Articles | Volume 19, issue 12
https://doi.org/10.5194/tc-19-6381-2025
https://doi.org/10.5194/tc-19-6381-2025
Research article
 | 
01 Dec 2025
Research article |  | 01 Dec 2025

Extended seasonal prediction of Antarctic sea ice concentration using ANTSIC-UNet

Ziying Yang, Jiping Liu, Mirong Song, Yongyun Hu, Qinghua Yang, Ke Fan, Rune Grand Graversen, and Lu Zhou

Related authors

MErSiM v1.0: resolving biases in global silicate weathering model with a data-driven surface erosion module
Jiaxi Zhao, Yonggang Liu, and Yongyun Hu
Geosci. Model Dev., 19, 6857–6878, https://doi.org/10.5194/gmd-19-6857-2026,https://doi.org/10.5194/gmd-19-6857-2026, 2026
Short summary
Transient simulation of Holocene climatic and isotopic variations across global monsoon regions
Xiaoxu Shi, Xiya Liu, Jiping Liu, Martin Werner, Hu Yang, Francisco W. Cruz, Chaoyuan Yang, and Gerrit Lohmann
EGUsphere, https://doi.org/10.5194/egusphere-2026-3894,https://doi.org/10.5194/egusphere-2026-3894, 2026
This preprint is open for discussion and under review for Climate of the Past (CP).
Short summary
Dust emission, loading, and deposition throughout the Phanerozoic simulated by CESM1.2 coupled with BIOME4
Qi Cui, Qifan Lin, Jiaqi Guo, Yonggang Liu, Yue Liu, Xiujuan Bao, Xiang Li, Zhibo Li, Haoyue Zuo, Shuai Yuan, Yihui Chen, Shiyan Zhang, Jian Zhang, and Yongyun Hu
EGUsphere, https://doi.org/10.5194/egusphere-2026-3925,https://doi.org/10.5194/egusphere-2026-3925, 2026
This preprint is open for discussion and under review for Climate of the Past (CP).
Short summary
Effects of snow redistribution parameterization on simulated snow thickness validated by MOSAiC observations
Fengguan Gu, Changwei Liu, Bo Han, Qinghua Yang, and Jiping Liu
EGUsphere, https://doi.org/10.5194/egusphere-2026-2179,https://doi.org/10.5194/egusphere-2026-2179, 2026
Short summary
Snow depth distributions on sea ice of different ages and thicknesses from regional field campaigns
Lanqing Huang, Julienne Stroeve, Thomas Newman, Robbie Mallett, Rosemary Willatt, Lu Zhou, Malin Johansson, Carmen Nab, and Alicia Fallows
The Cryosphere, 20, 2825–2849, https://doi.org/10.5194/tc-20-2825-2026,https://doi.org/10.5194/tc-20-2825-2026, 2026
Short summary

Cited articles

Abernathey, R. P., Cerovecki, I., Holland, P. R., Newsom, E., Mazloff, M., and Talley, L. D.: Water-mass transformation by sea ice in the upper branch of the Southern Ocean overturning, Nat. Geosci., 9, 596–601, https://doi.org/10.1038/ngeo2749, 2016. 
Bianco, E., Iovino, D., Masina, S., Materia, S., and Ruggieri, P.: The role of upper-ocean heat content in the regional variability of Arctic sea ice at sub-seasonal timescales, The Cryosphere, 18, 2357–2379, https://doi.org/10.5194/tc-18-2357-2024, 2024. 
Bintanja, R., van Oldenborgh, G. J., Drijfhout, S. S., Wouters, B., and Katsman, C. A.: Important role for ocean warming and increased ice-shelf melt in Antarctic sea-ice expansion, Nat. Geosci., 6, 376–379, https://doi.org/10.1038/ngeo1767, 2013. 
Bourassa, M. A., Gille, S. T., Bitz, C., Carlson, D., Cerovecki, I., Clayson, C. A., Cronin, M. F., Drennan, W. M., Fairall, C. W., Hoffman, R. N., Magnusdottir, G., Pinker, R. T., Renfrew, I. A., Serreze, M., Speer, K., Talley, L. D., and Wick, G. A.: High-Latitude Ocean and Sea Ice Surface Fluxes: Challenges for Climate Research, Bulletin of the American Meteorological Society, 94, 403–423, https://doi.org/10.1175/BAMS-D-11-00244.1, 2013. 
Download
Short summary
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.
Share