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Conference Paper

Quantifying the Antarctic marginal ice zone with unsupervised statistical methods

Authors

Day,  Noah
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Bennetts,  Luke
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

O'Farrell,  Siobhan
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Alberello,  Alberto
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Day, N., Bennetts, L., O'Farrell, S., Alberello, A. (2023): Quantifying the Antarctic marginal ice zone with unsupervised statistical methods, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-2410


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018347
Abstract
This work presents a new method for categorising Antarctic sea ice regions to measure the width of the marginal ice zone (MIZ). The Antarctic MIZ is generally described as the area of sea ice affected by ocean surface waves and acts as an interface between the open Southern Ocean and the consolidated inner pack. Standalone CICE6 (with atmospheric, oceanic and wave forcing) was used to create a dataset consisting of variables describing the ice cover (sea ice concentration, age, thickness, etc.) as well as dynamic and thermodynamic processes. An unsupervised statistical method identified distinct sea ice regions (including the MIZ) and quantified the respective dominant physical processes. The impact of wave attenuation on the MIZ extent was measured and validated with recent altimetry observations. Floe size was found to enhance our MIZ classification to include high concentration pancake fields, which form over winter in the presence of waves. These results support the inclusion of floe size within sea ice modelling, and the importance of multivariate approaches to describe sea ice.