This model approaches the question of “how is the total Greenhouse effect modified by increased concentrations of atmospheric Greenhouse gases?”. The model attempts to quantify the amount that the total Greenhouse effect has been augmented by the higher levels of various Greenhouse gases in the atmosphere since pre-industrial times.
is to treat the world as it is and then look if there is any relationship between changes in the total Greenhouse effect and other observed metrics including global atmospheric temperatures at different levels, average global concentrations of Greenhouse gases and world population.
No prior causal relationship is assumed.
The models of the Anointed (see elsewhere on this website for use of the term ‘Anointed’ after Thomas Sowell) all start out by incorporating, axiomatically, the causal relationship ‘Anthropogenic emissions cause Global Warming’.
This leads to the logic trap of circular reasoning. Circular reasoning or ‘confirmatory modelling’ starts out at the finishing line by assuming the proposed causality step is absolutely true. The modelling then proceeds to prove that the starting assertion is true, so the models verify that the assertion is true. This is classic circular reasoning and is so logically flawed that it is incredible to me that scientists accept these models are valid.
In laypersons term it was observed that there was a spell of hot weather. At the same time it was noted that carbon dioxide levels had increased. This led to some people (notably Hanson & Bolin - see 'What is Climate Change?' page) to postulate 'it is obviously carbon dioxide causing this hot weather' and make outrageous predictions (all wrong).
The rest, as they say, is history - all based on a foundless coincidental postulate.
The Cardinal model make no assertion of causality.
It tries to describe how increased levels of Greenhouse gases and many other factors might be associated with increase in the total Greenhouse effect – as observed.
The page 'The Greenhouse Effect' introduces some of the concepts that are modelled using empirical parameters in the Cardinal Model.
Included in the Cardinal Model, for example, are the globally observed (monthly) average carbon dioxide level. Other atmospheric gases observed ground-level concentrations are included. The linking of these concentrations to the total Greenhouse effect is done through a series of empirical parameters. Empirical parameter modelling of observations is the process of building a mathematical model based on observed data, rather than on a priori physical or theoretical principles. The parameters in such models are quantifiable measures derived from observations together with valid theory and are used to fit the model to the data and characterise how the ‘system under consideration’ works.
The parameters link to concepts like effective density, Planck radiation, photon flux density, effective emission altitude, saturation zone, broadening/overlap zone, atmospheric layering, Arrhenius and other form responses some of which are derivd from consideration of Schwarzschild’s Radiative Transfer Equation.
The model proceeds to find values for all these empirical parameters by a process of variance minimisation – trying to fit the model’s outcome to the real world – as observed.
The Cardinal model incorporates 35 parameters which have been found to be non-trivial.
The ‘goodness of fit’ of the moel with the derived empirical parameters that minimise the variance of the model to the real world is nearly perfect.
Because of this near-perfect fit there is very high confidence that the values obtained for the empirical parameters are a good and valid representation of reality.
That the data used in the model contains slight measurement error (particularly for inhomogeneously distributed gases) is inevitable.
Increasing the number of empirical parameters in order to provide a better fit found no non-trivial additions.
The fit is so close (monthly error of 0.01WM-2 or less) that there is little room for increased accuracy.