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 (SVD-based 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 bottom-up 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.
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from the Commonwealth Scientific and Industrial Research Organisation (CSIRO) network, the Environment Canada (EC) network, and a National Institute of Water and Atmospheric research (NIWA) site. We assume a measurement uncertainty of 0.4 ppb at all flask sampling sites based on recommendations from the data providers. In addition to the flask-based air samples, we use high-frequency N2O measurements (discrete hourly or hourly averaged) from the NOAA Chromatograph for Atmospheric Trace Species (CATS) network (Hall et al., 2007), the Advanced Global Atmospheric Gases Experiment (AGAGE) network (Prinn et al., 2000), and the University of Minnesota tall tower (KCMP tall tower; Griffis et al., 2013; Chen et al., 2016). The hourly measurement uncertainty at these sites is approximately 0.3, 0.6, and 1 ppb, respectively.
Acknowledgements. This work was supported by NOAA (grant no. NA13OAR4310086 and NA13OAR4310081) and the Minnesota Supercomputing Institute. The KCMP measurements were made with support from the USDA (grant no. 2013-67019-21364). We thank E. Kort and S. Wofsy for providing the HIPPO N2O measurements. We thank Environment Canada for providing data from the Alert, Churchill, Estevan Point, East Trout Lake, Fraserdale, and Sable Island Sites. We thank R. Martin and S. Nichol for providing data from the Arrival Heights NIWA station. We thank J. Muhle and C. Harth (UCSD-SIO), P. Fraser (CSIRO), R. Wang (GaTech), and other members of the AGAGE team for providing AGAGE data. The AGAGE Mace Head, Trinidad Head, Cape Matatula, Ragged Point, and Cape Grim stations are supported by NASA grants to the Massachusetts Institute of Technology and Scripps Institution of Oceanography, the Department of Energy and Climate Change (DECC, UK) contract to the University of Bristol, and CSIRO and the Australian Bureau of Meteorology. We thank C. Adam Schlosser for work on the MIT IGSM.
© Author(s) 2018. This work is distributed under the Creative Commons Attribution 3.0 License.