气候变化领域集成服务门户(CCPortal): Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design

CCPortal

DOI	10.5194/acp-20-1795-2020
	Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design
	Super I.; Dellaert S.N.C.; Visschedijk A.J.H.; Van Der Gon H.A.C.D.
发表日期	2020
ISSN	1680-7316
起始页码	1795
结束页码	1816
卷号	20 期号:3
英文摘要	Quantification of greenhouse gas emissions is receiving a lot of attention because of its relevance for climate mitigation. Complementary to official reported bottomup emission inventories, quantification can be done with an inverse modelling framework, combining atmospheric transport models, prior gridded emission inventories and a network of atmospheric observations to optimize the emission inventories. An important aspect of such a method is a correct quantification of the uncertainties in all aspects of the modelling framework. The uncertainties in gridded emission inventories are, however, not systematically analysed. In this work, a statistically coherent method is used to quantify the uncertainties in a high-resolution gridded emission inventory of CO2 and CO for Europe. We perform a range of Monte Carlo simulations to determine the effect of uncertainties in different inventory components, including the spatial and temporal distribution, on the uncertainty in total emissions and the resulting atmospheric mixing ratios. We find that the uncertainties in the total emissions for the selected domain are 1 % for CO2 and 6 % for CO. Introducing spatial disaggregation causes a significant increase in the uncertainty of up to 40 % for CO2 and 70 % for CO for specific grid cells. Using gridded uncertainties, specific regions can be defined that have the largest uncertainty in emissions and are thus an interesting target for inverse modellers. However, the largest sectors are usually the best-constrained ones (low relative uncertainty), so the absolute uncertainty is the best indicator for this. With this knowledge, areas can be identified that are most sensitive to the largest emission uncertainties, which supports network design. © 2020 Author(s).
语种	英语
scopus关键词	atmospheric pollution; carbon dioxide; carbon monoxide; computer simulation; data inversion; emission inventory; Monte Carlo analysis; network design; numerical model; pollution monitoring; quantitative analysis; uncertainty analysis; Europe
来源期刊	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141546
作者单位	Department of Climate Air and Sustainability, TNO, P.O. Box 80015, TA Utrecht, 3508, Netherlands
推荐引用方式 GB/T 7714	Super I.,Dellaert S.N.C.,Visschedijk A.J.H.,et al. Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design[J],2020,20(3).
APA	Super I.,Dellaert S.N.C.,Visschedijk A.J.H.,&Van Der Gon H.A.C.D..(2020).Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design.Atmospheric Chemistry and Physics,20(3).
MLA	Super I.,et al."Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design".Atmospheric Chemistry and Physics 20.3(2020).