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Volume 15, issue 5
Atmos. Chem. Phys., 15, 2903-2914, 2015
https://doi.org/10.5194/acp-15-2903-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 15, 2903-2914, 2015
https://doi.org/10.5194/acp-15-2903-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 13 Mar 2015

Research article | 13 Mar 2015

Biases in atmospheric CO2 estimates from correlated meteorology modeling errors

S. M. Miller1, M. N. Hayek1, A. E. Andrews2, I. Fung3, and J. Liu4 S. M. Miller et al.
  • 1Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
  • 2Global Monitoring Division, Earth Systems Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA
  • 3Department of Earth and Planetary Sciences, University of California Berkeley, Berkeley, CA, USA
  • 4Earth Science Division, Jet Propulsion Laboratory, NASA, Pasadena, CA, USA

Abstract. Estimates of CO2 fluxes that are based on atmospheric measurements rely upon a meteorology model to simulate atmospheric transport. These models provide a quantitative link between the surface fluxes and CO2 measurements taken downwind. Errors in the meteorology can therefore cause errors in the estimated CO2 fluxes. Meteorology errors that correlate or covary across time and/or space are particularly worrisome; they can cause biases in modeled atmospheric CO2 that are easily confused with the CO2 signal from surface fluxes, and they are difficult to characterize. In this paper, we leverage an ensemble of global meteorology model outputs combined with a data assimilation system to estimate these biases in modeled atmospheric CO2. In one case study, we estimate the magnitude of month-long CO2 biases relative to CO2 boundary layer enhancements and quantify how that answer changes if we either include or remove error correlations or covariances. In a second case study, we investigate which meteorological conditions are associated with these CO2 biases.

In the first case study, we estimate uncertainties of 0.5–7 ppm in monthly-averaged CO2 concentrations, depending upon location (95% confidence interval). These uncertainties correspond to 13–150% of the mean afternoon CO2 boundary layer enhancement at individual observation sites. When we remove error covariances, however, this range drops to 2–22%. Top-down studies that ignore these covariances could therefore underestimate the uncertainties and/or propagate transport errors into the flux estimate.

In the second case study, we find that these month-long errors in atmospheric transport are anti-correlated with temperature and planetary boundary layer (PBL) height over terrestrial regions. In marine environments, by contrast, these errors are more strongly associated with weak zonal winds. Many errors, however, are not correlated with a single meteorological parameter, suggesting that a single meteorological proxy is not sufficient to characterize uncertainties in atmospheric CO2. Together, these two case studies provide information to improve the setup of future top-down inverse modeling studies, preventing unforeseen biases in estimated CO2 fluxes.

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