From: William Hamilton <willeugenehamilton@gmail.com>

Date: Sat Dec 12 2009 - 16:42:04 EST

Date: Sat Dec 12 2009 - 16:42:04 EST

On Fri, Dec 11, 2009 at 1:43 PM, John Burgeson (ASA member) <

hossradbourne@gmail.com> wrote:

*> William makes a compelling argument here why the climate models may be
*

*> incorrect.
*

*>
*

*> Let's use Dick Cheny's "1% rule," which he used to justify the Iraq
*

*> invasion. This rule states that if there is even a very small chance
*

*> of a catastropic event then one is justified in taking measures
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*> against it. In the Iraq case, Cheney was speaking of thepossibility of
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*> WMDs.
*

*>
*

*> The obvious application to you and to me is the seatbelt rule. The
*

*> chances of me being in a catastropic accident are very very small
*

*> (I've driven and been driven for over 70 years and it hasn't happened
*

*> yet). Yey I put on my seat belt and happily pay the extra $$ for an
*

*> airbag in my vehicular contrivance -- because of the 1% rule. It is a
*

*> good rule.
*

*>
*

*> So -- apply this to the IPCC reports. They run multiple models and all
*

*> say we are in serious trouble. Maybe they are all wrong. Maybe the
*

*> science is not as robust as some people think. Let's take the most
*

*> optimistic view of all this we can. Is there even a 1% chance the IPCC
*

*> is right and the denialists are wrong? Even a 0.1% chance?
*

*>
*

*> It seems to me -- in this case -- we ought to be taking all measures
*

*> to prevent a global catastrophe. Even if it costs us something. Even
*

*> if it costs us a lot.
*

*>
*

However, this rule can work the other way as well: If there is a 1% chance

that people will die because of extraordinary measures we take to limit

greenhouse gases, then we ha better be *very* sure we know what we are

doing, and the need for it, before we proceed.

*>
*

*>
*

*>
*

*> On 11/30/09, William Hamilton <willeugenehamilton@gmail.com> wrote:
*

*> > Hi John
*

*> >
*

*> > I'm not a mathematician, but I spent 33 years developing computer models
*

*> of
*

*> > various systems. Most of the systems I modeled were pretty well-behaved,
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*> but
*

*> > in the mid 70's I developed a radar bomb-scoring system (RBS) for the
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*> Navy.
*

*> > The Navy uses huge quantities of ordnance in training. Not only is this
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*> > costly it leaves tons of unexploded ordnance around that eventually has
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*> to
*

*> > be cleaned up. And the noise annoys civilians, making it necessary to do
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*> > training in remote locations. They wanted me to write a program that
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*> would
*

*> > track an aircraft making a practice bomb run and simulate the bomb
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*> > ballistics to determine whare the bomb would land. In addition to being
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*> able
*

*> > to detect the moment when the bomb was released, we had to determine or
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*> > estimate the bomb's initial velocity with respect to the aircraft and
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*> > correctly model the bomb's aerodynamics, which were highly nonlinear.
*

*> There
*

*> > was an additional complication: some bombs, known as cluster bombs,
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*> > consisted of a shell containing a number of smaller "bomblets". At a
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*> > specified altitude the shell would open and the bomblets would be
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*> released.
*

*> > Of course the bomblets had completely different aerodynamics from the
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*> shell,
*

*> > so the solution had to be stopped an restarted with new initial
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*> conditions.
*

*> >
*

*> > We tested the RBS system by having aircraft drop real practice bombs
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*> (bombs
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*> > without explosives but with the correct mass and aerodynamics) and
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*> comparing
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*> > the real impact point with the predicted. As I remember the results were
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*> > close enough to be usable, but had the bombing been from any higher
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*> altitude
*

*> > or had the aircraft been taking evasive action they wouldn't have been.
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*> > Variations in barometric pressure and humidity affected the results too.
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*> >
*

*> > But trying to predict climate years in advance is a great deal more
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*> > difficult than trying to predict a bomb trajectory from an aircraft
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*> flying a
*

*> > few hundred to a few thousand feet above the deck.
*

*> >
*

*> > I've spent some time looking at computer models of climate. Most of them
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*> are
*

*> > too complicated to be easily analyzed. Even a retiree has limited time
*

*> :-).
*

*> > And most of them are too big to be run on the equipment available to me:
*

*> a
*

*> > macbook pro. But the complexity of the models argues for careful
*

*> assessment
*

*> > by people not having a vested interest in the accuracy of the models. I
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*> > don't know whether this has been done.
*

*> >
*

*> > However there is one paper by Tobias and Weiss: Resonant Interactions
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*> > between Solar Activity and Climate that you can get at
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*> >
*

*> http://ams.allenpress.com/perlserv/?request=get-document&doi=10.1175%2F1520-0442%282000%29013%3C3745%3ARIBSAA%3E2.0.CO%3B2
*

*> > that uses the Lorenz equations to model the earth's climate. They find a
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*> > stochastic resonance phenomenon that can result in warming of the earth's
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*> > climate with very small variation in solar activity. Now one might
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*> rightly
*

*> > question the simplification of using the Lorenz Differential equations to
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*> > model the earth's climate, but still the model points out a possible
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*> > connection between solar activity and earth's climate. I made a fairly
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*> > extensive study of the CCSM climate model, one of the models commonly
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*> used
*

*> > by the GW community, and solar input is just a constant, so that model
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*> > doesn't model solar variation at all. The GISS model is somewhat more
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*> > difficult to analyze, and I haven't yet determined how or whether they
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*> model
*

*> > solar variation.
*

*> >
*

*> > Another approach to studying climate dynamics is that used by Scafetta.
*

*> His
*

*> > web site is http://www.fel.duke.edu/~scafetta/<http://www.fel.duke.edu/%7Escafetta/>on which he has reprinted
*

*> > many of his papers. In addition there is a video of a talk he gave at the
*

*> > EPA back in February that is worth watching. Among other things Scafetta
*

*> has
*

*> > done extensive analysis of the statistics of climate data and solar
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*> output
*

*> > and found "echoes" of the solar input in the climate data, leading him to
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*> > conclude that solar variation is exciting global warming. (I'm not doing
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*> > justice to Scafett' research, which is extensive)
*

*> >
*

*> > So, while I admit the possibility that the various climate models used by
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*> > IPCC could be correct, I am very leery of basing policy decision on them
*

*> > until more analysis of the models and their input data and results is
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*> done
*

*> > by an impartial party.
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*> >
*

*> >
*

*> > On Mon, Nov 30, 2009 at 6:34 AM, John Walley <john_walley@yahoo.com>
*

*> wrote:
*

*> >
*

*> >> I found this to be very interesting. I wonder if any of the
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*> mathematicians
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*> >> on the list have any comment?
*

*> >>
*

*> >> John
*

*> >>
*

*> >> November 30, 2009
*

*> >> The Mathematics of Global Warming
*

*> >> By Peter Landesman
*

*> >>
*

*> http://www.americanthinker.com/2009/11/the_mathematics_of_global_warm.html
*

*> >>
*

*> >> The forecasts of global warming are based on the mathematical solutions
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*> of
*

*> >> equations in models of the weather. But all of these solutions are
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*> >> inaccurate. Therefore no valid scientific conclusions can be made
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*> >> concerning
*

*> >> global warming. The false claim for the effectiveness of mathematics is
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*> an
*

*> >> unreported scandal at least as important as the recent climate data
*

*> fraud.
*

*> >> Why is the math important? And why don't the climatologists use it
*

*> >> correctly?
*

*> >>
*

*> >> Mathematics has a fundamental role in the development of all physical
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*> >> sciences. First the researchers strive to understand the laws of nature
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*> >> determining the behavior of what they are studying. Then they build a
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*> >> model
*

*> >> and express these laws in the mathematics of differential and difference
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*> >> equations. Next the mathematicians analyze the solutions to these
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*> >> equations
*

*> >> to improve the understanding of the scientist. Often the mathematicians
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*> >> can
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*> >> describe the evolution through time of the scientist's model.
*

*> >>
*

*> >> The most famous successful use of mathematics in this way was Isaac
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*> >> Newton's demonstration that the planets travel in elliptical paths
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*> around
*

*> >> the sun. He formulated the law of gravity (that the rate of change of
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*> the
*

*> >> velocity between two masses is inversely proportional to the square of
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*> the
*

*> >> distance between them) and then developed the mathematics of
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*> differential
*

*> >> calculus to demonstrate his result.
*

*> >>
*

*> >> Every college physics student studies many of the simple models and
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*> their
*

*> >> successful solutions that have been found over the 300 years after
*

*> Newton.
*

*> >> Engineers constantly use models and mathematics to gain insight into the
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*> >> physics of their field.
*

*> >>
*

*> >> However, for many situations of interest, the mathematics may become too
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*> >> difficult. The mathematicians are unable to answer the scientist's
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*> >> important
*

*> >> questions because a complete understanding of the differential equations
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*> >> is
*

*> >> beyond human knowledge. A famous longstanding such unsolved problem is
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*> >> the
*

*> >> n-body problem: if more than two planets are revolving around one
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*> >> another,
*

*> >> according to the law of gravity, will the planets ram each other or will
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*> >> they drift out to infinity?
*

*> >>
*

*> >> Fortunately, in the last fifty years computers have been able to help
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*> >> mathematicians solve complex models over short time periods. Numerical
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*> >> analystshave developed techniques to graph solutions to differential
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*> >> equations and thus to yield new information about the model under
*

*> >> consideration. All college calculus students use calculators to find
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*> >> solutions to simple differential equations called integrals.
*

*> Space-travel
*

*> >> is possible because computers can solve the n-body problem for short
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*> times
*

*> >> and small n. The design of the stealth jet fighter could not have been
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*> >> accomplished without the computing speed of parallel processors. These
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*> >> successes have unrealistically raised the expectations for the
*

*> application
*

*> >> of mathematics to scientific problems.
*

*> >>
*

*> >> Unfortunately, even assuming the model of the physics is correct,
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*> >> computers
*

*> >> and mathematicians cannot solve more difficult problems such as the
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*> >> weather
*

*> >> equations for several reasons. First, the solution may require more
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*> >> computations than computers can make. Faster and faster computers push
*

*> >> back
*

*> >> the speed barrier every year. Second, it may be too difficult to
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*> collect
*

*> >> enough data to accurately determine the initial conditions of the model.
*

*> >> Third, the equations of the model may be non-linear. This means that no
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*> >> simplification of the equations can accurately predict the properties of
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*> >> the
*

*> >> solutions of the differential equations. The solutions are often
*

*> unstable.
*

*> >> That is a small variation in initial conditions lead to large variations
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*> >> some time later. This property makes it impossible to compute solutions
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*> >> over
*

*> >> long time periods.
*

*> >>
*

*> >> As an expertin the solutions of non-linear differential equations, I can
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*> >> attest to the fact that the more than two-dozen non-linear differential
*

*> >> equations in the models of the weather are too difficult for humans to
*

*> >> have
*

*> >> any idea how to solve accurately. No approximation over long time
*

*> periods
*

*> >> has any chance of accurately predicting global warming. Yet
*

*> approximation
*

*> >> is exactly what the global warming advocates are doing. Each of the
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*> more
*

*> >> than 30 modelsbeing used around the world to predict the weather is just
*

*> a
*

*> >> different inaccurate approximation of the weather equations. (Of course
*

*> >> this is only an issue if the model of the weather is correct. It is
*

*> >> probably
*

*> >> not because the climatologists probably do not understand all of the
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*> >> physical processes determining the weather.)
*

*> >>
*

*> >> Therefore, logically one cannot conclude that any of the predictions are
*

*> >> correct. To base economic policy on the wishful thinking of these
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*> >> so-called
*

*> >> scientists is just foolhardy from a mathematical point of view. The
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*> >> leaders
*

*> >> of the mathematical community, ensconced in universities flush with
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*> global
*

*> >> warming dollars, have not adequately explained to the public the above
*

*> >> facts.
*

*> >>
*

*> >> President Obama should appoint a Mathematics Czar to consult before he
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*> >> goes
*

*> >> to Copenhagen.
*

*> >>
*

*> >> Peter Landesman mathmaze@yahoo.comis the author of the 3D-maze
*

*> >> bookSpacemazes for children to have fun while learning mathematics.
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*> >>
*

*> >>
*

*> >>
*

*> >>
*

*> >>
*

*> >> To unsubscribe, send a message to majordomo@calvin.edu with
*

*> >> "unsubscribe asa" (no quotes) as the body of the message.
*

*> >>
*

*> >
*

*> >
*

*> >
*

*> > --
*

*> > William E (Bill) Hamilton Jr., Ph.D.
*

*> > Member American Scientific Affiliation
*

*> > Austin, TX
*

*> > 248 821 8156
*

*> >
*

*>
*

*>
*

*> --
*

*> Burgy
*

*>
*

*> www.burgy.50megs.com
*

*>
*

-- William E (Bill) Hamilton Jr., Ph.D. Member American Scientific Affiliation Austin, TX 248 821 8156 To unsubscribe, send a message to majordomo@calvin.edu with "unsubscribe asa" (no quotes) as the body of the message.Received on Sat Dec 12 16:42:26 2009

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