识别气候系统变率动力模式的旋转谱主成分分析（rsPCA）
Rotated spectral principal component analysis (rsPCA) for identifying dynamical modes of variability in climate systems
摘要：
与经典PCA相比，谱PCA（sPCA）具有识别特定频段内有组织的时空模式和提取动态模式的优点。然而，PC的频率分辨率和鲁棒性之间不可避免的权衡导致对噪声和过拟合的高度敏感性，这限制了对sPCA结果的解释。我们在这里提出了一个简单的非参数实现的sPCA使用连续解析Morlet小波作为一个强大的估计的互谱矩阵具有良好的频率分辨率。为了提高结果的可解释性，当几个模式的相似幅度存在于同一频段内，我们提出了一个旋转的特征向量，优化的空间平滑度在相位域。所开发的方法，称为旋转谱PCA（rsPCA），模拟传播波的合成数据进行测试，并显示出令人印象深刻的性能，即使在数据中的高水平的噪声。应用于太平洋海表温度（SST）的历史时间序列，该方法准确地捕捉厄尔尼诺-南方涛动（ENSO）在低频率（2至7年的周期）。在高频率（次年度周期性），在其中几个类似的振幅的extratrophic模式被确定，rsPCA成功地unmixed的基本模式，揭示空间相干模式与强大的传播动力学。识别更高频率的时空气候模式，为季节到亚季节的预测和气候模式的诊断分析提供了希望。
英文摘要：
Spectral PCA (sPCA), in contrast to classical PCA, offers the advantage of identifying organized spatio-temporal patterns within specific frequency bands and extracting dynamical modes. However, the unavoidable tradeoff between frequency resolution and robustness of the PCs leads to high sensitivity to noise and overfitting, which limits the interpretation of the sPCA results. We propose herein a simple non-parametric implementation of the sPCA using the continuous analytic Morlet wavelet as a robust estimator of the cross-spectral matrices with good frequency resolution. To improve the interpretability of the results when several modes of similar amplitude exist within the same frequency band, we propose a rotation of eigenvectors that optimizes the spatial smoothness in the phase domain. The developed method, called rotated spectral PCA (rsPCA), is tested on synthetic data simulating propagating waves and shows impressive performance even with high levels of noise in the data. Applied to historical sea surface temperature (SST) time series over the Pacific Ocean, the method accurately captures the El Niño-Southern Oscillation (ENSO) at low frequency (2 to 7 years periodicity). At high frequencies (sub-annual periodicity), at which several extratropical patterns of similar amplitude are identified, the rsPCA successfully unmixes the underlying modes, revealing spatially coherent patterns with robust propagation dynamics. Identification of higher frequency space-time climate modes holds promise for seasonal to subseasonal prediction and for diagnostic analysis of climate models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
