The First Red Pill - How Can You Weaponize the Lunar Atmospheric Tides When it Comes to Climate?

As Morpheus so famously said to Thomas Anderson in the movie "The Matrix":

"This your last chance. After this, there is no turning back. You take the blue pill, the story ends. You wake up in your bed and believe whatever you want to. You take the red pill, you stay in Wonderland, and I show you how deep the rabbit hole goes. Remember, all I'm offering is the truth. Nothing more."

This is the first of series of multiple posts that offers you the opportunity to EITHER, take the blue pill and continue to believe what the Australian Bureau of Meteorology (BOM) tells you about Australia's climate OR take the red pill and open your mind to an alternative reality.

RED PILL 1 The influence of cycles in the atmospheric lunar tides upon the Earth's atmospheric pressure can be re-inforced (i.e weaponized) if they constructively interfere with the annual seasonal cycle.

Q1. How Can You Weaponize the Lunar Atmospheric Tides When it Comes to Climate?

The most significant large-scale systematic variations of the atmospheric pressure, on an inter-annual to decadal time scale, are those caused by the seasons. These variations are predominantly driven by the change in the level of solar insolation with latitude that is produced by the effects of the Earth's obliquity (i.e. the tilt of its rotation axis with respect to the Earth-Sun plane) and its annual motion around the Sun (i.e. its orbit).

This raises an important question: What is the most effective way for cycles in the lunisolar atmospheric tides to influence the Earth's atmospheric pressure, on inter-annual to decadal time scales?

One way is that the lunisolar atmospheric tides can act independently of the variations in atmospheric pressure caused by the seasonal changes in the level of solar insolation with latitude.

If this was true, you would expect to see long-term periodicities in the atmospheric pressure records that would match periodicities of the most extreme peak lunar tides.

An alternative way is that the lunar atmospheric tides could act in "resonance" with (i.e. subordinate to) the atmospheric pressure changes caused by the far more dominant solar-driven seasonal cycles.

With this type of simple “resonance” model, it is not so much in what years do the atmospheric lunar tides reach their maximum strength, but whether or not there are peaks in the strength of the lunar atmospheric tides that re-occur at the same time within the annual seasonal cycle.

So the answer our original question is that, strong peaks in the lunar tidal forces that slowly drift through the seasons, with each advancing year, may not be as effective at influencing the Earth's atmospheric pressure distribution, as weaker tidal peaks that appear at precisely the same time during the seasonal year (hereafter referred to as seasonal peak tides).

In essence, the influence of cycles in the atmospheric lunar tides upon the Earth's atmospheric can be re-inforced (i.e weaponized) if they constructively interfere with the annual seasonal cycles.

   

Keeping Track of the Latest MJO Event - Update 25/10/2019

A. Location

B. 1000 hPa Near-Surface Wind Map

C. 850 hPa - Wind Map Showing Embedded Equatorial Rossby Wave in MJO

 

Keeping Track of the Current Madden Julian Oscillation (MJO) Event

19/10/2019 06 UT

A. Location

B. 1000 hPa Near-Surface Wind Map

70 hPa - 18km Altitude Wind Map - Showing an embedded Kelvin Wave in the MJO

Temporary Post on Transits of Venus

A Luni-Solar Connection to Weather and Climate III: Sub-Centennial Time Scales

Wilson, I.R.G. and Sidorenkov, N.S., 2019, A Luni-Solar Connection to Weather and Climate III: Sub-Centennial Time Scales, The General Science Journal, 7927

https://www.gsjournal.net/Science-Journals/Research%20Papers-Astrophysics/Download/7927

The following figure shows the raw HadCRUT4 monthly (Land + Sea) world mean temperature anomaly (WMTA) data from 1850 to 2017 (grey line – Climatic Research Unit, University of East Anglia, 2017).

https://crudata.uea.ac.uk/cru/data/temperature/

Following the method used by Copeland and Watts (2009), a Hodrick Prescott filter (Hodrick and Prescott 1981 - using λ = 129,0000) is applied to the raw WMTA data to produce a smoothed temperature anomaly curve (Excel Plugin 2019). The resulting smoothed anomaly curve is superimposed upon the raw WMTA data in the figure below (red line).

The Hodrick Prescott filter is designed to separate a time-series data into a trend component and a cyclical component using a technique that is equivalent to a cubic spline smoother. It acts as a low-pass filter that smooths out short-term temperature fluctuations, leaving behind the unattenuated long-term oscillatory signals (Copland and Watts 2009). Given the specific value of λ used here, this effectively translates to a band-pass that eliminates all the oscillatory temperature signals that have periods ≤ 7.0 years (Copland and Watts 2009).

      Investigations of climate change generally involve the study of "forcings" upon the climate system. These are expressed in power terms that are measured in W m^-2. This means that the best way to study temporal changes in these "forcings" is to look at time series of the first differences in the total energies that are associated with each forcing. Similarly, the mean temperature of the Earth's atmosphere is a measure of its total energy content. Thus, the best way to study the changes in the climate "forcings" that impact the mean temperature of the Earth's atmosphere is to look at time-series of the first difference in world-mean temperature, rather than time-series of the temperature itself [Goodman 2013].

 

     Following this train of logic, the first difference curve of the smoothed trend component of the WMTA time series is calculated in degrees Celsius per month. The resultant first difference curve (multiplied by an arbitrary factor of 150) is plotted in the figure below (blue curve). Superimposed upon this is the raw temperature anomaly data (light-grey curve) and the smoothed trend component (red curve), displayed in units of degrees Celsius.

The blue dashed curve in the figure below shows a superposition of a sine wave of amplitude 1.0 unit and period 9.1 tropical years with a sine wave of amplitude 2.0 units and period 10.1469 (= 9 FMC’s) tropical years. Note that the units used are degrees Celsius per month times 1000. The actual function used in this figure is:

where t is the date expressed in decimal Gregorian years [N.B. For the purposes of this study, this curve will be referred to as the lunar tidal forcing curve – i.e. LTF curve].

Overlaying this is a red curve which is simply a reproduction of the difference plot of the smoothed component of the WMTA from the earlier figure [N.B. For the purposes of this study, this curve will be referred to as the difference of the smoothed temperature anomaly curve – i.e. DSTA. It has units measured in degrees Celsius per month. In addition, the DSTA curve values have been scaled-up by a factor of 1000 to roughly match the variance of the LTF curve. This is done to aid the comparison between these two curves.

     A comparison of the DSTA and LTF curves shows that that the timing of the peaks in the LTF curve closely match those seen in the DSTA curve for two 45-year periods. 

     The first going from 1865 to 1910 and the second from 1955 to 2000. Note that these years are delineated by the black vertical lines in this figure. During these two epochs, the aligned peaks of the LTF and the DSTA curves are separated from adjacent peaks by roughly the 9.6 years, which is close to the mean of 9.1 and 10.1469 years. This is in stark contrast to the 45-year period separating these two epochs (i.e. from 1910 to 1955), and the period after the year 2000, where the close match between the timing of the peaks in LTF and DSTA curves breaks down, with the DSTA peaks becoming separated from their neighboring peaks by approximately 20 years.

     Hence, the variations in the rate of change of the smoothed HadCRUT4 temperature anomalies closely follow a “forcing” curve that is formed by the simple sum of two sinusoids, one with a 9.1-year period which matches that of the lunar tidal cycle, and the other with a period of 10.1469-year that matches that of half the Perigean New/Full moon cycle. This is precisely what you would expect if the natural periodicities associated with the Perigean New/Full moon tidal cycles were driving the observed changes in the world mean temperature on decadal time scales.

References:

Copeland, B. and Watts, A., 2009, Evidence of a Luni-Solar Influence on the Decadal and Bi-decadal Oscillations in Globally Averaged Temperature Trends, retrieved at:

http://wattsupwiththat.com/2009/05/23/evidence-of-a-lunisolar-influence-on-decadal-and-bidecadal-oscillations-in-globally-averaged-temperature-trends/

Ref: https://climategrog.wordpress.com/2013/03/01/61/

Lunar-Solar Influence on SST March 1^st, 2013, Greg Goodman

Hodrick, R.  Prescott, E.,  1997, Postwar US business cycles: an empirical investigation.  Journal of Money, Credit, and Banking. 29(1): pp. 1-16. Reprint of University of Minneapolis discussion paper 451, 1981.

Are the Strongest Lunar Perigean Spring Tides Commensurate with the Transit Cycle of Venus?

The following graph inspired my 2014 paper entitled:

Wilson, I.R.G. Are the Strongest Lunar Perigean Spring Tides Commensurate with the Transit Cycle of Venus?,  Pattern Recogn. Phys., 2, 75-93

Received: 25/Jul/2014 - Revised: 10/Sep/2014 - Accepted: 18/Sep/2014 - Published: 28/Nov/2014

It was accepted for publication in the second volume of the Journal Pattern Recognition in Physics (PRP) on the 18th of November 2014. The publishers of the PRP, Copernicus Publications, decided to close the journal in 2014, despite having accepted my paper for publication. It temporarily appeared on-line (i.e. was published) and then removed. In protest, I refused to pay the publication fee until they put my paper back up on-line. They never did.  I didn't realize that a publisher could accept a paper for publication, publish it and then remove it from publication, without giving any rational reason for their actions.

This graph shows the remarkable alignment between the dates for the transits of Venus over a 700-year period between 1600 and 2300 A.D. and the repetition pattern for the most extreme Perigean Spring tides that are closest to the nominal date of the Perihelion of the Earth's orbit. 

Abstract: 

This study identifies the strongest perigean spring tides that reoccur at roughly the same time in the seasonal calendar and shows how their repetition pattern, with respect to the tropical year, is closely synchronized with the 243-year transit cycle of Venus. It finds that whenever the pentagonal pattern for the inferior conjunctions of Venus and the Earth drifts through one of the nodes of Venus’ orbit, the 31/62 year perigean spring tidal cycle simultaneously drifts through almost exactly the same days of the Gregorian year, over a period from 1 to 3000 A.D. Indeed, the drift of the 31/62 year tidal cycle with respect to the Gregorian calendar almost perfectly matches the expected long-term drift between the Gregorian calendar and the tropical year. If the mean drift of the 31/62 perigean spring tidal cycle is corrected for the expected long-term drift between the Gregorian calendar and the tropical year, then the long-term residual drift between: 

a) the 243-year drift-cycle of the pentagonal pattern for the inferior conjunctions of Venus and the Earth with respect to the nodes of Venus’s orbit and 

b) the 243-year drift-cycle of the strongest seasonal peak tides on the Earth (i.e. the 31/62 perigean spring tidal cycle) with respect to the tropical year,

is approximately equal to -7 ± 11 hours, over the 3000-year period. The large relative error of the final value for the residual drift means that this study cannot rule out the possibility that there is no long-term residual drift between the two cycles i.e. the two cycles are in perfect synchronization over the 3000 year period. However, the most likely result is a long-term residual drift of -7 hours, over the time frame considered.

Keywords: Solar System — Planetary Orbits — Lunar Tides