Tuesday, December 29, 2015
Dave Smith critiques Bob Armstrong's Luft Gesheft
- "the deep relationship between equilibrium temperatures and gravity" -
The only obvious one is the change in temperature of a gas with adiabatic expansion or compression. This then leads to a Lapse Rate - the rate at which atmospheric temperature decreases with an increase in altitude (see Lapse Rate in Wikipedia).
In reality, due to the inhomogeneity of the atmosphere and interactions with radiation the Lapse Rate is just an idealised figure (see Atmospheric temperature in Wikipedia). There is certainly no simple application when comparing different planets.
As to greenhouse gases, they actually interfere with the idealised calculations of lapse rates as they assume some sort of even warming, and do not calculate radiative heat transfers. In short increases in CO2 result in increased warmth in the troposphere with cooling in the stratosphere and above.
- "But I see no evidence that the journeyman "climate scientist" has anywhere near that background in "thermostatics"" -
They most certainly do, this sort of physics is the simple basics of what they need to understand. Bob seems to be making some bizarre claim of a greater understanding of climate physics than climate physicists.
From this he also appears to be hinting at the impossibility of radiative forcing changing the temperature by assuming that simplistic idealised physics are the be all and end all of reality.
Climate models use complex calculations of radiative heat transfer, vertical mixing and inhomogeneities, and cloud and aerosol formation.
Thermostatics is the study of systems in thermal equilibrium. Lapse rates are an example of that.
- "notion of orthogonal function decomposition" -
There are many ways of looking at this, but in essence it is the attempt to change a function dependent on multiple variables where those variables have some interaction with each other, into one where the variables are chosen such that the results can be expressed as the sum or the product of functions of those variables.
For example if we have y=f(x1, x2) and how x2 affect y is dependent on the value of x1 then orthogonal function decomposition will result in y = f1(X1) + f2(X2), where X1 and X2 are new data inputs based on x1 and x2. It's called orthogonal because of the concept of dot products being zero if they are orthogonal.
See 'Empirical orthogonal functions' in Wikipedia.
- "the computationally useful 279k gray body temperature in our orbit" -