SGER: Exploring Arctic Ice Thickness Distribution with Wavelets

本研究探讨了利用小波分析评估北极浮冰分布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，但尚未用于研究浮冰。它们提供了样本方差的精确分解，对于空间序列，根据位置和规模索引的系数。这些信息可以反过来用于统计评估方差的空间变异性。因为它们提供了这些信息，它们是一个潜在的有用工具，用于表征和研究多尺度、非平稳特征，如浮冰。通过将小波分析应用于基于海底观测的大型现有数据库，本研究试图证明，小波可以成为确定哪些空间尺度对北极浮冰厚度总体变化贡献最大的有力工具。了解这些统计特性对于评估观测到的变化是否反映了气候变化或仅仅是由于采样的不确定性至关重要。这一示范的预期成功完成将推动更广泛的研究工作，以解决浮冰对气候变化的反应。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，并将因此促进将其纳入大学数据分析课程。

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.