Yet if we consult Tol's paper--the very one cited by Nordhaus in support of the above quotation--we find that most economic studies find global warming will confer net benefits on humanity at least through the years 2050 - 2060. Only after we get at least another 2 degrees Celsius of warming (and that is compared to a recent baseline, not a preindustrial benchmark), do most studies in this literature say that the damages to certain parts of the world begin to overwhelm the benefits to other parts of the world.
As I mentioned in my IER post, I hope the average reader will agree with me that Nordhaus's summary of Tol's findings was extremely misleading (perhaps unintentionally). I daresay the average person, relying on mainstream media treatment of the issue, has been led to believe that "the consensus" of experts believes climate change is right now causing incredible damage and will only get worse as time passes.
And yet, the very person Nordhaus singled out as the leading scholar in the field, shows that the majority of the best available studies show global warming leading to net benefits at least for another four decades. (italics his)
Here's the new ground. It's on the difference between how the climate people and how economists think about confidence intervals. And it's a huge difference:
In a standard economic regression analysis, we typically approach things the way one is taught in high school when learning basic statistics. Namely, you set up a null hypothesis that is the opposite of the causal relationship you (the researcher) actually think exists. Then, if there is an apparent relationship in the data (such that you get a positive value on the coefficient for a certain term in a least-squares regression, say) you can see if the result holds up at a 90 percent, 95 percent, or 99 percent confidence interval.
In this normal context, the higher the confidence interval, it means the more confident you are that the apparent relationship between two measured variables isn't spurious. You are in effect saying, "If there really weren't any relationship between variable X and variable Y, then I wouldn't be getting this type of result 99 percent of the time. Therefore, I reject the null hypothesis--which says there is no relationship--and think that there really is a relationship."
Yet in charts of climate model projections, the "confidence interval" works the other way around. Here, the higher the number, the less confident we can be that an apparent match between the model and nature is due to the underlying accuracy of the model. To put it in other words, here the null hypothesis is that "this suite of climate models is accurately simulating global temperature." Thus if we make it harder to reject the null (by ramping up the confidence level), then it gives more wiggle room for the models.
Specifically, the "95% range" in the second graph above comes from looking at all of the observed "runs" of the suite of climate models, and then plotting the gray boundary that captures the realizations of 95% of the runs centered around the average. Ironically then, the less agreement there is between the individual climate models, then the wider the gray zone would be, and the harder it would be for Nature to "falsify" the suite of climate models. (italics his)