Abstract
Abstract. We quantify how well column-integrated CO2 measurements from the Orbiting Carbon Observatory (OCO) should be able to constrain surface CO2 fluxes, given the presence of various error sources. We use variational data assimilation to optimize weekly fluxes at a 2°×5° resolution (lat/lon) using simulated data averaged across each model grid box overflight (typically every ~33 s). Grid-scale simulations 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 rejection due to clouds and thick aerosols. Finally, we examine the impact of three types of systematic errors: measurement biases due to aerosols, transport errors, and mistuning errors caused by assuming incorrect statistics. When only random measurement errors are considered, both nadir- and glint-mode data give error reductions over the land of ~45% for the weekly fluxes, and ~65% for seasonal fluxes. Systematic errors reduce both the magnitude and spatial extent of these improvements by about a factor of two, however. Improvements nearly as large are achieved over the ocean using glint-mode data, but are degraded even more by the systematic errors. Our ability to identify and remove systematic errors in both the column retrievals and atmospheric assimilations will thus be critical for maximizing the usefulness of the OCO data.
Highlights
The global carbon cycle plays a key role in the climatic response to anthropogenic forcing, yet our understanding of its dominant processes is still too weak to make accurate long-term predictions (IPCC, 2007)
RMSprior quantifies the initial difference between the prior and true flux models; no attempt to incorporate the information provided by the current in situ measurement network into RMSprior has been made, since Baker et al (2006b) suggest that its constraint is weak at the 2◦×5◦ resolution examined here
The root mean square (RMS) values for the estimated 7-day fluxes given here are computed across the full year; RMS statistics for seasonal means computed from the 7-day fluxes are given
Summary
The global carbon cycle plays a key role in the climatic response to anthropogenic forcing, yet our understanding of its dominant processes is still too weak to make accurate long-term predictions (IPCC, 2007). Atmospheric CO2 measurements have revealed much of what we know about the functioning of the global carbon cycle. As our data coverage has increased, inverse methods have been used to optimize global sources and sinks of CO2 and the process models that compute them (Enting et al, 1995; Bousquet et al, 2000; Rodenbeck et al, 2003; Baker et al, 2006a; Rayner et al, 2005). The “top-down” atmospheric inverse approach to validating carbon models has been only marginally successful: where the data are most dense, fluxes may be estimated at continental scales (Baker et al, 2006a), but not at the regional scales where they would be most useful for identifying flaws in the carbon models.
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