Pindyck, from MIT, is a leading expert in this area, here is part of his summary conclusion:
It would certainly be nice if the problems with IAMs [integrated assessment models] simply boiled down to an imprecise knowledge of certain parameters, because then uncertainty could be handled by assigning probability distributions to those parameters and then running Monte Carlo simulations. Unfortunately, not only do we not know the correct probability distributions that should be applied to these parameters, we don’t even know the correct equations to which those parameters apply. Thus the best one can do at this point is to conduct a simple sensitivity analysis on key parameters, which would be more informative and transparent than a Monte Carlo simulation using ad hoc probability distributions. This does not mean that IAMs are of no use. As I discussed earlier, IAMs can be valuable as analytical and pedagogical devices to help us better understand climate dynamics and climate–economy interactions, as well as some of the uncertainties involved. But it is crucial that we are clear and up-front about the limitations of these models so that they are not misused or oversold to policymakers. Likewise, the limitations of IAMs do not imply that we have to throw up our hands and give up entirely on estimating the SCC [social costs of carbon] and analyzing climate change policy more generally.