Global monthly averaged CO2 fluxes recovered using a geostatistical inverse modeling approach: 2. Results including auxiliary environmental data

[1] Geostatistical inverse modeling has been shown to be a viable alternative to synthesis Bayesian methods for estimating global continental-scale CO2 fluxes. This study extends the geostatistical approach to take advantage of spatially and temporally varying auxiliary data sets related to CO2 flux processes, which allow the inversion to capture more grid-scale flux variability and better constrain fluxes in areas undersampled by the current atmospheric monitoring network. Auxiliary variables are selected for inclusion in the inversion using a hypothesis-based variable selection method, and are then used in conjunction with atmospheric CO2 measurements to estimate global monthly fluxes for 1997 to 2001 at a 3.75 5 resolution. Results show that the inversion is able to infer realistic relationships between the selected variables and flux, with leaf area index and the fraction of canopy-intercepted photosynthetically active radiation (fPAR) capturing a large portion of the biospheric signal, and gross domestic product and population densities explaining approximately three quarters of the expected fossil fuel emissions signal. The extended model is able to better constrain estimates in regions with sparse measurements, as confirmed by a reduction in the a posteriori uncertainty at the grid and aggregated continental scales, as compared to the inversion presented in the companion paper (Mueller et al., 2008).