Inverse modelling of European N2O emissions: assimilating observations from different networks
- 1European Commission Joint Research Centre, Institute for Environment and Sustainability, 21027 Ispra (Va), Italy
- 2Energy research Centre of the Netherlands (ECN), Petten, The Netherlands
- 3Finnish Meteorological Institute (FMI), Helsinki, Finland
- 4Hungarian Meteorological Service, Budapest, Hungary
- 5Umweltbundesamt (UBA), Messstelle Schauinsland, Kirchzarten, Germany
- 6School of Chemistry, University of Bristol, Bristol, UK
- 7Laboratoire des Sciences du Climat et de l'Environment (LSCE), Gif sur Yvette, France
- 8Edinburgh University, Edinburgh, UK
- 9Swiss Federal Laboratories for Materials Science and Technology (Empa), Duebendorf, Switzerland
- 10Max Planck Institute for Biogeochemistry, Jena, Germany
- 11NOAA Earth System Research Laboratory, Global Monitoring Division, Boulder, CO, USA
- 12Max Planck Institute for Chemistry, Mainz, Germany
- 13Wageningen University and Research Centre (WUR), Wageningen, The Netherlands
Abstract. We describe the setup and first results of an inverse modelling system for atmospheric N2O, based on a four-dimensional variational (4DVAR) technique and the atmospheric transport zoom model TM5. We focus in this study on the European domain, utilizing a comprehensive set of quasi-continuous measurements over Europe, complemented by N2O measurements from the Earth System Research Laboratory of the National Oceanic and Atmospheric Administration (NOAA/ESRL) cooperative global air sampling network. Despite ongoing measurement comparisons among networks parallel measurements at a limited number of stations show that significant offsets exist among the different laboratories. Since the spatial gradients of N2O mixing ratios are of the same order of magnitude as these biases, the direct use of these biased datasets would lead to significant errors in the derived emissions. Therefore, in order to also use measurements with unknown offsets, a new bias correction scheme has been implemented within the TM5-4DVAR inverse modelling system, thus allowing the simultaneous assimilation of observations from different networks. The N2O bias corrections determined in the TM5-4DVAR system agree within ~0.1 ppb (dry-air mole fraction) with the bias derived from the measurements at monitoring stations where parallel NOAA discrete air samples are available. The N2O emissions derived for the northwest European and east European countries for 2006 show good agreement with the bottom-up emission inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Moreover, the inverse model can significantly narrow the uncertainty range reported in N2O emission inventories for these countries, while the lack of measurements does not allow to reduce the uncertainties of emission estimates in southern Europe.
Several sensitivity experiments were performed to test the robustness of the results. It is shown that also inversions without detailed a priori spatio-temporal emission distributions are capable to reproduce major regional emission patterns within the footprint of the existing atmospheric network, demonstrating the strong constraints of the atmospheric observations on the derived emissions.