What came out of the hearing has people asking if the same problems affecting Enron, WorldCom, Global Crossing, etc…are now troubling environmental science.
Puffy federal documents create consequences. In this case, look no further than a recent California law requiring the first statewide reductions on global warming emissions from cars. It cites many findings from the “U.S. National Assessment” of global warming, one of the documents that provoked the House Oversight hearing.
Science operates by a hard and fast ethic. Theory must conform to reality and be tested by reality. In the case of climate science, our “theories” are huge computer models that project various amounts of warming for the next 100 years.
In my review of the Assessment, I discovered two very disturbing facts. The Assessment team considered several computer models, but chose the two that predicted the most extreme changes in temperature and rainfall over the United States. That’s prima facie evidence for some type of bias, which isn’t surprising. The team was vetted through four Clinton administration committees, one of which was headed by Al Gore. Worse, these models couldn’t beat a table of random numbers, or two dice on a crap table, when it came to predicting U.S. temperatures.
The Assessment team was required to publicly comment on the science reviews, and swept this criticism under the rug. Behind the scenes they were much more fearful, and replicated my experiment. They found out that the computer models they were using couldn’t even simulate 25‐year temperature averages over the United States as the greenhouse effect changed in the last 100 years.
How, then, could these models be used to assess climate change in the next 100 years?
It gets worse. The temperature and precipitation data these models spit out are then input to other sectors of the U.S. economy, such as agriculture, forestry, and water supplies.
In science, random numbers are garbage, and that’s what went in. Refuse in science is what we call “transitive.” Start with it and you end with it. Garbage out.
Tom Karl, who heads the National Climatic Data Center in Asheville, North Carolina, and co‐chaired the production of the Assessment, was the lucky person who had to come to Washington to defend these shenanigans. His dancing was about as painful as you would expect when a noted scientist has to defend something so wrong.
The core defense of the federal establishment was that they were not making “predictions” of future U.S. climate with these models. Instead they were “plausible projections.” Are the most extreme estimates of U.S. temperature and rainfall changes “plausible”?
Can someone explain to a reporter, seeing these dire results spit out by extremist computer models, that there’s a lick of difference between a “prediction” and a “projection”? Does anyone believe that the political impact of either, if based upon bad models, isn’t the same, i.e., bad legislation?
This rings the same bell that Bill Clinton did when he said, “That depends on what the definition of the word ‘is’ is.” Predictions, projections, forecasts…they’re all the same to any reporter, or, for that matter, to any professor. And when they’re based upon computer models that can’t beat dice dancing in Atlantic City, they’re junk.
This story isn’t going away. It surfaced on MSNBC’s ” Hardball” last week, and it will to bubble up every time someone comes up with a new law on climate change in the United States.
It’s no accident that all of this wound up in front of the Oversight and Investigations Subcommittee. Recently it targeted corporations that cook books and hide things from shareholders.
Forecasting something as important as future U.S. climate based upon models that do not work is as deceptive as stating assets that a company does not have. In this case it is the federal science establishment and we, citizens of the United States, who are the investors.
It’s not just CEO’s and CFO’s who inflate results to jack up their currency. The corruption has now spread to science.