The Historical Process – Cycles of War

(originally posted 17/05/07 on Wobbly Universe blog by Ray Tomes)

In the early 1900s, Alexander Chizhevsky (sometimes spelled Tchijevsky or various other ways) made very comprehensive investigations in historical processes, especially wars, to try and determine the causes of the processes. He found that existing methods of analysis could not predict major events that were about to happen.

Unfortunately for us in the English speaking world, most of Chizhevsky’s writing is in Russian or French and has never been translated into English. Some parts have been, and the investigation into war has been reported in English. Some of this material has also been reported by Raymond Wheeler in America, but I think that Chizhevsky was the originator. It would be good to know the full story on any links between these two.

Chizhevsky first began to observe that conflicts in Russia tended to be cyclic. After publishing several papers on this he expanded his study to world wide conflicts and made an index of battles for the last 2500 years. It showed clearly that there were 9 fairly regular peaks in conflicts each century, or averaging 11.1 years apart. There were other fluctuations, great sweeping rises and falls over longer periods, but this one cycle stood out.

It so happens that the sunspot cycle also has an average period of 11.1 years, so it was natural to begin to look at the connection. He found that the peak in human aggression fairly much coincided with the peak in sunspots. It is natural to wonder as to whether the Sun is somehow causing or contributing to human behaviour.

There are some additional facts available to us now that help to understand this process. I have never seen this fully laid out exactly like this before (excluding things that I have written), but here are a collection of facts that we can connect the dots on:

1. The quantity of different types of radiation vary over the sunspot cycle.

2. There is a continuous stream of radiation of various types, called the “Solar Wind” that arrives at the Earth.

3. Fluctuations in the stream of radiation at the Earth affects the earth’s geomagnetic field.

4. There is an oscillation of the Earth’s entire electromagnetic field, called the “Schumann Resonance” which oscillates about 7.5 times per second because that is how fast electromagnetic waves travel around the Earth.

5. Bursts of Solar radiation can get the Schumann resonance really ringing. Although the 7.5 Hz mode is the main one, there are also oscillations at near multiples of this: 15, 22.5, 30 Hz and sometimes at lower frequencies such as 3 Hz.

6. Human brain wave frequencies that are commonly studied cover the range from about 0.3 to 40 Hz, and the Schumann resonance is right in the middle of that range. The brain is a largely electrical organ in its functioning.

7. Outside electromagnetic fields can affect the brain by entrainment or in other ways. For example it is known that 10 Hz waves will make human reaction times faster while 3 Hz waves will slow it down.

8. Accidents are more frequent when there are strong ELF or ULF (Extra Low Frequency or Ultra Low Frequency) waves such as 3 Hz. This can be understood as resulting from a slowing down of human reaction times – people are less able to deal with tripping up or sudden events and so more likely to have an accident.

I do not know of evidence that moods are affected by ELF / ULF waves, but would expect that to be the case. Certainly we may say that the evidence is that when the Sun gets uppity, so do people, probably because of electromagnetic waves affecting our brain operation.

My guess is that we all have stored up resentments that we are bottling up, and that when a bit of extra electrical activity is brought to bear on our brains, these then overflow just as they would when the last straw was added by events in our lives. Then all sorts of stuff bursts forth. However when this comes from the whole Earth resonating, it means that many people get agitated at the same time. That is just the recipe for international conflict or social unrest.

war-sun

For further reading please see:

Chizhevsky’s paper (1.4 MB PDF file) as published by the Foundation for the Study of Cycles.

Edward Dewey’s War cycle analysis (0.1 MB PDF file). Dewey found other cycles in Chizhevsky,s data also, of which the strongest were 53.5, 23.8, 17.4, 11.2 and 6.0 years.

For 4 years from early 2007 until late 2010, Ray Tomes ran a blog called “Wobbly Universe” on his personal web site. With software changes that blog stopped working. Over the coming weeks or months these old articles will be reposted to CRI blog.

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Harmonic Cloud Patterns on Jupiter

(originally posted 28/06/07 on Wobbly Universe blog by Ray Tomes)

At the NASA web site the following description is found about a polar map of Jupiter.

This map of Jupiter is the most detailed global color map of the planet ever produced. The round map is a polar stereographic projection that shows the south pole in the center of the map and the equator at the edge. It was constructed from images taken by Cassini on Dec. 11 and 12, 2000, as the spacecraft neared Jupiter during a flyby on its way to Saturn.

The map shows a variety of colorful cloud features, including parallel reddish-brown and white bands, the Great Red Spot, multi-lobed chaotic regions, white ovals and many small vortices. Many clouds appear in streaks and waves due to continual stretching and folding by Jupiter’s winds and turbulence. The bluish-gray features along the north edge of the central bright band are equatorial “hot spots,” meteorological systems such as the one entered by NASA’s Galileo probe. Small bright spots within the orange band north of the equator are lightning-bearing thunderstorms. The polar region shown here is less clearly visible because Cassini viewed it at an angle and through thicker atmospheric haze.

Image Credit: NASA/JPL/Space Science Institute

jupiter-pole       Jupiter polar map produced by NASA from photos taken December 11-12, 2000.

It is clear that there are several types of features that repeat around that planet at fairly regular intervals. There are regular white spots about half way between the pole and equator, and much closer regular notches nearer to the equator.

Several of these features are marked on the map below, and in some places where the pattern is not clear it is continued until it links with the same pattern further around.

jupiter-pole-harmonics       Jupiter polar map showing harmonics of 12, 24, 72 and 144.

The smaller wave pattern is made clearer in the following magnification. There is a Full size image of Jupiter available at this same scale at NASA.

jupiter-72        A magnification shows the regularity of the wave identified as the 72nd harmonic.

It so happens that there is an explanation based on the harmonic formation of waves in a non-linear system that predicts that certain harmonics should be much stronger than others. It is called The Harmonics Theory and was developed by me, Ray Tomes. Here is a graphic showing the predicted relative power in various harmonics:

h1-100       Predicted Strong Harmonics from 1 to 100 according to the Harmonics Theory

Quite clearly, 12, 24 and 72 (also 144 not shown on this graph) are expected to be strong harmonics. The reason that certain harmonics are stronger than others is that they can be formed in more ways. This type of behaviour is not observed in essentially linear systems such as a guitar string, where no harmonics are especially strong relative to their neighbours. But in 2 and 3 dimensional structures, especially nearly closed systems where standing waves can last for a long time, not only do harmonics form, but harmonics of harmonics.

This means that a wave that divides the planets circumference in 2 for example will itself have divisions which will therefore divide the planets circumference in 4, 6, 8, 10, 12 and so on. Likewise a wave that divides the planet into 3 will form harmonics that divide it into 6, 9, 12 and so on. Because some numbers can be factorised in many more ways than others, these strong harmonics will be more evident when we see pictures of Jupiter or any other nearly closed system which can sustain standing waves for long periods of time.

Harmonics theory also makes predictions of much smaller waves (larger order harmonics) and these predictions are also found in cloud patterns on Earth. When applied to the entire universe, the theory explains why there are certain scales of distance at which structures prefer to form and these include galaxies, stars, planets, moons as well as cells, atoms and nuclear particles. Based on the predicted and observed patterns, it can be concluded that the universe is much larger than many cosmologists currently believe.

For 4 years from early 2007 until late 2010, Ray Tomes ran a blog called “Wobbly Universe” on his personal web site. With software changes that blog stopped working. Over the coming weeks or months these old articles will be reposted to CRI blog.

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3.029 and 3.606 minute cycles in Solar Wind Speed

The ACE satellite measures the solar wind speed (among other things) of particles traveling from the Sun towards the Earth. Located at the L1 libration point it is always around 1.5 million km in the direction of the Sun and so can give about an hours warning of space weather changes. The solar wind speed is typically in the range of 300 to 700 km/s.

This 1 minute interval real time data remains available for only a short period of time. The  present analysis was done on data recorded on 11th July 2015 and there are 1440 (less about 20 or so missing) measurements at 1 minute intervals. The analysis was done using our free CATS software.

Firstly the rapidly fluctuating solar wind speed is shown.  solarwindspeed-20150711It can be seen that minute by minute there are substantial changes in solar wind speed. A spectral analysis of the solar wind speed shows a number of cycles significant at p=0.05 level as listed in this table:

no. of    Cycle period    Date of peak    Cycle            Bartell’s
cycles    minutes                                  Strength        Test (p=)

399.28    3.60649    2015.5252621    0.098406    0.00000000
475.39    3.02909    2015.5252634    0.076864    0.00000000
398          3.61809    2015.5252620    0.054579    0.00001875
400.83    3.59255    2015.5252648    0.050971    0.00014225
402.19    3.58040    2015.5252665    0.045818    0.00079066
49.32    29.19708    2015.5253121    0.070705    0.00204380
395.79    3.63829    2015.5252675    0.039579    0.00348685
217.41    6.62343    2015.5252699    0.042097    0.00369764
476.69    3.02083    2015.5252647    0.036243    0.00429099
50.53    28.49792    2015.5252634    0.060138    0.00621892
215.95    6.66821    2015.5252676    0.038759    0.00634786
474.01    3.03791    2015.5252625    0.032432    0.00820103
403.29    3.57063    2015.5252618    0.034605    0.00867797
74.88    19.23077    2015.5252801    0.046984    0.01230259
194.41    7.40703    2015.5252621    0.038716    0.01238090
32.64    44.11765    2015.5252903    0.042015    0.01393355
48.11    29.93141    2015.5253090    0.055255    0.01451557
161.24    8.93079    2015.5252751    0.043495    0.01474822
324.51    4.43746    2015.5252652    0.036788    0.01493206
482.48    2.98458    2015.5252647    0.020699    0.01656507
172.13    8.36577    2015.5252655    0.038061    0.01716400
76.21    18.89516    2015.5252839    0.050419    0.01754671
39.99    36.009    2015.5253088    0.057387    0.01863100
565.36    2.54705    2015.5252626    0.032063    0.02572346
160.07    8.99606    2015.5252717    0.03406    0.02682052
25.42    56.64831    2015.5252675    0.043156    0.02765430
120.2    11.98003    2015.5252833    0.036602    0.02871458
14.77    97.49493    2015.5253709    0.071089    0.03136794
222.09    6.48386    2015.5252709    0.035618    0.03216350
394.53    3.64991    2015.5252657    0.030707    0.03295080
230.47    6.2481    2015.5252618    0.031044    0.03326301
211.72    6.80144    2015.5252714    0.034033    0.03358752
210.13    6.8529    2015.5252671    0.031237    0.03360750
477.93    3.01299    2015.5252662    0.028155    0.03678022
274.73    5.24151    2015.5252660    0.031051    0.03685700
83.61    17.22282    2015.5252927    0.047398    0.03700348
47.23    30.4891    2015.5253041    0.057521    0.03791188
78.49    18.34629    2015.5252890    0.048698    0.03937316
23.15    62.20302    2015.5253658    0.065049    0.04028760
554.02    2.59918    2015.5252628    0.026363    0.04334227
247.27    5.82359    2015.5252616    0.029738    0.04570371
173.65    8.29254    2015.5252681    0.033344    0.04915963

The first two listed cycles in particular are extremely significant and would not occur even one time in a hundred million by chance alone. The Bartell’s test shows the significance of cycles as a probability. They did occur in two different days that I analysed, with extremely similar periods.

The part of the spectrum near to these two cycles is shown because there are other significant cycles nearby:

solarwindspeed-20150711-spectrum-3-minutesA graph of the autocorrelation of the minute by minute changes of the solar wind speed up to 200 minutes lag is shown:

solarwindspeed-20150711-beats-19-minutesThis shows how the change in solar wind speed compares to the corresponding change at lags of up to 200 minutes and it can be clearly seen that there is a cycle of 3 minutes or so, and also that there is a modulation of 19 minutes in the autocorrelation. The two dominant cycles periods will produce beats of 18.92 minutes just as we observe. Note that there are two actual cycles near to 19 minutes also.

The author has often found cycles of near 3 and 6 minutes in the solar system previously. Along with 80 and 160 minutes these periods are rather common. We may note that the inner planets are roughly spaced at 3 light minutes apart in distance from the Sun, and the outer planets at 80 light minutes apart.

In my previous post to CRI Blog, I reported on Kotovs method of analysing planetary distances from the Sun gives spacings which are multiples or fractions of 0.376, 0.734, 5.01 and 10.06 AU. Light travels 1 AU in 8.317 minutes, so these distances can be converted to periods of an electromagnetic (or gravitational) standing wave of 3.13, 6.10, 41.7 and 83.7 minutes. In the solar wind speed as well as many cycles around 3 minutes or so there are a number at a little over 6 minutes.

Two periods of 3.17 and 6.34 minutes have also been found by the author in fluctuations in the rate of radioactive decay of Plutonium as measured by Biophysics laboratory at Russian Academy of Sciences in Pushchino. These periods are not perfectly steady but wander about by around 5%.

 

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Kotov’s Method of Commensurability

Valery Kotov and colleagues  have made much study of 160 minute oscillations in a wide variety of astronomical phenomena, but especially the Sun. In some cases he uses a commensurability measure to determine the best fit values for oscillations. The main purpose of this article is to explain Kotov’s method of commensurability and the way that I use it to produce graphs that are a type of spectrum derived from a set of discrete measurements, usually of time periods or distances.

For this purpose, I am going to use the distances of the 9 traditional planets from the Sun in AU (astronomical units). Unlike the Titus Bode method where the fit is to a more or less  geometric series, this looks for something more like an arithmetic series. It does this by having a test value that is varied in tiny steps and for each value a measure of commensurability is determined. The test value is divided into each of the data values and the absolute difference from the nearest integer is found. In the case where the data value is less than the test value, the division is done the other way around so that the result is greater than one, then the difference from an integer is found. The sum of these “errors” is determined. In my method I then use the reciprocal of the sum. That then makes the graph look like a typical spectrum with peaks at the possibly interesting test values.

So with our example of the 9 traditional planet mean distances from the Sun the result is a graph that looks like this:

pladistquaThe two strongest peaks are at very close to 5 and 10 AUs. This is hardly surprising because the 4 outer planets are at rather close to 10, 20, 30 and 40 aAU from the Sun. With Jupiter at close to 5 AU we can easily understand these peaks. But we do see the arithmetic series 10, 20, 30 and 40 AU. For the inner planets there are also two peaks at around 0.35 and 0.7 AU. Again they make an approximate series at 0.35 AU intervals, but the regularity is less in this case. There is also a peak at about 120 AU. If there is meaning in this process then we might expect to find some additional matter concentration floating about at this distance from the Sun.

Here are the tables of fits to the best test values.

planets-qua-tableThe interesting thing is that although the 5 and 10 AU spacings obviously fit the 5 outer planets, they also fit 3 of the 4 inner planets. Only Mars is not near an integer fraction of 5 and 10 AU. I have also shown the 515 AU distance which commutes the best of all with the planets, having a sum of differences from integer of only 0.797.

Kotov has shown with many examples that there is a pervading 160 minute oscillation throughout the solar system, galaxy and even further afield. He has found this period in binary stars, planetary distances, and galactic cores as well as various solar phenomena. I have found further examples, and agree with him that this is a universal wave phenomenon. I refer generally to 3, 6, 80 and 160 minutes waves.

The solar system also shows a shorter wave of around 0.12 AU. If these waves are real then there should be consequences beyond the data that was feed in and indeed there is.

The asteroids density with distance show peaks at spacings of 0.12 AU. There are additional objects between and beyond the outer planets and these also favour multiples of 5 AU. So we can reasonably conclude that this method has shown us something real that is happening in the solar system (and beyond). I close with the opening graph but having added exact ratios between values showing that the waves have harmonic relationships to each other.

pladistquarat

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The Matamak Conference on Cycles in 1931

Copley Amory invited a group of about 30 scientists to be his guests at a conference at the mouth of the Matamak river in Canada where he had a summer house. The subject was various biological cycles, as it was known that different animals had huge variations in numbers over cycles of about 4 and 10 years. These various fluctuations were important to humans because they affected agriculture in many ways. Related matters of weather and climate cycles were also discussed and, surprisingly, the 11 year sunspot cycle did not dominate matters.

This report was filed by Ellsworth Huntington of Yale University working with an editorial committee and with the approval of the conference.

Matamak-Conference

Some years later, Edward R Dewey, who had been studying economic cycles, was to find out about this conference and in a short space of time had contacted the organisers and the Foundation for the Study of Cycles was born.

 

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Ray Tomes Speaking at NPA Conference in Baltimore

The Natural Philosophy Alliance (NPA) is an organisation that is determined to stick to sound scientific principles based in empirical research rather than sticking with some theory and ignoring the facts that disagree with it. NPA is holding a conference in Baltimore from November 19 to 21, 2014. Ray Tomes will be one of the many speakers, giving two papers about how the Harmonics Theory came about and its successful predictions and explanations.

Ray’s two talks are available here as PDF documents for download.

NPA-Harmonics Theory and how it came about

NPA-Predictions of the Harmonics Theory

 

Posted in Astronomy-General, astronomy-Planets, astronomy-Solar, cycles-Common, Cycles-General, Economy-General, economy-Markets, economy-Stock_Markets, Geology, Harmonics, Physics-General, physics-Geophysics, physics-Wave_Structure, Researchers-General | Tagged , , , , , , , , , , , , , , | 3 Comments

Sunspot Number reconstruction by Leif Svalgaard and proxy cycles of 104 and 208 years

In an article on WUWT, “Solar Activity – Past, Present, Future“, Leif Svalgaard describes the problems of historical sunspot records because of changes in instrumentation and observers. He produces a new series which attempts to correct for the various problems. He has made the data available to me to perform a cycles analysis. This proves to be interesting as it enables linking our understanding of the telescopic record with the proxy records from C14 and Be10.

Before I performed a cycles analysis on the adjusted sunspot numbers, I took the square root of them. This has the result of making the 11 year cycle more symmetrical, and making typical fluctuations near minima and near maxima of about equal amounts. It does not have much effect on this particular analysis. Here is what the series looks like then:
It can be seen that the range of each cycle since about 1750 is quite similar. This is the result of using square root of sunspot numbers.

Next, a spectral analysis was done on this series using CATS. This allows finer resolution than with such tools as Fourier analysis or FFT. The location of peaks can be determined with high precision as shown here:

When analysis of shorter periods of sunspots are performed, the second highest peak here often appears only as a bump on the shoulder of the highest peak. Because of the longer period the peaks are able to be resolved clearly.

A number of researchers, myself included, have suggested that three of the periods found here might be related to planetary motions affecting the Sun. The periods are Jupiter’s period of 11.86 years, the Jupiter-Saturn conjunction period of 9.93 years, and the Jupiter-Venus-Earth syzygy cycle of 11.07 years (also happens to correspond to Jupiter + Neptune frequency). The increased precision of these estimates is almost able to rule out some of these suspected matches. In particular, the 10.01 year period should have uncertainty of +/-0.025 and it differs from 9.93 years by 0.08 years.  Hard to say.

In order of strength the cycles periods are:
11.05, 10.49, 10.01, 11.79 years.
The interesting thing about these periods is the beats between them.
11.05 and 10.49 years gives 207 years beats.
11.05 and 10.01 years gives 106 year beats.
The other various pairs give beats of 220, 177, 95 and 66 years.

See previous articles about C14 cycles analysis and Be10 cycles analysis as these two series are considered to be proxies for the sunspot cycle. We see the strongest beats are very close to, and others are generally clustering around the C14 and Be10 periods of 208 and 104 years. This is very suggestive. The modern sunspot cycle has a high autocorrelation after a lag of 210 years.

We can use the 104 and 208 year cycles to make a crude cycles forecast. For this purpose the wilder fluctuations pre-1750 have been omitted. The result:

It can be seen that the 104 and 208 year lagged sunspot numbers give a reasonably good fit to the present weak cycle 24.

The most important conclusion is that the 104 and 208 year cycles are closely related to the beat cycles of the closely spaced strong cycles near 11 years. This type of behaviour is quite commonly found in cycles analysis.

This suggests that we will not return to strong solar cycles again until the 2040s.

 

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