Aggregated Particle-size distributions for tephra-deposit model forecasts

Abstract:

The accuracy of models that forecast atmospheric transport and deposition of tephra to anticipate hazards during volcanic eruptions is limited by the fact that fine ash tends to aggregate and fall out more rapidly than the individual constituent particles. Aggregation is generally accounted for by representing fine ash as aggregates with density ρ_agg and a log-normal size range with median μ_agg and standard deviation σ_agg. Values of these parameters likely vary with eruption type, grain size, and atmospheric conditions. To date, no studies have examined how the values vary from one eruption or deposit to another. In this study, we used the Ash3d tephra model to simulate four deposits: 18 May 1980 Mount St. Helens, 16-17 September 1992 Crater Peak (Mount Spurr), Alaska, 17 June 1996 Ruapehu, and 23 March 2009 Mount Redoubt volcano. In 158 simulations, we systematically varied μ_agg (1-2.3Φ) and σ_agg (0.1-0.3Φ), using ellipsoidal aggregates with =600 kg m^-3 and a shape factor F≡((b+c)/2a)=0.44 . We evaluated the goodness of fit using three statistical comparisons: modeled versus measured (1) mass load at individual sample locations; (2) mass load versus distance along the dispersal axis; and (3) isomass area. For all deposits, the best-fit μ_agg ranged narrowly between ~1.6-2.0Φ (0.33-0.25mm), despite large variations in erupted mass (0.25-50 Tg), plume height (8.5-25 km), mass fraction of fine (<0.063mm) ash (3-59%), atmospheric temperature, aggregation mechanism, and water content between these eruptions. This close agreement suggests that the aggregation process may be modeled as a discrete process that is agnostic to the eruptive style or magnitude of eruption. This result paves the way to a simple, computationally-efficient parameterization of aggregation that is suitable for use in operational deposit forecasts. Further research may indicate whether this narrow range also reflects physical constraints on processes in the evolving cloud.