Climate Modelers are Wizard of Oz’s Spawn

If you look closely, it’s not demonstrated science but the climate models that are the basis for all the forecasts of catastrophe will result from manmade global warming.  The models, cited by the IPCC in their reports, supposedly demonstrated that the global temperatures recorded from 1978 to 1998 could only have occurred because of additional atmospheric CO2 from the increased use of fossil fuels.  Thus we are to believe that they have modeled the atmosphere so when the models look to the future they must give accurate projections.

But we know that these same models do not forecast worth a damn.  How can it be the models that all showed agreement with the past don’t get the future right? But perhaps more importantly why is it that the future forecasts don’t agree with one another.  That mystery is explain by Warren Meyer in his 9 June 2011 posting in Forbes:

When I looked at historic temperature and CO2 levels, it was impossible for me to see how they could be in any way consistent with the high climate sensitivities that were coming out of the IPCC models.  

My skepticism was increased when several skeptics pointed out a problem that should have been obvious.  The ten or twelve IPCC climate models all had very different climate sensitivities — how, if they have different climate sensitivities, do they all nearly exactly model past temperatures?  If each embodies a correct model of the climate, and each has different climate sensitivity, only one (at most) should replicate observed data.  But they all do. 

The answer to this paradox came in a 2007 study by climate modeler Jeffrey Kiehl. To understand his findings, we need to understand a bit of background on aerosols. Aerosols are man-made pollutants, mainly combustion products, which are thought to have the effect of cooling the Earth’s climate.

What Kiehl demonstrated was that these aerosols are likely the answer to my old question about how models with high sensitivities are able to accurately model historic temperatures.  When simulating history, scientists add aerosols to their high-sensitivity models in sufficient quantities to cool them to match historic temperatures.  Then, since such aerosols are much easier to eliminate as combustion products than is CO2, they assume these aerosols go away in the future, allowing their models to produce enormous amounts of future warming.

Specifically, when he looked at the climate models used by the IPCC, Kiehl found they all used very different assumptions for aerosol cooling and, most significantly, he found that each of these varying assumptions were exactly what was required to combine with that model’s unique sensitivity assumptions to reproduce historical temperatures.  In my terminology, aerosol cooling was the plug variable.

The problem, of course, is that matching history is merely a test of the model — the ultimate goal is to accurately model the future, and arbitrarily plugging variable values to match history is merely gaming the test, not improving accuracy.

This is why, when run forward, these models seldom do a very credible job predicting the future.  None, for example, predicted the flattening of temperatures over the last decade.  And when we look at the results of these models, or at least their antecedents, from twenty years ago, they are nothing short of awful.  NASA’s James Hansen famously made a presentation to Congress in 1988 showing his model runs for the future, all of which show 2011 temperatures well above what we actually measure today.

Meyer adds that: “Rather than real science, the climate models are in some sense an elaborate methodology for disguising our uncertainty.  They take guesses at the front-end and spit them out at the back-end with three-decimal precision.  In this sense, the models are closer in function to the light and sound show the Wizard of Oz uses to make himself seem more impressive, and that he uses to hide from the audience his shortcomings.”

So there we have it, the modelers jigger the system with enough variables to have the predetermined variables such as the positive feedback that boosts CO2 effect by a multiple of 3 or 4 be over ridden when doing the back cast and then drop the jiggering (in this case, aerosols) for future forecasts.


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