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

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.