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From statistics guru Ross McKitrick:

A few years ago a biologist I know looked at how climate change might affect the spread of a particular invasive insect species. He obtained climate-model projections for North America under standard greenhouse-gas scenarios from two modelling labs, and then tried to characterize how the insect habitat might change. To his surprise, he found very different results depending on which model was used. Even though both models were using the same input data, they made opposite predictions about regional climate patterns in North America.

 This reminded me of a presentation I’d seen years earlier about predicted changes in U.S. rainfall patterns under global warming. The two models being used for a government report again made diametrically opposite predictions. In region after region, if one model predicted a tendency toward more flooding, the other tended to predict drying.

 Just how good are climate models at predicting regional patterns of climate change? I had occasion to survey this literature as part of a recently completed research project on the subject. The simple summary is that, with few exceptions, climate models not only fail to do better than random numbers, in some cases they are actually worse.

That is just the summary. Read the whole thing.

The point is that the models simply do not work when it comes to predicting changes in global (or even regional) climate. But it is predictions based on these same models which have been used to justify crippling legislation like the carbon dioxide tax, and spending billions of dollars to solve a problem which doesn’t exist.

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