Exploiting CO :CO correlations to improve inverse analyses of carbon fluxes

Observed correlations between atmospheric concentrations of CO and CO represent potentially powerful information for improving CO surface flux estimates through coupled CO -CO inverse analyses. We explore the value of these correlations in improving estimates of regional CO fluxes in East Asia by using aircraft observations of CO and CO from the TRACE-P campaign over the NW Pacific in March, 2001. Our inverse model uses regional CO and CO surface fluxes as the state vector, separating biospheric and combustion contributions to CO . CO -CO error correlation coefficients are included in the inversion as off-diagonal entries in the a priori and observation error covariance matrices. Observed correlations between atmospheric CO and CO concentrations imply corresponding error correlations in the chemical transport model used as the forward model for the inversion. These error correlations in excess of 0.7, as derived from the TRACE-P data, enable a coupled CO -CO inversion to achieve significant improvement over a CO -only inversion for quantifying regional fluxes of CO , and for separating the biospheric and combustion components. We also derive error correlations in a priori combustion source estimates of CO and CO by propagating error estimates of fuel consumption rates and emission factors. However, we find that these correlations are weak because CO source uncertainties are mostly determined by emission factors.