Top-down constraints on global N2O emissions at optimal resolution: application of a new dimension reduction technique.

We present top-down constraints on global monthly N₂O emissions for 2011 from a multi-inversion approach and an ensemble of surface observations. The inversions employ the GEOS-Chem adjoint and an array of aggregation strategies to test how well current observations can constrain the spatial distribution of global N₂O emissions. The strategies include (1) a standard 4D-Var inversion at native model resolution (4° 5°), (2) an inversion for six continental and three ocean regions, and (3) a fast 4D-Var inversion based on a novel dimension reduction technique employing randomized singular value decomposition (SVD). The optimized global flux ranges from 15.9 TgNyr^-1 (SVDbased inversion) to 17.5-17.7 TgNyr^-1 (continental-scale, standard 4D-Var inversions), with the former better capturing the extratropical N₂O background measured during the HIAPER Pole-to-Pole Observations (HIPPO) airborne campaigns. We find that the tropics provide a greater contribution to the global N₂O flux than is predicted by the prior bottomup inventories, likely due to underestimated agricultural and oceanic emissions. We infer an overestimate of natural soil emissions in the extratropics and find that predicted emissions are seasonally biased in northern midlatitudes. Here, optimized fluxes exhibit a springtime peak consistent with the timing of spring fertilizer and manure application, soil thawing, and elevated soil moisture. Finally, the inversions reveal a major emission underestimate in the US Corn Belt in the bottom-up inventory used here.We extensively test the impact of initial conditions on the analysis and recommend formally optimizing the initial N₂O distribution to avoid biasing the inferred fluxes. We find that the SVD-based approach provides a powerful framework for deriving emission information from N₂O observations: by defining the optimal resolution of the solution based on the information content of the inversion, it provides spatial information that is lost when aggregating to political or geographic regions, while also providing more temporal information than a standard 4D-Var inversion. [ABSTRACT FROM AUTHOR]