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.