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describe all aspects of man
s interactions with environmental bodies and with their
physical, biological and chemical systems. One such way has its origin in the
studies of the Computer Centre of the Russian Academy of Sciences in Moscow
(Krapivin et al. 1982). A global model of this type is formed on basis of the detailed
description of the climate system with the consideration of a small set of biospheric
components. This strategy of global modeling is adhered to in the Potsdam Institute
for Climate Impact Research studies (Boysen 2000) where the Moscow Global
Model prototypes are developed. More than 30 climate models are being developed
in different countries as attempts to bring forth new trends in the science of global
change (Demirchian, Kondratyev 2004; Claussen et al. 1999). Unfortunately glo-
bal- and regional-scale studies on the processes and impacts of global change using
this approach have not produced results that are enough satisfactory. That is why
another approach to the global modeling problem has been developed by many
authors (Krapivin 1993; Sellers et al. 1996; Kondratyev et al. 2002; Degermendzhi
et al. 2009). This approach is known as evolutionary modeling.
The traditional approaches to building a global model encounter some dif
'
culties
of the algorithmic description with respect to many socio-economic, ecological and
climatic processes so that one has to deal with information uncertainty. These
approaches to global modeling simply ignore such uncertainty and consequently the
structure of the resultant models does not adequately re
ect the real processes.
Evolutionary modeling makes it possible to remove this drawback by the synthesis
of a combined model whose structure is subject to adaptation against the back-
ground of the history of a system of the biosphere and climate components. The
implementation of such a model can also be combined in various classes of models
using conventional software and hardware and special-purpose processors of the
evolutionary type. The form of such combination is diverse, depending on the
spatial-temporal completeness of the databases.
The experience in global modeling abounds in examples of insolvable problems
encountered when looking for ways to describe the scienti
fl
c and technological
advances and human activity in its diverse manifestations. No lesser dif
culties
arise in modeling climate described by a superimposition of processes with different
temporal variability rates. As to completeness of description in the global model, it
is impossible to clearly delineate the bounds of information availability and the
extent of the required spatial and structural detail. Therefore, without going into
natural-philosophical analysis of global problems, and skirting the issue of the
ultimate solution to global modeling, we will con
ne ourselves to the discussion of
only one of the possible approaches. This approach will demonstrate in which way
evolutionary modeling implemented on special processors can help overcome
computing, algorithmic and other dif
culties of global modeling. All of this does
imply that a search for effective models of the traditional type can apt be considered
perspective. At present, the building of global biogeocenotic models is not seen as
dif
cult. Many such models have been created, and the gathering of information to
support them is under way. The history of the interaction of the biosphere with the
climate system and human society is not known suf
ciently, which is one of the
hurdles, e.g.
in the description of climatic cycles. To build a global model
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