Session Details

[S5-S6-O]S5:Approaches to Sea Ice and Climate Predictions, Process Studies for Advancing Understanding and Forecasting, and Their Utilization for Stakeholders / S6:Sea ice research and machine learning: Opportunities to fill knowledge gaps

Thu. Oct 30, 2025 9:30 AM - 11:00 AM JST
Thu. Oct 30, 2025 12:30 AM - 2:00 AM UTC
Room 1
Chair: Gaelle Veyssiere (British Antarctic Survey)
S5 : This session addresses the critical role of sea ice in the Earth's climate system, highlighting recent Arctic and Antarctic trends observed via satellite. It seeks contributions focused on enhancing sea ice prediction through integrated approaches, including observations, modeling, and theoretical studies. Emphasis is placed on improving forecast accuracy and exploring practical applications in marine ecosystems, socio-economic planning, and Arctic shipping. The session also welcomes interdisciplinary insights from the humanities and social sciences to tackle the complex implications of sea ice change.
S6 : Sea ice is a vital component of Earth's climate system, and improving its monitoring and modeling is essential for understanding environmental change in polar regions and informing global mitigation and adaptation strategies. Despite recent advances, significant observational and modeling gaps remain. This session highlights the growing role of Machine Learning (ML) in enhancing sea ice research by improving predictive accuracy, addressing data voids, and correcting model biases across various climate scenarios. It will showcase interdisciplinary approaches-including remote sensing, in-situ measurements, and physical modeling-that integrate ML techniques to better forecast sea ice dynamics. Bringing together experts in sea ice physics, biology, biogeochemistry, remote sensing, and data science, the session aims to foster cross-disciplinary collaboration, spotlight innovative methodologies, and outline challenges and opportunities in this fast-evolving field.

Explanation of purpose

[S5-S6-O-01]Monitoring Beaufort Sea Ice Drift During the Freeze Season Using FY-3D Thermal Infrared and FY-3E Low-Light Imagery

*Xue Wang1,2,3, Ran Lu1, Xingle Pan1, Yuwei Hou1, Meng Yang1 (1. Sun Yat-sen University (China), 2. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (China), 3. Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education (China))

[S5-S6-O-02]Retrieving Arctic snow depth with machine learning and multi-source remote sensing data

*Mengmeng Li1 (1. Zhengzhou University (China))

[S5-S6-O-03]Use of L-band Radiometry for Sea Ice Thickness Estimation: From Field Campaigns to Satellite Evaluation

*Carolina Gabarró1, Ferran Hernández-Macià1, Marcus Huntemann2, Gunnar Spreen2, Randy K. Scharien3, Pedro Elosegui1, Eva De Andres1 (1. Institute of Marine Science, ICM-CSIC (Spain), 2. Institute of Environmental Physics, University of Bremen (Germany), 3. University of Victoria (Canada))

[S5-S6-O-04]Melting processes of the marginal ice zone inferred from floe size distributions measured with a drone in the southern Sea of Okhotsk

*Takenobu Toyota1, Yuriko Arihara1, Takuji Waseda2, Masato Ito3, Jun Nishioka1 (1. Hokkaido University (Japan), 2. The University of Tokyo (Japan), 3. National Institute of Polar Research (Japan))

[S5-S6-O-05]Spring sea ice thickness as a predictor of summer sea ice distribution in the Arctic Ocean

*Noriaki Kimura1, Hiroyasu Hasumi1 (1. The University of Tokyo (Japan))

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