Temperature drives CO2, NOT the other way round
Data from across the globe shows that temperature change always precedes CO2 change so it is impossible for the later CO2 change to be the cause of the earlier temperature change.
Weekly Mauna Loa CO2 data shows that the draconic 27.2 day and the 29.5 day synodic periods of the Moon are reflected in the variation in the rate of change of the CO2 concentration. This confirms that the small temperature change that occurs when the Moon passes between the Sun and the Earth thereby reducing the radiation received from the Sun and causing a diminished Earth temperature. CO2 is not causing the Moon to rotate around the Earth!
The major question to be answered is why have so many scientists told lies? I believe, as a result of the PSI articles, that the same forces of evil are at work with the fake pandemic and fake vaccine.
Atmospheric CO2 concentration
Here in Figure 1 is the monthly atmospheric CO2 concentration measured at the Mauna Loa Observatory, Hawaii, Latitude19.5̊ N, Longitude 155̊ W, elevation 3397m, for the 64 year period from March 1958 to September 2021.
The monthly CO2 concentration had an average rate of increase over the 64 year period from March 1958 to September 2021 of 1.59 ppm pa. For the 5 year period March 1958 to March 1963 the rate was 0.68 ppm pa and for the 5 year period September 2016 to September 2021 the rate was 2.55 ppm pa, that is, the rate of increase has steadily accelerated over time to be almost four times greater than it was 59 years earlier. The range is from a minimum of 312.44 to a maximum of 418.95 ppm.
The amplitude of the seasonal variation was estimated to range from 5.2 ppm to 8.03 ppm, increasing in amplitude over time, in an irregular fashion. The maxima occurred, on average, in early May, which is the beginning of Summer, and the minima in late September, at the end of Summer. The greatest seasonal variation took place between September 2015 and April 2016.
This means that the CO2 concentration rose during the cool of Winter and fell during the heat of Summer, which is out of phase with the UN IPCC claim that increased CO2 concentration causes an increase in temperature. Nor does the UN IPCC hypothesis provide an explanation for the steady increase in the rate of increase of the CO2 concentration.
Temperature and CO2 concentration
Here is 514 months of empirical data, showing a distinct lack of a relationship between the Tropics satellite lower troposphere temperature [ Ref.1] and the seasonally adjusted atmospheric CO2 concentration at the Mauna Loa Observatory.
Figure 2 shows the monthly satellite lower troposphere temperature for the Tropics zone, 20̊ South to 20̊ North, in blue, and the relevant monthly CO2 concentration in red after removal of the seasonal variation so as to match the residual temperature series. The range for the monthly CO2 concentration is from 335.78 ppm to 416.42 ppm. The range for the Tropics temperature is from -0.99̊ Celsius to +1.15̊ Celsius with respect to a 30 year average base value. The clear and obvious difference between the two raises the possibility that there may be no common causal factor whereby the CO2 concentration drives the temperature as claimed by the UN IPCC.
Calculation of the Ordinary Linear Regression between the two time series gave a correlation coefficient of 0.471 from the 514 monthly data pairs. This is a measure of the relationship between the background linear trend of each of the time series as shown by an almost identical correlation of 0.470 between the temperature and the time. The correlation between the CO2 concentration and the time was 0.995, that is, the seasonal adjusted CO2 concentration time series was practically a linear trend with respect to time. Any pair of linear trends, no matter what their source, will have a high correlation coefficient of about 1.0 which is necessarily of no causal significance as a background linear trend with respect to time can be calculated for any time series.
Detrending of the pair of time series in order to assign a statistical significance to the correlation coefficient gave a value of 0.042 showing that the above correlation of 0.471 was mainly the result of the positive linear trend for each series. Statistical tests of both time series indicated that neither series was a random Normal statistical distribution. The Spearman Rank test gave a value of 0.045 with a 31 percent probability that the correlation could be zero.
An autocorrelation test of both detrended series confirmed that neither series was a random sequence, that is, each measured value was partly dependent, in time, on the previous value. The Durbin-Watson test of the joint time series gave a result of 0.28 which indicates positive autocorrelation. The autocorrelation was estimated to be 0.86.
This mandated the application of a First Order Autoregressive Model for the analysis of the combined time series whereby the transformed series gave a correlation coefficient of 0.051 with a 27 percent probability that the correlation coefficient could be zero from the Spearman Rank test. Calculation of the cross correlation between the detrended pair of Tropics zone temperature and seasonally adjusted CO2 concentration showed that the CO2 changes lagged the temperature changes by eleven months.
Applying the above procedure using the Tropics Land component of the satellite lower troposphere temperature gave a correlation of 0.57 between the temperature and the seasonal adjusted CO2 concentration. On detrending, the correlation was 0.049, the Durbin-Watson test was 0.44 and the autocorrelation was estimated to be 0.78.
Applying the First Order Autoregressive Model gave a correlation of 0.04 with a 40 percent probability that the correlation could be zero from the Spearman Rank test. Calculation of the cross correlation between the detrended pair of Tropics Land temperature and seasonally adjusted CO2 concentration showed that the CO2 changes lagged the temperature changes by eleven months.
Applying the procedure using the Tropics Ocean component of the satellite lower troposphere temperature gave a correlation of 0.43 between the temperature and the seasonal adjusted CO2 concentration. On detrending, the correlation was 0.04, the Durbin-Watson test was 0.27 and the autocorrelation was estimated to be 0.87.
Applying the First Order Autoregressive Model gave a correlation of 0.051 with an 22 percent probability that the correlation could be zero from the Spearman Rank test. Calculation of the cross correlation between the detrended pair of Tropics Ocean temperature and seasonally adjusted CO2 concentration also showed that the temperature changes preceded the CO2 changes by eleven months.
In summary, the correlation between the satellite lower troposphere temperature and the CO2 concentration for the Mauna Loa Observatory was of the order of 0.05 with an indeterminate probability as to whether or not the relationship is significant. Cross correlation determined that the changes in CO2 concentration lagged the temperature change by eleven months so it could not possibly be the cause of the earlier temperature changes.
The result was supported by analysis of data from:
(1) Macquarie Island in the Southern Ocean at Latitude 54.48̊ South, Longitude 158.97̊ East, altitude 12 m,
(2) Mt Waliguan, Tibetan Plateau, China, Lat. 36.28̊ N, Long. 100.9̊ E, altitude 3810 m,
(3) Point Barrow, Alaska,
(4) South Pole Station, Antarctica, and
(5) Cape Grim, Tasmania,
as may be seen on the accompanying pages at www.climateauditor.com .
In every case, as the probabilities of a positive correlation coefficient were not statistically significant, the UN IPCC proposition that increased CO2 caused increased temperature could not be supported and the conclusion must be that the null hypothesis applies, namely that the correlation coefficients were zero.
The above conclusion is totally at odds with the statements from the United Nations climate body, the Intergovernmental Panel on Climate Change. The Policymakers Summary from Climate Change, The IPCC Scientific Assessment, 1990, being the, then, final Report of Working Group 1 of the IPCC, opened with the statement, page XI:
“EXECUTIVE SUMMARY
We are certain of the following:
- there is a natural greenhouse effect which already keeps the Earth warmer than it would otherwise be
- emissions resulting from human activities are substantially increasing the atmospheric concentrations of the greenhouse gases carbon dioxide, methane, chlorofluorocarbons (CFCs) and nitrous oxide. These increases will enhance the greenhouse effect, resulting on average in an additional warming of the Earth’s surface. The main greenhouse gas, water vapour, will increase in response to global warming and further enhance it.” – end quote.
After decades of research into the relationship between the atmospheric CO2 concentration and temperature, the latest, Fifth Assessment Report, 2015, of the IPCC, the Synthesis Report, Summary for Policymakers, page 8, made the claim:
“SPM 2.1 Key drivers of future climate
Cumulative emissions of CO2 largely determine global mean surface warming by the late 21st century and beyond. …….” – end quote.
Temperature and Rate of Change of CO2 concentration
Here is 42 years of empirical data clearly showing a positive relationship between the detrended variables, satellite temperature and the rate of change of atmospheric CO2 concentration at the Mauna Loa Observatory.
Figure 3 shows the monthly satellite lower troposphere temperature for the Tropics zone, in blue, and the annual change in CO2 concentration in red. The obvious correlation between the two raises the possibility that there may be some common causal factor whereby the temperature drives the rate of change of CO2 concentration. It is not possible for the rate of change of CO2 to cause the temperature level as a time rate of change does not define a base.
For example a rate of 2 ppm per annum could be from 0 to 2 ppm in 12 months, 456 to 458 ppm in 12 months or any other pair of numbers that differ by 2.
Note that the satellite temperature data is supplied as a residual after adjustment for the estimated seasonal variation. This makes it directly comparable to the annual rate of change of CO2 concentration as taking the annual rate eliminates the seasonal variation. The range, after detrending, for the Tropics Zone temperature is from -0.72̊ Celsius to +1.26̊ Celsius with respect to a 30 year average base value and the range for the CO2 annual increment is -1.57 to 1.90 ppm per annum.
Calculation of the Ordinary Linear Regression between the two time series gave a correlation coefficient of 0.65 from the 508 monthly data pairs. Detrending of the time series in order to determine the statistical significance gave a correlation coefficient of 0.54. However the Durbin-Watson test of the time series gave a value of 0.86 indicating positive autocorrelation which means that Ordinary Linear Regression is inapplicable.
The autocorrelation was estimated to be 0.57. When applied to transform both time series, that is, applying a First Order Autoregressive Model, it resulted in a correlation coefficient of 0.26 with a minuscule probability of the order of 10^-9 that the coefficient could be zero from the Spearman Rank test.
It follows that this synthesis of empirical data conclusively reveals that CO2 has not caused temperature change over the past 42 years but that the rate of change in CO2 concentration has been influenced to a statistically significant degree by the temperature level. Note that it is not likely for a rise in CO2 concentration to cause the temperature to increase and for the temperature level to control the rate of change of CO2 concentration as this would mean that there was a positive feedback loop causing both CO2 concentration and temperature to rise continuously and the oceans could have evaporated long ago.
Support for this thesis is seen in a statistical analysis of the annual rate of change of the monthly CO2 concentration with respect to the 13 month average lower troposphere temperature for Macquarie Island in the Southern Ocean and for Mt Waliguan, Tibetan Plateau, China.
Chronological Sequence
The above conclusions are supported by the sequence of events recorded at Mauna Loa Observatory for the major 1997 -‘98 El Nino event displayed in Figure 4 below.
The graph displays the time relationship between atmospheric CO2 concentration at the Mauna Loa Observatory, Hawaii, from the Scripps Institution, compared to the satellite lower troposphere Tropics-Land temperature provide by the University of Alabama, Huntsville, for the major 1997-‘98 El Nino event.
The maximum in the annual increment of the temperature, at October 1997, preceded the maximum in the annual increment in the CO2 concentration, at March 1998, by 5 months revealing that the CO2 change could not possibly have caused the temperature change. The maximum in the annual increment in the CO2 concentration occurred as the temperature reached its maximum, confirming the conclusion in the previous section.
Figure 4: CO2 annual increment relative to temperature average and annual increment.
Cumulative temperature vs. CO2 concentration
If the temperature determines the rate of generation of CO2, that is, d/dt CO2 equals a constant multiplied by the temperature level then, mathematically, this implies that the CO2 concentration at a given time is proportional to the integral of the temperature up to that time relative to a base value at which no CO2 is generated. This would be the temperature at which there is a balance between the various sources and sinks of CO2 at the Earth’s surface.
Figure 5 shows the sum with respect to time of the global temperature values relative to a base of -0.99, the minimum value from the global list as a calibrated base is not known, with the range minimum to maximum adjusted to match that of the seasonally adjusted CO2 range. The resulting near linear trend in the cumulative satellite temperature closely matches that for the seasonally adjusted CO2 trend as expected under the hypothesis that the temperature drives the rate of generation of CO2.
Applying the Spearman Rank test, as the distributions are not Normal Distributions, gave a Spearman Rank statistic of 0.999895 with a probability that was too small to be represented by a machine number for the correlation between the seasonally adjusted CO2 concentration and the cumulative temperature to be zero.
The conclusion that the temperature controls the rate of change of CO2 concentration explains the well known fact that CO2 change lags temperature change over a large time range. Ice core data has revealed that the cycle of ice ages and inter-glacial warm periods show CO2 change lagging temperature change by several centuries to more than a millennium while modern CO2 and global data shows lags of 9.5 to 10 months for atmospheric temperature and 11 to 12 months for sea surface temperature, Humlum et el., 2013 [Ref.5].
Cross correlation of the CO2 concentration at Mauna Loa and satellite lower tropospheric Tropics Land and Ocean temperatures showed that CO2 change lagged the temperature change by 9 or 10 months. If temperature controls the rate of change of CO2 concentration, local maxima in the CO2 rate must correspond to temperature maxima which, mathematically, must occur after the maxima in the rate of change of temperature. Likewise the CO2 concentration maxima must post-date the maxima in the CO2 rate and thus post-date the corresponding temperature maxima.
Put simply, CO2 has not caused global warming.
Conclusion
There is no statistical evidence for the claim by the UN IPCC that a rise in CO2 concentration causes a rise in the temperature of the lower troposphere but there is highly significant evidence that the temperature determines the rate of change of CO2 concentration.
Added to that is a secondary conclusion that there is a prominent 42 month cycle for the temperature due to a repeated occurrence of a configuration of the Solar system which is expressed in the Earth’s climate as the El Niño event. It also causes the same cycle in the rate of change of CO2 concentration.
Furthermore other cycles in the temperature and the CO2 rate spectra may relate to orbital cycles of the planets indicating that, at least in terms of months and years, the orientation of the planets with respect to the Sun determine changes in the Earth’s temperature which result in coincident changes in the rate of change of CO2. That is, an important factor in the Earth’s climate change arises from the continually changing position of the Moon and the planets relative to the Earth and the Sun and has nothing whatsoever to do with the concentration of CO2 in the atmosphere as this is a consequence of the natural climate change.
References:
[1] The satellite lower troposphere temperature data is freely available from the University of Alabama, Huntsville, on Dr Roy Spencer’s Web site at:
http://www.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt
[2] The CO2 concentration data for the Mauna Loa Observatory is freely available from the Scripps Institute via the Web page:
https://scrippsco2.ucsd.edu/data/atmospheric_co2/mlo.html
Files: monthly_in_situ_co2_mlo.csv and weekly_in_situ_co2_mlo.csv
[5] Ole Humlum, Kjell Stordahl, Jan-Erik Solheim, “The phase relation between atmospheric carbon dioxide and global temperature”, Global and Planetary Change 100 (2013) 51-69.
[6] Identification of the driving forces of climate change using the longest instrumental temperature record. Geli Wang, Peicai Yang & Xiuji Zhou
Scientific Reports 7, Article number: 46091 (2017), doi:10.1038/srep46091
[7] T.W. Crowther, et el, “Quatifying global soil carbon losses in response to warming” Nature, Vol. 540, 104-108, 01 December 2016, Letter
Bold emphasis added
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Carbon Bigfoot
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https://www.thegatewaypundit.com/2021/11/bidens-marxist-treasury-nominee-says-quiet-part-loud-fossil-fuel-industry-want-go-bankrupt-want-tackle-climate-change-video/
As reported in Gateway Pundit
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Herb Rose
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Hi Bevan,
What you say is true but irrelevant. There are far to many people pretending to be scientist who are earning money by telling their sponsors what they want to hear. Money speaks louder than evidence and MSM goes with the money. Data and reason will not overcome dollars for these pseudo scientist so appealing to them with facts will have no effect. In order to accomplish change the argument must be addressed to the mass of scientifically ignorant people who are being fleeced and paying the bill. It must be done so they can comprehend what is being said and understand that they are the victims of this fraud. Data and scientific terms will not work only by referencing things they experience can you hope to make them become aware of what is happening and end this farce.
Herb
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Alan
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I think you are basically correct. The pseudo-scientists will not debate anybody who opposed their view because of the money as you say, but it will also mean they will have to admit they are lying and they will not do that either.
The masses will not listen either because they don’t have even a basic knowledge of science. There will only be a change when costs hit them hard and by then it will all be too late because the policies will have destroyed our reliable and cheap energy supplies and they cannot be replaced quickly.
The pandemic shows how easy it has been to control people and make them act in ways they would never normally consider.
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Jerry Krause
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Hi Herb, Alan and PSI Readers,
SCIENCE is not about debate, SCIENCE is about observations (measurements) about which there can be no debate.
I address to particularly to Herb. I have been asking you questions to which you have not responded. So I ask you another question: WHY don’t you respond???
Have a good day, Jerry
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Jerry Krause
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Hi PSI Readers,
Because I cannot remember specific examples of the past question I have asked Herb to which he has not responded, I give an example.
In weather and climate a very fundamental factor is TEMPERATURE. This article is about AVERAGE TEMPERATURES for a year. I believe we all know that the TEMPERATURE at any location on the surface of the EARTH is not CONSTANT for an entire YEAR. So we all should know that these AVERAGE TEMPERATURES do not actually EXIST!!!
So WHY do we continually read about them here at PSI???
The maximum-minimum TEMPERATURE during any period of time (an hour, a day, a month) are all actually measured TEMPERATURES at the location at which they have been measured.
I have an inside-outside common weather machine which reports the maximum-minimum teachers since midnight. So in the morning I look at these two actually measured temperatures and they tell me (because of previous experiences) what has happened during that time period. If the difference between these two temperatures is maybe as great as 7F for a 5 hour period I know the sky has been cloudless most of this period. That assumes the minimum temperature is the present temperature. But if the present temperature is 7F greater I am reasonably certain that the wind has shifted from the north to the south.
I could go on but I only ask: What factors might be involved if the difference is 0 or 1F???
Have a good day, Jerry
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Carbon Bigfoot
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Jerry always enjoy your comments. Being a connoisseur of many websites on this subject I don’t know if Bob Tisdale has ever posted here. Mostly I see his comments on WUWT. In 2019 Bob compiled Graphs of 100 years of NOAA Contiguous U.S, Climate Data (2018)… as Bob suggested this is a book NOAA should have published. Tisdale’s work is called:
Extremes and Averages in Contiguous U.S. Climate available from Amazon: https://www.amazon.com/Books-Bob-Tisdale/s?rh=n%3A283155%2Cp_27%3ABob+Tisdale
The presentations begin with the adjustments to the average temperature (TAVG) made by NOAA. I read but can’t extract Bob’s copyright but I wouldn’t do so without his permission.
Supports your position Jerry….mostly.
Make it a positive day Carbon
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Bevan
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Jerry, my study did not involve yearly averages, it used monthly data and there is a reference to my study of weekly data.
As for average temperatures, sorry Jerry but they must exist. The temperature cannot get from minimum to maximum without, at least once, passing through the average temperature.
Statistical sampling theory endeavours to determine the basic properties of a population such as mean, standard deviation, distribution, etc. The advantage of using a mean is that its standard deviation is inversely proportional to the square root of the number of samples. Thus a monthly mean has a standard deviation of the inverse of the square root of 30, ie. 0.18 times the population standard deviation, thereby providing a better idea of the overall mean value to be expected for the whole population.
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Jerry Krause
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Hi Bevan and PSI Readers,
This comment is just to acknowledge I overlooked the fact of the monthly averages. For we all must acknowledge such mistakes!!! I will study your graphs and probably have a comment about what can be seen that I overlooked. And I will look at the links (information) that you and others have provided.
This seems a very good conversation you have begun and I certainly want to do my part in keeping it going.
Have a good day, Jerry
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Jerry Krause
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Hi Beven,
Relative to FIG. 2, you wrote: “Figure 2 shows the monthly satellite lower troposphere temperature for the Tropics zone, 20̊ South to 20̊ North, in blue, and the relevant monthly CO2 concentration in red after removal of the seasonal variation so as to match the residual temperature series.”
Could you explain fully what “after removal of the seasonal variation so as to match the residual temperature series” involved.
Have a good day, Jerry
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Bevan
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Thank You Jerry,
Herewith an extract from the header to the data file ‘monthly_in_situ_co2_mlo.csv’:
The data file below contains 10 columns. Columns 1-4 give the dates in several redundant ”
” formats. Column 5 below gives monthly Mauna Loa CO2 concentrations in micro-mol CO2 per ”
” mole (ppm), reported on the 2012 SIO manometric mole fraction scale. This is the ”
” standard version of the data most often sought. The monthly values have been adjusted ”
” to 24:00 hours on the 15th of each month. Column 6 gives the same data after a seasonal ”
” adjustment to remove the quasi-regular seasonal cycle. The adjustment involves ”
” subtracting from the data a 4-harmonic fit with a linear gain factor. Column 7 is a ”
” smoothed version of the data generated from a stiff cubic spline function plus 4-harmonic ”
” functions with linear gain. Column 8 is the same smoothed version with the seasonal ”
” cycle removed. Column 9 is identical to Column 5 except that the missing values from ”
” Column 5 have been filled with values from Column 7. Column 10 is identical to Column 6 ”
” except missing values have been filled with values from Column 8. Missing values are ”
” denoted by -99.99
End of copy.
Figure 2 shows the seasonally adjusted CO2 data from column 10.
Wishing You Well, Bevan
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Jerry Krause
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Hi Bevan and PSI Readers,
A problem with these comments is that Bevan would need to write another article to do what I ask. Fig 1 is amazing for its yearly oscillation as the carbon dioxide slowly in crease from year to year. Certainly no one should question these measurements.
While I expect there would be no yearly oscillation of the world’s population, I suspect it might be yearly increasing. So, using an appropriate scale for this population I would like to see a comparison the carbon dioxide and population for these years. For people and animals breathe out carbon dioxide from the food they eat. And I know that in the USA the yields per acre have increased dramatically from 1960 to 2021 because of improved farming practices. And I suspect the same is the case for other major food producing regions.
Bevan, maybe you could make such a graph and report here as a comment if there appears to be a coloration between increasing carbon dioxide and world population.
Have a good day, Jerry
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Jerry Krause
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Hi Bevan,
First, I try keep comment lines as long as possible while keeping the comments in the same vicinity.
Thank you for your 1:33pm comment. While I don’t understand what is being described I do accept the validity of the measurements of the figures and that nothing is being distorted to ‘fit’ this or that idea. For I expect your information is standard statistical analysis. And that the monthly average is the actual average for the 15th of each month. Which works in the case of carbon dioxide because the values of carbon dioxide concentrations change only slightly from hour to hour or from day to day during a given month.
This is also true about a soil temperatures at a depth of one meter which shows no daily oscillation but does shows a slow change from month to month. So there is an yearly temperature oscillation which does observably change a little from year to year for several real factors which have nothing to do with atmospheric carbon dioxide (etc). But we know that the air temperature and its oscillation can change drastically from one day to the next. From which drastic change from day to day, you conclude that there can be no greenhouse effect of atmospheric greenhouse gas molecules (whatever these molecules may be). However, this reasoning does not prove there is absolutely no greenhouse effect of these molecules.
What absolutely proves there is no GHE of gaseous atmospheric molecules is (and I repeat and repeat) that the GHE idea (theory) predicts that any measured air temperature would be about 33C (58F) if the atmosphere contained NO GHG molecules.
However, when the air’s temperature and the air’s dew point temperature are measured at the same place and time, the air temperature has never been found to be less than the dew point temperature. Hence, it is the air’s dew point temperature which determines the air’s minimum possible temperature. But, I hasten to state that the measured air temperature can be greater than its dew point temperature.
It’s that SIMPLE. Thus confirming Einstein’s comment: “If you can’t explain it simply, you don’t understand it well enough.
Have a good day, Jerry
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Bevan
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Jerry, unfortunately your absolute proof of a greenhouse effect is complete fiction using an inappropriate mathematical model which is certainly not one of Einstein’s “simple explanations”.
For more detail see my web site at :
http://www.climateauditor.com
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jerry Krause
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Hi Bevan,
“Jerry, unfortunately your absolute proof of a greenhouse effect is complete fiction using an inappropriate mathematical model which is certainly not one of Einstein’s “simple explanations”.” (Nov 12, 2021 at 3:44 am)
What “mathematical model” did I use??? And I certainly did not imply in any way that the Greenhouse Effect of atmospheric carbon dioxide and other similar gas molecules (GHE) was a valid theory. I referred you and PSI readers to observed (measured) facts whose validity can not be questioned. Which facts force the conclusion that the atmosphere’s dew point temperature limits the atmosphere’s MINIMUM TEMPERATURE!!!
E = m c^2 is SIMPLE. And the atomic bombs prove its validity.
Have a good day, Jerry
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Alan
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Isn’t maths wonderful, but how many times have we seen graphs of temperature and carbon dioxide displayed and been told that carbon dioxide is cause the temperature changes. Al Gore did this but I the person who has discredited science more is Prof Brian Cox. He appeared on the Australian TV programme Q&A claiming the same. He know that correlation cannot be seen by observation and that mathematical analysis is available. He also knows that even if there is correlation it does not mean causation. He has done more to damage the credibility of science than any other person.
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Allan Shelton
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Bevan…….
You are a great statistical analyst I am sure.
Now… would please do an analysis of the death rate of a country such as the USA, the UK, or Canada, showing the total death rate from all causes for the las t decade or two?
If Covid is really a pandemic, then it should show up in the stats. No???
Using ALL causes of death should eliminate any conflicting data from flu death or whatever.
Thank ahead of time for your effort.
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Bevan
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Allan Shelton, not being trained in biology or medicine, I have been making an effort to learn and understand what mRNA gene therapy is and what the COVID19 virus is about.
However a statistical analysis of death rates seems pointless when the health officials, the politicians and particularly the media, do not openly advise the public of the deaths and adverse reactions from the fake ‘vaccine’. Thus we have nasty, spiteful attacks in the media about the unvaccinated by persons who obviously do not have any idea as to the health risk that they have taken in being injected by the experimental fake ‘vaccine’.
Remember that it was 1956 when the first malformed baby was born to an employee of the German company that created the thalidomide medication. Then it was not until 1961, five years later, that the drug was removed from distribution and eight months after that babies deformed by thalidomide ceased to be born.
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Jerry Krause
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Hi PSI Reader,
Bevan proves again one must read and read to LEARN what has HISTORICALLY HAPPENED.
Have a good day, Jerry
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coronistan.blogspot.com
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Evil climate denier…
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Roger Higgs
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Bevan, excellent article, thank you.
Roger
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Jav
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I’m a amateur history buff,especially when it comes to ancient times and I have seen the same correlation done by archeologist and geologist. Guess what they say the same thing. Warming preceded co2 levels all along. I think A l Gore made a fortune out of nothing other than panic porn.
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Google
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Google
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