Machine Learning of ENSO

This topic will gain steam in the coming years. The following paper generates quite a good cross-validation for SOI, shown in the figure below.

  1. Xiaoqun, C. et al. ENSO prediction based on Long Short-Term Memory (LSTM). IOP Conference Series: Materials Science and Engineering, 799, 012035 (2020).

The x-axis appears to be in months and likely starts in 1979, so it captures the 2016 El Nino (El Nino is negative for SOI). Still have no idea how the neural net arrived at the fit other than it being able to discern the cyclic behavior from the historical waveform between 1979 and 2010. From the article itself, it appears that neither do the authors.

The following are fragments that I am working on.

High-resolution (5-day) MJO model fit

Back extrapolation to historical SOI

The forcing for SOI and high-res MJO is aligned, not degrading at all regions that are outside the training interval (prior to 1980)/

The LTE modulation over the entire span (above) and just over the post 1979 MJO data interval (below)

Power spectrum of forcing shows the expected 13.66 day tropical fortnightly signal, but nearly inseparable from the next strongest 27.55 day anomalistic monthly signal. With the annual impulse mixing these show up as 3.795 year and 3.917 year periods, which thus gives rise to a long beat frequency of 121 years, which is likely the low-frequency peak.

IMPA=399, IMPB=400, SCALING=0.1, SAMPLING=365, REF_TIME=1880

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