WOMBAT: A fully Bayesian global flux-inversion framework

TL;DR

WOMBAT is a comprehensive Bayesian framework for trace gas flux inversion that effectively handles biased and correlated errors, providing accurate flux estimates and uncertainty quantification from diverse observational data.

Contribution

It introduces a fully Bayesian hierarchical model with correlated error consideration, online bias correction, and uncertainty quantification, advancing flux inversion methods.

Findings

WOMBAT achieves flux estimates comparable to established models.

It provides more accurate out-of-sample predictions than previous methods.

The framework effectively handles biased and correlated errors in data.

Abstract

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 correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and uncertainties on non-fossil-fuel CO$_2$ 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, Atmos. Chem. Phys., vol. 19). We also find that our predictions of out-of-sample retrievals from the Total Column Carbon Observing Network are, for the most part, more accurate than those made by the MIP participants. Subsequent versions of the OCO-2 datasets will be ingested into WOMBAT as they become available.