SEPTEMBER 2005: Exploiting CO2:CO correlations in inversion analyses improve estimates of CO2 surface fluxes

Observed correlations between atmospheric concentrations of CO2 and CO represent potentially powerful information for improving CO2 surface flux estimates through coupled CO2-CO inverse analyses. Our inverse model uses regional CO2 and CO surface fluxes as the state vector, separating biospheric and combustion contributions to CO2. CO2-CO error correlation coefficients are included in the inversion as off-diagonal entries in the a~priori and observation error covariance matrices.

In Palmer et al, [2005], we explore the value of these correlations in improving estimates of regional CO2 fluxes in East Asia by using aircraft observations of CO2 and CO from the TRACE-P campaign over the NW Pacific in March, 2001.

Observed correlation coefficients between atmospheric CO2 and CO concentrations (>0.7) imply corresponding error correlations in the chemical transport model used as the forward model for the inversion. Figure: Using these error correlations in a joint CO2-CO inversion significantly improves over a CO2-only inversion (CO2:CO error correlation=0) for quantifying regional fluxes of CO2, and for separating the biospheric and combustion components.