Sengupta, S. K. and J. Boyle,1993: Statistical intercomparison
of global climate models: A common principal component approach. PCMDI
Report 13, Program for Climate Model Diagnosis and Intercomparison, Lawrence
Livermore National Laboratory, 41 pp.
Variables describing atmospheric circulation and other climate parameters
derived from various GCMs and obtained from observations can be represented
on a spatio-temporal grid (lattice) structure. The primary objective of
this paper is to explore existing as well as new statistical methods to
analyze such data structures for the purpose of model diagnostics and intercomparison
from a statistical perspective. Among the several statistical methods considered
here, a new method based on common principal components appears most promising
for the purpose of intercomparison of spatiotemporal data structures arising
in the task of model/model and model/data intercomparison. A strategy for
such an intercomparison is outlined in two steps: first, the commonalty
of spatial structures in two (or more) fields is captured in the common
principal vectors, and second, the corresponding principal components obtained
as time series are then compared on the basis of similarities in their
temporal evolution.