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Abstract. Following the disruption to European airspace caused by the eruption of Eyjafjallajökull in 2010 there has been a move towards producing quantitative predictions of volcanic ash concentration using volcanic ash transport and dispersion simulators. However, there is no formal framework for determining the uncertainties of these predictions and performing many simulations using these complex models is computationally expensive. In this paper a Bayesian linear emulation approach is applied to the Numerical Atmospheric-dispersion Modelling Environment (NAME) to better understand the influence of source and internal model parameters on the simulator output. Emulation is a statistical method for predicting the output of a computer simulator at new parameter choices without actually running the simulator. A multi-level emulation approach is applied using two configurations of NAME with different numbers of model particles. Information from many evaluations of the computationally faster configuration is combined with results from relatively few evaluations of the slower, more accurate, configuration. This approach is effective when it is not possible to run the accurate simulator many times and when there is also little prior knowledge about the influence of parameters. The approach is applied to the mean ash column loading in 75 geographical regions on 14 May 2010. Through this analysis it has been found that the parameters that contribute the most to the output uncertainty are initial plume rise height, mass eruption rate, free tropospheric turbulence levels and precipitation threshold for wet deposition. This information can be used to inform future model development and observational campaigns and routine monitoring. The analysis presented here suggests the need for further observational and theoretical research into parameterisation of atmospheric turbulence. Furthermore it can also be used to inform the most important parameter perturbations for a small operational ensemble of simulations. The use of an emulator also identifies the input and internal parameters that do not contribute significantly to simulator uncertainty. Finally, the analysis highlights that the faster, less accurate, configuration of NAME can, on its own, provide useful information for the problem of predicting average column load over large areas.

Abstract. Extratropical cyclones are the most damaging
natural hazard to affect western Europe. Serial clustering occurs when many
intense cyclones affect one specific geographic region in a short period of
time which can potentially lead to very large seasonal losses. Previous
studies have shown that intense cyclones may be more likely to cluster than
less intense cyclones. We revisit this topic using a high-resolution climate
model with the aim to determine how important clustering is for windstorm-related losses. The role of windstorm clustering is investigated using a quantifiable metric
(storm severity index, SSI) that is based on near-surface meteorological
variables (10 m wind speed) and is a good proxy for losses. The SSI is used
to convert a wind footprint into losses for individual windstorms or seasons.
918 years of a present-day ensemble of coupled climate model simulations from
the High-Resolution Global Environment Model (HiGEM) are compared to
ERA-Interim reanalysis. HiGEM is able to successfully reproduce the
wintertime North Atlantic/European circulation, and represent the large-scale
circulation associated with the serial clustering of European windstorms. We
use two measures to identify any changes in the contribution of clustering to
the seasonal windstorm loss as a function of return period. Above a return period of 3 years, the accumulated seasonal loss from HiGEM is
up to 20 % larger than the accumulated seasonal loss from a set of random
resamples of the HiGEM data. Seasonal losses are increased by
10 %–20 % relative to randomized seasonal losses at a return period
of 200 years. The contribution of the single largest event in a season to the
accumulated seasonal loss does not change with return period, generally
ranging between 25 % and 50 %. Given the realistic dynamical representation of cyclone clustering in HiGEM,
and comparable statistics to ERA-Interim, we conclude that our estimation of
clustering and its dependence on the return period will be useful for
informing the development of risk models for European windstorms,
particularly for longer return periods.