Articles | Volume 18, issue 1
https://doi.org/10.5194/essd-18-147-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-147-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
06 Jan 2026
Data description article |
| 06 Jan 2026
| 06 Jan 2026
A six-year circum-Antarctic icebergs dataset (2018–2023)
Zilong Chen
School of Geospatial Engineering and Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 51908, China
Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
Xuying Liu
Institute of Artificial Intelligence, Shaoxing University, Shaoxing 312000, China
Zhenfu Guan
School of Geospatial Engineering and Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 51908, China
State Key Laboratory of Remote Sensing and Digital Earth, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
School of Geospatial Engineering and Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 51908, China
Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
Xiao Cheng
CORRESPONDING AUTHOR
School of Geospatial Engineering and Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 51908, China
Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
State Key Laboratory of Remote Sensing and Digital Earth, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
School of Geospatial Engineering and Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 51908, China
Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
Lei Zheng
School of Geospatial Engineering and Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 51908, China
Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
Jiping Liu
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 51908, China
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Ziyu Yan, Yufang Ye, Georg Heygster, Bin Cheng, Carolina Gabarró, Ferran Hernández-Macià, and Xiao Cheng
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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.
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We present a new framework to model frontal ablation of marine-terminating glaciers by linking two global glacier models with a simple physics-based calving scheme and Bayesian calibration. Applied to 71 Svalbard glaciers (2000–2019) and projected to 2100, it reproduces seasonal front changes but overestimates ablation due to uncertain geometry and velocity. This efficient approach can improve regional to global simulations.
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
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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.
Fan Gao, Qiang Shen, Hansheng Wang, Tong Zhang, Liming Jiang, Yan Liu, C. K. Shum, Yan An, and Xu Zhang
The Cryosphere, 20, 1947–1965, https://doi.org/10.5194/tc-20-1947-2026, https://doi.org/10.5194/tc-20-1947-2026, 2026
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Basal ice-shelf melting critically impacts Antarctic ice sheet evolution. Our testing of two melting schemes showed starkly diverging projections despite near-identical initial states, especially for West Antarctica. By 2100, the predicted sea-level contributions differed by 57%. Initial setup changes hidden sub-ice properties (e.g., friction, temperature), modifying ice flow. Accurately representing melt and refining setup are thus essential to reduce projections uncertainty.
Whyjay Zheng, Wesley Van Wychen, Tian Li, and Tsutomu Yamanokuchi
The Cryosphere, 20, 1699–1714, https://doi.org/10.5194/tc-20-1699-2026, https://doi.org/10.5194/tc-20-1699-2026, 2026
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Siran Chen, Jiping Liu, and Xianxian Han
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Fukai Peng, Xiaoli Deng, Yunzhong Shen, and Xiao Cheng
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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.
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
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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.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
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Sea ice models are mainly based on non-moving structured grids, which is different from buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in Community Ice CodE (CICE). We validate the sea ice tracking with buoys and evaluate the sea ice deformation in high-resolution simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.
Yan Sun, Shaoyin Wang, Xiao Cheng, Teng Li, Chong Liu, Yufang Ye, and Xi Zhao
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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.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
The Cryosphere, 18, 1215–1239, https://doi.org/10.5194/tc-18-1215-2024, https://doi.org/10.5194/tc-18-1215-2024, 2024
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We present a new atmosphere–ocean–wave–sea ice coupled model to study the influences of ocean waves on Arctic sea ice simulation. Our results show (1) smaller ice-floe size with wave breaking increases ice melt, (2) the responses in the atmosphere and ocean to smaller floe size partially reduce the effect of the enhanced ice melt, (3) the limited oceanic energy is a strong constraint for ice melt enhancement, and (4) ocean waves can indirectly affect sea ice through the atmosphere and the ocean.
Tian Li, Konrad Heidler, Lichao Mou, Ádám Ignéczi, Xiao Xiang Zhu, and Jonathan L. Bamber
Earth Syst. Sci. Data, 16, 919–939, https://doi.org/10.5194/essd-16-919-2024, https://doi.org/10.5194/essd-16-919-2024, 2024
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Our study uses deep learning to produce a new high-resolution calving front dataset for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, containing 124 919 terminus traces. This dataset offers insights into understanding calving mechanisms and can help improve glacier frontal ablation estimates as a component of the integrated mass balance assessment.
Xiaoxu Shi, Martin Werner, Hu Yang, Roberta D'Agostino, Jiping Liu, Chaoyuan Yang, and Gerrit Lohmann
Clim. Past, 19, 2157–2175, https://doi.org/10.5194/cp-19-2157-2023, https://doi.org/10.5194/cp-19-2157-2023, 2023
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The Last Glacial Maximum (LGM) marks the most recent extremely cold and dry time period of our planet. Using AWI-ESM, we quantify the relative importance of Earth's orbit, greenhouse gases (GHG) and ice sheets (IS) in determining the LGM climate. Our results suggest that both GHG and IS play important roles in shaping the LGM temperature. Continental ice sheets exert a major control on precipitation, atmospheric dynamics, and the intensity of El Niño–Southern Oscillation.
Haihan Hu, Jiechen Zhao, Petra Heil, Zhiliang Qin, Jingkai Ma, Fengming Hui, and Xiao Cheng
The Cryosphere, 17, 2231–2244, https://doi.org/10.5194/tc-17-2231-2023, https://doi.org/10.5194/tc-17-2231-2023, 2023
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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.
Tian Li, Geoffrey J. Dawson, Stephen J. Chuter, and Jonathan L. Bamber
The Cryosphere, 17, 1003–1022, https://doi.org/10.5194/tc-17-1003-2023, https://doi.org/10.5194/tc-17-1003-2023, 2023
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The Totten and Moscow University glaciers in East Antarctica have the potential to make a significant contribution to future sea-level rise. We used a combination of different satellite measurements to show that the grounding lines have been retreating along the fast-flowing ice streams across these two glaciers. We also found two tide-modulated ocean channels that might open new pathways for the warm ocean water to enter the ice shelf cavity.
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.
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.
Tian Li, Geoffrey J. Dawson, Stephen J. Chuter, and Jonathan L. Bamber
Earth Syst. Sci. Data, 14, 535–557, https://doi.org/10.5194/essd-14-535-2022, https://doi.org/10.5194/essd-14-535-2022, 2022
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Accurate knowledge of the Antarctic grounding zone is important for mass balance calculation, ice sheet stability assessment, and ice sheet model projections. Here we present the first ICESat-2-derived high-resolution grounding zone product of the Antarctic Ice Sheet, including three important boundaries. This new data product will provide more comprehensive insights into ice sheet instability, which is valuable for both the cryosphere and sea level science communities.
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.
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.
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Short summary
Our study uses Google Earth Engine to create a dataset of Antarctic icebergs in the Southern Ocean (south of 55° S) from October 2018 to 2023. The dataset includes icebergs larger than 0.04 km², with details on their locations, sizes, and shapes. It shows significant changes in iceberg number and area, mainly driven by major ice shelf calving events – especially in the Weddell Sea. This resource fills key gaps in understanding iceberg impacts on the ocean and climate.
Our study uses Google Earth Engine to create a dataset of Antarctic icebergs in the Southern...
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