Submitted as: development and technical paper 12 Jul 2021

Submitted as: development and technical paper | 12 Jul 2021

Review status: a revised version of this preprint is currently under review for the journal GMD.

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

Andrew Zammit-Mangion1,, Michael Bertolacci1,, Jenny Fisher2, Ann Stavert3, Matthew L. Rigby4, Yi Cao1, and Noel Cressie1 Andrew Zammit-Mangion et al.
  • 1School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia
  • 2School of Earth and Life Sciences, University of Wollongong, Wollongong, Australia
  • 3Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale, Australia
  • 4School of Chemistry, University of Bristol, Bristol, UK
  • These authors contributed equally to this work.

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 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, Atmos. Chem. Phys., vol. 19). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network are, for the most part, more accurate than those made by the MIP participants.

Andrew Zammit-Mangion et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-181', Scot Miller, 25 Aug 2021
  • RC2: 'Comment on gmd-2021-181', Anonymous Referee #2, 30 Aug 2021
  • AC1: 'Comment on gmd-2021-181', Andrew Zammit-Mangion, 04 Oct 2021

Andrew Zammit-Mangion et al.

Data sets

Code for reproducing results and figures in manuscript Michael Bertolacci, Andrew Zammit-Mangion, Yi Cao, Jenny Fisher, Ann Stavert

Model code and software

Intermediate files for reproducing results in manuscript Michael Bertolacci, Jenny Fisher, Yi Cao, Ann Stavert, Matthew Rigby

Andrew Zammit-Mangion et al.


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Short summary
We present a statistical framework for estimating the sources and sinks (flux) of carbon dioxide from satellite data. The framework is statistical, and yields measures of uncertainty alongside all estimates of flux and other parameters in the underlying model. It also allows us to generate other insights, such as the size of errors and biases in the data. The primary aim of this research was to develop a fully statistical flux inversion framework for use by atmospheric scientists.