Estimation of continuous anthropogenic CO2: model-based evaluation of CO2, CO, δ13C(CO2) and Δ14C(CO2) tracer methods
Abstract. We investigate different methods for estimating anthropogenic CO2 using modeled continuous atmospheric concentrations of CO2 alone, as well as CO2 in combination with the surrogate tracers CO, δ13C(CO2) and Δ14C(CO2). These methods are applied at three hypothetical stations representing rural, urban and polluted conditions. We find that, independent of the tracer used, an observation-based estimate of continuous anthropogenic CO2 is not yet feasible at rural measurement sites due to the low signal-to-noise ratio of anthropogenic CO2 estimates at such settings. The tracers δ13C(CO2) and CO provide an accurate possibility to determine anthropogenic CO2 continuously, only if all CO2 sources in the catchment area are well characterized or calibrated with respect to their isotopic signature and CO to anthropogenic CO2 ratio. We test different calibration strategies for the mean isotopic signature and CO to CO2 ratio using precise Δ14C(CO2) measurements on monthly integrated as well as on grab samples. For δ13C(CO2), a calibration with annually averaged 14C(CO2) grab samples is most promising, since integrated sampling introduces large biases into anthropogenic CO2 estimates. For CO, these biases are smaller. The precision of continuous anthropogenic CO2 determination using δ13C(CO2) depends on measurement precision of δ13C(CO2) and CO2, while the CO method is mainly limited by the variation in natural CO sources and sinks. At present, continuous anthropogenic CO2 could be determined using the tracers δ13C(CO2) and/or CO with a precision of about 30 %, a mean bias of about 10 % and without significant diurnal discrepancies. Hypothetical future measurements of continuous Δ14C(CO2) with a precision of 5 ‰ are promising for anthropogenic CO2 determination (precision ca. 10–20 %) but are not yet available. The investigated tracer-based approaches open the door to improving, validating and reducing biases of highly resolved emission inventories using atmospheric observation and regional modeling.