A process-based model of forest ecosystems driven by meteorology

Although anthropogenic climate change is generally acknowledged as a reality, tree level models that respond to the boundary conditions expected to change as a result of global warming are largely non-existent. Consequently, a process-based model of individual tree growth driven by meteorology was developed. The model, Forest v5.1 predicts the growth of deciduous and coniferous species for the Great Lakes Region of North America. The model uses a daily time step and was written with two overriding design tenets in mind: (i) model drivers must mimic controls on plant growth as they exist in nature and (ii) model initialization must be achievable through the use of typical forest inventory field plot data. Forest v5.1 predicts the carbon, nutrient and water cycle as these influence tree growth and with particular emphasis on light interception and assimilation. Model outputs are both in dimension as well as biomass. Sensitivity analysis shows the importance of parameters that characterize maximum photosynthetic potential and scaling factors. A comparison of observed and predicted growth trajectories for 25 years indicates tree diameter development exhibits useful levels of precision (-0.12 to 0.11 cm year^-1) relative to an empirical model. The model, using HadCM2-generated weather, projects that water use efficiency will increase as a result of climate change. Net basal area increment increases on average by 0.17 m² ha^-1 year^-1 relative to a projection of current climate.