ALBERT

Description: We perform a series of observing system simulation experiments (OSSEs) to quantify how well surface CO2 fluxes may be estimated using column-integrated CO2 data from the Orbiting Carbon Observatory (OCO), given the presence of various error sources. We use variational data assimilation to optimize weekly fluxes at 2°×5° (lat/lon) using simulated data averaged only across the ~33 s that OCO takes to cross a typical 2°×5° model grid box. Grid-scale OSSEs of this sort have been carried out before for OCO using simplified assumptions for the measurement error. Here, we more accurately describe the OCO measurements in two ways. First, we use new estimates of the single-sounding retrieval uncertainty and averaging kernel, both computed as a function of surface type, solar zenith angle, aerosol optical depth, and pointing mode (nadir vs. glint). Second, we collapse the information content of all valid retrievals from each grid box crossing into an equivalent multi-sounding measurement uncertainty, factoring in both time/space error correlations and data availability due to clouds and thick aerosols (calculated from MODIS data). Finally, we examine the impact of three types of systematic errors: measurement biases due to aerosols, transport errors, and errors caused by assuming incorrect error statistics. When only random measurement errors are considered, both nadir- and glint-mode data give error reductions of ~50% over the land for the weekly fluxes, and ~65% for seasonal fluxes. Systematic errors reduce both the magnitude and extent of these improvements by up to a factor of two, however. Flux improvements over the ocean are significant only when using glint-mode data and are smaller than those over land; when the assimilation is mistuned, slow convergence makes even these improvements difficult to achieve. The OCO data may prove most useful over the tropical land areas, where our current flux knowledge is weak and where the measurements remain fairly accurate even in the face of systematic errors.