SPATIAL MODELLING OF NEW ZEALAND TEMPERATURE NORMALS

Abstract

Spatial modelling and prediction of normals of monthly temperatures in relation to geographical factors is examined for a data set of 100 New Zealand climate stations, most of which have 30-year records. Results are given for maximum and minimum temperature and for mean temperature and diurnal range. Latitude and altitude are well established as predictors of temperature. The application of a partial thin-plate smoothing spline model shows that there is no advantage in using this model to represent the temperature response as a surface in the latitude and longitude field. using additive models to examine non-linear responses, we find that the normals are linear with altitude and mostly exponential with distance-to-sea. Based on these findings, multiple regression analysis and the Akaike Information Criterion are used to find optimal prediction models. New predictors are identified as: distance-to-sea in non-linear exponential form to capture the oceanic influences in coastal regions; a regional index that divides the country into two parts based on exposure to, or sheltering from, the predominant west to south-westerly moist air flow; and a forest index that models the lower temperatures experienced at forest stations. The resulting regression models for the monthly mean temperature normals explain 86–95 per cent of the variance and give root mean square residuals within the range of 0ċ 43°C to 0ċ 62°C. We find that the use of the annual mean temperature normal as a predictor results in substantially improved models, which implies that there are non-random factors affecting site temperature regimes that remain unmodelled by the existing predictors. The magnitude, sign and seasonal variation of the coefficients of the regression models provide insights into spatial temperature variability and its associated climate processes.