Section: New Results

Monitoring network design

In this section, we report studies that are related to the evaluation of monitoring networks and to new monitoring strategies. This year, network designs techniques have been applied to the inverse modeling of CO2 fluxes.

Network design for mesoscale inversions of CO2 sources and sinks

Participants : Thomas Lauvaux [Pennsylvania State University, USA] , Andy Schuh [Colorado State University, USA] , Marc Bocquet, Lin Wu, Scott Richardson [Pennsylvania State University, USA] , Natasha Miles [Pennsylvania State University, USA] , Ken Davis [Pennsylvania State University, USA] .

Recent 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 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 kilometers 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 kilometers away from the observation sites.

Potential of the International Monitoring System radionuclide network for inverse modeling

Participants : Mohammad Reza Koohkan, Marc Bocquet, Lin Wu, Monika Krysta [The Preparatory Commission for the Comprehensive Nuclear Test-Ban Treaty Organization, UNO] .

The International Monitoring System (IMS) radionuclide network enforces the Comprehensive Nuclear-Test-Ban Treaty, which bans nuclear explosions. We have evaluated the potential of the IMS radionuclide network for inverse modeling of the source, whereas it is usually assessed by its detection capability. To do so, we have chosen the degrees of freedom for the signal (DFS), a well established criterion in remote sensing, in order to assess the performance of an inverse modeling system. Using a multiscale data assimilation technique, we have computed optimal adaptive grids of the source parameter space by maximizing the DFS. This optimization takes into account the monitoring network, the meteorology over one year (2009) and the relationships between the source parameters and the observations derived from the FLEXPART Lagrangian transport model. Areas of the domain, where the grid-cells of the optimal adaptive grid are large, emphasize zones where the retrieval is more uncertain, whereas areas, where the grid-cells are smaller and denser, stress regions where more source variables can be resolved. The observability of the globe through inverse modeling is studied in strong, realistic and small model error cases. The strong error and realistic error cases yield heterogeneous adaptive grids, indicating that information does not propagate far from the monitoring stations, whereas in the small error case, the grid is much more homogeneous.

In all cases, several specific continental regions remain poorly observed such as Africa as well as the tropics, because of the trade winds.

The northern hemisphere is better observed through inverse modeling (more than 60% of the total DFS), mostly because it contains more IMS stations. This unbalance leads to a better performance of inverse modeling in the northern hemisphere winter. The methodology is also applied to the subnetwork composed of the stations of the IMS network that measure noble gases.