ESD Ideas article for review

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The simple idea is that tidal forces play a bigger role in geophysical behaviors than previously thought, and thus helping to explain phenomena that have frustrated scientists for decades.

The idea is simple but the non-linear math (see figure above for ENSO) requires cracking to discover the underlying patterns.

The rationale for the ESD Ideas section in the EGU Earth System Dynamics journal is to get discussion going on innovative and novel ideas. So even though this model is worked out comprehensively in Mathematical Geoenergy, it hasn’t gotten much publicity.

Complexity vs Simplicity in Geophysics

In our book Mathematical GeoEnergy, several geophysical processes are modeled — from conventional tides to ENSO. Each model fits the data applying a concise physics-derived algorithm — the key being the algorithm’s conciseness but not necessarily subjective intuitiveness.

I’ve followed Gell-Mann’s work on complexity over the years and so will try applying his qualitative effective complexity approach to characterize the simplicity of the geophysics models described in the book and on this blog.

from Deacon_Information_Complexity_Depth.pdf

Here’s a breakdown from least complex to most complex

1. Say we are doing tidal analysis by fitting a model to a historical sea-level height (SLH) tidal gauge time-series. That’s essentially an effective complexity of 1 because it just involves fitting amplitudes and phases from known lunisolar sinusoidal tidal cycles.

Figure 1: Conventional tidal analysis by fitting amplitudes and phases of known tidal periods

This image has been resized to fit in the page. Click to enlarge.

2. The same effective complexity of 1 applies for the differential length-of-day (dLOD) time-series, as it involves straightforward additive tidal cycles.

Figure 2 : Long-period tidal cycles map directly to dLOD.
The long-term modulation shown follows the 18.6 year nodal cycle

This image has been resized to fit in the page. Click to enlarge.

3. The Chandler wobble model developed in Chapter 13 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 cycle pass). Just a bit more complex than #1 or #2 but the complexity already may be too great for geophysicists to accept, as the consensus instead argues for a stochastic forcing stimulating a resonance.

Figure 3 : Chandler Wobble model consisting of a lunisolar tidal forcing.
The beat frequency is annual (sun) nodal against lunar nodal cycle.

This image has been resized to fit in the page. Click to enlarge.

4. The QBO model described in Chapter 11 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. But instead of a bandpass filter for the Chandler wobble, the QBO model applies an integrating filter to create more of a square-wave-like time-series. Again, this is too complex for consensus atmospheric physics to accept.

Figure 4 : QBO model has a fundamental cycle of ~2.33 years.
This is an aliasing of lunar nodal cycle against the annual cycle

This image has been resized to fit in the page. Click to enlarge.

5. The ENSO model described in Chapter 12 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 physics “complexity” is certain to be above the heads of ocean scientists and climate scientists, who are still scratching their heads over #3 and #4.

Figure 5 : Higher complexity of ENSO model due to nonlinear modulation of the LTE solution

This image has been resized to fit in the page. Click to enlarge.

The ENSO model is complex due to the non-linearity of the solution. The cyclic tidal factors can create harmonics from both the inverse cubic gravitational pull and from the LTE solution, and together with the annual impulse modulation creates an additional nasty aliasing that requires painstaking analysis to reveal.

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.

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

“Battles of new ideas against conventional wisdom are common in science, aren’t they?”

“It’s very interesting how these certain negative principles get embedded in science sometimes. Most challenges to scientific orthodoxy are wrong. A lot of them are crank. But it happens from time to time that a challenge to scientific orthodoxy is actually right. And the people who make that challenge face a terrible situation. Getting heard, getting believed, getting taken seriously and so on. And I’ve lived through a lot of those, some of them with my own work, but also with other people’s very important work. Let’s take continental drift, for example. American geologists were absolutely convinced, almost all of them, that continental drift was rubbish. The reason is that the mechanisms that were put forward for it were unsatisfactory. But that’s no reason to disregard a phenomenon. Because the theories people have put forward about the phenomenon are unsatisfactory, that doesn’t mean the phenomenon doesn’t exist. But that’s what most American geologists did until finally their noses were rubbed in continental drift in 1962, ’63 and so on when they found the stripes in the mid-ocean, and so it was perfectly clear that there had to be continental drift, and it was associated then with a model that people could believe, namely plate tectonics. But the phenomenon was still there. It was there before plate tectonics. The fact that they hadn’t found the mechanism didn’t mean the phenomenon wasn’t there. Continental drift was actually real. And evidence was accumulating for it. At Caltech the physicists imported Teddy Bullard to talk about his work and Patrick Blackett to talk about his work, these had to do with paleoclimate evidence for continental drift and paleomagnetism evidence for continental drift. And as that evidence accumulated, the American geologists voted more and more strongly for the idea that continental drift didn’t exist. The more the evidence was there, the less they believed it. Finally in 1962 and 1963 they had to accept it and they accepted it along with a successful model presented by plate tectonics….”

https://scienceblogs.com/pontiff/2009/09/16/gell-mann-on-conventional-wisd

With all that, progress is being made in earth geophysics by looking at other planets. My high-school & college classmate Dr. Alex Konopliv of NASA JPL has lead the first research team to detect the Chandler wobble on another planet (this case for Mars), see “Detection of the Chandler Wobble of Mars From Orbiting Spacecraft” Geophysical Research Letters(2020).

In the body of the article, a suggestion is made as to the source of the forcing for the Martian Chandler wobble. The Martian moon Phobos is quite small and cycles the planet in ~7 hours, so this may not have the impact that the Earth’s moon has on our Chandler Wobble.

The wobble is small, about 10 cm on average.

Since a Mars year is 687 Earth days, only the 3rd harmonic (229 days) is close to the measured wobble of 206.9 days. With the Earth, it’s quite simple how the nodal lunar cycle interferes with the annual cycle to line up exactly with the Earth’s 433 day Chandler wobble (see Figure 3 up-thread in this post) , creating that wobble as a forced response, but nothing like that on Mars, which may be a natural response wobble.

Gravitational Pull

In Chapter 12 of the book, we provide an empirical gravitational forcing term that can be applied to the Laplace’s Tidal Equation (LTE) solution for modeling ENSO. The inverse squared law is modified to a cubic law to take into account the differential pull from opposite sides of the earth.

excerpt from Mathematical Geoenergy (Wiley/2018)

The two main terms are the monthly anomalistic (Mm) cycle and the fortnightly tropical/draconic pair (Mf, Mf’ w/ a 18.6 year nodal modulation). Due to the inverse cube gravitational pull found in the denominator of F(t), faster harmonic periods are also created — with the 9-day (Mt) created from the monthly/fortnightly cross-term and the weekly (Mq) from the fortnightly crossed against itself. It’s amazing how few terms are needed to create a canonical fit to a tidally-forced ENSO model.

The recipe for the model is shown in the chart below (click to magnify), following sequentially steps (A) through (G) :

(A) Long-period fortnightly and anomalistic tidal terms as F(t) forcing
(B) The Fourier spectrum of F(t) revealing higher frequency cross terms
(C) An annual impulse modulates the forcing, reinforcing the amplitude
(D) The impulse is integrated producing a lagged quasi-periodic input
(E) Resulting Fourier spectrum is complex due to annual cycle aliasing
(F) Oceanic response is a Laplace’s Tidal Equation (LTE) modulation
(G) Final step is fit the LTE modulation to match the ENSO time-series

The tidal forcing is constrained by the known effects of the lunisolar gravitational torque on the earth’s length-of-day (LOD) variations. An essentially identical set of monthly, fortnightly, 9-day, and weekly terms are required for both a solid-body LOD model fit and a fluid-volume ENSO model fit.

Fitting tidal terms to the dLOD/dt data is only complicated by the aliasing of the annual cycle, making factors such as the weekly 7.095 and 6.83-day cycles difficult to distinguish.

If we apply the same tidal terms as forcing for matching dLOD data, we can use the fit below as a perturbed ENSO tidal forcing. Not a lot of difference here — the weekly harmonics are higher in magnitude.

Modified initial calibration of lunar terms for fitting ENSO

So the only real unknown in this process is guessing the LTE modulation of steps (F) and (G). That’s what differentiates the inertial response of a spinning solid such as the earth’s core and mantle from the response of a rotating liquid volume such as the equatorial Pacific ocean. The former is essentially linear, but the latter is non-linear, making it an infinitely harder problem to solve — as there are infinitely many non-linear transformations one can choose to apply. The only reason that I stumbled across this particular LTE modulation is that it comes directly from a clever solution of Laplace’s tidal equations.

for full derivation see Mathematical Geoenergy (Wiley/2018)

Reversing Traveling Waves

For the solution to Laplace’s Tidal Equation described in Chapter 12, the spatial and temporal results are separable, leading to a non-linear standing-wave time-series formulation:

sin(kx) sin(A sin(wt) )

By analogy to a linear standing-wave formulation, a solution such as

sin(kx) sin(wt)

with the following traveling wave solution (propagating in the +x direction):

sin(kx-wt)

becomes the following in the non-linear LTE solution mode:

sin(kxA sin(wt) )

This is also a traveling wave, but with the characteristic property of being able to periodically reverse direction from +x to –x depending on the value of A and w. As an intuitive aid, a standing wave can be considered as the superposition of two traveling waves traveling in opposite directions:

sin(kxA sin(wt) ) + sin(kx + A sin(wt) )

Here the cross terms cancel after applying the trig identity on sums, and the separable standing-wave result similar to the first equation results. But, whenever there is an imbalance of +x and -x travelling waves, a periodic reversing traveling-wave/standing-wave mix results. This is shown in the following animation, where a mix of nonlinear traveling-waves and standing-waves show the periodic reversal in direction quite clearly.

This reversal is actually observed in ocean measurements, as exemplified in this recent research article:

From their Figure 3, one can see this reversing process as the trajectory of a measured Argo float drift:

If that is not clear enough, the red arrows in the following annotated figure show the direction of the float motion. The drifting floats may not always exactly follow a trajectory as dictated by the velocity of a traveling wave, as this is partly a phase velocity with limited lateral volume displacement, but clearly a large wave-train such as a Tropical Instability Wave will certainly move a float. At least some of this is due to eddy behavior as the reversal is a natural consequence of a circular vortex motion of a large eddy.

Applying the LTE model to complete spatio-temporal data sets such as what Figure 3 is derived from would likely show an interesting match, adding value to the latest ENSO results, but this will require some digging into the data availability.

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.

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Combinatorial Tidal Constituents

For the tidal forcing that contributes to length-of-day (LOD) variations [1], only a few factors contribute to a plurality of the variation. These are indicated below by the highlighted circles, where the V0/g amplitude is greatest. The first is the nodal 18.6 year cycle, indicated by the N’ = 1 Doodson argument. The second is the 27.55 day “Mm” anomalistic cycle which is a combination of the perigean 8.85 year cycle (p = -1 Doodson argument) mixed with the 27.32 day tropical cycle (s=1 Doodson argument). The third and strongest is twice the tropical cycle (therefore s=2) nicknamed “Mf”.

Tidal Constituents contributing to dLOD from R.D. Ray [1]

These three factors also combine as the primary input forcing to the ENSO model. Yet, even though they are strongest, the combinatorial factors derived from multiplying these main harmonics are vital for generating a quality fit (both for dLOD and even more so for ENSO). What I have done in the past was apply the recommended mix of first- and second-order factors that appear in the dLOD spectra for the ENSO forcing.

Yet there is another approach that makes no assumption of the strongest 2nd-order factors. In this case, one simply expands the primary factors as a combinatorial expansion of cross-terms to the 4th level — this then generates a broad mix of monthly, fortnightly, 9-day, and weekly harmonic cycles. A nested algorithm to generate the 35 constituent terms is :

(1+s+p+N')^4

Counter := 1;
for J in Constituents'Range loop
 for K in Constituents'First .. J loop 
  for L in Constituents'First .. K loop 
   for M in Constituents'First .. L loop 
      Tf := Tf + Coefficients (Counter) * Fundamental(J) * 
            Fundamental(K) * Fundamental(L) * Fundamental (M); 
      Counter := Counter + 1; 
   end loop; 
  end loop; 
 end loop; 
end loop;

This algorithm requires the three fundamental terms plus one unity term to capture most of the cross-terms shown in Table 3 above (The annual cross-terms are automatic as those are generated by the model’s annual impulse). This transforms into a coefficients array that can be included in the LTE search software.

What is missing from the list are the evection terms corresponding to 31.812 (Msm) and 27.093 day cycles. They are retrograde to the prograde 27.55 day anomalistic cycle, so would need an additional 8.848 year perigee cycle bring the count from 3 fundamental terms to 4.

The difference between adding an extra level of harmonics, bringing the combinatorial total from 35 to 126, is not very apparent when looking at the time series (below), as it simply adds shape to the main fortnightly tropical cycle.

Yet it has a significant effect on the ENSO fit, approaching a CC of 0.95 (see inset at right for the scatter correlation). Note that the forcing frequency spectra in the middle right inset still shows a predominately tropical fortnightly peak at 0.26/yr and 0.74/yr.

These extra harmonics also helps in matching to the much more busy SOI time-series. Click on the chart below to inspect how the higher-K wavenumbers may be the origin of what is thought to be noise in the SOI measurements.

Is this a case of overfitting? Try the following cross-validation on orthogonal intervals, and note how tight the model matches the data to the training intervals, without degrading too much on the outer validation region.

I will likely add this combinatorial expansion approach to the LTE fitting software on GitHub soon, but thought to checkpoint the interim progress on the blog. In the end the likely modeling mix will be a combination of the geophysical calibration to the known dLOD response together with a refined collection of these 2nd-order combinatorial tidal constituents. The rationale for why certain terms are important will eventually become more clear as well.

References

  1. Ray, R.D. and Erofeeva, S.Y., 2014. Long‐period tidal variations in the length of day. Journal of Geophysical Research: Solid Earth119(2), pp.1498-1509.

El Nino Modoki

El Nino Modoki Possible To Increase Winter Snow Chances – Just In ...
Tri-lobes of Modoki

In Chapter 12 of the book we model — via LTE — the canonical El Nino Southern Oscillation (ENSO) behavior, fitting to closely-correlated indices such as NINO3.4 and SOI. Another El Nino index was identified circa 2007 that is not closely correlated to the well-known ENSO indices. This index, referred to as El Nino Modoki, appears to have more of a Pacific Ocean centered dipole shape with a bulge flanked by two wing lobes, cycling again as an erratic standing-wave.

If in fact Modoki differs from the conventional ENSO only by a different standing-wave wavenumber configuration, then it should be straightforward to model as an LTE variation of ENSO. The figure below is the model fitted to the El Nino Modoki Index (EMI) (data from JAMSTEC). The cross-validation is included as values post-1940 were used in the training with values prior to this used as a validation test.

The LTE modulation has a higher fundamental wavenumber component than ENSO (plus a weaker factor closer to a zero wavenumber, i.e. some limited LTE modulation as is found with the QBO model).

The input tidal forcing is close to that used for ENSO but appears to lead it by one year. The same strength ordering of tidal factors occurs, but with the next higher harmonic (7-day) of the tropical fortnightly 13.66 day tide slightly higher for EMI than ENSO.

The model fit is essentially a perturbation of ENSO so did not take long to optimize based on the Laplace’s Tidal Equation modeling software. I was provoked to run the optimization after finding a paper yesterday on using machine learning to model El Nino Modoki [1].

It’s clear that what needs to be done is a complete spatio-temporal model fit across the equatorial Pacific, which will be amazing as it will account for the complete mix of spatial standing-wave modes. Maybe in a couple of years the climate science establishment will catch up.

References

[1] Pal, Manali, et al. “Long-Lead Prediction of ENSO Modoki Index Using Machine Learning Algorithms.” Scientific Reports, vol. 10, 2020, doi:10.1038/s41598-019-57183-3.

Double-Sideband Suppressed-Carrier Modulation vs Triad

An intriguing yet under-reported finding concerning climate dipole cycles is the symmetry in power spectra observed. This was covered in a post on auto-correlations. The way that this symmetry reveals itself is easily explained by a mirror-folding about one-half some selected carrier frequency, as shown in Fig. 1 below.

Figure 1 : ENSO amplitude spectrum is mirror-folded about 1/2 the annual frequency.
Both the data and model align with their mirrored counterparts, as seen in highlighted box.
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Length of Day II

This is a continuation from the previous Length of Day post showing how closely the ENSO forcing aligns to the dLOD forcing.

Ding & Chao apply an AR-z technique as a supplement to Fourier and Max Entropy spectral techniques to isolate the tidal factors in dLOD

The red data points are the spectral values used in the ENSO model fit.

The top panel below is the LTE modulated tidal forcing fitted against the ENSO time series. The lower panel below is the tidal forcing model over a short interval overlaid on the dLOD/dt data.

That’s all there is to it — it’s all geophysical fluid dynamics. Essentially the same tidal forcing impacts both the rotating solid earth and the equatorial ocean, but the ocean shows a lagged nonlinear response as described in Chapter 12 of the book. In contrast, the solid earth shows an apparently direct linear inertial response. Bottom line is that if one doesn’t know how to do the proper GFD, one will never be able to fit ENSO to a known forcing.

Triad Waves

In Chapter 12 of the book, we discuss tropical instability waves (TIW) of the equatorial Pacific as the higher wavenumber (and higher frequency) companion to the lower wavenumber ENSO (El Nino /Southern Oscillation) behavior. Sutherland et al have already published several papers this year that appear to add some valuable insight to the mathematical underpinnings to the fluid-mechanical relationship.

“It is estimated that globally 1 TW of power is transferred from the lunisolar tides to internal tides[1]. The action of the barotropic tide over bottom topography can generate vertically propagating beams near the source. While some fraction of that energy is dissipated in the near field (as observed, for example, near the Hawaiian Ridge [2]), most of the energy becomes manifest as low-mode internal tides in the far field where they may then propagate thousands of kilometers from the source [3]. An outstanding question asks how the energy from these waves ultimately cascades from large to small scale where it may be dissipated, thus closing this branch of the oceanic energy budget. Several possibilities have been explored, including dissipation when the internal tide interacts with rough bottom topography, with the continental slopes and shelves, and with mean flows and eddies (for a recent review, see MacKinnon et al. [4]). It has also been suggested that, away from topography and background flows, internal modes may be dissipated due to nonlinear wave-wave interactions including the case of triadic resonant instability (hereafter TRI), in which a pair of “sibling” waves grow out of the background noise field through resonant interactions with the “parent” wave”

see reference [2]
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