Document Type

Article

Publication Date

7-28-2016

Department

Department of Geological and Mining Engineering and Sciences

Abstract

Volcanic ash transport and dispersion (VATD) models are used to forecast tephra deposition during volcanic eruptions. Model accuracy is limited by the fact that fine-ash aggregates (clumps into clusters), thus altering patterns of deposition. In most models this is accounted for by ad hoc changes to model input, representing fine ash as aggregates with density ρagg, and a log-normal size distribution with median μagg and standard deviation σagg. Optimal values may vary between eruptions. To test the variance, we used the Ash3d tephra model to simulate four deposits: 18 May 1980 Mount St. Helens; 16-17 September 1992 Crater Peak (Mount Spurr); 17 June 1996 Ruapehu; and 23 March 2009 Mount Redoubt. In 192 simulations, we systematically varied μagg and σagg, holding ρagg constant at 600 kgm-3. We evaluated the fit using three indices that compare modeled versus measured (1) mass load at sample locations; (2) mass load versus distance along the dispersal axis; and (3) isomass area. For all deposits, under these inputs, the best-fit value of μagg ranged narrowly between ∼2.3 and 2.7φ (0.20-0.15 mm), despite large variations in erupted mass (0.25-50 Tg), plume height (8.5- 25 km), mass fraction of fine ( < 0.063 mm) ash (3-59 %), atmospheric temperature, and water content between these eruptions. This close agreement suggests that aggregation may be treated as a discrete process that is insensitive to eruptive style or magnitude. This result offers the potential for a simple, computationally efficient parameterization scheme for use in operational model forecasts. Further research may indicate whether this narrow range also reflects physical constraints on processes in the evolving cloud.

Publisher's Statement

© Author(s) 2016. Publisher’s version of record: https://doi.org/10.5194/acp-16-9399-2016

Publication Title

Atmospheric Chemistry and Physics

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

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