Paper: Scientists ‘obsessed’ with climate models that don’t work

By |2016-05-21T07:44:01+00:00May 21st, 2016|Climate|3 Comments

The federal government has spent billions of taxpayer dollars funding the “single minded process” of developing climate models, according to two experts — models that have been over-predicting global warming for at least six decades.

“Billions of research dollars are being spent in this single minded process,” climate scientist Patrick Michaels and policy expert David Wojick wrote in an analysis published on the science blog Watts Up With That.

“In the meantime the central scientific question – the proper Daily Caller  New Foundationattribution of climate change to natural versus human factors – is largely being ignored,” Michaels, a Cato Institute scientist, and Wojick wrote.

Michaels and Wojick were examining just how much of climate science was dedicated to modeling the climate. The two used Google Scholar to find more than 900,000 peer-reviewed journal articles with the words model, modeled or modeling. Of those journal articles, 55 percent had to do with climate science.

From this, Michaels and Wojick argued climate science was too obsessed with climate models — many of which have not correctly predicted global warming over the last six decades.

“Climate science appears to be obsessively focused on modeling,” they wrote. “Modeling can be a useful tool, a way of playing with hypotheses to explore their implications or test them against observations. That is how modeling is used in most sciences.”

“But in climate change science modeling appears to have become an end in itself,” they wrote. “In fact it seems to have become virtually the sole point of the research. The modelers’ oft stated goal is to do climate forecasting, along the lines of weather forecasting, at local and regional scales.”

Michaels and fellow Cato climate scientist Chip Knappenberger found the models had been over-predicting warming for more than 60 years.

Michaels and Wojick argue this is a big problem for climate science. The government has spent billions of dollars over the years on climate models instead of bettering their understanding of how the climate actually works.

“Climate modeling is not climate science. Moreover, the climate science research that is done appears to be largely focused on improving the models,” they wrote. “In doing this it assumes that the models are basically correct, that the basic science is settled. This is far from true.”

“The models basically assume the hypothesis of human-caused climate change,” they wrote. “Natural variability only comes in as a short term influence that is negligible in the long run. But there is abundant evidence that long term natural variability plays a major role climate change. We seem to recall that we have only very recently emerged from the latest Pleistocene glaciation, around 11,000 years ago.”

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This article originally appeared in The Daily Caller


  1. Raveendran NARAYANAN May 22, 2016 at 7:54 AM

    “Why I am different from 31, 487 CLIMATE AND GLOBAL WARMING SCIENTISTS? ”
    If need arises will AIR CONDITION PLANET MARS TOO. !!!!!!!

  2. VACornell June 9, 2016 at 8:38 PM

    Michaels is right…
    From 20,000 to 11,000 years ago that mile of ice over Chicago melted…thank God?
    Or thank the continued “warm” from then to now
    WE DO NOT need modeling…we need science in its true sence.
    Pat and Dave are correct…press on!

  3. David Courard-Hauri April 25, 2017 at 11:01 AM

    Your “models” and “reality” lines are just made up. I’m surprised that you can get away with nobody challenging them, but I suppose that’s what happens when you post into an echo chamber. If anyone cares about the facts, just go look up one of the older IPCC reports. For example, you can grab the whole 1995 report here: . Check out the projected surface temperature change graph (Figure 19 in the technical summary) and compare against any of the surface temperature datasets. Looks to me like the models got it right. What goes through your mind when you’re faking a graph like this? Do you think: “It’s too confusing to explain my side with real data, so I’ll jam this together because it at least ought to be the way the world I would like should work”, or do you cherry-pick data that colleagues have finessed without asking any questions?

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