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Oh boy have I. I went through it extensively way back in the day, when we were at the old old old place. That experience was part of my coming to the conclusion that the entire endeavor is simply an impossible task. You mention some of the problems; there are others. I agree that his Nobel was a scientific travesty and sad state of affairs.
I would echo this, but with one very minor modification:
You simply state that it's "catastropic". Whereas I think it's pretty much impossible to actually model the complex interactive feedbacks... especially when it comes to their intersection with political/economic systems.
One of the problems with the whole sort of analysis you do in this paragraph is that everyone does timescale-separated coupled dynamical systems backwards in the case of political/economic-climate coupling. As I alluded to in my comment, the dynamics of political/economic systems are fast, much much much faster than the dynamics of climate, even with human activity. If one spends time with the theory of such coupled systems (the canonical text being Khalil's book), which I have done extensively for non-climate-related professional reasons and prior to engaging with any economic-climate models, then one understands the proper way to go about analyzing such systems. And, well, nobody does it the proper way. Why not? In my view, it's because they can't. It's impossible. Rather than the problem lying with human psychology, the problem is that the math doesn't math that way.
We could probably have a debate over the details of some of this. I don’t know what your background is, but I’m just an advanced layman who’s more than just a little bit literate on these topics. I agree that mathematically modeling this is hard. Very hard, in fact; but you can still do it well enough to extract valuable data from this. Just a note on such models ln how I see it.
If you look at global circulation models (GCM’s), they use physical data on the chemical composition of the ground, atmosphere, air and sea temperatures, wind velocities, rainfall, river flows, et al., the key variable in all these being greenhouse gases (‘GHG’s’ which we all know) because they absorb and re-radiate heat back to the Earth. The most important GHG is water vapor; but the problem is it’s inherently localized; making it the least controllable and hardest to obtain data on a global average; and it accounts for between 1/3rd or 2/3rd’s of the GHG effect. The next one is CO2, followed by methane and a few other industrial chemicals. If you compare things on a simple molecular basis, methane is more potent than CO2, but it doesn’t remain in the atmosphere for as long (about 5-6 years if I recall), compared to roughly a century for CO2. There’s much less methane in the atmosphere than CO2 but that could change if emissions from the methane clathrates in the sediment under the east Siberian shelf begin to accelerate.
This might be more to your point perhaps but yes it’s known that GCM’s have weaknesses. The first is that they assume vertical atmospheric thermal convection without wholly taking to account the horizontal component of circulation that arises from ocean currents and mixing. The other is that they divide the surface of the globe into icosahedrons but the minimum size of the grid area (with the exception of the poles) is way too large. You can’t simulate small scale behavior within just a few kilometers (which is nowhere near the 10’s of 100’s of meters necessary to predict local precipitation patterns or storm paths). That’s one of just a handful of problems which is why they also rely on comparative models, but the general trend of things has accelerated even faster than scientists forecast after factoring in the uncertainty of their models.
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