EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Ice Lead Network Analysis

Julia Kaltenborn1,2, Venkatesh Ramesh1,3, and Thomas Wright1,2
Julia Kaltenborn et al.
  • 1Mila, Montreal, Canada
  • 2School of Computer Science, McGill University, Montreal, Canada
  • 3DIRO, Université de Montréal, Montreal, Canada

Ice lead analysis is an essential task for evaluating climate change processes in the Arctic. Ice leads are narrow cracks in the sea-ice, which build a complex network. While detecting and modeling ice leads has been performed in numerous ways based on airborne images, the dynamics of ice leads over time remain hidden and largely unexplored. These dynamics could be analyzed by interpreting the ice leads as more than just airborne images, but as what they really are: a dynamic network. The lead’s start, end, and intersection points can be considered nodes, and the leads themselves as edges of a network. As the nodes and edges change over time, the ice lead network is constantly evolving. This new network perspective on ice leads could be of great interest for the cryospheric science community since it opens the door to new methods. For example, adapting common link prediction methods might make data-driven ice lead forecasting and tracking feasible.
To reveal the hidden dynamics of ice leads, we performed a spatio-temporal and network analysis of ice lead networks. The networks used and presented here are based on daily ice lead observations from Moderate Resolution Imaging Spectroradiometer (MODIS) between 2002 and 2020 by Hoffman et al. [1].
The spatio-temporal analysis of the ice leads exhibits seasonal, annual, and overall trends in the ice lead dynamics. We found that the number of ice leads is decreasing, and the number of width and length outliers is increasing overall. The network analysis of the ice lead graphs reveals unique network characteristics that diverge from those present in common real-world networks. Most notably, current network science methods (1) exploit the information that is embedded into the connections of the network, e.g., in connection clusters, while (2) nodes remain relatively fixed over time. Ice lead networks, however, (1) embed their relevant information spatially, e.g., in spatial clusters, and (2) shift and change drastically. These differences require improvements and modifications on common graph classification and link prediction methods such as Preferential Attachment and EvolveGCN on the domain of ice lead dynamic networks.
This work is a call for extending existing network analysis toolkits to include a new class of real-world dynamic networks. Utilizing network science techniques will hopefully further our understanding of ice leads and thus of Arctic processes that are key to climate change mitigation and adaptation.


We would like to thank Prof. Gunnar Spreen, who provided us insights into ice lead detection and possible challenges connected to the project idea. Furthermore, we would like to thank Shenyang Huang and Asst. Prof. David Rolnick for their valuable feedback and support. J.K. was supported in part by the DeepMind scholarship, the Mitacs Globalink Graduate Fellowship, and the German Academic Scholarship Foundation.


[1] Jay P Hoffman, Steven A Ackerman, Yinghui Liu, and Jeffrey R Key. 2019. The detection and characterization of Arctic sea ice leads with satellite imagers. Remote Sensing 11, 5 (2019), 521.

How to cite: Kaltenborn, J., Ramesh, V., and Wright, T.: Ice Lead Network Analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8945,, 2022.


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