Because the ENSO model generates precise temporal harmonics via a non-linear solution to Laplace’s Tidal Equations, it may in practice be trivially easy to verify. By only using higher-frequency harmonics (T<1.25y) during spectral training (with a small window of low-frequency signal to stabilize the solution, T>11y), the model essentially fills in the missing bulk of the signal frequency spectrum, 1.25y < T < 11y. This is shown below in Figure 1.

Fig. 1: Bottom panel of amplitude ENSO SOI spectra shows the training windows. A primarily low-amplitude spectral signal is used to fit the model (using least-squares on the error signal). Upper spectra shows the expanded view of the out-of-band fit. This rich spectra is all due to the non-linear harmonic solution of the ENSO Laplace’s Tidal Equation solution.
This agreement is statistically unlikely (nee impossible) to occur unless the out-of-band signal had knowledge of the fundamental harmonics (i.e. the highest amplitude terms in the meat of the spectra) that are contributing to the higher harmonics.
Figure 2 is the underlying temporal fit. Although not as good a fit as what we can achieve using more of the primary Fourier terms, it is still striking.

Fig. 2: Temporal model fit using only Fourier frequency terms shorter than 1.25 years and longer than 11 years. The correlation coefficient is 0.7 here
The consensus claim is that ENSO is a chaotic process with no long-term coherence. Yet, this shows excellent agreement with a forced lunisolar model showing very long-term coherence. An issue to raise is: why has the obvious deterministic forcing model been abandoned as a plausible physical mechanism so long ago?
Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model
Appears to be a good predictor yet they use wind stress as a regression variable (possibly not independent) and it is only 6 months in advance as shown below.
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A full model fit to shortest component 0.4y.
The residual of the fit across the entire spectrum (Nyquist T = 1/6 y) appears to be flat white noise
No use trying to improve the fit beyond this point —
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Interesting Fourier analysis for paleo data here:
Autumn-winter minimum temperature changes in the southern Sikhote-Alin mountain range of northeastern Asia since 1529 AD
Note below the strong biennial component right at the Nyquist limit, and that they do not even discuss in the text!
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