Evaluating Transport Model Uncertainty on Atmospheric Flux Inversions of CO2

The world invests significant resources every year on remote sensing platforms that aim to quantify the carbon cycle, whether through measurements of atmospheric composition or the land surface. Composition measurements – such as CO₂ and CH₄ – cannot be used directly but must be interpreted through inverse models of different complexity to estimate surface sinks and emissions, from global to urban scales. Flux inversion models aim to quantify the upwind fluxes associated with the downwind concentrations using knowledge of the atmospheric transport between. Therefore, inverse models and their associated inferred flux estimates are critically dependent upon an assumed transport operator.

We use multiple lines of evidence to explore the variability in the global transport of long-lived trace gases like CO2 and SF6. We present results from the CATRINE and OCO-2 SF6 Model Intercomparison Projects (MIPs) which explore the variability in the transport of long lived gases across Chemical Transport Models and their high-resolution General Circulation Model parent models. Additionally, we use the first dual transport atmospheric inversion framework, WOMBATv3, to further explore the relationship between inferred fluxes of CO2 and inversion model transport assumptions, e.g. GEOS-Chem and TM5.