Using CO2:CO Correlations to Improve Inverse Analyses of Carbon Fluxes
Palmer, Paul I.
Jones, Dylan B.A.
Streets, David G.
Vay, Stephanie A.
Sachse, Glen W.Note: Order does not necessarily reflect citation order of authors.
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CitationPalmer, Paul I., Parvadha Suntharalingham, Dylan B. A. Jones, Daniel J. Jacob, David G. Streets, Qingyan Fu, Stephanie A. Vay, Glen W. Sachse. 2006. Using CO2:CO correlations to improve inverse analyses of carbon fluxes. Journal of Geophysical Research 111: D12318.
AbstractObserved correlations between atmospheric concentrations of CO2 and CO represent potentially powerful information for improving CO2 surface flux estimates through coupled CO2-CO inverse analyses. 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. 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. We derive error correlations in a priori combustion source estimates of CO2 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. Observed correlations between atmospheric CO2 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 CO2-CO inversion to achieve significant improvement over a CO2-only inversion for quantifying regional fluxes of CO2.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:3743789
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