Guest essay by Bevan Dockery
Here is 38 years of empirical data clearly showing a relationship between the satellite temperature and the rate of change of atmospheric CO2 concentration at the Mauna Loa Observatory.
Figure 1. Mauna Loa Observatory
Figure 1 shows the monthly lower tropospheric satellite temperature for the Tropics-Land component 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 removal of the estimated seasonal variation. This makes it comparable to the annual rate of change of CO2 concentration as taking the annual rate eliminates the seasonal variation.
Calculation of the Ordinary Linear Regression between the two time series gave a correlation coefficient of 0.65 from the 448 monthly data pairs. Detrending of the time series in order to determine the statistical significance gave a correlation coefficient of 0.56 with 446 degrees of freedom. However the Durbin-Watson test of the time series gave a value of 1.08 indicating positive autocorrelation which means that Ordinary Linear Regression is inapplicable. The autocorrelation was estimated to be 0.53. When applied to the transformed time series, that is, applying a First Order Autoregressive Model, it resulted in a correlation coefficient of 0.25 with 445 degrees of freedom and a t statistic of 5.38, implying an infinitesimal probability that the coefficient is equal to zero from a two-sided t-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 0.14 with 445 degrees of freedom and a t statistic of 3.06, implying a probability of 0.2% that the coefficient is equal to zero from a two-sided t-test.
It follows that this synthesis of empirical data conclusively reveals that CO2 has not caused temperature change over the past 38 years but that the rate of change in CO2 concentration may have been influenced to a statistically significant degree by the temperature level. Note that it is not possible 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 would have evaporated long ago.
Support for this thesis is seen in a statistical analysis of the monthly CO2 concentration with respect to the lower tropospheric temperature for Macquarie Island in the Southern Ocean at Latitude 54̊ 29ʹ South, Longitude 158̊ 58ʹ East. Applying a First Order Autoregressive Model to the various components of the temperature, Global, Southern Hemisphere, Tropics, and Southern Extension and their Land and Ocean components gave correlation coefficients ranging from a minimum of 0.01 for 284 degrees of freedom, t statistic 0.15, probability of zero correlation 88% for the Southern Hemisphere zone, 90̊S to 0̊, to a maximum of 0.55, 284 deg. of free., t statistic 10.97, infinitesimal probability of zero correlation for the Tropics temperature zone, 20̊S to 20̊N.
This 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 interglacial warm periods shows CO2 change lagging temperature change by several centuries to more than a millennium while modern CO2 and temperature data shows lags of 9 to 12 months, Humlum et el., 2013 . Cross correlation of annual changes in each of CO2 concentration at Mauna Loa and satellite lower tropospheric Tropics – Land temperature showed that CO2 change lagged temperature change by 5 months. As 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 does not cause global warming.
The CO2 concentration data for the Mauna Loa Observatory is freely available from the Scripps Institute via the Web page:
The satellite temperature data for the Tropics zone is freely available from the University of Alabama, Huntsville, Dr Roy Spencer’s Web site at:
The CO2 concentration data for Macquarie Island is available at: http://ds.data.jma.go.jp/gmd/wdcgg/pub/data/current/co2/monthly/mqa554s00.csiro.as.fl.co2.nl.mo.dat
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:
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.
Here again is 38 years of empirical data, this time showing a distinct lack of a relationship between the satellite temperature and the atmospheric CO2 concentration.
Figure 2. Mauna Loa Observatory
Figure 2 shows the monthly lower tropospheric satellite temperature for the Tropics-Land component in blue and the monthly CO2 concentration in red after removal of the seasonal variation so as to match the residual temperature series. 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 IPCC.
Calculation of the Ordinary Linear Regression between the two time series gave a correlation coefficient of 0.49 from the 454 monthly data pairs. This is a measure of the relationship between the background linear trend of each of the time series as shown by the almost identical correlation between the temperature and the time of 0.50. The correlation between the CO2 concentration and the time was 1.00, that is, the CO2 concentration time series was practically a linear trend as is the 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 every time series has a background linear trend with respect to time.
Detrending of the time series in order to determine the statistical significance gave a correlation coefficient of 0.0015 with 452 degrees of freedom. However, the Durbin-Watson test of the time series gave a value of 2.40 which indicates negative autocorrelation. The autocorrelation was estimated to be -0.79. Applying a First Order Autoregressive Model to the two transformed time series resulted in a correlation coefficient of 0.002 with 451 degrees of freedom and a t statistic of 0.047 implying a probability of 96% that the correlation coefficient is equal to zero from the two-sided t-test.
Once again the Macquarie Island data support this result. The Island is in the Southern Extension zone of the satellite lower tropospheric 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 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 -0.06, 296 deg. of free, t statistic -0.98, probability of zero correlation 33%. For the Land component, the correlation coefficient was -0.02, 296 deg. of free, t statistic -0.39, probability of zero correlation 70%. For the Ocean component, the correlation coefficient was -0.07, 296 deg. of free, t statistic -1.14, probability of zero correlation 26%.
The negative correlations imply that an increase in CO2 concentration caused a decrease in temperature, the complete opposite of the IPCC thesis. However as the probabilities were not statistically significant, this could not be supported and the conclusion must be that the correlation coefficients were zero in agreement with the Mauna Loa result.
In conclusion, this synthesis of empirical data reveals that increases in the CO2 concentration has not caused temperature change over the past 38 years across the Tropics-Land area of the Globe. However, the rate of change in CO2 concentration may have been influenced to a statistically significant degree by the temperature level. As the Tropics is the zone of greatest average temperature, it must consequently produce the greatest rate of increase in CO2 concentration causing that CO2 to spread North and South towards the Poles. This is supported by data from CO2 stations across the Globe whereby temperature events, such as El Nino, increasingly lead the matching CO2 event with increasing CO2 station latitude.
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 .
Note that, as a consequence, the CO2 concentration will not fall until after the temperature falls below a critical value. This is predicted to be a surface temperature of zero degrees Centigrade at which point water freezes and is no longer available to support the continued regeneration of the biogenetic sources that create CO2. This may explain the large time lag between the long term temperature changes and the corresponding later changes in CO2 concentration seen in ice core records.
 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.
 T.W. Crowther, et el, “Quatifying global soil carbon losses in response to warming” Nature, Vol. 540, 104-108, 01
Bevan Dockery, B.Sc.(Hons), Grad. Dip. Computing, retired geophysicist.
formerly: Fellow of the Australian Institute of Geoscientists,
Member of the Australian Society of Exploration Geophysicists,
Member of the Society of Exploration Geophysicists,
Member of the European Association of Exploration Geophysicists,
Member of the Australian Institute of Mining and Metallurgy.