GIGObuster, I’ll repeat this first part just once more. I have said repeatedly that the models have not been tested using V&V and SQA. I have not said that they have not been tested. In fact, I have pointed out a test, prediction of the monsoon precipitation. They failed, they did worse than models from 1932. Why is it so hard for you to understand that?
Next, the Nature study showed that the models were not independent, so they can’t be used to check each other as you seem to think. You believe that since the people who produce the models say that they are all just peachy keen, the question is settled … right.
Finally, you keep bringing up proofs that the world is warming … why? Everyone agrees that the world is warming, and has been for the last three centuries or so. That’s not the question, so stop with the proofs that it’s warming, already. We know that.
The questions are:
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how much of the warming is due to humans, and
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what is the mechanism for whatever percentage of the warming is caused by humans? Land use changes? Black carbon on the snow? Brown haze over Asia? CO2? Methane from ruminants? Change from forest to agriculture? Changes in cosmic rays? Irrigation increasing the humidity? Increased dust from construction and agriculture? Some combination of the above?, and
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if we are causing some amount of warming, is this a danger? (Warming since the Little Ice Age has been a net benefit to humans, too cold kills more people than too warm) and
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if we find we are causing a significant amount of the warming, and if we can figure out exactly what mechanism is causing the majority of the anthropogenic portion of the warming, and if we decide that (unlike warming in the past) the costs will outweigh the benefits, is there a cost-effective way to reduce the effect we are having?
Those are the questions, and to date, we don’t know the answers. The difficulty is, we don’t know how the climate works. We have no general theory of climate, like we have a theory of gravity or fundamental particles. We regularly discover new, unknown forcings. Plankton, for example, cause clouds … who knew? Given the size of the ocean, is this a significant forcing? We don’t know. Heck for some forcings, we don’t even know the sign of the effect, much less the size.
Take aerosols. What are we talking about? Well, sea spray, and sulfates, and black carbon, and organic carbon, and nitrates, and biogenic aerosols, and mineral dust … the effect of all of these on clouds is very poorly understood, we lack both observational data and a theoretical framework. The models do a very poor job of modeling these effects:
[QUOTE=IPCC FAR, Section 2.4.5.5]
Modelling the cloud albedo effect from first principles has proven difficult because the representation of aerosol-cloud and convection-cloud interactions in climate models are still crude (Lohmann and Feichter, 2005). Clouds often do not cover a complete grid box and are inhomogeneous in terms of droplet concentration, effective radii and LWP, which introduces added complications in the microphysical and radiative transfer calculations.
Model intercomparisons (e.g., Lohmann et al., 2001; Menon et al., 2003) suggest that the predicted cloud distributions vary significantly between models, particularly their horizontal and vertical extents; also, the vertical resolution and parametrization of convective and stratiform clouds are quite different between models (Chen and Penner, 2005). Even high-resolution models have difficulty in accurately estimating the amount of cloud liquid and ice water content in a grid box.
It has proven difficult to compare directly the results from the different models, as uncertainties are not well identified and quantified. All models could be suffering from similar biases, and modelling studies do not often quote the statistical significance of the RF estimates that are presented. Ming et al. (2005b) demonstrated that it is only in the mid-latitude NH that their model yields a RF result at the 95% confidence level when compared to the unforced model variability.
There are also large differences in the way that the different models treat the appearance and evolution of aerosol particles and the subsequent cloud droplet formation. Differences in the horizontal and vertical resolution introduce uncertainties in their ability to accurately represent the shallow warm cloud layers over the oceans that are most susceptible to the changes due to anthropogenic aerosol particles.
A more fundamental problem is that GCMs do not resolve the small scales (order of hundreds of metres) at which aerosol-cloud interactions occur. Chemical composition and size distribution spectrum are also likely insufficiently understood at a microphysical level, although some modelling studies suggest that the albedo effect is more sensitive to the size than to aerosol composition (Feingold, 2003; Ervens et al., 2005; Dusek et al., 2006). Observations indicate that aerosol particles in nature tend to be composed of several compounds and can be internally or externally mixed. The actual conditions are difficult to simulate and possibly lead to differences among climate models. The calculation of the cloud albedo effect is sensitive to the details of particle chemical composition (activation) and state of the mixture (external or internal).
The relationship between ambient aerosol particle concentrations and resulting cloud droplet size distribution is important during the activation process; this is a critical parametrization element in the climate models. It is treated in different ways in different models, ranging from simple empirical functions (Menon et al., 2002a) to more complex physical parametrizations that also tend to be more computationally costly (Abdul-Razzak and Ghan, 2002; Nenes and Seinfeld, 2003; Ming et al., 2006).
Finally, comparisons with observations have not yet risen to the same degree of verification as, for example, those for the direct RF estimates; this is not merely due to model limitations, since the observational basis also has not yet reached a sound footing.
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So, given all of those problems with simulating the aerosol/cloud interactions, how do we estimate the effect of aerosols on radiative forcing? We adjust the postulated forcing to best agree with the historical temperature record, and then take the modeled forcing as representing reality … I’m sure you can see the various problems with this approach. At least, I hope you can.
The ugly reality is that we lack both a theoretical framework and observational data for many, perhaps most, aspects of the climate. This is not surprising, since the climate system is unimaginably complex, and goes on at all scales from the molecular level to planetwide. That’s why the models perform so poorly on anything but replicating the historical temperature trends that they are tuned for – because, as the IPCC FAR notes, modeling from first principles is difficult, so we have to guess at the values, and adjust the parameters, until the models are somewhat like the reality.
Which is fine, and there are things to learn from that. But the climate is an interconnect system, so if there is an error in any one of the myriad processes going on in the climate, the other guesses and parameters that we have made will be wrong. We tweak the threshold relative humidity parameter to get the modeled albedo right, and as a result, the cloud cover is wrong. Which means that our parameters for the cloud/albedo interations will be off. Which mean that … and so on.
Consider the “missing carbon sink” problem. When we try to model the carbon cycle, we find that there is a whole lot of carbon that is leaving the atmosphere but we don’t know where it is going. There have been lots of guesses where it ends up, but we don’t know. Now, when we model the climate, we have to assume that the carbon goes somewhere … so we figure it goes into the soil, maybe, or into the plants, maybe, or into the ocean, maybe … but any one of these assumptions creates changes in all of the other parts of the model. So we adjust various parameters until the model works … but does that mean we’ve made the right assumption? Does that indicate which one of these assumptions is right?
We don’t know. You should practice saying that. It’s one of the most important statements that a scientist can make, and is applicable to far more of the climate system than we would like. For example, why does the RSS data currently show that the tropospheric temperature has been falling for the last five years, while the surface temperature seems to be still rising?
We don’t know. And since the models are not based on physical principles, but on parameterized estimations, they can’t answer the question either – none of them predict that the troposphere would cool as the surface warms. Why do you think that is happening, one cooling while the other warms?
Me … I don’t know.
w.