Articles | Volume 19, issue 2
https://doi.org/10.5194/tc-19-849-2025
© Author(s) 2025. 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-19-849-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Feb 2025
Research article |
| 25 Feb 2025
| 25 Feb 2025
Reconstructing ice phenology of a lake with complex surface cover: a case study of Lake Ulansu during 1941–2023
Puzhen Huo
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China
Peng Lu
CORRESPONDING AUTHOR
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China
Bin Cheng
Finnish Meteorological Institute, Helsinki, 00101, Finland
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China
Qingkai Wang
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China
Xuewei Li
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China
Zhijun Li
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, 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.
Jialiang Zhu, Tao Li, Peng Lu, Yilin Liu, and Xiaoyu Wang
The Cryosphere, 20, 351–368, https://doi.org/10.5194/tc-20-351-2026, https://doi.org/10.5194/tc-20-351-2026, 2026
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The albedo of refreezing melt pond is observed and effect of multiple factors on it is studied. The refreezing melt ponds are categorized into 5 types according to surface state that dominates albedo. Based on it, we found that ratio between albedo in certain bands can be used to distinguish snow-covered pond from unponded ice. Besides, properties such as pond depth and ice lid have various effect on different types of ponds, which is also examined using in-situ data and modelling.
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.
Miao Yu, Peng Lu, Hang Zhang, Fei Xie, Lei Wang, Qingkai Wang, and Zhijun Li
EGUsphere, https://doi.org/10.5194/egusphere-2024-2155, https://doi.org/10.5194/egusphere-2024-2155, 2024
Preprint archived
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The ice microstructure was observed by continuous sampling and a imaging system. The newly formed bubbles in the middle ice layer were partly thermally driven. Gas bubbles of the ice surface are significantly affected by net shortwave radiation. Variation in the inclusion size distribution was attributed to the merging process.
Dunwang Lu, Jianqiang Liu, Lijian Shi, Tao Zeng, Bin Cheng, Suhui Wu, and Manman Wang
The Cryosphere, 18, 1419–1441, https://doi.org/10.5194/tc-18-1419-2024, https://doi.org/10.5194/tc-18-1419-2024, 2024
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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.
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.
Hang Zhang, Miao Yu, Peng Lu, Jiaru Zhou, Qingkai Wang, and Zhijun Li
EGUsphere, https://doi.org/10.5194/egusphere-2023-1758, https://doi.org/10.5194/egusphere-2023-1758, 2023
Preprint archived
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The Monte Carlo (MC) model is employed to investigate the influence of the melt pond and floe size on the apparent optical properties. The ratio of albedo Kα and transmittance KT of linear combination to MC model are proposed to determine the accuracy of the linear combination. New parameterization results for Kα and KT of different latitude and melting stage are provided. The results can be used correct the in situ data got by linear combination with floe size smaller than 20 m.
Yaodan Zhang, Marta Fregona, John Loehr, Joonatan Ala-Könni, Shuang Song, Matti Leppäranta, and Zhijun Li
The Cryosphere, 17, 2045–2058, https://doi.org/10.5194/tc-17-2045-2023, https://doi.org/10.5194/tc-17-2045-2023, 2023
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There are few detailed studies during the ice decay period, primarily because in situ observations during decay stages face enormous challenges due to safety issues. In the present work, ice monitoring was based on foot, hydrocopter, and boat to get a full time series of the evolution of ice structure and geochemical properties. We argue that the rapid changes in physical and geochemical properties of ice have an important influence on regional climate and the ecological environment under ice.
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.
Qian Yang, Xiaoguang Shi, Weibang Li, Kaishan Song, Zhijun Li, Xiaohua Hao, Fei Xie, Nan Lin, Zhidan Wen, Chong Fang, and Ge Liu
The Cryosphere, 17, 959–975, https://doi.org/10.5194/tc-17-959-2023, https://doi.org/10.5194/tc-17-959-2023, 2023
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A large-scale linear structure has repeatedly appeared on satellite images of Chagan Lake in winter, which was further verified as being ice ridges in the field investigation. We extracted the length and the angle of the ice ridges from multi-source remote sensing images. The average length was 21 141.57 ± 68.36 m. The average azimuth angle was 335.48° 141.57 ± 0.23°. The evolution of surface morphology is closely associated with air temperature, wind, and shoreline geometry.
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.
Qingkai Wang, Yubo Liu, Peng Lu, and Zhijun Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-31, https://doi.org/10.5194/tc-2023-31, 2023
Revised manuscript not accepted
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We intended to bring a new sight for the Arctic sea ice change by updating the knowledge of mechanical properties of summer Arctic sea ice. We find the flexural strength of summer Arctic sea ice was dependent on sea ice porosity rather than brine volume fraction, which unified the physical parameter affecting sea ice mechanical properties to sea ice porosity. Arctic sea ice strength has been weakening in recent summers by evaluating the strength using the previously published sea ice porosities.
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.
Qingkai Wang, Zhaoquan Li, Peng Lu, Yigang Xu, and Zhijun Li
The Cryosphere, 16, 1941–1961, https://doi.org/10.5194/tc-16-1941-2022, https://doi.org/10.5194/tc-16-1941-2022, 2022
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A large area of landfast sea ice exists in the Prydz Bay, and it is always a safety concern to transport cargos on ice to the research stations. Knowing the mechanical properties of sea ice is helpful to solve the issue; however, these data are rarely reported in this region. We explore the effects of sea ice physical properties on the flexural strength, effective elastic modulus, and uniaxial compressive strength, which gives new insights into assessing the bearing capacity of landfast sea ice.
Wenfeng Huang, Wen Zhao, Cheng Zhang, Matti Leppäranta, Zhijun Li, Rui Li, and Zhanjun Lin
The Cryosphere, 16, 1793–1806, https://doi.org/10.5194/tc-16-1793-2022, https://doi.org/10.5194/tc-16-1793-2022, 2022
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Thermal regimes of seasonally ice-covered lakes in an arid region like Central Asia are not well constrained despite the unique climate. We observed annual and seasonal dynamics of thermal stratification and energetics in a shallow arid-region lake. Strong penetrated solar radiation and high water-to-ice heat flux are the predominant components in water heat balance. The under-ice stratification and convection are jointly governed by the radiative penetration and salt rejection during freezing.
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 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.
We developed a new method for retrieving lake ice phenology for a lake with complex surface...
