Objective evaluation of surface- and satellite-driven CO2 atmospheric inversions.

We study an ensemble of six multi-year global Bayesian CO₂ atmospheric inversions that vary in terms of assimilated observations (either column retrievals from one of two satellites or surface air sample measurements) and transport model. The time series of inferred annual fluxes are first compared with each other at various spatial scales. We then objectively evaluate the small inversion ensemble based on a large dataset of accurate aircraft measurements in the free troposphere over the globe, that are independent from all assimilated data. The measured variables are connected with the inferred fluxes through mass-conserving transport in the global atmosphere and are part of the inversion results. Large-scale annual fluxes estimated from the bias-corrected land retrievals of the second Orbiting Carbon Observatory (OCO-2) differ from the prior fluxes much, but are similar to the fluxes estimated from the surface network within the uncertainty of these surface-based estimates. The OCO-2- and surface-based inversions have similar performance when projected in the space of the aircraft data, but relative strengths and weaknesses of the two flux estimates vary within the Northern and Tropical parts of the continents. The verification data also suggests that the more complex and more recent transport model does not improve the inversion skill. In contrast, the inversion using bias-corrected retrievals from the Greenhouse Gases Observing Satellite (GOSAT) or, to a larger extent, a non-Bayesian inversion that simply adjusts a recent bottom-up flux estimate with the annual growth rate diagnosed from marine surface measurements, estimate much different fluxes and fit the aircraft data less. Our study highlights a way to rate global atmospheric inversions. It suggests that some satellite retrievals can now provide inversion results that are, despite their uncertainty, comparable in credibility to traditional inversions using the accurate but sparse surface network and that are therefore complementary for studies of the global carbon budget. [ABSTRACT FROM AUTHOR]