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Numerical Models and Running Rexes



Right now I believe that I am in a particularly good position that allows me to 
make a few short comments about numerical models.

In my atmospheric dynamics courses, Iâve been constantly learning about how 
numerical models for climate data modeling and weather prediction are made, how 
they function, and what they are able to reliably produce for describing the 
dynamical workings of the atmosphere. Why should this matter to the DML???  
Simply put, Iâm learning just how ârealâ numerical models of natural 
entities truly are.

There is a profoundly important concept that every single one of my professors 
continually mentions in regards to ANY type of numerical model... For even the 
best of the best, in order to "get it right", numerical models are composed of 
a core of respectability, surrounded by a varying amount of voodoo (fudge 
factors). General, broad-sweeping numerical models have a very high degree of 
confidence for their outputs. Numerical models designed to interpret the over 
all global climatic effects caused by glaciations, are just swell. The driving 
variables of these numerical models are all well known and can be independently 
verified using numerous different approaches, including looking at deep-sea 
cores to measuring the albedo from the Antarctic ice-sheet. On the other 
hand... Devising a numerical model that has a fairly high degree of accuracy 
and describes the "weather" caused by glaciation events, is not so swell. Iâm 
sure you understand why. (Remember, climate is the synthe!
si!
s or "statistics" of weather in a particular region. Weather is the 
"instantaneous condition" of the ocean and atmosphere at a "select location 
within" a region.) Look at it this wayâ. There is a reason why meteorologists 
can tell you that there are going to be thunderstorms across the entire state 
of Ohio todayâ. but they canât tell you that the storm presently at 
grid-point A9 is going to be a storm that is going to produce an F3 tornado 
once it moves into Mahoning County at 4:17pm.

So where am I going with this? What I am getting at is simple. When you are 
dealing with numerical models thatâs purpose is to describe some specific, 
NOT general, natural entity, the subsequent loss in generality of the inputs 
results in the loss in accuracy of the outputs. I know this is confusing, but 
follow me here... When you start to become specific in numerical models, 
unknowns, already known and unknown, amplify already understood uncertainties, 
which in turn amplify already understood errors, while at the same time 
introducing brand new errors, which are also known and unknown. These now 
amplified errors are compounded beneath the fabric of your now "detailed" 
variables and uncertainties, both known and unknown. Error is built on top of 
error. The end result is a numerical model thatâs output confidence has been 
rapidly eroded away. How do you deal with all of this? Voodoo is the answer... 
Simplify your equations. Simplify your constraints. Simplify your variabl!
es!
. Approximate. Atmospheric dynamics is governed by only a handful of equations. 
Problem is, the equations are unsolvable. How do you get around that little 
bump in the road? Linearize them. Too bad that what you get out can model an 
air mass but canât model a storm within that air mass.

And you know what else helps? Being able to look at your subject and test the 
accuracy of your numerical model against it. Itâs fantastic that Mr. 
Hutchinson was able to produce an accurate numerical model of an ostrich. 
Problem is, tyrannosaurs were not ostriches, just as a hurricane is not a 
extratropical cyclone, even though both are powerful storms with very similar 
structure. Tiny differences end up making a world of difference. (And with a 
bit of intentional imagery, one even evolves from the other.) So, it rightly 
follows that using the same numerical model to describe both of them will 
produce erroneous results. Not completely wrong results.. but results in error. 
Then why is it that a numerical model with set parameters, variable constraints 
and sensitivity analysis ideal for describing the locomotory capabilities of an 
ostrich, is thought of as being the numerical model that is ideal for 
describing the locomotory capabilities of a different, yet similar animal, n!
am!
ely a tyrannosaur? As Mr. Hutchinson said, "Our model included all of the 
tyrannosaur anatomy that was needed, and T.rex was still found wanting." Mr. 
Hutchinson also said that anatomists suffer from tunnel vision... They are 
mired in "untested assumptions about the relationship of anatomy and locomotor 
performance." Mr. Hutchinson says, "I think biomechanics is what is needed to 
test anatomical specializations and see how important they really are; the work 
has not been done to my satisfaction." And finally, Mr. Hutchinson says, "But 
computer models will always be a useful supplement to any other line of 
evidence, particularly when it comes to extinct animals, because they allow you 
to test hypotheses indirectly without relying on time machines, analogy, or 
mere speculation and anecdotes.  You just need to be cautious when using them.  
The fact that scientists have used the same methods we used in the Nature paper 
for decades of studying living animals gives me strong confi!
de!
nce in the applicability of these modeling methods to ext!
inct anim
als, particularly when carefully approached with sensitivity analysis." I 
suppose the anatomist could simply respond by saying "As for sensitivity 
analysis, of course you are going to know the limits for the ostrich... Go and 
dissect one to find out... Go and dissect a few more while you are at it... 
Hell, go to town on extant every animal you can find. Be sure of yourself. 
Study those soft tissues. Make sure all those equations work for body forms 
that exist today.  But excuse me... when it comes to extinct animals for which 
no exact extant analogue exists to test your numerical model against, why are 
you so sure that you have accounted for all of that extinct animalâs 
anatomical specializations and their importance? You can freely go ahead and 
say that this is turning into speculations and anecdotes... but exactly how are 
you going to dissect a tyrannosaur to see if you included all of what Mr. 
Hutchinson calls "strings and sealing wax and other fancy stuff"?" This is al!
l !
answered by Occamâs Razor right? But what did I say above when it came to 
simplifying the goods when it came to numerical weather models? And by the 
way... when it comes to the weather, numerical models are not a very good 
supplement to other lines of evidence.... Looking out the window is. The 
anatomist could also cite examples of failed numerical models with the talk of 
diving whales, swimming tuna, jumping frogs, and running turkeys.  Numerical 
modeling works best if you have solid foundations for its premises... but not 
so great for trying to deduce the premises in the first place. Who is the one 
with tunnel vision again? I forgot.

I tend to think that my thoughts can be pushed aside as Mr. Paulâs were... 
The consequences of not understanding how this particular numerical model 
works. And for me, this is probably true. Being that I cannot articulate what I 
want to say as good as Mr. Paul does, doesn't help me either. But in the same 
breath, I suppose I could also pick up the mantle of BAND and Mr. Feduccia to 
proclaim that Dinosaurologists think that birds are dinosaurs because they 
donât understand avian anatomy and digit development and evolution. I suppose 
I could also set a near-unreachable criteria for usurping my conclusions... 
something akin to asking for a trackway of a running "Tyrannosaurus rex" would 
be suffice. 

Kris