WOMBAT v1.0: a fully Bayesian global flux-inversion framework

WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.

Automated summary

WOMBAT is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases, extending the conventional framework by considering correlated errors and online bias correction, providing uncertainty quantification on all unknowns in the statistical model. Experiments show the importance of these extensions when data are biased and have spatio-temporally correlated errors; using an atmospheric transport model, WOMBAT can obtain posterior means and variances on CO2 fluxes comparable to those reported in the Model Intercomparison Project, and its predictions of out-of-sample retrievals from TCCON data are generally more accurate than those made by MIP participants.