Tsonis Anastasios A, Wang Geli, Lu Wenxu, Kravtsov Sergey, Essex Christopher, Asten Michael W
Atmospheric Sciences Group, Department of Mathematical Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA.
Hydrologic Research Center, San Diego, CA 92127, USA.
Entropy (Basel). 2021 Apr 13;23(4):459. doi: 10.3390/e23040459.
Proxy temperature data records featuring local time series, regional averages from areas all around the globe, as well as global averages, are analyzed using the Slow Feature Analysis (SFA) method. As explained in the paper, SFA is much more effective than the traditional Fourier analysis in identifying slow-varying (low-frequency) signals in data sets of a limited length. We find the existence of a striking gap from ~1000 to about ~20,000 years, which separates intrinsic climatic oscillations with periods ranging from ~60 years to ~1000 years, from the longer time-scale periodicities (20,000 year+) involving external forcing associated with Milankovitch cycles. The absence of natural oscillations with periods within the gap is consistent with cumulative evidence based on past data analyses, as well as with earlier theoretical and modeling studies.
利用慢特征分析（SFA）方法对具有本地时间序列、全球各地区域平均值以及全球平均值的代理温度数据记录进行了分析。正如论文中所解释的，在识别有限长度数据集中的慢变（低频）信号方面，SFA比传统的傅里叶分析要有效得多。我们发现，从大约1000年到约20000年存在一个显著的间隔，它将周期从约60年到约1000年的内在气候振荡与涉及与米兰科维奇循环相关的外部强迫的更长时间尺度的周期性（20000年以上）区分开来。该间隔内不存在周期的自然振荡，这与基于过去数据分析的累积证据以及早期的理论和模型研究一致。
