Emissions of methane in Europe inferred by total column measurements

Using five long-running ground-based atmospheric observatories in Europe, we demonstrate the utility of long-term, stationary, ground-based measurements of atmospheric total columns for verifying annual methane emission inventories. Our results indicate that the methane emissions for the region in Europe between Orléans, Bremen, Białystok, and Garmisch-Partenkirchen are overestimated by the state-of-the-art inventories of the Emissions Database for Global Atmospheric Research (EDGAR) v4.2 FT2010 and the high-resolution emissions database developed by the Netherlands Organisation for Applied Scientific Research (TNO) as part of the Monitoring Atmospheric Composition and Climate project (TNO-MACC_III), possibly due to the disaggregation of emissions onto a spatial grid. Uncertainties in the carbon monoxide inventories used to compute the methane emissions contribute to the discrepancy between our inferred emissions and those from the inventories.

emissions inventories that contain information about the specific point and area sources of the known emissions, and timely and long-term measurements of greenhouse gases in the atmosphere to verify that the emissions reduction targets are met.
Because atmospheric methane is well-mixed and has a lifetime of about 12 years (Stocker et al., 2013), it is transported far from its emission source, making source attribution efforts challenging from atmospheric measurements alone. Atmospheric measurements are often assimilated into "flux inversion" models to locate the sources of the emissions (e.g., Houweling et al.,5 2014) but rely on model wind fields to drive transport, and tend to have spatial resolutions that do not resolve sub-regional scales. Methane measurement schemes that constrain emissions on local and regional scales are thus important to help identify the sources of the emissions and to verify inventory analyses. Regional or country-scale emissions are important to public policy as those emissions are reported annually to the United Nations Framework Convention on Climate Change (UNFCCC).
The atmospheric measurement techniques that are used to estimate methane emissions include measurements made in situ, 10 either on the ground, from tall towers, or from aircraft. Remote sensing techniques are also used, either from space or from the ground. The spatial scale of the sensitivity to emissions differs by the measurement technique: surface in situ measurements provide information about local emissions on urban scales (e.g., McKain et al., 2015;Hopkins et al., 2016), aircraft in situ measurements can provide information about regional and synoptic-scale fluxes (e.g., Jacob et al., 2003;Kort et al., 2008Kort et al., , 2010Wofsy, 2011;Baker et al., 2012;Frankenberg et al., 2016;Karion et al., 2016). Satellite remote sensing techniques 15 provide information useful for extracting emissions information on larger scales (regional to global) (e.g., Silva et al., 2013;Schneising et al., 2014;Alexe et al., 2015;Turner et al., 2015), and for large point or urban sources (e.g., Kort et al., 2012Kort et al., , 2014Nassar et al., 2017). Several studies have shown the importance of simultaneous measurements of co-emitted species (e.g., C 2 H 6 and CH 4 ; CO and CO 2 Aydin et al., 2011;Simpson et al., 2012;Peischl et al., 2013;Silva et al., 2013;Hausmann et al., 2016;Wunch et al., 2016;Jeong et al., 2017) or co-located measurements (e.g., Wunch et al., 2009Wunch et al., , 2016 showing 20 the added analytical power of the combination of atmospheric tracer information. Ground-based remote sensing instruments have been used to estimate methane emissions on urban (e.g., Wunch et al., 2009;Hase et al., 2015;Wunch et al., 2016) and sub-urban (e.g., Chen et al., 2016;Viatte et al., 2017) scales. In Hase et al. (2015), Viatte et al. (2017), andChen et al. (2016), the authors have placed mobile ground-based remote sensing instruments around a particular emitter of interest (e.g., a city, dairy, or neighbourhood) and have designed short-term campaigns to measure the difference between upwind and 25 downwind atmospheric methane abundances. From these differences, the authors have computed emission fluxes. However, there is a network of non-mobile ground-based remote sensing instruments that have been collecting long-term measurements of atmospheric greenhouse gas abundances. These instruments were not placed intentionally around an emitter of interest, but collectively, they ought to contain information about nearby emissions. To date, there have been no studies that have attempted to extract regional methane emissions information from these existing ground-based remote sensing observatories. 30 In this paper, we will describe our methods for computing the emissions of methane using five stationary ground-based remote sensing instruments located in Europe in §2. Our results, and comparisons to the state-of-the-art inventories are shown in §3, and we summarize our results in §4.
Our study area is the region between five long-running atmospheric observatories situated in Europe. Three of the stations are in Germany: Bremen (Notholt et al., 2014), Karlsruhe (Hase et al., 2014), and Garmisch (Sussmann and Rettinger, 2014). The other two are in Poland (Białystok, Deutscher et al., 2014), and France (Orléans, Warneke et al., 2014). Each station measures the vertical column-averaged dry-air mole fraction of carbon dioxide (X CO2 ), carbon monoxide (X CO ), methane (X CH4 ), and 5 other trace gas species. The locations are shown in Figure 1, overlaid on a night lights image from NASA to provide a sense of the population density of the area. These observatories are part of the Total Carbon Column Observing Network (TCCON, Wunch et al., 2011), and have been tied to the World Meteorological Organization trace-gas scale through comparisons with vertically integrated, calibrated in situ profiles over the observatories (Wunch et al., 2010;Messerschmidt et al., 2011;Geibel et al., 2012;Wunch et al., 2015).

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Following a similar method to Wunch et al. (2009Wunch et al. ( , 2016, we estimate emissions of methane from the data recorded from the TCCON observatories, coupled with gridded inventories of carbon monoxide within the region. We compute changes (or "anomalies") in X CH4 and X CO that we will refer to as ∆X CH4 and ∆X CO , and then compute the slopes relating ∆X CH4 to ∆X CO . From the computed slopes (α), we can infer emissions of methane (E CH4 ) if emissions of carbon monoxide (E CO , in mass per unit time) are known, using the following relationship: where m CH 4 m CO is the ratio of the molecular masses of CH 4 and CO. In the Wunch et al. (2009Wunch et al. ( , 2016 papers, measurements from a single atmospheric observatory were used to infer emissions, because the unique dynamics of the region advected the polluted airmass into and out of the study area diurnally. In this paper, we rely on several stations to provide measurements of the boundary of the study region to measure CO and CH 4 emitted 20 between the stations. This analysis relies on a few assumptions about the nature of the emissions. First, that the lifetimes of the gases of interest are longer than the transport time within the region. This is the case both for methane, which has an atmospheric lifetime of 12 years, and for carbon monoxide, which has an atmospheric lifetime of a few weeks. Second, we assume that typical emissions are consistent over time periods longer than a few days so that they are advected together. The nature of the emissions in this region (mostly residential and industrial energy needs) supports this assumption. Third, we 25 assume that the spatial distribution of the emissions is similar for CH 4 and CO, as confirmed by the inventory maps (Fig.   A3). This method does not require that carbon monoxide and methane are co-emitted (as they generally do not have the same emissions sources).
To compute anomalies and slopes, we first filter the data to minimize the impacts of data sparsity and air mass differences between stations (Appendix A). Then, for each station, the daily median value is subtracted from each measurement. This 30 reduces the impact of the station altitude and any background seasonal cycle from aliasing into the results. Subsequently, we compute the differences in the X CH4 and X CO abundances measured at the same solar zenith and solar azimuth angles on the same day at two TCCON stations. By computing anomalies at the same solar zenith angles, we minimize any impact that airmass-dependent biases could have on the calculated anomalies. This analysis is repeated for all combinations of pairs of stations within the study area. The vertical sensitivity of the TCCON measurements is explicitly taken into account by dividing the anomalies by the surface layer column averaging kernel value, as we assume that the anomalies are due to emissions near the surface. The slopes computed for each year for each pair of stations are shown in Figure 2.
The farthest distance between the European TCCON stations included in this study is between Orléans and Białystok (1580 km). Climatological annual mean surface wind speeds from the NCEP/NCAR reanalysis (Kalnay et al., 1996) within the 5 study area are about 6 km · h −1 (Fig. A1). The air from Orléans will quickly mix vertically from the surface where the winds aloft are more rapid than at the surface (see Appendix B). Thus, air from Orléans would normally reach Białystok in a few days. To determine whether these anomalies are consistent throughout the transport time through the study area, we compute anomalies between sites lagged by up to 14 days. The slopes of the anomalies do not change significantly or systematically with the lag time (Appendix B; Fig. A2), presumably because the atmospheric composition within the study area is relatively 10 well-mixed or because the emissions are relatively consistent from day to day within the study area.
Previous papers have used carbon dioxide instead of carbon monoxide to infer methane emissions. We choose to compute emissions using measurements of X CO instead of X CO2 in this work because the natural CO 2 fluxes in the region are large compared with the anthropogenic emissions, and they have a strong diurnal and seasonal cycle. The distance between the stations is large enough that local (sub-daily) uptake of CO 2 differs from station to station, significantly obscuring the re-15 lationships between methane and carbon dioxide, and thus the anomaly slopes, especially in the summer months. While the emissions inventory of anthropogenic CO 2 may be more accurate than the CO inventory in the region, the presence of these large natural fluxes of CO 2 precludes its use in the anomaly slope calculation. The accuracy of our method, then, is limited by the accuracy of the carbon monoxide emission inventory. Fires could provide a large flux of CO without a large CH 4 flux, and this should also be taken into consideration in these types of analyses. In our study area, fluxes from fires are small. 20

Inventories
To obtain an estimate of carbon monoxide emissions (E CO ) within the study area, we use gridded inventories, and sum the emissions within the study area to compare with our emissions inferred from the TCCON measurements (see Appendix  Using country-level emissions reported through 2015 from the European Environment Agency (EEA, 2015), we extrapolate the EDGAR and TNO-MACC gridded inventory CO emissions for the study area through 2015. This facilitates more direct comparisons with the TCCON measurements, which begin with sufficient data for our study in 2009. We extrapolate the emissions by scaling the total emissions from the countries that are intersected by the area of interest (Germany, Poland, Belgium, France, Luxembourg, Czech Republic) to the last reported year of emissions from the inventory. We then assume that the same scaling factor applies for each subsequent year. The details of the extrapolation method are in Appendix D and Figs.
The time series of the reported emissions from 2000-2015 are shown in Fig. 3. The inventories and scaled country-level reported emissions for this region suggest that emissions of CO and CH 4 have decreased by about 40% and 20%, respectively, 5 between 2000 and 2015. The TNO-MACC_III carbon monoxide emissions are on average 15% higher than the EDGAR v.4.3.1 emissions in the study area. The total TNO-MAC_III and EDGAR methane emissions agree to within 2% in the study area.
An earlier version of the EDGAR carbon monoxide inventory was evaluated by Stavrakou and Müller (2006) and Fortems-Cheiney et al. (2009), who assimilated satellite measurements of CO using the EDGAR v3.3FT2000 CO emissions inventory as the a priori. Stavrakou and Müller (2006) found that over Europe, the a posteriori emissions increase by less than 15% The more recent EDGAR v4.3.1 CO emissions in our study are 24% lower than the EDGAR v3.3FT2000 CO emissions for the year 2000, so it may be that the EDGAR v4.3.1 CO emissions are significantly underestimated. However, assimilations 15 of CO are known to be very sensitive to the chemistry described in the model: most notably the OH chemistry (Protonotariou et al., 2010;Yin et al., 2015). Therefore it is difficult to determine how much of the discrepancy between versions of the model is from the inventory or the model chemistry.
The EDGAR methane inventory has been evaluated in several previous studies. It has been shown to overestimate regional CH 4 emissions (e.g., Wunch et al., 2009;Wecht et al., 2014), but to underestimate oil and gas emissions (e.g., Miller et al., 20 2013;Buchwitz et al., 2017). However, recent methane isotope analysis by Röckmann et al. (2016) has suggested that the EDGAR inventory overestimates fossil fuel-related emissions. The study area of interest here has little oil and gas production, except for some test sites in Poland (USEIA, 2015), no commercial shale gas industry, and few pipelines.

Model Experiment
To test whether the anomaly method described in §2 can accurately infer methane emissions, we conducted a modeling exper-  Silva, 2015). The biomass burning in the study area produces less than 2% of the 5 total anthropogenic emissions of CO.
We used identical OH fields (from version v7-02-03 of GEOS-Chem) for the CO and CH 4 simulations so that the chemical losses of methane and carbon monoxide are consistent, and ran tagged CO experiments so that we could identify the source of the emissions. The model atmospheric carbon monoxide and methane profiles were integrated to compute simulated X CO and X CH4 . These were interpolated to the locations of the TCCON stations in this study, and anomalies and slopes were 10 computed following the method applied to the observed atmospheric data. We then applied Equation 1 to our anomaly slopes to compute methane emissions from the known CO emissions, accounting for only the CO emissions from anthropogenic, biomass burning, and biofuel sources. We neglect sources of CO emissions from the oxidation of CH 4 and VOCs because the column enhancements for those emissions are relatively spatially uniform across Europe and thus should not contribute significantly to the anomalies. The resulting annual CH 4 emissions agree well with the model emissions: the inferred emissions 15 from the anomaly analysis are higher than the model emissions by less than 2% percent (Fig. 4).
While the inferred annual emissions agree well with the modeled annual emissions, the seasonal pattern of the emissions inferred from the anomaly analysis differs from that of the model. The anomaly analysis overestimates emissions in the winter and underestimates emissions in the summer. This may be due to small spatial inhomogeneities in the column enhancements from VOC (biogenic) emissions that influence the anomaly analysis most in summertime when VOC emissions are largest. 20 Including the VOC emissions in the total carbon monoxide emissions leads us to infer annual methane emissions that are overestimated by 15%, increasing the inferred summertime emissions without significantly changing the inferred wintertime emissions.
This modeling experiment shows that for this region of Europe, where VOC and methane oxidation emissions lead to relatively spatially uniform column enhancements, and fire emissions are small, we can successfully use the anomaly method 25 described in §2 to infer annual methane emissions.

Results and Discussion
To compute methane emissions, we apply equation 1 to our anomaly slopes and the inventory-reported carbon monoxide emissions in the study region (Fig. 5). If we choose the mean of the reported CO emissions from EDGAR v4.3.1 and TNO-MACC_III, the methane emissions we compute within the study area based on the TCCON measurements are 30 1.7 ± 0.3 Tg · yr −1 in 2009, with a non-monotonic decrease to 1.2 ± 0.3 Tg · yr −1 in 2015. The uncertainties quoted here are from the standard errors on the data slope fitting only; we have not included uncertainties from the inventories. The magnitude of methane emissions we compute from the TCCON data are, on average, about 2.3 times lower than the methane emissions reported by EDGAR, and about 2 times lower than the methane emissions reported by TNO-MACC_III.
Our method of inferring methane emissions depends critically on the carbon monoxide inventory. The carbon monoxide emissions for 2010 in the study area from our GEOS-Chem model run, derived from EMEP emissions, were 6.4 Tg, about 35% higher than the average of the EDGAR and TNO-MACC_III emissions for that year. This magnitude underestimate has 5 also been suggested by Stavrakou and Müller (2006) and Fortems-Cheiney et al. (2009) using independent data. Using the GEOS-Chem carbon monoxide emissions increases the methane emissions inferred by the anomaly analysis to 2.4 ± 0.3 Tg in 2010. This value remains lower than the EDGAR and TNO-MACC_III methane emissions estimates for 2010, which are 3 Tg, but by only 20%. Therefore, we find that the inventories likely overestimate methane emissions, but the accuracy of our results relies on the accuracy of the carbon monoxide inventory.

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Although the EDGAR and TNO-MACC_III inventories agree to within 15% in carbon monoxide emissions and 2% in methane emissions in the study region, they spatially distribute these emissions differently. Maps of the spatial differences between the TNO-MACC_III and EDGAR emissions are shown in Fig. 6 for carbon monoxide and Fig. 7 for methane. EDGAR estimates larger emissions of carbon monoxide from the main cities in the study region and the surrounding areas. This is clearly visible from the difference map (Fig. 6), where cities such as Hamburg, Berlin, Prague, Wrocław, Warsaw, Munich, Paris, and 15 Vienna appear in blue. However, the overall carbon monoxide emissions from TNO-MACC_III in the study area are higher than EDGAR, and this comes from regions between the main cities, particularly in Poland and eastern France.
The differences between EDGAR and TNO-MACC_III methane emissions also show that the EDGAR emissions estimates near large cities are significantly larger (Fig. 7). In contrast to the carbon monoxide spatial distribution, the TNO-MACC_III methane emissions are generally smaller everywhere, except for discrete point sources. 20 Comparing 2010 reported country-level carbon monoxide emissions with the inventories shows reasonable agreement, which is expected since the inventories use country-level reports as input. The sum of the carbon monoxide emissions within the entire countries of Germany, Poland, France, Luxembourg, Belgium, and Czech Republic differ between EDGAR and TNO-MACC_III by 18%, with EDGAR estimates lower than those from TNO-MACC_III. Emissions from Germany, most of which are included in the study area, differ by only 6% between EDGAR and TNO-MACC_III, again with EDGAR estimates lower 25 than TNO-MACC_III. The national carbon monoxide emissions reported to the Convention on Long-range Transboundary Air Pollution (LRTAP Convention, https://www.eea.europa.eu/ds_resolveuid/0156b7a0ca47485593e7754c52c24afd, EEA, 2015) agree to within a few percent of the TNO-MACC_III country-level emissions (e.g., 5.5% for Germany in 2010).
The differences between 2010 country-level emissions estimates are larger for methane: EDGAR estimates are larger than TNO-MACC_III estimates by 36% when summing all countries intersected by the study area, and 8% when considering only 30 German emissions. The TNO-MACC_III country-level emissions estimates agree to within a few percent of the UNFCCC (http://di.unfccc.int/time_series) country-level reported methane emissions (e.g., 8% for Germany in 2010).
The differences between the EDGAR and TNO-MACC_III inventories suggest that the spatial distribution of emissions is less certain than the larger-scale emissions, since the total carbon monoxide and methane emissions between the inventories agree to within 15% and 2% respectively in the study area, but these estimates can disagree by a factor of two on city scales. 35 If we assume that the country-scale methane emissions are correctly reported in EDGAR and TNO-MACC_III, our results indicate that the methane emissions in the region are incorrectly spatially distributed in the inventories. It could be that point or urban sources outside the study area, but within the countries intersected by the study area, emit a larger proportion of the country-level emissions than previously thought.

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Using co-located measurements of methane and carbon monoxide from five long-running ground-based atmospheric observing stations, we have shown that in the area of Europe between Orléans, Bremen, Białystok, and Garmisch, the inventories likely overestimate methane emissions, and point to a large uncertainty in the spatial distribution (i.e., the spatial disaggregation) of country-level emissions. However, the magnitude of our inferred methane emissions relies heavily on the EDGAR v4.3.1 and TNO-MACC_III carbon monoxide inventories, and thus there is a need for rigorous validation of the carbon monoxide 10 inventories.
This study demonstrates the potential of clusters of long term ground-based stationary monitoring of total columns of atmospheric greenhouse and tracer gases. It also shows the potential of having co-located measurements of multiple pollutants to derive better estimates of emissions. These types of observing systems can help policymakers verify that greenhouse gas emissions are reducing at a rate necessary to meet regulatory obligations. The atmospheric measurements are agnostic to the 15 source (and country of origin) of the methane, measuring only what is emitted into the atmosphere in a given area. Thus they can help validate and reveal inadequacies in the current inventories, and in particular, how country-wide emission reports are disaggregated on a grid. To enhance these results, simultaneous measurements of complementary atmospheric trace gases, such as ethane, acetylene, nitrous oxide, nitrogen dioxide, ammonia, and isotopes would help distinguish between sources of methane. This would provide additional, valuable information that would likely improve inventory disaggregation. The filtering method was designed to remove days of data for which the atmospheric air mass was inconsistent between sites (e.g., a front was passing through or there were significant stratospheric incursions into the troposphere), and for years in which there were too few simultaneous measurements at a pair of TCCON stations to compute robust annually-representative anomalies.
To address the consistency of the air mass between sites, we retained days on which the retrievals of X HF were between 50 ppt and 100 ppt, and deviated by less than 10 ppt of the median X HF value for all sites on that day. HF is a trace gas that exists only in the stratosphere, and thus serves as a tracer of tropopause height (Washenfelder et al., 2003;Saad et al., 2014).
Since the concentration of CH 4 decreases significantly above the tropopause in the mid-latitudes, its total column dry-air mole fraction (X CH4 ) is sensitive to the tropopause height. Filtering out days on which X HF varies significantly between sites also 5 ensures that the anomalies (and thus the slopes) are minimally impacted by stratospheric variability. This filter removed less than 5% of the data.
To ensure that the anomalies are representative of the full year, we require that each year has 400 coincident measurements across at least three seasons.
Appendix B: Transport time between stations 10 Figure A1 shows the annual change in monthly mean climatological wind speeds from the NCEP/NCAR reanalysis (Kalnay et al., 1996). These are interpolated to surface pressure and 850 hPa pressures (~1500 m geopotential height) from model (sigma) surfaces and cover January 1948 through March 2017. Vertical mixing into the boundary layer occurs on the time scale of a day or two (Jacob, 1999), and thus the relevant wind speed is between the surface and 850 hPa. The annual mean surface wind speed is 6 km · hr −1 , which gives a mean transit time between Orléans and Białystok of 11 days. The annual mean 850 15 hPa winds are 17 km · hr −1 , which give a shorter mean transit time between Orléans and Białystok of 4 days.
To test whether the transport time impacts the anomalies, we computed the slopes for time lags between sites of 0 − 14 days. Figure A2 shows a small change in anomaly slope as a function of the lag used to calculate the anomalies. This figure shows that the transport time between TCCON stations is of negligible importance to the slopes and lends weight to the decision to compute anomalies from data recorded at two TCCON stations on the same day.

Appendix D: Projecting inventory emissions beyond 2010
Using data from the European Environment Agency National Database (European Environment Agency, 2016), we extrapolate the inventory CO and CH 4 emissions for the study area through 2015. This is done by summing the total emissions for the 30 five countries that are intersected by the study area (France, Belgium, Germany, Poland, Luxembourg, Czech Republic), and normalizing the emissions to the last year of the inventory (2010 for EDGAR, 2011 for TNO-MACC_III). Figures A4 and A5 show the process for the EDGAR and TNO-MACC_III CO and CH 4 emissions, respectively.
The top panel of Fig. A4 shows the reported country-level emissions for the years 1990-2015, their sum (black stars), and the sum of the inventory emissions for the years available (20002000-2011 in squares.

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The second and third panels show the ratio of the country-level emissions to the area emissions, normalized to 1 for the last year available in the inventory. These panels show that the ratio of the summed country total emissions to the emissions from the area of interest is less variable from year to year than the emissions reported for individual countries. Thus, we choose to extrapolate the area emissions using the country total emissions, scaled to the last year of the inventory for the study area.
The bottom panel shows the results of using a single scaling factor to estimate the study area emissions from the country-10 level emissions for each year. We use the summed study area emissions for the years available, and the extrapolated emissions through 2015 for subsequent analysis (e.g., Figs. 3 and 5).  8, 3059-3068, doi:10.5194/amt-8-3059-2015, http://www.atmos-meas-tech.net/8/3059/2015/, 2015. Hausmann, P., Sussmann, R., and Smale, D.: Contribution of oil and natural gas production to renewed increase in atmospheric methane (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014): Top-down estimate from ethane and methane column observations, Atmospheric Chemistry andPhysics, 16, 3227-3244, doi:10.5194/acp-16-3227-2016, 2016. Houweling, S., Krol, M., Bergamaschi, P., Frankenberg, C., Dlugokencky, E. J., Morino, I., Notholt, J., Sherlock, V., Wunch, D., Beck, V., Gerbig, C., Chen, H., Kort, E. A., Röckmann, T., and Aben, I.: A multi-year methane inversion using SCIAMACHY, accounting for Jacob, D. J., Crawford, J. H., Kleb, M. M., Connors, V. S., Bendura, R. J., Raper, J. L., Sachse, G. W., Gille, J. C., Emmons, L., and Heald, C. L.: Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft mission: Design, execution, and first results, Journal of Geophysical Research, 108, 9000, doi:10.1029/2002JD003276, http://doi.wiley.com/10.1029/2002JD003276, 2003 Estimating methane emissions from biological and fossil-fuel sources in 5 the San Francisco Bay Area, Geophysical Research Letters, pp. 486-495, doi:10.1002/2016GL071794, http://doi.wiley.com/10.1002/ 2016GL071794, 2017    Warm (red) colours indicate that the TNO-MACC_III inventory is larger than the EDGAR inventory; cool (blue) colours indicate that the EDGAR inventory is larger than TNO-MACC_III.    Figure A4. This four-panel plot shows the methodology for scaling the country-level reported emissions of CO to extrapolate the gridded inventory emissions to 2015. The top panel shows the CO emissions reported by the European Environment Agency (EEA) for the countries contained within the study area (Germany, France, Czech Republic, Belgium, Luxembourg, and Poland). The black stars with a joining line represent the summed total from the five countries. The EDGAR (green) and TNO-MACC_III (orange) inventories summed within the study area are plotted with squares joined by solid lines. The second panel shows the ratio between the individual country totals and the EDGAR area total, normalized to produce an emission ratio of 1 in 2010. The quantity with the least interannual variability in the ratio is from the country total (black stars with line). The third panel shows the ratio between the individual country totals and the TNO-MACC_III area total, normalized to produce an emission ratio of 1 in 2011. The quantity with the least interannual variability in the ratio is, again, from the country total. The bottom panel shows the scaled country total, normalized to produce the EDGAR CO emissions for 2010 and the TNO-MACC_III CO emissions for 2011. This permits us to compute a sensible emission for the study area through to 2015.  Figure A5. This four-panel plot shows the methodology for scaling the country-level emissions of CH4 reported to the UNFCCC to extrapolate the gridded inventory emissions to 2015. The panels and symbols follow the same description as in Fig. A4.