Guest Post by Willis Eschenbach
One of my great pleasures is to come across a new dataset. Turn me loose on new observations of this magical world, and there’s no telling where I’ll end up. Thanks to a recent article here on WUWT I got to thinking about water vapor. Some research found the RSS 1° gridded “total precipitable water” (TPW) dataset. Total precipitable water (TPW) is the mass (or sometimes the depth) of water in a 1 metre by 1 metre column from the surface to the top of the atmosphere, if it all fell as rain. The RSS dataset has the TPW (for the ice-free ocean areas only) since 1988. Figure 1 shows the average values, in kilograms of water per square metre. Note that the RSS dataset only covers the ice-free oceans.
Now, there are a few interesting things about Figure 1. First, you can see why they call it the “wet tropics”. There’s lots of water in the air.
Next, the horizontal red band just above the equator delineates the effect of the band of thunderstorms perpetually boiling along the length of the inter-tropical convergence zone (ITCZ).
You can also see why CO2 is called a “well-mixed” greenhouse gas, and water vapor is not. The amount of water in the air varies from the poles to the tropics by more than an order of magnitude.
Seeing Figure 1 made me think that I could estimate the change in the poorly-named “greenhouse effect” due to a given change in water vapor. Ramanathan proposed that the magnitude of the clear-sky atmospheric greenhouse effect could be measured as the amount of upwelling longwave radiation (ULR) from the surface that is absorbed by the atmosphere. Ramanathan also observed that the variation in the strength of the clear-sky greenhouse effect was an effect of the variations in water vapor.
To show the close relationship between variations in the atmospheric absorption of the surface radiation, and the total water vapor seen in Figure 1, Figure 2 shows the atmospheric absorption as revealed by the CERES data:
Figure 2. Average atmospheric absorption of upwelling surface longwave radiation, clear-sky CERES data. Calculated as the amount of longwave (infrared) emitted by the surface minus the amount observed at the top of the atmosphere.
Seeing those two figures gave me the idea that I could actually measure the amount of change in downwelling radiation from a given change in precipitable water vapor. So here is a scatterplot graph relating the two:
Figure 3. Scatterplot of Total Precipitable Water (logarithmic, horizontal scale) versus Atmospheric Absorption (vertical scale). Dashed vertical line shows global average value. Dotted lines show the range of the global average value over the period.
This is quite an impressively tight result, particularly given that the two variables (absorption and TPW) are from totally different datasets. I note that this is experimental validation of the IPCC’s statement about the underlying physics, viz:
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. IPCC AR5 WGI Box 8.1
More than just validating the IPCC claim of a generalized logarithmic relationship, however, this has allowed us to actually quantify the relation between the two. It also allows us to differentiate that relationship in order to determine the slope of the atmospheric absorption as a function of water vapor. That slope turns out to be 62.8 / TPW. At the average TPW value in Figure 3 of 29 kg/m^2, this gives us a slope of 62.8 / 29.0 = 2.2 W/m2 increase in absorption per kg/m2 change in TPW.
That is to say, we get a bit over two watts per square metre of increased absorption for every additional kilo of atmospheric water per square metre.
Now, that is an interesting finding which we can combine with the following look at the change in global average total precipitable water since 1988:
Some things of interest. First, in the bottom panel you can see the effect on TPW of the El Nino episodes in 1997/98, 2010/11, and 2015/16. You can also see that we haven’t quite recovered from the most recent episode.
Next, there is a clear trend in the TPW data. The total change over the period is ~ 1.5 kg/m^2, centered around the long-term mean of 28.7 kg/m^2.
And utilizing the relationship between water content and atmospheric absorption derived above, this indicates an increase in downwelling radiation of 3.3 W/m2 over the period.
Now, please note that this 3.3 W/m2 increased forcing from the long-term increase in water vapor since 1988 is in addition to the IPCC-claimed 2.3 W/m2 increase since 1750 in all other forcings (see Figure SPM-5, IPCC AR5 SPM). The IPCC counts as forcings the long-term changes in the following: CO2, CH4, Halocarbons, N2O, CO, NMVOC, NOx, mineral dust, SO2, NH3, organic carbon, black carbon, land use, and changes in solar irradiance … but not the long-term changes in water vapor.
This leads us to a curious position where we have had a larger change in forcing from water vapor since 1988 than from all the other IPCC-listed forcings since 1750 … so where is the corresponding warming?
Sunny today, I’m going for a walk …
My Usual Request: We can minimize misunderstandings by being specific. If you disagree with me or anyone, please quote the exact words you disagree with, so we can all understand the exact nature of your objections. I can defend my own words. I cannot defend someone else’s interpretation of some unidentified words of mine.
My Other Request: If you believe that e.g. I’m using the wrong method or the wrong dataset, please educate me and others by demonstrating the proper use of the right method or identifying the right dataset. Simply claiming I’m wrong about methods or data doesn’t advance the discussion unless you can point us to the right way to do it.