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"INFERRING CHANGES TO THE GLOBAL CARBON CYCLE WITH WOMBAT V2.0, A HIERA" by Michael Bertolacci, Andrew Zammit-Mangion et al.

The natural cycles of the surface-to-atmosphere fluxes of carbon dioxide (CO2) and other important greenhouse gases are changing in response to human influences. These changes need to be quantified to understand climate change and its impacts, but this is difficult to do because natural fluxes occur over large spatial and temporal scales and cannot be directly observed. Flux inversion is a technique that estimates the spatiotemporal distribution of a gas’ fluxes using observations of the gas’ mole fraction and a chemical transport model. To infer trends in fluxes and identify phase shifts and amplitude changes in flux seasonal cycles, we construct a flux-inversion system that uses a novel spatially-varying time-series decomposition of the fluxes. We incorporate this decomposition into the Wollongong Methodology for Bayesian Assimilation of Trace-gases (WOMBAT, Zammit-Mangion et al., Geosci. Model Dev., 15, 2022), a Bayesian hierarchical flux-inversion framework that yields posterio ....

Wollongong Methodology , Bayesian Assimilation , Model Dev , Orbiting Carbon , Carbon Cycle , Carbon Dioxide , Lux Inversion ,

Satellite Tracks CO2 Emissions from 100 Nations in Pilot Project

Satellite Tracks CO2 Emissions from 100 Nations in Pilot Project
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France General , United Kingdom , Glasgow City , United States , United Arab Emirates , Nicholasm Deutscher , Paul Jones , Andrew Zammit Mangion , Theo Farrell , Brendan Byrne , Nicholas Deutscher , David Griffith , Michael Bertolacci , Noel Cressie , Propulsion Laboratory , School Of Mathematics , University Centre For Environmental Informatics , Australian Research Council Discovery , School Of Earth , Centre For Environmental Informatics , Life Sciences , University Of Wollongong , University Of Wollongong Centre For Environmental Informatics , Global Stocktake , United Arab , Professor Noel Cressie ,

"WOMBAT v1.0: A fully Bayesian global flux-inversion framework" by Andrew Zammit-Mangion, Michael Bertolacci et al.

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 p ....

Column Carbon Observing Network , Bayesian Assimilation , Orbiting Carbon , Model Intercomparison Project , Total Column Carbon Observing Network ,