Abstract
Abstract. We present inverse modelling (top down) estimates of European methane (CH4) emissions for 2006–2012 based on a new quality-controlled and harmonised in situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.8 (20.2–29.7) Tg CH4 yr−1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006–2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr−1 (2006) to 18.8 Tg CH4 yr−1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3–8.2) Tg CH4 yr−1 from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. The estimated average regional biases range between −40 and 20 % at the aircraft profile sites in France, Hungary and Poland.
Highlights
Atmospheric methane (CH4) is the second most important long-lived anthropogenic greenhouse gas (GHG) after carbon dioxide (CO2) and contributed ∼ 17 % to the direct anthropogenic radiative forcing of all long-lived GHGs in 2016, relative to 1750 (NOAA Annual Greenhouse Gas Index, AGGI; Butler and Montzka, 2017)
Taking into account the estimates of the WETCHIMP ensemble brings the results of the six inverse models that derive high emissions into the upper uncertainty range of the sum of anthropogenic emissions and wetland emissions, while the emissions derived by NAME fall in the lower range (Fig. 3b)
This analysis suggests broad consistency between bottom-up and top-down emission estimates, albeit with a clear tendency (6 of 7 models) towards the upper range of the bottom-up inventories for the total CH4 emissions from the EU-28. This behaviour is apparent for western and southern Europe, while for eastern Europe several models are close to or above the upper uncertainty bound (NAME is very close to the mean), and for northern Europe several models are in the lower range of the combined United Nations Framework Convention on Climate Change (UNFCCC) and WETCHIMP inventory
Summary
Atmospheric methane (CH4) is the second most important long-lived anthropogenic greenhouse gas (GHG) after carbon dioxide (CO2) and contributed ∼ 17 % to the direct anthropogenic radiative forcing of all long-lived GHGs in 2016, relative to 1750 (NOAA Annual Greenhouse Gas Index, AGGI; Butler and Montzka, 2017). In situ measurements at regional surface monitoring stations can directly monitor the atmospheric mole fractions within the boundary layer, providing strong constraints on regional emissions These regional monitoring stations have been set up over the past years, especially in the United States (Andrews et al, 2014) and Europe Levin et al, 1999; Lopez et al, 2015; Popa et al, 2010; Schmidt et al, 2014; Vermeulen et al, 2011) The measurements from these stations were used in a number of inverse modelling studies to estimate emissions at regional and national scales (Bergamaschi et al, 2010, 2015; Ganesan et al, 2015; Henne et al, 2016; Kort et al, 2008; Manning et al, 2011; Miller et al, 2013). We examine in more detail the potential contribution of natural emissions (such as peatlands, wetlands, or wet soils) using seven different wetland inventories from the Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP) (Melton et al, 2013; Wania et al, 2013)
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