An example of a prediction:
How does anyone know which way the ENSO behavior is heading if there is not a clear understanding of the underlying mechanism? [1]
For the prediction quoted above, the closer one gets to an peak or valley, the safer it is to make a dead reckoning guess. For example, I can say a low tide is coming if it is coming off a high tide — even if I have no idea what causes tides.
Yet, if we understand the mechanism behind ocean tides — that it is due to the gravitational pull of the sun and the moon — we can do a much better job of prediction.
The New York Times climate change reporter Justin Gillis suggests that climate science can make predictions as well as geophysicists can predict eclipses:
https://www.nytimes.com/2017/08/18/climate/should-you-trust-climate-science-maybe-the-eclipse-is-a-clue.html. And there is this:
Yet, if climate scientists can’t figure out the mechanism behind a behavior such as ENSO, everyone is essentially in the same boat, fishing for a basic understanding.
So what happens if we can formulate the messy ENSO behavior into a basic geophysics problem, something on the complexity of tides? We are nowhere near that according to the current research literature, unless this finding — which has been a frequent topic here — turns out to be true.
In this case, the recent solar eclipse is in fact a clue. The precise orbit of the moon is vital to determining the cycles of ENSO. If this assertion is true, one day we will likely be able to predict when the next El Nino occurs, with the accuracy of predicting the next eclipse.
Footnote:
[1] Consider one common explanation invoking winds. In fact, shifts in the prevailing winds is not a mechanism because any shift or reversal requires a mechanism itself, see for example the QBO.
GeoEnergy Math 
Shorter: Tides, yes. Eclipses, yes. ENSO, no. Why?
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Geophysics constraint is to set correlation coefficient to at least 0.75 for the lunar forcing calibration. Let the amplitudes vary within that constraint on an ENSO training interval. It will reproduce the profile outside the training:
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“QBO may help trigger eastward flow”
Now you’re talking. QBO is clearly forced by the nodal lunar cycle, which is also the main ingredient behind ENSO forcing. This provides either a common mode or a cooperative mechanism, whereby QBO winds and lunar forcing reinforce ENSO.
Not widely appreciated, but tides are a significant contribution to ocean circulation:
“In any case, tidal mixing (including internal waves due to tidal flow) may be the most important energy source driving the thermohaline circulation. Without tidal mixing, there would be virtually no stratification or motion in the deep ocean. For example, a large basin without midocean ridges, or an imaginary planet earth without the moon would have a dramatically different thermohaline circulation and climate. “
from:
Mixing and Energetics of the Oceanic Thermohaline Circulation:
Journal of Physical Oceanography: Vol 29, No 4
http://journals.ametsoc.org/doi/full/10.1175/1520-0485(1999)029%3C0727:MAEOTO%3E2.0.CO%3B2
Perfectly reasonable to assume that lunar tidal force can impact ENSO just as it impacts the entire ocean.
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Its all about demonstrated accuracy of prediction.
Scientists have been predicting eclipses for a long time and have been proven right many many times.
Media pundits representing scientists have been predicting an ice free arctic for quite a long time now and have been proven wrong time and time again. The latest prediction is for an ice free arctic by September 2017 – wrong again!
This is why people are so interested in the prediction powers of your ENSO model – if you make a prediction and it proves accurate…
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“This is why people are so interested in the prediction powers of your ENSO model – if you make a prediction and it proves accurate…”
Ignoring for the moment that no one is interested in this ENSO model, one of the worst bits of scientific folklore is that you need to make a prediction of the future to prove that a model is correct.
Consider that we don’t need to create a human being artificially to scientifically understand that genes encode all the information necessary to create one naturally.
What is necessary (and this is the way that science actually works) is for someone to (1) either demonstrate that the model is incorrect or (2) come up with a better model.
It should be easy to show (1) that the model is incorrect because all one needs to do is to take the known lunar tidal cycles and then demonstrate that these cycles can not produce the long-term ENSO pattern observed. This is the easiest way to reject the model if the model is somehow flawed. If the cycles match up, then it becomes harder to reject. So if you can’t easily reject the model, you have to resort to (2) and come up with an alternative model that works better.
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In the context of your heading, _why_ do people trust predictions of eclipses? Because of a demonstrated history of ever improving predictions or because the people making predictions are “scientists”?
How about football score predicting algorithms? Stock market predicting algorithms? There are many models that can be tuned to be accurate historically but which fail when attempting to reasonably predict the future. It would be difficult to falsify those which have successfully replicated a past season except by assessing their ability to predict the next season, at which point a new model usually needs to be developed, adding in new factors not allowed for. But if factors are used which in reality have no bearing on history other than mere coincidence, then the model will rapidly fail.
Given the detail provided on this site, I am interested in this work because it makes compelling sense, and the mechanical and physical processes involved are quite clear.
If (as I believe) the physical processes are as predictable at a macro level as you have thus far demonstrated, then predicting ENSO should become as routine as predicting tides.
In addition I have learned quite a bit about orbital physics and been triggered to do additional learning in that area because of some of the things I have read about here, so thanks for that.
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“How about football score predicting algorithms? Stock market predicting algorithms? There are many models that can be tuned to be accurate historically but which fail when attempting to reasonably predict the future.”
Please come up with something related to the hard sciences. The two you picked are both related to human game theory, and it has been proven that any predictions related to game theory are useless in practice.
https://www.technologyreview.com/s/515481/gaming-the-system/
Seriously, try to come up with a good example, as it will strengthen your counter-argument.
The one I can think of off the top of my head is earthquake prediction. But even there, it has recently (last year) been shown that earthquakes are triggered by lunar forcing as well!
Ide, Satoshi, Suguru Yabe, and Yoshiyuki Tanaka. “Earthquake potential revealed by tidal influence on earthquake size-frequency statistics.” Nature Geoscience 9.11 (2016): 834-837.
van der Elst, Nicholas J., et al. “Fortnightly modulation of San Andreas tremor and low-frequency earthquakes.” Proceedings of the National Academy of Sciences (2016): 201524316.
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OK, I take you point and don’t really want to belabor that. I get that the examples I used are from a different domain, and ones which historically have proven to have little long term prediction capability.
The distinction between deterministic and stochastic rears its head again, particularly where deterministic systems can be so complex with so many contributing elements that to all but the most detailed analysis they can appear to be stochastic.
ENSO on a casual glance may appear to be stochastic, but you have effectively isolated a number of deterministic elements which combined together make a very convincing case that it is in fact a completely deterministic system.
Oh and thanks for the extra reading references.
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Yes, there could be a few contributing factors that are missing from the model. I tried to cover the ways that a seasonal, draconic, and anomalistic cycle can interact, but there may be others.
The limit of agreement may be a correlation coefficient of 0.8. That’s essentially the correlation between NINO34 and SOI. It could be that last 0.2 is due to weather events that impact local measuring areas, i.e. these are definitely stochastic. But since I am seeing model CCs of 0.77, it may not be worthwhile to chase any additional secondary factors. That could be overfitting to the stochastic elements.
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