New paper confirms the gravito-thermal greenhouse effect on 6 planets including Earth, falsifies CAGW

An important new paper published in Advances in Space Research determines that the Earth surface temperature (as well as the surface temperatures of 5 other rocky planets in our solar system) can be very accurately determined (R² = 0.9999! & tiny standard error σ=0.0078) solely on the basis of two variables: 

1) atmospheric pressure at the surface, and 

2) solar irradiance at the top of the atmosphere, 

and without any consideration of any greenhouse gas concentrations or 'radiative forcing' from greenhouse gases whatsoever. 

Thus, the paper adds to the works of at least 40 others (partial list below) who have falsified the Arrhenius radiative theory of catastrophic global warming from increased levels of CO2, and also thereby demonstrated that the Maxwell/Clausius/Carnot/Boltzmann/Feynman atmospheric mass/gravity/pressure greenhouse theory is instead the correct explanation of the 33C greenhouse effect on Earth, and which is independent of "radiative forcing" from greenhouse gases. 

Using observed data from the planets Earth, Venus, the Moon, Mars, Titan, and Triton, the authors,

"apply the Dimensional Analysis (DA) methodology to a well-constrained data set of six celestial bodies representing highly diverse physical environments in the solar system, i.e. Venus, Earth, the Moon, Mars, Titan (a moon of Saturn), and Triton (a moon of Neptune). Twelve prospective relationships (models) suggested by DA are investigated via non-linear regression analyses involving dimensionless products comprised of solar irradiance, greenhouse-gas partial pressure/density and total atmospheric pressure/density as forcing variables, and two temperature ratios as dependent (state) variables. One non-linear regression model is found to statistically outperform the rest by a wide margin. Our analysis revealed that GMATs [Global Mean Atmospheric Temperatures] of rocky planets can accurately be predicted over a broad range of atmospheric conditions [0% to over 96% greenhouse gases] and radiative regimes only using two forcing variables: top-of-the-atmosphere solar irradiance and total surface atmospheric pressure [a function of atmospheric mass & gravity]. The new model displays characteristics of an emergent macro-level thermodynamic relationship heretofore unbeknown to science that deserves further investigation and possibly a theoretical interpretation."

Fig. 4. 

Dependence of the relative atmospheric thermal enhancement (T_s/T_na) on mean surface air pressure according to Eq. (10a) derived from data representing a broad range of planetary environments in the Solar System. Saturn’s moon Titan has been excluded from the regression analysis leading to Eq. (10a). Error bars of some bodies are not clearly visible due to their small size relative to the scale of the axes. See Table 2 for the actual error estimates.

"The above comparisons indicate that Eq. (10b) rather accurately reproduces the observed variation of mean surface temperatures across a wide range of planetary environments characterized in terms of solar irradiance (from 1.5 W m^-2 to 2,602 W m^-2), total atmospheric pressure (from near vacuum to 9,300 kPa), and greenhouse-gas concentrations (from 0.0% to over 96% per volume). While true that Eq. (10a) is only based on data from 6 planetary bodies, one should keep in mind that these represent all objects in the Solar System meeting our criteria (discussed in Section 2.3) for the quality of available data. The fact that only one of the investigated twelve non-linear regressions yielded a tight relationship suggests that Model 12 might be describing a macro-level thermodynamic property of planetary atmospheres heretofore unbeknown to science . A function of such predictive skill spanning the breadth of the Solar System may not be just a result of chance. Indeed, complex natural systems consisting of myriad interacting agents have been known to exhibit emergent behaviors at higher levels of hierarchical organization that are amenable to accurate modeling using top-down statistical approaches (e.g. Stolk et al. 2003). Equation (10) also displays several other characteristics that lend further support to the above conjecture."

Comparison of the two best-performing regression models according to statistical scores presented inTable 5. Vertical axes use linear scale to better illustrate the difference in skills between the models.
Added: The top model incorporates greenhouse gas partial pressures and has a standard error over 20 times worse than the bottom model which does not consider greenhouse gas concentrations or radiative forcing whatsoever. 

Fig. 5. 

Absolute differences between predicted average global surface temperatures (Eq. 10b) and observed GMATs (Table 2) for studied celestial bodies. Titan represents an independent data point, since it was excluded from the non-linear regression analysis leading to Eq. (10a). 

Added: The surface temperatures of 5 planets are determined within hundredths of degrees C using the Eqn 10a as a sole function of surface pressure and solar insolation. 

Fig. 7. 

a)   Dry adiabatic response of the air/surface temperature ratio to pressure changes in the free atmosphere according to Poisson’s formula (Eq. 12). The reference pressure is arbitrarily assumed to be p_o=100${}_{}\mathrm{}$ kPa;b) The SB radiation law expressed as a response of a blackbody temperature ratio to variation in photon pressure (see text for details). Note the similarity in shape between these two curves and the one portrayed in Fig. 4 depicting Eq. (10a).

The authors have used a new empirical non-linear regression method of determining the gravito-thermal greenhouse effect on 6 planets, and "might be describing a macro-level thermodynamic property of planetary atmospheres heretofore unbeknown to science," but are apparently unaware of and do not cite any of the over 36 scientific works/papers (partial list below) which have described the theoretical basis of the same 33C Maxwell/Clausius/Carnot gravito-thermal effect of atmospheric pressure, some of which also utilize the Poisson relation as illustrated in Fig 7. from the paper above. 

Only one possible explanation of the 33C 'greenhouse' effect temperature gradient on Earth can be possible, otherwise the greenhouse effect would be twice as large (i.e. 66C):

1) The 33C Arrhenius radiative greenhouse theory from greenhouse gases (which confuses the cause with the effect and fails to explain the planetary temperatures of Venus, Earth, Mars, Titan, Jupiter, Saturn, Uranus, Neptune, etc.)

OR

2) The 33C Maxwell/Clausius/Carnot gravito-thermal effect, proven by this new paper and the works/papers of at least 36 others (and very accurately predicts the surface and atmospheric temperatures of all rocky planets with an atmosphere in our solar system):

The HS greenhouse equation
The Maxwell/Clausius et al gravito-thermal 'greenhouse effect'
Richard Feynman
Boltzmann
Chilingar et al
1976 US Standard Atmosphere
International Standard Atmosphere & here
Hans Jelbring
Connolly & Connolly
Nikolov & Zeller
Mario Berberan-Santos et al
Claes Johnson and here
Velasco et al
Huffman
Giovanni Vladilo et al

Heinz Thieme

Stephen Wilde

Alberto Miatello

Gerhard Gerlich and Ralf D. Tscheuschner

Verity Jones
William C. Gilbert & here
The Barometric Formulae
Richard C. Tolman
Lorenz & McKay
Peter Morecombe
Ozawa et al
Murry Salby
Goran Ahlgren
Joe Postma
Charles Anderson
Wing and Cronin
Kimoto
Kalmanovitch/quantum physics

Pangburn

Pauli exclusion principle/quantum physics

Planck's quantum theory of blackbody radiation

Miskolczi
Kristian
Siddons
Poisson
Carnot
Helmholtz

Emergent Model for Predicting the Average Surface Temperature of Rocky Planets with Diverse Atmospheres

• Den Volokin^, , 
• Lark ReLlez

 Show more

doi:10.1016/j.asr.2015.08.006t

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Highlights

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Dimensional Analysis is used to model the average temperature of planetary bodies.

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The new model is derived via regression analysis of measured data from 6 bodies.

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Planetary bodies used by the model are Venus, Earth, Moon, Mars, Titan and Triton.

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Two forcing variables are found to accurately predict mean planetary temperatures.

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The predictor variables include solar irradiance and surface atmospheric pressure.

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Abstract

The Global Mean Annual near-surface Temperature (GMAT) of a planetary body is an expression of the available kinetic energy in the climate system and a critical parameter determining planet’s habitability. Previous studies have relied on theory-based mechanistic models to estimate GMATs of distant bodies such as extrasolar planets. This ‘bottom-up’ approach oftentimes relies on case-specific parameterizations of key physical processes (such as vertical convection and cloud formation) requiring detailed measurements in order to successfully simulate surface thermal conditions across diverse atmospheric and radiative environments. Here, we present a different ‘top-down’ statistical approach towards the development of a universal GMAT model that does not require planet-specific empirical adjustments. Our method is based on Dimensional Analysis (DA) of observed data from the Solar System. DA provides an objective technique for constructing relevant state and forcing variables while ensuring dimensional homogeneity of the final model. Although widely utilized in some areas of physical science to derive models from empirical data, DA is a rarely employed analytic tool in astronomy and planetary science. We apply the DA methodology to a well-constrained data set of six celestial bodies representing highly diverse physical environments in the solar system, i.e. Venus, Earth, the Moon, Mars, Titan (a moon of Saturn), and Triton (a moon of Neptune). Twelve prospective relationships (models) suggested by DA are investigated via non-linear regression analyses involving dimensionless products comprised of solar irradiance, greenhouse-gas partial pressure/density and total atmospheric pressure/density as forcing variables, and two temperature ratios as dependent (state) variables. One non-linear regression model is found to statistically outperform the rest by a wide margin. Our analysis revealed that GMATs of rocky planets can accurately be predicted over a broad range of atmospheric conditions and radiative regimes only using two forcing variables: top-of-the-atmosphere solar irradiance and total surface atmospheric pressure. The new model displays characteristics of an emergent macro-level thermodynamic relationship heretofore unbeknown to science that deserves further investigation and possibly a theoretical interpretation.