SGER: Exploring Arctic Ice Thickness Distribution with Wavelets

本研究探讨了利用小波分析评估北极冰盖分布的空间变异性的可行性。近年来，小波已被用于分析各种地球物理过程，但尚未被用于研究浮冰。它们提供了空间序列的样本方差的精确分解，其依据的是根据位置和尺度索引的系数。这些信息又可以用来对方差中的空间变异性进行统计评估。因为它们提供了这些信息，所以它们是一个潜在的有用工具，可以用来描述和研究多尺度的、非平稳的特征，如积冰。这项工作试图通过将小波分析应用于现有的从海底观测得出的大型数据库来证明，小波可以成为确定哪些空间尺度对北极冰盖厚度的总体变化做出最大贡献的强大工具。了解这些统计特性对于评估观测到的变化是反映气候变化还是仅仅由于抽样的不确定性是至关重要的。预计这一演示的成功完成将为更广泛的研究工作提供动力，以解决积冰对气候变异性的反应。这项工作还将有助于宣传小波在研究其他地球物理过程中的潜在用途，并通过这样做，促进将其纳入大学的数据分析课程。

This research explores the feasibility of using wavelet analysis to assess spatial variability in the distribution of arctic pack ice. Wavelets have been used in recent years to analyze a variety of geophysical processes, but they have not yet been used to study pack ice. They provide an exact decomposition of the sample variance, for a spatial series, in terms of coefficients that are indexed according to both location and scale. This information can be used in turn to statistically assess spatial variability in the variance. Because they provide this information, they are a potentially useful tool for use in characterizing and studying multi-scale, nonstationary features such as pack ice. This work seeks to demonstrate, through application of wavelet analysis to a large existing database derived from submarine-based observations, that wavelets can be a powerful tool for determining which spatial scales make the greatest contributions to overall variance in arctic pack ice thickness. Knowledge of these statistical properties is essential for assessing whether observed variations reflect climate change or are due merely to sampling uncertainty. The anticipated successful completion of this demonstration will provide the impetus for a broader research effort that will address pack ice response to climate variability. This work will also help to publicize the potential uses of wavelets in studying other geophysical processes and will, in so doing, facilitate their integration into university courses on data analysis.