Introduction
The Hype
The Impacts
Interactive Timeline
La Niņa
Prediction Methods
> benefits of predictions
> TOPEX/Poseidon
> TOPEX/Poseidon data
> uses for predictions
> types of models
> obstacles
The Preparation
About the Site
Archives
Search
Learn Anything?
Forum
______________________
Predictive Models
Statistical & Dynamical
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.
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.
Previous Section: Uses for Predictions