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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 1
Atmos. Chem. Phys., 10, 83–94, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 10, 83–94, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  07 Jan 2010

07 Jan 2010

High resolution modeling of CO2 over Europe: implications for representation errors of satellite retrievals

D. Pillai1, C. Gerbig1, J. Marshall1, R. Ahmadov2,*, R. Kretschmer1, T. Koch1, and U. Karstens1 D. Pillai et al.
  • 1Max Planck Institute for Biogeochemistry, P.O. Box 100164, 07701 Jena, Germany
  • 2NOAA Earth System Research Laboratory, Boulder, Colorado, USA
  • *also at: Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, USA

Abstract. Satellite retrievals for column CO2 with better spatial and temporal sampling are expected to improve the current surface flux estimates of CO2 via inverse techniques. However, the spatial scale mismatch between remotely sensed CO2 and current generation inverse models can induce representation errors, which can cause systematic biases in flux estimates. This study is focused on estimating these representation errors associated with utilization of satellite measurements in global models with a horizontal resolution of about 1 degree or less. For this we used simulated CO2 from the high resolution modeling framework WRF-VPRM, which links CO2 fluxes from a diagnostic biosphere model to a weather forecasting model at 10×10 km2 horizontal resolution. Sub-grid variability of column averaged CO2, i.e. the variability not resolved by global models, reached up to 1.2 ppm with a median value of 0.4 ppm. Statistical analysis of the simulation results indicate that orography plays an important role. Using sub-grid variability of orography and CO2 fluxes as well as resolved mixing ratio of CO2, a linear model can be formulated that could explain about 50% of the spatial patterns in the systematic (bias or correlated error) component of representation error in column and near-surface CO2 during day- and night-times. These findings give hints for a parameterization of representation error which would allow for the representation error to taken into account in inverse models or data assimilation systems.

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