Constraining Surface Carbon Dioxide Fluxes Using Advanced Ensemble-Based Data Assimilation Techniques (Invited Presentation)

Monitoring of the carbon cycle, including the emission and uptake of important greenhouse gases such as carbon dioxide (CO₂) and methane, is of outmost importance both to better predict future climate and to support future international climate agreements. However, direct measurements of surface carbon fluxes are usually representative of only a small region due to the nature of measurement methods and the high spatial variability of the surface fluxes. A key challenge for the carbon community is how to upscale surface carbon fluxes to climate- and policy-relevant scales.

Atmospheric inversions provide a way forward to constrain e.g. surface CO₂ fluxes at the regional to global scales using observed atmospheric CO₂ concentration and an atmospheric transport model to relate the CO₂ concentrations to the fluxes. The atmospheric transport acts as the essential link between the observations and the quantity of interest, in this case the surface fluxes; thus, it is important to quantify the uncertainties associated with the atmospheric transport, or we may overconstrain the solution. Here we present recent results from a new regional-scale ensemble-based data assimilation system with the aim at constraining CO₂ fluxes over North America. Through observing system simulation experiments, we explore the predictability of CO₂ transport in the atmosphere, and how transport errors impact the posterior CO₂ fluxes and their error covariance structure. The ultimate goal is to understand how uncertainties in the atmospheric transport limits our ability to constrain the surface CO₂ fluxes, both in the practical sense given our current observing network, and in more ideal cases with near-perfect knowledge of the CO₂ concentration state.