Preprints
https://doi.org/10.5194/acp-2021-538
https://doi.org/10.5194/acp-2021-538

  02 Aug 2021

02 Aug 2021

Review status: this preprint is currently under review for the journal ACP.

Evaluation of interactive and prescribed agricultural ammonia emissions for simulating atmospheric composition in CAM-Chem

Julius Vira1,2, Peter Hess1, Money Ossohou3,4, and Corinne Galy-Lacaux5 Julius Vira et al.
  • 1Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA
  • 2Finnish Meteorological Institute, Helsinki, Finland
  • 3University of Man, Man, Côte d’Ivoire
  • 4Laboratoire des Sciences de Matière, de l’Environnement et de l’Energie Solaire, Université Félix Houphouët-Boigny, Abidjan, Côte d’ivoire
  • 5Laboratoire d’Aérologie, Université de Toulouse, CNRS, Observatoire Midi Pyrénées, Toulouse, France

Abstract. Ammonia (NH3) plays a central role in the chemistry of inorganic secondary aerosols in the atmosphere. The largest emission sector for NH3 is agriculture, where NH3 is volatilized from livestock wastes and fertilized soils. Although the NH3 volatilization from soils is driven by the soil temperature and moisture, many atmospheric chemistry models prescribe the emission using yearly emission inventories and climatological seasonal variations. Here we evaluate an alternative approach where the NH3 emissions from agriculture are simulated interactively using the process model FANv2 (Flow of Agricultural Nitrogen, version 2) coupled to the Community Atmospheric Model with Chemistry (CAM-chem). We run a set of six-year global simulations using the NH3 emission from FANv2 and three global emission inventories (EDGAR, CEDS and HTAP) and evaluate the model performance using a global set of multi-component (atmospheric NH3 and NH4+, and NH4+ wet deposition) in-situ observations. Over East Asia, Europe, and North America, the simulations with different emissions perform similarly when compared with the observed geographical patterns. The seasonal distributions of NH3 emissions differ between the inventories, and the comparison to observations suggests that both FANv2 and the inventories would benefit from more realistic timing of fertilizer applications. The largest differences between the simulations occur over data-scarce regions. In Africa, the emissions simulated by FANv2 are 200–300 % higher than in the inventories, and the available in-situ observations from Western and Central Africa, as well as NH3 retrievals from the IASI instrument, are consistent with the higher NH3 emissions as simulated by FANv2. Overall, in simulating ammonia and ammonium concentrations over regions with detailed regional emission inventories, the inventories based on these details (HTAP, CEDS) capture the atmospheric concentrations and their seasonal variability the best. However these inventories can not capture the impact of meteorological variability on the emissions, nor can these inventories couple the emissions to the biogeochemical cycles and their changes with climate drivers. Finally, we show with sensitivity experiments that the simulated time-averaged nitrate concentration in air is sensitive to the temporal resolution of the NH3 emissions. Over the CASTNET monitoring network covering the U.S., resolving the NH3 emissions hourly instead monthly reduced the positive model bias from approximately 80 % to 60 % of the observed yearly mean nitrate concentration. This suggests that some of the commonly reported overestimation of aerosol nitrate over the U.S. may be related to unresolved temporal variability in the NH3 emissions.

Julius Vira et al.

Status: open (extended)

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Julius Vira et al.

Julius Vira et al.

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Short summary
Ammonia is an important trace gas due to its role in atmospheric chemistry and due to its adverse effects on sensitive ecosystems. Here we used a new model to assess the global ammonia emissions from agriculture, which is the largest emission source. The model results agree with earlier estimates over industrialized regions, but the model predicts much higher emissions over the sub-Saharan Africa. The available observations from surface stations and satellites support the higher emissions.
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