I've followed Gell-Mann's work on complexity over the years and will now try my hand at using his approach to describe the simplicity of the models developed in this long thread.

![](https://pbs.twimg.com/media/Eg8i9OvXYAEsR8z.png)

Each model fits the data applying a concise algorithm -- the key being its conciseness but not necessarily subjective intuitiveness.

Here's a quick breakdown :

#1. Say I was doing tidal analysis and fitting a model to a SLH tidal gauge time-series. That's essentially an effective complexity of **1** because it just involves fitting known sinusoid amplitudes and phases.

![](https://imagizer.imageshack.com/img922/8716/LClDrl.png)

#2. Same effective complexity of **1** for the dLOD, as it is straightforward additive tidal cycles.

#3. The Chandler wobble model that I developed has an effective complexity of **2** because it takes a single monthly tidal forcing and it multiplies it by a semi-annual nodal impulse (one for each nodal pass). Just a bit more complex than #1 or #2 but evidently too difficult for geophysicists to handle in this day and age.

![](https://imagizer.imageshack.com/img924/9381/BnYSgd.png)

#4. The QBO model that I developed is also estimated at an effective complexity of **2** as it is impulse modulated by nearly the same mechanism as for the Chandler wobble of #3. Instead of a bandpass filter for #3 (Chandler wobble) it uses an integrating filter to create more of a square-wave-like time-series. Again, this is apparently at the breaking point of understanding for the atmospheric physicists

![](https://imagizer.imageshack.com/img923/7210/7FQPAA.png)

#5. The ENSO model that I developed is an effective complexity of **3** because it adds the nonlinear Laplace's Tidal Equation (LTE) modulation to the square-wave-like fit of #4 (QBO), tempered by being calibrated by the tidal forcing model for #2 (dLOD). Of course this additional level of "complexity" is certain to be above the heads of ocean scientists and climate scientists, who are still scratching their heads over #3 and #4.

![](https://imagizer.imageshack.com/img922/9074/17Yhbw.png)

By comparison, most GCMs of climate behaviors have effective complexities much more than this because (as Gell-Man defined it) the shortest algorithmic description would require pages and pages of text to express. To climate scientists, perhaps the massive additional complexity of a GCM is preferred over the intuition required for enabling incremental complexity.

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Since started with a Gell-Mann citation, may as well stick one here at the end:

![](https://imagizer.imageshack.com/img923/7452/PfQefQ.png)