Articles | Volume 18, issue 3
https://doi.org/10.5194/tc-18-1419-2024
© Author(s) 2024. 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-18-1419-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Mar 2024
Research article |
| 28 Mar 2024
| 28 Mar 2024
Retrieval of sea ice drift in the Fram Strait based on data from Chinese satellite HaiYang (HY-1D)
Dunwang Lu
National Marine Environmental Forecasting Center, Beijing 100081, China
National Satellite Ocean Application Service, Beijing 100081, China
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China
Jianqiang Liu
National Satellite Ocean Application Service, Beijing 100081, China
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China
deceased
Lijian Shi
CORRESPONDING AUTHOR
National Satellite Ocean Application Service, Beijing 100081, China
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China
Tao Zeng
National Satellite Ocean Application Service, Beijing 100081, China
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China
Bin Cheng
Finnish Meteorological Institute, Helsinki 00101, Finland
Suhui Wu
National Satellite Ocean Application Service, Beijing 100081, China
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China
Manman Wang
National Marine Environmental Forecasting Center, Beijing 100081, China
National Satellite Ocean Application Service, Beijing 100081, China
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China
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Mengmeng Li, Jianwei Ma, Yang Li, Juha Karvonen, Bin Cheng, Yingfei Wang, Haili Li, Yafei Nie, and Zheng Duan
EGUsphere, https://doi.org/10.5194/egusphere-2026-2504, https://doi.org/10.5194/egusphere-2026-2504, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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We retrieved Arctic snow depth (2012–2021) using satellite data and four machine learning methods. A simple linear regression performed best against independent aircraft data, while complex models overfit. A measurement at 89 GHz was key for older ice. Using our snow depth data greatly reduced errors in ice thickness estimates. This provides a reliable Arctic snow depth dataset.
Ziyu Yan, Yufang Ye, Georg Heygster, Bin Cheng, Carolina Gabarró, Ferran Hernández-Macià, and Xiao Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2026-1680, https://doi.org/10.5194/egusphere-2026-1680, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Passive microwave retrieval of snow depth remains constrained by snow and ice conditions. By combining the Snow Microwave Radiative Transfer model with global sensitivity analysis, this study quantifies how snow grain size, density, and sea ice type affect snow depth retrievability across channels. The small grain size of new snow limits retrieval performance and increases its sensitivity to sea ice type, while the use of the 89 GHz channel may help address this limitation.
Xiyue Zhang, Minghu Ding, Diyi Yang, Bin Cheng, Ian Allison, Kongju Zhu, Yuande Yang, Xiangbin Cui, Petra Heil, Chuanjin Li, and Cunde Xiao
EGUsphere, https://doi.org/10.5194/egusphere-2026-2150, https://doi.org/10.5194/egusphere-2026-2150, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Stake measurements underestimate Antarctic surface mass balance (SMB) due to firn compaction. Using a firn densification model, we correct 2008-2024 stake records from Dome A. The corrected mean annual SMB is 24.14 kg m-2 yr-1, 8.8 % higher than uncorrected values, whereas RACMO2.4p1 gives 17.50 kg m-2 yr-1. The net vapor flux is equivalent to 5.7 % of the total mass input. This framework provides observational benchmarks for climate models, and supports oldest‑ice drilling at Dome A.
Yubing Cheng, Bin Cheng, Roberta Pirazzini, Amy R. Macfarlane, Timo Vihma, Wolfgang Dorn, Ruzica Dadic, Martin Schneebeli, Stefanie Arndt, and Annette Rinke
The Cryosphere, 19, 6001–6021, https://doi.org/10.5194/tc-19-6001-2025, https://doi.org/10.5194/tc-19-6001-2025, 2025
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We study snow density from the MOSAiC expedition. Several snow density schemes were tested and compared with observation. A thermodynamic ice model was employed to assess the impact of snow density and precipitation on the thermal regime of sea ice. The parameterized mean snow densities are consistent with observations. Increased snow density reduces snow and ice temperatures, promoting ice growth, while increased precipitation leads to warmer snow and ice temperatures and reduced ice thickness.
Fangru Mu, Chengfei Jiang, Bin Cheng, Keguang Wang, Caixin Wang, Yuhan Chen, Zhiyuan Shao, and Jiechen Zhao
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.
Cecilia Äijälä, Yafei Nie, Lucía Gutiérrez-Loza, Chiara De Falco, Siv Kari Lauvset, Bin Cheng, David Anthony Bailey, and Petteri Uotila
Geosci. Model Dev., 18, 4823–4853, https://doi.org/10.5194/gmd-18-4823-2025, https://doi.org/10.5194/gmd-18-4823-2025, 2025
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The sea ice around Antarctica has experienced record lows in recent years. To understand these changes, models are needed. MetROMS-UHel is a new version of an ocean–sea ice model with updated sea ice code and the atmospheric data. We investigate the effect of our updates on different variables with a focus on sea ice and show an improved sea ice representation as compared with observations.
Puzhen Huo, Peng Lu, Bin Cheng, Miao Yu, Qingkai Wang, Xuewei Li, and Zhijun Li
The Cryosphere, 19, 849–868, https://doi.org/10.5194/tc-19-849-2025, https://doi.org/10.5194/tc-19-849-2025, 2025
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We developed a new method for retrieving lake ice phenology for a lake with complex surface cover. The method is particularly useful for mixed-pixel satellite data. We implement this method on Lake Ulansu, a lake characterized by complex shorelines and aquatic plants in northwestern China. In connection with a random forest model, we reconstructed the longest lake ice phenology in China.
Yurii Batrak, Bin Cheng, and Viivi Kallio-Myers
The Cryosphere, 18, 1157–1183, https://doi.org/10.5194/tc-18-1157-2024, https://doi.org/10.5194/tc-18-1157-2024, 2024
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Atmospheric reanalyses provide consistent series of atmospheric and surface parameters in a convenient gridded form. In this paper, we study the quality of sea ice in a recently released regional reanalysis and assess its added value compared to a global reanalysis. We show that the regional reanalysis, having a more complex sea ice model, gives an improved representation of sea ice, although there are limitations indicating potential benefits in using more advanced approaches in the future.
Miao Yu, Peng Lu, Matti Leppäranta, Bin Cheng, Ruibo Lei, Bingrui Li, Qingkai Wang, and Zhijun Li
The Cryosphere, 18, 273–288, https://doi.org/10.5194/tc-18-273-2024, https://doi.org/10.5194/tc-18-273-2024, 2024
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Variations in Arctic sea ice are related not only to its macroscale properties but also to its microstructure. Arctic ice cores in the summers of 2008 to 2016 were used to analyze variations in the ice inherent optical properties related to changes in the ice microstructure. The results reveal changing ice microstructure greatly increased the amount of solar radiation transmitted to the upper ocean even when a constant ice thickness was assumed, especially in marginal ice zones.
Zhaoqing Dong, Lijian Shi, Mingsen Lin, Yongjun Jia, Tao Zeng, and Suhui Wu
The Cryosphere, 17, 1389–1410, https://doi.org/10.5194/tc-17-1389-2023, https://doi.org/10.5194/tc-17-1389-2023, 2023
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We try to explore the application of SGDR data in polar sea ice thickness. Through this study, we find that it seems difficult to obtain reasonable results by using conventional methods. So we use the 15 lowest points per 25 km to estimate SSHA to retrieve more reasonable Arctic radar freeboard and thickness. This study also provides reference for reprocessing L1 data. We will release products that are more reasonable and suitable for polar sea ice thickness retrieval to better evaluate HY-2B.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
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.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Marcel Nicolaus, Fanyi Zhang, Benjamin Rabe, Long Lin, Julia Regnery, and Donald K. Perovich
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-25, https://doi.org/10.5194/tc-2023-25, 2023
Manuscript not accepted for further review
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To characterize the freezing and melting of different types of sea ice, we deployed four IMBs during the MOSAiC second drift. The drifting pattern, together with a large snow accumulation, relatively warm air temperatures, and a rapid increase in oceanic heat close to Fram Strait, determined the seasonal evolution of the ice mass balance. The refreezing of ponded ice and voids within the unconsolidated ridges amplifies the anisotropy of the heat exchange between the ice and the atmosphere/ocean.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
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We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Yu Liang, Haibo Bi, Haijun Huang, Ruibo Lei, Xi Liang, Bin Cheng, and Yunhe Wang
The Cryosphere, 16, 1107–1123, https://doi.org/10.5194/tc-16-1107-2022, https://doi.org/10.5194/tc-16-1107-2022, 2022
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A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that an anomalously high advection of energy and water vapor prevailed during spring (April to June) 2020 over regions with noticeable sea ice retreat. The large-scale atmospheric circulation and cyclones act in concert to trigger the exceptionally warm and moist flow. The convergence of the transport changed the atmospheric characteristics and the surface energy budget, thus causing a severe sea ice melt.
Bin Cheng, Yubing Cheng, Timo Vihma, Anna Kontu, Fei Zheng, Juha Lemmetyinen, Yubao Qiu, and Jouni Pulliainen
Earth Syst. Sci. Data, 13, 3967–3978, https://doi.org/10.5194/essd-13-3967-2021, https://doi.org/10.5194/essd-13-3967-2021, 2021
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Climate change strongly impacts the Arctic, with clear signs of higher air temperature and more precipitation. A sustainable observation programme has been carried out in Lake Orajärvi in Sodankylä, Finland. The high-quality air–snow–ice–water temperature profiles have been measured every winter since 2009. The data can be used to investigate the lake ice surface heat balance and the role of snow in lake ice mass balance and parameterization of snow-to-ice transformation in snow/ice models.
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Short summary
We retrieved sea ice drift in Fram Strait using the Chinese HaiYang 1D Coastal Zone Imager. The dataset is has hourly and daily intervals for analysis, and validation is performed using a synthetic aperture radar (SAR)-based product and International Arctic Buoy Programme (IABP) buoys. The differences between them are explained by investigating the spatiotemporal variability in sea ice motion. The accuracy of flow direction retrieval for sea ice drift is also related to sea ice displacement.
We retrieved sea ice drift in Fram Strait using the Chinese HaiYang 1D Coastal Zone Imager. The...
