NUMERICAL MODELS, INTEGRATED CIRCUITS AND GLOBAL WARMING THEORY

American Thinker, 28 February 2007

 HYPERLINK "http://www.americanthinker.com/2007/02/numerical_models_integrated_ci.html" http://www.americanthinker.com/2007/02/numerical_models_integrated_ci.html

By Jerome J. Schmitt

Global warming theory is a prediction based on complex mathematical

models developed to explain the dynamics of the atmosphere. These models

must account for a myriad of factors, and the resultant equations are so

complex they cannot be solved explicitly or "analytically" but rather

their solutions must be approximated "numerically" with computers. The

mathematics of global warming should not be compared with the explicit

calculus used, for example, by Edmund Halley to calculate the orbit of

his eponymous comet and predict its return 76 years later.

Although based on scientific "first principles", complex numerical

models inevitably require simplifications, judgment calls, and

correction factors.  These subjective measures may be entirely

acceptable so long as the model matches the available data -- acceptable

because the model is not intended to be internally consistent with all

the laws of physics and chemistry, but rather to serve as an expedient

means to anticipate behavior of the system in the future. However,

problems can arise when R&D funding mechanisms inevitably "reward"

exaggerated and alarming claims for the accuracy and implications of

these models.  

Many other scientific fields besides climatology use similar models,

based on the same or related laws of nature, to explain and predict what

will happen in other complex systems.  Most famously, the US Department

of Energy's nuclear labs use supercomputer simulations to help design

atomic weapons. Most of this work is secret but we know, of course, that

the models are "checked" occasionally with underground test explosions.

The experimental method is an essential tool

A much better analogue to climate science is found in the semiconductor

industry. Integrated circuits and many other building blocks of modern

electronics are manufactured by creating artificial atmospheres or

"climates" within which chemical vapor deposition (CVD) forms

nanometer-scale thin solid films on silicon wafer surfaces. In CVD,

metal vapor precursors entrained in carrier gases are used to deposit

metal films on surfaces in a condensation process not unlike formation

of dew or frost on a lawn.  In such CVD processes, premature formation

of metal particles is unwanted and needs to be controlled and prevented;

such particle formation is akin to precipitation of rain drops in the

atmosphere

The semiconductor process industry uses numerical models to predict the

behavior of gases and vapors in order to deposit these substances on

substrates, and thereby manufacture integrated circuits. I am not a

climatologist or meteorologist but I have studied fluid mechanics and

gasdynamics and have a general understanding of computer models used in

process engineering.  Such models are used to analyze industrial

processes with which I am familiar.  Indeed the mathematics for such

models is generalized.  And industry's experience with numerical process

models sheds light on their strengths and limitations.

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. Chemical Engineering is

at the heart of what Intel and other semiconductor manufacturers

accomplish.  

Let's consider how these process engineering mathematical models are

actually used in industry. Intel and its competitors (as well as their

key suppliers) employ many chemical engineers who are familiar with such

process models, some of whom specialize solely in mathematical modeling.

Often a new technical challenge will emerge in which a process must be

changed (such as for scale-up to accommodate larger silicon wafers) or

adjusted to accommodate a new material composition.  

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.

The fact that these artificial "climates" are closed systems far simpler

than the global climate, have the advantage of the experimental method,

and are subject to precise controls, and yet are frequently wrong,

should lend some humility to those who make grand predictions about the

future of the earth's atmosphere.

So serious are the problems, sometimes, that it is not unheard of for an

experimental reactor to be scrapped entirely in favor of starting from

scratch in designing the process and equipment. Often a design

adjustment predicted to improve performance actually does the opposite.

This does not mean that process models are useless, for they undergird

the engineer's understanding of what is happening in the process and

help him or her make adjustments to fix the problem.  But it means that

they cannot be relied upon by themselves to predict results. These new

adjustments and related information are then used to improve the models

for future use in a step by step process tested time and again against

experimental reality.  

In actuality, the semiconductor industry is well familiar with the

limits of process modeling and would never make a decision to purchase

equipment or adjust their manufacturing processes based on predictions

derived from models alone. They would rightly expect extensive

experimental data to support such a decision in order to assure the

ability to reliably and economically manufacture high quality materials

and devices.  

Climate Models

As with all fluid mechanics models, the flow field of a climate model

(i.e. the entire atmosphere) is divided into three-dimensional grids of

small volume elements designated by latitude, longitude and altitude.

Each volume element of the grid is then characterized with parameters

such as pressure, temperature, wind velocity, etc., and equations that

relate these factors.  Air and energy that leave one volume element

enters the adjacent one. When summed across all volume elements, the

model keeps track of the flows of air and energy in the entire

atmosphere. Many factors must be accounted (see below). Boundary

conditions must be set: in this case, the boundary of the atmosphere is

land or ocean surface on the bottom, and some boundary in space on the

top; these yield rules (e.g. air cannot flow into the surface of the

earth). Then, Initial Conditions must be set: this means that the grid's

equations are "populated" with the known values of the parameters

characterizing the atmosphere such as pressure, temperature, and

humidity profiles measured today.  

Finally, the computer calculation can commence: A unit of time (a

second, minute, day) is assumed to pass and the computer calculates the

next "state" of the model based on the initial conditions, the boundary

conditions and the other equations of the model. This process is

repeated again and again, with the new state being the initial condition

for calculating the subsequent state, until e.g. 100 years has passed. 

Errors can accumulate rapidly.  Let's list some of the factors that must

be included (by no means an exhaustive list):

Solar flux 

Gravity, Pressure 

Temperature 

Density

Humidity

Earth's rotation

Surface temperature

Currents in the Ocean (e.g., Gulf Stream)

Greenhouse gases 

CO2 dissolved in the oceans

Polar ice caps

Infrared radiation

Cosmic rays (ionizing radiation)

Earth's magnetic field

Evaporation

Precipitation

Cloud formation

Reflection from clouds

Reflection from snow

Volcanoes

Soot formation

Trace compounds

And many, many others

Even if mathematics could be developed to accurately model each of these

factors, the combined model would be infinitely complex requiring some

simplifications.  Simplifications in turn amount to judgment calls by

the modeler. Can we ignore the effects of trace compounds?  Well, we

were told that trace amounts of chlorofluoro compounds had profound

effects on the ozone layer, necessitating the banning of their use in

refrigerators and as aerosol spray propellants.  Can we ignore cosmic

rays? Well, they cause ions (electrically charged molecules) which

affect the ozone layer and also catalyze formation of rain-drops and

soot particles.  

As with all models, it is perilous to ignore factors in the absence of

complete experimental data which might have otherwise have significant

effect.

Perhaps most critically, the role of precipitation in climate seems to

be understated in the numerical global climate models. Roy W. Spencer,

principal research scientist at the Global Hydrology and Climate Center

of the National Space Science and Technology Center in Huntsville, AL,

writes that the role of precipitation is not fully accounted for in

global warming models. In my view, that's like an economist admitting

his theory of the money supply doesn't fully account for the role of the

Federal Reserve.

Unless we know how the greenhouse-limiting properties of precipitation

systems change with warming, we don't know how much of our current

warmth is due to mankind, and we can't estimate how much future warming

there will be, either. To solve the global-warming puzzle, we first need

to learn much more about the precipitation-system puzzle. 

What little evidence we now have suggests that precipitation systems act

as a natural thermostat to reduce warming.

Approximating the experimental method

While mankind cannot experiment on the global climate, these models can

be used retroactively to see how well they "model" the past.  The UN's

2001 Climate Change report distorted the historical record by

eliminating the Medieval Warm Period in the famous "Hockey Stick Curve"

which, by many accounts, unreasonably accentuated temperature rise in

the 20th century.  Such distortion of the historical data undercuts the

credibility of the models themselves, since this is the only

"experimental data" available for testing the fidelity of the models to

the actual climate. 

Why on earth would climate scientists "massage the data" to produce

doomsday predictions? The answer requires looking at the rewards

available to these researchers.

Catastrophe and careers

Vannevar Bush's seminal 1944 policy paper unleashed the Federal

government's unprecedented post-war investment in R&D in the hard

sciences and engineering. Science was seen as the way to avoid (or at

least win) another catastrophic war.  

The golden era of federal funding resulted in unprecedented employment

opportunities for hard science Ph.D.s.  Fresh graduates could easily

find tenure track employment at universities expanding their hard

sciences program. The enormous dividends from this investment make up

our modern technological world.  However, the munificence of the federal

funding caused a certain, shall we say, insouciance about resources:

"Why use lead when gold will do?" became an informal motto at Lawrence

Livermore National Lab.

Inevitably, the growth in congressional funding tapered off and in the

late 1980s the competition for R&D sponsorship began to tighten.  Fresh

Ph.D.s often had to look to the private sector for employment (heaven

forefend!).  Grant writers were required to start highlighting the

potential "practical payoffs" of their proposed work.  Since there was

little need for better atomic weapons in the post-cold war era, High

Energy Physics lost its central status in the funding universe.  Many

mathematical physicists became refugees to allied fields (some of them

even became "quants" on Wall Street). But others found employment

elsewhere, including in climate science.

In this competitive environment, one can imagine climate modelers

justifying their work by citing the possibility of global change, the

further study of which requires, of course, "more research". One can

further imagine that in the inchoate communication between university

researcher, funding agency, congressional staffer and congressmen that

"possibility" eventually became "probability" and then "probability"

morphed into "certainty" of global warming, especially if there was

potential for political advantage.  

This has resulted in an inadvertent funding-feedback mechanism that now

resonates in largely unjustified alarm and also seeks to quash

scientific dissidents who indirectly threaten to throttle the funding

spigots.

The practical experience of numerical modeling in allied fields such as

semiconductor process modeling should cause us to question the claimed

accuracy for Global Climate Models. The UN's distortion of historical

climate data should further undermine our faith in climate models

because such models can only be "tested" against accurate historical

data.  

In my view, we should adopt the private sector's practice of placing

extremely limited reliance on numerical models for major investment

decisions in the absence of confirming test data, that is, climate data

which can be easily collected just by waiting.  

Jerome Schmitt is president of NanoEngineering Corporation, and has

worked in the process equipment and instrument engineering industries

for nearly 25 years.

Copyright 2007, American Thinker