A model-based assessment of the TrOCA approach for estimating anthropogenic carbon in the ocean
Abstract. The quantification of the amount of anthropogenic carbon (Cant) that the ocean has taken up from the atmosphere since pre-industrial times is a challenging task because of the need to deconvolute this signal from the natural, unperturbed concentration of dissolved inorganic carbon (DIC). Nonetheless, a range of techniques have been devised that perform this separation using the information implicit in other physical, biogeochemical, and man-made ocean tracers. One such method is the TrOCA approach, which belongs to a group of back-calculation techniques, but relative to other methods employs a simple parameterization for estimating the preformed, pre-industrial concentration, the key quantity needed to determine Cant. Here we examine the theoretical foundation of the TrOCA approach and test its accuracy by deconvoluting the known distribution of Cant from an ocean general circulation model (OGCM) simulation of the industrial period (1864–2004). We reveal that the TrOCA tracer reflects the air-sea exchange of both natural and anthropogenic CO2 as well as that of O2. Consequently, the determination of the anthropogenic CO2 flux component requires an accurate determination not only of the contribution of the natural (pre-industrial) CO2 flux component, but also of the O2 flux component. The TrOCA method attempts to achieve this by assuming that the concentration changes invoked by these two air-sea flux components scale with temperature and alkalinity. While observations support a strong exponential scaling of the oxygen flux component with temperature, there exists no simple relationship of the natural CO2 flux component with temperature and/or alkalinity. This raises doubts whether the sum of these two components can be adequately parameterized with a single function. The analyses of the model support this conclusion, even when Cant is deconvoluted using parameter values that were optimized on the basis of the synthetic dataset from the model. Application of an optimal, but globally uniform set of parameters for the estimation of Cant results in a global positive bias in the inventory of more than a factor of two, suggesting that a "universal" TrOCA parameterisation is not achieveable. Even the application of regionally specific sets of parameters causes, on average, a global positive bias of more than 50%. This is substantially larger than the potential positive bias of 7% identified for the ΔC* method using a similar model-based assessment method.