Improving the temporal and spatial distribution of CO_2 emissions from global fossil fuel emission data sets

摘要

Through an analysis of multiple global fossil fuel CO_2 emission data sets, Vulcan emission data for the United States, Canada's National Inventory Report, and NO_2 variability based on satellite observations, we derive scale factors that can be applied to global emission data sets to represent weekly and diurnal CO_2 emission variability. This is important for inverse modeling and data assimilation of CO_2, 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 CO_2 is perturbed by up to 1.5-8 ppm and column-averaged CO_2 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 CO_2 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 CO_2, which are not accounted for in population-based global emission data sets. The resulting products of these analyses are global 0.25°×0.25° gridded scale factor maps that can be applied to global fossil fuel CO_2 emission data sets to represent weekly and diurnal variability and 1° ×1° scale factor maps to redistribute spatially emissions from two common global data sets to account for differences in per capita emissions within Canada.