Constraining the CO<sub>2</sub> budget of the corn belt: exploring uncertainties from the assumptions in a mesoscale inverse system

Abstract

We performed an atmospheric inversion of the CO₂ fluxes over Iowa and the surrounding states, from June to December 2007, at 20 km resolution and weekly timescale. Eight concentration towers were used to constrain the carbon balance in a 1000 × 1000 km² domain in this agricultural region of the US upper midwest. The CO₂ concentrations of the boundaries derived from CarbonTracker were adjusted to match direct observations from aircraft profiles around the domain. The regional carbon balance ends up with a sink of 178 TgC±35 TgC over the area for the period June–December, 2007. Potential bias from incorrect boundary conditions of about 0.4 ppm over the 7 months was corrected using mixing ratios from four different aircraft profile sites operated at a weekly time scale, acting as an additional source of uncertainty of 18 TgC. We used two different prior flux estimates, the SiBCrop model and the inverse flux product from the CarbonTracker system. We show that inverse flux estimates using both priors converge to similar posterior estimates (10 TgC difference), in our reference inversion, but some spatial structures from the prior fluxes remain in the posterior fluxes, revealing the importance of the prior flux resolution and distribution despite the large amount of atmospheric data available. The retrieved fluxes were compared to eddy flux towers in the corn and grassland areas, revealing an improvement in the seasonal cycles between the two compared to the prior fluxes, despite large absolute differences due to representation errors. The uncertainty of 35 TgC (about 35 gC m²) was derived from the posterior uncertainty obtained with our reference inversion of about 25 to 30 TgC and from sensitivity tests of the assumptions made in the inverse system, for a mean carbon balance over the region of −178 TgC, slightly weaker than the reference. Because of the potential large bias (~20 TgC in this case) due to choice of background conditions, proportional to the surface but not to the regional flux, this methodology seems limited to regions with a large signal (sink or source), unless additional observations can be used to constrain the boundary inflow

Optimal representation of source-sink fluxes for mesoscale carbon dioxide inversion with synthetic data

“…This leads to long time aggregations for inversions, e.g. over one week [Schuh et al, 2010;Lauvaux et al, 2011]. In this study, the mean 15-day fluxes are to be inverted.…”

“…[57] It has been found by preliminary tests by Lauvaux et al [2011] that the spatial correlation structure for crops is rather short. Inversion results with correlation lengths longer than 100 km are very similar.…”

“…The transport errors due to the imprecision in horizontal transport, vertical mixing, and sub-grid physics may persist across hours, which probably leads to spatiotemporal correlations in R. In this regard, Gerbig et al [2003] used exponential decaying models for a parameterization of spatial correlations. Based on an ensemble of atmospheric transports [Lauvaux et al, 2009a], Lauvaux et al [2011] introduced temporal correlations within 12 hours in the observational error to account for its impact on inversions. In general, such observational spatiotemporal correlations would make the inversions less constrained by the observations [Lauvaux et al, 2011].…”

“…Based on an ensemble of atmospheric transports [Lauvaux et al, 2009a], Lauvaux et al [2011] introduced temporal correlations within 12 hours in the observational error to account for its impact on inversions. In general, such observational spatiotemporal correlations would make the inversions less constrained by the observations [Lauvaux et al, 2011].…”

“…New observations have been increasingly collected from extended networks or satellites [Lauvaux et al, 2011;Chevallier et al, 2007], and continuous observations from towers may provide additional gains [Law et al, 2003;Peylin et al, 2005].…”