctic, Haiyang Xuebao, 39, 115–127,
https://doi.org/10.3969/j.issn.0253-4193.2017.11.011, 2017 (in Chinese).
Zhao, J., Yang, Q., Cheng, B., Leppäranta, M., Hui, F., Xie, S., Chen,
M., Yu, Y., Tian, Z., Li, M., and Zhang, L.: Spatial and temporal evolution
of landfast ice near Zhongshan Station, East Antarctica, over an annual
cycle in 2011/2012, Acta Oceanol. Sin., 38, 51–61,
https://doi.org/10.1007/s13131-018-1339-5, 2019.
Zhao, J., Cheng, B., Vihma, T., Heil, P., Hui, F., Shu, Q., Zhang, L., and
Yang, Q.: Fast Ice Prediction System (FIPS) for land-fast sea ice at Prydz
Bay, East Antarctica: an operational service for CHINARE, Ann. Glaciol., 61,
271–283, https://doi.org/10.1017/aog.2020.46, 2020.
Short summary
The oceanic characteristics beneath sea ice significantly affect ice growth and melting. The high-frequency and long-term observations of oceanic variables allow us to deeply investigate their diurnal and seasonal variation and evaluate their influences on sea ice evolution. The large-scale sea ice distribution and ocean circulation contributed to the seasonal variation of ocean variables, revealing the important relationship between large-scale and local phenomena.
The oceanic characteristics beneath sea ice significantly affect ice growth and melting. The...
