Possible representation errors in inversions of satellite CO2 retrievals
Possible representation errors in inversions of satellite CO2 retrievals
复制标题
卫星 CO2 反演中可能出现的表示错误
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
J.
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文献类型：
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作者：
K. Corbin;A. Denning;Lixin Lu;Jih;Ian Baker;K. Corbin;A. Denning;L. Lu;J.
[1]由于全球空间采样和庞大的数据量，卫星CO2浓度可用于反演模型，以提高我们对碳循环的理解。使用柱测量来表示传输模型网格柱可能会将空间、局部晴空和时间采样误差引入反演：足迹小于网格单元，仅在晴空中检索总柱浓度，并且仅一次采样混合比。为了研究这些误差，我们使用了一个耦合的生态系统-大气云解析模型，利用显式微物理学，从1 km 2和25 km 2的像素中创建了精细（101 ° × 1°）和粗糙（104 ° × 4°）网格列上的CO2场。我们在2001年8月进行了两次模拟：一次在北美中部，一次在巴西亚马逊。通过从10公里宽的模拟卫星测量值中减去域平均柱浓度，计算卫星和网格柱浓度之间的差异。空间和当地晴空误差小于0.5 ppm的细网格列，然而，这些错误变得很大，并在北美的粗网格列的偏见。为了避免这些错误，传输模型应该以高分辨率运行。使用卫星测量来表示双月平均值在所有情况下都产生了很大的误差（>1 ppm）。在北美的模拟中，误差是负偏的（约为-0.4 ppm），这表明逆模型不能使用卫星测量来表示时间平均值。模拟的代表性错误并没有出现，因为在多云与晴天条件下的生态系统代谢的差异，相反，他们反映了大规模的CO2梯度在中纬度地区的组织沿着锋面边界和区域云层掩盖。在这里介绍的旱季热带模拟中没有发现这样的边界，并且在一般的热带地区可能不那么普遍。为了避免产生误差，反演必须准确地模拟天气尺度的大气传输，并且必须在观测的时间和地点同化CO2浓度。
[1] Owing to global spatial sampling and sheer data volume, satellite CO2 concentrations can be used in inverse models to enhance our understanding of the carbon cycle. Using column measurements to represent a transport model grid column may introduce spatial, local clear-sky, and temporal sampling errors into inversions: the footprint is smaller than a grid cell, total column concentrations are only retrieved in clear skies, and the mixing ratios are only sampled at one time. To investigate these errors, we used a coupled ecosystem-atmosphere cloud-resolving model to create CO2 fields over fine (∼1° × 1°) and coarse (∼4° × 4°) grid columns from 1 km2 and 25 km2 pixels that utilized explicit microphysics. We performed two simulations in August 2001: one in central North America and one in the Brazilian Amazon. Differences between satellite and grid column concentrations were calculated by subtracting the domain mean column concentration from 10-km-wide simulated satellite measurements. Spatial and local clear-sky errors were less than 0.5 ppm for the fine grid column; however, these errors became large and biased over the coarse grid column in North America. To avoid these errors, transport models should be run at high resolution. Using satellite measurements to represent bimonthly averages created large (>1 ppm) errors for all cases. The errors were negatively biased (approximately −0.4 ppm) in the North American simulation, indicating that inverse models cannot use satellite measurements to represent temporal averages. Simulated representation errors did not arise because of differences in ecosystem metabolism in cloudy versus sunny conditions; rather, they reflected large-scale CO2 gradients in midlatitudes that were organized along frontal boundaries and masked under regional cloud cover. Such boundaries were not found in the dry-season tropical simulation presented here and may be less prevalent in the tropics in general. To avoid incurring errors, inversions must accurately model synoptic-scale atmospheric transport and CO2 concentrations must be assimilated at the time and place observed.