Microclimf: fast modelling of microclimate across real landscapes in R

Ilya MacLean

1. 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.
2. I present microclimf, a mechanistic microclimate model designed for computationally efficient, gridded estimation of microclimate, 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. Outputs include temperature, humidity, wind speed and radiation fluxes.
3. Validation across diverse environments—including boreal and tropical forests—showed strong agreement with in-situ temperature measurements (RMSE 0.69–2.9 °C), demonstrating the model’s utility for ecological applications requiring fine-scale climatic data.
4. The package addresses the need for improved estimation of regional and landscape--scale predictions of the conditions experienced by organisms, thereby facilitating more robust understanding and prediction of species responses to climatic changes.