ALBERT

Description: [1]  Estimates of surface fluxes of carbon dioxide (CO 2 ) can be derived from atmospheric CO 2 concentration measurements through the solution of an inverse problem, but the sparseness of the existing CO 2 monitoring network is often cited as a main limiting factor in constraining fluxes. Existing methods for assessing or designing monitoring networks either primarily rely on expert knowledge, or are sensitive to the large number of modeling choices and assumptions inherent to the solution of inverse problems. This study proposes a monitoring network evaluation and design approach based on the quantification of the spatial variability in modeled atmospheric CO 2 . The approach is used to evaluate the 2004-2008 North American network expansion, and to create two hypothetical further expansions. The less stringent expansion guarantees a monitoring tower within one correlation length of each location (1 CL), requiring an additional 8 towers relative to 2008. The more stringent network includes a tower within one half of a correlation length (½ CL) and requires 35 towers beyond the 1 CL network. The two proposed networks are evaluated against the network in 2008, which temporarily had the most continuous monitoring sites in North America thanks to the Mid-Continent Intensive project. Evaluation using a synthetic data inversion shows a marked improvement in the ability to constrain both continental- and biome-scale fluxes, especially in areas that are currently under-sampled. The proposed approach is flexible, computationally inexpensive, and provides a quantitative design tool that can be used in concert with existing tools to inform atmospheric monitoring needs.

Description: [1]  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 CO 2 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 CO 2 transport model. We propose an approach where the differences between two modeled CO 2 concentration fields, based on different but plausible CO 2 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 CO 2 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 4-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 CO 2 concentration estimates. Observations from the Greenhouse gases Observing SATellite (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 4-dimensional CO 2 fields against independent CO 2 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.