Articles | Volume 19, issue 7
https://doi.org/10.5194/gmd-19-2985-2026
© 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/gmd-19-2985-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Apr 2026
Model description paper |
| 17 Apr 2026
| 17 Apr 2026
The coupled Southern Ocean–Sea ice–Ice shelf Model (SOSIM v1.0): configuration and evaluation
Chengyan Liu
CORRESPONDING AUTHOR
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
Zhaomin Wang
CORRESPONDING AUTHOR
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
Dake Chen
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
Xianxian Han
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
Hengling Leng
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing, 100081, China
Liangjun Yan
School of Marine Science, Sun Yat-sen University, Zhuhai, 519082, China
Xiang Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
Craig Stevens
Earth Sciences New Zealand, Wellington, 6021, New Zealand
Andrew McC. Hogg
Research School of Earth Sciences and ARC Centre of Excellence for Climate System Science, Australian National University, Canberra, ACT 2600, Australia
Kazuya Kusahara
Japan Agency for Marine-Earth Science and Technology, Yokohama, 237-0061, Japan
Kaihe Yamazaki
Institute of Marine and Antarctic Studies, University of Tasmania, Hobart, TAS 7001, Australia
Kay I. Ohshima
Institute of Low Temperature Science, Hokkaido University, Sapporo, 060-0819, Japan
Meng Zhou
Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education and School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China
Xiao Cheng
School of Geospatial Engineering and Science, Sun Yat-sen University, 519082, Zhuhai, China
Dongxiao Wang
School of Marine Science, Sun Yat-sen University, Zhuhai, 519082, China
Changming Dong
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Jiping Liu
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China
Qinghua Yang
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China
Xichen Li
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Ruibo Lei
Polar Research Institute of China, Shanghai, 200136, China
Minghu Ding
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Zhaoru Zhang
Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education and School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China
Dujuan Kang
Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education and School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China
Di Qi
Polar and Marine Research Institute, Jimei University, Xiamen, 361021, China
Tongya Liu
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, China
Jihai Dong
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China
College of Surveying and Geo-Informatics, Tongji University, Shanghai, 200092, China
Ru Chen
School of Marine Science and Technology, Tianjin University, Tianjin, 300072, China
Tong Zhang
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Xiaoming Hu
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China
Bo Han
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China
Haibo Bi
Key Laboratory of Marine Geology and Environment, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China
First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, 266061, China
Longjiang Mu
Laoshan Laboratory, Qingdao, 266100, China
Shiming Xu
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
Hu Yang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
Hailong Liu
Yunnan Key Laboratory of Meteorological Disasters and Climate Resources in the Greater Mekong Subregion, Yunnan University, Kunming, 650500, China
Tingfeng Dou
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
Zhixuan Feng
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
Lei Zheng
School of Geospatial Engineering and Science, Sun Yat-sen University, 519082, Zhuhai, China
Xueyuan Tang
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Guitao Shi
School of Geographic Sciences and State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
Yongqing Cai
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Bingrui Li
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Yang Wu
School of Information Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, China
Xia Lin
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Wenjin Sun
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Yu Liu
Marine Science and Technology College, Zhejiang Ocean University, Zhoushan, 316000, China
Kai Yu
Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, 210098, China
Yu Zhang
College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai, 201306, China
Weizeng Shao
College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai, 201306, China
Xiaoyu Wang
Frontier Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China, Qingdao, 266100, China
Shaojun Zheng
Key Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, Guangdong Ocean University, Zhanjiang, 524088, China
Chengyi Yuan
Department of Marine Technology, College of Marine and Environmental Sciences, Tianjin University of Science and Technology, Tianjin, 300457, China
Chunxia Zhou
Chinese Antarctic Centre of Surveying and Mapping, Wuhan University, Wuhan, 430079, China
Jian Liu
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China
Yue Xia
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China
Xiaoyu Pan
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China
Jiabao Zeng
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China
Kechen Liu
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, 410073, China
Jiahao Fan
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China
Chen Cheng
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
Qi Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
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We propose an innovative multi-satellite DEM adjustment model (MDAM) that removes biases in elevation between sub-DEMs across ice shelves. Our results reveal quantitative 3D structural and mélange features of a ~50 km long rift. For first time, we found that while the mélange elevation decreased from 2014–2021, the mélange inside the rift experienced a rapid expansion, attributing to newly calved shelf ice from rift walls, associated rift widening, and other rift-mélange interaction factors.
Benjamin K. Galton-Fenzi, Richard Porter-Smith, Sue Cook, Eva Cougnon, David E. Gwyther, Wilma G. C. Huneke, Madelaine G. Rosevear, Xylar Asay-Davis, Fabio Boeira Dias, Michael S. Dinniman, David Holland, Kazuya Kusahara, Kaitlin A. Naughten, Keith W. Nicholls, Charles Pelletier, Ole Richter, Hélène Seroussi, and Ralph Timmermann
The Cryosphere, 19, 6507–6525, https://doi.org/10.5194/tc-19-6507-2025, https://doi.org/10.5194/tc-19-6507-2025, 2025
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Quantifying melt and freeze beneath Antarctica’s floating ice shelves is vital to understanding present-day ice-sheet behavior and its potential to contribute to future sea-level rise. We compare 10 ice-shelf/ocean computer simulations with satellite data, providing the first multi-model estimate of melting and refreezing driven by the ocean. This new estimate offers a valuable tool for assessing ice-shelf roles in current and future ice-sheet changes, informing coastal adaptation strategies.
Guokun Lyu, Longjiang Mu, Armin Koehl, Ruibo Lei, Xi Liang, and Chuanyu Liu
Geosci. Model Dev., 18, 9451–9468, https://doi.org/10.5194/gmd-18-9451-2025, https://doi.org/10.5194/gmd-18-9451-2025, 2025
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In the sea ice-ocean models, errors in the parameters and missing spatiotemporal variations contribute to the deviations between the simulations and the observations. We extended an adjoint method to optimize spatiotemporally varying parameters together with the atmosphere forcing and the initial conditions using satellite and in-situ observations. Seasonally, this scheme demonstrates a more prominent advantage in mid-autumn and show great potential for accurately reproducing the Arctic changes.
Ziying Yang, Jiping Liu, Mirong Song, Yongyun Hu, Qinghua Yang, Ke Fan, Rune Grand Graversen, and Lu Zhou
The Cryosphere, 19, 6381–6402, https://doi.org/10.5194/tc-19-6381-2025, https://doi.org/10.5194/tc-19-6381-2025, 2025
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Antarctic sea ice has changed rapidly in recent years. Here we developed a deep learning model trained by multiple climate variables for extended seasonal Antarctic sea ice prediction. Our model shows high predictive skills up to 6 months in advance, particularly in predicting extreme events. It also shows skillful predictions at the sea ice edge and year-to-year sea ice changes. Variable importance analyses suggest what variables are more important for prediction at different lead times.
Zhengyi Song, Yudi Pan, Jiangtao Li, Hongrui Peng, Yiming Wang, Yuande Yang, Kai Lu, Xueyuan Tang, and Xiaohong Zhang
The Cryosphere, 19, 6341–6353, https://doi.org/10.5194/tc-19-6341-2025, https://doi.org/10.5194/tc-19-6341-2025, 2025
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Obtaining the physical properties of ice sheets is important. In this study, we use seismic ambient noise to obtain the shallow S-wave velocity structure at the Dome A region. The result agrees with the ice-core data nearby and reveals radial anisotropy in the firn layer. This study demonstrates that cultural seismic noise provides an effective and environmentally friendly way for the imaging of near-surface structures in Antarctica.
Libang Xu, Bin Jia, Yu Liu, Xue'en Chen, and Donglin Guo
EGUsphere, https://doi.org/10.5194/egusphere-2025-4290, https://doi.org/10.5194/egusphere-2025-4290, 2025
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We compared two methods to calculate ice-ocean drag coefficient in Bohai Sea. Results demonstrate that in the thin ice environment, the ice-bottom surface skin drag and the ice floe edge form drag are the main components. One method better predicts ice extent, the other better predicts ice season duration. Higher ice-ocean drag melts ice from below and cools water to form new ice. Our findings improve regional ice forecasts, enhancing safety for shipping and coastal industries.
Claire K. Yung, Madelaine G. Rosevear, Adele K. Morrison, Andrew McC. Hogg, and Yoshihiro Nakayama
The Cryosphere, 19, 5827–5861, https://doi.org/10.5194/tc-19-5827-2025, https://doi.org/10.5194/tc-19-5827-2025, 2025
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Ocean models are used to understand how the ocean interacts with the Antarctic Ice Sheet, but they are too coarse in resolution to capture the small-scale ocean processes driving melting and require a parameterisation to predict melt. Previous parameterisations ignore key processes occurring in some regions of Antarctica. We develop a parameterisation with the feedback of stratification on melting and test it in idealised and regional ocean models, finding changes to melt rate and circulation.
Runzhuo Fang, Jinfeng Ding, Wenjuan Gao, Xi Liang, Zhuoqi Chen, Chuanfeng Zhao, Haijin Dai, and Lei Liu
Earth Syst. Sci. Data, 17, 6049–6069, https://doi.org/10.5194/essd-17-6049-2025, https://doi.org/10.5194/essd-17-6049-2025, 2025
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Integrated Multi-source Polar Mesoscale Cyclone Tracks (IMPMCT) is a dataset containing a 24-year record (2001–2024) of polar storms in the Nordic Seas. These storms, called Polar Mesoscale Cyclones (PMCs), sometimes cause extreme winds and waves, threatening marine operations. IMPMCT combines remote sensing measurements and reanalysis data to construct a comprehensive PMCs archive. It includes 1110 PMCs tracks, 16 001 cloud patterns, and 4472 wind records, providing fundamental data for advancing our understanding of their development mechanisms.
Gokhan Danabasoglu, Frederic S. Castruccio, Burcu Boza, Alice M. Barthel, Arne Biastoch, Adam Blaker, Alexandra Bozec, Diego Bruciaferri, Frank O. Bryan, Eric P. Chassignet, Yao Fu, Ian Grooms, Catherine Guiavarc'h, Hakase Hayashida, Andrew McC. Hogg, Ryan M. Holmes, Doroteaciro Iovino, Andrew E. Kiss, M. Susan Lozier, Gustavo Marques, Alex Megann, Franziska U. Schwarzkopf, Dave Storkey, Luke van Roekel, Jon Wolfe, Xiaobiao Xu, and Rong Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-5406, https://doi.org/10.5194/egusphere-2025-5406, 2025
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A comparison of simulated and observed overturning transports across the Overturning in the Subpolar North Atlantic Program sections for the 2014–2022 period is presented. Eighteen ocean simulations participate in the study. The simulated transports are in general agreement with observations. Analyzing overturning circulations in both depth and density space together provides a more complete picture of the overturning properties. The study serves as a benchmark for evaluation of ocean models.
Xiaojing Lv, Zhao Liu, Yuxuan Li, Shaoqing Zhang, Haohuan Fu, Xiaohui Duan, Shiming Xu, Yang Gao, Yujing Fan, Lifeng Yan, Haopeng Huang, Haitian Lu, Lingfeng Wan, Haoran Lin, Qixin Chang, Chenlin Li, Quanjie He, Yangyang Yu, Qinghui Lin, Sheng Jia, Tengda Zhao, Weiguo Liu, and Guangwen Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-5297, https://doi.org/10.5194/egusphere-2025-5297, 2025
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This study introduces a highly-efficient optimization approach that integrates automated and fine-grained optimizations for kilometer-level Earth System Models on heterogeneous many-core supercomputers. Our optimization achieves full parallel coverage for code segments exceeding 1 % of runtime. The optimized 5-km/3-km coupled model reaches 222 Simulated Days Per Day. This work signifies a pivotal advancement in ESMs, providing a robust platform for HR climate simulations.
Siqi Liu, Shiming Xu, Wenkai Guo, Yanfei Fan, Lu Zhou, Jack Landy, Malin Johansson, Weixin Zhu, and Alek Petty
The Cryosphere, 19, 5175–5199, https://doi.org/10.5194/tc-19-5175-2025, https://doi.org/10.5194/tc-19-5175-2025, 2025
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In this study, we explore the potential of using synthetic aperture radars (SAR) to predict the sea ice height measurements by the airborne campaign of Operation IceBridge. In particular, we predict the meter-scale sea ice height with the statistical relationship between the two, overcoming the resolution limitation of SAR images from Sentinel-1 satellites. The prediction and ice drift correction algorithms can be applied to the extrapolation of ICESat-2 measurements in the Arctic region.
Qi'an Chen, Hongzhou Xu, Dongxiao Wang, Bo Hong, Chunlei Liu, Zheyang Zhang, Huichang Jiang, Wei Song, Tong Long, Ling Wang, Sumin Liu, and Rongjie Chen
Ocean Sci., 21, 2631–2642, https://doi.org/10.5194/os-21-2631-2025, https://doi.org/10.5194/os-21-2631-2025, 2025
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NIWs (near-inertial waves) have horizontal spatial scales of 10–100 km and slow group velocities, making them to interact strongly with mesoscale eddies. In the northwestern SCS (South China Sea), large portion of eddy propagate westward and may interact with local NIWs. In this study, four moorings were deployed in this area and they captured a vivid case of interaction between a cyclonic eddy and NIWs. Our paper provides direct evidence of strong interaction between eddy and NIWs in the northwestern SCS.
Shenjie Zhou, Pierre Dutrieux, Claudia F. Giulivi, Adrian Jenkins, Alessandro Silvano, Christopher Auckland, E. Povl Abrahamsen, Michael Meredith, Irena Vaňková, Keith Nicholls, Peter E. D. Davis, Svein Østerhus, Arnold L. Gordon, Christopher J. Zappa, Tiago S. Dotto, Ted Scambos, Kathryn L. Gunn, Stephen R. Rintoul, Shigeru Aoki, Craig Stevens, Chengyan Liu, Sukyoung Yun, Tae-Wan Kim, Won Sang Lee, Markus Janout, Tore Hattermann, Julius Lauber, Elin Darelius, Anna Wåhlin, Leo Middleton, Pasquale Castagno, Giorgio Budillon, Karen J. Heywood, Jennifer Graham, Stephen Dye, Daisuke Hirano, and Una Kim Miller
Earth Syst. Sci. Data, 17, 5693–5706, https://doi.org/10.5194/essd-17-5693-2025, https://doi.org/10.5194/essd-17-5693-2025, 2025
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We created the first standardised dataset of in-situ ocean measurements time series from around Antarctica collected since 1970s. This includes temperature, salinity, pressure, and currents recorded by instruments deployed in icy, challenging conditions. Our analysis highlights the dominance of tidal currents and separates these from other patterns to study regional energy distribution. This unique dataset offers a foundation for future research on Antarctic ocean dynamics and ice interactions.
Robert G. Bingham, Julien A. Bodart, Marie G. P. Cavitte, Ailsa Chung, Rebecca J. Sanderson, Johannes C. R. Sutter, Olaf Eisen, Nanna B. Karlsson, Joseph A. MacGregor, Neil Ross, Duncan A. Young, David W. Ashmore, Andreas Born, Winnie Chu, Xiangbin Cui, Reinhard Drews, Steven Franke, Vikram Goel, John W. Goodge, A. Clara J. Henry, Antoine Hermant, Benjamin H. Hills, Nicholas Holschuh, Michelle R. Koutnik, Gwendolyn J.-M. C. Leysinger Vieli, Emma J. MacKie, Elisa Mantelli, Carlos Martín, Felix S. L. Ng, Falk M. Oraschewski, Felipe Napoleoni, Frédéric Parrenin, Sergey V. Popov, Therese Rieckh, Rebecca Schlegel, Dustin M. Schroeder, Martin J. Siegert, Xueyuan Tang, Thomas O. Teisberg, Kate Winter, Shuai Yan, Harry Davis, Christine F. Dow, Tyler J. Fudge, Tom A. Jordan, Bernd Kulessa, Kenichi Matsuoka, Clara J. Nyqvist, Maryam Rahnemoonfar, Matthew R. Siegfried, Shivangini Singh, Vjeran Višnjević, Rodrigo Zamora, and Alexandra Zuhr
The Cryosphere, 19, 4611–4655, https://doi.org/10.5194/tc-19-4611-2025, https://doi.org/10.5194/tc-19-4611-2025, 2025
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The ice sheets covering Antarctica have built up over millenia through successive snowfall events which become buried and preserved as internal surfaces of equal age detectable with ice-penetrating radar. This paper describes an international initiative working together on these archival data to build a comprehensive 3-D picture of how old the ice is everywhere across Antarctica and how this is being used to reconstruct past and to predict future ice and climate behaviour.
Alexandra Gossart, Alena Malyarenko, Liv Cornelissen, Craig Stevens, Una Miller, Christopher J. Zappa, Nancy Luca, Pasquale Castagno, and Giorgio Budillon
EGUsphere, https://doi.org/10.5194/egusphere-2025-4332, https://doi.org/10.5194/egusphere-2025-4332, 2025
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Polynyas are areas of open water that form within the sea ice during the Antarctic winter. They occur along the coastline and act as hotspots for air–sea interactions and sea-ice formation. We use the P-SKRIPSv2 model to simulate air–sea–ice–ocean interactions in the Ross Sea. Model results are compared with observations, and we test how changes in air–sea–ice coupling affect the system. Our findings show that P-SKRIPSv2 provides a valuable tool to study polynyasa and understand their dynamics.
Haifeng Huo, Hui Xu, Jixiu Wu, Tao Li, Jingjin Liu, Enzhao Xiao, and Xueyuan Tang
EGUsphere, https://doi.org/10.5194/egusphere-2025-4768, https://doi.org/10.5194/egusphere-2025-4768, 2025
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Through a series of direct shear tests, this study analyzes the variation of shear strength parameters (cohesion and internal friction angle) in compacted snow under different conditions of density, sintering time, and temperature. A Genetic Algorithm-Back Propagation neural network model was subsequently developed to establish systematic benchmark values for these parameters. This work provides essential data and a predictive framework for the reliable design of snow structures in cold regions.
Tong Zhang, Wei Yang, Yuzhe Wang, Chuanxi Zhao, Qingyun Long, and Cunde Xiao
The Cryosphere, 19, 4487–4498, https://doi.org/10.5194/tc-19-4487-2025, https://doi.org/10.5194/tc-19-4487-2025, 2025
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This study investigates the 2018 Sedongpu glacier detachment in Southeastern Tibet using a two-dimensional ice flow model that includes an ice stiffness and basal slip positive feedback mechanism. The model simulates rapid transitions in glacier flow, triggering detachment when ice stress exceeds yield strength. The results, including ice speed and duration, align with observations, demonstrating the potential for early warning of similar hazards in the region.
Shuyu Zhao, Tian Feng, Xuexi Tie, Biao Tian, Xiao Hu, Bo Hu, Dong Yang, Sinan Gu, and Minghu Ding
Atmos. Chem. Phys., 25, 12483–12496, https://doi.org/10.5194/acp-25-12483-2025, https://doi.org/10.5194/acp-25-12483-2025, 2025
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This study investigated how cloud–radiation interactions influence ozone formation in a warming climate. Using measurements, reanalysis data, and models, we found that cloud–radiation interactions can worsen O3 pollution, and climate warming will amplify the influence. We highlight that climate change will pose greater challenges for China's O3 pollution prevention and control, and actions such as reducing O3 precursors emissions and mitigating climate change are urgently needed.
Yanjun Li, Violaine Coulon, Javier Blasco, Gang Qiao, Qinghua Yang, and Frank Pattyn
The Cryosphere, 19, 4373–4390, https://doi.org/10.5194/tc-19-4373-2025, https://doi.org/10.5194/tc-19-4373-2025, 2025
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We incorporate ice damage processes into an ice-sheet model and apply the new model to Thwaites Glacier. The upgraded model more accurately captures the observed ice mass loss of Thwaites Glacier over 1990–2020. Our simulations show that ice damage has a notable impact on the ice mass loss, grounding-line retreat, ice velocity, and ice thickness of the Thwaites Glacier basin. This study highlights the necessity for incorporating ice damage into ice-sheet models.
Liv Cornelissen, Sukyoung Yun, Jasmin McInerney, Brett Grant, Fiona Elliot, Seung-Tae Yoon, Christopher J. Zappa, Won Sang Lee, and Craig Stevens
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-540, https://doi.org/10.5194/essd-2025-540, 2025
Revised manuscript accepted for ESSD
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We present a decade-long mooring time series from southern Terra Nova Bay, Ross Sea, begun in December 2014 as the “DITx” array. Three sites around the Drygalski Ice Tongue record temperature, salinity, pressure, and currents. The data highlight seasonal cycles and variability, informing studies of water mass formation, ice–ocean interactions, glaciology, and regional ecosystems.
Chaofeng Wang, Zhiqiang Xu, Guangfu Luo, Xiaoyu Wang, Yan He, Musheng Lan, Tiancheng Zhang, and Wuchang Zhang
Ocean Sci., 21, 1873–1889, https://doi.org/10.5194/os-21-1873-2025, https://doi.org/10.5194/os-21-1873-2025, 2025
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Our study provides a comprehensive assessment of microzooplankton ciliate trait structure, focusing on size spectrum, biodiversity and biotic–abiotic interplay based on 175 stations (1117 samples) across five temperature zones, which offered an ideal paradigm to study the plankton response to future climate change.
Rui Li, Xing Liu, Yubing Shen, Yumeng Shao, Yining Gao, Ziwei Yao, Xi Liu, and Guitao Shi
Atmos. Chem. Phys., 25, 9263–9274, https://doi.org/10.5194/acp-25-9263-2025, https://doi.org/10.5194/acp-25-9263-2025, 2025
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We reveal for the first time the global variations of PAHs and derivatives in marine air. We found that marine aerosols in East China Sea (ECS) and Western Pacific (WP) were significantly affected by coal and engine combustion, while those in Bismarck Sea (BS) and East Australian Sea (EAS) were mainly influenced by wildfire and coal combustion. The Antarctic Ocean (AO) was dominated by biomass burning and local shipping emissions. This finding helps elucidate the mechanism of the global PAH cycle.
Xinxin Wang, Jiuke Wang, Wenfang Lu, Changming Dong, Hao Qin, and Haoyu Jiang
Geosci. Model Dev., 18, 5101–5114, https://doi.org/10.5194/gmd-18-5101-2025, https://doi.org/10.5194/gmd-18-5101-2025, 2025
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Large-scale wave modeling is essential for science and society, typically relying on resource-intensive numerical methods to simulate wave dynamics. In this study, we introduce a rolling AI-based method for modeling global significant wave height. Our model achieves accuracy comparable to traditional numerical methods while significantly improving speed, making it operable on standard laptops. This work demonstrates AI's potential to enhance the accuracy and efficiency of global wave modeling.
Fanyi Zhang, Ruibo Lei, Meng Qu, Na Li, Ying Chen, and Xiaoping Pang
The Cryosphere, 19, 3065–3087, https://doi.org/10.5194/tc-19-3065-2025, https://doi.org/10.5194/tc-19-3065-2025, 2025
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We reconstructed sea ice drift trajectories and identified optimal deployment areas for Lagrangian observations in the central Arctic Ocean. The trajectories revealed a preference for ice advection towards the Transpolar Drift region over the Beaufort Gyre, with endpoints influenced by large-scale atmospheric circulation patterns. This study provides critical support for the planning and implementation of Lagrangian observations relying on ice floes in the central Arctic Ocean under changing environmental conditions.
Fei Zhu, Meilin Zhu, and Lu An
EGUsphere, https://doi.org/10.5194/egusphere-2025-2579, https://doi.org/10.5194/egusphere-2025-2579, 2025
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Using on-site measurements and a new detection method, we found the 2022 summer heatwave caused unprecedented melting at the source of China’s Yangtze River and Tibet’s largest lake. Over 52 days, melting accounted for 65.2 % of the year’s ice loss—doubling previous summers. This extreme event, driven by persistent heat and unusual weather patterns, shows how rapidly vital water sources can disappear.
Hu Yang, Xiaoxu Shi, Xulong Wang, Qingsong Liu, Yi Zhong, Xiaodong Liu, Youbin Sun, Yanjun Cai, Fei Liu, Gerrit Lohmann, Martin Werner, Zhimin Jian, Tainã M. L. Pinho, Hai Cheng, Lijuan Lu, Jiping Liu, Chao-Yuan Yang, Qinghua Yang, Yongyun Hu, Xing Cheng, Jingyu Zhang, and Dake Chen
Clim. Past, 21, 1263–1279, https://doi.org/10.5194/cp-21-1263-2025, https://doi.org/10.5194/cp-21-1263-2025, 2025
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For 1 century, the hemispheric summer insolation is proposed as a key pacemaker of astronomical climate change. However, an increasing number of geologic records reveal that the low-latitude hydrological cycle shows asynchronous precessional evolutions that are very often out of phase with the summer insolation. Here, we propose that the astronomically driven low-latitude hydrological cycle is not paced by summer insolation but by shifting perihelion.
Chunhua Qiu, Zhenyang Du, Haibo Tang, Zhenhui Yi, Jiawei Qiao, Dongxiao Wang, Xiaoming Zhai, and Wenbo Wang
Earth Syst. Sci. Data, 17, 3189–3202, https://doi.org/10.5194/essd-17-3189-2025, https://doi.org/10.5194/essd-17-3189-2025, 2025
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The high-resolution autonomous underwater vehicle (AUV) dataset for the South China Sea (SCS) provides 13 491 temperature and salinity profiles and covers 463 d of experiments. To our knowledge, the resolution and length of this dataset are enough to detect the asymmetry, vertical tilt, and temporal evolution of mesoscale eddies (MEs) and the corresponding submesoscale processes. The dataset is expected to improve the accuracy of current and biogeochemistry numerical models. More projects conducting AUV experiments will be promoted in the future.
Xiaoqiao Wang, Zhaoru Zhang, Chuan Xie, Xi Zhao, Chuning Wang, Heng Hu, and Yuanjie Chen
The Cryosphere, 19, 2229–2245, https://doi.org/10.5194/tc-19-2229-2025, https://doi.org/10.5194/tc-19-2229-2025, 2025
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Global bottom water originates from high-salinity shelf water (HSSW), formed by intense sea ice production (SIP) in the Southern Ocean. This study uses numerical outputs for the Ross Sea to examine the extreme HSSW event in 2007, when atmospheric circulations enhanced SIP, resulting in the highest HSSW volume in a decade. However, salinity was low, owing to increased meltwater. The findings highlight the complex interplay between SIP and ice shelf melting, with key implications for ocean processes.
Mingyue Nong, Xuying Liu, Teng Li, Baogang Zhang, Qi Liang, Lei Zheng, Tiancheng Zhao, and Xiao Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-1884, https://doi.org/10.5194/egusphere-2025-1884, 2025
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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.
Arnaud F. Valcarcel, Craig L. Stevens, Joanne M. O'Callaghan, and Sutara H. Suanda
Ocean Sci., 21, 965–987, https://doi.org/10.5194/os-21-965-2025, https://doi.org/10.5194/os-21-965-2025, 2025
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This paper describes underwater robotic measurements in an energetic strait. The data show how energy is transferred from winds and tides to turbulent processes. Boundary layers of strong turbulence affected the water from surface to seafloor across an unusually deep extent, except when fresher or warmer waters moved into the region. Numerical models revealed that turbulent energy transport allowed boundary layers to interact. This phenomenon may impact the biological structure of coastal seas.
Kai Man, Xichen Li, Jürg Luterbacher, Lei Geng, Naiming Yuan, Yurong Hou, Yonghao Wang, and Yujie Miao
EGUsphere, https://doi.org/10.5194/egusphere-2025-1381, https://doi.org/10.5194/egusphere-2025-1381, 2025
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The West Antarctic Ice Sheet shows opposing snow accumulation trends: decreasing in the west and increasing in the east. Our study reveals that tropical ocean temperature shifts – Pacific cooling and Atlantic warming – drive changes in winds and moisture, boosting snowfall in the east while reducing it in the west. Using ice cores and models, we highlight how distant ocean changes shape Antarctic Ice Sheet, crucial for predicting future sea level rise.
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.
Xiaoyu Wang, Longjiang Mu, and Xianyao Chen
Ocean Sci., 21, 577–586, https://doi.org/10.5194/os-21-577-2025, https://doi.org/10.5194/os-21-577-2025, 2025
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The East Siberian Sea has nearly 80 % of the subsea permafrost worldwide. The cold layer with a temperature around −1.5 ºC above the seafloor prevents heat transporting from above to melt permafrost and release methane from sediments. However, we observed a warming trend at the seafloor caused by wave-induced vertical mixing in the shelf. The intensified mixing can transport enormous heat downward, leading to warming of more than 3 °C at the bottom, putting the subsea permafrost at high risk.
Zhaoru Zhang, Chuan Xie, Chuning Wang, Yuanjie Chen, Heng Hu, and Xiaoqiao Wang
Geosci. Model Dev., 18, 1375–1393, https://doi.org/10.5194/gmd-18-1375-2025, https://doi.org/10.5194/gmd-18-1375-2025, 2025
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A coupled fine-resolution ocean–ice model is developed for the Ross Sea and adjacent regions in Antarctica, a key area for the formation of global ocean bottom water, the Antarctic Bottom Water (AABW), which affects global ocean circulation. The model has a high skill level in simulating sea ice production driving the AABW source water formation and AABW properties when assessed against observations. A model experiment shows a significant impact of ice shelf melting on the AABW characteristics.
Katsuro Katsumata, Shigeru Aoki, Kay I. Ohshima, and Michiyo Yamamoto-Kawai
Ocean Sci., 21, 419–436, https://doi.org/10.5194/os-21-419-2025, https://doi.org/10.5194/os-21-419-2025, 2025
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Ship-based observations provide data of seawater properties like temperature, salinity, nutrients, and various gases, but some important world oceans have still not been covered. A voyage in 2019/20 in the southwest Indian Ocean along approximately 55° E from 30° S to Antarctica attempted to fill one such data-sparse region. The measured cross section of the Antarctic Circumpolar Current and accompanying eddies demonstrates various oceanic behaviours including fronts and eddy mixing.
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.
Han Zhang, Dake Chen, Tongya Liu, Di Tian, Min He, Qi Li, Guofei Wei, and Jian Liu
Earth Syst. Sci. Data, 16, 5665–5679, https://doi.org/10.5194/essd-16-5665-2024, https://doi.org/10.5194/essd-16-5665-2024, 2024
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This paper provides a cross-shaped moored array dataset (MASCS 1.0) of observations that consist of five buoys and four moorings in the northern South China Sea from 2014 to 2015. The moored array is influenced by atmospheric forcings such as tropical cyclones and monsoon as well as oceanic tides and flows. The data reveal variations of the air–sea interface and the ocean itself, which are valuable for studies of air–sea interactions and ocean dynamics in the northern South China Sea.
Anyao Jiang, Xin Meng, Yan Huang, and Guitao Shi
The Cryosphere, 18, 5347–5364, https://doi.org/10.5194/tc-18-5347-2024, https://doi.org/10.5194/tc-18-5347-2024, 2024
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Landlocked lakes are crucial to the Antarctic ecosystem and sensitive to climate change. Limited research on their distribution prompted us to develop an automated detection process using deep learning and multi-source satellite imagery. This allowed us to accurately determine the landlocked lake open water (LLOW) area in Antarctica, generating high-resolution time series data. We find that the changes in positive and negative degree days predominantly drive variations in the LLOW area.
Litao Dai, Xingchen Liu, Lu An, and Rongxing Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 101–106, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-101-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-101-2024, 2024
Jian Liu, Jingjing Yu, Chuyong Lin, Min He, Haiyan Liu, Wei Wang, and Min Min
Earth Syst. Sci. Data, 16, 4949–4969, https://doi.org/10.5194/essd-16-4949-2024, https://doi.org/10.5194/essd-16-4949-2024, 2024
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The Japanese Himawari-8 and Himawari-9 (H8/9) geostationary (GEO) satellites are strategically positioned over the South China Sea (SCS), spanning from 3 November 2022 to the present. They mainly provide cloud mask, fraction, height, phase, optical, and microphysical property; layered precipitable water; and sea surface temperature products within a temporal resolution of 10 min and a gridded resolution of 0.05° × 0.05°.
Lu Zhou, Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Shiming Xu, Weixin Zhu, Sahra Kacimi, Stefanie Arndt, and Zifan Yang
The Cryosphere, 18, 4399–4434, https://doi.org/10.5194/tc-18-4399-2024, https://doi.org/10.5194/tc-18-4399-2024, 2024
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Snow over Antarctic sea ice, influenced by highly variable meteorological conditions and heavy snowfall, has a complex stratigraphy and profound impact on the microwave signature. We employ advanced radiation transfer models to analyse the effects of complex snow properties on brightness temperatures over the sea ice in the Southern Ocean. Great potential lies in the understanding of snow processes and the application to satellite retrievals.
Yi Zhou, Xianwei Wang, Ruibo Lei, Arttu Jutila, Donald K. Perovich, Luisa von Albedyll, Dmitry V. Divine, Yu Zhang, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2024-2821, https://doi.org/10.5194/egusphere-2024-2821, 2024
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This study examines how the bulk density of Arctic sea ice varies seasonally, a factor often overlooked in satellite measurements of sea ice thickness. From October to April, we found significant seasonal variations in sea ice bulk density at different spatial scales using direct observations as well as airborne and satellite data. New models were then developed to indirectly predict sea ice bulk density. This advance can improve our ability to monitor changes in Arctic sea ice.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
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In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev., 17, 6847–6866, https://doi.org/10.5194/gmd-17-6847-2024, https://doi.org/10.5194/gmd-17-6847-2024, 2024
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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.
Weixin Zhu, Siqi Liu, Shiming Xu, and Lu Zhou
Earth Syst. Sci. Data, 16, 2917–2940, https://doi.org/10.5194/essd-16-2917-2024, https://doi.org/10.5194/essd-16-2917-2024, 2024
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In the polar ocean, wind waves generate and propagate into the sea ice cover, forming marginal ice zones (MIZs). Using ESA's CryoSat-2, we construct a 12-year dataset of the MIZ in the Atlantic Arctic, a key region for climate change and human activities. The dataset is validated with high-resolution observations by ICESat2 and Sentinel-1. MIZs over 300 km wide are found under storms in the Barents Sea. The new dataset serves as the basis for research areas, including wave–ice interactions.
Xiaofeng Wang, Lu An, Peter L. Langen, and Rongxing Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-2024, 691–696, https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-691-2024, https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-691-2024, 2024
Yi Zhou, Xianwei Wang, Ruibo Lei, Luisa von Albedyll, Donald K. Perovich, Yu Zhang, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2024-1240, https://doi.org/10.5194/egusphere-2024-1240, 2024
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This study examines how the density of Arctic sea ice varies seasonally, a factor often overlooked in satellite measurements of sea ice thickness. From October to April, using direct observations and satellite data, we found that sea ice density decreases significantly until mid-January due to increased porosity as the ice ages, and then stabilizes until April. We then developed new models to estimate sea ice density. This advance can improve our ability to monitor changes in Arctic sea ice.
Yihao He, Xiayan Lin, Guoqing Han, Yu Liu, and Han Zhang
Ocean Sci., 20, 621–637, https://doi.org/10.5194/os-20-621-2024, https://doi.org/10.5194/os-20-621-2024, 2024
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Based on multiple datasets, we investigate the influence of two anticyclonic eddies on upper-ocean responses to Typhoon Kalmaegi in the northern South China Sea. This nuanced response is intricately linked to factors including the relative position of the eddies and the TCs, the eddies' intensity, and the background current. This work will further improve the accuracy of TC forecasts and enhance the simulation capabilities of air–sea coupled models.
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
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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.
Zhengyi Hu, Wei Jiang, Yuzhen Yan, Yan Huang, Xueyuan Tang, Lin Li, Florian Ritterbusch, Guo-Min Yang, Zheng-Tian Lu, and Guitao Shi
The Cryosphere, 18, 1647–1652, https://doi.org/10.5194/tc-18-1647-2024, https://doi.org/10.5194/tc-18-1647-2024, 2024
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The age of the surface blue ice in the Grove Mountains area is dated to be about 140 000 years, and one meteorite found here is 260 000 years old. It is inferred that the Grove Mountains blue-ice area holds considerable potential for paleoclimate studies.
Sheng Dong, Lei Fu, Xueyuan Tang, Zefeng Li, and Xiaofei Chen
The Cryosphere, 18, 1241–1257, https://doi.org/10.5194/tc-18-1241-2024, https://doi.org/10.5194/tc-18-1241-2024, 2024
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Subglacial lakes are a unique environment at the bottom of ice sheets, and they have distinct features in radar echo images that allow for visual detection. In this study, we use machine learning to analyze radar reflection waveforms and identify candidate subglacial lakes. Our approach detects more lakes than known inventories and can be used to expand the subglacial lake inventory. Additionally, this analysis may also provide insights into interpreting other subglacial conditions.
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.
Tong Zhang, William Colgan, Agnes Wansing, Anja Løkkegaard, Gunter Leguy, William H. Lipscomb, and Cunde Xiao
The Cryosphere, 18, 387–402, https://doi.org/10.5194/tc-18-387-2024, https://doi.org/10.5194/tc-18-387-2024, 2024
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The geothermal heat flux determines how much heat enters from beneath the ice sheet, and thus impacts the temperature and the flow of the ice sheet. In this study we investigate how much geothermal heat flux impacts the initialization of the Greenland ice sheet. We use the Community Ice Sheet Model with two different initialization methods. We find a non-trivial influence of the choice of heat flow boundary conditions on the ice sheet initializations for further designs of ice sheet modeling.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
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Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
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.
Kazuya Kusahara, Daisuke Hirano, Masakazu Fujii, Alexander D. Fraser, Takeshi Tamura, Kohei Mizobata, Guy D. Williams, and Shigeru Aoki
The Cryosphere, 18, 43–73, https://doi.org/10.5194/tc-18-43-2024, https://doi.org/10.5194/tc-18-43-2024, 2024
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This study focuses on the Totten and Moscow University ice shelves, East Antarctica. We used an ocean–sea ice–ice shelf model to better understand regional interactions between ocean, sea ice, and ice shelf. We found that a combination of warm ocean water and local sea ice production influences the regional ice shelf basal melting. Furthermore, the model reproduced the summertime undercurrent on the upper continental slope, regulating ocean heat transport onto the continental shelf.
Vigan Mensah, Koji Fujita, Stephen Howell, Miho Ikeda, Mizuki Komatsu, and Kay I. Ohshima
EGUsphere, https://doi.org/10.5194/egusphere-2023-2492, https://doi.org/10.5194/egusphere-2023-2492, 2023
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We estimated the volume of freshwater released by sea ice, glaciers, rivers, and precipitation into Baffin Bay and the Labrador Sea, and their changes over the past 70 years. We found that the freshwater volume has risen in Baffin Bay due to increased glacier melting, and dropped in the Labrador Sea because of the decline in sea ice production. We also infer that freshwater from the Arctic Ocean has been exported to our study region for the past 30 years, possibly as a result of global warming.
Andrew C. Ross, Charles A. Stock, Alistair Adcroft, Enrique Curchitser, Robert Hallberg, Matthew J. Harrison, Katherine Hedstrom, Niki Zadeh, Michael Alexander, Wenhao Chen, Elizabeth J. Drenkard, Hubert du Pontavice, Raphael Dussin, Fabian Gomez, Jasmin G. John, Dujuan Kang, Diane Lavoie, Laure Resplandy, Alizée Roobaert, Vincent Saba, Sang-Ik Shin, Samantha Siedlecki, and James Simkins
Geosci. Model Dev., 16, 6943–6985, https://doi.org/10.5194/gmd-16-6943-2023, https://doi.org/10.5194/gmd-16-6943-2023, 2023
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We evaluate a model for northwest Atlantic Ocean dynamics and biogeochemistry that balances high resolution with computational economy by building on the new regional features in the MOM6 ocean model and COBALT biogeochemical model. We test the model's ability to simulate impactful historical variability and find that the model simulates the mean state and variability of most features well, which suggests the model can provide information to inform living-marine-resource applications.
Qiyan Ji, Lei Han, Lifang Jiang, Yuting Zhang, Minghong Xie, and Yu Liu
Ocean Sci., 19, 1561–1578, https://doi.org/10.5194/os-19-1561-2023, https://doi.org/10.5194/os-19-1561-2023, 2023
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Accurate wave forecasts are essential to marine engineering safety. The research designs a model with combined signal decomposition and multiple neural network algorithms to predict wave parameters. The hybrid wave prediction model has good robustness and generalization ability. The contribution of the various algorithms to the model prediction skill was analyzed by the ablation experiments. This work provides a neoteric view of marine element forecasting based on artificial intelligence.
Fanyi Zhang, Ruibo Lei, Mengxi Zhai, Xiaoping Pang, and Na Li
The Cryosphere, 17, 4609–4628, https://doi.org/10.5194/tc-17-4609-2023, https://doi.org/10.5194/tc-17-4609-2023, 2023
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Atmospheric circulation anomalies lead to high Arctic sea ice outflow in winter 2020, causing heavy ice conditions in the Barents–Greenland seas, subsequently impeding the sea surface temperature warming. This suggests that the winter–spring Arctic sea ice outflow can be considered a predictor of changes in sea ice and other marine environmental conditions in the Barents–Greenland seas, which could help to improve our understanding of the physical connections between them.
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.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
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To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Shijie Peng, Qinghua Yang, Matthew D. Shupe, Xingya Xi, Bo Han, Dake Chen, Sandro Dahlke, and Changwei Liu
Atmos. Chem. Phys., 23, 8683–8703, https://doi.org/10.5194/acp-23-8683-2023, https://doi.org/10.5194/acp-23-8683-2023, 2023
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Due to a lack of observations, the structure of the Arctic atmospheric boundary layer (ABL) remains to be further explored. By analyzing a year-round radiosonde dataset collected over the Arctic sea-ice surface, we found the annual cycle of the ABL height (ABLH) is primarily controlled by the evolution of ABL thermal structure, and the surface conditions also show a high correlation with ABLH variation. In addition, the Arctic ABLH is found to be decreased in summer compared with 20 years ago.
Ying Chen, Ruibo Lei, Xi Zhao, Shengli Wu, Yue Liu, Pei Fan, Qing Ji, Peng Zhang, and Xiaoping Pang
Earth Syst. Sci. Data, 15, 3223–3242, https://doi.org/10.5194/essd-15-3223-2023, https://doi.org/10.5194/essd-15-3223-2023, 2023
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The sea ice concentration product derived from the Microwave Radiation Image sensors on board the FengYun-3 satellites can reasonably and independently identify the seasonal and long-term changes of sea ice, as well as extreme cases of annual maximum and minimum sea ice extent in polar regions. It is comparable with other sea ice concentration products and applied to the studies of climate and marine environment.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
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.
Qi Shu, Qiang Wang, Chuncheng Guo, Zhenya Song, Shizhu Wang, Yan He, and Fangli Qiao
Geosci. Model Dev., 16, 2539–2563, https://doi.org/10.5194/gmd-16-2539-2023, https://doi.org/10.5194/gmd-16-2539-2023, 2023
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Ocean models are often used for scientific studies on the Arctic Ocean. Here the Arctic Ocean simulations by state-of-the-art global ocean–sea-ice models participating in the Ocean Model Intercomparison Project (OMIP) were evaluated. The simulations on Arctic Ocean hydrography, freshwater content, stratification, sea surface height, and gateway transports were assessed and the common biases were detected. The simulations forced by different atmospheric forcing were also evaluated.
Tongya Liu and Ryan Abernathey
Earth Syst. Sci. Data, 15, 1765–1778, https://doi.org/10.5194/essd-15-1765-2023, https://doi.org/10.5194/essd-15-1765-2023, 2023
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Nearly all existing datasets of mesoscale eddies are based on the Eulerian method because of its operational simplicity. Using satellite observations and a Lagrangian method, we present a global Lagrangian eddy dataset (GLED v1.0). We conduct the statistical comparison between two types of eddies and the dataset validation. Our dataset offers relief from dilemma that the Eulerian eddy dataset is nearly the only option for studying mesoscale eddies.
Bin Xiao, Fangli Qiao, Qi Shu, Xunqiang Yin, Guansuo Wang, and Shihong Wang
Geosci. Model Dev., 16, 1755–1777, https://doi.org/10.5194/gmd-16-1755-2023, https://doi.org/10.5194/gmd-16-1755-2023, 2023
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A new global surface-wave–tide–circulation coupled ocean model (FIO-COM32) with a resolution of 1/32° × 1/32° is developed and validated. Both the promotion of the horizontal resolution and included physical processes are shown to be important contributors to the significant improvements in FIO-COM32 simulations. It is time to merge these separated model components (surface waves, tidal currents and ocean circulation) and start a new generation of ocean model development.
Yi Zhou, Yu Zhang, Changsheng Chen, Lele Li, Danya Xu, Robert C. Beardsley, and Weizeng Shao
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-40, https://doi.org/10.5194/tc-2023-40, 2023
Revised manuscript not accepted
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This study used an improved retracking algorithm, considered the corrected radar penetration rates, and included the new snow depth from the Feng Yun-3B satellite to enhance the accuracy of Arctic sea ice thickness derived from the CryoSat-2 satellite. This comprehensive optimization was the first to improve the sea ice thickness retrieval. Compared with the sea ice product derived by the Alfred Wegener Institute, the optimization cases could successfully reduce the errors above 20 %.
Guokun Lyu, Armin Koehl, Xinrong Wu, Meng Zhou, and Detlef Stammer
Ocean Sci., 19, 305–319, https://doi.org/10.5194/os-19-305-2023, https://doi.org/10.5194/os-19-305-2023, 2023
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Data assimilation techniques are important for combining observations with numerical models. Here, we approximate the adjoint of viscous-plastic dynamics (adjoint-VP) to replace the adjoint of free-drift dynamics (adjoint-FD) for developing an advanced Arctic Ocean and sea ice modeling and adjoint-based assimilation system. We find that adjoint-VP provides a better ocean and sea ice estimation than adjoint-FD, considering the residual errors and adjustments of the atmospheric states.
Xiaoqiao Wang, Zhaoru Zhang, Michael S. Dinniman, Petteri Uotila, Xichen Li, and Meng Zhou
The Cryosphere, 17, 1107–1126, https://doi.org/10.5194/tc-17-1107-2023, https://doi.org/10.5194/tc-17-1107-2023, 2023
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The bottom water of the global ocean originates from high-salinity water formed in polynyas in the Southern Ocean where sea ice coverage is low. This study reveals the impacts of cyclones on sea ice and water mass formation in the Ross Ice Shelf Polynya using numerical simulations. Sea ice production is rapidly increased caused by enhancement in offshore wind, promoting high-salinity water formation in the polynya. Cyclones also modulate the transport of this water mass by wind-driven currents.
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.
Yan Zhang, Xuantong Wang, Yuhao Sun, Chenhui Ning, Shiming Xu, Hengbin An, Dehong Tang, Hong Guo, Hao Yang, Ye Pu, Bo Jiang, and Bin Wang
Geosci. Model Dev., 16, 679–704, https://doi.org/10.5194/gmd-16-679-2023, https://doi.org/10.5194/gmd-16-679-2023, 2023
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We construct a new ocean model, OMARE, that can carry out multi-scale ocean simulation with adaptive mesh refinement. OMARE is based on the refactorization of NEMO with a third-party, high-performance piece of middleware. We report the porting process and experiments of an idealized western-boundary current system. The new model simulates turbulent and temporally varying mesoscale and submesoscale processes via adaptive refinement. Related topics and future work with OMARE are also discussed.
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.
Long Lin, Ruibo Lei, Mario Hoppmann, Donald K. Perovich, and Hailun He
The Cryosphere, 16, 4779–4796, https://doi.org/10.5194/tc-16-4779-2022, https://doi.org/10.5194/tc-16-4779-2022, 2022
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Ice mass balance observations indicated that average basal melt onset was comparable in the central Arctic Ocean and approximately 17 d earlier than surface melt in the Beaufort Gyre. The average onset of basal growth lagged behind the surface of the pan-Arctic Ocean for almost 3 months. In the Beaufort Gyre, both drifting-buoy observations and fixed-point observations exhibit a trend towards earlier basal melt onset, which can be ascribed to the earlier warming of the surface ocean.
Jinfei Wang, Chao Min, Robert Ricker, Qian Shi, Bo Han, Stefan Hendricks, Renhao Wu, and Qinghua Yang
The Cryosphere, 16, 4473–4490, https://doi.org/10.5194/tc-16-4473-2022, https://doi.org/10.5194/tc-16-4473-2022, 2022
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The differences between Envisat and ICESat sea ice thickness (SIT) reveal significant temporal and spatial variations. Our findings suggest that both overestimation of Envisat sea ice freeboard, potentially caused by radar backscatter originating from inside the snow layer, and the AMSR-E snow depth biases and sea ice density uncertainties can possibly account for the differences between Envisat and ICESat SIT.
Sergey Kravtsov, Ilijana Mastilovic, Andrew McC. Hogg, William K. Dewar, and Jeffrey R. Blundell
Geosci. Model Dev., 15, 7449–7469, https://doi.org/10.5194/gmd-15-7449-2022, https://doi.org/10.5194/gmd-15-7449-2022, 2022
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Climate is a complex system whose behavior is shaped by multitudes of processes operating on widely different spatial scales and timescales. In hierarchical modeling, one goes back and forth between highly idealized process models and state-of-the-art models coupling the entire range of climate subsystems to identify specific phenomena and understand their dynamics. The present contribution highlights an intermediate climate model focussing on midlatitude ocean–atmosphere interactions.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, https://doi.org/10.5194/gmd-15-6399-2022, 2022
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We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Madelaine Rosevear, Benjamin Galton-Fenzi, and Craig Stevens
Ocean Sci., 18, 1109–1130, https://doi.org/10.5194/os-18-1109-2022, https://doi.org/10.5194/os-18-1109-2022, 2022
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Understanding ocean-driven melting of Antarctic ice shelves is critical for predicting future sea level. However, ocean observations from beneath ice shelves are scarce. Here, we present unique ocean and melting data from the Amery Ice Shelf, East Antarctica. We use our observations to evaluate common methods of representing melting in ocean–climate models (melting
parameterisations) and show that these parameterisations overestimate melting when the ocean is warm and/or currents are weak.
Yuejin Ye, Zhenya Song, Shengchang Zhou, Yao Liu, Qi Shu, Bingzhuo Wang, Weiguo Liu, Fangli Qiao, and Lanning Wang
Geosci. Model Dev., 15, 5739–5756, https://doi.org/10.5194/gmd-15-5739-2022, https://doi.org/10.5194/gmd-15-5739-2022, 2022
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The swNEMO_v4.0 is developed with ultrahigh scalability through the concepts of hardware–software co-design based on the characteristics of the new Sunway supercomputer and NEMO4. Three breakthroughs, including an adaptive four-level parallelization design, many-core optimization and mixed-precision optimization, are designed. The simulations achieve 71.48 %, 83.40 % and 99.29 % parallel efficiency with resolutions of 2 km, 1 km and 500 m using 27 988 480 cores, respectively.
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.
A. Zhao, Y. Cheng, D. Lv, M. Xia, R. Li, L. An, S. Liu, and Y. Tian
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 805–811, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-805-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-805-2022, 2022
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.
Sutao Liao, Hao Luo, Jinfei Wang, Qian Shi, Jinlun Zhang, and Qinghua Yang
The Cryosphere, 16, 1807–1819, https://doi.org/10.5194/tc-16-1807-2022, https://doi.org/10.5194/tc-16-1807-2022, 2022
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The Global Ice-Ocean Modeling and Assimilation System (GIOMAS) can basically reproduce the observed variability in Antarctic sea-ice volume and its changes in the trend before and after 2013, and it underestimates Antarctic sea-ice thickness (SIT) especially in deformed ice zones. Assimilating additional sea-ice observations with advanced assimilation methods may result in a more accurate estimation of Antarctic SIT.
Yunhe Wang, Xiaojun Yuan, Haibo Bi, Mitchell Bushuk, Yu Liang, Cuihua Li, and Haijun Huang
The Cryosphere, 16, 1141–1156, https://doi.org/10.5194/tc-16-1141-2022, https://doi.org/10.5194/tc-16-1141-2022, 2022
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We develop a regional linear Markov model consisting of four modules with seasonally dependent variables in the Pacific sector. The model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the earlier pan-Arctic model.
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.
Brandon J. Bethel, Wenjin Sun, Changming Dong, and Dongxia Wang
Ocean Sci., 18, 419–436, https://doi.org/10.5194/os-18-419-2022, https://doi.org/10.5194/os-18-419-2022, 2022
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Ocean surface waves forced by tropical cyclones can cause tremendous damage to offshore structures and coastal communities. As such, forecasting their evolution is of utmost importance. This study investigates the usage of a long short-term memory neural network to forecast hurricane-forced waves in the Caribbean Sea. Results strongly suggest that forecasts can be performed to a high degree of accuracy up to 12 h at minimal computational expense and even outperform a wave model.
Bin Xiao, Fangli Qiao, Qi Shu, Xunqiang Yin, Guansuo Wang, and Shihong Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-52, https://doi.org/10.5194/gmd-2022-52, 2022
Revised manuscript not accepted
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A new global surface wave-tide-circulation coupled ocean model FIO-COM32 with resolution of 1/32° × 1/32° is developed and validated. Both the promotion of the horizontal resolution and included physical processes are proved to be important contributors to the significant improvements of FIO-COM32 simulations. It should be the time to merge these separated model components (surface wave, tidal current and ocean circulation) for new generation ocean model development.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev., 15, 1155–1176, https://doi.org/10.5194/gmd-15-1155-2022, https://doi.org/10.5194/gmd-15-1155-2022, 2022
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We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
Guokun Lyu, Nuno Serra, Meng Zhou, and Detlef Stammer
Ocean Sci., 18, 51–66, https://doi.org/10.5194/os-18-51-2022, https://doi.org/10.5194/os-18-51-2022, 2022
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This study explores the Arctic sea level variability depending on different timescales and the relation to temperature, salinity and mass changes, identifying key parameters and regions that need to be observed coordinately. The decadal sea level variability reflects salinity changes. But it can only reflect salinity change at periods of greater than 1 year, highlighting the requirement for enhancing in situ hydrographic observations and complicated interpolation methods.
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.
Lin Li, Aiguo Zhao, Tiantian Feng, Xiangbin Cui, Lu An, Ben Xu, Shinan Lang, Liwen Jing, Tong Hao, Jingxue Guo, Bo Sun, and Rongxing Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-332, https://doi.org/10.5194/tc-2021-332, 2021
Preprint withdrawn
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No subglacial lakes have been reported in Princess Elizabeth Land (PEL), East Antarctica. In this study, thanks to a new suite of airborne geophysical observations in PEL, including RES and gravity data collected during the Chinese National Antarctic Research Expedition, we detected a large subglacial lake of ~45 km in length, ~11 km in width, and ~250 m in depth. These findings will help us understand ice sheet stability in the PEL region.
Hakase Hayashida, Meibing Jin, Nadja S. Steiner, Neil C. Swart, Eiji Watanabe, Russell Fiedler, Andrew McC. Hogg, Andrew E. Kiss, Richard J. Matear, and Peter G. Strutton
Geosci. Model Dev., 14, 6847–6861, https://doi.org/10.5194/gmd-14-6847-2021, https://doi.org/10.5194/gmd-14-6847-2021, 2021
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Ice algae are tiny plants like phytoplankton but they grow within sea ice. In polar regions, both phytoplankton and ice algae are the foundation of marine ecosystems and play an important role in taking up carbon dioxide in the atmosphere. However, state-of-the-art climate models typically do not include ice algae, and therefore their role in the climate system remains unclear. This project aims to address this knowledge gap by coordinating a set of experiments using sea-ice–ocean models.
Alexander D. Fraser, Robert A. Massom, Mark S. Handcock, Phillip Reid, Kay I. Ohshima, Marilyn N. Raphael, Jessica Cartwright, Andrew R. Klekociuk, Zhaohui Wang, and Richard Porter-Smith
The Cryosphere, 15, 5061–5077, https://doi.org/10.5194/tc-15-5061-2021, https://doi.org/10.5194/tc-15-5061-2021, 2021
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Landfast ice is sea ice that remains stationary by attaching to Antarctica's coastline and grounded icebergs. Although a variable feature, landfast ice exerts influence on key coastal processes involving pack ice, the ice sheet, ocean, and atmosphere and is of ecological importance. We present a first analysis of change in landfast ice over an 18-year period and quantify trends (−0.19 ± 0.18 % yr−1). This analysis forms a reference of landfast-ice extent and variability for use in other studies.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725, https://doi.org/10.5194/tc-15-4703-2021, https://doi.org/10.5194/tc-15-4703-2021, 2021
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Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
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.
Minghu Ding, Tong Zhang, Diyi Yang, Ian Allison, Tingfeng Dou, and Cunde Xiao
The Cryosphere, 15, 4201–4206, https://doi.org/10.5194/tc-15-4201-2021, https://doi.org/10.5194/tc-15-4201-2021, 2021
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Measurement of snow heat conductivity is essential to establish the energy balance between the atmosphere and firn, but it is still not clear in Antarctica. Here, we used data from three automatic weather stations located in different types of climate and evaluated nine schemes that were used to calculate the effective heat diffusivity of snow. The best solution was proposed. However, no conductivity–density relationship was optimal at all sites, and the performance of each varied with depth.
Linlu Mei, Vladimir Rozanov, Evelyn Jäkel, Xiao Cheng, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2781–2802, https://doi.org/10.5194/tc-15-2781-2021, https://doi.org/10.5194/tc-15-2781-2021, 2021
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This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 2 of two companion papers and shows the results and validation. The paper performs the new retrieval algorithm on the Sea and Land
Surface Temperature Radiometer (SLSTR) instrument and compares the retrieved snow properties with ground-based measurements, aircraft measurements and other satellite products.
Tongwen Wu, Rucong Yu, Yixiong Lu, Weihua Jie, Yongjie Fang, Jie Zhang, Li Zhang, Xiaoge Xin, Laurent Li, Zaizhi Wang, Yiming Liu, Fang Zhang, Fanghua Wu, Min Chu, Jianglong Li, Weiping Li, Yanwu Zhang, Xueli Shi, Wenyan Zhou, Junchen Yao, Xiangwen Liu, He Zhao, Jinghui Yan, Min Wei, Wei Xue, Anning Huang, Yaocun Zhang, Yu Zhang, Qi Shu, and Aixue Hu
Geosci. Model Dev., 14, 2977–3006, https://doi.org/10.5194/gmd-14-2977-2021, https://doi.org/10.5194/gmd-14-2977-2021, 2021
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This paper presents the high-resolution version of the Beijing Climate Center (BCC) Climate System Model, BCC-CSM2-HR, and describes its climate simulation performance including the atmospheric temperature and wind; precipitation; and the tropical climate phenomena such as TC, MJO, QBO, and ENSO. BCC-CSM2-HR is our model version contributing to the HighResMIP. We focused on its updates and differential characteristics from its predecessor, the medium-resolution version BCC-CSM2-MR.
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Short summary
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.
We developed a high-resolution computer model to simulate how the ocean, sea ice, and ice...
