Geoenergy

Atmospheric Science

/ geoenergymath / 2 Comments

I don’t immediately trust the research published by highly cited atmospheric scientists. By my count many of them seem more keen on presenting their personal views rather than advancing the field. Off the top of my head, Richard Lindzen, Murry Salby, Roy Spencer, Tim Dunkerton, Roger Pielke, Cliff Mass, Judith Curry are all highly cited but come across as political and/or religious zealots. One guy on the list, Dunkerton, is also a racist, who happened to make the Washington Post twice : “Physicist ousted from research post after sending offensive tweet to Hispanic meteorologist” and “Atmospheric scientist loses honor, membership over ethics violation“. Awful stuff and he hasn’t stopped spouting off on Twitter.

Granted that Dunkerton says dumb stuff on Twitter but his highly cited research is also off-base. That’s IMO only because recent papers by others in the field of atmospheric science do continue to cite his ideas as primary, if not authoritative. For example, from a recently published paper “The Gravity Wave Activity during Two Recent QBO Disruptions Revealed by U.S. High-Resolution Radiosonde Data”, citations 1 & 12 both refer to Dunkerton, and specifically to his belief that the QBO period is a property of the atmospheric medium itself

Straight-forward to debunk this Dunkerton theory since the length of the cycle directly above the QBO layer is semi-annual and thus not a property of the medium but of the semi-annual nodal forcing frequency. If we make the obvious connection to the other nodal forcing — that of the moon — then we find the QBO period is fixed to 28 months. I have been highlighting this connection to the authors of new QBO papers under community review, often with some subsequent feedback provided such as here: https://doi.org/10.5194/acp-2022-792-CC1 . Though not visible yet in the comments, I received some personal correspondence that showed that the authors under peer-review are taking the idea seriously and attempting to duplicate the calculations. They seem to be methodical in their approach, asking for clarification and further instructions where they couldn’t follow the formulation. They know about the GitHub software, so hopefully that will be of some help.

In contrast, Dunkerton also knows about my approach but responds in an inscrutable (if not condescending) way. Makes you wonder if scientists such as Dunkerton and Lindzen are bitter and taking out their frustrations via the media. Based on their doggedness, they may in fact be intentionally trying to impede progress in climate science by taking contrarian stances. In my experience, the top scientists in other research disciplines don’t act this way. YMMV

UPDATE 3/17/2023

More activity related to my review comment https://doi.org/10.5194/acp-2022-792-CC1

As a review, this was after commenting earlier this year on a Copernicus open-science research article on atmospheric cycles and QBO (with proposed links to sunspots and ENSO) that was undergoing a review, making a suggestion to consider analyses I had presented and published 4 years ago and also prior to that.

Thought that was that and was happy to see that the authors indicated they would revise the manuscript and perhaps advance understanding. But then several days ago, the editor interceded and essentially demanded that the authors not cite my research work. Apparently, the authors were influenced by the editor’s instructions, as they immediately removed my cite and replaced it with a citation to a review article that the editor preferred. The discussion on the article was then closed with no way for me to rebut.

This was all after I spent several hours working with the primary author as they worked to replicate my analysis, sending emails back and forth several times. The editor claimed that my contribution was “a new idea that has not been published in a recognized journal and received peer review”. This is not the case as I said above: Google Scholar citations all ignored.

Also see this post I contributed to the Peak Oil Barrel blog : https://peakoilbarrel.com/predicting-stratospheric-winds/

Steven Koonin & Unsettled: Cooking oil

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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:

Page 3

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

Page 7
Page 10
Page 24

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

Page 33

But then back to cooking oil! Twice!

Page 119
Page 172

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

Page 227
Page 243

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

Index

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.

QBO Aliased Harmonics

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In Chapter 12, we described the model of QBO generated by modulating the draconic (or nodal) lunar forcing with a hemispherical annual impulse that reinforces that effect. This generates the following predicted frequency response peaks:

From section 11.1.1 Harmonics

The 2nd, 3rd, and 4th peaks listed (at 2.423, 1.423, and 0.423) are readily observed in the power spectra of the QBO time-series. When the spectra are averaged over each of the time series, the precisely matched peaks emerge more cleanly above the red noise envelope — see the bottom panel in the figure below (click to expand).

Power spectra of QBO time-series — the average is calculated by normalizing the peaks at 0.423/year.
Each set of peaks is separated by a 1/year interval.

The inset shows what these harmonics provide — essentially the jagged stairstep structure of the semi-annual impulse lag integrated against the draconic modulation.

It is important to note that these harmonics are not the traditional harmonics of a high-Q resonance behavior, where the higher orders are integral multiples of the fundamental frequency — in this case at 0.423 cycles/year. Instead, these are clear substantiation of a forcing response that maintains the frequency spectrum of an input stimulus, thus excluding the possibility that the QBO behavior is a natural resonance phenomena. At best, there may be a 2nd-order response that may selectively amplify parts of the frequency spectrum.

See my latest submission to the ESD Ideas issue : ESDD – ESD Ideas: Long-period tidal forcing in geophysics – application to ENSO, QBO, and Chandler wobble (copernicus.org)

The SAO and Annual Disturbances

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In Chapter 11 of the book Mathematical GeoEnergy, we model the QBO of equatorial stratospheric winds, but only touch on the related cycle at even higher altitudes, the semi-annual oscillation (SAO). The figure at the top of a recent post geometrically explains the difference between SAO and QBO — the basic idea is that the SAO follows the solar tide and not the lunar tide because of a lower atmospheric density at higher altitudes. Thus, the heat-based solar tide overrides the gravitational lunar+solar tide and the resulting oscillation is primarily a harmonic of the annual cycle.

Figure 1 : The SAO modeled with the GEM software fit to 1 hPa data along the equator
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Why couldn’t Lindzen figure out QBO?

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Background: see Chapter 11 of the book.

In research articles published ~50 years ago, Richard Lindzen made these assertions:

“For oscillations of tidal periods, the nature of the forcing is clear”

Lindzen, Richard S. “Planetary waves on beta-planes.” Mon. Wea. Rev 95.7 (1967): 441-451.

and

“5. Lunar semidiurnal tide

One rationale for studying tides is that they are motion systems for which we know the periods perfectly, and the forcing almost as well (this is certainly the case for gravitational tides). Thus, it is relatively easy to isolate tidal phenomena in the data, to calculate tidal responses in the atmosphere, and to compare the two. Briefly, conditions for comparing theory and observation are relatively ideal. Moreover, if theory is incapable of explaining observations for such a simple system, we may plausibly be concerned with our ability to explain more complicated systems.

Lunar tides are especially well suited to such studies since it is unlikely that lunar periods could be produced by anything other than the lunar tidal potential. “

Lindzen, R.S. and Hong, S.S., 1974. “Effects of mean winds and horizontal temperature gradients on solar and lunar semidiurnal tides in the atmosphere“. Journal of the atmospheric sciences, 31(5), pp.1421-1446.
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EGU 2020 Notes

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Notes from the EGU 2020 meeting from last week. The following list of presentation pages are those those that I have commented on, with responses from authors or from chat sessions included. What’s useful about this kind of virtual meeting is that the exchange is concrete and there’s none of the ambiguity in paraphrasing one-on-one discussions that take place at an on-site gathering.

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The Oil Shock Model and Compartmental Models

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Chapter 5 of the book describes a model of the production of oil based on discoveries followed by a sequence of lags relating to decisions made and physical constraints governing the flow of that oil. As it turns out, this so-named Oil Shock Model is mathematically similar to the compartmental models used to model contagion growth in epidemiology, pharmaceutical/drug deliver systems, and other applications as demonstrated in Appendix E of the book.

One aspect of the 2020 pandemic is that everyone with any math acumen is becoming aware of contagion models such as the SIR compartmental model, where S I R stands for Susceptible, Infectious, and Recovered individuals. The Infectious part of the time progression within a population resembles a bell curve that peaks at a particular point indicating maximum contagiousness. The hope is that this either peaks quickly or that it doesn’t peak at too high a level.

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Wind Energy Dispersion

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In Chapter 11 of the book, we derive the distribution of wind speeds and show what role the concept of maximum entropy plays into the formulation. It’s a simple derivation and one that can be extended by layering more dispersion on the variability, in effect superposing more uncertainty on the Rayleigh or Weibull distribution that is typically used to quantify wind speed distribution. This is often referred to as superstatistics, first described by Beck, C., & Cohen, E. (2003) in Physica A: Statistical Mechanics and Its Applications, 322, 267–275.

A recent article uploaded to arXiv [1] gives an alternate treatment to the one we described. This follows Beck’s original approach more than our simplified formulation but each is an important contribution to understanding and applying the math of wind variability. The introduction to their article is valuable in providing a rationale for doing the analysis.

“Mitigating climate change demands a transition towards renewable electricity generation, with wind power being a particularly promising technology. Long periods either of high or of low wind therefore essentially define the necessary amount of storage to balance the power system. While the general statistics of wind velocities have been studied extensively, persistence (waiting) time statistics of wind is far from well understood. Here, we investigate the statistics of both high- and low-wind persistence. We find heavy tails and explain them as a superposition of different wind conditions, requiring q-exponential distributions instead of exponential distributions. Persistent wind conditions are not necessarily caused by stationary atmospheric circulation patterns nor by recurring individual weather types but may emerge as a combination of multiple weather types and circulation patterns. Understanding wind persistence statistically and synoptically, may help to ensure a reliable and economically feasible future energy system, which uses a high share of wind generation. “

[1]Weber, J. et al. “Wind Power Persistence is Governed by Superstatistics”. arXiv preprint arXiv:1810.06391 (2019).

SSAO and MSAO

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In Chapter 11 of the book, we concentrated on the mechanism behind the QBO of stratospheric equatorial winds. In a related topic (but only briefly touched on in the book), there is interesting data from a presentation on the equatorial-only Semi-Annual Oscillation (SAO) of the upper stratosphere and lower mesosphere wind pattern [1]. The distinction between QBO and the SAO is that the QBO has a longer periodic cycle and exists at altitudes lower in the stratosphere than the SAO.

[1] T. Hirooka, T. Ohata, and N. Eguchi, “Modulation of the Semiannual Oscillation Induced by Sudden Stratospheric Warming Events,” in ISWA2016, Tokyo, Japan, 2016, p. 16.

— presentation slides from International Symposium on the Whole Atmosphere

What’s interesting at the core fundamental level is that the SAO is understood by consensus to be forced by a semi-annual cycle (a resonant condition happening to match 1/2 year is just too coincidental) whereas there is no consensus behind the mechanism behind the QBO period (the tidal connection is only available from Chapter 12). To make the mathematical connection, the following shows how the SAO draws from the QBO tidal model.

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