Seasonal changes in interrelationships between climatic variables

The correlation patterns between air temperature and insolation were investigated for four sites ranging from northern to southern Australia and one site in Venezuela. This investigation was considered as a prior important step for the stochastic generation of these climatic variables as input data for crop models. The use of sunshine hours when insolation data were not available to estimate these correlation patterns was investigated. Data from one location, having simultaneously measured records of both solar radiation and sunshine hours, were used in this study. The correlation coefficients between fractional cloudiness estimated from solar radiation or sunshine hours and the maximum temperature on a given day (TX) and between fractional cloudiness and the difference between the maximum temperature on the same day and the minimum temperature on the next day (TD), were calculated for each 14-day period in the year and for each location. Most of these correlation coefficients displayed seasonal variations specific to each location. A methodology is described whereby these correlations can be preserved whilst synthetically generating weather data for input to models of plant growth. It has been suggested that the preservation of these intercorrelations is important for realism in predictions of crop production.