Background error covariance estimation for atmospheric CO2 data assimilation
A. Chatterjee, R. J. Engelen, S. R. Kawa, C. Sweeney and A. M. Michalak
In atmospheric CO2 data assimilation, observations and a background state (i.e. short-range forecast) are combined based on an assessment of their respective errors / uncertainties. We present a novel approach for parameterizing the background error statistics for CO2 data assimilation, and apply it to a system used at the European Centre for Medium-Range Weather Forecasts. The new approach leads to more accurate CO2 fields, as evaluated against independent CO2 observations, and is also applicable to other trace gas assimilation applications, especially ones where the background errors are influenced by both atmospheric transport and emissions patterns.
In any data assimilation framework, the background error covariance statistics play the critical role of filtering the observed information and determining the quality of the analysis. For atmospheric CO2 data assimilation, however, the background errors cannot be prescribed via traditional forecast or ensemble-based techniques as these fail to account for the uncertainties in the carbon emissions and uptake, or for the errors associated with the CO2 transport model. We propose an approach where the differences between two modeled CO2 concentration fields, based on different but plausible CO2 flux distributions and atmospheric transport models, are used as a proxy for the statistics of the background errors. The resulting error statistics: (1) vary regionally and seasonally to better capture the uncertainty in the background CO2 field, and (2) have a positive impact on the analysis estimates by allowing observations to adjust predictions over large areas. A state-of-the-art four-dimensional variational (4D-VAR) system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to illustrate the impact of the proposed approach for characterizing background error statistics on atmospheric CO2 concentration estimates. Observations from the Greenhouse gases Observing SATellite “IBUKI” (GOSAT) are assimilated into the ECMWF 4D-VAR system along with meteorological variables, using both the new error statistics and those based on a traditional forecast-based technique. Evaluation of the four-dimensional CO2 fields against independent CO2 observations confirms that the performance of the data assimilation system improves substantially in the summer, when significant variability and uncertainty in the fluxes are present.
Chatterjee, A., R. J. Engelen, S. R. Kawa, C. Sweeney, A. M. Michalak (2013), Background error covariance estimation for atmospheric CO2 data assimilation, Journal of Geophysical Research – Atmospheres, 118, 10140–10154, doi:10.1002/jgrd.50654.