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.

气候对生态模式和过程的影响很大，范围从地方到全球。对气候的生态响应的研究通常依赖于气象站的数据，气象站的温度和湿度可能与生物所居住的微环境的温度和湿度有很大差异。为了解决这个问题，我们提供了一个模型，该模型利用第一原理物理学来预测地球上任何地面位置的树冠之上，内部和之下的微气候，可作为R软件包免费提供。该模型可以两种模式之一运行。首先，将树冠内部和下方的热和蒸汽交换建模为瞬态过程，因此考虑了精细的时间分辨率变化。在第二种情况下，假设处于稳态状态，使每小时间隔或更长时间的条件能够以更高的计算效率进行估算。我们从全球多个位置进行了经验性的冠层下热测量，以验证模型的两种模式，从而得出了每小时预测的瞬态和稳态模式的平均绝对误差分别为2.77°C和2.79°C。除了小气候模型外，还提供了一些功能来协助数据同化，以及提供用于捕获各种栖息地的不同参数设置，即使在对研究位置知之甚少的情况下也可以灵活应用。该模型的模块化设计采用了生态研究人员熟悉的编程语言，可以轻松访问特定地点的气候强迫模型，从而更紧密地统一微气象学和生态学领域。