The ability of current global models to simulate the transport of CO2 by mid-latitude, synoptic-scale weather systems (i.e. CO2 weather) is important for inverse estimates of regional and global carbon budgets but remains unclear without comparisons to targeted measurements. Here, we evaluate ten models that participated in the Orbiting Carbon Observatory-2 model intercomparison project (OCO-2 MIP version 9) with intensive aircraft measurements collected from the Atmospheric Carbon Transport (ACT)-America mission. We quantify model-data differences in the spatial variability of CO2 mole fractions, mean winds, and boundary layer depths in 27 mid-latitude cyclones spanning four seasons over the central and eastern United States. We find that the OCO-2 MIP models are able to simulate observed CO2 frontal differences with varying degrees of success in summer and spring, and most underestimate frontal differences in winter and autumn. The models may underestimate the observed boundary layer-to-free troposphere CO2 differences in spring and autumn due to model errors in boundary layer height. Attribution of the causes of model biases in other seasons remains elusive. Transport errors, prior fluxes, and/or inversion algorithms appear to be the primary cause of these biases since model performance is not highly sensitive to the CO2 data used in the inversion. The metrics presented here provide new benchmarks regarding the ability of atmospheric inversion systems to reproduce the CO2 structure of mid-latitude weather systems. Controlled experiments are needed to link these metrics more directly to the accuracy of regional or global flux estimates.

We use 148 airborne vertical profiles of CO2 for frontal cases from the summer 2016 Atmospheric Carbon and Transport-America (ACT-America) campaign to evaluate the skill of ten global CO2 in situ inversion models from the version 7 Orbiting Carbon Observatory-2 (OCO-2) Model Intercomparison Project (MIP). Model errors (model posterior-observed CO2 dry air mole fractions) were categorized by region (Mid-Atlantic, Midwest, and South), frontal sector (warm or cold), and transport model (predominantly Tracer Model 5 (TM5) and Goddard Earth Observing System-Chemistry (GEOS-Chem)). All inversions assimilated the same CO2 observations. Overall, the median inversion profiles reproduce the general structures of the observations (enhanced / depleted low-level CO2 in warm / cold sectors), but 1) they underestimate the magnitude of the warm / cold sector mole fraction difference, and 2) the spread among individual inversions can be quite large (> 5 ppm). Uniquely in the Mid-Atlantic, inversion biases segregated according to atmospheric transport model, where TM5 inversions biases were-3 to-4 ppm in warm sectors, while those of GEOS-Chem were +2 to +3 ppm in cold sectors. The large spread among the mean posterior CO2 profiles is not explained by the different atmospheric transport models. These results show that the inversion systems themselves are the dominant cause of this spread, and that the aircraft campaign data are clearly able to identify these large biases. Future controlled experiments should identify which inversions best reproduce midlatitude CO2 mole fractions, and how inversion system components are linked to system performance.

We evaluate terrestrial net ecosystem-atmosphere exchange (NEE) of CO2 from nine global inversion systems that inferred fluxes from four CO2 observational sources. We use 98 flights in the central and eastern U.S. from the ACT-America aircraft mission to conduct this sub-continental, seasonal-scale evaluation. We use Lagrangian particle dispersion modeling (FLEXPARTv10.4-ERA-Interim) to compare observed and simulated regional biogenic CO2 mole fractions. We find a positive bias (modeled CO2 > observed) in the summer and negative bias (modeled CO2 < observed) in dormant seasons across most flux products, suggesting that the seasonal strength of CO2 NEE is underestimated in these inverse models. Fluxes inferred from OCO-2 v9 satellite land nadir/glint observations yield an error level that is similar to fluxes inferred from in-situ data. Large bias errors are observed in the croplands and eastern forests. Future experiments are needed to determine if these seasonal biases are associated with biases in net annual flux estimates.

Quantification of regional terrestrial carbon dioxide (CO2) fluxes is critical to our understanding of the carbon cycle. We evaluate inverse estimates of net ecosystem exchange (NEE) of CO2 fluxes in temperate North America, and their sensitivity to the observational data used to drive the inversions. Specifically, we consider the state-of-the-science CarbonTracker global inversion system, which assimilates (i) in situ measurements (’IS’), 29 (ii) the Orbiting Carbon Observatory-2 (OCO-2) v9 column CO 2 (XCO2) retrievals over land (’LNLG’), (iii) OCO-2 v9 XCO 2 retrievals over ocean (’OG’), and (iv) a combination of all these observational constraints (’LNLGOGIS’). We use independent CO2 observations from the Atmospheric Carbon and Transport (ACT)-America aircraft mission to evaluate the inversions. We diagnose errors in the flux estimates using the differences between modeled and observed biogenic CO2 mole fractions, influence functions from a Lagrangian transport model, and root-mean-square error (RMSE) and bias metrics. The IS fluxes have the smallest RMSE among the four products, followed by LNLG. Both IS and LNLG outperform the OG and LNLGOGIS inversions with regard to RMSE. Regional errors do not differ markedly across the four sets of posterior fluxes. The CarbonTracker inversions appear to overestimate the seasonal cycle of NEE in the Midwest and Western Canada, and overestimate dormant season NEE across the Central and Eastern US. The CarbonTracker inversions may overestimate annual NEE in the Central and Eastern US. The success of the LNLG inversion with respect to independent observations bodes well for satellite-based inversions in regions with more limited in situ observing networks.