Recent global policy agreements have led to renewed efforts to reduce greenhouse gas emissions to cap global temperature rise (e.g., Conference of the Parties 21, COP 21;; ). This, in turn, has motivated countries to seek methods of reducing their greenhouse gas emissions. In Europe, methane emissions account for a significant fraction (about 11 % by mass of CO2 equivalent) of the total greenhouse gas emissions . The lifetime of atmospheric methane is significantly shorter than for carbon dioxide, its 100-year global warming potential is significantly larger, and it is at near steady state in the atmosphere; therefore, significant reductions in methane emissions are an effective short-term strategy for reducing greenhouse gas emissions . Emission reduction strategies that include both methane emission reductions and carbon dioxide reductions are thought to be among the most effective at slowing the increase in global temperatures . Thus, it is important to know exactly how much methane is being emitted and the geographic and temporal source of the emissions. This requires an approach that combines state-of-the-art 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 , 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., but rely on model wind fields to drive transport, and they also tend to have spatial resolutions that do not resolve subregional 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 national-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, 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 with the measurement technique: surface in situ measurements provide information about local emissions on urban scales e.g.,, and aircraft in situ measurements can provide information about regional- and synoptic-scale fluxes e.g.,. Satellite remote sensing techniques provide information useful for extracting emission information on larger scales (regional to global) e.g., and for large point or urban sources e.g.,. Several studies have shown the importance of simultaneous measurements of co-emitted species e.g., C2H6 and CH4 or CO and CO2, or co-located measurements e.g.,, showing 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., and sub-urban e.g., scales. In , , and , the authors have placed mobile ground-based remote sensing instruments around a particular emitter of interest (e.g., a city, dairy, or neighborhood) and have designed short-term campaigns to measure the difference between upwind and downwind atmospheric methane abundances. From these differences the authors have computed emission fluxes. However, there is a network of nonmobile 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 emission information from these existing ground-based remote sensing observatories.

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 Sect. . Our results and comparisons to the state-of-the-art inventories are shown in Sect. , and we summarize our results in Sect. .

Our study area is the region between five long-running atmospheric observatories situated in Europe. Three of the stations are in Germany: Bremen , Karlsruhe , and Garmisch-Partenkirchen . The other two are in Poland Białystok, and France Orléans,. Each station measures the vertical column-averaged dry-air mole fraction of carbon dioxide (XCO2), carbon monoxide (XCO), methane (XCH4), and other trace gas species. The locations are shown in Fig. , overlaid on a nighttime light image from the National Aeronautics and Space Administration (NASA) to provide a sense of the population density of the area. These observatories are part of the Total Carbon Column Observing Network TCCON, and have been tied to the World Meteorological Organization trace-gas scale through comparisons with vertically integrated, calibrated in situ profiles over the observatories .

Following a similar method to , 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 XCH4 and XCO that we will refer to as ΔXCH4 and ΔXCO, and we then compute the slopes relating ΔXCH4 to ΔXCO. From the computed slopes (α), we can infer emissions of methane (ECH4) if emissions of carbon monoxide (ECO, in mass per unit time) are known, using the following relationship: 1ECH4=αmCH4mCOECO, where mCH4mCO is the ratio of the molecular masses of CH4 and CO.

In , measurements from a single atmospheric observatory were used to infer emissions because the unique dynamics of the region advected the polluted air mass 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 CH4 emitted 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 assume that the spatial distribution of the emissions is similar for CH4 and CO, as confirmed by the inventory maps (Fig. ). This method does not require carbon monoxide and methane to be 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 impact of data sparsity and air mass differences between stations (Appendix ). Then, for each station, the daily median value is subtracted from each measurement. This reduces the impact of the station altitude and any background seasonal cycle from aliasing into the results. Subsequently, we compute the differences in the XCH4 and XCO 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 air-mass-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 and each pair of stations are shown in Fig. .

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 National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) reanalysis within the study area are about 6 kmh-1 (Fig. ). The air from Orléans will quickly mix vertically from the surface where the winds aloft are more rapid than at the surface (see Appendix ). 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 ; Fig. ), presumably because the atmospheric composition within the study area is relatively 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 XCO instead of XCO2 in this work because the natural CO2 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 CO2 differs from station to station, significantly obscuring the relationships between methane and carbon dioxide, and thus the anomaly slopes, especially in the summer months. While the emissions inventory of anthropogenic CO2 may be more accurate than the CO inventory in the region, the presence of these large natural fluxes of CO2 precludes its use in the anomaly slope calculation. The accuracy of our method, therefore, is limited by the accuracy of the carbon monoxide emission inventory. Fires could provide a large flux of CO without a large CH4 flux, and this should also be taken into consideration in these types of analyses. In our study area fluxes from fires are small.

To compute methane emissions, we apply Eq. () to our anomaly slopes and the inventory-reported carbon monoxide emissions in the study region (Fig. ). 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 1.7±0.3 Tgyr-1 in 2009, with a non-monotonic decrease to 1.2±0.3 Tgyr-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 also been suggested by and 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.

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.  for carbon monoxide and Fig.  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. ), where cities such as Hamburg, Berlin, Prague, Wrocław, Warsaw, Munich, Paris, and 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. ). In contrast to the carbon monoxide spatial distribution, the TNO-MACC_III methane emissions are generally smaller everywhere, except for discrete point sources.

Comparing country-level carbon monoxide emissions reported in 2010 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 the 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 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, last access: 15 November 2017, 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 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, last access: 15 November 2017) 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 2 on city-level scales.

If we assume that the national-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.

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-Partenkirchen, 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 inventories.

This study demonstrates the potential of clusters of long-term ground-based stations monitoring 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 policy makers verify that greenhouse gas emissions are reducing at a rate necessary to meet regulatory obligations. The atmospheric measurements are agnostic to the 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.