|
 
 |
 Types
of Models
Scientists build
theoretical models for several reasons. One of these is to try to understand the
underlying cause of a phenomenon. Biologists, for instance, build models of how animals
and plants grow to better understand the growth processes which are taking place. The
second reason to build models is to predict future events. For instance, if a model can
simulate ocean temperature and height in a previous El Niņo episode, it may also be able
to simulate these same parameters in future El Niņos.
El Niņo models take ocean
parameters [wind speed, ocean temperature, atmospheric pressure, rainfall, sea height
data, etc.] for the past few months. The models predict what will happen in the future.
The model data will hopefully answer the following questions. Will an El Niņo develop?
Will it be large or small? Will there be a following La Nina episode, and how large will
that be?
There are two main
types of forecasts. First there are statistical forecasts, based on historical records.
Second are dynamical forecasts, based of forward integration of numerical models of the
coupled ocean-atmosphere system. Each has its strengths and weaknesses, and the results
from these can be quite different.
| Statistical Models |
Statistical forecasts correlate observed weather
conditions with occurrences of El Niņo. Typically, sea surface interactions (SST) in the
key regions of the equatorial Pacific are used to define "El Niņo
periods". Alternatively an index known as the "Southern Oscillation
Index" (SOI) is used, based on the surface pressure difference between Tahiti and
Darwin. The advantage of the SOI over SST is that the SOI records go back at least a
century, while we have only a few decades of SST observations in mid-ocean. Then the
correlation of one of these indices with, for example, rainfall in California, is the
basis for a forecast of the likelihood of reoccurrence of heavy rains in that region
during an El Niņo winter. These are probably the most common type of forecast that is
seen on the media. In some regions, such as the US Gulf Coast, these correlations are
quite robust and the statistical forecast is fairly reliable. In others the correlations
are weak and/or marginal.
The strength of statistical
forecasts is that they are based on events that actually did occur. However, they can fail
because El Niņo is not an exact, repeating phenomenon. We observe that different events
evolve in different patterns, can occur at different times of the year, and so on. In
addition, there are many climate oscillations occurring simultaneously, and the present
weather at any location is the sum of these oscillations and the interactions between
them. Therefore, it is not straightforward to isolate the specific effects of El
Niņo by averaging over previous events. All these things result in blurring the
statistics and reducing the confidence in such a forecast.
Another problem with
statistical forecasts is that we do not have good, long-term records of many of the
important quantities of interest. Once you go back further than the mid-1950s, the ocean
records are sparse and ambiguous, making it hard to determine which are strong El Niņo
years and which are weak ones. However, if attention is limited to the period of
"good" data, then there are really only a handful of events, and the statistics
become quite unreliable. Many of the differences among statistical forecasts
reported in the media are due to the choice of different averaging periods. |
The bottom picture is an acutal image
from a satellite that shows how much the computer modeled image [top] underestimated this
El Niņo. Shades of reds show the degree of sea level above average.
| Dynamical Models |
Dynamical forecasts are based on hydrodynamical
equations numerically integrated forward from present observed conditions. These computer
models range from relatively simple representations to complex models such as are used in
weather forecasting. During the 1980s it appeared as if El Niņo could be explained by
planetary waves bouncing around the Pacific, and this could be depicted easily in a
computer model. However, this theory failed to predict the events of 1990s, proving to us
that we must incorporate the full complexity of the ocean-atmospheric system in the
simulation. This is a task of utmost difficulty since it compounds the problems of
ordinary weather forecasting by the addition of numerous interactions between the ocean
and the atmosphere.
A major difficulty in this
type of forecasting is that we cannot simulate every molecule of air and water. Thus, at
many times, these simulations turn out be crude, blunt grid mesh representations of the
earth. Furthermore, due to computer speed and storage, these grids have spacing of
typically tens to hundreds of kilometers. Take, for example, the representation of clouds
in such models. The grid is far too coarse to resolve individual clouds, and therefore,
many clouds are combined to act as a whole. To correctly predict the amount of water
and heat released by a could, we have to know the actual speed and humidity of rising air.
Thus, the amount of precipitation produced by a group of individual clouds is not the same
as that which would be produced by a cloud that had the average properties of the whole
region. Much current research is devoted to figuring out how to represent complex
interactions like these in a way that computers can work with.
Nevertheless, as techonlogically-inclined
students, it is our belief that as computer become faster and as our understanding of the
physical processes becomes better, we will rely more and more on the dynamical forecasts.
They have the tremendous advantage of working forward from the actual present
observed conditions, and so avoid the problem of statistically averaging over a number of
events that differ in important details. In addition, for low-frequency events like El
Niņo, it will take decades or centuries to accumulate sufficient realizations to really
improve statistical confidence. This maybe so because this field offers the opportunity
for scientists to make significant progresses by advancing the understanding of physical
processes within the coupled system, as we have already seen over the past several years. |
|
