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
Related authors
No articles found.
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
Short summary
Short summary
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.
Jialiang Zhu, Tao Li, Peng Lu, Yilin Liu, and Xiaoyu Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2011, https://doi.org/10.5194/egusphere-2025-2011, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-1164, https://doi.org/10.5194/egusphere-2025-1164, 2025
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Guangyu Zuo, Dawei Gui, Qiongqiong Cai, H. Jakob Belter, and Wangxiao Yang
The Cryosphere, 15, 1321–1341, https://doi.org/10.5194/tc-15-1321-2021, https://doi.org/10.5194/tc-15-1321-2021, 2021
Short summary
Short summary
Quantification of ice deformation is useful for understanding of the role of ice dynamics in climate change. Using data of 32 buoys, we characterized spatiotemporal variations in ice kinematics and deformation in the Pacific sector of Arctic Ocean for autumn–winter 2018/19. Sea ice in the south and west has stronger mobility than in the east and north, which weakens from autumn to winter. An enhanced Arctic dipole and weakened Beaufort Gyre in winter lead to an obvious turning of ice drifting.
Cited articles
Ambrosetti, W. and Barbanti, L.: Deep water warming in lakes: an indicator of climatic change, J. Limnol., 58, 1–9, https://doi.org/10.4081/jlimnol.1999.1, 1999.
Anilkumar, R., Bharti, R., Chutia, D., and Aggarwal, S. P.: Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques, The Cryosphere, 17, 2811–2828, https://doi.org/10.5194/tc-17-2811-2023, 2023.
Antonova, S., Duguay, C., Kääb, A., Heim, B., Langer, M., Westermann, S., and Boike, J.: Monitoring bedfast ice and ice phenology in lakes of the Lena River Delta using TerraSAR-X backscatter and coherence time series, Remote Sens., 8, 903, https://doi.org/10.3390/rs8110903, 2016.
Bandhauer, M., Isotta, F., Lakatos, M., Lussana, C., Båserud, L., Izsák, B., Szentes, O., Tveito, O. E., and Frei, C.: Evaluation of daily precipitation analyses in E-OBS (v19.0e) and ERA5 by comparison to regional high-resolution datasets in European regions, Int. J. Climatol., 42, 727–747, https://doi.org/10.1002/joc.7269, 2021.
Bartosiewicz, M., Ptak, M., Woolway, R. I., and Sojka, M.: On thinning ice: Effects of atmospheric warming, changes in wind speed and rainfall on ice conditions in temperate lakes (Northern Poland), J. Hydrol., 597, 125724, https://doi.org/10.1016/j.jhydrol.2020.125724, 2021.
Bellerby, T., Taberner, M., Wilmshurst, A., Beaumont, M., Barrett, E., Scott, J., and Durbin, C.: Retrieval of land and sea brightness temperatures from mixed coastal pixels in passive microwave data, IEEE T. Geosci. Remote, 36, 1844–1851, https://doi.org/10.1109/36.729355, 1998.
Benson, B. J., Magnuson, J. J., Jensen, O. P., Card, V. M., Hodgkins, G., Korhonen, J., Livingstone, D. M., Stewart, K. M., Weyhenmeyer, G. A., and Granin, N. G.: Extreme events, trends, and variability in Northern Hemisphere lake-ice phenology (1855–2005), Climatic Change, 112, 299–323, https://doi.org/10.1007/s10584-011-0212-8, 2011.
Blagrave, K. and Sharma, S.: Projecting climate change impacts on ice phenology across Midwestern and Northeastern United States lakes, Climatic Change, 176, 119, https://doi.org/10.1007/s10584-023-03596-z, 2023.
Breiman, L.: Random Forests, Machine Learning, 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001.
Brodzik, M., Long, D., Hardman, M., Paget, A., and Armstrong, R.: MEaSUREs calibrated enhanced-resolution passive microwave daily EASE-grid 2.0 brightness temperature ESDR, version 1, Digital Media, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/MEASURES/CRYOSPHERE/NSIDC-0630.001, 2016.
Cai, Y., Ke, C.-Q., and Duan, Z.: Monitoring ice variations in Qinghai Lake from 1979 to 2016 using passive microwave remote sensing data, Sci. Total Environ., 607–608, 120–131, https://doi.org/10.1016/j.scitotenv.2017.07.027, 2017.
Cai, Y., Ke, C.-Q., Li, X., Zhang, G., Duan, Z., and Lee, H.: Variations of lake ice phenology on the Tibetan Plateau from 2001 to 2017 based on MODIS data. J. Geophys. Res.-Atmos., 124, 825–843, https://doi.org/10.1029/2018JD028993, 2019.
Cai, Y., Duguay, C. R., and Ke, C.-Q.: A 41-year (1979–2019) passive-microwave-derived lake ice phenology data record of the Northern Hemisphere, Earth Syst. Sci. Data, 14, 3329–3347, https://doi.org/10.5194/essd-14-3329-2022, 2022.
Caldwell, T. J., Chandra, S., Albright, T. P., Harpold, A. A., Dilts, T. E., Greenberg, J. A., Sadro, S., and Dettinger, M. D.: Drivers and projections of ice phenology in mountain lakes in the western United States, Limnol. Oceanogr., 66, 995–1008, https://doi.org/10.1002/lno.11656, 2020.
Cao, X., Lu, P., Leppäranta, M., Arvola, L., Huotari, J., Shi, X., Li, G., and Li, Z.: Solar radiation transfer for an ice-covered lake in the central Asian arid climate zone, Inland Waters, 11, 89–103, https://doi.org/10.1080/20442041.2020.1790274, 2021.
Cheng, Y., Cheng, B., Zheng, F., Vihma, T., Kontu, A., Yang, Q., and Liao, Z.: Air/snow, snow/ice and ice/water interfaces detection from high-resolution vertical temperature profiles measured by ice mass-balance buoys on an Arctic lake, Ann. Glaciol., 61, 309–319, https://doi.org/10.1017/aog.2020.51, 2020.
Du, J., Kimball, J. S., Duguay, C., Kim, Y., and Watts, J. D.: Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015, The Cryosphere, 11, 47–63, https://doi.org/10.5194/tc-11-47-2017, 2017.
Duan, H., Ma, R., and Hu, C.: Evaluation of remote sensing algorithms for cyanobacterial pigment retrievals during spring bloom formation in several lakes of East China, Remote Sens. Environ., 126, 126–135, https://doi.org/10.1016/j.rse.2012.08.011, 2012.
Gebre, S., Boissy, T., and Alfredsen, K.: Sensitivity of lake ice regimes to climate change in the Nordic region, The Cryosphere, 8, 1589–1605, https://doi.org/10.5194/tc-8-1589-2014, 2014.
Guo, H., Yang, S., Tang, X., Li, Y., and Shen, Z.: Groundwater geochemistry and its implications for arsenic mobilization in shallow aquifers of the Hetao Basin, Inner Mongolia, Sci. Total Environ., 393, 131–144, https://doi.org/10.1016/j.scitotenv.2007.12.025, 2008.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Howell, S. E. L., Brown, L. C., Kang, K.-K., and Duguay, C. R.: Variability in ice phenology on Great Bear Lake and Great Slave Lake, Northwest Territories, Canada, from SeaWinds/QuikSCAT: 2000–2006, Remote Sens. Environ., 113, 816–834, https://doi.org/10.1016/j.rse.2008.12.007, 2009.
Huang, W., Zhang, J., Leppäranta, M., Li, Z., Cheng, B., and Lin, Z.: Thermal structure and water-ice heat transfer in a shallow ice-covered thermokarst lake in central Qinghai-Tibet Plateau, J. Hydrol., 578, 124122, https://doi.org/10.1016/j.jhydrol.2019.124122, 2019.
Huang, W., Zhao, W., Zhang, C., Leppäranta, M., Li, Z., Li, R., and Lin, Z.: Sunlight penetration dominates the thermal regime and energetics of a shallow ice-covered lake in arid climate, The Cryosphere, 16, 1793–1806, https://doi.org/10.5194/tc-16-1793-2022, 2022.
Huo, P.: Lake Ulansu Ice Phenology Data (1941–2023), Zenodo [data set], https://doi.org/10.5281/zenodo.10848522, 2024.
Huo, P., Lu, P., Cheng, B., Zhang, L., Wang, Q., and Li, Z.: Monitoring ice phenology in lake wetlands based on optical satellite data: A case study of Wuliangsu Lake, Water, 14, 3307, https://doi.org/10.3390/w14203307, 2022.
Imrit, M. A. and Sharma, S.: Climate change is contributing to faster rates of lake ice loss in lakes around the Northern Hemisphere, J. Geophys. Res.-Biogeosci., 126, e2020JG006134, https://doi.org/10.1029/2020jg006134, 2021.
Ivanova, N., Pedersen, L. T., Tonboe, R. T., Kern, S., Heygster, G., Lavergne, T., Sørensen, A., Saldo, R., Dybkjær, G., Brucker, L., and Shokr, M.: Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations, The Cryosphere, 9, 1797–1817, https://doi.org/10.5194/tc-9-1797-2015, 2015.
Jewson, D. H., Granin, N. G., Zhdanov, A. A., and Gnatovsky, R. Y.: Effect of snow depth on under-ice irradiance and growth of Aulacoseira baicalensis in Lake Baikal, Aquat. Ecol., 43, 673–679, https://doi.org/10.1007/s10452-009-9267-2, 2009.
Jiang, J. M. and You, X. T.: Where and when did an abrupt climatic change occur in China during the last 43 years?, Theor. Appl. Climatol., 55, 33–39, https://doi.org/10.1007/bf00864701, 1996.
Johnson, M. T., Ramage, J., Troy, T. J., and Brodzik, M. J.: Snowmelt detection with Calibrated, Enhanced-Resolution Brightness Temperatures (CETB) in Colorado Watersheds, Water Resour. Res., 56, e2018WR024542, https://doi.org/10.1029/2018wr024542, 2020.
Kang, K.-K., Duguay, C. R., and Howell, S. E. L.: Estimating ice phenology on large northern lakes from AMSR-E: algorithm development and application to Great Bear Lake and Great Slave Lake, Canada, The Cryosphere, 6, 235–254, https://doi.org/10.5194/tc-6-235-2012, 2012.
Kirillin, G. B., Shatwell, T., and Wen, L.: Ice-covered lakes of Tibetan Plateau as solar heat collectors, Geophys. Res. Lett., 48, e2021GL093429, https://doi.org/10.1029/2021gl093429, 2021.
Kropáček, J., Maussion, F., Chen, F., Hoerz, S., and Hochschild, V.: Analysis of ice phenology of lakes on the Tibetan Plateau from MODIS data, The Cryosphere, 7, 287–301, https://doi.org/10.5194/tc-7-287-2013, 2013.
L'Abée-Lund, J. H., Vøllestad, L. A., Brittain, J. E., Kvambekk, Å. S., and Solvang, T.: Geographic variation and temporal trends in ice phenology in Norwegian lakes during the period 1890–2020, The Cryosphere, 15, 2333–2356, https://doi.org/10.5194/tc-15-2333-2021, 2021.
Latifovic, R. and Pouliot, D.: Analysis of climate change impacts on lake ice phenology in Canada using the historical satellite data record, Remote Sens. Environ., 106, 492–507, https://doi.org/10.1016/j.rse.2006.09.015, 2007.
Lazhu, Yang, K., Hou, J., Wang, J., Lei, Y., Zhu, L., Chen, Y., Wang, M., and He, X.: A new finding on the prevalence of rapid water warming during lake ice melting on the Tibetan Plateau, Sci. Bull., 66, 2358–2361, https://doi.org/10.1016/j.scib.2021.07.022, 2021.
Leppäranta, M., Terzhevik, A., and Shirasawa, K.: Solar radiation and ice melting in Lake Vendyurskoe, Russian Karelia, Hydrol. Res., 41, 50–62, https://doi.org/10.2166/nh.2010.122, 2010.
Leppäranta, M., Luttinen, A., and Arvola, L.: Physics and geochemistry of lakes in Vestfjella, Dronning Maud Land, Antarct. Sci., 32, 29–42, https://doi.org/10.1017/S0954102019000555, 2020.
Li, B., Feng, Q., Li, Y., Li, Z., Wang, F., Wang, X., and Guo, X.: Stable oxygen and carbon isotope record from a drill core from the Hetao Basin in the upper reaches of the Yellow River in northern China and its implications for paleolake evolution, Chem. Geol., 557, 119798, https://doi.org/10.1016/j.chemgeo.2020.119798, 2020.
Li, X., Peng, S., Xi, Y., Woolway, R. I., and Liu, G.: Earlier ice loss accelerates lake warming in the Northern Hemisphere, Nat. Commun., 13, 5156, https://doi.org/10.1038/s41467-022-32830-y, 2022.
Lin, X., Wu, X., Chao, J., Ge, X., Tan, L., Liu, W., Sun, Z., and Hou, J.: Effects of combined ecological restoration measures on water quality and underwater light environment of Qingshan Lake, an urban eutrophic lake in China, Ecol. Indic., 163, 112107, https://doi.org/10.1016/j.ecolind.2024.112107, 2024.
Lu, P., Cao, X., Li, G., Huang, W., Leppäranta, M., Arvola, L., Huotari, J., and Li, Z.: Mass and heat balance of a lake ice cover in the Central Asian arid climate zone, Water, 12, 2888, https://doi.org/10.3390/w12102888, 2020.
Magee, M. R., Wu, C. H., Robertson, D. M., Lathrop, R. C., and Hamilton, D. P.: Trends and abrupt changes in 104 years of ice cover and water temperature in a dimictic lake in response to air temperature, wind speed, and water clarity drivers, Hydrol. Earth Syst. Sci., 20, 1681–1702, https://doi.org/10.5194/hess-20-1681-2016, 2016.
Marengo, J. A. and Camargo, C. C.: Surface air temperature trends in Southern Brazil for 1960–2002, Int. J. Climatol., 28, 893–904, https://doi.org/10.1002/joc.1584, 2007.
Mishra, V., Cherkauer, K. A., Bowling, L. C., and Huber, M.: Lake Ice phenology of small lakes: Impacts of climate variability in the Great Lakes region, Glob. Planet. Change, 76, 166–185, https://doi.org/10.1016/j.gloplacha.2011.01.004, 2011.
Murfitt, J. and Duguay, C. R.: 50 years of lake ice research from active microwave remote sensing: Progress and prospects, Remote Sens. Environ., 264, 112616, https://doi.org/10.1016/j.rse.2021.112616, 2021.
Newton, A. M. W. and Mullan, D. J.: Climate change and Northern Hemisphere lake and river ice phenology from 1931–2005, The Cryosphere, 15, 2211–2234, https://doi.org/10.5194/tc-15-2211-2021, 2021.
Nõges, P. and Nõges, T.: Weak trends in ice phenology of Estonian large lakes despite significant warming trends, Hydrobiologia, 731, 5–18, https://doi.org/10.1007/s10750-013-1572-z, 2013.
Nunziata, F., Li, X., Marino, A., Shao, W., Portabella, M., Yang, X., and Buono, A.: Microwave satellite measurements for coastal area and extreme weather monitoring, Remote Sens., 13, 3126, https://doi.org/10.3390/rs13163126, 2021.
Ramon, J., Lledó, L., Torralba, V., Soret, A., and Doblas-Reyes, F. J.: What global reanalysis best represents near-surface winds?, Q. J. Roy. Meteor. Soc., 145, 3236–3251, https://doi.org/10.1002/qj.3616, 2019.
Ruan, Y., Zhang, X., Xin, Q., Qiu, Y., and Sun, Y.: Prediction and analysis of lake ice phenology dynamics under future climate scenarios across the inner Tibetan Plateau, J. Geophys. Res.-Atmos., 125, e2020JD033082, https://doi.org/10.1029/2020jd033082, 2020.
Sharma, S., Blagrave, K., Magnuson, J. J., O'Reilly, C. M., Oliver, S., Batt, R. D., Magee, M. R., Straile, D., Weyhenmeyer, G. A., Winslow, L., and Woolway, R. I.: Widespread loss of lake ice around the Northern Hemisphere in a warming world, Nat. Clim. Change, 9, 227–231. https://doi.org/10.1038/s41558-018-0393-5, 2019.
Sharma, S., Richardson, D. C., Woolway, R. I., Imrit, M. A., Bouffard, D., Blagrave, K., Daly, J., Filazzola, A., Granin, N., Korhonen, J., Magnuson, J., Marszelewski, W., Matsuzaki, S. I. S., Perry, W., Robertson, D. M., Rudstam, L. G., Weyhenmeyer, G. A., and Yao, H.: Loss of ice cover, shifting phenology, and more extreme events in Northern Hemisphere lakes, J. Geophys. Res.-Biogeo., 126, e2021JG006348, https://doi.org/10.1029/2021jg006348, 2021.
Shi, N. and Zhu, Q.: An abrupt change in the intensity of the East Asian summer monsoon index and its relationship with temperature and precipitation over east China, Int. J. Climatol., 16, 757–764, https://doi.org/10.1002/(sici)1097-0088(199607)16:7<757::Aid-joc50>3.0.Co;2-5, 1996.
Smits, A. P., Gomez, N. W., Dozier, J., and Sadro, S.: Winter climate and lake morphology control ice phenology and under-ice temperature and oxygen regimes in mountain lakes, J. Geophys. Res.-Biogeo., 126, e2021JG006277, https://doi.org/10.1029/2021jg006277, 2021.
Su, L., Che, T., and Dai, L.: Variation in ice phenology of large lakes over the Northern Hemisphere based on passive microwave remote sensing data, Remote Sens., 13, 1389, https://doi.org/10.3390/rs13071389, 2021.
Sun, B., Li, C. Y., and Zhu, D. N.: Changes of Ulansuhai Lake in past 150 years based on 3S technology, 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, 2993–2997, https://doi.org/10.1109/rsete.2011.5964944, 2011.
Wang, X., Feng, L., Gibson, L., Qi, W., Liu, J., Zheng, Y., Tang, J., Zeng, Z., and Zheng, C.: High-resolution mapping of ice cover changes in over 33,000 lakes across the north temperate zone, Geophys. Res. Lett., 48, e2021GL095614, https://doi.org/10.1029/2021gl095614, 2021.
White, I., Xu, T., Zeng, J., Yu, J., Ma, X., Yang, J., Huo, Z., and Chen, H.: Changing climate and implications for water use in the Hetao Basin, Yellow River, China, Proc. IAHS, 383, 51–59, https://doi.org/10.5194/piahs-383-51-2020, 2020.
Woolway, R. I., Kraemer, B. M., Lenters, J. D., Merchant, C. J., O'Reilly, C. M., and Sharma, S.: Global lake responses to climate change, Nat. Rev. Earth Environ., 1, 388–403, https://doi.org/10.1038/s43017-020-0067-5, 2020.
Wu, Y., Duguay, C. R., and Xu, L.: Assessment of machine learning classifiers for global lake ice cover mapping from MODIS TOA reflectance data, Remote Sens. Environ., 253, 112206, https://doi.org/10.1016/j.rse.2020.112206, 2021.
Wu, Y., Guo, L., Zhang, B., Zheng, H., Fan, L., Chi, H., Li, J., and Wang, S.: Ice phenology dataset reconstructed from remote sensing and modelling for lakes over the Tibetan Plateau, Sci. Data, 9, 743, https://doi.org/10.1038/s41597-022-01863-9, 2022.
Xu, Y., Long, D., Li, X., Wang, Y., Zhao, F., and Cui, Y.: Unveiling lake ice phenology in Central Asia under climate change with MODIS data and a two-step classification approach, Remote Sens. Environ., 301, 113955, https://doi.org/10.1016/j.rse.2023.113955, 2024.
Yang, Q., Song, K., Hao, X., Wen, Z., Tan, Y., and Li, W.: Investigation of spatial and temporal variability of river ice phenology and thickness across Songhua River Basin, northeast China, The Cryosphere, 14, 3581–3593, https://doi.org/10.5194/tc-14-3581-2020, 2020a.
Yang, Q., Shi, X., Li, W., Song, K., Li, Z., Hao, X., Xie, F., Lin, N., Wen, Z., Fang, C., and Liu, G.: Fusion of Landsat 8 Operational Land Imager and Geostationary Ocean Color Imager for hourly monitoring surface morphology of lake ice with high resolution in Chagan Lake of Northeast China, The Cryosphere, 17, 959–975, https://doi.org/10.5194/tc-17-959-2023, 2023.
Yang, W., Xu, M., Li, R., Zhang, L., and Deng, Q.: Estimating the ecological water levels of shallow lakes: a case study in Tangxun Lake, China, Sci. Rep., 10, 5637, https://doi.org/10.1038/s41598-020-62454-5, 2020b.
Yuan, X., Yang, K., Lu, H., He, J., Sun, J., and Wang, Y.: Characterizing the features of precipitation for the Tibetan Plateau among four gridded datasets: Detection accuracy and spatio-temporal variabilities, Atmos. Res., 264, 105875, https://doi.org/10.1016/j.atmosres.2021.105875, 2021.
Zhang, S. and Pavelsky, T. M.: Remote sensing of lake ice phenology across a range of lakes sizes, ME, USA, Remote Sens., 11, 1718, https://doi.org/10.3390/rs11141718, 2019.
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...
