Carbon source/sink information provided by column CO 2 measurements from the Orbiting Carbon Observatory
Carbon source/sink information provided by column CO 2 measurements from the Orbiting Carbon Observatory
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DOI：
10.5194/acp-10-4145-2010
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发表时间：
2008-11
影响因子：
6.3
通讯作者：
D. Baker;H. Bösch;S. Doney;D. O'Brien;D. Schimel
中科院分区：
文献类型：
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作者：
D. Baker;H. Bösch;S. Doney;D. O'Brien;D. Schimel
抽象。考虑到各种误差源的存在，我们量化了轨道碳观测站（OCO）的列综合CO2测量应该能够约束地表CO2通量。我们使用变分资料同化来优化每周通量在2°×5°分辨率（纬度/经度），使用模拟数据平均在每个模型网格框飞越（通常每33秒）。这种网格规模的模拟已经进行了OCO使用简化的假设测量误差。在这里，我们以两种方式更准确地描述OCO测量。首先，我们使用新的估计的单探测检索的不确定性和平均内核，都计算作为一个函数的表面类型，太阳天顶角，气溶胶光学厚度，和指向模式（最低点与闪烁）。其次，我们崩溃的信息内容的所有有效检索从每个网格框交叉到一个等效的多探测测量的不确定性，在时间/空间误差的相关性和数据拒绝由于云和厚气溶胶的因素。最后，我们研究了三种类型的系统误差的影响：由于气溶胶的测量偏差，传输误差，以及由于假设不正确的统计数据引起的失调误差。当只考虑随机测量误差时，天底和闪烁模式数据在陆地上每周通量的误差减少约45%，季节通量的误差减少约65%。然而，系统误差使这些改进的幅度和空间范围都减少了大约两倍。使用闪烁模式数据在海洋上实现了几乎同样大的改进，但由于系统误差而退化得更多。因此，我们的能力，以确定和消除系统误差的列检索和大气同化将是至关重要的OCO数据的有用性最大化。
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.