The effects on a dataset of smoothing by successive correction have been in
vestigated. The resulting spatial resolution is estimated using a distribut
ion of ship reports from a sample month. Although the smoothing uses the sa
me characteristic radii over the whole globe, the resulting resolution is s
patially variable and, in data-sparse regions, will show large month-to-mon
th variability with changes in the distribution of the ship tracks. The cli
matological dataset, which is gridded at 1 degrees, is shown to have a typi
cal resolution of 3 degrees. In some regions the resolution is much coarser
.
Using sea surface temperature as an example, it is shown that the successiv
e correction procedure as used, for example, in a recent climatological dat
aset, is not successful in removing all of the noise in data-sparse regions
. Additionally, the well-defined intermonthly variability in the main shipp
ing lanes, where there are many observations, is degraded by the influence
of poorer-quality data in the surrounding regions. This typically increases
the intermonthly variability estimates in the shipping lanes by a factor o
f 2. Further, the reduction of intermonthly variability, by up to a factor
of 6, in highly variable regions such as the Gulf Stream, is greater than c
an be accounted for by noise in the individual ship reports. This reduction
is due to the removal of small-scale variability by the smoothing process.
Removal of coherent and persistent small-scale variability has an effect o
n the temporal and spatial characteristics of the data. It is suggested tha
t smoothing by successive correction, although commonly used, is poorly sui
ted to such spatially inhomogenous data as those from the merchant ships.
However, the effect of successive correction on variability analysis using
empirical orthogonal functions (EOFs) is shown to be small for the most sig
nificant modes of variability identified in the Gulf Stream region. This is
because the EOF analysis picks out the large-scale variability in the high
est-order modes. However, too large a fraction of the total variance explai
ned is ascribed to the large-scale modes of variability. Variability with s
mall spatial scales is more likely to be significant if raw data are used i
n the EOF analysis. Little significance should be given to EOF modes with s
patial scales similar to the size of gaps between shipping lanes; this vari
es from region to region.