Abstract
Abstract. National annual total CO2 emissions from combustion of fossil fuels are likely known to within 5–10 % for most developed countries. However, uncertainties are inevitably larger (by unknown amounts) for emission estimates at regional and monthly scales, or for developing countries. Given recent international efforts to establish emission reduction targets, independent determination and verification of regional and national scale fossil fuel CO2 emissions are likely to become increasingly important. Here, we take advantage of the fact that precise measurements of 14C in CO2 provide a largely unbiased tracer for recently added fossil-fuel-derived CO2 in the atmosphere and present an atmospheric inversion technique to jointly assimilate observations of CO2 and 14CO2 in order to simultaneously estimate fossil fuel emissions and biospheric exchange fluxes of CO2. Using this method in a set of Observation System Simulation Experiments (OSSEs), we show that given the coverage of 14CO2 measurements available in 2010 (969 over North America, 1063 globally), we can recover the US national total fossil fuel emission to better than 1 % for the year and to within 5 % for most months. Increasing the number of 14CO2 observations to ∼ 5000 per year over North America, as recently recommended by the National Academy of Science (NAS) (Pacala et al., 2010), we recover monthly emissions to within 5 % for all months for the US as a whole and also for smaller, highly emissive regions over which the specified data coverage is relatively dense, such as for the New England states or the NY-NJ-PA tri-state area. This result suggests that, given continued improvement in state-of-the art transport models, a measurement program similar in scale to that recommended by the NAS can provide for independent verification of bottom-up inventories of fossil fuel CO2 at the regional and national scale. In addition, we show that the dual tracer inversion framework can detect and minimize biases in estimates of the biospheric flux that would otherwise arise in a traditional CO2-only inversion when prescribing fixed but inaccurate fossil fuel fluxes.
Highlights
The terrestrial biosphere and the oceans have taken up roughly half the anthropogenic emissions of CO2, with the remainder contributing to the observed increase in atmospheric CO2 concentration from ∼ 280 ppm in the early 1800s to ∼ 395 ppm in 2013 (Ballantyne et al, 2012)
True terrestrial fluxes were based on the Carnegie Ames Stanford Approach (CASA) Global Fire Emissions Database (GFED) 3 model
For the “perfect transport” Observation System Simulation Experiments (OSSEs), we evaluate the posterior correlation between fossil fuel CO2 emissions (Ffos) and Fbio to assess the degree to which these fluxes can be retrieved independently using (a) CO2 data only and (b) using 14CO2 and CO2 data together
Summary
The terrestrial biosphere and the oceans have taken up roughly half the anthropogenic emissions of CO2, with the remainder contributing to the observed increase in atmospheric CO2 concentration from ∼ 280 ppm in the early 1800s to ∼ 395 ppm in 2013 (Ballantyne et al, 2012). While CO2 observations from sampling networks over large, industrialized land areas will be influenced by emissions from combustion of fossil fuels, they are often dominated by seasonally and diurnally varying fluxes of the terrestrial biosphere. Statistics on fossil fuel consumption and assumed combustion efficiencies (cf Andres et al, 2012) with an assigned uncertainty of zero Under these conditions, any deviation of the prescribed fossil fuel CO2 fluxes from their true values can be expected to result in errors in the retrieved estimates of the terrestrial biospheric exchange fluxes. It is frequently necessary to extrapolate emissions inventories forward in time to correspond with the times of atmospheric observations Such extrapolations might reasonably account for changes in population but will not capture changes in fossil fuel use associated with, for example, protracted regional heat and cold waves. We repeat (b) but with different models of atmospheric transport to generate and assimilate the synthetic observations, in order to evaluate the potential impact of transport model error on our emissions estimates
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