On 5 Jul 2005 15:51:36 -0700, wrote:



Larry Jaques wrote:
On 4 Jul 2005 12:01:09 -0700, the opaque
clearly wrote:

Larry Jaques wrote:
"How can we make our point with so little data to go on? Aha, make the
increments so small the data (with which we want to scare folks) is
off the charts!" Oh, and "Let's estimate data about 10x longer than
we have ANY data for.)

SPLORF! I realize that is not your only criticism but it is hilarious
that you would base ANY criticism on the tic spacing on the temeprature
axis. If they spaced the tics 10 degrees apart the plot would look the
same, it would just be harder to convert the picture to numbers.

Graph range has been used to hide data more than once, bubba.

Sure, had the authore chosen a range from, say -100 C to + 100 C the
chart would be inscrutable. As it is, the range appears tobe
chosen as any sensible person would, to fit the data on the page
within comfortable margins.

BTW, why'd you change the subject from tic-spacing to range? Perhaps
you DO realize the tic spacing is arbitrary, just like the choice
of origin?


When the @#$% was the subject ever tic spacing? The issue is the
represented data and the range of the data that is based upon very gross
observables being used to predict global average temperature fluctuations
based upon ice core samples, tree ring size, and contemporary cultural
documentation going back the past millennia. Those gross measurements
(again, which could be influenced by more than just temperature) were then
used to compute numbers with very small predicted increments. The
precision presented is not the precision that one would expect from such
gross measures. Had you explored the web site at which you found the
chart, you would have found that this was a conclusion from a paper by Mann
in 1998 that used the data that was summarized in that chart to predict
future global warming. The paper by Mann is one of the keystones of the
global warming adherents (not just a dog and pony show chart). The chart
is simply a summary of the Mann's "research" and conclusions. There are
numerous objections to Mann's methods and his refusal to turn over *all*
of his data or algorithms http://www.climateaudit.org/index.php?p=234
despite being funded by the NSF. Further, problems with his methodology
are documented in http://www.numberwatch.co.uk/2003%20October.htm#bathtub
as well as other areas on the site. He deliberately omitted data that
corresponded to a midiaeval warm period, thus making his predictions for
the future look like the largest jump in history. Again, even if this
chart was only for consumption by politicians and policy makers, it was a
deliberately distorted conclusion that could only be intended to engender a
specific response regarding global warming. In order to get his infamous
2.5C temperature rise prediction, he used trend of the numbers to pad the
data fit rather than padding with the mean of the data. (again documented
on the numberwatch page).

Here
they go the opposite direction to support falsehoods and hysteria.

The graph in question looks to me to have bene prepared for some
sort of dog and pony show. If it was created by a climatologist
in the first place, I'll bet it was created to show to reporters
and politicians (and also bet that they didn't understand it anyways.)


.... and if it was so created, it was created in order to drive a specific
conclusion and input to direct public policy. That is not a trivial, wave
your hands and dismiss-it kind of action. The politicians who used it
certainly understood the conclusions that Mann was trying to assert. The
fact that he omitted the medieval warm period further indicates that this
was not a harmless use of the data from an innocent scientist.


It has been over a decade since I last attended a coloquium given
by a climatologist. At that time predictions were being made based
on climate models--not by looking at a graph and imagining it extended
beyond the right margin.


Where do you think that climatologists get the bases for their climate
models? Where do you think they get data that they can use to fine-tune
those models and validate them?



For example, this fellow (sorry I do not remember his name) explained
that one of the objections to a Kyoto type agreement (this was
before Kyoto) came about because some models predicted that average
annual rainfall in Siberia would decrease over about the next fifty
years but then increase over the following 100. So the Soviets
(this was back when there were still Soviets) were concerned about
not stabilizing global change at a time when Siberia was near the
dryest part of the expected changes.


So, since it's been over a decade, were their models correct? Has
rainfall in Siberia been decreasing? From a quick perusal of the web, it
appears that significant flooding has occurred in Siberia in recent years
due to heavy rains as well as spring melt.


Note also that Siberia getting drier for fifty years and then
getting wetter for a hunderd years after is a nonlinear change.
The prediction was not being made by simply extending a plot.


No, it was made by running a computer model. Do you know what goes into
computer models and simulations? Do you have any idea how much data and
effort is required to get a computer model to make predictions that are
reliable? I do; as I mentioned before, I've been involved in the area of
development, and integration & test for a considerable time. I know how
difficult it is to get a model to generate accurate predictions even when I
have control of a significant proportion of the test environment. To
believe that climatologists have the ability to generate models that
predict the future performance of such a complex system as the Earth's
climate yet cannot predict even short term with any significant degree of
accuracy is a stretch of epic proportions to say the least.


People who write as if the predictions made by climatologists
are based on extrapolating from dog and pony show style visual
aids a

1) Not very honest.
or
2) Not very bright.
or
3) Have been misled by people fitting 1) and/or 2) above.


People who think that climatologists who generate such charts are not
attempting to influence policy and opinion are
1) Not very honest
2) Not very bright
3) Have mislead themselves into believing that said climatologists are
simply objective scientists publishing reduced graphs that are being used
for purposes that they did not envision.

That Mann does not fall under the title of naive scientist can be found
in http://www.washtimes.com/commentary/20030825-090130-5881r.htm



I've never worked on a Climate model but have no doubt that
Climatologists rely on tried and true statistical methods
to fit data to their models and to made predictions from
those models just like any other scientist.


Very well, and where are these climatologists getting *their* data to
validate their models? Generating models is easy, generating models that
produce accurate results is not.

If they underestimate the uncertainties in their data, or
overestimate the degrees of freedom in their models their
reduced chi-squares will be too small, just like they were
when Gregor Mendel's data were fitted to his theory. (Not
by Mendel himself, he didn't do chi squares). While Mendel's
theory of genetics overestimated the degrees of freedom, his
data fit modern genetic theory quite well.

If someone has a scientifically valid theory, they will have
the math to support it. The same is true for a scientifically
valid criticism of a theory.


Statistics does *not* make the math for a model. Statistics can be used
to validate the precision, or distribution of outcomes of a model run in a
Monte-Carlo sense, comparing the dispersion of the monte-carlo runs to the
dispersion of real data, but that assumes one has sufficient real data with
which to perform such a comparison and that the diversity of the variables
being modified in the model are sufficiently represented in the data set to
which the model is being compared. If all one is relying upon to predict
future events is past data being statistically processed, one has done
nothing beyond glorified curve fitting and extrapolation beyond the data
set. The real math behind models and simulations should be the
first-principals physics and chemistry that are properly applied to the
problem being modeled. Therein lies the rub, there are so many variables
and degrees of freedom (in a true modeling definition of that phrase), that
validating the first principals models to the degree that one could trust a
model to predict future climate changes is, at this time, insufficient.
Using such models in making public policy that can have devastating
economical effects upon peoples' lives would be a travesty. Finally, even
given that you have climatalogical models that have some degree of
precision, there is still the pesky problem of proving that human activity
is to blame for the phenomena being observed as root cause changes to the
future climate predictions.



If instead, their criticism is that the tic spacing on a graph
is too close, well, that conclusion is left as an exercise for
the reader.

Your statement above indicates that either you don't get it, or are being
deliberately obtuse regarding the referenced paper and the infamous "hockey
stick" chart. Think of it this way, the chart shown is the equivalent to
the final output from one of your revered climatologist's models that
predicts global average temperature will increase by 2.5C per decade
(Mann's original paper apparently stated 1C per decade, but the number was
later revised to 2.5C). This is the equivalent to your climatologists'
model prediction that rain in Siberia would decrease over the next 50
years, then increase over the next 100.


Fred, this is my last post on this subject, as it is clear that a) you
really don't get it and b) for all of your feigned objectivity and previous
comments upon how you take an objective view of all sides and then look at
the available, data; you have shown that you look at that data only from a
particular worldview. You are welcome to the last word, I have better
things to do with my time.



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If you're gonna be dumb, you better be tough

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