Atmos. Chem. Phys. Discuss., 11, 341-386, 2011
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This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
Optimizing global CO emissions using a four-dimensional variational data assimilation system and surface network observations
P. B. Hooghiemstra1,2, M. C. Krol1,2,3, J. F. Meirink4, P. Bergamaschi5, G. R. van der Werf6, P. C. Novelli7, I. Aben2,6, and T. Röckmann1
1Institute for Marine and Atmospheric Research Utrecht, University of Utrecht, Utrecht, The Netherlands
2Netherlands Institute for Space Research (SRON), Utrecht, The Netherlands
3Wageningen University and Research Centre, Wageningen, The Netherlands
4Royal Netherlands Meteorological Institute (KNMI), de Bilt, The Netherlands
5European Commission Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy
6Faculty of Earth and Life Sciences, VU University, Amsterdam, The Netherlands
7Global Monitoring Division, Earth System Research Laboratory, NOAA, Boulder, Colorado, USA

Abstract. We apply a four-dimensional variational (4D-VAR) data assimilation system to optimize carbon monoxide (CO) emissions for 2003 and 2004 and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. The system is designed to assimilate large (satellite) datasets, but in the current study only a limited amount of surface network observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) is used to test the 4D-VAR system. By design, the system is capable to adjust the emissions in such a way that the posterior simulation reproduces background CO mixing ratios and large-scale pollution events at background stations. Uncertainty reduction up to 60% in yearly emissions is observed over well-constrained regions and the inferred emissions compare well with recent studies. However, with the limited amount of data from the surface network, the system becomes data sparse. This results in a large solution space and the 4D-VAR system has difficulties in separating anthropogenic and biogenic sources in particular. In addition we show that uncertainties in the model such as biomass burning injection height and the OH distribution largely influence the inversion results. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument version 4 (V4) shows only a slight improved agreement over the well-constrained Northern Hemisphere. However, the model with optimized emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 40%. This is caused by a reduction in SH CO sources mainly due to surface stations on the high southern latitudes.

Citation: Hooghiemstra, P. B., Krol, M. C., Meirink, J. F., Bergamaschi, P., van der Werf, G. R., Novelli, P. C., Aben, I., and Röckmann, T.: Optimizing global CO emissions using a four-dimensional variational data assimilation system and surface network observations, Atmos. Chem. Phys. Discuss., 11, 341-386, doi:10.5194/acpd-11-341-2011, 2011.
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