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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Marta Stentella, Ghislain Picard, Petra Heil, Jacqueline Boutin, Emmanuel Dinnat, and Stuart Corney
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Passive microwave radiometers are the primary tool for monitoring Antarctic sea ice, but their reliability decreases in the marginal ice zone between the pack ice and the open ocean. We simulate satellite observations to assess the impact of varying physical parameters on sea ice concentration retrievals. The largest errors are due to flooded/wet snow, thin ice, and roughened ocean surfaces. These findings can improve our interpretation of satellite observations and forecast sea ice changes.
Shuo Wei, Lei Zheng, Qi Liang, Teng Li, and Xiao Cheng
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Supraglacial lakes on Antarctica affect ice stability. Because tracking their rapid changes is difficult, we combined satellite images using artificial intelligence to map these lakes every eight days from 2000 to 2023. We found that most lakes are very short-lived and primarily form near the edges of the ice. This record helps scientists understand fast-melting processes and their impact on future global sea level rise.
Chengyan Liu, Zhaomin Wang, Dake Chen, Xianxian Han, Hengling Leng, Xi Liang, Liangjun Yan, Xiang Li, Craig Stevens, Andrew McC. Hogg, Kazuya Kusahara, Kaihe Yamazaki, Kay I. Ohshima, Meng Zhou, Xiao Cheng, Dongxiao Wang, Changming Dong, Jiping Liu, Qinghua Yang, Xichen Li, Ruibo Lei, Minghu Ding, Zhaoru Zhang, Dujuan Kang, Di Qi, Tongya Liu, Jihai Dong, Lu An, Ru Chen, Tong Zhang, Xiaoming Hu, Bo Han, Haibo Bi, Qi Shu, Longjiang Mu, Shiming Xu, Hu Yang, Hailong Liu, Tingfeng Dou, Zhixuan Feng, Lei Zheng, Xueyuan Tang, Guitao Shi, Yongqing Cai, Bingrui Li, Yang Wu, Xia Lin, Wenjin Sun, Yu Liu, Kai Yu, Yu Zhang, Weizeng Shao, Xiaoyu Wang, Shaojun Zheng, Chengyi Yuan, Chunxia Zhou, Jian Liu, Yang Liu, Yue Xia, Xiaoyu Pan, Jiabao Zeng, Kechen Liu, Jiahao Fan, Chen Cheng, and Qi Li
Geosci. Model Dev., 19, 2985–3033, https://doi.org/10.5194/gmd-19-2985-2026, https://doi.org/10.5194/gmd-19-2985-2026, 2026
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We developed a high-resolution computer model to simulate how the ocean, sea ice, and ice shelves interact around Antarctica. This helps us understand their critical role in global climate and sea-level rise. Our model successfully captures essential features like major currents and seasonal ice changes. Despite some remaining biases, it provides a useful tool for predicting future changes in this vital and rapidly evolving region.
Chen Jiang, Lejiang Yu, Timo Vihma, and Jiechen Zhao
Atmos. Chem. Phys., 26, 4275–4288, https://doi.org/10.5194/acp-26-4275-2026, https://doi.org/10.5194/acp-26-4275-2026, 2026
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This study analyzes the spatial and temporal trends of Antarctic fast ice extent from 2000 to 2018. We also investigate the physical processes through which mid- and low-latitude sea surface temperature anomalies affect fast ice via atmospheric planetary waves, along with seasonal variations in climate forcing mechanisms.
Xinlong Liu, Stuart Paul Corney, Rachel Louise Tilling, and Petra Heil
EGUsphere, https://doi.org/10.5194/egusphere-2026-662, https://doi.org/10.5194/egusphere-2026-662, 2026
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Antarctic sea-ice thickness from satellites depends on how snow is treated. We compared four widely used CryoSat-2 radar products for winters 2013–2018 on the same map grid. All show similar large-scale patterns, but each gives systematically higher or lower values, with the biggest differences in the snowier western Weddell Sea. Year-to-year changes are similar across products, yet these offsets can shift thickness estimates, so users should account for product choice and snow assumptions.
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Ice cover on small water bodies is highly sensitive to climate change and influences ecosystems, water, and the carbon cycle. We produced a satellite-based ice fraction dataset for small water bodies on the Arctic Coastal Plain from 2017 to 2023. The dataset captures freeze-up and break-up timing and reveals spatial variability. It will support studies of climate–ice interactions and improve models of water and carbon processes.
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We observe harmonics of the main incoming wave peak in sea ice motion data. These harmonics match non-linear 3-wave interactions predicted from the dispersion relation. This may indicate that wave in ice triads are empirically observed, which suggests that non-linear energy transfers play a role in wave in ice propagation.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-3214, https://doi.org/10.5194/egusphere-2025-3214, 2025
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Icebergs pose risks to ships and are an important part of the polar environment. We developed an iceberg detection algorithm based on the Swin transformer model (IDAS-Transformer). The IDAS-Transformer is capable of handling complex surface characteristic mixtures, including fast ice, pack ice, and open water, to identify icebergs.
Mingyue Nong, Xuying Liu, Teng Li, Baogang Zhang, Qi Liang, Lei Zheng, Tiancheng Zhao, and Xiao Cheng
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Preprint archived
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We extracted nearshore small icebergs in front of Dalk Glacier using UAV high-resolution data and directly obtained geometric parameters of the icebergs and analyzed their distribution patterns. The area/volume relationship of our icebergs aligns with the medium to large icebergs in existing ocean model. The study demonstrates UAVs' effectiveness in polar research and the importance of including all iceberg sizes in ocean modeling for better environmental impact predictions.
Fukai Peng, Xiaoli Deng, Yunzhong Shen, and Xiao Cheng
Earth Syst. Sci. Data, 17, 1441–1460, https://doi.org/10.5194/essd-17-1441-2025, https://doi.org/10.5194/essd-17-1441-2025, 2025
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A new reprocessed altimeter coastal sea level dataset, International Altimetry Service 2024 (IAS2024), for monitoring sea level changes along the world’s coastlines is presented. The evaluation and validation results confirm the reliability of this dataset. The altimeter-based virtual stations along the world’s coastlines can be built using this dataset to monitor the coastal sea level changes where tide gauges are unavailable. Therefore, it is beneficial for both oceanographic communities and policymakers.
Mukund Gupta, Heather Regan, Younghyun Koo, Sean Minhui Tashi Chua, Xueke Li, and Petra Heil
The Cryosphere, 19, 1241–1257, https://doi.org/10.5194/tc-19-1241-2025, https://doi.org/10.5194/tc-19-1241-2025, 2025
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The sea ice cover is composed of floes, whose shapes set the material properties of the pack. Here, we use a satellite product (ICESat-2) to investigate these floe shapes within the Weddell Sea in Antarctica. We find that floes tend to become smaller during the melt season, while their thickness distribution exhibits different behavior between the western and southern regions of the pack. These metrics will help calibrate models and improve our understanding of sea ice physics across scales.
Yan Sun, Shaoyin Wang, Xiao Cheng, Teng Li, Chong Liu, Yufang Ye, and Xi Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2760, https://doi.org/10.5194/egusphere-2024-2760, 2025
Preprint archived
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This manuscript proposes to combine semantic segmentation of ice region using a U-Net model and multi-stage detection of ice pixels using the Multi-textRG algorithm to achieve fine ice-water classification. Novel proccessings for the HV/HH polarization ratio and the GLCM textures, as well as the usage of regional growing, largely improve the method accuracy and robustness. The proposed algorithm framework achieved automated sea-ice labelling.
Yan Sun, Shaoyin Wang, Xiao Cheng, Teng Li, Chong Liu, Yufang Ye, and Xi Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-1177, https://doi.org/10.5194/egusphere-2024-1177, 2024
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.
Na Li, Ruibo Lei, Petra Heil, Bin Cheng, Minghu Ding, Zhongxiang Tian, and Bingrui Li
The Cryosphere, 17, 917–937, https://doi.org/10.5194/tc-17-917-2023, https://doi.org/10.5194/tc-17-917-2023, 2023
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The observed annual maximum landfast ice (LFI) thickness off Zhongshan (Davis) was 1.59±0.17 m (1.64±0.08 m). Larger interannual and local spatial variabilities for the seasonality of LFI were identified at Zhongshan, with the dominant influencing factors of air temperature anomaly, snow atop, local topography and wind regime, and oceanic heat flux. The variability of LFI properties across the study domain prevailed at interannual timescales, over any trend during the recent decades.
Yetang Wang, Xueying Zhang, Wentao Ning, Matthew A. Lazzara, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Paolo Grigioni, Petra Heil, Elizabeth R. Thomas, David Mikolajczyk, Lee J. Welhouse, Linda M. Keller, Zhaosheng Zhai, Yuqi Sun, and Shugui Hou
Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, https://doi.org/10.5194/essd-15-411-2023, 2023
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Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
Yufang Ye, Yanbing Luo, Yan Sun, Mohammed Shokr, Signe Aaboe, Fanny Girard-Ardhuin, Fengming Hui, Xiao Cheng, and Zhuoqi Chen
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
Preprint withdrawn
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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.
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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...
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