Abstract
ABSTRACTRecent instrumental deployments of regional observation networks of atmospheric CO2 mixing ratios have been used to constrain carbon sources and sinks using inversion methodologies. In this study, we performed sensitivity experiments using observation sites from the Mid Continent Intensive experiment to evaluate the required spatial density and locations of CO2 concentration towers based on flux corrections and error reduction analysis. In addition, we investigated the impact of prior flux error structures with different correlation lengths and biome information. We show here that, while the regional carbon balance converged to similar annual estimates using only two concentration towers over the region, additional sites were necessary to retrieve the spatial flux distribution of our reference case (using the entire network of eight towers). Local flux corrections required the presence of observation sites in their vicinity, suggesting that each tower was only able to retrieve major corrections within a hundred of kilometres around, despite the introduction of spatial correlation lengths (~100 to 300 km) in the prior flux errors. We then quantified and evaluated the impact of the spatial correlations in the prior flux errors by estimating the improvement in the CO2 model-data mismatch of the towers not included in the inversion. The overall gain across the domain increased with the correlation length, up to 300 km, including both biome-related and non-biome-related structures. However, the spatial variability at smaller scales was not improved. We conclude that the placement of observation towers around major sources and sinks is critical for regional-scale inversions in order to obtain reliable flux distributions in space. Sparser networks seem sufficient to assess the overall regional carbon budget with the support of flux error correlations, indicating that regional signals can be recovered using hourly mixing ratios. However, the smaller spatial structures in the posterior fluxes are highly constrained by assumed prior flux error correlation lengths, with no significant improvement at only a few hundreds of kilometres away from the observation sites.
Highlights
The remaining fraction of atmospheric carbon from anthropogenic emissions corresponds to about 45% of the total emissions, due to absorption mechanisms on the continents and the oceans (Raupach et al, 2008; LeQuéré et al, 2009)
We evaluated the gain from the inversion in terms of mixing ratio mismatch with Leave-One-Out Cross-Validation (LOOCV)
Because the two are basically related to the prior flux error structures, we investigate the impact of different correlation structures on the flux corrections and the error reduction
Summary
The remaining fraction of atmospheric carbon from anthropogenic emissions corresponds to about 45% of the total emissions, due to absorption mechanisms on the continents and the oceans (Raupach et al, 2008; LeQuéré et al, 2009). (Canadell et al, 2007) Their contribution remains poorly constrained at the continental and regional levels using inverse approaches despite consistency at larger scales (Ciais et al, 2010). Process-based approaches and statistical regression methods for parameter optimisation have been used to constrain the carbon pools and the net flux from the terrestrial vegetation (Ricciuto et al, 2011), Tellus B 2012. Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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