Guest Post by Willis Eschenbach
Well, another productive ramble through the CERES dataset, which never ceases to surprise me. This time my eye was caught by a press release about a new (paywalled) study by Gordon et al. regarding the effect of water vapor on the climate:
From 2002 to 2009, an infrared sounder aboard NASA’s Aqua satellite measured the atmospheric concentration of water vapor. Combined with a radiative transfer model, Gordon et al. used these observations to determine the strength of the water vapor feedback. According to their calculations, atmospheric water vapor amplifies warming by 2.2 plus or minus 0.4 watts per square meter per degree Celsius. (See Notes for sources)
Hmmm, sez I, plus or minus 0.4 W/m2? I didn’t know if that was big or small, so I figured I’d take a look at what the CERES data said about water vapor. As the inimitable Ramanathan pointed out, the distribution of water vapor in the atmosphere is shown by the variations in the clear-sky atmospheric absorption of upwelling longwave.
Figure 1. Distribution of Atmospheric Water Vapor, as shown by absorption of upwelling surface longwave (LW) radiation, in watts per square metre (W/m2). In areas of increased water vapor, a larger amount of the upwelling radiation is absorbed in clear-sky conditions. Absorption is calculated as the upwelling surface longwave radiation minus the upwelling top-of-atmosphere (TOA) longwave radiation. The difference between the two is what is absorbed. Contours are at 10 W/m2 intervals.
As Ramanathan saw, there’s only one greenhouse gas (GHG) that shows that kind of spatial variability of absorption, and that’s water vapor. The rest of the GHGs are too well mixed and change too slowly to be responsible for the variation we see in atmospheric absorption of upwelling surface radiation.. OK, sez I, I can use that information to figure out the change in clear-sky absorption per degree of change in temperature. However, I wanted an answer in watts per square metre … and that brings up a curious problem. Figure 2 shows my first (unsuccessful) cut at an answer. I simply calculated the change in absorption (in W/m2) that results from a one-degree change in temperature.
Figure 2. Pattern of changes in clear-sky atmospheric absorption, per 1°C increase in temperature. This is the pattern after the removal of the monthly seasonal variations.
The problem with Figure 2 is that if there is a 1°C increase in temperature, we expect there to be an increase in watts absorbed even if there is absolutely no change in the absorption due to water vapor. In other words, at 1°C higher temperature we should get more absorption (in W/m2) even if water vapor is fixed, simply because at a higher temperature, more longwave is radiated upward by the surface. As a result, more upwelling longwave will be radiated will be absorbed. So I realized that Figure 2 was simply misleading me, because it includes both water vapor AND direct temperature effects.
But how much more radiation should we get from a surface temperature change of 1°C? I first considered using theoretical blackbody calculations. After some reflection, I realized that I didn’t have to use a theoretical answer, I could use the data. To do that, instead of average W/m2 of absorption, I calculated the average percentage of absorption for each gridcell, as shown in Figure 3.
Figure 3. As in Figure 1, showing the distribution of water vapor, but this time shown as the percentage of upwelling surface longwave radiation which is absorbed in clear-sky conditions. Contours are at intervals of 2%, highest contour is 40%. Contours omitted over the land for clarity.
This is an interesting plot in and of itself, because it shows the variations in the efficiency of the clear-sky atmospheric greenhouse effect in percent. It is similar to Figure 1, but not identical. Note that the clear-sky greenhouse effect in the tropics is 30-40%, while at the poles it is much smaller. Note also how Antarctica is very dry. You can also see the Gobi desert in China and the Atacama desert in Peru. Finally, remember this does not include the manifold effects of clouds, as it is measuring only the clear-sky greenhouse effect.
Back to the question of water vapor feedback, using percentages removes the direct radiative effect of the increase in temperature. So with that out of the way, I looked at the relationship between the percentage of absorption of upwelling LW, and the temperature. Figure 4 shows the average temperature and the average absorption of upwelling LW (%):
Figure 4. Scatterplot of 1°x1° gridcell average atmospheric absorption and average temperature. The green data points are land gridcells, and the blue points show ocean gridcells. N (number of observations) = 64,800.
As you can see, the relationship between surface temperature and percentage of absorption is surprisingly linear. It is also the same over the land and the ocean, which is not true of all variables. The slope of the trend line (gold dashed line in Figure 4) is the change in percentage of absorption per degree of change in temperature. The graph shows a ~ 0.4% increase in absorption per °C of warming.
Finally, to convert this percentage change in absorption to a global average water vapor feedback in watts per square metre per °C, we simply need to multiply the average upwelling longwave (~ 399 W/m2) times 0.443%, which is the change in percentage per degree C. This gives us a value for the change in absorption of 1.8 ± .001 W/m2 per degree C.
Finally, recall what the authors said above, that “atmospheric water vapor amplifies warming by 2.2 plus or minus 0.4 watts per square meter per degree Celsius.” That means that the CERES data does not disagree with the conclusions of the authors above. However, it is quite a bit smaller—the Gordon et al. value is about 20% larger than the CERES value.
Which one seems more solid? I’d say the CERES data, for a couple of reasons. First, because the trend is so linear and is stable over such a wide range. Second, because the uncertainty in the trend is so small. That indicates to me that it is a real phenomenon with the indicated strength, a 1.8 W/m2 increase in absorbed TOA radiation.
Finally, according to Gordon et al. there is both a short-term and a long-term effect. They say
By forcing a radiative transfer model with the observed distribution of water vapor, we can understand the effect that the water vapor has on the TOA irradiance. Combining information on how global mean surface temperature affects the total atmospheric moisture content, we provide an estimate of the feedback that water vapor exerts in our climate system. Using our technique, we calculate a short-term water vapor feedback of 2.2 W m–2 K–1. The errors associated with this calculation, associated primarily with the shortness of our observational time series, suggest that the long-term water vapor feedback lies between 1.9 and 2.8 W m-2 K–1.
So … which one is being measured in this type of analysis? I would argue that the gridcells in each case represent the steady-state, after all readjustments and including all long-term effects. As a result, I think that we are measuring the long-term water-vapor feedback.
That’s the latest news from CERES, the gift that keeps on giving.
Best to all,
w.
NOTES:
Ut Solet
If you disagree with something I (or anyone) says, please quote my words exactly. I can defend my own words, or admit their errors, and I’m happy to do so as needed. I can’t defend your (mis)understanding of my words. If you quote what I said, we can all be clear just what it is that you think is incorrect.
Data and Paper
Press Release here.
Paywalled paper: An observationally based constraint on the water-vapor feedback, Gordon et al., JGR Atmospheres
R Code: CERES Water Vapor (zipped folder 750 mb)
CERES Data: CERES TOA (220 Mb) and CERES Surface (115 Mb)
[UPDATE]
An alert reader noted that I had simplified the actual solution, saying:
Since one of the feedbacks is T^4 it would probably come out as T^3 in a percentage plot and this curve has strong upwards curvature.
To which I replied:
Not really, although you are correct that expressing it as a percentage removes most of the dependence on temperature, but not quite all of the dependence on temperature. As a result, as you point out the derivative would not be a straight line. Here’s the math. The absorption as a percentage, as noted above, is
(S- TOA)/S
with S being upwelling surface LW and TOA upwelling LW.
This simplifies to
1 – TOA/S
But as you point out, S, the surface upwelling LW, is related to temperature by the Stefan-Boltzmann equation, viz
S = sigma T4
where S is surface upwelling LW, sigma is the Stefan Boltzmann constant, and T is temperature. (As is usual in such calculations I’ve assumed the surface LW emissivity is 1. It makes no significant difference to the results.)
In addition, the TOA upwelling longwave varies linearly with T. This was a surprise to me. One of the interesting parts of the CERES dataset investigation is seeing who varies linearly with temperature, and who varies linearly with W/m2. In this case TOA can be well expressed (to a first order) as a linear function of T of the form mT+b.
This means that (again to a first order) I am taking the derivative of
1 – (m T + b) / (sigma T4)
which solves to
(4 b + 3 m T)/(sigma T5)
Over the range of interest, this graphs out as
Recall that my straight-line estimate was 0.44% per degree, the average of the values shown above. In fact, the more nuanced analysis the commenter suggested shows that it varies between about 0.38% and 0.5% per degree.
An excellent article, thank you. However, there are more significant digits in the error bar than the number (1.8 plus or minus 0.001 W/m^2).
To my eye Figure 4 looks logarithmic which I think would be expected due to saturation.
TimTheToolMan says:
March 24, 2014 at 1:16 am
Thanks, Tim, but … no. There are two groups of CERES datasets. The TOA datasets are measured. The surface datasets are calculated from the TOA datasets, and validated against a variety of other sources..
However, I’ve compared the CERES upwelling surface longwave dataset to the surface longwave calculated from the observed temperatures and guess what? They are very close, certainly close enough for our purposes.
So while you are kind of correct (some of the CERES data is calculated not measured), it’s a difference that makes very little difference.
Regards,
w.
Willis,
There appears to be an inflection point in the data. In your Figure 4, focusing in on the ocean data … the blue dots appear to turn upward right around 30C.
Is there enough data to reliably estimate the relationship independently above and below the inflection point?
JohnB
I don’t know and I’ve never tried to to work it out.
I can see the Atacama desert, but Gobi not so much. Gobi is on the border of China and (mostly in) Mongolia I thought.
Also. how come I can’t make out the vast deserts of Nth Africa, Arabia and Australia? Are there so much water vapour above these intense dry places? I’m confused.
I have to agree. Where, exactly, is the water vapour above these intense dry places? The average humidity of the Sahara, for example, is around 25%, and most of that comes from its comparatively few rains (it is around 4 or 5% humid most of the time). I’m not certain what kind of average the 25% is, but there is nothing LIKE a 25% relationship between the nearly 100% humid equatorial ocean.
Humidity is of course a questionable measure in and of itself, as it is usually expressed as relative humidity, water vapor content relative to saturation. Cold air and warm air with exactly the same water vapor content can have very different relative humidity. It is still surprising to me that the Sahara and Namib do not stand out like a sore thumb. Parts of the Namib dessert receive 19mm of rain and 35 mm of fog/dew precipitation per year — where in NC we get that much rain in a day and that much dew precipitation in a week or two. Yet NC is considered drier than the Namib as far as any of the maps above are concerned. Then there is the US midwest, considered to be much, much drier (as far as water-vapor-linked atmospheric absorption is concerned) than the Sahara.
I have to say, Willis, that this fails mere common sense checks for data consistency. I’m not certain what is really being graphed here on the maps, but I’m pretty sure that it isn’t what any of these arguments claim it is.
Indeed, one can compare the direct spectrographs provided in Petty’s book with this data to see the puzzle. He provides the spectrograph looking down over the Sahara, and in the dry air the CO_2 bands stand out but everywhere else the atmosphere is nearly transparent. He also provides direct spectrographs looking down over tropical ocean, where there is an overwhelming contribution from water vapor and even the CO_2 bands are reduced to perturbations on a broad background of absorption. The spectrographs just don’t seem consistent with the maps above, suggesting that the normalization isn’t being done right. I don’t know offhand how to fix it or how it is wrong, but I’d be very suspicious of any map that shows substantial water-vapor absorption above places that get less than 5 inches of total precipitation a year.
I’ve long thought that the GHE can be better studied in places like the Namib and the Sahara than anywhere else on Earth, largely because they lack the confounding effect of water vapor. Water vapor is not a good direct proxy for the GHE because it is a dual contribution. Yes, it increases absorption and hence back radiation, but it also is the vehicle for direct transport of latent heat up from the surface to heights from which it can be radiated without passing through the greenhouse layer. Finally, it is the precursor to clouds, where the direct variation of the effective albedo usually trumps the variation of GHE. Altogether water vapor produces a mix of feedbacks from different, nonlinearly coupled processes that affect the total radiation being given off by any patch of surface and the pathways by which incident heat leave (humid air also absorbs and scatters more SW energy on the way down and prevent as much from reaching the surface in the first place, for example). That’s why deserts are good. All of this is turned off.
rgb
rgbatduke says:
March 24, 2014 at 9:23 am
Short of going to the South Pole, as did the putative gravity-wave detecting BICEP2 team, the best place to study the GHE would be the Atacama Desert of northern Chile, where so many observatories are located, thanks to extreme dryness of the air above mountains there.
Willis Eschenbach said:
“As the inimitable Ramanathan pointed out, the distribution of water vapor in the atmosphere is shown by the variations in the clear-sky atmospheric absorption of upwelling longwave.”
“Finally, recall what the authors said above, that “atmospheric water vapor amplifies warming by 2.2 plus or minus 0.4 watts per square meter per degree Celsius.” ”
“As a result, I think that we are measuring the long-term water-vapor feedback.”
———————
Sam Cogar asks:
Looking at the Figure 3 graphic, it is obvious that the H2O vapor ppm is the greatest at the Equator and decreases toward either Pole.
And if I accept this as reasonably accurate figures, to wit:
“The percentage water vapor in surface air varies from .01% at -42℃ (-44℉) to 4.24% when the dew point is 30℃ (86℉).” Src: http://en.wikipedia.org/wiki/Water_vapor
And looking again at your Figure 3 graphic, I then have to assume that the H2O vapor ppm between the Equator and the extent of the Temperate Zones, … @ur momisugly 66.5° north latitude & 66.5° south latitude, ……. should average between 4.24% at the Equator to say 1.5% in said Temperate Zones. And if expressed in ppm then the H2O vapor averages between 42,400 ppm and 15,000 ppm respectively.
Now given the above stated fact about “H2O vapor amplification” then that means it requires a minimum average of 15,000 ppm to 20,000 ppm of H2O vapor (1.5 – 2%) to amplify atmospheric warming by 2.2 plus or minus 0.4 watts per square meter per degree Celsius.
Now if the current atmospheric CO2 is at 0.040% ….. or 400 ppm, … then the H2O vapor at 15,000 ppm is 37.5 times greater than the CO2 ….. and at 20,000 ppm it is 50 times greater than the CO2. (106 > @ur momisugly 4.24%)
Now considering all the above, just how much would, could, does 400 ppm of CO2 amplify atmospheric warming in watts per square meter per degree Celsius.
And if one is using a thermometer to measure near-surface temperatures then how does one know which one (1) of the gasses in the air is responsible for the increase in temperature and/or how much of the temperature increase during any given time frame?
Or is my thinking totally FUBAR ….. and thus my question irrelevant?
Is there a reason for the Sahara desert not responding in the same way as the Atacama and Gobi deserts?
Keitho says:
March 24, 2014 at 10:40 am
The Namib & Atacama share cold eastern boundary currents flowing north from Antarctic waters, which produce fog but very little to no rain. The Gobi is not as dry, but suffers from a continental climate, with a rain shadow formed by the Himalayas’ blocking clouds from the Indian Ocean.
Greg says:
March 24, 2014 at 2:51 am
Not sure I follow this one.
Mmmm … in fitting a straight line between the proportional rise and the temperature, I’m taking the derivative of the % absorbed WRT temperature. The percentage absorbed is:
A = (S – TOA) / S
where A is the percentage absorbed, S is the upwelling longwave surface radiation, and TOA is the TOA lw radiation.
I’m not clear why taking that derivative dA/dT is equivalent to a geometric mean.
Some non-linearity is always present in the real world. In this case, unlike most such graphs, the surprising thing was the linearity.
Not really, although you are correct that expressing it as a percentage removes most of the dependence on temperature, but not quite all of the dependence on temperature. As a result, as you point out the derivative would not be a straight line. Here’s the math. The absorption as a percentage, as noted above, is
(S- TOA)/S
with S upwelling surface LW and TOA upwelling LW.
This simplifies to
1 – TOA/S
But as you point out, S, the surface upwelling LW, is related to temperature by the Stefan-Boltzmann equation, viz
S = sigma T4
where S is surface upwelling LW, sigma is the Stefan Boltzmann constant, and T is temperature. (As is usual in such calculations I’ve assumed the surface LW emissivity is 1. It makes no significant difference to the results.)
In addition, the TOA upwelling longwave varies linearly with T. This was a surprise to me. One of the interesting parts of the CERES dataset investigation is seeing who varies linearly with temperature, and who varies linearly with W/m2. In this case TOA can be well expressed (to a first order) as a linear function of T of the form mT+b.
This means that I am taking the derivative of
1 – (m T + b) / (sigma T4)
which solves to
(4 b + 3 m T)/(sigma T5)
Over the range of interest, this graphs out as
Recall that my straight-line estimate was 0.44% per degree. In fact, the more nuanced analysis you suggested shows that it varies between about 0.38% and 0.5% per degree.
Thanks for an interesting question,
w.
rgbatduke says:
March 24, 2014 at 9:23 am
…
I’ve long thought that the GHE can be better studied in places like the Namib and the Sahara than anywhere else on Earth, largely because they lack the confounding effect of water vapor. Water vapor is not a good direct proxy for the GHE because it is a dual contribution. …
I suspect that what is really clear from this discussion is that without water vapour there is no significant GHE. CO2 certainly traps a little LWIR energy, but only in proportion to its concentration, which is negligible on earth. Water vapour does the yeoman work in keeping the planet habitable. This question has some very important implications since, off the planet, we can trace some rough parallels between Earth and Mars for example over much of the solar system’s early history. Recently, one objection to the anthropic CO2 effect as a contributor to recent warming was that among other neighbors, Mars has also experienced observable warming during the last few decades.
Richard says: “Inserting a high cloud in the model is like adding a second atmospheric layer; it enhances the greenhouse effect.”. How sure are you? During the day time, the cloud backscatters a substantial part of solar irradiance , which varies from 1321 W/m2 to 1412 W/m2. During the night it backscatters the upwelling IR (about 400 W/m2 – there are huge variations). Whether the overall effect is cooling or warming is far from clear. If you make assumptions like “the top of a low cloud has a temperature close to that of the surface due to convection”, you introduce a huge error – check with any glider pilot.
Alex says:
March 24, 2014 at 7:10 am
Alex, what I am investigating in this post is what is called the “clear-sky greenhouse effect”. Using clear-sky data is the only way I know of to investigate that, and no, that’s not cherry picking in any sense.
w.
rgbatduke says:
March 24, 2014 at 9:23 am
Sorry, my friend, but you’ll have to get your common sense recalibrated. As I mentioned, I’m not the inventor of this idea that the distribution of the clear-sky GHE is a reasonable estimate of the distribution of the atmospheric water vapor. As far as I know, that was Ramanathan, here’s his graph:
The source of the graph is here, it’s an interesting read. Note that his graph of the GHE, although displayed using much larger gridcells, is quite similar to my results.
HOWEVER, if you still think that the variations in the atmospheric absorption of upwelling LW are from something other than variations in water vapor … then what might that something be?
w.
johnmarshall wrote, “None of the above explains why dry deserts are HOTTER than water vapour rich rainforest.”
Steve Keohane replied, “Isn’t it because the sunlight is not absorbed by the atmosphere and heats the surface and cools radically at night, a 50°F cycle. It is the water vapor giving more mass to the atmosphere that stabilizes the temperature.”
Close. Humid air is actually lighter than dry air, not heavier, because H2O vapor molecules have molecular weight of only 18, compared to 32 for O2 and 28 for N2.
But surface moisture and vegetation do moderate temperatures. When water evaporates from open water and moist surfaces (mostly in the daytime), and by plant transpiration (only in the daytime for most plants), the process of evaporation removes “heat of evaporation,” cooling the surface from which it evaporates. So in the desert, where there’s little water to evaporate in the daytime, the day/night temperature swings are greater.
The heat absorbed by evaporation is carried by the rising moist air into the middle troposphere (because the moist air is lighter!), where it is released when the water vapor condenses into clouds, rain & snow. The additional cloudiness also tends to cool the surface during the daytime, and reduce heat loss at night, both of which reduce surface temperature excursions. But that doesn’t happen much in deserts, either, because they have few clouds.
Duster wrote, “…without water vapour there is no significant GHE. CO2 certainly traps a little LWIR energy, but only in proportion to its concentration, which is negligible on earth.”
That’s incorrect. In fact, the reason that additional CO2 has so little GHG effect is that there is already so much of it in the atmosphere.
MODTRAN calculates that 50% of the warming effect of current (400 ppm) CO2 level would be accomplished by just 20 ppm CO2 (for a tropical atmosphere w/ constant relative humidity). The NCAR radiation code says that 40 ppm CO2 would be needed to get 50% of the current CO2-caused warming, rather than 20 ppm, but, either way, the lesson is clear: we’re well past the point of diminishing returns w/r/t the warming effect of CO2.
For a more intuitive approach, think of CO2 as a coloring agent. It tints the atmosphere in the infrared.
It doesn’t take much of a coloring agent to dramatically color an otherwise clear fluid or gas. Just as the first drop of blue food coloring added to a bowl of water has a dramatic effect on its color, but additions of more drops of blue food coloring have a diminishing effects, so GHGs “color” the atmosphere in the IR region, and have diminishing effects as their concentrations go up. That’s why GHGs with very low concentrations are said to be more “potent” as GHGs: because the wavelengths (colors!) which they block are not already mostly blocked.
By way of analogy, consider a liter of pure water in a clear, 10x10x10 cm cubic jar or box, and one drop of food coloring. Our (99% N2+O2) atmosphere is like the water, and CO2 is like the food coloring, except that the “color” of CO2 is from absorption in the invisible IR range.
If you add one drop of food coloring to the liter of water, it will quite noticeably tint the whole liter. But one drop is only about 0.05 ml, so one drop in one liter is 0.05 / 1000 = 0.00005 = just 50 ppm.
But consider: although the atmosphere is less dense than liquid water, it is miles thick. The full thickness of the atmosphere is about the same mass as a 30 foot deep layer of water.
Your cubic jar of colored water is only about four inches thick. So to get an equivalent thickness to the Earth’s atmosphere, you’ll have to stack up 90 of those jars of colored water in a 30-foot-long row.
Now, look through (or shine a light through) the row of 90 jars of colored water. Imagine how deep the color will be, from just 50 ppm food coloring.
That’s why just a few ppm of a trace gas can significantly affect the spectrum of the light which passes through the Earth’s atmosphere, and have a potentially significant so-called “greenhouse” effect.
rgb, I thought further about the question I asked you, regarding what is causing the variation in absorption, viz:
One possibility is that we are also seeing the variation in CO2 absorption with temperature, due to the location of the absorption bands … this is of course another possible source of feedback.
However, it doesn’t change the underlying question or results. We want to find out what is happening with clear-sky absorption as temperature rises. Whether CO2 or water vapor or both are involved is not very significant, as the net feedback (change in % absorbed per degree of warming) is the main issue, not the sources of the net feedback.
w.
Willis,
Very interesting work
The revised graphic of upwelling long-wave versus temperature (OLS slope of 1.93 watts/degree) implies a clear-sky sensitivity to CO2 forcing of ~3.71/1.93 = 1.9 C per doubling of CO2. But the data shows enough curvature to suggest warmer regions have a significantly greater increase in absolute (not percentage) clear-sky upwelling per degree of warming than do cold regions. It might be interesting to weight the average slope by surface area (that is, there is a lot more surface area in the tropics than above 60 degrees). The weighting could be just a multilication by the sine of the latitude for each cell. This would I think give a better estimate of the average sensitivity, and by eyebal, I would guess and area weighted clear sky sensitivity of about 1.7-1.8C per doubling. FWIW, this seems to be not far from empirical estimates of sensitivity.
Of course, determining the net sensitivity requires and accurate estimate of the net influence of clouds…. both reflection of sunlight and blocking of upwelling longwave. No easy task.
Given the relationship between temperature and absorption, is Figure 3 actually representing temperature more than water vapour distribution?
@richard \at 3:52 am
RE: cirrus clouds
Does water vapor have the same IR absorption and emission spectra
as do ice crystals (i.e. water vapor in solid state) in cirrus clouds
and condensed water droplets in stratus clouds?
rgb, further to the possibility of variations in CO2 absorption, I took a look at MODTRAN. Using MODTRAN, I determined that in the absence of water, that the percentage of absorption by CO2 is indeed temperature dependent. The approximate relation is
Percentage Absorption = .14 * Temperature (K) -24.7
The range of interest in Figure 4 is perhaps -50°C to +35°C. At -50°C (Antarctic), CO2 alone absorbs only about 5% of the upwelling surface LW. On the other hand, at +35°C (Sahara desert), CO2 alone absorbs more than three times as much, about 17% of the upwelling LW.
This may explain some part of the difference in Figure 3 between the atmospheric absorption in areas of dry and cold (Antarctic) versus dry and warm (Sahara).
As always, my thanks for your contributions,
w.
markx asked, “How is the ‘absorbed upwelling LW’ calculated? (Is that by taking into account known surface temperatures and calculating theoretical upwelling vs measured upwelling LW?)”
I have the same question.
I need to ask some dumb questions:
What is measured?
What is calculated?
And what keep us from engaging in a circular calculation in the analysis that is some other model’s assumptions?
At best the CERES instruments in the Terra, Aqua, Aura, TRMM satellites measures LW from TOA. It is supplemented with data converted from geostationary GOES. (From “Evidence That Clouds….” thread Oct. 8-10, 2013)
A lot of calculations goes into making the CERES dataset with hourly data.
How much of it is calculate using models? Are the models in some doubt?
From where does the CERES dataset get its upwelling Surface LW?
When you subtract TOA LW from the upwelling surface LW, how do we know we aren’t just evaluating the assumptions NCAR used to generate the calculated upwelling surface LW?
Willis, my question @ur momisugly #comment-1597561, as seems usual, has gone missing.
However I think it highly pertinent to the subject at hand so I will ask it again.
How do you distinguish between spontaneous emission of IR from water vapour as a so called “GHG” and IR from latent heat emitted by the process of condensation of water vapour, at the TOA.
Thanks.