Lower humidity, less rain, less CO2 transported from high to surface. Both greenhouse gasses decrease at the top. How does this effect altitude versus temperature ?

Box 8.1 of 4AR Chapter 8 page 632 states:

“The radiative effect of absorption by water vapour is roughly proportional to the logarithm of its concentration, so it is the fractional change in water vapour concentration, not the absolute change, that governs its strength as a feedback mechanism. Calculations with GCMs suggest that water vapour remains at an approximately constant fraction of its saturated value (close to unchanged relative humidity (RH)) under global-scale warming (see Section 8.6.3.1). Under such a response, for uniform warming, the largest fractional change in water vapour, and thus the largest contribution to the feedback, occurs in the upper troposphere.”

“In looking at Anthony’s plots, you need to take note of the vertical axis scales. The range at 1000 mb is .45 gm/kg; at 300 mb it is .045 gm/kg, ten times less. The decrease at high altitudes is much less than the increase at low altitudes.”

That’s how it looks at the extremes, but for the range from 700-400 mb the decrease is a bit bigger, and this pressure range corresponds to a much greater altitude range than the region where specific humidity is decreasing. However, since atmospheric density is lower at higher altitudes ( http://www.auf.asn.au/metimages/atmosdensity.gif ), a change in specific humidity at higher altitude corresponds to a smaller change in actual water vapor concentration. With all this in mind, you can’t really tell whether the graphs suggest a net increase or decrease in the total amount of water vapor in the atmosphere just by eyeballing it; somebody would have to take the source data and do some actual number-crunching on it.

I forgot to note that the pressure and temperature distributions in the atmosphere above the surface are based on the usual lapse-rate model with surface temperature = 288.15 K and pressure = 101325.0 Pa.

If I have correctly evaluated the equation here’s is what I get for relative humidity for several values of pressure.

Pressure RH
(mb) (-)
1013 0.77
925 0.70
850 0.64
800 0.60
700 0.53
600 0.45
400 0.29
300 0.21
200 0.14
100 0.06

These are eyeball-grade values, I did not bother to interpolate in the table that I built.

The values are pretty good relative to those in the plot, except at high altitude.

“In the absence of large changes in relative humidity, the observed warming of the troposphere (see Section 3.4.1) implies that the specific humidity in the upper troposphere should have increased.”

I am not after falsification. I am not sure that its an achievable goal. My approach is to look at the way evidence is used. As a preliminary observation, there seems to be a pattern in this area of deprecating observations that do not fit the assumptions of GCMs, and RH is a prime example. That is not a falsification in itself, but the inconsistency in the story downgrades the evidence for warming due to enhanced greenhouse effect (EGE) provided by the models, I think.

Better evidence would be provided if the models had a consistent story between themselves, and with the observations. There are a number of areas where almost all thue models are at variance with the observations, important to evidencing EGE the upper troposphere where temperature and RH contradict them. Of course, models are wrong in lots of ways and you expect refinements. Its a problem though when you draw faulty conclusions as a result.

But I don’t want to take this OT to GCMs. The LBL codes are also of major importance, particularly in this upper-atmosphere issue.