Empirical modeling of spatial and temporal variation in warm season nocturnal air temperatures in two North Idaho mountain ranges, USA

Accurate, fine spatial resolution predictions of surface air temperatures are critical for understanding many hydrologic and ecological processes. This study examines the spatial and temporal variability in nocturnal air temperatures across a mountainous region of Northern Idaho. Principal components analysis (PCA) was applied to a network of 70 Hobo temperature loggers systematically distributed across 2 mountain ranges. Four interpretable modes of variability were observed in average nighttime temperatures among Hobo sites: (1) regional/synoptic; (2) topoclimatic; (3) land surface feedback; (4) canopy cover and vegetation. PC time series captured temporal variability in nighttime temperatures and showed strong relationships with regional air temperatures, sky conditions and atmospheric pressure. PC2 captured the topographic variation among temperatures. A cold air drainage index was created by predicting PC2 loadings to elevation, slope position and dissection indices. Nightly temperature maps were produced by applying PC time series back to the PC2 loading surface, revealing complex temporal and spatial variation in nighttime temperatures. Further development of both physically and empirically based daily temperature models that account for synoptic atmospheric controls on fine-scale temperature variability in mountain ecosystems are needed to guide future monitoring efforts aimed at assessing the impact of climate change.

Research highlights▶ Spatial and temporal variability in air temperatures are independently identified using PCA. ▶ Temporal variability in nocturnal air temperatures is modeled using physically based variables. ▶ Spatial variability is predicted to 30 m digital elevation model indices. ▶ The resulting PC2 loading surface is a physiographic index describing relative vulnerability of landscape positions to cold air drainage at night. ▶ Temporal and spatial models are combined using a PCA reconstruction technique to visualize spatial and temporal variability in nighttime temperatures. This method shows some potential for modeling spatial and temporal variation in cold air drainage in regions of complex topography.