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- Title
WOMBAT v1.0: a fully Bayesian global flux-inversion framework.
- Authors
Zammit-Mangion, Andrew; Bertolacci, Michael; Fisher, Jenny; Stavert, Ann; Rigby, Matthew; Cao, Yi; Cressie, Noel
- 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 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). 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.
- Subjects
ATMOSPHERIC transport; ATMOSPHERIC models; CARBON dioxide; STATISTICAL models; SIMULATION methods &; models; TRACE gases; STATISTICAL bias; INVERSIONS (Geometry)
- Publication
Geoscientific Model Development, 2022, Vol 15, Issue 1, p45
- ISSN
1991-959X
- Publication type
Article
- DOI
10.5194/gmd-15-45-2022