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.
Urederra (10:06:46) :
“Could anybody who has read the original study explain what is the difference between this and the ‘normal’ parametrization?”
A “normal” parameter is from Earth. A “super” parameter is from the planet Krypton.
E.M.Smith (10:40:36) :
“This emphasizes that an AGCM is a system whose mean behavior can reflect unanticipated and unphysical interactions between its component parameterizations.
Or in short form: The circulation models are bogus and busted and give you bad results because the prameterized code is bogus and flakey in strange ways.”
Exactly. To me this is the real meat of the 2006 report. Superparameterization is just another model that may or may not be an improvement, but the unquestionably non-physical AGCM calculations (tuned to give the desired results) take the models straight to the realm of stopped-clock accuracy.
http://www.heartland.org/publications/NIPCC%20report/PDFs/Chapter%202.1.pdf
“Global Atmosphere—Coupled Ocean-Atmosphere Response Experiment, Sud et al. (1999) demonstrated that deep convection in the tropics acts as a thermostat to keep sea surface temperature (SST) oscillating between approximately 28° and 30°C. Their analysis suggests that as SSTs reach 28°-29°C, the cloud-base airmass is charged with the moist static energy needed for clouds to reach the upper troposphere, at which point the cloud cover reduces the amount of solar radiation received at the surface of the sea, while cool and dry downdrafts promote ocean surface cooling by increasing sensible and latent heat fluxes there. This “thermostat-like control,” as Sud et al. describe it, tends “to ventilate the tropical ocean efficiently and help contain the SST between 28°-30°C.” The phenomenon would also be expected to prevent SSTs from rising any higher in response to enhanced CO2-induced radiative forcing.”
Stephen Wilde:
Thanks for the reply and I really liked your original comment. I just wanted to explain that the first thing that popped into my mind when I read that sentence in your original comment is, “But what about Virginia Beach in the summer?” Hence, the nitpick.
I think your overall explanation has merit and I’d like to see you expand on it a bit. Your idea might even cover vulcanism it would seem. As for me, I think the positions of the continents and their topography give the ‘why’ of climate and your explanation gives the ‘how’ of climate.
I’m looking forward to an expanded post from you either here or on E.M. Smith’s blog.
George E. Smith (14:48:40) :
Holy cow George. You gave me a lot to chew on there. I was trying express a simple thought. Namely, if you interpose a barrier between something warm (the sun) and cold (the earth) the cold thing is gonna get colder. And so, overall, how could clouds do anything but tend to cool? That’s my basic simple minded thought – absent considerations of nighttime.
George E. Smith (14:48:40) :
Careful – some of what you’re saying matches a dry area like California, but things can be quite different elsewhere like here in New Hampshire where we normally get 3-4″ of precip every month.
High clouds tend not to be connected with ground moisture unless convective conditions exist, and often warm and moist conditions near ground drive that. However, a lot of altostratus and altocumulus comes from approaching warm fronts, passing extratropical storms, etc. During the day such cloud cover suppresses convection, on cloudy nights there’s even less. BTW, clouds from an approaching warm front can be hundreds of miles ahead of the ground level manisfestation of the front.
On a cloudy night I’d agree that a cool night feels cooler than a warm night (you might want to try that sentence again!). A clear dry night with light wind is ideal for radiational cooling. On long winter nights when the cooling curve is close to exponential I can often spot where the wind has picked up a bit and stirred up the inversion (temperature increase and wind in the log) or where high clouds (approaching storm) has blocked the sky and reflects the IR back to the ground (sudden decrease in the cooling slope). A meteorogist has saved some traces where a stray cloud moves by and interrupts cooling only briefly.
1366 is the incident (Sun overhead) value, 342 is the average over the entire Earth (surface area 4 x pi x r^2, area intercepted pi x r^2).
E.M.Smith (11.51.17),
All very well and complicated possibilities – wouldn’t it be easier to acknowledge that it’s the mice changing the parameters of their experiment with Earth? 🙂
I’ll talk about Jack Eddy’s work tomorrow in my radio broadcast. I will change my talking on “Women in Science” for the next week.
Often I see references to Lindzen or someone consenting that some specific rise in C02 will have a corresponding rise in temp, and the one that comes to mind is doubling the current 385ppm to get 1 degree. It seems many assume this means a doubling of 385ppm gets us 1 degree higher than we are today. But in a system of many independent variables with feedbacks back and forth among them, the actual temp after that rise would depend upon the sum of effects of that collection of variables as a whole. The net result could put us anywhere.
Anthony,
The observed 5% decrease in cloud cover is the main cause of global warming, why is nobody looking at it as the cause.
please look at the Ole Humlum climate and clouds page at http://www.climate4you.com its awesome!
Cloud changes correspond almost perfectly with temperature changes and the observed forcing (including negative feedback) is much larger than CO2 forcing.
Cheers
Ric Werme (16:13:42) :
1366 is the incident (Sun overhead) value, 342 is the average over the entire Earth (surface area 4 x pi x r^2, area intercepted pi x r^2).
My problem with that is this.
1) do they include all the other confounders?
The extremities of the presented disc receive far less insolation for the reasons of incidence, extra atmosphere, refraction, reflection & probably others I’ve not thought of!
DaveE.
I forgot there wasn’t a 2) LOL
DaveE.
Excellent post and intelligent comments. However, I feel that for all the science being debated between the AGW proponent and skeptics, the issue is more political than anything else. Whether you admit it or not, the congress is corrupted. Just look at how they pass the bailout awhile ago, despite the opposition from the majority of Americans. Same for the coming carbon tax and cap-and-trade. As long as the congress follows their marching order and vote for whatever they’re commanded to, any discussion in science is futile.
Mike Ramsey (15:27:29) :
http://www.heartland.org/publications/NIPCC%20report/PDFs/Chapter%202.1.pdf
“Global Atmosphere—Coupled Ocean-Atmosphere Response Experiment, Sud et al. (1999) demonstrated that deep convection in the tropics acts as a thermostat to keep sea surface temperature (SST) oscillating between approximately 28° and 30°C.
Indeed there are several such mechanisms that appear to function as a part of this “thermostat.” AGU published a paper: “Unexpected consequences of increasing CO2 and ocean acidity on marine production of DMS and CH2ClI: Potential climate impacts”
http://www.agu.org/pubs/crossref/2007/2006GL028139.shtml
demonstrating increased marine microorganism gases which may impact cloud formation and albedo. Beautifully complex, if not outright clever.
George E. Smith:
As a surfer, my observation has been that a hurricane does not need to come overhead to cool the water at my location. The groundswell itself is enough to cool the inshore water by as much as 2 degrees Celsius in 24 hours. So, although you can surf without a wetsuit in late August and early September at various surf breaks on the U.S. northeastern coast, by the time hurricane waves have been coming through for a day or so you frequently need to shift back to a wetsuit of some kind.
The cooling of the water, in those circumstances, generally takes place without a single hurricane cloud coming within 300 miles of shore.
Thus, from what I have seen personally, I think you might be underestimating the power of stirring.
“How do they get away with ignoring clouds negative feedback?”
SPF 10,000 Sun Blocker?
If clouds are formed and how they are formed depends on the following factors.
1. The temperature characteristic of the source area of a specific air mass.
2. The relative geographic position and season.
3. The characteristics of the land mass, flat land, mountenous land, woods, deserts, cities and industrialized area’s, farmland etc.
4. The wind speed and wind direction.
5. The temperature of the land or ocean.
6. The air pressure. (and changes in air pressure)
7. humidity
8. the availablity of condensation nuclea. (dust particles, biomaterial, cosmic rays?
An airmass can be stable, unstable or indifferent.
In a standard atmosphere the air temperature is going down 0,65 degree Celsius for every 100 meter altitude.
At a specific altitude there is an inversion. An inversion is an area where the temperature goes up by increasing altitude instead of going down.
You can see an inversion when the clouds reach a maximum altitude and flatten off. The altitude of an inversion varies during the day and if sufficient heat is absorbed by the land, an inversion can disappear.
This happens at the ITCZ where Cumulonimbus clouds reach altitudes of 15.000 meters.
In the morning the day often starts with a ground inversion and an inversion at a higher altitude.
The sun heats up the earth and the earth heats up the air.
The local temperature depends on the heat absorbing characteristics of the undergroud, the wind speed and direction.
The air that is heated up builds a bubble which is sticking to the gound untill it reaches a certain volume and gets triggered by an impulse.
An impulse can be a passing truck, a tree line or a small hill in a flat landscape.
The heated and expanded air takes of like a hot air balloon.
The rising speed depends on the relative differens in temperature between the heated air and the surrounding air.
As the rising air cools down and the condesation level is reached, a cloud is formed. If there still is a heat surplus the cloud will build untill it reaches inversion level.
Mr. Watts:
I always thought the picture in your web site header was an excellent example of this issue; a picture from the Space Shuttle I expect. I am quite surprised there is any debate, or disregard, of this phenomenon.
Great Blog; keep up the good work.
REPLY: Believe it or not, you are the first person to comment on it – thanks – Anthony
When a cloud fades and disappears, surely the energy released in its formation is re-adsorbed. It would only have an effect when it rains, and when it reflects.
OTOH the current atmosphere warms the surface by 150 W/m2 on average, warming it by about 30 degrees C. This suggests an obvious ratio of 30/150 or 0.15K/WattM-2. The “low” figure of 0.4 in the OT seems rather high to me, and for me would require some explanation of why it is so high. A climate model that calculates this figure to be more than an order of magnitude larger requires some serious evidence. However the state of climate science is such that serious evidence against it being an order of magnitude greater is required.
Some excellent points above and some bizarre ones esp. with the non-weather analogies. Despite the disdain for models here, they provide accurate results for different cloud scenarios. The reason they are not useful for AGW predictions is that the models cannot predict the cloud scenario accurately unless they accurately model future weather (ie the weather in a world with more CO2) in sufficient detail. Since computational power essegntially limits model resolution (both vertical and horizontal), weather effects including cloud are parameterized at smaller scales. Those parameterizations can be somewhat validated by performing high res modeling on a small scale in high CO2 scenarios. Or you can just put in some garbage and see what happens!
Here are a lot of modeling abstracts to get an idea:
http://www.ccsm.ucar.edu/publications/PhD%20and%20Masters%20Theses.htm
Another way to get an idea about clouds is to look at IR satellite pictures. They are generally false color where white means less outgoing IR and dark means more. High clouds are white so less IR out, so global warming. Low and mid clouds are ambiguous, but lower ones tend to be dark (cooling). Lower ones also tend to be diurnal which is definitely cooling. The models generally back that up, higher clouds are warming, lower clouds are cooling, mid clouds are ambiguous.
The key notion is that how the clouds allow or block IR output to space is more important to global warming or global cooling than how they block sunlight from reaching earth. That is why the the idea that “bright white” clouds are cooling after or near a thunderstorm is a bit of misattribution. The coolness has likely come from downdrafts or evaporation, not the shade from the cloud. In fact those high clouds from the thunderstorm cause “global warming”. But here is where the models help a lot, the thunderstorms, or any concentrated convection actually cause global cooling.
The case of concentrated convection is complicated because clouds are not the determining factor but the fact that CC causes drying of the upper troposphere which is cooling. Hurricanes cause global cooling for the same reason and because of the large cloud-free subsidence zones around them allowing IR radiation to space even though the high clouds of the hurricane itself are IR trapping and warming.
It also depends on what is underneath the clouds. Ice is rare so not really a factor. Land radiates more IR than ocean so clouds over land tend to be more warming than clouds over ocean. Most of what clouds do depends on clouds in the tropics (ie whether convection is concentrated or not) and most of that modeling requires model fidelity at a small scale (ie mesoscale) because that is the scale at which there will be the greatest differences between the current world and a world with more CO2.
Sorry, I was not finished with my earlier posting.
The rising air is replaced by air that is succed in by the drop in air pressure that is caused when the pocket of heated air takes of from the ground.
Sometimes this effect is visible by dust devils or flying parasols and even entire terraces in front of a resraurant can take to the air.
The surrounding area of the rising aircollumn that keeps a cumulus cloud aloft
makes a downward movement.
The energy involved in the process is enormous.
There are day’s when the rising air currents make speeds between 4 and 10 meters per second.
In Cumulonimbus clouds that form in extremely unstable airmasses, the speed of up- and down currents can reach 25 to 35 meters per second.
Rising and sinking currents can occure in a very small areas and they are responsible for the big hail up to the size of a tennis ball.
We know about soaring pilots in Bitterwasser (Namibia) that made a flight into a CB at the end of the day just to cool a pack of beer.
One of them (a crazy German) flew a metal glider (Blanick) made in Poland and lost half a wing. He made a safe landing and enjoyed his beer.
Other pilots are less fortunate.
During the day, with the inversion in place, the clouds start to spread out under the inversion at a level where they block the sun in such a manner that the heating process comes to a rest. After some time the clouds dissolve and the process starts all over again.
From my own experience, I know that different landscapes produce thermals (rising air currents) at different times during the day.
The first significant source of rising air currents are dry sand area’s which heat up very quickly. Extremely strong currents can be found at the boundary area of dry sand or rocks and water. Later during the day, villages, cities, rail stations and shopping mauls whith huge parking lots generate thermals. At the end of the day, cities, and later the woods which have been absorbing the heat during the day perform a slow release where air currents still climb with speeds between 0.5 and 1.5 meter per second.
Soaring pilots today have huge databases where to find the best thermals depending on the wind direction.
Anyhow, the process of rising air currents is a continuous process which also happens on cloudless days as long as the sun heats the earth.
The relative difference in temperaure is what makes this natural air mixing machine. Just remember, relative warm air always goes up.
The effect of any CO2 in this process is entirely irrelevant.
One remark:
Some posters believe that the convective process stops when the sun goes down. This is not always the case.
If the air is sufficient unstable and energy is retracted form the land or water, heavy storms also occure by night, including tornado’s.
If models are only accurate at the mesoscale level, and inaccurate at any higher level, then the best thing to do with them is ignore them.
Another way of looking at it. Suppose there was an anti-greenhouse effect of about 10 percent of the long-wave warming from the atmosphere, about 20 watts/m2. Using the 1.5K/Wm2 sensitivity figure, that would mean cooling below the black-body radiation equilibrium temperature of the Earth.
Stephen Wilde (06:17:18) :
Stephen Wilde (10:33:30) :
Anthony, if he is willing to write it, it might be nice to post?
just one vote here lol
Tim
Hi Peter,
I agree we should ignore climate models. On the 10% anti-greenhouse effect, do you mean 10% of outgoing LW reflected back or trapped in the atmosphere by clouds? I think the bulk of LW is trapped or lost to space by GH gases, mostly water vapor and not clouds. The atmosphere sends 10 times as much energy to space as the ground which is why high clouds versus low clouds is such an important factor. High cloud tops are cold and low cloud tops are warm, a fact I should have mentioned above. Warmer cloud tops send more IR to space.
Clouds are less important than the weather that creates them since it is the upper tropospheric moisture that really matters.
Eric (skeptic) (19:07:59) :
Despite the disdain for models here, they provide accurate results for different cloud scenarios. The reason they are not useful for AGW predictions is that the models cannot predict the cloud scenario accurately unless they accurately model future weather (ie the weather in a world with more CO2) in sufficient detail. Since computational power essegntially limits model resolution (both vertical and horizontal), weather effects including cloud are parameterized at smaller scales.
The argument for bigger and bigger computer facilities can be used, but is perpendicular to whether a model is good or not. If the model is inherently bad in its initial assumptions it will be bad with infinite computational capacity.
I am convinced that the climate models by construction cannot be projected to the time scales that are presumed possible. I have two arguments, one dovetailing into the other:
1) There is an underlying assumption that the solutions of the fluid dynamics equations used are well behaved and stable. This hidden assumption is what would legalize the use of averages over grid boxes for many of the variables that are being integrated over when doing a time stepping. This is a wrong assumption for a system that is based on a chaotic substratum. Turbulences, convections etc.
2) argument 1) is demonstrated in the inability of GC models to predict weather for more than a weak or so. It is not the computer power, it is the unpredictability, instability, multiple solutions available of the chaotic weather system. As climate models are glorified weather models the result is the same. Use of simplifying first order approximation of solutions of chaotic systems are bound to diverge from reality after a certain number of time steps. For weather, it is a week that proves it, for climate evidently a decade .
Jeff F (19:06:54) :
Hmm, that’s rather rude of us. And I even spent some time marveling at it. The storm isn’t associated with a cold front, so the anvil isn’t being blown well ahead of the storm. Hence the central convection is surrounded by anvil.
The small cumulus is interesting in its own right. The clouds seem to reach a certain height and then start growing quickly. Is that an example of what I think is called “marginally stable” air?
You do a good job picking photos to go along with the stories.