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Atmospheric Chemistry and Physics An Interactive Open Access Journal of the European Geosciences Union

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Atmos. Chem. Phys., 11, 4705-4723, 2011
© Author(s) 2011. This work is distributed
under the Creative Commons Attribution 3.0 License.
Optimizing global CO emission estimates 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, The Netherlands
2Netherlands Institute for Space Research, Utrecht, The Netherlands
3Wageningen University, Wageningen, The Netherlands
4Royal Netherlands Meteorological Institute, de Bilt, The Netherlands
5Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy
6Faculty of Earth and Life Sciences, Free University, Amsterdam, The Netherlands
7National Oceanic and Atmospheric Administration, Climate Monitoring and Diagnostics Laboratory, Boulder, 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 for 2004. However, with the limited amount of data from the surface network, the system becomes data sparse resulting in a large solution space. Sensitivity studies have shown that model uncertainties (e.g., vertical distribution of biomass burning emissions and the OH field) and the prior inventories used, influence the inferred emission estimates. Also, since the observations only constrain total CO emissions, the 4D-VAR system has difficulties in separating anthropogenic and biogenic sources in particular. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from the prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument version 4 (V4) shows a slight improved agreement over the well-constrained Northern Hemisphere and in the tropics (except for the African continent). However, the model simulation with posterior emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 10 %. 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 emission estimates using a four-dimensional variational data assimilation system and surface network observations, Atmos. Chem. Phys., 11, 4705-4723, doi:10.5194/acp-11-4705-2011, 2011.
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