The role of statistical analysis in the process of establishing and
utilizing climate and other environmental
models is discussed. The role of empirical evidence is made explicit
by having not only dynamical equations but observational equations as well.
In ``quasi-realistic models'', statistical thinking is encoded in the
parameterizations, and is required for extracting experimental evidence
and for validation. Data assimilation techniques are used to systematically
combine observational evidence and quasi-realistic models. While quasi-realistic
models serve as complex substitute reality, is dynamical knowledge represented
trough simplified models. These ``cognitive'' idealized models have to
be fitted to observational data when adapted to real situations.
Literature:
von Storch, H., and F.W. Zwiers, 1999: Statistical Analysis in Climate
Research, Cambridge University Press, ISBN 0 521 45071 3, 494 pp.
von Storch, H., and A. Navarra (Eds.), 1999: Analysis of Climate Variability:
Applications of Statistical
Techniques, Springer Verlag, 2nd updated extended edition (ISBN
3-540-66315-0), 342 pp,
von Storch, H., 2000: Statistics - an indispensable tool in dynamical
modelling. In: H. von Storch and G. Flöser (Eds): Models in Environmental
Research. Springer Verlag (in press)