Jim, The CF is the characteristic or resonant frequency factor of the mean-valued wave equation (aka the *a* term in comment #89). Clarke said this was about 4.25 years and I am using 4.36 years for this fit.

The 0.42 actually represents the correlation coefficient between the model and data.

I indicated that there appears to be a 50 year cycle to represent multi-decadal changes. The MW of 0.1253 is ~2$\pi$/50. This is a well-known variation in the Pacific ocean and perhaps the same mechanism behind the long term Pacific decadal oscillation.

There is also an observed long term precession in the earth known as the [Markowitz wobble](http://hpiers.obspm.fr/eop-pc/models/PM/Markowitz.html) of a few decades. But now they are saying this is an artifact of measurement so I doubt the 50 year cycle is due to that.

I was looking again at the data behind the UEP and it is quite noisy. Below I show the raw data (which has the opposite sign of what I am fitting to). The standard deviation fluctuates quite a bit and has about the same magnitude as the signal. This means that it is quite difficult to get a significant variance reduction against a model fit, as one is battling the noise while trying to isolate the signal.

![UEP](http://imagizer.imageshack.us/a/img539/5597/CEAViP.gif)

I know this isn't quite the same as trying to extract signals from SETI data, but as the combination of applying machine learning and narrowing possible physical correlations improves it gets more interesting. Why isn't anyone else doing this? SETI essentially hijacks thousands of computers to try to find something that may not exist, yet there is more than likely something hiding in the ENSO signal that will have some practical benefit if we can just isolate it.

The 0.42 actually represents the correlation coefficient between the model and data.

I indicated that there appears to be a 50 year cycle to represent multi-decadal changes. The MW of 0.1253 is ~2$\pi$/50. This is a well-known variation in the Pacific ocean and perhaps the same mechanism behind the long term Pacific decadal oscillation.

There is also an observed long term precession in the earth known as the [Markowitz wobble](http://hpiers.obspm.fr/eop-pc/models/PM/Markowitz.html) of a few decades. But now they are saying this is an artifact of measurement so I doubt the 50 year cycle is due to that.

I was looking again at the data behind the UEP and it is quite noisy. Below I show the raw data (which has the opposite sign of what I am fitting to). The standard deviation fluctuates quite a bit and has about the same magnitude as the signal. This means that it is quite difficult to get a significant variance reduction against a model fit, as one is battling the noise while trying to isolate the signal.

![UEP](http://imagizer.imageshack.us/a/img539/5597/CEAViP.gif)

I know this isn't quite the same as trying to extract signals from SETI data, but as the combination of applying machine learning and narrowing possible physical correlations improves it gets more interesting. Why isn't anyone else doing this? SETI essentially hijacks thousands of computers to try to find something that may not exist, yet there is more than likely something hiding in the ENSO signal that will have some practical benefit if we can just isolate it.