Abstract Many ecological studies require climate data, but readily available datasets are poor surrogates for the conditions that organisms experience in nature. Understanding the climatic conditions experienced by organisms requires modelling microclimate rather than relying on coarse, station‐based climate data. I present microclimf, a mechanistic microclimate model designed for computationally efficient, gridded estimation of microclimate, principally temperature, within and below vegetation canopies. The model is written in C++ with an R front end and requires only readily available spatial datasets as inputs. It incorporates a simplified Lagrangian canopy model, an optional snow model and routines for efficient large‐area processing at user‐defined spatial and temporal resolutions. In addition to temperature, outputs include humidity, wind speed and radiation fluxes. Temperature validation across diverse environments—including boreal and tropical forests—showed strong agreement with in situ temperature measurements (RMSE 0.69°C–2.9°C), demonstrating the model's utility for ecological applications requiring fine‐scale climatic data. The package addresses the need for improved estimation of regional and landscape‐scale predictions of the conditions experienced by organisms, thereby facilitating a more robust understanding and prediction of species responses to climatic changes.
Ilya M. D. Maclean (Fri,) studied this question.