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Optimizing Global Co Emissions Using a Four-dimensional Variational Data Assimilation System and Surface Network Observations : Volume 11, Issue 1 (06/01/2011)

By Hooghiemstra, P. B.

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Book Id: WPLBN0003995845
Format Type: PDF Article :
File Size: Pages 46
Reproduction Date: 2015

Title: Optimizing Global Co Emissions Using a Four-dimensional Variational Data Assimilation System and Surface Network Observations : Volume 11, Issue 1 (06/01/2011)  
Author: Hooghiemstra, P. B.
Volume: Vol. 11, Issue 1
Language: English
Subject: Science, Atmospheric, Chemistry
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Van Der Werf, G. R., Hooghiemstra, P. B., Aben, I., Röckmann, T., Meirink, J. F., Bergamaschi, P.,...Krol, M. C. (2011). Optimizing Global Co Emissions Using a Four-dimensional Variational Data Assimilation System and Surface Network Observations : Volume 11, Issue 1 (06/01/2011). Retrieved from

Description: Institute for Marine and Atmospheric Research Utrecht, University of Utrecht, Utrecht, The Netherlands. 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.

Optimizing global CO emissions using a four-dimensional variational data assimilation system and surface network observations

Arellano, Jr., A. F., Kasibhatla, P., Giglio, L., van der Werf, G., and Randerson, J.: Top-down estimates of global CO sources using MOPITT measurements, Geophys. Res. Lett., 31, L01104, doi:10.1029/2003GL018609, 2004.; Arellano, Jr., A. F., Kasibhatla, P., Giglio, L., van der Werf, G., Randerson, J., and Collatz, G.: Time-dependent inversion estimates of global biomass burning CO emissions using {M}easurement of {P}ollution in the {T}roposphere ({MOPITT}) measurements, J. Geophys. Res., 111, D09303, doi:10.1029/2005JD006613, 2006.; Bergamaschi, P., Hein, R., Heimann, M., and Crutzen, P. J.: Inverse modeling of the global CO cycle 1. {I}nversion of CO mixing ratios, J. Geophys. Res., 105, 1909–1927, 2000.; Bergamaschi, P., Krol, M., Dentener, F., Vermeulen, A., Meinhardt, F., Graul, R., Ramonet, M., Peters, W., and Dlugokencky, E. J.: Inverse modelling of national and European CH4 emissions using the atmospheric zoom model TM5, Atmos. Chem. Phys., 5, 2431–2460, doi:10.5194/acp-5-2431-2005, 2005.; Bergamaschi, P., Frankenberg, C., Meirink, J.-F., Krol, M., Villani, G., Houweling, S., Dentener, F., Dlugokencky, E. J., and Engel, A.: Inverse modeling of global and regional CH4 emissions based on recently revised SCIAMACHY retrievals, J. Geophys. Res., 114, D22301, doi:10.1029/2009JD012287, 2009.; Bergamaschi, P., Krol, M., Meirink, J. F., Dentener, F., Segers, A., van Aardenne, J., Monni, S., Vermeulen, A., Schmidt, M., Ramonet, M., Yver, C., Meinhardt, F., Nisbet, E. G., Fisher, R., O'Doherty, S., and Dlugokencky, E. J.: Inverse modeling of {E}uropean CH4 emissions 2001–2006, J. Geophys. Res., 115, 4703–4715, 2010.; Chevallier, F., Fortems, A., Bousquet, P., Pison, I., Szopa, S., Devaux, M., and Hauglustaine, D. A.: African CO emissions between years 2000 and 2006 as estimated from MOPITT observations, Biogeosciences, 6, 103–111, doi:10.5194/bg-6-103-2009, 2009.; Deeter, M., Emmons, L., Francis, G., Edwards, D., Gille, J., Warner, J., Khattatov, B., Ziskin, D., Lamarque, J.-F., Ho, S.-P., Yudin, V., Attie, J.-L., Packman, D., Chen, J., Mao, D., and Drummond, J.: Operational carbon monoxide retrieval algorithm and selected results for the MOPITT instrument, J. Geophys. Res., 108, D14, doi:10.1029/2002JD003186, 2003.; Deeter, M. N., Edwards, D. P., and Gille, J. C.: Retrievals of carbon monoxide profiles from MOPITT observations using lognormal a priori statistics, J. Geophys. Res., 112, D11311, doi:10.1029/2006JD007999, 2007.; Deeter, M. N., Edwards, D. P., Gille, J. C., Emmons, L. K., Francis, G., Ho, S.-P., Mao, D., Master


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