Abstract. We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF 6 ) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF 6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods.

Abstract. European CH 4 and N 2 O emissions are estimated for 2006 and 2007 using four inverse modelling systems, based on different global and regional Eulerian and Lagrangian transport models. This ensemble approach is designed to provide more realistic estimates of the overall uncertainties in the derived emissions, which is particularly important for verifying bottom-up emission inventories.We use continuous observations from 10 European stations (including 5 tall towers) for CH 4 and 9 continuous stations for N 2 O, complemented by additional European and global discrete air sampling sites. The available observations mainly constrain CH 4 and N 2 O emissions from northwestern and eastern Europe. The inversions are strongly driven by the observations and the derived total emissions of larger countries show little dependence on the emission inventories used a priori.Three inverse models yield 26-56 % higher total CH 4 emissions from north-western and eastern Europe compared Published by Copernicus Publications on behalf of the European Geosciences Union. P. Bergamaschi et al.: Top-down estimates of European CH 4 and N 2 O emissionsto bottom-up emissions reported to the UNFCCC, while one model is close to the UNFCCC values. In contrast, the inverse modelling estimates of European N 2 O emissions are in general close to the UNFCCC values, with the overall range from all models being much smaller than the UNFCCC uncertainty range for most countries. Our analysis suggests that the reported uncertainties for CH 4 emissions might be underestimated, while those for N 2 O emissions are likely overestimated.

Abstract. We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDF) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgement. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF6) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties, than traditional methods.

Abstract. Airborne and ground-based measurements of methane (CH 4 ), carbon dioxide (CO 2 ) and boundary layer thermodynamics were recorded over the Fennoscandian landscape (67-69.5 • N, 20-28 • E) in July 2012 as part of the MAMM (Methane and other greenhouse gases in the Arctic: Measurements, process studies and Modelling) field campaign. Employing these airborne measurements and a simple boundary layer box model, net regional-scale (∼ 100 km) fluxes were calculated to be 1.2 ± 0.5 mg CH 4 h −1 m −2 and −350 ± 143 mg CO 2 h −1 m −2 . These airborne fluxes were found to be relatively consistent with seasonally averaged surface chamber (1.3 ± 1.0 mg CH 4 h −1 m −2 ) and eddy covariance (1.3 ± 0.3 mg CH 4 h −1 m −2 and −309 ± 306 mg CO 2 h −1 m −2 ) flux measurements in the local area. The internal consistency of the aircraft-derived fluxes across a wide swath of Fennoscandia coupled with an excellent statistical comparison with local seasonally averaged ground-based measurements demonstrates the potential scalability of such localised measurements to regional-scale representativeness. Comparisons were also made to longerterm regional CH 4 climatologies from the JULES (Joint UK Land Environment Simulator) and HYBRID8 land surface models within the area of the MAMM campaign. The average hourly emission flux output for the summer period Based on these observations both models were found to significantly underestimate the CH 4 emission flux in this region, which was linked to the under-prediction of the wetland extents generated by the models.

Abstract. High frequency, in situ observations from 11 globally distributed sites for the period 1994-2014 and archived air measurements dating from 1978 onward have been used to determine the global growth rate of 1,1-difluoroethane (HFC-152a, CH 3 CHF 2 ). These observations have been combined with a range of atmospheric transport models to derive global emission estimates in a topdown approach.