Why Computer Models are Crap

Next time you read an article about global warming that states the computer models predicted something remember this article, which offers proof positive that the computer models used by so-called scientists to predicts unprecedented global warming are crap.

From Scientific American we get this:

When it comes to assigning blame for the current economic doldrums, the quants who build the complicated mathematic financial risk models, and the traders who rely on them, deserve their share of the blame. [See“A Formula For Economic Calamity” in the November 2011 issue]. But what if there were a way to come up with simpler models that perfectly reflected reality? And what if we had perfect financial data to plug into them?

Incredibly, even under those utterly unrealizable conditions, we’d still get bad predictions from models.

That’s right, even a computer model that is “perfect” isn’t worth the electricity used to run it. The problem is calibration. In a nutshell, take a “perfect” model and tweak it any and it is no longer going to predict anything correctly.

The problem, of course, is that while these different versions of the model might all match the historical data, they would in general generate different predictions going forward–and sure enough, his calibrated model produced terrible predictions compared to the “reality” originally generated by the perfect model. Calibration–a standard procedure used by all modelers in all fields, including finance–had rendered a perfect model seriously flawed. Though taken aback, he continued his study, and found that having even tiny flaws in the model or the historical data made the situation far worse. “As far as I can tell, you’d have exactly the same situation with any model that has to be calibrated,” says Carter.

Calibration, tweaking, adjusting, cheating, lying, stealing, or whatever you want to call it, produces crap. Crap financial models or crap climate models, it’s all the same crap.

Note, to reiterate. it doesn’t matter if it agreed with past data, a process called hindcasting, which is a big line you hear from climate modelers, it still comes up with crap predictions! Getting one to agree with the weather in 1900 means nothing according to this article.

The next time you read some horrific article about how global warming is going to make the sea-level rise, cause droughts, floods, and just about anything else you can name including 3-eyed cows and cooties, remember that the climate model used to predict it is crap. Perhaps it was even tweaked to arrive at a preconceived conclusion.

Proof? The computer models used to predict tomorrow’s weather aren’t even right all the time.  So, how can they predict what’s going to happen 20-30-100 years in the future?

Read more details about this at Scientific American.

Advertisements

3 Comments

Filed under Climate Alarmism, Climate Change, Climate Disruption, Climate Modeling, Climategate, CO2, Co2 Insanity, Global Warming, Government, IPCC, NASA, NOAA

3 responses to “Why Computer Models are Crap

  1. Jerry

    I first understood that computer modeling was garbage when they claimed, from computer results, that a butterfly flapping its wings across the ocean could influence our weather. I am quite happy that I learned the idea of ratio and proportion early on.

  2. Steven

    Good work by Scientific American. Mathematical modeling of complex physical systems is inherently unreliable in extrapolation beyond it’s domain of validity. Such models do not obey causality. See my blog post on this exact subject from August of this year. Here’s the link to my article. http://syntheticinformation.blogspot.com/p/what-is-modeling-is-climate-modeling.html This means that such models cannot predict the future in the way IPCC and AGW advocates claim.

  3. Steven

    This morning I sent an email to the editors of Scientific American complementing them on the article by David Freedman. I also informed them, in an entirely friendly way, of my prior publication on the same subject. That is, the inherent unreliability of models, and implications for climate modeling in particular. It would be nice for my work to be recognized in some appropriate way by Sci. Am. We await their collegial response.