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Comparing the Carbontracker and Tm5-4Dvar Data Assimilation Systems for Co2 Surface Flux Inversions : Volume 15, Issue 6 (25/03/2015)

By Babenhauserheide, A.

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Book Id: WPLBN0003997239
Format Type: PDF Article :
File Size: Pages 50
Reproduction Date: 2015

Title: Comparing the Carbontracker and Tm5-4Dvar Data Assimilation Systems for Co2 Surface Flux Inversions : Volume 15, Issue 6 (25/03/2015)  
Author: Babenhauserheide, A.
Volume: Vol. 15, Issue 6
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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Peters, W., Houweling, S., Basu, S., Babenhauserheide, A., & Butz, A. (2015). Comparing the Carbontracker and Tm5-4Dvar Data Assimilation Systems for Co2 Surface Flux Inversions : Volume 15, Issue 6 (25/03/2015). Retrieved from

Description: IMK-ASF, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. Data assimilation systems allow for estimating surface fluxes of greenhouse gases from atmospheric concentration measurements. Good knowledge about fluxes is essential to understand how climate change affects ecosystems and to characterize feedback mechanisms. Based on assimilation of more than one year of atmospheric in-situ concentration measurements, we compare the performance of two established data assimilation models, CarbonTracker and TM5-4DVar, for CO2 flux estimation. CarbonTracker uses an Ensemble Kalman Filter method to optimize fluxes on ecoregions. TM5-4DVar employs a 4-D variational method and optimizes fluxes on a 6° × 4° longitude/latitude grid. Harmonizing the input data allows analyzing the strengths and weaknesses of the two approaches by direct comparison of the modelled concentrations and the estimated fluxes. We further assess the sensitivity of the two approaches to the density of observations and operational parameters such as temporal and spatial correlation lengths.

Our results show that both models provide optimized CO2 concentration fields of similar quality. In Antarctica CarbonTracker underestimates the wintertime CO2 concentrations, since its 5-week assimilation window does not allow for adjusting the far-away surface fluxes in response to the detected concentration mismatch. Flux estimates by CarbonTracker and TM5-4DVar are consistent and robust for regions with good observation coverage, regions with low observation coverage reveal significant differences. In South America, the fluxes estimated by TM5-4DVar suffer from limited representativeness of the few observations. For the North American continent, mimicking the historical increase of measurement network density shows improving agreement between CarbonTracker and TM5-4DVar flux estimates for increasing observation density.

Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO2 surface flux inversions

Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., Krummel, P., Steele, P., Langenfelds, R., Torn, M., Biraud, S., Stephens, B., Andrews, A., and Worthy, D.: Global CO2 fluxes estimated from GOSAT retrievals of total column CO2, Atmos. Chem. Phys., 13, 8695–8717, doi:10.5194/acp-13-8695-2013, 2013.; Bergamaschi, P., Krol, M., Meirink, J. F., Dentener, F., Segers, A., van Aardenne, J., Monni, S., Vermeulen, A. T., Schmidt, M., Ramonet, M., Yver, C., Meinhardt, F., Nisbet, E. G., Fisher, R. E., O'Doherty, S., and Dlugokencky, E. J.: Inverse modeling of European CH4 emissions 2001–2006, J. Geophys. Res.-Atmos., 115, D22309, doi:10.1029/2010JD014180, 2010.; Bruhwiler, L. M. P., Michalak, A. M., Peters, W., Baker, D. F., and Tans, P.: An improved Kalman Smoother for atmospheric inversions, Atmos. Chem. Phys., 5, 2691–2702, doi:10.5194/acp-5-2691-2005, 2005.; Bruhwiler, L. M. P., Michalak, A. M., and Tans, P. P.: Spatial and temporal resolution of carbon flux estimates for 1983–2002, Biogeosciences, 8, 1309–1331, doi:10.5194/bg-8-1309-2011, 2011.; Chatterjee, A. and Michalak, A. M.: Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO2 data assimilation, Atmos. Chem. Phys., 13, 11643–11660, doi:10.5194/acp-13-11643-2013, 2013.; Chevallier, F., Fisher, M., Peylin, P., Serrar, S., Bousquet, P., Bréon, F.-M., Chédin, A., and Ciais, P.: Inferring CO2 sources and sinks from satellite observations: method and application to TOVS data, J. Geophys. Res., 110, 24309, doi:10.1029/2005JD006390, 2005.; Chevallier, F., Ciais, P., Conway, T. J., Aalto, T., Anderson, B. E., Bousquet, P., Brunke, E. G., Ciattaglia, L., Esaki, Y., Fröhlich, M., Gomez, A., Gomez-Pelaez, A. J., Haszpra, L., Krummel, P. B., Langenfelds, R. L., Leuenberger, M., Machida, T., Maignan, F., Matsueda, H., Morguí, J. A., Mukai, H., Nakazawa, T., Peylin, P., Ramonet, M., Rivier, L., Sawa, Y., Schmidt, M., Steele, L. P., Vay, S. A., Vermeulen, A. T., Wofsy, S., and Worthy, D.: CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements, J. Geophys. Res., 115, D21307, doi:10.1029/2010JD013887, 2010.; Enting, I. G.: Green's Function Methods of Tracer Inversion, vol. 114, American Geophysical Union, Washington, DC, 19–31, 2000.; Errico, R. M.: What is an adjoint model?, B. Am. Meteorol. Soc., 78, 2577–2591, 1997.; European Centre for Medium-Range Weather Forecasts: ERA Interim, via the ECMWF Data Server, available at: (last access: 20 March 2015), 2013.; Fairbairn, D., Pring, S. R., Lorenc, A. C., and Roulstone, I.: A comparison of 4DVar with ensemble data assimilation methods, Q. J. Roy. Meteor. Soc., 281–294, doi:10.1002/qj.2135, 2013.; Feng, L., Palmer, P. I., Yang, Y., Yantosca, R. M., Kawa, S. R., Paris, J.-D., Matsueda, H., and Machida, T.: Evaluating a 3-D transport model of atmospheric CO2 using ground-based, aircraft, and space-borne data, Atmos. Chem. Phys., 11, 2789–2803, doi:10.5194/acp-11-2789-2011, 2011.; Foken, T., Aubinet, M., and Leuning, R.: The Eddy Covariance Method, Springer, the Netherlands, doi:


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