Natural Hazards and Earth System Sciences (2020-11-01)

Extension of the WRF-Chem volcanic emission preprocessor to integrate complex source terms and evaluation for different emission scenarios of the Grimsvötn 2011 eruption

  • M. Hirtl,
  • M. Hirtl,
  • B. Scherllin-Pirscher,
  • M. Stuefer,
  • D. Arnold,
  • D. Arnold,
  • R. Baro,
  • C. Maurer,
  • M. D. Mulder

Journal volume & issue
Vol. 20
pp. 3099 – 3115


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Volcanic eruptions may generate volcanic ash and sulfur dioxide (SO2) plumes with strong temporal and vertical variations. When simulating these changing volcanic plumes and the afar dispersion of emissions, it is important to provide the best available information on the temporal and vertical emission distribution during the eruption. The volcanic emission preprocessor of the chemical transport model WRF-Chem has been extended to allow the integration of detailed temporally and vertically resolved input data from volcanic eruptions. The new emission preprocessor is tested and evaluated for the eruption of the Grimsvötn volcano in Iceland 2011. The initial ash plumes of the Grimsvötn eruption differed significantly from the SO2 plumes, posing challenges to simulate plume dynamics within existing modelling environments: observations of the Grimsvötn plumes revealed strong vertical wind shear that led to different transport directions of the respective ash and SO2 clouds. Three source terms, each of them based on different assumptions and observational data, are applied in the model simulations. The emission scenarios range from (i) a simple approach, which assumes constant emission fluxes and a predefined vertical emission profile, to (ii) a more complex approach, which integrates temporarily varying observed plume-top heights and estimated emissions based on them, to (iii) the most complex method that calculates temporal and vertical variability of the emission fluxes based on satellite observations and inversion techniques. Comparisons between model results and independent observations from satellites, lidar, and surface air quality measurements reveal the best performance of the most complex source term.