[xcamel]

FReepmail me to get on or off
Click on POGW graphic for full GW rundown

---

Wow...

[Tolik]

Global Warming ping

[listenhillary]

Excellent article.

[facedown]

Although based on scientific "first principles", complex numerical models inevitably require simplifications, judgment calls, and correction factors.

As a scientist, we have a name for this. It's called a WAG.

That's a wild-a$$ guess, for those of your in Rio Linda.

[Tolik]

In case you missed it : Skeptics and Deniers of Global Warming. Its not a settled science. Debate continues. 10 part series

[Fitzcarraldo]

Just don't build too close to the coast and dress appropriately for the weather. That should get a person by.

[null and void]

Andrew Grove PhD is a giant in the history of semiconductors. A founder of Intel, Grove famously presided as CEO over its enormous growth during the 1980s and 1990s. Few realize that his academic training is as a Chemical Engineer, not an Electrical Engineer.

Yup. My electronics engineering training has been virtually no help in my career in semiconductor processing.

[RightWhale]

Computational sciences have given new life to countless careers that would otherwise have been converted to used car sales.

[Lonesome in Massachussets]

Thomas Sowell's first job as an economist after graduating from Harvard was working for AT&T making economic predictions, using mathematical models. His boss showed him were the previous models (stacks of punch cards) where kept, so he could use them as a starting point. Young Thomas says "Oh, great, then we can see how well we've done!" His boss's reaction made it clear that he was *never* to bring up that idea again.

Actually, regression analysis allows one to place reliable bounds on the accuracy of a model, like when astronomers say that there is a 1 in 45,000 chance of a certain asteriod striking earth in 2036 (or whatever).

The IPCC seems to be paying homage to notion of error bounds by saying that there is a 90% chance of some {poorly defined unfavorable} event. What the author of this piece is talking about is what is known as validation. Historical data is perfectly good for validating a model, provided you can establish *all* the relevant input parameters and their associated uncertainties. I am personally unimpressed by anything I've read or seen about global warming to date. The alarmist scenarios are just too convenient for the politcal Left.

[CharlesWayneCT]

Ping to read when I'm having trouble sleeping.....

[kalee]

ping

[Old Professer]

Counterpoint: "We don't have time to wait, we must act now."

Read this small piece from today's Tennessean by Beverly Keel the celebrity columnist for Gore's views yesterday:

http://tennessean.com/apps/pbcs.dll/article?AID=/20070228/NEWS01/702280434

[chaosagent]

bookmarked

[sportutegrl]

http://en.wikipedia.org/wiki/Chaos_theory

It would seem to me that chaos theory would suggest that it is impossible to predict with any degree of certainty any claims of future global warming specificially due to man. Too many variables, too many assumptions or simplifications/etc. in variables.

[lmailbvmbipfwedu]

Might I direct your attention to

Pikey, O.H., Pilkey-Jarvis, 2007, Useless Arithmetic, Columbia University Press, ISBN 0-231-13212-3.

For your reading enjoyment. BTW the subtitle of this book is "Why Environmental Scientists Can't Predict The Future"

[Publius6961]

Another keeper!

I particularly loved these two paragraphs, Which should be self evident to anyone with just a conversant familiarity with the hard sciences:

Almost all semiconductor manufacturing processes occur in closed vessels. This permits the engineers to precisely control the input chemicals (gases) and the pressure, temperature, etc. with high degree of precision and reliability.

Closed systems are also much easier to model as compared to systems open to the atmosphere (that should tell us something already). Computer models are used to inform the engineering team as the design the shape, temperature ramp, flow rates, etc, etc, (i.e. the thermodynamics) of the new reactor.

Nonetheless, despite the fact that 1) the chemical reactions are highly studied, 2) there exists extensive experience with similar reactors, much of it recorded in the open literature, 3) the input gases and materials are of high and known purity, and 4) the process is controlled with incredible precision, the predictions of the models are often wrong, requiring that the reactor be adjusted empirically to produce the desired product with quality and reliability.

There was an article recently by a statistician, who pointed out that use of statistics analysis by amateurs can quickly lead to non-sensical results.
For example, if 3 assumptions are made which are 99% correct, the results can only be correct, significantly less than 99%.
When just a dozen factors are 99% correct, the results are only about 50% reliable.

With climate having dozens, perhaps hundreds of relevant factors, most of which are guessed at or ignored altogether, how reliable can these computer "model predictions" be?

[neverdem]

thanks, bfl

[ggekko60506]

Great Post.

The author's analysis is devastating critique of the methodological fallacies that lay behind Climate Modeling as it is currently practiced.

There is also another major flaw in the current practice that the author did not mention that should warrant the summary rejection of the use these models in Public Policy.

The builders of the models on which the IPCC and other Reports have been based have refused to publish the input parameters and algorithms used to generate predictive results. Peer review is the backbone of scientific integrity and the lack of peer review outside the small coterie of model builders is a big of a red flag as can be imagined for any type of predictive model.

When the builder of the infamous "hockey stick" model was asked to publish his model assumptions he played the "political persecution" card. The highly and odd defensive reaction is indicative of how badly these models have been cooked for political purposes.

[IncPen]

... ping

[Former Proud Canadian]

Mark