Microclimc: A mechanistic model of above, below and within-canopy microclimate

• Published in: Ecological modelling 2021-07, Vol.451, p.109567, Article 109567
• Main Authors: Maclean, Ilya M.D., Klinges, David H.
• Format: Article
• Language: English
• Subjects: Biophysical ecology; Climate; Evapotranspiration; Mechanistic model; R package; Temperature
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2021.109567

•Climate experienced by organisms differs from data used in most ecological studies, which typically use data derived from weather stations.•We present an ecologically-relevant model for predicting the climate experienced by organisms.•The model uses first-principles physics and can thus be applied in any terrestrial environment.•The model was verified and validated with data from four widely geographically distributed forest sites.•The model provides reasonably accurate estimates of microclimate. Climate strongly influences ecological patterns and processes at scales ranging from local to global. Studies of ecological responses to climate usually rely on data derived from weather stations, where temperature and humidity may differ substantially from that in the microenvironments in which organisms reside. To help remedy this, we present a model that leverages first principles physics to predict microclimate above, within, and below the canopy in any terrestrial location on earth, made freely available as an R software package. The model can be run in one of two modes. In the first, heat and vapour exchange within and below canopy are modelled as transient processes, thus accounting for fine temporal-resolution changes. In the second, steady-state conditions are assumed, enabling conditions at hourly intervals or longer to be estimated with greater computational efficiency. We validated both modes of the model with empirical below-canopy thermal measurements from several locations globally, resulting in hourly predictions with mean absolute error of 2.77 °C and 2.79 °C for the transient and steady-state modes respectively. Alongside the microclimate model, several functions are provided to assist data assimilation, as well as different parameterizations to capture a variety of habitats, allowing flexible application even when little is known about the study location. The model's modular design in a programming language familiar to ecological researchers provides easy access to the modelling of site-specific climate forcing, in an attempt to more closely unify the fields of micrometeorology and ecology.