Identification of the Madden-Julian Oscillation With Data-Driven Koopman Spectral Analysis

被引:0
|
作者
Lintner, Benjamin R. R. [1 ,2 ]
Giannakis, Dimitrios [3 ]
Pike, Max [1 ]
Slawinska, Joanna [3 ]
机构
[1] Rutgers State Univ, Dept Environm Sci, New Brunswick, NJ 08854 USA
[2] Rutgers Inst Earth Ocean & Atmospher Sci, New Brunswick, NJ 08854 USA
[3] Dartmouth Coll, Dept Math, Hanover, NH USA
基金
美国国家科学基金会;
关键词
tropical dynamics; climate variability; spectral analysis; data analysis; algorithms and implementation; Koopman operator; Madden-Julian Oscillation; climate data analysis; DYNAMICAL-SYSTEMS; MJO; WAVES;
D O I
10.1029/2023GL102743
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, is commonly identified using the realtime multivariate MJO (RMM) index based on joint empirical orthogonal function (EOF) analysis of near-equatorial upper and lower level zonal winds and outgoing longwave radiation. Here, in place of conventional EOFs, we apply an operator-theoretic formalism based on dynamic systems theory (the Koopman operator) to extract an analog to RMM that exhibits certain features that refine the characterization and predictability of the MJO. In particular, the spectrum of Koopman operator eigenfunctions, with eigenvalues corresponding to mode periods, contains a leading intraseasonal mode with period of similar to 50 days. Moreover, the amplitude of this leading intraseasonal eigenfunction exhibits a seasonal modulation clearly peaked in boreal winter. Finally, the phase space formed by the complex Koopman MJO eigenfunction exhibits a smoother temporal evolution and higher degree of autocorrelation than RMM, which may contribute to enhanced predictive skill.
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页数:10
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