Seeing the forest through the trees: Recovering large-scale carbon flux biases in the midst of small-scale variability

[1] This paper investigates the effect of fine-scale spatial variability in carbon fluxes upon regional carbon flux inversion estimates in North America using simulated data from 1 May through 31 August 2004 and a hypothetical sparse network of eight towers in North America. A suite of random smooth regional carbon flux patterns are created and then obscured with random fine-scale spatial flux ‘‘noise’’ to mimic the effect of fine-scale heterogeneity in carbon fluxes found in nature. Five hundred and forty grid-scale atmospheric inversions are run using the synthetic data. We find that, regardless of the particular fine spatial scale carbon fluxes used (noise), the inversions can improve a priori carbon flux estimates significantly by capturing the large-scale regional flux patterns. We also find significant improvement in the root-mean-square error of the model are possible across a wide range of spatial decorrelation length scales. Errors associated with the inversion decrease as estimates are sought for larger and larger areas. Results show dramatic differences between postaggregated fine-scale inversion results and preaggregated coarse-scale inversion results confirming recent warnings about the ‘‘preaggregation’’ of inversion regions.