CMG: Assessing Ice Type Distributions and Characteristic Scales Using Wavelets

已提供资金发展评估冰厚分布变化所需的统计理论，并将这一理论应用于对1976年至1997年潜艇巡航的冰流测量数据进行小波分析。这项工作将促进对北极海冰及其分布如何在空间和时间上变化的认识和理解。由于冰厚数据具有显著的自相关性，其分布与高斯（正态）分布明显不同，因此不能使用评估分布变化的标准统计检验。通过将冰测量根据厚度划分为不同类型（新冰、第一年冰、多年冰和脊冰），所提出的努力简化了非高斯性的性质。这种划分导致了二进制值的序列，从而产生了一个简单的非高斯分布。然后，冰型分布差异的试验可作为更复杂的冰厚分布差异试验的替代。此外，小波分析的最大观测分量可用于定义冰型的特征尺度。这一特征尺度的时空变化提供了北极海冰变化的另一特征，与冰型分布捕获的变化相辅相成。拟议的努力还将为评价特征尺度的变化建立一个严格的统计理论。海冰分布的变化通常被视为气候变化的一个指标。更好地描述这种时空变异性将改善气候变率的一个重要信号。

Funds are provided to develop the statistical theory required to assess changes in ice thickness distributions and to apply this theory to the wavelet analysis of ice draft measurements from submarine cruises spanning the years 1976 to 1997. This effort will advance the knowledge and understanding of Arctic sea ice and how its distribution varies both spatially and temporally. Because ice thickness data are significantly autocorrelated and have distributions that differ markedly from the Gaussian (normal) distribution, standard statistical tests for assessing changes in distributions cannot be used. The proposed effort simplifies the nature of the non-Gaussianity by partitioning the ice measurements into different types according to thickness (new, first year, medium multiyear and ridged). This partitioning leads to series that are binary-valued, thus yielding a simple non-Gaussian distribution with which to deal. Tests of differences in ice type distributions then serve as surrogates for tests of differences in the more complicated ice thickness distributions. In addition, the largest observed component of the wavelet analysis can be used to define a characteristic scale for an ice type. Spatial and temporal variations in this characteristic scale provide another characterization of how Arctic sea ice varies, which is complementary to variations captured by ice type distributions. The proposed effort will also establish a rigorous statistical theory for evaluating changes in characteristic scales. Variations in sea ice distribution is often viewed as and indicator of climate variation. Better characterization of this spatial and temporal variability will improve an important signal of climate variability.