Optimization of a biochemical model with eddy covariance measurements in black spruce forests of Alaska for estimating CO2 fertilization effects

Abstract

Abstract Understanding how high-latitude terrestrial productivity and evapotranspiration change in association with rising atmospheric CO2 concentration ([CO2]), also known as ‘CO2 fertilization’, is important for predicting future climate change. To quantify the magnitude of this fertilization effect, we have developed a big-leaf model that couples photosynthesis and stomatal conductance processes. This model was inverted by inputting eddy covariance CO2 and H2O fluxes from four black spruce forests in Alaska to infer spatially representative ecophysiological parameters using a global optimization technique. Inferred seasonal variations in a maximum carboxylation rate at 25 °C per unit leaf area and stomatal conductance suggest greater photosynthetic capacity per unit leaf area during the mid-growing season, compared to spring and autumn. The interannual variability of parameters suggest that warm summers stimulate photosynthetic capacity and dry summers force stomatal regulation. Based on the model with optimized parameters, small but clear increases in gross primary productivity (GPP) and decreases in latent heat flux (LE) were estimated to be associated with rising [CO2] from 2002 to 2014 (p