Abstract
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). 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.
Highlights
Atmospheric carbon dioxide (CO2) is a leading driver of global warming (e.g. Peters et al, 2013)
While we know that the land and oceans absorb more than half of the CO2 emitted by human activities (e.g. Dlugokencky and Tans, 2020), the geographical and temporal patterns of these sinks remain unclear (e.g. Crowell et al, 2019)
To evaluate the estimated fluxes in the Orbiting Carbon Observatory-2 (OCO-2) Model Intercomparison Project (MIP), each participant was asked to use the 30 min average Total Column Carbon Observing Network (TCCON) retrievals of column-averaged CO2 as validation data, and compare them to the column-averaged CO2 predicted values obtained by applying the process model to the estimated fluxes with the same chemical transport model (CTM) used for the inversion
Summary
Atmospheric carbon dioxide (CO2) is a leading driver of global warming (e.g. Peters et al, 2013). A. Zammit-Mangion et al.: WOMBAT v1.0: a fully Bayesian global flux-inversion framework an atmospheric transport model and a spatio-temporal model for the fluxes Two other causes of model misspecification worth noting are an incorrectly specified initial global mole-fraction field and flux components assumed known in the inversion (i.e. assumed degenerate at their prior mean), such as anthropogenic emissions (e.g. Feng et al, 2019). The latter can be seen as a special case of (i) above, while the effect of the former can generally be minimised by using a realistic initial condition
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