Environmental Engineering Reference
In-Depth Information
Atmospheric models designed to simulate
climate change also depend upon numerical
prediction, but they differ in a number of
important ways from the weather forecasting
models. For example, over the short time periods
(4-5 days) covered by the weather forecasting
models, environmental elements such as
vegetation, oceans, ice sheets and glaciers can be
considered as static or unchanging, and therefore
contributing little to atmospheric change. Over
the longer time scales (10-50 years) involved in
in the climate models, however, these elements
would be expected to change, and that change
must be incorporated in the models. This may
be simple if the change remains linear, but
difficulties arise when response to change initiates
feedback in the earth/atmosphere system. Many
of the uncertainties in the results of climate
modelling can be traced to the inability of the
models to deal adequately with climate feedback
mechanisms.
The spatial resolution of climate models also
tends to be coarser than that of weather models.
Modern computer systems are remarkably
sophisticated, but their use is at times constrained
by such factors as operational speed, memory
capacity and cost. For example, the additional
calculations introduced to accommodate the
longer time scale and greater number of
environmental variables incorporated in the
climate models increases the computer memory
and time required. This in turn increases the cost
of running the model. A reduction in the number
of grid points at which the calculations are made
helps to keep these elements at manageable levels,
but it also produces a coarser resolution in the
model. Typical climate models have a resolution
which is three to six times coarser than that of
weather forecasting models. Although this still
allows adequate representation of macro-scale
climate features, it gives only limited results at
the regional level (Cubasch and Cess 1990). In
an attempt to improve regional or meso-scale
resolution, Australian scientists have developed
a method in which a meso-scale model is 'nested'
or 'embedded' within a macro-scale global
climate model. This nested model, driven by the
global model which surrounds it, is programmed
to provide much finer detail at the regional level
than is possible with the standard model alone.
Since the extra information is required for only
a limited area, and the running of the global
model is unaffected, both the additional
computer time needed and the costs remain
manageable. Tests suggest that this is the best
method currently available for achieving
increased spatial resolution, although it requires
further development before it can be used for
climate prediction (Henderson-Sellers 1991).
Several important climatic processes take place
at regional scales, and are therefore missed by
the macro-scale grids of most climate models.
However, since they affect the predictions
produced by the models, they must be included.
This is done through a process of
parameterization, in which statistical
relationships are established between the small
scale processes and the grid scale variables. Since
the latter can be calculated by the models, the
values of the small scale processes can then be
estimated (Hengeveld 1991). For example,
existing models cannot deal with individual
clouds, but average cloudiness in a particular
grid-box can be predicted using temperature and
humidity values calculated by the model
(Schneider 1987). Other variables that require
parameterization include radiation, evaporation
and land surface processes.
Climate models take various forms, and
involve various levels of complexity, depending
upon the application for which they are
designed. A simple model, for example, may
provide only one value, such as the average
temperature of the earth. Increasing levels of
sophistication produce one- and two-
dimensional models and the complex three-
dimensional general circulation models, which
depend upon the full use of numerical prediction
to produce results.
One dimensional (1-D) models provide
information on change along a vertical line
stretching from the earth's surface up into the
atmosphere. The main inputs into these models
are incoming solar radiation and returning
