Atmospheric Chemistry and Physics Discussions www.atmos-chem-phys-discuss.net 1680-7367 1680-7375 9 3 2009 10.5194/acpd-9-11783-2009 http://www.atmos-chem-phys-discuss.net/9/11783/2009/ http://www.atmos-chem-phys-discuss.net/9/11783/2009/acpd-9-11783-2009.html http://www.atmos-chem-phys-discuss.net/9/11783/2009/acpd-9-11783-2009.pdf 11783 11810 2009-05-12 Error correlation between CO₂ and CO as constraint for CO₂ flux inversions using satellite data H. Wang hwang@cfa.harvard.edu D. J. Jacob M. Kopacz D. B. A. Jones P. Suntharalingam J. A. Fisher R. Nassar S. Pawson J. E. Nielsen School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA Harvard-Smithsonian Center For Astrophysics, Cambridge, MA, USA Department of Physics, University of Toronto, Toronto, Ontario, Canada School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom Department of Geography, University of Toronto, Toronto, Ontario, Canada NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, MD, USA Inverse modeling of CO₂ 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 CO₂–CO inversion to improve CO₂ flux estimates, by exploiting the CTM transport error correlations between CO₂ and CO. We estimate the error correlation globally and for different seasons by a paired-model method (comparing CTM simulations of CO₂ 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 CO₂ and CO columns for the same forecast time). We find strong positive and negative error correlations (<i>r</i>²&gt;0.5) between CO₂ 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. 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