Simulating climatic responses of vegetation production with ecological optimality theories

Ecosystem gross primary productivity (GPP) is a fundamental ecosystem function. Accurately predicting variations in GPP in response to environmental changes is a key task for the modelling community. Although sharing the same photosynthesis module, land models show divergent predictions of GPP in response to environmental changes. One reason causing the uncertainties is that key parameters of the photosynthesis module, e.g., stomatal conductance, maximum rate of Rubisco carboxylation (Vcmax), are fixed default values or estimated with empirical functions. To solve this issue, here we integrate three well-accepted optimality theories for plant photosynthesis, i.e., (1) plants maximize carbon gain for a unit of water loss through optimizing stomatal conductance, (2) plants minimize the summed cost of maintaining transpiration and carboxylation through optimizing partial pressure of CO₂ in the intercellular space (Ci), and (3) vegetation allocates resources in a coordinated manner to operate close to the intersection of the light-limited and Rubisco-limited carbon assimilation rates. By bridging these theories, the key parameters of photosynthesis are estimated with analytical equations, without additional empirical constraining functions. Thereafter, an optimality-based model (Opt model) is developed to estimate GPP in this study. The Opt model is driven by leaf area index and easily accessible climate factors, with minimized number of parameters to be calibrated. Comparing with flux tower observations, widely-used GPP models and satellite products, the Opt model shows satisfactory performance in predicting GPP at both site level and global scale. Notably, it captures the sensitivity of GPP in response to climate variabilities and elevated atmospheric CO₂, showing a great potential to be incorporated into land models for predicting ecosystem functioning and its feedback to climate under global changes.