Experimenting with linking to slide presentations instead of a trad blog post. The PDF linked below is an eye-opener as the NINO34 fit is the most parsimonious ever, at the expense of a higher LTE modulation (explained here). The cross-validation involves far fewer tidal factors than dealt with earlier, the two factors used (Mf and Mm tidal factors) rivaling the one factor used in QBO (described here).

Continue reading# Steven Koonin & Unsettled: Cooking oil

Koonin who was chief scientist for BP, barely touches the elephant in the room (significant global oil depletion) in his anti-climate science diatribe “*Unsettled*: *What Climate Science Tells Us, What It Doesn’t, and Why It Matters*“. Checking Google Books for references to * oil*, it started out promising, thinking he would discuss

*why*renewable energy was important, independent of any climate change considerations:

Instead he discusses cooking oil, spread over several pages, in reference to a Richard Feynman parable on deceptive advertising.

On page 33, a mention of crude (not cooking) oil, although the context is missing, perhaps referring to methane concentrations?

But then back to cooking oil! Twice!

After 200 some pages, a few factual statements on supply and demand for fossil fuels and the difficulty of carbon capture.

That’s it. The book’s index only points to page 243 relevant to oil, which is consistent with Google Book’s search.

The book is a smokescreen, with the intention of smearing climate science so as to avoid discussing the obvious No Regrets strategy for addressing rapidly declining oil reserves. No discussion of this on Rogan’s podcast with Koonin either. Oil companies do not want this discussed so they can continue to squeeze investment $$$ to find the meager and scant remaining reserves.

Koonin’s book is a bait and switch, which is to put the emphasis on the least existential crisis. Today we are globally using over 35 billion barrels of oil per year, but discovering less than 5 billion per year. That’s equivalent to having an annual income of $5,000 while spending as if you earn $35,000 — not close to sustainable after the savings you have runs out.

# Moonfall and glacially slow geophysics advances

This blog is late to the game in commenting on the physics of the Hollywood film **Moonfall** — but does that really matter? Geophysics research and glacially slow progress seem synonymous at this point. In social media, unless one jumps on the event of the day within an hour, it’s considered forgotten. However, difficult problems aren’t unraveled quickly, and that’s what he have when we consider the Moon’s influence on the Earth’s geophysics. Yes, tides are easy to understand, but any other impact of the Moon is considered warily, perhaps over the course of decades, not as part of the daily news & entertainment cycle.

My premise: The movie **Moonfall** is a more pure climate-science-fiction film than **Don’t Look Up**. *Discuss*.

# The Tragedy of QBO

Trying to understand QBO may lead to madness, if the plights of Richard Lindzen (Macbeth) and Timothy Dunkerton (Hamlet) are any indication. It was first Lindzen — the primary theorist behind QBO — in his quest for scientific notoriety that led to lofty pretentiousness and eventually bad blood with his colleagues. Now it’s the Lindzen-acolyte Dunketon’s turn, avenging his “uncle” with troubling behavior

https://www.washingtonpost.com/weather/2022/02/06/dunkerton-offensive-tweet-nwra-ams/

The behavior of QBO is yet to be explained, but it may be simpler than imagined by Lindzen and Dunkerton

# Understanding is Lacking

Regarding the gravity waves concentrically emanating from the Tonga explosion

“It’s really unique. We have never seen anything like this in the data before,” says Lars Hoffmann, an atmospheric scientist at the Jülich Supercomputing Centre in Germany.

https://www.nature.com/articles/d41586-022-00127-1

and

“That’s what’s really puzzling us,” says Corwin Wright, an atmospheric physicist at the University of Bath, UK. “It must have something to do with the physics of what’s going on, but we don’t know what yet.”

https://www.nature.com/articles/d41586-022-00127-1

The discovery was prompted by a tweet sent to Wright on 15 January from Scott Osprey, a climate scientist at the University of Oxford, UK, who asked: “Wow, I wonder how big the atmospheric gravity waves are from this eruption?!” Osprey says that the eruption might have been unique in causing these waves because it happened very quickly relative to other eruptions. “This event seems to have been over in minutes, but it was explosive and it’s that impulse that is likely to kick off some strong gravity waves,” he says. The eruption might have lasted moments, but the impacts could be long-lasting. Gravity waves can interfere with a cyclical reversal of wind direction in the tropics, Osprey says, and this could affect weather patterns as far away as Europe. “We’ll be looking very carefully at how that evolves,” he says.

https://www.nature.com/articles/d41586-022-00127-1

This (*“cyclical reversal of wind direction in the tropics”*) is referring to the QBO, and we will see if it has an impact in the coming months. Hint: the QBO from the last post is essentially modeling gravity waves arising from the tidal forcing as driving the cycle. Also, watch the LOD.

Perhaps the lacking is in applying this simple scientific law: for every action there is a reaction. Always start from that, and also consider: an object that is in motion, tends to stay in motion. Is the lack of observed Coriolis effects to first-order part of why the scientists are mystified? Given the variation of this force with latitude, the concentric rings perhaps were expected to be distorted according to spherical harmonics.

# Cross-Validation of Geophysics Behaviors

The fit to the ENSO model looks like this

The forcing spectrum like this, with the aliased draconic (27.212d) factor circled:

For QBO, we remove all the lunar factors except for the draconic, as this is the only declination factor with the same spherical group symmetry as the semi-annual solar declination.

And after modifying the annual (ENSO spring-barrier) impulse into a semi-annual impulse with equal and opposite excursions, the resultant model matches well (to first order) the QBO time series.

Although the alignment isn’t perfect, there are indications in the structure that the fit has a deeper significance. For example, note how many of the shoulders in the structure align, as highlighted below in yellow

The peaks and valleys do wander about a bit and might be a result of the sensitivity to the semi-annual impulse and the fact that this is only a monthly resolution. The chart below is a detailed fit of the QBO using data with a much finer daily resolution. As you can see, slight changes in the seasonal timing of the semi-annual pulse are needed to individually align the 70 and 30 hBar QBO time-series data.

This will require further work, especially in considering recently reported perturbations in the QBO periodicity, but it is telling that a shared draconic forcing of the ENSO and QBO models suggests an important cross-validation of the underlying causal mechanism.

*Another potential geophysical cross-validation …*

The underlying forcing of the ENSO model shows both an 18-year Saros cycle (which is an eclipse alignment cycle of all the tidal periods), along with a 6-year anomalistic/draconic interference cycle. This modulation of the main anomalistic cycle appears in both the underlying daily and monthly profile, shown below before applying an annual impulse. The 6-year is clearly evident as it aligns with the x-axis grid 1880, 1886, 1892, 1898, etc.

The bottom inset shows that a similar 6-year cycle consistently appears in length-of-day (LOD) analyses, this particular trace from a recent paper: [ Leonid Zotov *et al* 2020 *J. Phys.: Conf. Ser.* **1705** 012002 ].

The 6-year cycle in the LOD is not aligned as strictly as the tidal model and it tends to wander, but it seems a more plausible and parsimonious explanation of the modulation than for example in this paper (where the 6-year LOD cycle is *“similarly detected in the variations of C22 and S22, the degree-2 order-2 Stokes coefficients of the Earth’s gravitational field”*).

Cross-validation confidence improves as the number of mutually agreeing alignments increase. Given the fact that controlled experiments are impossible to perform, this category of analyses is the best way to validate the geophysical models.

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# Is the equatorial LTE solution a 1-D gyre?

The analytical solution to Laplace’s Tidal Equation along the 1-D equatorial wave guide not only appears odd, but it acts odd, showing a Mach-Zehnder-like modulation which can be quite severe. It essentially boils down to a sinusoidal modulation of a forcing, belonging to a class of non-autonomous functions. The standing wave comes about from deriving a separable spatial component.

*sin*(* f(t) *) *sin*(*kx*)

The underlying structure of the solution shouldn’t be surprising, since as with Mach-Zehnder, it’s fundamentally related to a path integral formulation known from mathematical physics. As derived via quantum mechanics (originally by Feynman), one temporally integrates an energy Hamiltonian over a path allowing the wave function to interfere with itself over all possible wavenumber (*k*) and spatial states (*x*).

Because of the imaginary value *i* in the exponential, the result is a sinusoidal modulation of some (potentially complicated) function. Of course, the collective behavior of the ocean is not a quantum mechanical result applied to fluid dynamics, yet the topology of the equatorial waveguide can drive it to *appear *as one, see the breakthrough paper “Topological Origin of Equatorial Waves” for a rationale. (The curvature of the spherical earth can also provide a sinusoidal basis due to a trigonometric projection of tidal forces, but this is rather weak — not expanding far beyond a first-order expansion in the Taylor’s series)

Moreover, the rather strong interference may have a physical interpretation beyond the derived mathematical interpretation. In the past, I have described the modulation as wave breaking, in that the maximum excursions of the inner function *f*(*t*) are folded non-linearly into itself via the limiting sinusoidal wrapper. This is shown in the figure below for progressively increasing modulation.

In the figure above, I added an extra dimension (roughly implying a toroidal waveguide) which allows one to visualize the wave breaking, which otherwise would show as a progressively more rapid up-and-down oscillation in one dimension.

Perhaps coincidentally (or perhaps not) this kind of sinusoidal modulation also occurs in heuristic models of the *double-gyre* structure that often appears in fluid mechanics. In the excerpt below, note the *sin*(*f*(*t*)) formulation.

The interesting characteristic of the structure lies in the more rapid cyclic variations near the edge of the gyre, which can be seen in an animation (Jupyter notebook code here).

Whether the equivalent of a double-gyre is occurring via the model of the LTE 1-D equatorial waveguide is not clear, but the evidence of double-gyre wavetrains (Lagrangian coherent structures, Kelvin–Helmholtz instabilities), occurring along the equatorial Pacific is abundantly clear through the appearance of tropical instability (TIW) wavetrains.

These so-called coherent structures may be difficult to isolate for the time being, especially if they involve subtle interfaces such as thermocline boundaries :

Mercator analysis does show higher levels of waveguide modulation, so perhaps this will be better discriminated over time *(see figure below with the long wavelength ENSO dipole superimposed along with the faster TIW wavenumbers in dashed line, with the double-gyre pairing in green + dark purple)*, and something akin to a 1-D gyre structure will become a valid description of what’s happening along the thermocline. In other words, the wave-breaking modulation due to the LTE modulation is essentially the same as the vortex gyre mapped into a 1-D waveguide.

# Sea-Level Height as a proxy for ENSO

Sea-level height has several scales. At the daily scale it represents the well-known lunisolar tidal cycle. At a multi-decadal, long-term scale it represents behaviors such as global warming. In between these two scales is what often appears to be noisy fluctuations to the untrained eye. Yet it’s fairly well-accepted [1] that much of this fluctuation is due to the side-effects of alternating La Nina and El Nino cycles (aka ENSO, the El Nino Southern Oscillation), as represented by measures such as NINO34 and SOI.

To see how startingly aligned this mapping is, consider the SLH readings from Ft. Denison in Sydney Harbor. The interval from 1980 to 2012 is shown below, along with a fit used recently to model ENSO.

*(click to expand chart)*

I chose a shorter interval to somewhat isolate the trend from a secular sea-level rise due to AGW. The last point is 2012 because tide gauge data collection ended then.

As cross-validation, this fit is extrapolated backwards to show how it matches the historic SOI cycles

Much of the fine structure aligns well, indicating that *intrinsically *the dynamics behind sea-level-height at this scale are due to ENSO changes, associated with the inverted barometer effect. The SOI is essentially the pressure differential between Darwin and Tahiti, so the prevailing atmospheric pressure occurring during varying ENSO conditions follows the rising or lowering Sydney Harbor sea-level in a synchronized fashion. The change is 1 cm for a 1 mBar change in pressure, so that with the SOI extremes showing 14 mBar variation at the Darwin location, this accounts for a 14 cm change in sea-level, roughly matching that shown in the first chart. Note that being a differential measurement, SOI does not suffer from long-term secular changes in trend.

Yet, the unsaid implication in all this is that not only are the daily variations in SLH due to lunar and solar cyclic tidal forces, but so are these monthly to decadal variations. The longstanding impediment is that oceanographers have not been able to solve Laplace’s Tidal Equations that reflected the non-linear character of the ocean’s response to the long-period lunisolar forcing. Once that’s been analytically demonstrated, we can observe that both SLH and ENSO share essentially identical lunisolar forcing (see chart below), arising from that same common-mode linked mechanism.

Many geographically located tidal gauge readings are available from the Permanent Service for Mean Sea Level (PSMSL) repository so I can imagine much can be done to improve the characterization of ENSO via SLH readings.

**REFERENCES**

[1] F. Zou, R. Tenzer, H. S. Fok, G. Meng and Q. Zhao, “The Sea-Level Changes in Hong Kong From Tide-Gauge Records and Remote Sensing Observations Over the Last Seven Decades,” in *IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing*, vol. 14, pp. 6777-6791, 2021, doi: 10.1109/JSTARS.2021.3087263.

# Highly-Resolved Models of NAO and AO Indices

Revisiting earlier modeling of the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) indices with the benefit of updated analysis approaches such as negative entropy. These two indices in particular are intimidating because to the untrained eye they appear to be more noise than anything deterministically periodic. Whereas ENSO has periods that range from 3 to 7 years, both NAO and AO show rapid cycling often on a faster-than-annual pace. The trial ansatz in this case is to adopt a semi-annual forcing pattern and synchronize that to long-period lunar factors, fitted to a Laplace’s Tidal Equation (LTE) model.

Start with candidate forcing time-series as shown below, with a mix of semi-annual and annual impulses modulating the primarily synodic/tropical lunar factor. The two diverge slightly at earlier dates (starting at 1880) but the NAO and AO instrumental data only begins at the year 1950, so the two are tightly correlated over the range of interest.

The intensity spectrum is shown below for the semi-annual zone, noting the aliased tropical factors at 27.32 and 13.66 days standing out.

The NAO and AO pattern is not really that different, and once a strong LTE modulation is found for one index, it also works for the other. As shown below, the lowest modulation is sharply delineated, yet more rapid than that for ENSO, indicating a high-wavenumber standing wave mode in the upper latitudes.

The model fit for NAO (data source) is excellent as shown below. The training interval only extended to 2016, so the dotted lines provide an extrapolated fit to the most recent NAO data.

Same for the AO (data source), the fit is also excellent as shown below. There is virtually no difference in the lowest LTE modulation frequency between NAO and AO, but the higher/more rapid LTE modulations need to be tuned for each unique index. In both cases, the extrapolations beyond the year 2016 are very encouraging (though not perfect) cross-validating predictions. The LTE modulation is so strong that it is also structurally sensitive to the exact forcing.

Both NAO and AO time-series appear very busy and noisy, yet there is very likely a strong underlying order due to the fundamental 27.32/13.66 day tropical forcing modulating the semi-annual impulse, with the 18.6/9.3 year and 8.85/4.42 year providing the expected longer-range lunar variability. This is also consistent with the critical semi-annual impulses that impact the QBO and Chandler wobble periodicity, with the caveat that group symmetry of the global QBO and Chandler wobble forcings require those to be draconic/nodal factors and not the geographically isolated sidereal/tropical factor required of the North Atlantic.

It really is a highly-resolved model potentially useful at a finer resolution than monthly and that will only improve over time.

*(as a sidenote, this is much better attempt at matching a lunar forcing to AO and jet-stream dynamics than the approach Clive Best tried a few years ago. He gave it a shot but without knowledge of the non-linear character of the LTE modulation required he wasn’t able to achieve a high correlation, achieving at best a 2.4% Spearman correlation coefficient for AO in his Figure 4 — whereas the models in this GeoenergyMath post extend beyond 80% for the interval 1950 to 2016! )*

# Climate Dipoles as crystal-crypto

Climate scientists as a general rule don’t understand crystallography deeply (I do). They also don’t understand cryptography (that, I don’t understand deeply either). Yet, as the last post indicated, knowledge of these two scientific domains is essential to decoding dipoles such as the El Nino Southern Oscillation (ENSO). Crystallography is basically an exercise in signal processing where one analyzes electron & x-ray diffraction patterns to be able to decode structure at the atomic level. It’s mathematical and not for people accustomed to existing outside of *real space*, as diffraction acts to transform the world of 3-D into a *reciprocal space* where the dimensions are inverted and common intuition fails.

Cryptography in its common use applies a *key *to enable a user to decode a scrambled data stream according to the instruction pattern embedded within the key. If diffraction-based crystallography required a complex unknown key to decode from reciprocal space, it would seem hopeless, but that’s exactly what we are dealing with when trying to decipher climate dipole time-series -— we don’t know what the decoding key is. If that’s the case, no wonder climate science has never made any progress in modeling ENSO, as it’s an existentially difficult problem.

The breakthrough is in identifying that an analytical solution to Laplace’s tidal equations (LTE) provides a crystallography+cryptography analog in which we can make some headway. The challenge is in identifying the decoding key (an unknown forcing) that would make the reciprocal-space inversion process (required for LTE demodulation) straightforward.

According to the LTE model, the forcing has to be a combination of tidal factors mixed with a seasonal cycle (stages 1 & 2 in the figure above) that would enable the last stage (Fourier series a la diffraction inversion) to be matched to empirical observations of a climate dipole such as ENSO.

The forcing key used in an ENSO model was described in the last post as a predominately *Mm*-based lunar tidal factorization as shown below, leading to an excellent match to the NINO34 time series after a minimally-complex LTE modulation is applied.

Critics might say and justifiably so, that this is potentially an over-fit to achieve that good a model-to-data correlation. There are too many degrees of freedom (DOF) in a tidal factorization which would allow a spuriously good fit depending on the computational effort applied (see **Reference 1** at the end of this post).

Yet, if the forcing key used in the ENSO model was **reused as is **in fitting an independent climate dipole, such as the AMO, and this same key required little effort in modeling AMO, then the over-fitting criticism is invalidated. What’s left to perform is finding a distinct low-DOF LTE modulation to match the AMO time-series as shown below.

This is an example of a *common-mode cross-validation* of an LTE model that I originally suggested in an AGU paper from 2018. Invalidating this kind of analysis is exceedingly difficult as it requires one to show that the erratic cycling of AMO can be randomly created by a few DOF. In fact, a few DOFs of sinusoidal factors to reproduce the dozens of AMO peaks and valleys shown is virtually impossible to achieve. I leave it to others to debunk via an independent analysis.

addendum: LTE modulation comparisons, essentially the wavenumber of the diffraction signal:

This is the *forcing *power spectrum showing the principal * Mm *tidal factor term at period 3.9 years, with nearly identical spectral profiles for both ENSO and AMO.

According to the precepts of cryptography, decoding becomes straightforward once one knows the key. Similarly, nature often closely guards its secrets, and until the key is known, for example as with DNA, climate scientists will continue to flounder.

** References **

- Chao, B. F., & Chung, C. H. (2019). On Estimating the Cross Correlation and Least Squares Fit of One Data Set to Another With Time Shift.
*Earth and Space Science*, 6, 1409–1415. https://doi.org/10.1029/2018EA000548

“*For example, two time series with predominant linear trends (very low DOF) can have a very high ρ (positive or negative), which can hardly be construed as an evidence for meaningful physical relationship. Similarly, two smooth time series with merely a few undulations of similar timescale (hence low DOF) can easily have a high apparent ρ just by fortuity especially if a time shift is allowed. On the other hand, two very “erratic” or, say, white time series (hence high DOF) can prove to be significantly correlated even though their apparent ρ value is only moderate. The key parameter of relevance here is the DOF: A relatively high ρ for low DOF may be less significant than a relatively low ρ at high DOF and vice versa.*“

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