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Uncertainty Analysis of a European High-Resolution Emission Inventory of CO2 and CO to Support Inverse Modelling and Network Design Atmos. Chem. Phys., 20, 1795–1816, 2020 https://doi.org/10.5194/acp-20-1795-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design Ingrid Super, Stijn N. C. Dellaert, Antoon J. H. Visschedijk, and Hugo A. C. Denier van der Gon Department of Climate, Air and Sustainability, TNO, P.O. Box 80015, 3508 TA Utrecht, the Netherlands Correspondence: Ingrid Super ([email protected]) Received: 1 August 2019 – Discussion started: 25 September 2019 Revised: 14 January 2020 – Accepted: 16 January 2020 – Published: 14 February 2020 Abstract. Quantification of greenhouse gas emissions is re- 1 Introduction ceiving a lot of attention because of its relevance for cli- mate mitigation. Complementary to official reported bottom- Carbon dioxide (CO2) is the most abundant greenhouse gas up emission inventories, quantification can be done with an and is emitted in large quantities from human activities, es- inverse modelling framework, combining atmospheric trans- pecially from the burning of fossil fuels (Berner, 2003). A port models, prior gridded emission inventories and a net- reliable inventory of fossil fuel CO2 (FFCO2) emissions is work of atmospheric observations to optimize the emission important to increase our understanding of the carbon cycle inventories. An important aspect of such a method is a cor- and how the global climate will develop in the future. The im- rect quantification of the uncertainties in all aspects of the pact of CO2 emissions is visible on a global scale and inter- modelling framework. The uncertainties in gridded emission national efforts are required to mitigate climate change, but inventories are, however, not systematically analysed. In this cities are the largest contributors to FFCO2 emissions (about work, a statistically coherent method is used to quantify the 70 %, IEA, 2008). Therefore, emissions should be studied at uncertainties in a high-resolution gridded emission inventory different spatial and temporal scales to get a full understand- of CO2 and CO for Europe. We perform a range of Monte ing of their variability and mitigation potential. Carlo simulations to determine the effect of uncertainties One way of describing emissions is an emission inven- in different inventory components, including the spatial and tory, which is a structured set of emission data, distinguishing temporal distribution, on the uncertainty in total emissions different pollutants and source categories. Often, emission and the resulting atmospheric mixing ratios. We find that the inventories are based on reported emission data, for exam- uncertainties in the total emissions for the selected domain ple, from the National Inventory Reports (NIRs) (UNFCCC, are 1 % for CO2 and 6 % for CO. Introducing spatial disag- 2019), which are national, yearly emissions based on energy gregation causes a significant increase in the uncertainty of statistics. These country-level emissions can be spatially and up to 40 % for CO2 and 70 % for CO for specific grid cells. temporally disaggregated (scaled-down) to a certain level us- Using gridded uncertainties, specific regions can be defined ing proxies (e.g. the inventories of the Netherlands Organ- that have the largest uncertainty in emissions and are thus an isation for Applied Scientific Research (TNO); Denier van interesting target for inverse modellers. However, the largest der Gon et al., 2017; Kuenen et al., 2014). Other emission sectors are usually the best-constrained ones (low relative un- inventories are based on local energy consumption data and certainty), so the absolute uncertainty is the best indicator reported emissions, which are (dis)aggregated to the required for this. With this knowledge, areas can be identified that are spatial scale (e.g. Hestia, Gurney et al., 2011, 2019) or rely on most sensitive to the largest emission uncertainties, which (global) statistical data and a consistent set of (non-country- supports network design. specific) emission factors representing different technology levels, e.g. Emissions Database for Global Atmospheric Re- search (EDGAR) (http://edgar.jrc.ec.europa.eu, last access: Published by Copernicus Publications on behalf of the European Geosciences Union. 1796 I. Super et al.: Uncertainty analysis of a European high-resolution emission inventory 6 May 2019). Most inventories, including the one used in this files to calculate annual emissions. Therefore, knowledge of study, rely on a combination of methods, using large-scale uncertainties in temporal profiles helps to better quantify the data supplemented with local data. Gridded emission inven- uncertainty in these annual emissions. Finally, emission un- tories are essential as input for atmospheric transport models certainties can support atmospheric observation system de- to facilitate comparison with observations of CO2 concentra- sign, for example, for inverse modelling studies. An ensem- tions, as well as in inverse modelling as a prior estimate of ble of model runs can represent the spread in atmospheric the emission locations and magnitude. concentration fields due to the uncertainty in emissions. Lo- During the compilation of an emission inventory, uncer- cations with a large spread in atmospheric concentrations are tainties are introduced at different levels (e.g. magnitude, most sensitive to uncertainties in the emission inventory and timing or locations), and increasingly more attention is given are preferential locations for additional atmospheric mea- to this topic. Parties of the United Nations Framework Con- surements. To conclude, emission uncertainties are a criti- vention on Climate Change (UNFCCC) report their annual cal part of emission verification systems and require more emissions (disaggregated over source sectors and fuel types) attention. To better understand how uncertainties in underly- in a NIR (UNFCCC, 2019), which includes an assessment ing data affect the overall uncertainty in gridded emissions, of the uncertainties in the underlying data and an analysis of a family of 10 emission inventories is compiled within the the uncertainties in the total emissions following IPCC (In- CO2 Human Emissions (CHE) project, which is funded by tergovernmental Panel on Climate Change) guidelines. The the Horizon 2020 EU Research and Innovation programme simplest uncertainty analysis is based on simple equations (see data availability). The methodology used to create this for combining uncertainties from different sources (Tier 1 ap- family of emission inventories also forms the basis for the proach). A more advanced approach is a Monte Carlo simu- work described here. lation, which allows for non-normal uncertainty distributions In this paper, we illustrate a statistically coherent method (Tier 2 approach). The Tier 2 approach has been used by sev- to assess the uncertainties in a high-resolution emission in- eral countries, for example, Finland (Monni et al., 2004) and ventory, including uncertainties resulting from spatiotempo- Denmark (Fauser et al., 2011). ral disaggregation. For this purpose, we use a Monte Carlo These reports provide a good first step in quantifying emis- simulation to propagate uncertainties in underlying param- sion uncertainties, but the uncertainty introduced by using eters into the total uncertainty in emissions (like the Tier 2 proxies for spatial and temporal disaggregation is not consid- approach). We illustrate our methodology using a new high- ered. These are, however, an important source of uncertainty resolution emission inventory for a European region centred in the gridded emission inventories (Andres et al., 2016). In- over the Netherlands and Germany (Table 1, Fig. 1). We illus- verse modelling studies are increasingly focusing on urban trate the magnitude of the uncertainties in emissions and how areas, the main source areas of FFCO2 emissions, for which this affects simulated concentrations. The research questions emission inventories with a high spatiotemporal resolution are as follows: are used to better represent the variability in local emissions affecting local concentration measurements. Understanding 1. How large are uncertainties in total inventory emissions, the uncertainty at higher resolution than the country level is and how does this differ per sector and country? thus necessary, which means that the uncertainty caused by spatiotemporal disaggregation becomes important as well. 2. How do uncertainties in spatial proxy maps affect local The uncertainties in emission inventories are important measurements? to understand for several reasons. First, knowledge of un- certainties helps to pinpoint emission sources or areas that 3. How important is the uncertainty in temporal profiles require more scrutiny (Monni et al., 2004; Palmer et al., for the calculation of annual emissions from daytime 2018). Second, knowledge of uncertainties in prior emission (12:00–16:00 LT) emissions, which result from urban estimates is an important part of inverse modelling frame- inverse modelling studies using only daytime observa- works, which can be used for emission verification and in tions? support of decision-making (Andres et al., 2014). For ex- ample, if uncertainties are not properly considered, there is 4. What information can we gain from high-resolution a risk that the uncertainty range does not contain the ac- gridded uncertainty maps by comparing different re- tual emission
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