CMIP5 Model Temperature Results in Excel

But what we lack almost any evidence for is the idea that the changes in temperature follow the changes in forcing. And I have given a number of reasons to think that such a relationship doesn’t exist. These include the paltry response of global temperatures to volcanoes, the reversion of the temperature to the previous levels (or higher) following eruptions, the lack of any climate response to the 11-year sunspot cycles, the lack of temperature change from the ~ 5% increase in solar strength over the last half billion years, the ~ 30°C maximum of open ocean temperatures, and the like.

This is simply untrue, as the graph I post above makes perfectly clear. Not only is there evidence, but one can actually produce a remarkably accurate fit of the entirety of HadCRUT4 using only cCO_2 as input.
The physical basis for this model is enormously simple. It is the bog-standard radiative model that predicts a temperature forcing somewhere in the ballpark of 1 C per doubling, where I would assert that we don’t know the physics to do much better even with line by line computations e.g Modtran (as it is a hard problem already at this point, involving assumptions about temperature and line broadening and pressure in the entire atmosphere between the ground or sea surface and TOA escape). In addition, I assume that if there are any feedbacks, they are directly proportional to the cCO2 forcing, and hence follow the same logarithmic curve. Maybe water is net positive feedback, maybe it is negative feedback, maybe it can be considered separately from methane or aerosols or soot. I ignore it all. I actively ignore volcanoes as I have done computations (like you) that show that they are awesomely ignorable. I lump it all together and assume that it is some percent modification of the CO_2 driven forcing. It could double it! It could halve it! I don’t assume that I know what it will do, only that it the linear terms in the multivariate Taylor series of any response function are likely the most important and ultimately one has to sum over them and hence lose which medium makes what contribution.
In the end, 2.62 ln(cCO_2) works to describe the data very, very well. This is not a lack of evidence. It is pretty good evidence, as far as it goes. Furthermore, it decribes the data very well with no lag and little room for natural or unnatural variation outside of maybe 0.1 to 0.2 C of “noise” and possible systematic variation around it. It symmetrically splits the data and is neither warm nor cold biased. The big question is why we need any sort of more complex model, especially when the more complex models have many, many parameters and still don’t perform as well. Same reason that I conclude that I don’t need to worry about volcanic aerosols, as even R can barely find a reason to include them, and then only produces a tiny divot in temperatures if the volcano in question is VEI 5 or 6 (or, presumably, higher).
With that said, I am as hampered as you are by two things. One is that HadCRUT4 may be the BEST we can do (pun intended) or maybe BEST is, but our best is mostly likely terrible back to 1850 (neither of us believe HadCRUT4’s error bars in 1850) and probably more terrible across any times prior to that, no matter who is doing the computation and how. Nobody seems willing to acknowledge just how poorly we know global temperatures, global temperature “anomalies”, and how much worse our knowledge of things like specific atmospheric chemistry or state of the ocean in the still more remote past is. So I have no good reason to believe that my enormously simple and successful 164 year model will work all the way back to 1750, 1650, 1000, 0, 9000 BCE, or whatever. Somewhere in there there are truly ponderous things that drive the climate over very long time scales (and possibly, drive it rapidly due to nonlinear feedbacks) and my model accounts for none this and even if I tried to include it, there simply isn’t any reliable data to use to do the model building. At some point the error in the data becomes greater than 1 C, the error in the possible CO_2 concentration exceeds 10 ppm, Milankovitch can no longer be ignored, multivariate stuff we can’t even GUESS at could be dominant, state evolution comes into play…
So I in no way assert that my simple one+one parameter model is correct, only that it works to describe the data it was fit to very convincingly, certainly well enough that you can’t point to it and tell me that it doesn’t work! It does not fail a hypothesis test, although it does leave room for additional hypotheses as long as they are rather smaller in their aggregate effect. But it could be the other way around — the temperature might best be explained by the additional (unspecified) hypotheses and CO_2 could be a much smaller fraction of the total effect. The only thing I can say is that the additional hypotheses are a) unspecified; b) will have more parameters; and hence c) the inferrable “meaning” of the fit will take a hit from covariance as the input parameter list increases. Simple pictures are the best.
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