Comparing low‐frequency and intermittent variability in comprehensive climate models through nonlinear Laplacian spectral analysis
Comparing low‐frequency and intermittent variability in comprehensive climate models through nonlinear Laplacian spectral analysis
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通过非线性拉普拉斯谱分析比较综合气候模型中的低频和间歇性变化
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
A. Majda
中科院分区：
文献类型：
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作者：
D. Giannakis;A. Majda
非线性拉普拉斯谱分析（NLSA）是最近发展起来的一种用于高维数据时空分析的技术，它通过非线性数据流形上的自然正交基函数来表示时间模式。通过这样的基函数，通过图论算法有效地确定，NLSA捕获不连续性，罕见事件和其他非线性动力学特征，这些特征是通过线性方法无法访问的（例如，奇异谱分析（SSA）。本文利用NLSA方法对CCSM 3和ECHAM 5/MPI-OM模式的北太平洋SST月资料进行了分析。而不执行空间粗粒化（即，在滞后嵌入后，在高达1.6 × 105的环境空间维度中操作），或季节周期减法，该方法揭示了周期性，低频和间歇性时空模式的家族。间歇模式描述了西部和东部边界流的变化，以及亚热带环流的变化与年复一年的重新出现，没有被SSA捕获，但可能在预测背景下具有很高的意义，并在跨模型比较中具有很高的实用性。
Nonlinear Laplacian spectral analysis (NLSA) is a recently developed technique for spatiotemporal analysis of high‐dimensional data, which represents temporal patterns via natural orthonormal basis functions on the nonlinear data manifold. Through such basis functions, determined efficiently via graph‐theoretic algorithms, NLSA captures intermittency, rare events, and other nonlinear dynamical features which are not accessible through linear approaches (e.g., singular spectrum analysis (SSA)). Here, we apply NLSA to study North Pacific SST monthly data from the CCSM3 and ECHAM5/MPI‐OM models. Without performing spatial coarse graining (i.e., operating in ambient‐space dimensions up to 1.6 × 105after lagged embedding), or seasonal‐cycle subtraction, the method reveals families of periodic, low‐frequency, and intermittent spatiotemporal modes. The intermittent modes, which describe variability in the Western and Eastern boundary currents, as well as variability in the subtropical gyre with year‐to‐year reemergence, are not captured by SSA, yet are likely to have high significance in a predictive context and utility in cross‐model comparisons.