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Inferring Regional Sources and Sinks of Atmospheric Co2 from Gosat XCo2 Data : Volume 13, Issue 10 (10/10/2013)

By Deng, F.

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Book Id: WPLBN0003996424
Format Type: PDF Article :
File Size: Pages 62
Reproduction Date: 2015

Title: Inferring Regional Sources and Sinks of Atmospheric Co2 from Gosat XCo2 Data : Volume 13, Issue 10 (10/10/2013)  
Author: Deng, F.
Volume: Vol. 13, Issue 10
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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Bousserez, N., Deutscher, N. M., Fisher, J. B., A. Jone, D. B., Sussmann, R., Hase, F.,...Wofsy, S. C. (2013). Inferring Regional Sources and Sinks of Atmospheric Co2 from Gosat XCo2 Data : Volume 13, Issue 10 (10/10/2013). Retrieved from

Description: Department of Physics, University of Toronto, Toronto, ON, Canada. We have examined the utility of retrieved column-averaged, dry-air mole fractions of CO2 (XCO2) from the Greenhouse Gases Observing Satellite (GOSAT) for quantifying monthly, regional flux estimates of CO2, using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system. We focused on assessing the potential impact of biases in the GOSAT CO2 data on the regional flux estimates. Using different screening and bias correction approaches, we selected three different subsets of the GOSAT XCO2 data for the 4D-Var inversion analyses, and found that the inferred global fluxes were consistent across the three XCO2 inversions. However, the GOSAT observational coverage was a challenge for the regional flux estimates. In the northern extratropics, the inversions were more sensitive to North American fluxes than to European and Asian fluxes due to the lack of observations over Eurasia in winter and over eastern and southern Asia in summer. The regional flux estimates were also sensitive to the treatment of the residual bias in the GOSAT XCO2 data. The largest differences obtained were for Temperate North America and Temperate South America, for which the largest spread between the inversions was 1.02 Pg C and 0.96 Pg C, respectively. In the case of Temperate North America, one inversion suggested a strong source, whereas the second and third XCO2 inversions produced a weak and strong sink, respectively. Despite the discrepancies in the regional flux estimates between the three XCO2 inversions, the a posteriori CO2 distributions were in good agreement (with a mean difference between the three inversions of typically less than 0.5 ppm) with independent data from the Total Carbon Column Observing Network (TCCON), the surface flask network, and from the HIAPER Pole-to-Pole Observations (HIPPO) aircraft campaign. The discrepancy in the regional flux estimates from the different inversions, despite the agreement of the global flux estimates, suggests the need for additional work to determine the minimum spatial scales at which we can reliably quantify the fluxes using GOSAT XCO2. The fact that the a posteriori CO2 from the different inversions were in good agreement with the independent data although the regional flux estimates differed significantly, suggests that innovative ways of exploiting existing datasets, and possibly additional observations, are needed to better evaluate the inferred regional flux estimates.

Inferring regional sources and sinks of atmospheric CO2 from GOSAT XCO2 data

Andres, R. J., Gregg, J. S., Losey, L., Marland, G., and Boden, T. A.: Monthly, global emissions of carbon dioxide from fossil fuel consumption, Tellus B, 63, 309–327, doi:10.1111/j.1600-0889.2011.00530.x, 2011.; Baker, D. F., Doney, S. C., and Schimel, D. S.: Variational data assimilation for atmospheric CO2, Tellus B, 58, 359–365, doi:10.1111/j.1600-0889.2006.00218.x, 2006a.; Baker, D. F., Law, R. M., Gurney, K. R., Rayner, P., Peylin, P., Denning, A. S., Bousquet, P., Bruhwiler, L., Chen, Y. H., Ciais, P., Fung, I. Y., Heimann, M., John, J., Maki, T., Maksyutov, S., Masarie, K., Prather, M., Pak, B., Taguchi, S., and Zhu, Z.: TransCom 3 inversion intercomparison: impact of transport model errors on the interannual variability of regional CO2 fluxes, 1988–2003, Global Biogeochem. Cy., 20, GB1002, doi:10.1029/2004gb002439, 2006b.; Baldocchi, D.: Turner review no. 15. Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems, Aust. J. Bot., 56, 1–26, doi:10.1071/BT07151, 2008.; Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities, B. Am. Meteorol. Soc., 82, 2415–2434, 2.3.CO;2>doi:10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001.; Basu, S., Houweling, S., Peters, W., Sweeney, C., Machida, T., Maksyutov, S., Patra, P. K., Saito, R., Chevallier, F., Niwa, Y., Matsueda, H., and Sawa, Y.: The seasonal cycle amplitude of total column CO2: factors behind the model-observation mismatch, J. Geophys. Res., 116, D23306, doi:10.1029/2011jd016124, 2011.; 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. Discuss., 13, 4535–4600, doi:10.5194/acpd-13-4535-2013, 2013.; Blunden, J., Arndt, D. S., and Baringer, M. O.: State of the climate in 2010, B. Am. Meteorol. Soc., 92, S1–S236, doi:10.1175/1520-0477-92.6.s1, 2011.; 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.; Canadell, J. G., Le Quéré, C., Raupach, M. R., Field, C. B., Buitenhuis, E. T., Ciais, P., Conway, T. J., Gillett, N. P., Houghton, R. A., and Marland, G.: Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks, P. Natl. Acad. Sci. USA, 104, 18866–18870, doi:10.1073/pnas.0702737104, 2007.; Chen, J. M., Liu, J., Cihlar, J., and Goulden, M. L.: Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications, Ecol. Model.


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