Global monthly averaged CO2 fluxes recovered using a geostatistical inverse...

Mueller, K. L., S. M. Gourdji, and A. M. Michalak (2008), Global monthly averaged CO2 fluxes recovered using a geostatistical inverse modeling approach: 1. Results using atmospheric measurements, J. Geophys. Res., 113, D21114, doi:10.1029/2007JD009734.

This study presents monthly CO2 fluxes from 1997 to 2001 at a 3.75° latitude × 5° longitude resolution, inferred using a geostatistical inverse modeling approach. The approach focuses on quantifying the information content of measurements from the NOAA-ESRL cooperative air sampling network with regard to the global CO2 budget at different spatial and temporal scales. The geostatistical approach avoids the use of explicit prior flux estimates that have formed the basis of previous synthesis Bayesian inversions and does not prescribe spatial patterns of flux for large, aggregated regions. Instead, the method relies strongly on the atmospheric measurements and the inferred spatial autocorrelation of the fluxes to estimate sources and sinks and their associated uncertainties at the resolution of the atmospheric transport model. Results show that gridscale estimates exhibit high uncertainty and relatively little small-scale variability, but generally reflect reasonable fluxes in areas that are relatively well constrained by measurements. The aggregated continental-scale fluxes are better constrained, and estimates are consistent with results from previous synthesis Bayesian inversion studies for many regions. Observed differences at the continental scale are primarily attributable to the choice of a priori assumptions in the current work relative to those in other synthesis Bayesian studies. Overall, the results indicate that the geostatistical inverse modeling approach is able to estimate global fluxes using the limited atmospheric measurement network without relying on assumptions about a priori estimates of the flux distribution. As such, the method provides a means of isolating the information content of the atmospheric measurements, and thus serves as a valuable tool for reconciling top-down and bottom-up estimates of CO2 flux variability.

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Research Program: 
Interdisciplinary Science Program (IDS)
Modeling Analysis and Prediction Program (MAP)