Real pollution (not 'carbon' pollution) increases storm clouds

Science, I believe we may have some here.

Also, look at what happens when the heating fires/combustion gasses get out of control and there is clouds :
http://en.wikipedia.org/wiki/Pyrocumulonimbus
and i thought the science was settled?

Wow, those are serious numbers for the changes in forcing. One has to wonder if GCR-modulation models (if they are correct) have similarly amplified effects.
But overall, it is yet another key component in the physics in GCMs that could not only be incorrect, but egregiously incorrect. Even small changes in albedo make big changes in surface temperature averages, as it slices insolation “off the top” as it were, although the abstract above suggests that the change in surface forcing due to enhanced vertical heat transport through most of the opaque GHG layer of the atmosphere may be even more important.
It’s already been suggested that the inconsistency in the treatment and effect of aerosols is a major factor in the apparent failure of GCMs to reproduce the current flat climate (or each other). The article is just another piece of evidence supporting the idea that it is time to go back and rebuild the GCMs nearly from scratch, altering lots of things within them.
As I continue to meditate upon them, I cannot help but think that a second place they are likely to be making a serious problem is in their gridding and granularity. A lat/long grid is horribly biased due to the spherical polar coordinate Jacobean, and this alone can lead to large, and difficult to control, errors. It is also very difficult to rescale, making adaptive solutions that rescale to convergence difficult to write. I would very much want to see a uniform rescalable grid used — an icosahedral tessellation, for example — and implemented in a completely rescalable way for full adaptive operation.
Why, you might ask? Because of the used in radiative cooling computations. At any scale of the estimation of cooling due to thermal radiation, and in any sub-band of LWIR, spatiotemporal averaging of temperatures will produce a spurious warming. This is because the radiation rate from 1 square kilometer, half at 300 K and half at 290 K is strictly greater than the radiation rate from 1 square kilometer at the mean temperature of 295K. It’s not clear to me that there is any scale where this effect stops. The front yard of my house is often 5 to even 10 C warmer than the back yard — the front yard faces southwest with little shade and lots of concrete and asphalt, the back yard faces northwest (in the shade of the house in the afternoon) and has lots of trees. The radiation rate from the property is dominated by the hot front yard, not the cool back yard, and is not equal to the rate of an equal sized plot of land at the average temperature.
GCMs impose a granularity of (typically) 1 degree, without direct compensation for the Jacobean. The average temperature of that 1 degree not-quite-squared will strictly underestimate its radiative cooling unless the temperature is truly homogeneous across the area, yet that whole square is necessarily assigned a single average temperature in the GCMs.
The interesting question is: What is the renormalized correction factor, given the presumably measurable average spatiotemporal temperature inhomogeneity? Is it 1%? 5%? 0.1%? My feeling is that it is around 1%, perhaps a bit less, but that is order of a W/m^2, enough (applied as it would be nearly everywhere) to make a pretty substantial difference. Perhaps enough to explain the growing deficit in warming predicted by the GCMs? Don’t know. I might be able to do the computation, though, and find out subject to some assumptions.
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@Peter Melia: “However, we’ve had this pollution (which cools) for 2, perhaps 3 generations and for a greater part of that period the planet has been warming.”
Call it the EPAGW hypythosesis. As pollution controls got better, albedo lowered, the planet warmed. And that saved us from the catastrophic coming ice age predictions and gave us catastrophic warming predictions. Extensionally we can fault China’s recently hot economy and lax pollution controls from saving us from a climate disaster by undoing the harm the EPA has wrought.
Ballpark. Have fun with the notion anyways.

rgbatduke: “But overall, it is yet another key component in the physics in GCMs that could not only be incorrect, but egregiously incorrect. … suggests that the change in surface forcing due to enhanced vertical heat transport through most of the opaque GHG layer of the atmosphere may be even more important.”
All is as you say in your post. An additional concern I have is that there is a lot of discussion about line spectra and almost no discussion about the effects on the pressure and density at points in the atmospheric column. This may simply be something that isn’t discussed, well, anywhere. But so far as I know it hasn’t been looked at either. As certainly when temp increases, pressure does, and the density drops. Which reduces conductive transfer and increases radiative losses.
There seems to be a wholesale negligence on modeling the atmosphere as an atmosphere and not a chunk of dry ice.

Perhaps if the team had gone a little bit deeper into the research and asked themselves why and how those cloud droplet forming ice crystals were originally created, they might have come across some quite old research which shows a lot of the ice crystal formation in the upper troposphere is the direct outcome from the presence of ice forming bacteria and bacteria created chemical compounds which act as the nuclei around which super cooled water vapor in the upper atmosphere can condense around to form cloud droplets.
All these natural phenomena aren’t all pollution created as seems to be the current fashionable claim amongst the believers . Such a claim is naturally a direct spin off from mankind’s supposed grievous sinning against Gaia.
Nature is far, far more subtle about it’s secrets and revealing those secrets than that.
Still more of the unknown unknowns or the deeper they go into the science, the deeper it gets.

Well, we shall see, maybe , maybe not, but I am sure it is a mistake to be sure of anything based on one study, and most people here should understand why.

TheLastDemocrat sez:
feedback is a bi*c*

Is this new ? Some 40 years ago we were seeding clouds with silver iodide. Isn’t that the same physics ?

the effects on the pressure and density at points in the atmospheric column. This may simply be something that isn’t discussed, well, anywhere. But so far as I know it hasn’t been looked at either. As certainly when temp increases, pressure does, and the density drops. Which reduces conductive transfer and increases radiative losses.
There seems to be a wholesale negligence on modeling the atmosphere as an atmosphere and not a chunk of dry ice.
I do not think this is true. IIRC one of the reasons that one expects the GHE to increase (still) with CO_2 even though CO_2’s effect is long since saturated is line broadening at the higher partial pressures, especially at altitude. I’m not so sure I buy this argument but if you look at Grant Petty’s book on atmospheric radiation you’ll see that all of these sorts of effects are accounted for “as best they can” given that one has to sum/integrate over dense bands of levels and a range of pressures and temperatures. Some of the effects are indeed negligible — the time required for a CO_2 molecule to thermalize to the surrounding gas after absorbing an IR photon is much, much less than the emission rate, for example, which justifies treating the absorption and emission processes as simple thermal rates rather than resonant absorption/emission (although no doubt some of the latter occurs, it is orders of magnitude less important).
GCMs without any question contain all sorts of information on pressure, density, and temperature of the air parcels they manipulate. If you are interested in seeing what goes on in at least on GCM, there is an open source on (CAM 3) with online documentation here:
http://www.cesm.ucar.edu/models/atm-cam/docs/description/
This is not one of the most detailed GCMs, but it is one where you can download the actual source and look at it, with program documentation in hand on the side. One can fault the design (one can ALWAYS fault a design:-) in a number of places, but the assertion that they don’t handle elementary parameters like pressure, density, and temperature is simply not correct. Indeed, to fault this one has to get pretty specific — point to a particular place in section 4 where they do something wrong. The top article basically suggests that there may well be something seriously wrong in CAM 3’s (and other GCMs’) treatments of aerosols, cloud formation, and vertical transport based on actual measurements. I suggest equally specifically that there may be something wrong with using a discrete latitude/longitude grid, especially with comparatively weak or missing adaptivity (CAM 3 actually has a tiny bit of adaptivity in it to handle polar regions better) for the specific reason that radiation rates on thermally averaged cells will be strict lower bounds — not upper bounds — on the true rate, so that CAM 3 and any other GCM that assigns a single temperature to a comparatively large horizontal area (in any given slab or layer) will underestimate the cooling via the unblocked channels and underestimate the rate of radiative energy transport between slabs in the blocked layers (basically, overestimating the “radiative insulation” properties of any given slab) because more uniform temperatures lead to more warming with exactly the same insolation at all scales.
This is the game, if one wants to criticize the GCMs. One can perfectly legitimately point out that they aren’t working without specifying why, as that is a posterior conclusion based on comparison of their predictions and the actual data, but if one wishes to assert that they aren’t working for a specific reason, to be responsible one has to look at the actual code and see if the specific reason you suggest is implemented in the actual code in a way that is (or more properly, may be for some evidence-supported reason) incorrect. So it isn’t that GCMs don’t include the effects of latent heat transport — they obviously do (see “shallow/middle troposphere moist convection” in the CAM 3 documentation, for example). It MIGHT be that they don’t include it CORRECTLY.
It is an open question as to whether or not they are leaving some physics out entirely that ends up being important. The galactic cosmic ray hypothesis, for example, has some empirical support but it is so far not a slam dunk or sufficiently compelling to warrant inclusion in a model on anything other than a trial basis. It would actually be interesting to include it ON a trial basis — one can always insert provisional physics into a model just to test the model and see if it does better with it or without it, or if it gives the model additional explanatory power. This is itself a form of weak evidence, if it does. In a highly multivariate model, however, it is probably WEAK evidence because model predictions, especially of single outputs, are very probably highly covariate in the physical parameters, so that one can turn up CO_2 sensitivity and turn up the effect of aerosols at the same time and maintain good agreement with GASTA across some training set, but end up with highly disparate long-term predictions as eventually CO_2 continues increasing but aerosols don’t.
I suspect that it is this alone that is largely responsible for much of the error in the GCMs relative to the present — they’ve systematically exaggerated CO_2 sensitivity and maintained agreement with data across the 50’s through the 90’s by asserting a larger effect to pollution and volcanic aerosols, but as we moved past the fit region and CO_2 continued up with aerosols not increasing to match (and with volcanism if anything a bit diminished) the highest senstivity models have started to systematically diverge from the observed temperatures.
Is this indeed the explanation? Hard to say. There need not be ONE explanation. There is no doubt that GCMs contain both positive and negative forcing terms and achieve agreement by balancing them. There is little doubt that they assign quite different values to the effect of aerosols as there is no consensus value or model (and the top article shows how nonlinear any model must be to correctly account for all of the observable physics!) The sad truth is that while there is only one way for a program to be right there are countless ways for it to be wrong. Once a program has the complexity of something like CAM 3, not only are there countless ways for it to be wrong but they get to where no single human knows the entire code and few humans are willing to take even a major component of that code and monkey with it as you have to START by learning it all. It gets to be very “expensive” to make changes — one can spend most of a postdoctoral position just getting to understand what the existing code does and have little time to even THINK of making serious changes, retraining the code parameters, and then spending two years of CPU time running the program all over again to see if the changes don’t egregiously break the existing code and (perhaps) lead to some improvement.
I’ve downloaded and looked over the CAM 3 code. Sadly, however good or bad the code itself may be, the packaging of the code truly sucks. It would be a matter of weeks of work (for me) just to get it to BUILD, lest alone build and run on some small test program, and I’m a pretty damn good programmer (although I do hate Fortran, sigh:-). It just isn’t worth it — I have no grant for working on climate (so it is by definition a “hobby”, not a profession), I’m not getting paid to do it, I am getting paid to do a lot of other stuff that is very time consuming and have lots of other hobbies/projects that languish for lack of work on my part.
Porting a rather large Fortran program to C, organizing it so that it will automagically build across a range of platforms in both parallel and serial versions, replacing the lat/long tessellation with a scalable icosahedral tesselation, determining the granulation error in radiative transfer rates as a function of scale and estimates of per-cell spatiotemporal noise, correcting the aerosol, cloud, and vertical transport component (or somehow parameterizing it so that one can experiment with different rates based on empirical evidence as it comes in), adding an “optional” (parametric) component for GCR-modulated cloud nucleation rates, transforming the initialization data from lat/long to the icosahedral grid, fixing the single-slab ocean model to account for oceanic transport and more, better, figuring out how to correctly include projections of solar state (out as far as such projections themselves have some reasonable chance to be right) — I could spend the rest of my life working on this WITH A TEAM and a million dollars a year in grant money. A bit much too tackle for free and if I want to have a life of some sort on the side.
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