Aimdyn, Inc., Santa Barbara, CA, 93101, USA.
University of California, Santa Barbara, USA.
Sci Rep. 2020 Oct 1;10(1):16313. doi: 10.1038/s41598-020-73211-z.
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.
北极和南极的海冰覆盖是气候变化的一个重要指标，对环境、经济和安全都有重要影响。海冰时空动态的复杂性使得很难评估变化的时间性质——例如线性或指数——以及它们的精确地理位置。在这项研究中，我们应用了 Koopman 模态分解（KMD）对南北半球的海冰浓度卫星数据进行分析，以深入了解海冰行为的时空动态，并预测未来的海冰行为。我们观察到与北极和南极海冰浓度的均值和年变化相对应的空间模态，并观察到从早期到后期海冰浓度均值的下降，以及海冰浓度年变化显著的地点的相应转移。我们在两个半球都发现了指数衰减的空间模态，并讨论了它们的精确空间范围，还对未来的海冰浓度进行了预测。基于 Koopman 算子的数据驱动分解技术深入了解了传统方法中不明显的海冰浓度的时空动态。
