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dc.contributor.authorBaker, David F.  Concept link
dc.contributor.authorBosch, H.  Concept link
dc.contributor.authorDoney, Scott C.  Concept link
dc.contributor.authorO'Brien, D.  Concept link
dc.contributor.authorSchimel, David S.  Concept link
dc.date.accessioned2010-06-01T19:39:49Z
dc.date.available2010-06-01T19:39:49Z
dc.date.issued2010-05-03
dc.identifier.citationAtmospheric Chemistry and Physics 10 (2010): 4145-4165en_US
dc.identifier.urihttp://hdl.handle.net/1912/3554
dc.description© The Authors, 2010. This article is distributed under the terms of the Creative Commons Attribution 3.0 License. The definitive version was published in Atmospheric Chemistry and Physics 10 (2010): 4145-4165, doi:10.5194/acp-10-4145-2010.en_US
dc.description.abstractWe 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.en_US
dc.description.sponsorshipSD and DB acknowledge support from NASA grant NNG06G127G. DB also acknowledges initial support from NOAA Grant NA16GP2935.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoenen_US
dc.publisherCopernicus Publications on behalf of the European Geosciences Unionen_US
dc.relation.urihttps://doi.org/10.5194/acp-10-4145-2010
dc.rightsAttribution 3.0 Unported*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/*
dc.titleCarbon source/sink information provided by column CO2 measurements from the Orbiting Carbon Observatoryen_US
dc.typeArticleen_US
dc.identifier.doi10.5194/acp-10-4145-2010


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Attribution 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution 3.0 Unported