Another Wall Street Journal editorial attacks climate science: ‘The level of deception by the WSJ authors is absolutely astonishing’
By Barry Bickmore
22 February 2012 The Wall Street Journal posted yet another op-ed by 16 scientists and engineers, which even include a few climate scientists(!). Here is the editor’s note to explain the context.Editor’s Note: The authors of the following letter, listed below, are also the signatories of “No Need to Panic About Global Warming,” an op-ed that appeared in the Journal on January 27. This letter responds to criticisms of the op-ed made by Kevin Trenberth and 37 others in a letter published Feb. 1, and by Robert Byer of the American Physical Society in a letter published Feb. 6.
A relative sent me the article, asking for my thoughts on it. Here’s what I said in response.
Hi [Name Removed],
I don’t have time to do a full reply, but I’ll take apart a few of their main points. 1. The WSJ authors’ main point is that if the data doesn’t conform to predictions, the theory is “falsified”. They claim to show that global mean temperature data hasn’t conformed to climate model predictions, and so the models are falsified. But let’s look at the graph. They have a temperature plot, which wiggles all over the place, and then they have 4 straight lines that are supposed to represent the model predictions. The line for the IPCC First Assessment Report is clearly way off, but back in 1990 the climate models didn’t include important things like ocean circulation, so that’s hardly surprising. The lines for the next 3 IPCC reports are very similar to one another, though. What the authors don’t tell you is that the lines they plot are really just the average long-term slopes of a bunch of different models. The individual models actually predict that the temperature will go up and down for a few years at a time, but the long-term slope (30 years or more) will be about what those straight lines say. Given that these lines are supposed to be average, long-term slopes, take a look at the temperature data and try to estimate whether the overall slope of the data is similar to the slopes of those three lines (from the 1995, 2001, and 2007 IPCC reports). If you were to calculate the slope of the data WITH error bars, the model predictions would very likely be in that range. That brings up another point. All climate models include parameters that aren’t known precisely, so the model projections have to include that uncertainty to be meaningful. And yet, the WSJ authors don’t provide any error bars of any kind! The fact is that if they did so, you would clearly see that the global mean temperature has wiggled around inside those error bars, just like it was supposed to.
So before I go on, let me be blunt about these guys. They know about error bars. They know that it’s meaningless, in a “noisy” system like global climate, to compare projected long-term trends to just a few years of data. And yet, they did. Why? I’ll let you decide. 2. The WSJ authors say that, although something like 97% of actively publishing climate scientists agree that humans are causing “significant” global warming, there really is a lot of disagreement about how much humans contribute to the total. The 97% figure comes from a 2009 study by Doran and Zimmerman. So they don’t like Doran and Zimmerman’s survey, and they would have liked more detailed questions. After all, D&Z asked respondents to say whether they thought humans were causing “significant” temperature change, and who’s to say what is “significant”? So is there no real consensus on the question of how much humans are contributing? First, every single national/international scientific organization with expertise in this area and every single national academy of science, has issued a statement saying that humans are causing significant global warming, and we ought to do something about it. So they are saying that the human contribution is “significant” enough that we need to worry about it and can/should do something about it. This could not happen unless there was a VERY strong majority of experts. Here is a nice graphic to illustrate this point (H/T Adam Siegel). […]
