Exponentially decaying modes and long-term prediction of sea ice concentration using Koopman mode decomposition

Abstract Sea ice cover in the Arctic and Antarctic is an important indicator of changes in the climate, with important environmental, economic and security consequences. The complexity of the spatio-temporal dynamics of sea ice makes it difficult to assess the temporal nature of the changes—e.g. lin...

Full description

Bibliographic Details
Published in:Scientific Reports
Main Authors: Hogg, James, Fonoberova, Maria, Mezić, Igor
Other Authors: United States Department of Defense | United States Navy | Office of Naval Research
Format: Article in Journal/Newspaper
Language:English
Published: Springer Science and Business Media LLC 2020
Subjects:
Online Access:http://dx.doi.org/10.1038/s41598-020-73211-z
https://www.nature.com/articles/s41598-020-73211-z.pdf
https://www.nature.com/articles/s41598-020-73211-z
Description
Summary:Abstract Sea ice cover in the Arctic and Antarctic is an important indicator of changes in the climate, with important environmental, economic and security consequences. The complexity of the spatio-temporal dynamics of sea ice makes it difficult to assess the temporal nature of the changes—e.g. linear or exponential—and their precise geographical loci. In this study, Koopman Mode Decomposition (KMD) is applied to satellite data of sea ice concentration for the Northern and Southern hemispheres to gain insight into the temporal and spatial dynamics of the sea ice behavior and to predict future sea ice behavior. We observe spatial modes corresponding to the mean and annual variation of Arctic and Antarctic sea ice concentration and observe decreases in the mean sea ice concentration from early to later periods, as well as corresponding shifts in the locations that undergo significant annual variation in sea ice concentration. We discover exponentially decaying spatial modes in both hemispheres and discuss their precise spatial extent, and also perform predictions of future sea ice concentration. The Koopman operator-based, data-driven decomposition technique gives insight into spatial and temporal dynamics of sea ice concentration not apparent in traditional approaches.