scholarly journals Multivariate Nonstationary Oscillation Simulation of Climate Indices with Empirical Mode Decomposition

2019 ◽  
Author(s):  
Taesam Lee ◽  
Taha B.M.J. Ouarda
Keyword(s):  
Empirical Mode Decomposition ◽  
Climate Indices ◽  
Nonstationary Oscillation ◽  
Mode Decomposition

scholarly journals Evaluation of empirical mode decomposition for quantifying multi-decadal variations and acceleration in sea level records

2015 ◽  
Vol 22 (2) ◽  
pp. 157-166 ◽  
Author(s):  
D. P. Chambers
Keyword(s):  
Sea Level ◽  
Empirical Mode Decomposition ◽  
San Francisco ◽  
Decadal Variability ◽  
Tide Gauge ◽  
Random Noise ◽  
Climate Indices ◽  
Intrinsic Mode Functions ◽  
Tide Gauge Record ◽  
Mode Decomposition

Abstract. The ability of empirical mode decomposition (EMD) to extract multi-decadal variability from sea level records is tested using three simulations: one based on a series of purely sinusoidal modes, one based on scaled climate indices of El Niño and the Pacific decadal oscillation (PDO), and the final one including a single month with an extreme sea level event. All simulations include random noise of similar variance to high-frequency variability in the San Francisco tide gauge record. The intrinsic mode functions (IMFs) computed using EMD were compared to the prescribed oscillations. In all cases, the longest-period modes are significantly distorted, with incorrect amplitudes and phases. This affects the estimated acceleration computed from the longest periodic IMF. In these simulations, the acceleration was underestimated in the case with purely sinusoidal modes, and overestimated by nearly 100% in the case with prescribed climate modes. Additionally, in all cases, extra low-frequency modes uncorrelated with the prescribed variability are found. These experiments suggest that using EMD to identify multi-decadal variability and accelerations in sea level records should be used with caution.

scholarly journals Evaluation of empirical mode decomposition for quantifying multi-decadal variations in sea level records

2014 ◽  
Vol 1 (2) ◽  
pp. 1833-1854 ◽  
Author(s):  
D. P. Chambers
Keyword(s):  
Sea Level ◽  
Empirical Mode Decomposition ◽  
San Francisco ◽  
Tide Gauge ◽  
Random Noise ◽  
Climate Indices ◽  
Intrinsic Mode Functions ◽  
Tide Gauge Record ◽  
Multidecadal Variability ◽  
Mode Decomposition

Abstract. The ability of empirical mode decomposition (EMD) to extract multidecadal variability from sea level records is tested using three simulations: one based on a series of purely sinusoidal modes, one based on scaled climate indices of El Niño and the Pacific Decadal Oscillation (PDO), and the final one including a single month with an extreme sea level event. All simulations include random noise of similar variance to high-frequency variability in the San Francisco tide gauge record. The intrinsic mode functions (IMFs) computed using EMD were compared to the prescribed oscillations. In all cases, the longest-period modes are significantly distorted, with incorrect amplitudes and phases. This affects the estimated acceleration computed from the longest periodic IMF. In these simulations, the acceleration was underestimated in the case with purely sinusoidal modes, and overestimated by nearly 100% in the case with prescribed climate modes. Additionally, in all cases, extra low-frequency modes uncorrelated with the prescribed variability are found. These experiments suggest that using EMD to identify multidecadal variability and accelerations in sea level records should be used with caution.

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