WOMBAT: A fully Bayesian global flux-inversion framework

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 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, 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.

Computationally efficient methods for large-scale atmospheric inverse modeling

“…Fourth, calculating uncertainties in the estimated fluxes can be computationally prohibitive. The design of new methods to improve the computational feasibility of large atmospheric inverse problems has been the focus of numerous recent publications (see, e.g., Baker et al, 2006;Bousserez and Henze, 2018;Chatterjee and Michalak, 2013;Chatterjee et al, 2012;Chen et al, 2021a, b;Gourdji et al, 2012;Henze et al, 2007;Liu et al, 2021;Meirink et al, 2008;Miller et al, , 2014Yadav and Michalak, 2013;Zammit-Mangion et al, 2021).…”