Mauna Loa Observatory

CO2_vs_temp_2020June.pdf  22 June 2020

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 62 year period from March 1958 to May 2020.

mlo_mthco2

Figure 1. Monthly atmospheric CO2 concentration, Mauna Loa Observatory, Source:[ Ref. 2]

The source file, Ref. 2, lists the data in 10 columns. The columns used here were columns 3 and 4, the date in Excel and decimal format, column 9 being the measured CO2 concentration with missing values in-filled from a smoothed fit to the data and column 10 being the seasonally adjusted measurements again with missing values in-filled.

The monthly CO2 concentration had an average rate of increase over the 62 year period from March 1958 to May 2020 of 1.57 ppm pa. For the 5 year period March 1958 to March 1963 the rate was 0.69 ppm pa and for the 5 year period May 2015 to May 2020 the rate was 2.59 ppm pa, that is, the rate of increase has steadily accelerated over time to be almost four times greater than it was 57 years earlier. The data range is from a minimum of 312.43 to a maximum of 417.16 ppm.

The amplitude of the seasonal variation was estimated to range from 5.2 ppm to 8.01 ppm, again 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. 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.

Any proposition by the UN IPCC about the effect of CO2 on climate needs to be able to explain the source of the above variations.

Temperature and CO2 concentration

  Here is 498 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.
mlo_sat_temp_co2Figure 2. Mauna Loa Observatory, Source: [ Ref. 1] and [ Ref. 2]

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 335.77 ppm to 413.75 ppm. The range for the Tropics temperature is from -0.81̊ Celsius to +1.28̊ 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 0.477 from the 498 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 0.473 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 0.081 showing that the above correlation of 0.477 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 0.075 with a 9.6% probability that the correlation could be zero. The Durbin-Watson test of the joint time series gave a value of 0.27 which indicates positive autocorrelation. The autocorrelation was estimated to be 0.86. This mandated the application of a First Order Autoregressive Model to the combined time series whereby the transformed series gave a correlation coefficient of 0.063 with a 16% 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 five 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 0.081, 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 0.05 with a 30% 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 five months, again.

Applying the procedure using the Tropics Ocean component of the satellite lower troposphere temperature gave a correlation of 0.44 between the temperature and the CO2 concentration. On detrending, the correlation was 0.08, the Durbin-Watson test was 0.26 and the autocorrelation was estimated to be 0.87. Applying the First Order Autoregressive Model gave a correlation of 0.063 with an 16% 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 five 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 0.06 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 five months so it could not possibly be the cause of the earlier temperature changes.

The result was supported by data from Macquarie Island in the Southern Ocean at Latitude 54.48̊ South, Longitude 158.97̊ East, altitude 12 m. The Island is in the Southern Extension zone of the lower troposphere satellite temperature data, latitudes 90̊ South to 20̊ South. Analysis of the temperature data for the complete zone and its Land and Ocean components with respect to the CO2 concentration [Ref.3] showed that there was positive autoregression in each case requiring a First Order Autoregressive Model to be applied. The result for the whole zone was a correlation coefficient of -0.009, 308 deg. of free., t statistic -0.15, probability of zero correlation 88%. For the Land component, the correlation coefficient was -0.001, 308 deg. of free., t statistic -0.02, probability of zero correlation 98%. For the Ocean component, the correlation coefficient was -0.014, 308 deg. of free., t statistic -0.25, probability of zero correlation 81%.

Additional support is seen in a statistical analysis of the monthly CO2 concentration with respect to the satellite lower troposphere temperature for Mt Waliguan, Tibetan Plateau, China, Lat. 36.28̊ N, Long. 100.9̊ E, altitude 3810 m. Applying a First Order Autoregressive Model to the CO2 concentration [ Ref.4] for Mount Waliguan as the independent variable verses the Northern Extension zone satellite lower troposphere temperature, latitude 20̊ N to 90̊ N, gave results for the whole zone correlation coefficient of -0.13, 302 deg. of free., t statistic -2.30, probability of zero correlation 2.2%. For the Land component, the correlation coefficient was -0.12, 302 deg. of free., t statistic -2.18, probability of zero correlation 3%. For the Ocean component, the correlation coefficient was -0.13, 302 deg. of free, t statistic -2.32, probability of zero correlation 2.1%.

Further, applying a First Order Autoregressive Model to the CO2 concentration for Point Barrow, Alaska, as the independent variable verses the North Pole zone satellite temperature, latitude 60̊ N to 90̊ N, gave results for the whole of zone correlation coefficient of 0.06, 462 deg. of free., t statistic 1.23, probability of zero correlation 22.1%. For the Land component, the correlation coefficient was 0.08, 462 deg. of free., t statistic 1.66, probability of zero correlation 10%. For the Ocean component, the correlation coefficient was 0.02, 462 deg. of free., t statistic 0.50, probability of zero correlation 62%.

Further, applying a First Order Autoregressive Model to the CO2 concentration for the South Pole Station, as the independent variable verses the satellite lower troposphere temperature for the South Pole zone, latitude 60̊ S to 90̊ S, gave results for the whole of zone correlation coefficient of 0.007, 454 deg. of free., t statistic 0.15, probability of zero correlation 88%. For the Land component, the correlation coefficient was 0.027, 454 deg. of free., t statistic 0.58, probability of zero correlation 56%. For the Ocean component, the correlation coefficient was -0.019, 454 deg. of free., t statistic -0.41, probability of zero correlation 68%.

The same analysis applied to the CO2 concentration for Cape Grim, Tasmania, as the independent variable verses the satellite lower troposphere temperature for the Southern Extension zone, latitude 20̊ S to 90̊ S, gave a correlation coefficient of 0.018, 462 deg. of free., t statistic 0.39, probability of zero correlation 70% for the whole of the zone. For the Land component, the correlation coefficient was 0.013, 462 deg. of free., t statistic 0.27, probability of zero correlation 78%. For the Ocean component, the correlation coefficient was 0.015, 462 deg. of free., t statistic 0.33, probability of zero correlation 74%.

A negative correlation implies that an increase in CO2 concentration caused a decrease in temperature, the complete opposite of the UN IPCC thesis. However 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 41 years of empirical data clearly showing a positive relationship between the satellite temperature and the rate of change of atmospheric CO2 concentration at the Mauna Loa Observatory.

mlo_sat_TempLd_dco2

Figure 3. Mauna Loa Observatory CO2 annual increment & satellite Tropics Land temperature.

Figure 3 shows the monthly satellite lower troposphere temperature for the Land component of 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 for the Tropics Land temperature is from -0.88̊ Celsius to +1.32̊ Celsius with respect to a 30 year average base value. The range for the CO2 annual increment is 0.31 to 4.12 ppm per annum.

Calculation of the Ordinary Linear Regression between the two time series gave a correlation coefficient of 0.65 from the 492 monthly data pairs. Detrending of the time series in order to determine the statistical significance gave a correlation coefficient of 0.52. However the Durbin-Watson test of the time series gave a value of 0.90 indicating positive autocorrelation which means that Ordinary Linear Regression is inapplicable. The autocorrelation was estimated to be 0.55. When applied to transform both time series, that is, applying a First Order Autoregressive Model, it resulted in a correlation coefficient of 0.22 with a probability of the order of 10^-6 that the coefficient could be zero from the Spearman Rank test.

Applying a First Order Autoregressive Model to the Tropics-Ocean component of the satellite temperature compared to the annual change in CO2 concentration gave a correlation coefficient of 0.26 with a 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 41 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 at Latitude 54.48 deg South, Longitude 158.97 deg East, altitude 12 m. Applying a First Order Autoregressive Model to the various components of the satellite temperature, Southern Hemisphere, Tropics, and Southern Extension and their Land and Ocean components gave a maximum correlation coefficient of 0.30, 294 deg. of free., t statistic 5.34, infinitesimal probability of zero correlation for a two month lag of the CO2 annual rate of change relative to the 13 month average temperature for the Tropics zone, latitude 20S to 20N.

Additional support is seen in a statistical analysis of the annual rate of change of the monthly CO2 concentration with respect to the annual average satellite lower troposphere temperature for Mt Waliguan, Tibetan Plateau, China, Lat. 36.28̊ N, Long. 100.9̊ E, altitude 3810 m. Applying a First Order Autoregressive Model gave a maximum correlation among the various satellite temperature zones of 0.14 for 290 deg. of free., t statistic 2.5, probability of zero correlation of 1.3% for a two month lag of the CO2 annual rate of change relative to the annual average Tropics Land temperature.

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.

Chronological Sequence

MLo_El_Nino

Figure 4: CO2 annual increment relative to temperature average and annual increment.

The above 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. Statistical analysis of the complete data set extending from December 1978, when satellite measurements began, until May 2020, determined that a 5 month delay was the average throughout the 41 year period.

Further, it can be seen that the Average Temperature graph, being a plot of the 12 month running average temperature, corresponds with the overall variation in the CO2 annual increment. Again this is confirmed by analysis of the complete record which gave a statistically significant correlation between the two. It is not possible for a CO2 time rate of change to set the level of the average temperature but it is possible for the average temperature to cause the rate of change in the CO2 concentration in the same way that the temperature setting of a stove element determines the rate of evaporation of a pot of water. This supports the contention that the CO2 change has not caused the temperature change.

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 4 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.

Periodic cycles

Additional analysis of the Mauna Loa data gave the following autocorrelation function, Figure 5, for both the annual rate of change of the CO2 concentration (red line) and the satellite lower troposphere Tropics Land monthly temperature (blue line). The CO2 data covered the period March 1958 to January 2020 while the satellite Tropics temperature data was from December 1978 to January 2020. Considering the different lengths of the time series, the variables show a striking match in their periodic response. The fact that neither autocorrelation function decreases with increasing lag shows that neither time series is stationary in the statistical sense and dictates that the First Order Autocorrelation Model be applied as was done in calculating the above results.

Figure 5: Autocorrelation functions for Mauna Loa rate of change of CO2 concentration and Tropics Land satellite temperature.

The autocorrelation series are basically identical upon taking into account that the temperature series was 498 months long while the CO2 rate of change was 735 months long, resulting in better definition of its periodicity. The series is better defined by using the weekly data analysed in the accompanying page ‘Mauna Loa weekly data’ at: https://www.climateauditor.com/mauna-loa-weekly-co2-concentration-data/
This shows that the maxima all arise from the El Nino Southern Oscillation with the period estimated to be 1313 days. This result suggests that perturbations in the Sun’s irradiance, under the influence of the gravity effect of the planetary orbits largely control the temperature variation of the Earth which, in turn, controls the rate of change of the CO2 concentration in the atmosphere.

A matching response can also be seen in the Fourier Transform amplitude spectrum for each time series, as shown below:

Figure 6: Fourier Transform Amplitude spectrum – Satellite Lower Troposphere Tropics zone.

Figure 7: Fourier Transform Amplitude spectrum – Mauna Loa CO2 annual rate of change

The most prominent maximum on both spectra is at x-coordinate 12 on the satellite lower troposphere Tropics zone temperature and 24 on the CO2 rate spectrum, representing a period of 42.7 months resulting from the well known El Niño Southern Oscillation. It corresponds to the first broad maximum on the earlier correlogram, Figure 5 above. This is in agreement with the paper from Geli Wang et al [ Ref. 6] who used wavelet analysis to detect a 3.36 year cycle in the Central England Temperature dataset, which they attributed to the El Niño Southern Oscillation.

Remarkably, the 42 month period was known by the Babylonians and Israelites 2500 years ago, being mentioned in the Book of Daniel, Chapter 12.11 “…..1290 days …”, in the Old Testament and the Book of Revelation, Chapter 11.2 “….42 months …” and 11.3 “…. 1260 days …”, in the New Testament of the Bible.

Maxima from the CO2 rate of change amplitude spectrum to which a cause may be attributed are:-

 

x coord Amplitude Years Months Days
       2         1.47      42.67    512.0 15584.3
       9         2.00       9.48    113.8    3463.2 Jupiter-Mercury-Moon draconic
      24         4.11       3.56     42.7    1298.7  El Niño
      34         2.19       2.51     30.1     916.7
      58         1.59       1.47     17.7     537.4  20 x Moon draconic, Venus synodic
     100         0.57       0.85    10.2     311.7
     117         1.02       0.73      8.8     266.4  9 x Moon synodic
     130         0.90       0.66      7.9     239.8  Mercury-Moon sydonic
     185         0.51       0.46      5.5     168.5
     191         0.43       0.45      5.4     163.2  6 x Moon draconic
     201         0.58       0.42      5.1     155.1
     215         0.68       0.40      4.8     145.0
     290         0.64       0.29      3.5     107.5  Mercury-4 x Moon draconic
     326         0.41       0.26      3.1      95.6  3.5 x Moon draconic
     379         0.71       0.23      2.7      82.2  3 x Moon draconic
     392         0.47       0.22      2.6      79.5
     447         0.58       0.19      2.3      69.7  2.5 x Moon draconic 68.2 days

Accurate predictions as to the period and source of the local maxima in the amplitude spectra are not feasible due to the course sample interval of one month and the short time series of only 498 months for the temperature and 735 months for the CO2 annual rate of change.

This has been partly resolved by using the weekly Mauna Loa atmospheric CO2 concentration time series as proxy for the atmospheric temperature as shown on the accompanying page “Mauna Loa Weekly Data”. It is notable that both the synodic and draconic periods of the Moon are apparent in the weekly series. An explanation for the synodic period is that each New Moon reduces the incoming Sun’s radiation to the Earth and its atmosphere as it passes between the Sun and the Earth. The draconic period is due to the Moon’s elliptical plane being at an angle of 5.14° to the Earth’s elliptic relative to the Sun. As a result, when the Moon passes through one of the two nodal points, where the Moon’s ellipse intersects the Earth’s elliptic, it has the greatest influence in diminishing the irradiation of the Earth which, in turn, reduces the Earth’s surface temperature thereby causing a response in the rate of generation of CO2

Evidence that the 42 month cycle causes the El Niño event is seen in the responses over the South Pole as shown below. Once again the time series are of different lengths with the annual rate of change of the CO2 concentration (red line) covering the period June 1957 to December 2016 and the satellite lower troposphere South Pole Land monthly temperature (blue line) covering the period December 1978 to October 2017.

sth_pole_corrln

Figure 8: Autocorrelation functions for South Pole rate of change of CO2 concentration and South Pole Land satellite temperature.

There is an obvious difference between the time series. The periodic nature of the annual rate of change of the CO2 concentration repeats the wavelength of that from Mauna Loa while it is barely discernable for the South Pole satellite lower troposphere temperature series. The power spectra confirm the difference as seen here:

sth_pole_fft_temp

Figure 9: Fourier Transform Amplitude spectrum – Satellite Lower Troposphere South Pole Land

sth_pole_fft_dco2

 

Figure 10: Fourier Transform Amplitude Spectrum –South Pole CO2 annual rate of change

The peak in the amplitude spectrum for the annual rate of change of CO2 concentration remains at the wavelength of 42.7 months. However the power spectra for the satellite lower troposphere temperature has the 42.7 month peak in fourth spot with greater amplitude peaks occurring at wavelengths ( in decreasing amplitude ) of 64 months (which may be the synodic period for the Moon and Mercury and/or Jupiter ), 26.9 months (which may be the synodic period for Mars and/or Jupiter) and 10.7 months (which represents 11 synodic cycles of the Moon). The reduction in the 42.7 month peak is reasonable considering the fact that the Sun’s rays are practically tangential to the polar surface or do not impinge on part of that surface for months at a time and the Moon’s orbit is inclined at 5̊ to the elliptic.

The CO2 concentration over the South Polar region has been, on average, 2.2 ppm less than over the Tropics for the 58 years of recording during which time the concentration at the South Pole increased by 86.8 ppm and at Mauna Loa the increase was 88.3 ppm with the difference being statistically significant at the 99% level.

The clear similarity between the autocorrelation function and the power spectra for the two time series, temperature and rate of change of CO2 concentration, from the Equatorial zone support the original contention that the temperature drives the rate of change of CO2 concentration. As the Tropics has the highest average temperature it must produce CO2 at the greatest rate. That CO2 must diffuse North and South away from the Equator into the Polar regions. As the solubility of CO2 increases with decreasing temperature it must be precipitated at the Poles within the hail and snow. That is, there may be a continuous circulation of carbon from the Equatorial Zone, through the atmosphere as CO2, to the Poles where it is locked into the Polar ice sheets until those sheets move sufficiently far from the Pole to melt. The CO2 is then concentrated in sea water and may return to the Equatorial zone via the Earth’s oceans.

That is, the Tropics is a source for the atmospheric CO2 and the Polar regions are a sink. As the seasonal variation from photosynthesis can be as great as 20 ppm in amplitude, it is possible that the almost 2 ppm per annum increase in CO2 concentration over the past 38 years has arisen from biogenetic sources driven by the natural rise in temperature following the last ice age. The Tropics has the greatest profusion of life forms throughout the Globe, so this may be a feasible source for the increase in CO2 concentration for that period. That could include an increase in the population of soil microbes thereby increasing the fertility of the soil leading to the greening of the Earth as can now be seen in satellite imagery. This is supported by an extensive study of global soil carbon which, quote: “provides strong empirical support for the idea that rising temperatures will stimulate the net loss of soil carbon to the atmosphere” end quote, Crowther et el 2016 [ Ref.7].

Conclusion

The conclusion is that 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 the 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 the changes in the Earth’s temperature which result in coincident changes in the rate of change of CO2. That is, climate change is the result of 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 climate change.

4 thoughts on “Mauna Loa Observatory

  1. It delights me to see an analysis that takes into account our planet’s place in the solar system and all that is going on in it.
    I find the IPCC’s insistence on CO2 as the only variable to affect the Earth’s climate over millions of years to be simple minded at best and politically motivated at worst.
    15,000 years ago, where I am sitting right now, was covered in a kilometre of ice. Planes, trains and automobiles didn’t melt it away.
    Looking at your graphs, it looks as if your number crunching was done in R. Do you have a github page with source code?

    Like

  2. Thank you Finn for your kind remarks. I use Wolfram Mathematica for my number crunching and usually create a new .nb file for each study. I am still learning how to use Mathematica and have quite a way to go.

    Like

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