Before we plunge into creating and experimenting with models of
earth systems, it is extremely important to have a clear idea of why
we create models, what our goals are, and how we should view the
results of the models. Computer modeling may seem simple in that the
machine does all the difficult math for you and it spits out
impressive numerical results. But do not be seduced by such thinking.
One reason for rejecting this idea has to do with the fact that you
must do a great deal of thinking when you construct the model (more
on this later). Another reason is that it is not so easy to
understand how to interpret the results, or more to the point, it is
hard to decide what to conclude from the results of these computer
models.
The purpose of a model is not to replicate the real world - it is
clearly impossible to put the complexity of the real world into a
computer. Instead, the goal is usually to understand something about
the behavior of systems and also to understand the affects of changes
on the system - its response to changes. We try to get a sense for
which parts of the system control its behavior. We create and use
these models because the their real-life versions are so complex that
we cannot generally understand them without some kind of controlled
experimentation. At their best, models capture the essence of real
world systems, they exhibit similar, though not identical behaviors.
In some cases, a model may provide accurate predictions, but that is
generally not the goal.
I think that there are two polarized impulses regarding these
computer models. Some people have a tendency to think that the
computer does not make mistakes - its results are infallible and
should be accepted and believed, case closed. This is one extreme.
The other extreme is summed up as "garbage in, garbage out", but more
generally, this amounts to a universal rejection of all computer
modeling results on the grounds that there is no way that a computer
model can adequately represent all of the complexity of the real
world. I emphasize that these are extreme views and in reality, most
people wander back and forth between these two poles as they grapple
with the task of interpreting the results of computer models. In a
sense, these two poles also represent the range of response that some
people have to science - some maintain that science yields immutable
Truths and is infallible, while others summarily reject scientific
results because of uncertainty or disagreement among scientists. How
we deal with the results of computer models has to consider what the
model is intended to do.
For instance, let's imagine a model of plate tectonics in the very
early part of Earth's history. We obviously don't know too much about
the details of the dynamics at this time, but we might make some
assumptions and then investigate the implications of those
assumptions by experimenting with the computer model. This kind of
modeling has been done (see Appendix B) and one result is that normal
subduction should not have occurred until about 2 billion years ago
if the assumptions used in the model are more or less correct. Pay
attention to the wording in that last sentence. The modeling does not
prove anything, but it does suggest something interesting and
motivates some field work in ancient terranes to try to understand if
in fact the assumptions used in the model are wrong or alternatively,
if there was some other kind of subduction in the very early history
of the earth. So, this is a kind of model where the real physical
system is very poorly known and one strategy for advancing our
understanding is to investigate assumptions about how the system
operated.
This kind of model is probably the most common in the study of the
Earth because we still do not understand all of the processes and
connections well enough to express them in the form of an equation.
But these models are still very useful in generating questions and
exploring the implications of assumptions. The numerical output of
many of these models is less important that the general trends and
relative changes.
Another kind of model represents the opposite end of the spectrum
of models. We may have a model of a very simple system that involves
processes that are very well known and in this case, the computer
model is capable of generating numerical results that are very good
predictions of how the system behaves. One simple example might be
radioactive decay of some isotope. If the decay constant has been
determined from repeatable laboratory measurements, we can create a
simple model that will very accurately predict the history of decay
of some initial amount of that isotope.
A still different class of models is represented by one of the
global carbon cycle that we will use later in this book. The global
carbon cycle is clearly a huge, complex system, and is poorly
understood, although a great deal has been learned in just the past
few years. Many of the processes involved in this cycle are difficult
to express in the form of an equation that captures the behavior of a
process occurring all over the globe. But, many of the processes can
be described in a way that includes rate constants just like the ones
we used in the bathtub model. We also know something about how the
real global carbon cycle has behaved in the last few decades and it
is possible to adjust the rate constants so that the model can
reproduce the "known" behavior of the global carbon cycle. This kind
of model thus generates numerical results that are somewhere between
the other two types of models - we may not have a great deal of
confidence in the actual numbers generated by the model, but we have
reason to believe that those numbers may not be too far off. Again,
the reason for this modest confidence is that the model is capable of
reproducing the observed behavior of the system.
We have explored some complicated issues here, so let me summarize:
1) Models are made for a variety of reasons; the intent of the model must be kept in mind when thinking about the results.
2) Most of the models we will investigate in this book incorporate numerous simplifications and assumptions and we are really investigating the implications of these assumptions, and looking at relative changes and trends in the behavior of the model. We are more interested in the qualitative results rather than the actual quantitative output of the model.
3) If a model reproduces some of the behavior of the real-life system it is patterned after, we have some cause to cautiously accept the results.
4) A few models involve well known, easy-to-measure processes that can be expressed in simple equations; the results of these models represent precise predictions of the system's behavior that have a high probability of being correct.
5) In general, the main goal of modeling is to improve our
understanding of complex dynamics and to generate more questions or
to help us understand what the key questions are.
RETURN TO MAIN PAGE