Optimization of the seasonal cycles of simulated CO<sub>2</sub> flux by fitting simulated atmospheric CO<sub>2</sub> to observed vertical profiles
Abstract. An inverse of a combination of atmospheric transport and flux models was used to optimize model parameters of the Carnegie-Ames-Stanford Approach (CASA) terrestrial ecosystem model. The method employed in the present study is based on minimizing an appropriate cost function (i.e. the weighted differences between the simulated and observed seasonal cycles of CO2 concentrations). We tried to reduce impacts that the inaccuracy of a vertical mixing in a transport model has on the simulated amplitudes of seasonal cycles of carbon flux by using airborne observations of CO2 vertical profile aggregated to a partial column. Effect of the vertical mixing on optimized 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 15 locations. We found that the values of simulated growing season net flux (GSNF) and net primary productivity (NPP) are affected by the rate of vertical mixing in a transport model used in the optimization. Optimized GSNF and NPP are higher when optimized with partial column data as compared to the case with near-surface data only due to the weak vertical mixing in the transport model used in this study.