Optimization of the seasonal cycles of simulated CO₂ flux by fitting simulated atmospheric CO₂ to observed vertical profiles

Abstract. An inverse of a combination of atmospheric transport and flux models was used to optimize the Carnegie-Ames-Stanford Approach (CASA) terrestrial ecosystem model properties such as light use efficiency and temperature dependence of the heterotrophic respiration separately for each vegetation type. The method employed in the present study is based on minimizing the differences between the simulated and observed seasonal cycles of CO₂ concentrations. In order to compensate for possible vertical mixing biases in a transport model we use airborne observations of CO₂ vertical profile aggregated to a partial column instead of surface observations used predominantly in other parameter optimization studies. Effect of the vertical mixing on optimized net ecosystem production (NEP) was evaluated by carrying out 2 sets of inverse calculations: one with partial-column concentration data from 15 locations and another with near-surface CO₂ concentration data from the same locations. We confirmed that the simulated growing season net flux (GSNF) and net primary productivity (NPP) are about 14% higher for northern extra-tropical land when optimized with partial column data as compared to the case with near-surface data.