Improving the temporal and spatial distribution of CO2 emissions from global fossil fuel emission data sets
Improving the temporal and spatial distribution of CO2 emissions from global fossil fuel emission data sets
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DOI：
10.1029/2012jd018196
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发表时间：
2013-01-27
影响因子：
4.4
通讯作者：
Deng, Feng
中科院分区：
文献类型：
--
作者：
Nassar, Ray;Napier-Linton, Louis;Deng, Feng
通过对多个全球化石燃料CO2排放数据集、美国Vulcan排放数据、加拿大国家清单报告和基于卫星观测的NO2变化的分析，我们得出了可应用于全球排放数据集的尺度因子，以代表每周和昼夜CO2排放变化。这对于CO2的反演建模和数据同化非常重要，因为它们使用的是在这些时间尺度上可变的现场或卫星测量。应用每周和周日缩放的模型模拟表明，尽管远离污染源的影响较小，但在一些主要城市，地表大气CO2受到高达1.5-8 ppm的干扰，柱平均CO2受到0.1-0.5 ppm的干扰，这表明当这些时间变化模式未被代表时，城市地区的模型偏差很大。此外，我们还推导出比例因子来解释加拿大各省之间的人均二氧化碳排放量差异，这些差异来自人均能源使用的差异和不排放二氧化碳的方法所产生的能源比例，这些差异不包括在基于人口的全球排放数据集中。这些分析的结果是全球0.25度x0.25度网格比例因子图，可应用于全球化石燃料二氧化碳排放数据集，以表示每周和昼夜变化，以及1度x1度比例因子图，以重新分配两个共同的全球数据集的排放量，以说明加拿大境内人均排放量的差异。
Through an analysis of multiple global fossil fuel CO2 emission data sets, Vulcan emission data for the United States, Canada's National Inventory Report, and NO2 variability based on satellite observations, we derive scale factors that can be applied to global emission data sets to represent weekly and diurnal CO2 emission variability. This is important for inverse modeling and data assimilation of CO2, which use in situ or satellite measurements subject to variability on these time scales. Model simulations applying the weekly and diurnal scaling show that, although the impacts are minor far away from sources, surface atmospheric CO2 is perturbed by up to 1.5-8 ppm and column-averaged CO2 is perturbed by 0.1-0.5 ppm over some major cities, suggesting the magnitude of model biases for urban areas when these modes of temporal variability are not represented. In addition, we also derive scale factors to account for the large per capita differences in CO2 emissions between Canadian provinces that arise from differences in per capita energy use and the proportion of energy generated by methods that do not emit CO2, which are not accounted for in population-based global emission data sets. The resulting products of these analyses are global 0.25 degrees x 0.25 degrees gridded scale factor maps that can be applied to global fossil fuel CO2 emission data sets to represent weekly and diurnal variability and 1 degrees x 1 degrees scale factor maps to redistribute spatially emissions from two common global data sets to account for differences in per capita emissions within Canada.