A guide and tools for selecting and accessing microclimate data for mechanistic niche modeling

Abstract

Abstract Most ecological analyses and forecasts use weather station data or coarse interpolated, gridded air temperature data. Yet, these products often poorly capture the microclimates experienced by organisms that respond to fine‐scale spatial and temporal environmental variation near the surface. Sources of historic and projected future data with finer spatial and temporal resolution are proliferating. We qualitatively and quantitatively review and evaluate the available data on three core issues central to microclimate modeling: the quality of the input environmental data, the ability of algorithms to capture microclimatic processes given environmental forcing data, and how best to access microclimatic data. We show how differences between observed environmental conditions and those estimated using environmental forcing data, microclimate algorithms, and precomputed microclimate datasets can be substantial depending on the variable, location, and season. The choice of environmental dataset to parameterize biophysical models has ramifications for biological estimates, such as the duration of potential activity and incidence of thermal stress. New data sources offering high temporal and spatial resolution correspond well to observational data and have the potential to revolutionize understanding of the ecological implications of microclimate variability. We provide resources to help users select and access appropriate environmental data for biological applications, including users' guides and interactive visualization, to better infer how organisms experience climate variability and change.

Keywords

• biophysical
• climate change
• data comparison
• energy budget
• forecast
• hindcast