A general model for the seasonal to decadal dynamics of leaf area

Leaf phenology, represented at ecosystem scale by the seasonal dynamics of leaf area index (LAI), is a key control on the exchanges of CO ₂ , energy and water between the land and atmosphere. Robust simulation of leaf phenology is thus important for both dynamic global vegetation models (DGVMs) and land-surface representations in climate and Earth System models. There is no general agreement on how leaf phenology should be modelled. However, a recent theoretical advance posits a universal relationship between the time course of “steady-state” gross primary production (GPP) and LAI – that is, the mutually consistent LAI and GPP that would pertain if weather conditions were held constant. This theory embodies the concept that leaves should be displayed when their presence is most beneficial to plants, combined with the reciprocal relationship of LAI and GPP via (a) the Beer’s law dependence of GPP on LAI, and (b) the requirement for GPP to support the allocation of carbon to leaves. Here we develop a global prognostic leaf phenology model, combining this theoretical approach with a parameter-sparse terrestrial GPP model (the P model) that achieves good fit to GPP derived from flux towers in all biomes, and a scheme based on the P model that predicts seasonal maximum LAI as the lesser of an energy-limited rate (maximizing GPP) and a water-limited rate (maximizing the use of available precipitation). The exponential moving average method is used to represent the time lag between leaf allocation and modelled steady-state LAI. The model captures satellite-derived LAI dynamics across biomes at both site and global levels. Since this model outperforms the 15 DGVMs used in the TRENDY project, it could provide a basis for improved representation of leaf-area dynamics in vegetation and climate models.