Error correlation between CO sub(2) and CO as constraint for CO sub(2) flux inversions using satellite data

• Published in: Atmospheric chemistry and physics discussions 2009-05, Vol.9 (3), p.11783-11810
• Main Authors: Wang, H, Jacob, D J, Kopacz, M, Jones, DBA, Suntharalingam, P, Fisher, JA, Nassar, R, Pawson, S, Nielsen, JE
• Format: Article
• Language: English
• ISSN: 1680-7367; 1680-7375

Inverse modeling of CO sub(2) satellite observations to better quantify carbon surface fluxes requires a forward model such as a chemical transport model (CTM) to relate the fluxes to the observed column concentrations. Model transport error is an important source of observational error. We investigate the potential of using CO satellite observations as additional constraints in a joint CO sub(2)-CO inversion to improve CO sub(2) flux estimates, by exploiting the CTM transport error correlations between CO sub(2) and CO. We estimate the error correlation globally and for different seasons by a paired-model method (comparing CTM simulations of CO sub(2) and CO columns using different assimilated meteorological data sets for the same meteorological year) and a paired-forecast method (comparing 48- vs. 24-h CTM forecasts of CO sub(2) and CO columns for the same forecast time). We find strong positive and negative error correlations (r super(2)>0.5) between CO sub(2) and CO columns over much of the world throughout the year, and strong consistency between different methods to estimate the error correlation. Application of the averaging kernels used in the retrieval for thermal IR CO measurements weakens the correlation coefficients by 15% on average (mostly due to variability in the averaging kernels) but preserves the large-scale correlation structure. Results from a testbed inverse modeling application show that CO sub(2)-CO error correlations can indeed significantly reduce uncertainty on surface carbon fluxes in a joint CO sub(2)-CO inversion vs. a CO sub(2)-only inversion.