Articles | Volume 15, issue 2
https://doi.org/10.5194/acp-15-715-2015
https://doi.org/10.5194/acp-15-715-2015
Research article
 | 
19 Jan 2015
Research article |  | 19 Jan 2015

Top-down estimates of European CH4 and N2O emissions based on four different inverse models

P. Bergamaschi, M. Corazza, U. Karstens, M. Athanassiadou, R. L. Thompson, I. Pison, A. J. Manning, P. Bousquet, A. Segers, A. T. Vermeulen, G. Janssens-Maenhout, M. Schmidt, M. Ramonet, F. Meinhardt, T. Aalto, L. Haszpra, J. Moncrieff, M. E. Popa, D. Lowry, M. Steinbacher, A. Jordan, S. O'Doherty, S. Piacentino, and E. Dlugokencky

Abstract. European CH4 and N2O 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 CH4 and 9 continuous stations for N2O, complemented by additional European and global discrete air sampling sites. The available observations mainly constrain CH4 and N2O emissions from north-western 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 CH4 emissions from north-western and eastern Europe compared to bottom-up emissions reported to the UNFCCC, while one model is close to the UNFCCC values. In contrast, the inverse modelling estimates of European N2O 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 CH4 emissions might be underestimated, while those for N2O emissions are likely overestimated.

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