Constraining US and Global Sources of Nitrous Oxide Base on Field Observations and the GEOS – Chem Adjoint Model

The goal of the project is to improve scientific understanding of North American and global N2O surface sources. The project, combining forward and adjoint modeling with a global ensemble of airborne and ground-based observations, targets the above issues and will be a decisive step forward for understanding N2O emissions from the US and the globe. Research will be guided by the following core questions: 1) To what extent can current airborne and ground observation networks – �?� Constrain surface N2O emissions, their spatial distribution, and temporal variability? �?� Quantify the influence of STE on tropospheric N2O variability, and distinguish that signal from variability in surface fluxes? 2) What is the spatial and temporal distribution of N2O fluxes within North America, and – �?� What does this imply for our understanding of emission processes and dominant sources? �?� How do these emissions compare to fluxes from other global regions? 3) What observing strategy could reduce the key remaining uncertainties that are poorly addressed by existing networks? Work will include the following specific tasks: A) Develop and implement an N2O simulation for the GEOS-Chem global 3D chemical transport model (CTM) and its adjoint. B) Apply GEOS-Chem and its adjoint to quantify the extent to which ongoing and recent airborne (e.g., CARIBIC) and ground-based observations can constrain N2O emissions and stratospheric influence, at what spatial and temporal resolution, and the impact of sampling bias and aggregation errors on the flux estimates. C) Apply GEOS-Chem and its adjoint to interpret the observational ensemble in terms of new constraints on i) the spatial-temporal distribution of N2O emissions from North America and other global regions, and ii) the stratospheric influence on tropospheric N2O. D) Determine the emission processes or regions that are least well constrained by existing data, and employ a sensitivity analysis to identify a spatial/temporal sampling strategy to reduce that uncertainty.