CO2 storage in a 30-min period in a tall forest canopy often makes significant contributions to net ecosystem exchange (NEE) in the early morning and at night. When CO2 storage is properly measured and taken into account, underestimations of NEE on calm nights can be greatly reduced. Using CO2 data from a 12-level profile at the Missouri Ozark flux site (an oak-hickory forest in central Missouri, USA), we demonstrate that the lower canopy layer (below the thermal inversion) is a disproportionately large contributor to the total CO2 storage. This is because time derivative of CO2 density (Δc/Δt) generally shows increasing magnitude of mean and standard deviation with decreasing heights at night and from sunrise to 1000 h in both growing and dormant seasons. Effects of resolution and configuration in a profiling system on the accuracy of CO2 storage estimation are evaluated by comparing subset profiles to the 12-level benchmark profile. It is demonstrated that the effectiveness of a profiling system in estimating CO2 storage is not only determined by its number of sampling levels but, more importantly, by its vertical configuration. To optimize a profile, one needs to balance the influence of two factors, Δc/Δt and layer thickness, among all vertical sections within a forest. As a key contributor to the total CO2 storage, the lower canopy requires a higher resolution in a profile system than the layers above. However, if the upper canopy is oversparsely sampled relative to the lower canopy, the performance of a profile system might be degraded since, in such a situation, the influence of layer thickness dominates over that of Δc/Δt. We also find that because of different level of complexity in canopy structure, more sampling levels are necessary at our site in order to achieve the same level of accuracy as at a boreal aspen site. These results suggest that in order to achieve an adequate accuracy in CO2 storage measurements, the number of sampling levels in a profile and its design should be subject to the site properties, e.g., canopy architecture and the resulted thermodynamic and flow structures. If CO2 density from a single profile is averaged in time and then used in assessing CO2 storage to reduce random errors, biases associated with this averaging procedure become inevitable. Generally, larger window sizes used in averaging CO2 density generate poorer estimates of CO2 storage. If absolute errors are concerned, it appears that the more significant the CO2 storage is during a period, the larger effects the averaging procedure has.