1Laboratoire des Sciences du Climat et de l'Environnement (LSCE), UMR8212, Gif sur Yvette, France
2European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, Berkshire RG2 9AX, UK
3Atmospheric, Earth, and Energy Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, USA
4Goddard Earth Sciences and Technology Center, NASA Goddard Space Flight Center, Code 613.3, Greenbelt, MD 20771, USA
5Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
6SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, the Netherlands
7Institute for Marine and Atmospheric Research Utrecht (IMAU), Princetonplein 5, 3584 CC Utrecht, the Netherlands
8Wageningen University and Research Centre, Droevendaalsesteeg 4, 6708 PB Wageningen, the Netherlands
9Research Institute for Global Change/JAMSTEC, 3173–25 Show-machi, Yokohama, 2360001, Japan
10Center for Global Change Science, Building 54, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
11School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
Abstract. A modelling experiment has been conceived to assess the impact of transport model errors on the methane emissions estimated by an atmospheric inversion system. Synthetic methane observations, given by 10 different model outputs from the international TransCom-CH4 model exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the PYVAR-LMDZ-SACS inverse system to produce 10 different methane emission estimates at the global scale for the year 2005. The same set-up has been used to produce the synthetic observations and to compute flux estimates by inverse modelling, which means that only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes.
In our framework, we show that transport model errors lead to a discrepancy of 27 Tg CH4 per year at the global scale, representing 5% of the total methane emissions. At continental and yearly scales, transport model errors have bigger impacts depending on the region, ranging from 36 Tg CH4 in north America to 7 Tg CH4 in Boreal Eurasian (from 23% to 48%). At the model gridbox scale, the spread of inverse estimates can even reach 150% of the prior flux. Thus, transport model errors contribute to significant uncertainties on the methane estimates by inverse modelling, especially when small spatial scales are invoked. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher resolution models. The analysis of methane estimated fluxes in these different configurations questions the consistency of transport model errors in current inverse systems.
For future methane inversions, an improvement in the modelling of the atmospheric transport would make the estimations more accurate. Likewise, errors of the observation covariance matrix should be more consistently prescribed in future inversions in order to limit the impact of transport model errors on estimated methane fluxes.