Search

Fundamental knowledge on the determinants of air temperatures across spatial and temporal scales is essential in climate change mitigation and adaptation. Spatial-based statistical modelling provides an efficient approach for the analysis and prediction of air temperatures in human-modified environments at high spatial accuracy. The aim of the study was firstly, to analyse the environmental factors affecting extreme air temperature conditions in a coastal high-latitude city and secondly, to explore the applicability of generalized additive model (GAM) in the study of urban–rural temperatures. We utilized air temperature data from 50 permanent temperature logger stations and extensive geospatial environmental data on different scales from Turku, SW Finland. We selected five temperature situations (cases) and altogether 12 urban and natural explanatory variables for the analyses. The results displayed that (i) water bodies and topographical conditions were often more important than urban variables in controlling the spatial variability of extreme air temperatures, (ii) case specificity of the explanatory variables and their scales should be considered in the analyses and (iii) GAM was highly suitable in quantifying and visualizing the relations between urban–rural temperatures and environmental determinants at local scales. The results promote the use of GAMs in spatial-based statistical modelling of air temperature in future. [ABSTRACT FROM AUTHOR]