Geophysical systems are often assumed to follow Gaussian probability density functions; however, deviations from Gaussian behaviour can shed light on the underlying dynamics. For the large-scale motion of the Arctic sea ice, such deviations have been interpreted as signatures of structure in dynamic flow fields. In this study, we use higher-order moments (skewness and kurtosis) to identify spatiotemporal changes in the Beaufort Gyre (BG) and the Transpolar Drift (TPD) sea-ice drift patterns. Higher-order moments of satellite-derived ice drift speeds are examined over the winter period of 2006–2017 to describe the persistence of features like the BG and TPD, and their variation over time. Index patterns indicate that the periphery of the BG can be identified by a combination of high positive skewness and high kurtosis in the ice drift time series on an annual basis.

中文翻译：

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分析北极海冰漂移模式的高阶统计矩

通常假设地球物理系统遵循高斯概率密度函数；然而，与高斯行为的偏差可以揭示潜在的动态。对于北极海冰的大规模运动，这种偏差被解释为动态流场结构的特征。在这项研究中，我们使用高阶矩（偏度和峰度）来识别博福特环流（BG）和跨极漂移（TPD）海冰漂移模式的时空变化。在 2006-2017 年冬季期间检查了卫星衍生的冰漂移速度的高阶矩，以描述 BG 和 TPD 等特征的持续性，以及它们随时间的变化。