Fine-resolution (25 m) topoclimatic grids of near-surface (5 cm) extreme temperatures and humidities across various habitats in a large (200 × 300 km) and diverse region
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Abstract
The development of fine-resolution climate grids is an important priority in explaining species’ distributions at the regional scale and predicting how species may respond to variable and changing climates. Recent studies have demonstrated advantages of producing these grids using large networks of inexpensive climate loggers, as the resulting grids can capture local climatic variations over a range of environments. In this study we extend these methods to develop innovative fine-resolution (25 m) climate grids for a large region (∼200 × 300 km) of New South Wales, Australia. The key aspects of these grids is that they: (1) are based on near-surface (5 cm) observations to better reflect where many species live; (2) cover a wide variety of habitats including forests, woodlands and grasslands so that they are broadly applicable; (3) include both temperature and humidity, the latter of which has often been neglected in similar studies; (4) are developed using a variety of climate-forcing factors rather than relying only on elevation and geographic location; and (5) they focus on the extreme temperatures and humidities regardless of when these occur. Analyses showed that elevation was the dominant factor explaining mild temperatures (low maximums, high minimums), but cold air drainage, distance from coast, canopy cover and topographic exposure had more effect on the extreme maximum and minimum temperatures. Humidities were predominately determined by distance to coast, elevation, canopy cover and topography; however, the relationships were nonlinear and varied in both shape and effect size between dry and moist extremes. Extreme climates occur under specific weather conditions, and our results highlight how averaging climates over seasons or periods of consecutive days will include different weather patterns and obscure important trends. Regional-scale climate grids can potentially be further improved through a better understanding of how the effects of different climate-forcing factors vary under different weather conditions.
