CMG: Assessing Ice Type Distributions and Characteristic Scales Using Wavelets

提供资金，以发展所需的统计理论，以评估冰厚分布的变化，并将这一理论应用于小波分析的冰草案测量从潜艇巡航跨越1976年至1997年。 这项工作将增进对北极海冰及其时空分布变化的认识和了解。 由于冰厚数据具有显著的自相关性，并且具有与高斯（正态）分布明显不同的分布，因此无法使用用于评估分布变化的标准统计测试。 拟议的努力简化了非高斯性的性质划分为不同类型的冰测量根据厚度（新的，第一年，中多年和脊）。 这种划分导致序列是二进制值的，从而产生一个简单的非高斯分布来处理。 冰型分布差异的检验则作为更复杂冰厚分布差异检验的替代。 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。 这一特征尺度的空间和时间变化提供了北极海冰如何变化的另一个特征，这与冰型分布所反映的变化是互补的。 拟议的努力还将建立一个严格的统计理论，用于评估特征尺度的变化。 海冰分布的变化通常被视为气候变化的指标。 更好地描述这种空间和时间变异性将改善气候变异性的一个重要信号。

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.