The first 1-year-long estimate of the Paris region fossil fuel CO2 emissions based on atmospheric inversion
The first 1-year-long estimate of the Paris region fossil fuel CO2 emissions based on atmospheric inversion
复制标题
DOI：
10.5194/acp-16-14703-2016
复制
发表时间：
2016-11-25
影响因子：
6.3
通讯作者：
Ciais, Philippe
中科院分区：
文献类型：
--
作者：
Staufer, Johannes;Broquet, Gregoire;Ciais, Philippe
贝叶斯大气反演的能力，量化的巴黎地区的化石燃料的CO2排放量每月的基础上，运行了1年的CO2-MEGAPARIS实验（2010年8月至2011年7月）的一部分，网络的基础上，进行了分析。近地面监测站点（CO2梯度），位于东北和西南边缘的城市地区，每小时的CO2大气摩尔分数的差异，用于估计6小时平均化石燃料CO2排放量。反演依赖于CHIMERE运输模式运行在2公里× 2公里的水平分辨率，在2008年的化石燃料二氧化碳排放量的空间分布，从当地的库存建立在1公里× 1公里的水平分辨率由AIRPARIF空气质量机构，并从C-TESSEL陆面模型的生物CO2通量的空间分布。它纠正了AIRPARIF 2008年清单给出的化石燃料CO2排放的6小时平均预算的先前估计。我们发现，严格选择的CO2梯度是必要的可靠的反演结果，由于大的建模不确定性。特别是，如果风速大于3 ms（-1），并且逆风站点的模拟风在顺风和逆风站点之间的横断面的+/- 15度范围内，则本研究中分析的最稳健的数据选择仅使用午后梯度。这种严格的数据选择删除了92%的每小时观测值。尽管这使得几乎没有剩余的数据来约束排放，反演系统诊断出它们的同化显著降低了月排放的不确定性：2010年11月的9%降低到2010年10月的50%。反演的月平均排放量与独立的月平均气温有很好的相关性。此外，反演的年平均排放量与2010年AIRPARIF清单的独立修订一致，该清单比2008年清单更好地对应于测量期。反演的敏感性事先排放量估计，假设的空间分布的排放量，和大气传输模型的几个测试证明了反演化石燃料CO2排放量的测量约束的鲁棒性。然而，结果表明，显着的敏感性的描述的排放量的空间分布的反演系统，表明需要依靠高分辨率的本地库存，如AIRPARIF。虽然逆温通过同化CO2梯度约束排放，但当气团来自巴黎东北部的城市化和工业化地区时，远程CO2通量的不当建模影响会阻碍结果。本研究中使用的极端数据选择限制了连续监测巴黎化石燃料CO2排放的能力：特定月份（如2010年9月或11月）的反演结果受到CO2测量值过少的限制。反演的排放量对先前排放量日变化的高度敏感性突出了仅在下午同化数据所引起的局限性。此外，即使反演改善了城市排放量的季节变化和年度预算，在有限的合适天数内同化数据并不一定会产生对个别月份的可靠估计。通过改进数据处理，扩大数据选择范围，并通过扩大观测网络，可以克服这些局限性。
The ability of a Bayesian atmospheric inversion to quantify the Paris region's fossil fuel CO2 emissions on a monthly basis, based on a network of three surface stations operated for 1 year as part of the CO2-MEGAPARIS experiment (August 2010-July 2011), is analysed. Differences in hourly CO2 atmospheric mole fractions between the near-ground monitoring sites (CO2 gradients), located at the north-eastern and south-western edges of the urban area, are used to estimate the 6 h mean fossil fuel CO2 emission. The inversion relies on the CHIMERE transport model run at 2 km x 2 km horizontal resolution, on the spatial distribution of fossil fuel CO2 emissions in 2008 from a local inventory established at 1 km x 1 km horizontal resolution by the AIRPARIF air quality agency, and on the spatial distribution of the biogenic CO2 fluxes from the C-TESSEL land surface model. It corrects a prior estimate of the 6 h mean budgets of the fossil fuel CO2 emissions given by the AIRPARIF 2008 inventory. We found that a stringent selection of CO2 gradients is necessary for reliable inversion results, due to large modelling uncertainties. In particular, the most robust data selection analysed in this study uses only mid-afternoon gradients if wind speeds are larger than 3ms(-1) and if the modelled wind at the upwind site is within +/- 15 degrees of the transect between downwind and upwind sites. This stringent data selection removes 92% of the hourly observations. Even though this leaves few remaining data to constrain the emissions, the inversion system diagnoses that their assimilation significantly reduces the uncertainty in monthly emissions: by 9% in November 2010 to 50% in October 2010. The inverted monthly mean emissions correlate well with independent monthly mean air temperature. Furthermore, the inverted annual mean emission is consistent with the independent revision of the AIRPARIF inventory for the year 2010, which better corresponds to the measurement period than the 2008 inventory. Several tests of the inversion's sensitivity to prior emission estimates, to the assumed spatial distribution of the emissions, and to the atmospheric transport modelling demonstrate the robustness of the measurement constraint on inverted fossil fuel CO2 emissions. The results, however, show significant sensitivity to the description of the emissions' spatial distribution in the inversion system, demonstrating the need to rely on high-resolution local inventories such as that from AIRPARIF. Although the inversion constrains emissions through the assimilation of CO2 gradients, the results are hampered by the improperly modelled influence of remote CO2 fluxes when air masses originate from urbanised and industrialised areas north-east of Paris. The drastic data selection used in this study limits the ability to continuously monitor Paris fossil fuel CO2 emissions: the inversion results for specific months such as September or November 2010 are poorly constrained by too few CO2 measurements. The high sensitivity of the inverted emissions to the prior emissions' diurnal variations highlights the limitations induced by assimilating data only during the afternoon.Furthermore, even though the inversion improves the seasonal variation and the annual budget of the city's emissions, the assimilation of data during a limited number of suitable days does not necessarily yield robust estimates for individual months. These limitations could be overcome through a refinement of the data processing for a wider data selection, and through the expansion of the observation network.