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.
Hank (15:50:12) :
Well, sometimes you can be in a (cool) house that the sun is beating down on through a barrier (the roof) and it gets warmer inside than it is outside. How do you explain that?
Fascinating discussion.
I can’t add to it, but that picture of the cumulonimbus cloud is fantastic. Where was it taken?
Ric Werme (16:13:42) :
r=6371km
pi=3.1416
4 x pi x r x r = 510065664km² but this leads up the wrong path since 1366W/m² is the Top Of Atmosphere value.
A better way is to calculate how much energy is notionally received by earth at any given time. The first step is to calculate the area of the earths disk given the above value of “r” and “pi”.
pi x r x r = 127516117km² or 127516117×10^6m² then multiply it by the energy received over the area 1366W/m²
= 17.4187×10^16 W.
Now 4 x pi x r x r = 510065664km² gives surface area so
17.4187×10^16 W divided by 510065664×10^6 = 341.5 Approx.
The easy way is just to divide the energy input by 4 but I wanted to remind myself how we get there.
Eric,
“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.”
After much googling, I finally found this climate model here.
http://www.windows.ucar.edu/tour/link=/earth/climate/warming_clouds_albedo_feedback.html&edu=high
It has the following heat balance for the Earth’s surface:
Incoming:
Solar radiation absorbed by surface = 168W/m2
Atmospheric Back Radiation = 324 W/m2
Outgoing:
Thermals = 24 W/m2
Evaporation = 78 W/m2
Surface radiation to atmosphere = 350 W/m2
Surface radiation to space = 40 W/m2
The greenhouse effect means there is a bit more surface radiation to the atmosphere and a bit less radiation to space. My point is if the atmosphere was 10 percent less absorbing of surface radiation, then if the sensitivity of the Earth’s surface temperature is 1.6 K/W/m2 to the surface radiation to the atmosphere , then feeding in 10 percent of that 350 w/m2 figure, this would mean a decrease in surface temperature of 1.6 x 35 = a hell of a lot, less than the blackbody equilibrium temperature. So a figure of 1.6 for the sensitivity seems absurd to me.
Feedurile Wordpreess sunt furate ! http://gekrys77.wordpress.com/2009/06/13/atentie-blogeri-feedurile-wordpress-sunt-furate/
hunter (10:36:35) :
“No one doubts that CO2 in a sealed box will do what it does.
The question is how is this manifested on the vastly larger, vastly more complex, Earth climate system?”
I know that, but the person that wrote the comment was replying to an the question of another blogger that asked for real(physical proof). What I posted was his reply. I thought it silly and funny for the poster to use a high school project as his proof or reason for believing in AGW. The poster obviously forgot that the Real Earth atmosphere is not a sealed box and has 1000’s of variables involved in determining our current climate. I just posted it so that maybe some else would get a little chuckle out of it, like I did. 😉
Smallz
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
George E. Smith (13:54:01) :
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
I think we’re in violent agreement. 8^)
If it does not replicate the observed data, the model is invalid. Continuing to use it is sloppy science. Changing the observed data to match the model, and then claiming the model is “validated” or “verified” is fraud.
A note on gridding (or meshing). A big source of error in modeling and simulation is defining meshes that don’t have homogenous properties. Large problems are usually run for a number of time steps, intermediate results examined, and the model remeshed to obtain homogeneity, then continued. Some examples are studying how a vehicle structure plastically deforms to absorb energy, analyzing warheads, or studying terminal ballistics. (I’ve been involved in all three, so I’m well aware of the dangers of sloppy analytical technique.) I wouldn’t see any need for remeshing in a climate or cloud model, but proper meshing is critical.
I don’t recall if I read it here or on another site, but someone was describing meshing techniques in some of the GCMs. Apparently, some of them model the ocean in wedges from the equator 100s of kms long – WRONG. Others have meshes that include ocean, city and mountains – WRONG. Any “scientist” doing this would be more correctly understood as a child playing with tools he doesn’t understand.
A further observation on the GCMs. If I took on the program of building a climate model, based on my own experience, I’d have a staff of 15-20 Ph.D.s in solar physics, atmospheric science, optical physics, oceanography, thermodynamics, computational fluid dynamics, and finite element modeling. I’d have an additional staff of 10 or so software professionals. I wouldn’t promise the first version for 3 to 5 years. And, yes, the model would be independently validated (to ensure we actually built what we thought we’d built) and verified against observational data. If there’s a GCM out there developed using this approach, I haven’t heard of it.
oms (22:11:34) :
Hank (15:50:12) :
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?
Well, sometimes you can be in a (cool) house that the sun is beating down on through a barrier (the roof) and it gets warmer inside than it is outside. How do you explain that?
Dark shingles, and lack of air-flow, ie greenhouse effect. Open your windows. Don’t worry, the models have the same problem, no empirical evidence in an open-air experiment.
peter_ga,
One of the biggest uncertainties and one of the biggest leaps of faith/assumption in greenhouse theory is the ?K/W/m2 value.
The estimates for its value range from 0.1 to 1.0.
The climate models are presently using about 0.32C in hindcasts and for short-term predictions but the value increases over time up to 0.75C as the long-term equilibrium sets in. Hansen may have used 1.5C as the long-long-term value in his most recent paper.
And I’ve seen climate models use different values for different forcings such as 0.15C for volcanoes.
This is one of the big shortcomings in greenhouse theory that is not talked about much. I think they need to start over with this and develop a theory that is more robust and one that works in the same timelines that the energy being talked here works in [EM radiation that is and that works at the speed of light modulated by the ability of atmospheric, land and ocean molecules to store up this energy – in essence quantum physics/mechanics].
On average, a photon of light received from the Sun escapes back into space within 18 hours. In the cloud feedback example of this thread, the length of time that a photon is “reflected” (more accurately absorbed by a cloud H20 molecule and then emitted back into space) is less than 1 minute. It is possible that a photon absorbed by an ocean H20 molecule may spend more than a thousand years in the deep ocean. I don’t think the physics have been worked out for the proper ?K/Wm2 value at all, they are just guessing based on solving a few equations.
It is literally the second half of the equation for the temperature impact of global warming and we don’t have a solid foundation for its value.
oms
What I always heard was that the heat hits the roof (the barrier) and then once the barrier heats up it conducts in and heats the air inside. Also, since the air is trapped and can’t convect, heat builds up. That is the other part of what I had always heard, namely, that the heat inside a greenhouse (or attic) mostly results just because the air is trapped, and that outdoors as air heats up it convects upward. What I’m not clear on is to what degree heat that convects up to the upper parts of the atmosphere cools by anything other that temperature/volume considerations.
I suppose what my statement presupposed was that I was talking about heat transferred by the radiative process from sun to earth.
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 .
Hi anna,
I think the chaos argument is valid, but climate chaos has some different sources than weather chaos. I assume the sun is a chaotic system which makes us unable to predict the current cycle (or perhaps we just don’t know enough or aren’t taking the right measurements?). The effects on weather are somewhat unknown and possibly chaotic.
Then the circulations like ENSO and PDO are chaotic and coupled. Unfortunately they are also coupled to the atmosphere and the chaotic influences of weather. So presumably it goes butterfly somewhere -> hurricane -> puff of wind somewhere -> PDO shift. None of that will ever be predictable but I still believe that improvements in model fidelity will yield more accurate depictions (a weasel word instead of predictions) of climate under new forcings like more CO2.
Incoming:
Solar radiation absorbed by surface = 168W/m2
Atmospheric Back Radiation = 324 W/m2
Outgoing:
Thermals = 24 W/m2
Evaporation = 78 W/m2
Surface radiation to atmosphere = 350 W/m2
Surface radiation to space = 40 W/m2
Hi peter,
I see what you are saying. But is climate sensitivity really based on the surface radiation to atmosphere, or on atmosphere radiation to space? It seems to me that clouds (and of course water vapor and weather in general) affect both and the surface radiation to space. I don’t see how the 1.6 figure can only be applied to one quantity in your list and not the others (to a greater or lesser extent).
For example if the atmosphere is 10% less absorbing of surface radiation, wouldn’t the amount of energy of atmospheric back radiation also change?
Eric (skeptic) (21:01:34) :
“Clouds are less important than the weather that creates them since it is the upper tropospheric moisture that really matters”.
Eric, this statement is utter BS.
First of all, there is no tropospheric moisture in the troposphere.
If any it’s ice or ice needles from Cirrus clouds and the upper amsel of CB clouds. In general the temp at tropospheric level is minus 55 degree celcsus.
Clouds are the visual weather and the proces of heating causing the air to expand and rise (convection) is the
most important energy destructor of the entire weather system.
During Solar minimum the vertical height of the atmosphere is reduced and the upper atmosphere is colder. Would this not cause the vertical movement described to be stronger and carry more heat to be radiated to space?
Hi,
We have just added your latest post “Suggestions of “strong negative cloud feedbacks” in a warmer climate” to our Directory of Science . You can check the inclusion of the post here . We are delighted to invite you to submit all your future posts to the directory and get a huge base of visitors to your website.
Warm Regards
Scienz.info Team
http://www.scienz.info
Smallz7
Introduce a small amount of additional CO2 into one tank
I bet experiment doesn’t call for calculation of the pre-existing volume of CO2 in both tanks! A 10 litre bell jar of air will contain less than 5ml of CO2 – I wonder how much the temperature rises with another teaspoonful..?
Sorry Smallz79, I didn’t mean to demote you…
John W. (05:25:45):
Youre absolutely right about the professional, multi-disciplinary way to construct a climate model. But, then again, you’ve apparently never heard things discussed in a faculty club or a bureaucratic agency. Those guys know EVERYTHING!
By the way, the world’s largest carbon emitter has told the UN to take their climate treaties and stick them.
Climate pact in jeopardy as China refuses to cut carbon emissions
Here is a link to the NASA Earth Observatory Image of the Day post of 10th March 2008 titled “Cumulonimbus Cloud over Africa”, with an explanation of the impressive cloud formations seen in the superb picture at the head of this post. http://earthobservatory.nasa.gov/IOTD/view.php?id=8542
John W. (05:25:45) :
You provide the people and the funds, and I’m sure it will be done.
Hank (06:24:38) :
The barrier itself has a temperature, and it radiates downward and upward (in addition to conduction as you mentioned). If it were only conduction, then you might expect the top of the room to heat up in statically stable configuration (since only the air at the top can touch the hot roof) and the bottom to remain cool, (except for the very slow rate of downward heat diffusion).
Eventually, the air has to radiate heat to space, right? There’s nothing else to conduct to at the top boundary and yet heat does escape.
oms
eventually, yes. That seems to be the nub of the whole problem – how long… to what degree. Modelers have taken on an big task for themselves.
First of all, there is no tropospheric moisture in the troposphere.
If any it’s ice or ice needles from Cirrus clouds and the upper amsel of CB clouds. In general the temp at tropospheric level is minus 55 degree celcsus
Ron,
That’s not correct. Here’s the current upper troposphere plot of winds, heights, temperatures and dewpoints: http://weather.unisys.com/upper_air/ua_300.html The sky above me is completely clear (northern VA) is completely clear due to very dry air (300mb dewpoint is -63). That means I have global cooling in my local area.
There is gaseous water (water vapor) in that -41C air and there is always water vapor in -55C air. There will be water vapor in any air above absolute zero provided there was some to start out (e.g. ice crystals to sublime like when you watch a contrail disappear). The amount of water vapor will depend on air temperature and pressure.