Articles | Volume 14, issue 7
Atmos. Chem. Phys., 14, 3703–3727, 2014
Atmos. Chem. Phys., 14, 3703–3727, 2014

Research article 11 Apr 2014

Research article | 11 Apr 2014

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

F. Deng1, D. B. A. Jones1,2, D. K. Henze3, N. Bousserez3, K. W. Bowman2,4, J. B. Fisher4, R. Nassar5, C. O'Dell6, D. Wunch7, P. O. Wennberg7, E. A. Kort8, S. C. Wofsy9, T. Blumenstock10, N. M. Deutscher11,12, D. W. T. Griffith12, F. Hase10, P. Heikkinen13, V. Sherlock14, K. Strong1, R. Sussmann15, and T. Warneke11 F. Deng et al.
  • 1Department of Physics, University of Toronto, Toronto, ON, Canada
  • 2Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, USA
  • 3Department of Mechanical Engineering, University of Colorado, Boulder, CO, USA
  • 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 5Climate Research Division, Environment Canada, Toronto, ON, Canada
  • 6Department of Atmospheric Science, Colorado State University, Colorado, USA
  • 7California Institute of Technology, Pasadena, CA, USA
  • 8Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USA
  • 9Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
  • 10Karlsruhe Institute of Technology (KIT), Institute for Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
  • 11Institute of Environmental Physics, University of Bremen, Bremen, Germany
  • 12School of Chemistry, University of Wollongong, NSW, Australia
  • 13FMI-Arctic Research Center, Sodankylä, Finland
  • 14National Institute of Water and Atmospheric Research, Wellington, New Zealand
  • 15IMK-IFU, Garmisch-Partenkirchen, Germany

Abstract. 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 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 data sets, and possibly additional observations, are needed to better evaluate the inferred regional flux estimates.

Final-revised paper