Evidence I: The Simple Model of Global Warming 1 and the Global Effective CO2 Greenhouse Constant

Abstract The objective of this paper is to present the first empirical evidence (Evidence I) in a series of papers, which strongly supports the accuracy of the general linear model of GW (GLMGW) that was deduced in the first paper. The GLMGW claims that the global atmospheric surface temperature (GAST) is a multivariable linear function of the concentrations of atmospheric greenhouse gases (GHGs). Evidence I presents the theoretical derivation of the most simplified relationship between the global atmospheric surface temperature (GAST) and the global atmospheric CO2 concentration (GACC) based on the GLMGW. The result is referred to as the simple model of global warming 1 (SMGW1). The main premise of SMGW1 is that GAST can be described as a function of CO₂ alone, assuming that water vapor is linearly dependent on the atmospheric CO₂ concentration. All the other GHGs are assumed to have insignificant GHEs. It is empirically tested in this paper. The empirical test is based on NOAA’s global datasets of temperatures and atmospheric concentrations of CO₂, that is, the observed GAST and the observed Keeling Curve (GACC) for the time period 1959–2022. Evidence I, the most fundamental evidence in this entire work, shows that the observed GW (GAST) displays an unequivocal linear correlation with the observed global atmospheric concentration of CO2 (GACC). This result is the first strong indication in a series of evidence presented by this author showing via new theory, new methodology and empirical data that CO₂, which has been long time claimed, to be the indisputable single dominant root cause of the observed GW. Again, the result of the linear correlation is predicted by theoretical derivations in paper one, which are based on the absorption theory described by the Lambert–Beer–Bouguer law, which results in the GLMGW. Furthermore, the linear empirical correlation in combination with SMGW1 is transformed into a measurement model by means of a least-square regression method. The following are some of the characteristics of SMGW1. First, it should be regarded as a simplified validated engineering model to indirectly measure GW as a function of observed atmospheric CO₂ alone. Second, SMGW1 indirectly measures the average GAST(A) and absolute measurement uncertainty on the order of ± 0.015 K. The apparently random interannual variability observed in GAST is shown to be caused by atmospheric moisture in Evidence IV, and this variability is not accounted for in SMGW1. Third, the characteristic constant of SMGW1 is the global effective linear temperature constant of CO2 (or the global effective CO₂ greenhouse constant), which is equal to 0.010 K per ppm CO2. An effective greenhouse constant is vital in the estimation and prediction of GW progression. Fourth, by means of SMGW1, the average GASTA at the end of 2022 is estimated to be 1.39 ± 0.015 K, calculated from preindustrial time. Consequently, we were only 0.11 K from the 1.5K limit at the beginning of 2023. Fifth, SMGW1 measures a GW anomaly (GASTA) of approx. 1.22 ± 0.015 K for the time period 1850–2019. This result is also significantly greater than the IPCC GASTA estimates in AR6 for the same period, with the best estimate being 1.07 K (0.8-1.3K). The estimated temperature difference of 0.15 K (= 1.22–1.07) between SMGW1 and the IPCC corresponds to approximately 260–290 Gton of anthropogenic CO2 emissions (shown in Evidence II-III), which is a major difference. The temperature difference corresponds to nearly six to seven years of anthropogenic CO₂ emissions at the current rate of 41.5 Gton/yr. Sixth, SMGW1 is assumed to be linear in the whole measurement range of 0-420 ppm CO₂. This linear assumption is tested and proven to be correct in Evidence IV and V.