We all know that NORMAL temperature is the mean of 1960 to 1990. That’s what the temperature of the Earth is SUPPOSED to be! What it was always MEANT to be. That’s the RIGHT temperature.

(that was sarcastic)

The short answer based on a very simple two layer model of radiative transfer with all other things like clouds and aerosols being equal is that the troposphere is relatively opaque to long wave radiation and relatively transparent to short wave radiation while the stratosphere, while being relatively transparent overall, is more opaque to short wavelength (less than 350 nanometers wavelength) than to long wavelength radiation. Using a simple one dimensional gray atmosphere model it’s easy to show that if you increase the long wave optical density of the troposphere by adding CO2, long wave emission goes down and the temperature of the troposphere and the surface has to go up to restore balance.

There is no more efficient greenhouse gas than ozone. This makes the lower stratosphere relatively opaque to long wave radiation. The evidence for this lies in the strong temperature peak at 200hPa and above (yes, in the troposphere) in mid year at 30-40° S latitude in the south East Pacific.

This is a zone of down-welling air where stratospheric ozone finds its way into the troposphere. A strong peak of outgoing long wave radiation that is associated with seasonal cloud loss in the southern tropics warms the air at 200hPa. Look at figure 5 in my post at http://climatechange1.wordpress.com/

In the southern tropics the tropopause experiences a thermal maximum in August right round the globe.

Theoretical physics is no substitute for a hard look at historical data. It can be downloaded at http://www.cdc.noaa.gov/cgi-bin/Timeseries/timeseries1.pl

From memory,(always doubtful) the tropopause warmed until 1978 and has cooled since, along with the rest of the lower stratosphere.

1978 marked the transition from a weak solar cycle 20 to a strong cycle 21 and the start of a run of El Nino events.

Over the period of record since 1948 specific and relative humidity has declined at all levels in the atmosphere. The implication is that cloud cover has declined. We need look no further for an explanation of the gradual warming or the cooling that is now in train.

Thx for the info; much of the terminology is unfamiliar, but w/ the help of a statistician friend I was able to make it out, except: “even a century of data doesn’t establish a stable secular linear trend.” Assume that in this context, secular means century-to-century?

Did some research on your book suggestion, but was unable to determine if it’s a technical specialists’ book, or intended for the lay reader. Seems like that latter from the title, but before I shell out twenty bucks, it would be good to know . . .

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Re: DeWitt Payne (#170)

There is little question that this pattern of low altitude warming and high altitude cooling, all other things being equal, distinguishes ghg induced tropospheric warming from warming due to absorption of short wavelength radiation from an increase in solar radiation, which would warm the stratosphere as well as the troposphere and the surface.

And also little question that this combination obtains strictly from either GHG’s or variations in the “solar constant”?

thx,

PJ

Ok and thanks Geoff.

I’m experiencing problems with my antiquated browser when the number of comments at CA grows large. Let’s discuss your purely statistical questions off-line. I believe you have received my e-mail address along with the Australian compilation.

The short answer based on a very simple two layer model of radiative transfer with all other things like clouds and aerosols being equal is that the troposphere is relatively opaque to long wave radiation and relatively transparent to short wave radiation while the stratosphere, while being relatively transparent overall, is more opaque to short wavelength (less than 350 nanometers wavelength) than to long wavelength radiation. Using a simple one dimensional gray atmosphere model it’s easy to show that if you increase the long wave optical density of the troposphere by adding CO2, long wave emission goes down and the temperature of the troposphere and the surface has to go up to restore balance.

For the stratosphere, an increase in CO2 increases long wavelength emissivity and absorptivity significantly and has almost no effect on short wavelength absorptivity, but there isn’t much long wave to absorb compared to incoming solar radiation (which we are assuming isn’t changing), so the increase in emissivity at long wavelength causes a net loss in energy, particularly since the temperature increases with altitude in the stratosphere due to short wavelength absorption by oxygen and ozone and emission increases with temperature. So the stratosphere should cool until energy balance is again restored. With very little convective heat transfer in the stratosphere and very low heat capacity, the stratosphere equilibrates rapidly (weeks or months).

There is little question that this pattern of low altitude warming and high altitude cooling, all other things being equal, distinguishes ghg induced tropospheric warming from warming due to absorption of short wavelength radiation from an increase in solar radiation, which would warm the stratosphere as well as the troposphere and the surface.

The very question of linear temperature trends is moot. Given the strong multi-decadal components evident in the spectra of long series of yearly averages, even a century of data doesn’t establish a stable secular linear trend. If the interest is in tracking temperature variations on time-scales commensurate with human life-times, then linear-phase low-pass filters (Butterworth or Chebycheff) can be used to show those variations more clearly (see UC’s example here in #58). Linear regression tends toward quasi-stable results–decade to decade–only when computed over nearly two centuries of data. Thus, for example, the historic “De Bilt” series (from 1706) shows a 176-yr trend in the range of 0.2-0.3K/century throughout most of the 20th century. Being sensitive to start- and end-values, regressional methods are simply ill-suited for analyzing data with strong cyclical components.

The answer to your question about references on the tropospheric “hot spot” is, likewise, not simple. Inasmuch as this is a fiat of most models, the climate modelling literature would be the place to look. You will not find any explanation of this feature, however, in rigorous atmospheric physics tracts written by those who possess a mastery of the laws of thermodynamics. For a masterful account of how the climate system works, I would recommend John Dutton’s “The Ceaseless Wind.” Meanwhile, bear in mind that most GCM’s used for climate modelling do not even get the diurnal cycle right, because of over-damping by imputed “greenhouse” effects. And the one model (Russian) that does not conform with the rest, but agrees best with satellite observations, is dismissed by IPCC as “unrealistic.” Go figure!

The unreliable eye often sees cyclicities in climate data and the more reliable mappers and geodesy people and mathematicians find observable effects like the gravitational influence of solar bodies on orbits of others. Do you know if anyone had ever compiled the list of periodicities say for the last 500,000 years or so? Dividied into reasonably assured and speculative? You mention the Hale cycle for starters.

Your comment on regressional trends drew me off on a tanget. My apologies. Let’s use plant growth as a dependent variable and fertilizers (including CO2) as a group of inputs. In a classical factorial trial, one uses (say) 20 different nutrients at (say) 5 levels of concentration each, in all possible combinations, then to be more sure, triplicates the study. In a conventional non-linear multiple regression analysis where it is assumed that the fertilizers are not independent of each other, one can do a conventional maths dissection to indicate the strongest influences. But, it has been long been discussed that if certain nutrients are low or absent, there will be no additional response caused by the addition of other fertilizers. Is there a name for this type of statistical analysis where the responses are not smooth but can be stepped or halted entirely? I ask because of the many past posts on regressions, where I have not seen an example used for “if no more v, then no more response to w, x, y or z”. In a hypothetical, if molybdenum was limited in some dendro areas, then water, CO2, SO2 and various other nutrients would not cause a growth response and the growth rings would be affected accordingly.