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This paper reports a study of the full carbon (C-CO2) budget of the Australian continent, focussing on 1990–2011 in the context of estimates over two centuries. The work is a contribution to the RECCAP (REgional Carbon Cycle Assessment and Processes) project, as one of numerous regional studies. In constructing the budget, we estimate the following component carbon fluxes: net primary production (NPP); net ecosystem production (NEP); fire; land use change (LUC); riverine export; dust export; harvest (wood, crop and livestock) and fossil fuel emissions (both territorial and non-territorial). Major biospheric fluxes were derived using BIOS2 (Haverd et al., 2012), a fine-spatial-resolution (0.05°) offline modelling environment in which predictions of CABLE (Wang et al., 2011), a sophisticated land surface model with carbon cycle, are constrained by multiple observation types. The mean NEP reveals that climate variability and rising CO2 contributed 12 ± 24 (1σ error on mean) and 68 ± 15 TgC yr−1, respectively. However these gains were partially offset by fire and LUC (along with other minor fluxes), which caused net losses of 26 ± 4 TgC yr−1 and 18 ± 7 TgC yr−1, respectively. The resultant net biome production (NBP) is 36 ± 29 TgC yr−1, in which the largest contributions to uncertainty are NEP, fire and LUC. This NBP offset fossil fuel emissions (95 ± 6 TgC yr−1) by 38 ± 30%. The interannual variability (IAV) in the Australian carbon budget exceeds Australia's total carbon emissions by fossil fuel combustion and is dominated by IAV in NEP. Territorial fossil fuel emissions are significantly smaller than the rapidly growing fossil fuel exports: in 2009–2010, Australia exported 2.5 times more carbon in fossil fuels than it emitted by burning fossil fuels.

Precision requirements are determined for space-based column-averaged CO2 dry air mole fraction data. These requirements result from an assessment of spatial and temporal gradients in the relationship between precision and surface CO2 flux uncertainties inferred from inversions of the data, and the effects of biases on the fidelity of CO2 flux inversions. Observational system simulation experiments and synthesis inversion modeling demonstrate that the Orbiting Carbon Observatory mission design and sampling strategy provide the means to achieve these data precision requirements.

Monthly CO2 fluxes are estimated across 1988–2003 for 22 emission regions using data from 78 CO2 measurement sites. The same inversion (method, priors, data) is performed with 13 different atmospheric transport models, and the spread in the results is taken as a measure of transport model error. Interannual variability (IAV) in the winds is not modeled, so any IAV in the measurements is attributed to IAV in the fluxes. When both this transport error and the random estimation errors are considered, the flux IAV obtained is statistically significant at P ≤ 0.05 when the fluxes are grouped into land and ocean components for three broad latitude bands, but is much less so when grouped into continents and basins. The transport errors have the largest impact in the extratropical northern latitudes. A third of the 22 emission regions have significant IAV, including the Tropical East Pacific (with physically plausible uptake/release across the 1997–2000 El Niño/La Niña) and Tropical Asia (with strong release in 1997/1998 coinciding with large-scale fires there). Most of the global IAV is attributed robustly to the tropical/southern land biosphere, including both the large release during the 1997/1998 El Niño and the post-Pinatubo uptake.

In TransCom-3 (Level 1), atmospheric CO2 measurements from 76 monitoring stations for the period 1992–1996 and 16 atmospheric transport models were used to constrain annual mean CO2 fluxes over 11 land and 11 ocean regions. The tower measurements of atmospheric CO2 from Fraserdale, a continental site in northern Ontario, Canada are now available and processed for use in the TransCom-3 inverse modelling framework. In this short study, we show that by including this set of continental CO2 data, the estimated flux for the North American boreal region becomes nearly zero, a reduction of about 0.26 Pg C yr−1 from the previous estimate. The uncertainty of the estimated flux for this region is also reduced by ∼30%. All transport models show negative changes for boreal North America, with the strongest responses (∼−0.5 Pg C yr−1) shown by NIRE, NIES, CSU and SKYHI. Furthermore, models showing a strong response in boreal North America tend to show strong sensitivity in middle- and high-latitude Asian regions.

This paper explores various sources of error in atmospheric CO2 synthesis inversions using global circulation models. The estimation of prior, observation, model transport, and representation errors is described, and the latter two error sources are explored in more detail. Not accounting for these errors will act as a hard constraint on the inversion and will produce incorrect solutions to the problem as is shown in some example inversions. The magnitude of these errors falls generally between about 10% and 100% in the retrieved fluxes but can be even larger. This makes it highly desirable to avoid hard constraints and apply any prior information we have about the surface fluxes as a weak constraint to the inversion problem.