| Summary: | A procedure for model-assisted climate impact assessment is
developed. The approach combines data from observations and
atmospheric general circulation models (GCNs), and provides the basis
for a potentially valuable means of using information derived from
GCMs for climate impact assessments on local scales.
The first component of this procedure is an extension of the
'climate inverse' method of Kim al. (1984). Daily mesoscale
temperature and precipitation values are stochastically specifed on
the basis of observational data representing the average over an area
corresponding to a GCN grid element. Synthetic local data sets
generated in this manner resemble the corresponding observations with
respect to various spatial and temporal statistical measures.
A method for extrapolation to grid-scale 'scenarios' of a changed
climate on the basis of control and experimental integrations of a
GCM, in conjunction with observational data, is also presented. The
statistical characteristics of daily time series from each of these
data sources are portrayed in terms of the parameters of a
multivariate time-domain stochastic model. Significant differences
between the model data sets are applied to the corresponding
parameters derived from the observations, and synthetic data Bets
representing the inferred changed climate are generated using
Monte-Carlo simulations.
The use of the procedure is illustrated in a case study. The
potential climatic impacts of a doubling of atmospheric carbon dioxide
concentrations on three important North American grain cropping
regions is investigated using two 'physiological' crop models.
Although the specific results must be interpreted with caution, they
are moderately optimistic and demonstrate possible means by which
agricultural production may adapt to climatic changes. === Graduation date: 1987
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