Articles | Volume 33, issue 3
https://doi.org/10.5194/npg-33-455-2026
https://doi.org/10.5194/npg-33-455-2026
Research article
 | 
27 Aug 2026
Research article |  | 27 Aug 2026

Covariations between persistent synoptic features and record low Antarctic sea ice events via unsupervised regression learning

Andrew R. Axelsen, Terence J. O'Kane, Courtney R. Quinn, and Andrew P. Bassom

Model code and software

SH-DynamicsNotebooks (Version v1.0.1) A. R. Axelsen https://doi.org/10.5281/zenodo.16340913

CourtneyQuinn/FEM-BV-VAR_dynamics: v0.1.0 C. Quinn https://doi.org/10.5281/zenodo.4035644

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Short summary
Recent increases in the variability of Antarctic sea ice have elicited much interest and research on these changes. Here, we examine observations taken from three specific years (2016, 2021, and 2023) which either contain or precede a period of anomalously low sea ice extent. To understand the combined influence of weather systems, surface temperatures, and atmospheric pressure on sea ice formation and decay, we apply novel methods from machine learning and dynamical systems.
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