I thought this post on clouds and climate modeling below from Steve McIntyre’s Climate Audit was interesting, because it highlights the dreaded “negative feedbacks” that many climate modelers say don’t exist. Dr. Richard Lindzen highlighted the importance of negative feedback in a recent WUWT post.
One of the comments to the CA article shows the simplicity and obviousness of the existence of negative feedback in one of our most common weather events. Willis Eschenbach writes:
Cloud positive feedback is one of the most foolish and anti-common sense claims of the models.
This is particularly true of cumulus and cumulonimbus, which increase with the temperature during the day, move huge amounts of energy from the surface aloft, reflect huge amounts of energy to space, and fade away and disappear at night.
Spot on Willis, I couldn’t agree more. This is especially well demonstrated in the Inter Tropical Convergence Zone (ITCZ) The ITCZ has been in the news recently because early analysis of the flight path of Air France 447 suggests flying through an intense thunderstorm cell in the ITCZ may have been the fatal mistake. There is a huge amount of energy being transported into the upper atmosphere by these storms.
Here are some diagrams and photographs to help visualize the ITCZ heat transport process. First, here is what the ITCZ looks like from space. Note the bright band of cumulonimbus clouds from left to right.
Here is a pictorial showing a cross section of the ITCZ with a cumulonimbus cloud in the center.
And finally, a 3D pictorial showing ITCZ circulation and heat transport. Note the cloud tops produce a bright albedo, reflecting solar radiation.
And here is the post on Climate Audit
Cloud Super-Parameterization and Low Climate Sensitivity
“Superparameterization” is described by the Climate Process Team on Low-Latitude Cloud Feedbacks on Climate Sensitivity in an online meeting report (Bretherton, 2006) as:
a recently developed form of global modeling in which the parameterized moist physics in each grid column of an AGCM is replaced by a small cloud-resolving model (CRM). It holds the promise of much more realistic simulations of cloud fields associated with moist convection and turbulence.
Clouds have, of course, been the primary source of uncertainty in climate models since the 1970s. Some of the conclusions from cloud parameterization studies are quite startling.
The Climate Process Team on Low-Latitude Cloud Feedbacks on Climate Sensitivity reported that:
The world’s first superparameterization climate sensitivity results show strong negative cloud feedbacks driven by enhancement of boundary layer clouds in a warmer climate.
These strong negative cloud feedbacks resulted in a low climate sensitivity of only 0.41 K/(W m-2), described as being at the “low end” of traditional GCMS (i.e. around 1.5 deg C/doubled CO2.):
The CAM-SP shows strongly negative net cloud feedback in both the tropics and in the extratropics, resulting in a global climate sensitivity of only 0.41 K/(W m-2), at the low end of traditional AGCMs (e.g. Cess et al. 1996), but in accord with an analysis of 30-day SST/SST+2K climatologies from a global aquaplanet CRM run on the Earth Simulator (Miura et al. 2005). The conventional AGCMs differ greatly from each other but all have less negative net cloud forcings and correspondingly larger climate sensitivities than the superparameterization
They analyzed the generation of clouds in a few leading GCMs, finding that a GCM’s mean behavior can “reflect unanticipated and unphysical interactions between its component parameterizations”:
A diagnosis of the CAM3 SCM showed the cloud layer was maintained by a complex cycle with a few hour period in which different moist physics parameterizations take over at different times in ways unintended by their developers. A surprise was the unexpectedly large role of parameterized deep convection parameterization even though the cloud layer does not extend above 800 hPa. This emphasizes that an AGCM is a system whose mean behavior can reflect unanticipated and unphysical interactions between its component parameterizations.
Wyant et al (GRL 2006) reported some of these findings. Its abstract stated:
The model has weaker climate sensitivity than most GCMs, but comparable climate sensitivity to recent aqua-planet simulations of a global cloud-resolving model. The weak sensitivity is primarily due to an increase in low cloud fraction and liquid water in tropical regions of moderate subsidence as well as substantial increases in high-latitude cloud fraction.
They report the low end sensitivities noted in the workshop as follows:
We have performed similar +2 K perturbation experiments with CAM 3.0 with a semi-Lagrangian dynamical core, CAM 3.0 with an Eulerian dynamical core, and with the GFDL AM2.12b. These have λ’s of 0.41, 0.54, and 0.65 respectively; SP-CAM is about as sensitive or less sensitive than these GCMs. In fact, SPCAM has only slightly higher climate sensitivity than the least sensitive of the models presented in C89 (The C89 values are based on July simulations)…
The global annual mean changes in shortwave cloud forcing (SWCF) and longwave cloud forcing (LWCF) and net cloud forcing for SP-CAM are _1.94 W m_2, 0.17 W m_2, and _1.77 W m_2, respectively. The negative change in net cloud forcing increases G and makes λ smaller than it would be in the absence of cloud changes.
Wyant et al (GRL 2006) is not cited in IPCC AR4 chapter 8, though a companion study (Wyant et al Clim Dyn 2006) is, but only in the most general terms, no mention being made of low sensitivity being associated with superparameterization:
Recent analyses suggest that the response of boundary-layer clouds constitutes the largest contributor to the range of climate change cloud feedbacks among current GCMs (Bony and Dufresne, 2005; Webb et al., 2006; Wyant et al., 2006). It is due both to large discrepancies in the radiative response simulated by models in regions dominated by lowlevel cloud cover (Figure 8.15), and to the large areas of the globe covered by these regions…
the evaluation of simulated cloud fi elds is increasingly done in terms of cloud types and cloud optical properties (Klein and Jakob, 1999; Webb et al., 2001; Williams et al., 2003; Lin and Zhang, 2004; Weare, 2004; Zhang et al., 2005; Wyant et al., 2006).
(Bretherton 2006)
Dessler et al (GRL 2008) made no mention of strong negative cloud feedbacks under superparamterization, stating that sensitivity is “virtually guaranteed” to be at least several degrees C, unless “a strong, negative, and currently unknown feedback is discovered somewhere in our climate system”:
The existence of a strong and positive water-vapor feedback means that projected business-as-usual greenhouse gas emissions over the next century are virtually guaranteed to produce warming of several degrees Celsius. The only way that will not happen is if a strong, negative, and currently unknown feedback is discovered somewhere in our climate system.
There are a limited number of possibilities for such a possibility, but it is interesting that cloud super-parameterizations indicate a strong negative cloud feedback (contra the standard Soden and Held results.)
This is not an area that I’ve studied at length and I do have no personal views or opinions on the matters discussed in this thread.
References:
Bretherton, C.S., 2006. Low-Latitude Cloud Feedbacks on Climate Sensitivity. Available at: www.usclivar.org/Newsletter/VariationsV4N1/BrethertonCPT.pdf [Accessed June 12, 2009].
Wyant, M.C., Khairoutdinov, M. & Bretherton, C.S., 2006. Climate sensitivity and cloud response of a GCM with a superparameterization. Geophys. Res. Lett, 33, L06714. eos.atmos.washington.edu/pub/breth/papers/2006/SPGRL.pdf
Bretherton, C.S., 2006. Low-Latitude Cloud Feedbacks on Climate Sensitivity. Available at: www.usclivar.org/Newsletter/VariationsV4N1/BrethertonCPT.pdf [Accessed June 12, 2009].
Wyant, M.C., Khairoutdinov, M. & Bretherton, C.S., 2006. Climate sensitivity and cloud response of a GCM with a superparameterization. Geophys. Res. Lett, 33, L06714.
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jeroen (06:21:02) : The problem with models is that it only uses one given fact and extent that fact further in the future neglecting onther inputs.
BINGO! Give that person a Cupie Doll!
A model, by definition leaves out a lot of stuff and extends the other stuff to try to make up for it. (Some of the “stuff” it extends being hard coded Fudge Factors put in by programmers to simplify some hard bits they don’t understand, like clouds).
This is why the proper use of models is not to predict, but to inform our ignorance. You see where the model “goes off the rails” and that tells you what you don’t know and need to work on. Any other use is to “believe your own bull sheist”
All models are only predictive over a very narrow range where the parameters (Fudge Factors) are modestly valid. As soon as anything goes outside those built in hard coded assumptions, the model derails. Kind of like GIStemp right now with a large high anomaly when the world is stacking up snow so fast it’s looking like Christmas in June:
http://chiefio.wordpress.com/2009/05/23/south-hemisphere-record-early-snow/
A classic “derailment” in the making. Sit back, pop a cool one, and watch the show. For the next decade GISS will become ever more histrionic as their Favorite Toy diverges ever more strongly from reality. Then they will have an organizational “personality breakdown disorder” as a new person takes the helm and tosses out the trash…
This happens with astounding regularity in the stock trading world. Someone with the New Shiny Thing Stock Prediction Model promises to make millions, and it works for a bit until it doesn’t and they go through this cycle. The only difference here is the time scale. Instead of a 10 year cycle like in the stock market, it’s a 30 year PDO cycle and an 88 year or 176 year solar cycle (and maybe a bit of a 1500 year cycle…) so it all takes longer.
Per “superparameterization”:
I took this to mean that they had a plug number (a Fudge Factor parameter) that they knew wasn’t cutting it. They decided to make it variable in a range via modeling an underlaying event, but did not change how the parameter is used within the model. So instead of saying, for example, that
CloudFeedback = +2 (somethings)
it becomes
CloudFeedback = FunctionOfFoo(foo[time+1])
but you don’t change what you do with CloudFeedback in the model itself. So if the model has another bogusness, like saying
IceChange=CloudFeedback*0;
that would still be in the model and left for the next program review…
I could be wrong, but that is how I took the term to mean something. As a programmer, that is what I would expect the client to be asking me to do in the code if presented with this word. An actual climate scientist might want to comment on what they expected. If it turns out that we have a different idea of what it means, well, then you have a great example of how “bugs” get into programs and why they don’t always do what the designer thought he was asking them to do…
crosspatch (11:58:57)
Thank you.
What I say is consistent with every regional, local and global climate observation ever recorded.
Poleward and equatorward shifts in the air circulation systems explain them all.
The response of the air circulation systems to the fact of net global warming or net global cooling explains those shifts.
Variations in solar input over centuries combined with oceanic energy input/output over decades combined with chaotic weather variation from year to year cover every observation so far recorded.
That’s not to say that some other natural event couldn’t disrupt the pattern at any time.
Whatever, CO2 in the air is irrelevant.
George E. Smith (10:49:04) :
…
It amounts to writing an expression with a big enough array of adjustable fudge factors to get it to fit any observed set of data. So it is curve fitting ala King.
Doesn’t mean there is any causal physics associated with any of those parameters.
Scientists have proved before that you can fit observed measured data to as close as eight significant digits of precision; by doing nothing more than playing around with numbers.
Superparametrization is just as bogus; it is not going to realize any advance in climate physics; just make for prettier looking video games.
George, I have to disagree. In developing a model or simulation, we frequently use surface response curves to study the system as whole in context. If we’re interested in the sensitivity of the system do specific design details, we replace the surface with a physical model. i..e Superparametrization. The technique is not only legitimate, when it involves substituting actual hardware for the response surface, it’s called HardWare In the Loop (HWIL).
However, used also raised the point: “I just hope this ’superparametrization’ thingy doesn’t mean that these parameters don’t need to be validated.” You’re dead on, they absolutely do. In fact the whole point of superparametrization is replacement of the unvalidated with the validated parameters.
In this case, whether they use Lagrangian or Eulerian gridding, the meshing represents a small enough area of the real world that the cloud model can be validated and verified. I think we both agree that if they haven’t, the results are bogus.
E.M.Smith (12:33:31) :
…
Per “superparameterization”:
I took this to mean that they had a plug number (a Fudge Factor parameter) that they knew wasn’t cutting it. They decided to make it variable in a range via modeling an underlaying event, but did not change how the parameter is used within the model. So instead of saying, for example, that
CloudFeedback = +2 (somethings)
it becomes
CloudFeedback = FunctionOfFoo(foo[time+1])
See my response, above, to George. If they did as you described, they would be lying about superparameterizing the simulation. They have to use a physically descriptive model of the process.
hotrod (07:21:44) : As I commented on a couple of occasions before, large thunderstorms are incredible heat pumps that move vast amounts of heat energy to high altitude in a matter of minutes to easily be radiated away at the tropopause, while simultaneously creating a huge shroud of brilliant white cloud to shade the lower atmosphere and ground, along with the cooling effect of evaporation cooled air and rain.
Just to put some numbers on this… from:
http://www.aoml.noaa.gov/hrd/tcfaq/C5c.html
we have:
A fully developed hurricane can release heat energy at a rate of 5 to 20×10 to the 13 th watts and converts less than 10% of the heat into the mechanical energy of the wind. The heat release is equivalent to a 10-megaton nuclear bomb exploding every 20 minutes. According to the 1993 World Almanac, the entire human race used energy at a rate of 10 to the 13 th watts in 1990, a rate less than 20% of the power of a hurricane.
(I modified the quote to say “to the 13 th” since the superscripts got eaten in the cut / paste and I don’t know how to put them back. HTML is about my 15+ th computer language and I’m learning it against my will 😉
Now think about that for a moment. 1/5 the power of ONE hurricane is everything we do with all power all year long. And how many hurricanes are there in a year? But we all know it’s your gas fireplace that is dominating the system… that and your light bulbs…
Anyone know how well hurricanes are modeled in the climate models?
(Hint: They contain clouds, and clouds are a Fudge Factor…)
The water cycle is poorly understood from a thermodynamic perspective.
Water vapor evaporates from the ocean and is transported to land where it condenses and falls as snow. That is a lot of heat that has been transported – the heat of evaporation and the heat of fusion plus the heat in between the evaporation and fusion.
Note that the process from evaporation to freezing which leads to glaciers is a much more thermodynamically efficient process than just the ITCZ convection process so there is less waste heat staying in the system.
Another way to mpa this is to plot the seasonal Availability of the various regions of the Earth vs the moisture sources that feed into them. If Availability is going up, then we are seeing a net outflow of heat from the Earth that is getting more efficient.
Wade (07:42:22) : I find it funny how people can actually think a complex system is so simplistic. It reminds me of the time when I was in school learning physics. To help us learn, everything was ideal. But in the real world, nothing is ideal. It is almost like these people were never taught nothing is ideal.
Reminds me of an old Economist joke, used to get the newbies to understand that econometric models are nor reflective of reality:
Three scientists are stranded on a desert island with one big can of beans. A physicist, a chemist and an economist. They decide that each will apply the strongest tools in their arsenal of science to figure out a way to open the can of beans, having no can opener.
The physicist says: “I will tie a rock to a sapling, bend it down, and when released it will smash the can open”.
The chemist says: “Using sea water and other liquids I will make a corrosive fluid that will dissolve the lid, opening the can”
The economist says: “If we assume the can is open … “
Anthony I appreciate you have so much on your plate but FWIW I for one would like to see the theories of Stephen Wilde debated more fully on this excellent blog. Prima facie they make eminent sense to me but there may be other folks who would disagree and it would be useful to see the discourse IMHO.
Wade (07:42:22) : I find it funny how people can actually think a complex system is so simplistic
It is always simple, we are the complicated ones.
It is interesting if you look at this study and the latest study/story reported on RealClimate …
http://news.yahoo.com/s/ap/20090610/ap_on_sc/us_sci_diminishing_winds
… that for the two most important facets of the atmosphere, water vapour/clouds and winds, we have no idea what is going on and no way to properly model them, yet we are still supposed to consider the climate models as reasonably accurate approximations of the atmosphere.
Doesn’t hold water (whatever form, vapour, liquid or solid).
@ur momisugly Stephen Wilde (12:25:49) :
Thanks!
I’m thinking of making the “longer picture” comment into a posting on chiefio.wordpress.com and would like to know if I may include your comment here, with attribution, as part of that posting?
John W. (12:48:34) :
E.M.Smith (12:33:31) : CloudFeedback = FunctionOfFoo(foo[time+1])
See my response, above, to George. If they did as you described, they would be lying about superparameterizing the simulation. They have to use a physically descriptive model of the process.
I was presuming a “physically descriptive model of the process” as the “FunctionOfFoo”, or at least an good attempt at one… sorry for the unclarity.
See, what they base the positive feedback claims on is that at NIGHT, clouds are positive feedback, they reduce surface radiation to space, and because water vapor has higher heat content than, for instance, ice or snow, the effects are mistaken for the cause and vice versa. They completely ignore the shading effects of clouds in the daytime, as if every day is california sunshine the world over and it only rains or snows at night.
“”” E.M.Smith (12:52:30) :
hotrod (07:21:44) : As I commented on a couple of occasions before, large thunderstorms are incredible heat pumps that move vast amounts of heat energy to high altitude in a matter of minutes to easily be radiated away at the tropopause, while simultaneously creating a huge shroud of brilliant white cloud to shade the lower atmosphere and ground, along with the cooling effect of evaporation cooled air and rain.
Just to put some numbers on this… from:
http://www.aoml.noaa.gov/hrd/tcfaq/C5c.html
we have:
A fully developed hurricane can release heat energy at a rate of 5 to 20×10 to the 13 th watts and converts less than 10% of the heat into the mechanical energy of the wind. The heat release is equivalent to a 10-megaton nuclear bomb exploding every 20 minutes. According to the 1993 World Almanac, the entire human race used energy at a rate of 10 to the 13 th watts in 1990, a rate less than 20% of the power of a hurricane.
(Hint: They contain clouds, and clouds are a Fudge Factor…)
One other often missed fact is that after the passage of a hurricane across the ocean, the water behind it is left considerably colder. This is often mistakenly attributed to the hurricane “stirring up” the colder water from the depths. Not so, that cooling is the result of all those meagaton uields of energy that is in the hurricane.
The normal process of evaporation leaves the water surface film colder, because it is the high energy tail of the MB molecular distribution that is the first material to escape; thereby lowering the mean molecular energy and thus the temperature. Not to mention the 545 cal/grm of latent heat of evaporation that goes up into the storm along with the water.
Hurricanes are among the best on Nature’s refrigeration processes that cool the earth.
E.M. Smith (13:35:17)
Certainly. Please proceed.
The more my stuff is promulgated the sooner I will find out whether I’ve hit the mark or am talking nonsense.
That said I’ve been at this for 15 months now and as yet no killer rebuttal from the AGW community.
I was camping once and it was 70 during the day and 27°F at night. We camped by a river next to a hill. Very cold air came rushing down the hill at night, hit the warm water and formed fog. I saw every cloud formation within 2 feet of the surface I’ve ever seen, except cumulonimbus. New layer of cold air comes down and overlays the warm water, instantly forming fog 2 feet thick. Water flow provides shear. Stratus clouds form with rolling bands. Another layer comes in at a different angle and speed, creates vertical shear, and we saw hundreds of “tornadoes”, really realistic ones that you could see sucking up the lower layer and taking it higher. Another layer hits a stationary layer and pushes up cumulus clouds which have their bottoms quickly sheared away by the air layer below following the water flow, some with tornadoes at the tail. Every possible combination of layer thickness, speed, shear angles, etc was visible. It was so amazing I tried to video it but there was not enough light. I remember thinking how very simple the inputs were, and yet how chaotic and wild the resulting patterns were. I think some of the simpler patterns could be modeled as they were fairly predictable, but trying to get a realistic result with anything other than very small 3D grids would be impossible.
You could actually see how all of those strange cloud formations we see every day happen, except at about 50x speed. I only wish I had a better camera at the time, I watched it for about 3 hours.
This might be a job for the folks at Industrial Light & Magic. I’ll bet they could add a whole new level of insight into the modeling capabilities of 3D phenomena, then you could apply physics to that framework to arrive at a somewhat decent model, and finally compare it to real measurements.
I think the first order of business would be to shoot for a 3D, semi-transparent model that actually LOOKS like clouds forming. Adjust your model physics until the visual representation of each cloud type that is formed naturally from simple inputs looks visually correct over time from a virtual point on the ground. The visualization would probably be key to understanding whether you can model a single cloud, much less all of them. Once you have that, a numerical model that also LOOKS like a cloud, you would be well on your way to having at least some of the physics of cloud formation understood. THEN I think we could talk sensibly about modeling cloud feedbacks. The albedo and shadow aspects should come out fairly easily once the visual representation and scale looks reasonably close to real-world clouds… The rest is just superparameterization.
I’m going to use that in a meeting at work and see what happens.
“”” John W. (12:44:26) :
George E. Smith (10:49:04) :
…
It amounts to writing an expression with a big enough array of adjustable fudge factors to get it to fit any observed set of data. So it is curve fitting ala King.
Doesn’t mean there is any causal physics associated with any of those parameters.
Scientists have proved before that you can fit observed measured data to as close as eight significant digits of precision; by doing nothing more than playing around with numbers.
Superparametrization is just as bogus; it is not going to realize any advance in climate physics; just make for prettier looking video games.
George, I have to disagree. In developing a model or simulation, we frequently use surface response curves to study the system as whole in context. If we’re interested in the sensitivity of the system do specific design details, we replace the surface with a physical model. i..e Superparametrization. The technique is not only legitimate, when it involves substituting actual hardware for the response surface, it’s called HardWare In the Loop (HWIL).
However, used also raised the point: “I just hope this ’superparametrization’ thingy doesn’t mean that these parameters don’t need to be validated.” You’re dead on, they absolutely do. In fact the whole point of superparametrization is replacement of the unvalidated with the validated parameters.
In this case, whether they use Lagrangian or Eulerian gridding, the meshing represents a small enough area of the real world that the cloud model can be validated and verified. I think we both agree that if they haven’t, the results are bogus. “””
So John, I take it that after you have superparametricized your model and properly gridded it, that you can run the model and it will replicate the actual measured values that you read at each of those gridded points on planet earth; If it does not, why do you continue to use that model ?
George
Has anyone tried a Freedom of Information Act request to get this information?
If not, why not? It doesn’t take a big organization. It is helpful to have standing, i.e. scientific credentials, and legal help. There are lawyers on this board who might be willing to assist, I’ll bet.
Time to turn up the heat and hold those guys accountable. We taxpayers paid for those models, and that data, and we deserve to see every dirty little detail.
Maybe it’s time for a “Transparency Project,” a la the Surface-Stations one. . .
/Mr Lynn
Bill Illis (13:29:31)
When the globe is warming the jets are pushed poleward and compressed due to expansion of the equatorial air masses which gives them greater east/west force. In so far as they are still able to deviate poleward or equatorward then the increased force is maintained.
When the globe is cooling, as now, the equatorial air masses, having contracted, allow greater latitudinal movement of the jets which are then moving less fast. However there are greater poleward and equatorward movements of air.
When the globe is cooling the polar air masses move equatorward and try to extract more energy from the oceans in order to compensate for the net loss of energy to space.
When the globe is warming the equatorial air masses expand to try and push the net exess energy from the oceans into space..
All nice and logical and consistent with both observations and basic physics.
The reason for the observed decreasing wind speed is, in fact. global cooling.
Thus, could all this reasoning mean that the cause for the singular 97-98 El Nino was the 91 deep low in GCR ??
Mike Lorrey (13:47:53) :
See, what they base the positive feedback claims on is that at NIGHT, clouds are positive feedback, they reduce surface radiation to space,
Actually that is not positive feedback. An electrical analogue would be increasing the value of a resistor, (add clouds), across the output of a capacitor, (the Earth surface).
Dave.
David Ball: I agree.. but I don’t think many people will be interested in Hansen’s, Schmidt’s, or Mann’s data anymore quite soon.
“”” Hank (09:48:29) :
Seems like there are two situations – daytime and nightime:
In the daytime you have the heat of the sun radiating to the earth together with the heat of the earth reradiating heat outward. It doesn’t seem hard to see that if you insert a cloud barrier between the two you are not only shutting down the direct heat of the sun but you’re also shutting down that process that supplies heat to be re-radiated by the earth.
At night the situation is different; only the earth radiates at night and so the cloud barrier could be said to have a warming tendency. And it is my experience that cloudy winter nights where I live tend to be warmer than bitterly cold clear nights. Please notice though, that saying cloudy nights are warmer is a bit of a misstatement because what is really happening is that you are only slowing down cooling. Climatologically, nights are still cool and days are warm – on balance all that really happens at night is cooling. “””
Well Hank you are making the same error that everybody makes.
High clouds at night; balmy nights and warming; no high clouds at night dry and cold night.
It ALWAYS cools down at night clouds or no clouds; UNLESS warm air moves in from some other location.
But what is happening is somethign entirely different.
Those high clouds at night are NOT keeping the surface warmer; they are only there at all BECAUSE it was warmer st the surface during the day along with moisture to give some humidity; and the warmer it was at the surface for a given moisture level the high those clouds will be when they form at night because the hotter surface air and moisture will have to rise to a higher altitude before the dew point is reached.
And when it is cooler, and there isn’t any moisture during the day, it will get colder yet at night, but no high clouds will be formed because of the lack of moisture.
The temperatures and humidity are driving the clouds not the clouds driving the temperature.
And the reason it FEELS colder on a dry cool night is because the lack of humidity encourtages evaporation from your skin and cools YOU; but if it is warm and humid, then your porse don’t get to evaporate any sweat from your skin so you FEEL warmer.
When you cut all four legs off a frog; they will not jump; no matter how loudly you shout and scream at them.
That does NOT mean that frogs become stone deaf when you cut all four legs off.
And as to the subject of feedback; last night’s weather IS NOT climate; so in discussing the thermal effect of cloud cover; you have to consider the effect of an increase (or decrease) in total cloud cover over a meaningful CLIMATE SCALE TIME like how about 30 years like all other climate studies.
And increases in total global cloud cover over years or decades or centuries result in global cooling by reducing the total amount of solar flux that can reach the surface; either by albedo reflection back into space, or by blockage in the cloud; which cools the ground but warms the cloud so it rises higher.
Thermal radiation from the warmed cloud mass is radiated more or less isotropically, so about half of it goes up, and about half goes down.
And the fact that the atmosphere does very little observable warming of the gournd on a cold dry cloudless night demonstrates hwo totally ineffective CO2 is in warming the surfgace like a blanket. The CO2 hasn’t changed at all, and now it doesn’t have water molecules competing for absorption of the long wave IR emission from the surface; yet is still accomplishes virtually no observable heating.
Trying to piggy back water vapor feedback onto a lame duck CO2 GHG is just silly; the water vapor alone can do all of the blanket “warming” without any assistance from CO2 or any other “trigger” GHG.
These supercomputerparametrizations are simply trying to create an effect where there is none to be had.
The surface radiant emittance of long wave infrared radiation emitted from the earth’s surface varies by more than an order of magnitude from the hottest to the coldest surfaces and is more than a 12:1 ratio in the extreme cases. The 4th root of 12 is 1.86.
So if CO2 absorbed every bit of the surface emitted IR; the CO2 “forcing” of surface temperature rise would vary by 12:1 across the earth’s surface, and the possible maximum temperature rise caused by a doubling of CO2 would vary by something like that 1.86 factor from place to place.
So any claim of a constant “climate sensitivity” temperature rise caused by a doubling of CO2 is just plain silly so trying to pick a value for something that has a 12:1 forcing variation over the planet is hardly science.
And when was the last time that climatologist modellers mapped the actual “climate sensitivity” all over the earth or measured it using whatever griderization that they use in their cloud parameterization.
No wonder that the IPCC reports contain all these 3:1 fudge factors in their predictions; excuse me projections, of future catastrophic global warming.
When the NOAA official global energy balance budget that they plot sets the solar incoming radiation at 1366 or so W/m^2, instead of a fictitious 342; and adjusts all the other parametrizations as well; then maybe I’ll pay some attention to ccomputer models.
Of course that doesn’t help much because all of the ancient surface measurment data from over the oceans is bogus anyway; so climate history really doesn’t go back much earlier than 1978-80; so it doesn’t matter if the models are any good the data they use to parameterize them is rubbish.
George
I have to agree with Stumpy:
“common sense suggest clouds would be a negative feedback, otherwise the earth would have suffered runaway warming in the past.”
As I understand the current AGW theory, CO2 alone does not cause significant warming. The small warming it does create causes more water vapor which is also a greenhouse gas and which greatly amplifies the CO2 warming. But if that’s the case then why doesn’t a small increase in water vapor by itself cause a similar effect? More water vapor would produce warming which would cause more water vapor and more warming. Absent a negative feedback, the result should be runaway warming attributed to water vapor only.
Im In Miami, and in relation to rain and cooling.. I.E. cloud formation, as negative feedback… I witness it regularly.
the greatest swing in tempuratures on the planet from day to night happen in areas with the fewest total hours of canopy in percentage anually.
Deserts.
Now I always try to abide Mr Watts steadfast rules on Blog decorum, less we become Algorites ourselves, issuing ignorant diatribes filled with vitriol and spite.
But on this singular issue, I would say that those who believe the clouds are not the primary component for heat exchange in our climate engine, and carbon emissions are, are riding the short bus(bio diesel and solar power of course) to the AGW rally.