hls 
large when viewed as a whole, are quite small compared to changes in monthly 
anomaly patterns, say from one July to the next. Thus, as long as one com= 
pares only a few adjacent years, one may proceed without reference to the long 
term trend. For this reason straight monthly anomalies were computed from the 
temperatures of fige le It has been planned, however, to remove secular 
changes from any correlations with the atmosphere covering the whole period. 
It should be noted that analysis of the seeular trend by months or 
seagons could be utilized to normalize the data of fig 1. These are composed 
from records of varying length and periods, hence not fully comparabis. Nor= 
malization would produce some changes in the configuration of annual and 
menthly anomaly patternso 
Monuthiy Anomalies 
The eventual purpose of the program was to draw monthly anomaly charts 
and trace anomaly centers from month to month noting paths and changes in ine 
tensity. Two difficulties are to be overcome before this can be dones 
1) Due to poor or sparse observations large and fictitious oseiliac 
tions of anomaly can occurs These must be located and eliminatedo 
2) The strength of the anomalies may undergo seasonal variations; they 
may for instance decrease from winter to summer as in the atmosphere, If this 
is the case, the seasonal variation must be taken into account in following 
anomaly centers o 
The second problem was investigated by calculating the variability, or 
mean deviation, of the (uncorrected) monthly temperatures for each month in 
each squareo Fige 6 shows the result on graphs and fig. 7 on chartse Out= 
standing is the lack of important seasonal differences. Certainly ne organized 
decrease takes place from winter to summers some squares actually have largest 
