Spectral analysis of climate data
Spectral analysis of climate data
复制标题
气候数据的光谱分析
DOI：
10.1007/bf01931784
复制
发表时间：
1996
影响因子：
4.6
通讯作者：
M. Loutre
中科院分区：
文献类型：
--
作者：
Pascal Yiou;E. Baert;M. Loutre
气候变率在所有时间尺度上的复杂性要求使用几种精细的工具，从观测中揭示其主要动态。事实上，从动力系统理论的思想提供了新的方法来解释包含在气候时间序列的信息。这些方法属于四大类：傅立叶技术（Blackman-Tukey和Multi-Taper），最大熵技术，奇异谱技术和小波分析。通过对合成时间序列的数值实验，说明了它们各自的优点和局限性。由于气候数据在时间上的分布是不规则的，本文还比较了三种插值方法在这些时间序列上的应用。这些测试的目的是为了显示在气候数据上盲目使用数学或统计技术的陷阱。我们将这些方法应用于上世纪温度变化的“真实的”气候数据，以及末次冰期循环的东方冰芯氘记录。然后，我们将展示如何解释气候的动态可以在这些时间尺度上得出。
The complexity of climate variability on all time scales requires the use of several refined tools to unravel its primary dynamics from observations. Indeed, ideas from the theory of dynamical systems have provided new ways of interpreting the information contained in climatic time series.We review the properties of several modern time series analysis methods. Those methods belong to four main classes: Fourier techniques (Blackman-Tukey and Multi-Taper), Maximum Entropy technique, Singular-spectrum techniques and wavelet analysis. Their respective advantages and limitations are illustrated by numerical experiments on synthetic time series. As climate data can be irregularly spaced in time, we also compare three interpolating methods on those time series. Those tests are aimed at showing the pitfalls of the blind use of mathematical or statistical techniques on climate data.We apply those methods to ‘real” climatic data from temperature variations over the last century, and the Vostok ice core deuterium record over the last glacial cycle. Then we show how interpretations on the dynamics of climate can be derived on those time scales.