| 基于局部均值分解和交叉小波的全球平均海平面变化与ONI 指数多尺度特征研究 |
| Global Mean Sea Level Change and ONI Index Multiscale Characteristics Based on Local Mean Decomposition and Cross Wavelet |
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| DOI： |
| 中文关键词: 局部均值分解方法 交叉小波 海平面变化 ONI 指数 ENSO 相关性分析 |
| 英文关键词:local mean decomposition cross wavelet sea level change ONI Index ENSO correlation analysis |
| 基金项目:国家自然科学基金地区科学基金资助项目（42064001） |
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| 中文摘要: |
| 为研究全球平均海平面与ENSO（El Niño-Southern Oscillation） 的相关性问题， 本文提出了一种结合局部均值分解和交叉小波原理的分析方法， 揭示全球平均海平面和ENSO 的影响机理和因果关联。利用全球平均海平面的时间序列进行局部均值分解得到PF 分量和余量， 表示海平面变化的高频分量、低频分量和趋势分量。剔除高频分量的影响， 利用最小二乘线性拟合趋势分量， 得到1991—2000 年的全球平均海平面上升速率为3.6 mm/a。接着对PF 的低频分量进行距平变换再与ONI 指数（Oceanic Niño Index， ONI） 分别进行Morlet 连续小波变换得到小波功率谱， 再将变换的连续小波分别进行交叉小波变换得到交叉小波功率谱和凝聚谱， 通过交
叉小波功率谱和交叉小波凝聚谱揭示信号在时频空间的能量共振和协方差分布规律， 其中交叉小波功率谱体现了共同的高能量区的相关性， 交叉小波凝聚谱体现了共同的低能量区的相关性。结果表明， 该方法能在多尺度上分析海平面的变化， 并能分析ONI 指数与全球平均海平面的关系， 可为全球平均海平面演变规律分析和预测等方面提供有力工具。 |
| 英文摘要: |
| In order to study the correlation analysis between global mean sea level and ENSO, an analysis method combining local mean decomposition method and cross wavelet method is proposed, which can reveal the influence mechanism and causal relationship between global mean sea level and ENSO. The PF component and the margin are obtained by means of local mean decomposition based on the time series of global mean sea level, which represent the high frequency component，low frequency component and trend component of sea level change. The global mean sea level rise rate from 1993 to 2000 is 3.6 mm/a after removing the influence of high frequency component and using the least square method to fit the trend component. Then the low frequency component of PF analyzed separately Morlet transform again and ONI index continuous wavelet transform to get the wavelet power spectrum, then transform of continuous wavelet cross wavelet transform respectively get cross wavelet power spectrum and coagulation spectrum, by cross wavelet power spectrum and cross wavelet spectrum of signals in time -frequency space energy resonance and the distribution regularity of covariance, The cross wavelet power spectrum reflects the common high energy region correlation, the cross wavelet condensation spectrum reflects the common low energy region correlation. The results show that the proposed method can analyze the changes of sea level at multiple scales and the relationship between ONI index and global mean sea level, which provides a powerful tool for the analysis and prediction of global mean sea level evolution. |
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