Tj. Osborn et M. Hulme, DEVELOPMENT OF A RELATIONSHIP BETWEEN STATION AND GRID-BOX RAINDAY FREQUENCIES FOR CLIMATE MODEL EVALUATION, Journal of climate, 10(8), 1997, pp. 1885-1908
The validation of climate model simulations creates substantial demand
s for comprehensive observed climate datasets. These datasets need not
only to be historically and geographically extensive, but need also t
o be describing areally averaged climate, akin to that generated by cl
imate models. This paper addresses one particular difficulty found whe
n attempting to evaluate the daily precipitation characteristics of a
global climate model, namely the problem of aggregating daily precipit
ation characteristics from station to area. Methodologies are develope
d for estimating the standard deviation and rainday frequency of grid-
box mean daily precipitation time series from relatively few individua
l station time series. Temporal statistics of such areal-mean time ser
ies depend on the number of stations used to construct the areal means
and are shown to be biased (standard deviations too high, too few rai
ndays) if insufficient stations are available. It is shown that these
biases can be largely removed by using parameters that describe the sp
atial characteristics of daily precipitation anomalies. These spatial
parameters (the mean interstation correlation between station time ser
ies and the mean interstation probability of coincident dry days) are
calculated from a relatively small number of available station time se
ries for Europe, China, and Zimbabwe. The relationships that use these
parameters are able to successfully reproduce the statistics of grid-
box means from the statistics of individual stations. They are then us
ed to estimate the statistics of grid-box means as if constructed from
an infinite number of stations (for standard deviations) or 15 statio
ns (for rainday frequencies), even if substantially fewer stations are
actually available. These estimated statistics can be used for the ev
aluation of daily precipitation characteristics in climate model simul
ations, and an example is given using a simulation by the Commonwealth
Scientific and Industrial Research Organisation atmosphere general ci
rculation model. Applying the authors' aggregation methodology to obse
rved station data is a more faithful form of model validation than usi
ng unadjusted station time series.