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Atmospheric Environment 48 (2012) 143e151

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Atmospheric Environment

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The Eyjafjallajökull ash plume e Part 2: Simulating ash cloud dispersionwith REMOTE

C. O’Dowd a, S. Varghese a,*, D. Martin a, R. Flanagan a, A. McKinstry d, D. Ceburnis a, J. Ovadnevaite a,G. Martucci a, J. Bialek a, C. Monahan a, H. Berresheim a, A. Vaishya a, T. Grigas a, Z. McGrawa,S.G. Jennings a, B. Langmann b, T. Semmler c, R. McGrath c

a School of Physics & Centre for Climate and Air Pollution Studies, Ryan Institute, National University of Ireland, Galway, Irelandb Institute of Geophysics, University of Hamburg, Hamburg, GermanycMet Éireann, Glasnevin, Dublin 6, Irelandd Irish Centre for High-End Computing, National University of Ireland, Galway, Ireland

a r t i c l e i n f o

Article history:Received 21 February 2011Received in revised form12 October 2011Accepted 17 October 2011

Keywords:EyjafjallajokullVolcanic ashDispersionModelling

* Corresponding author.E-mail address: saji.varghese@nuigalway.ie (S. Var

1352-2310/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.atmosenv.2011.10.037

a b s t r a c t

The recent eruption of Iceland’s Eyjafjallajökull volcano caused extensive disruption across Europe. Inthis paper, we describe the volcanic ash parameterisation incorporated in the regional climate model(REMOTE) for forecasting volcanic ash dispersion. We investigate model sensitivity to emission param-eters including eruption column height and vertical release distribution. Model results over a number ofkey ash incursion events are assessed in terms of agreement with both ground based measurements andretrieved LIDAR data at a number of European sites.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Iceland’s Eyjafjallajökull volcano first erupted on 14th April2010. Volcanic ash was emitted up to a height of 11 km (Langmannet al., 2012). Prevailing weather conditions resulted in transport tocentral Europe where it remained up to the 20th April (Ansmannet al., 2010; Flentje et al., 2010). A number of further incidencesof ash incursion into European airspace occurred until the 20thMay. This caused havoc for European airlines, in terms of financiallosses, and for passengers, in terms of disrupted travel.

The London Volcanic Ash Advisory Center (VAAC) issued advi-sories on areas contaminated by ash based on model predictionsfrom the UK Meteorological Office’s NAME model. Based on theseadvisories, airspace was closed by the relevant national aviationauthority. This resulted in the cancellation of a number of flightsacross Europe and caused widespread disruption (Ansmann et al.,2010; Flentje et al., 2010). Once a threshold was specified by theengine manufacturers, the London VAAC moved from a total

ghese).

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avoidance forecast to a threshold forecast: 0.2 mg m�3 being thelimit below which aircraft may fly without special attention, and2 mg m�3 is the limit above which flights should be avoided. From18th May the safe limit was revised upwards to 4 mg m�3, withsome flights occurring under restricted circumstances at ashconcentrations between 2 and 4 mg m�3.

There are a number of dispersion models which have beendeployed for the simulation of volcanic ash. These include the UK’sNAME model (Witham et al., 2007), FLEXPART (Stohl and Seibert,1998), unified European Monitoring and Evaluation Programme(EMEP) model (Simpson et al., 2003), the European Air PollutionDispersion (EURAD) model (Kessler et al., 2001), FALL3D (Costaet al., 2006) and SILAM (Sofiev et al., 2009) and the CommunityMulti-scale Air Quality (CMAQ) model (Mebust et al., 2003).

Ground based in-situ and remote sensing measurements of theplume characteristics were conducted at the Mace Head Atmo-spheric Research Station. The Mace Head Atmospheric ResearchStation is located in County Galway on the Atlantic coast of Irelandat 53� 1903600N, 9�5401400W (O’Connor et al., 2008). As well as datafrom Mace Head model performance will also be assessed usingretrieved data from EARLINET aerosol Raman/polarization lidarsfrom three sites: Hamburg (53.6�N, 10.0�E, 25 m above sea level,

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asl), Leipzig (51.4�N,12.4�E, 125 m asl), and Maisach (48.2�N,11.3�E,515 m height asl, 25 km northwest of Munich) (Ansmann et al.,2010; Gasteiger et al., 2010).

2. Forecasting volcanic ash

2.1. The REMOTE model

The work outlined here addresses the further development,validation and utilisation of a three-dimensional on-line climatechemistry/aerosol model called REMOTE (REgional MOdel withTracer Extension) (Langmann, 2000; Marmer and Langmann, 2007)in ash plume simulation mode. REMOTE was developed from theREMO model which was originally developed as a climate modelsuitable for climate modeling (Jacob et al., 2001). REMOTE deter-mines the physical, photochemical and aerosol state of the modelatmosphere at every model time step, thus offering the possibilityto consider trace species effects on climate (e.g. Langmann, 2007).The dynamical part of the model is based on the former regionalweather forecast system of the GermanWeather Service. Beside theGerman Weather Service physical parameterisations, those of theglobal climate model ECHAM-4 (Roeckner et al., 1996) have beenimplemented in REMOTE. In addition REMOTE can be forced byanalysis data at its lateral boundaries.

After being released in the atmosphere, gas phase and aerosolphase species undergo transport processes (horizontal and verticaladvection (Smolarkiewitz, 1983), transport in convective clouds(Tiedtke, 1989), vertical turbulent diffusion and are removed fromthe atmosphere by sedimentation, dry and wet deposition.

Photochemical production and loss in REMOTE is determined bythe RADM II chemical scheme (Stockwell et al., 1990) by 163chemical reactions in the gas phase including a wide range ofhydrocarbon degradation reactions is implemented with a quasis-teady-state approximation (QSSA) solver (Hesstvedt et al., 1978).

For gaseous compounds, dry deposition fluxes are determinedafter Wesley (1989), whereas for aerosol particles, the size-dependent parameterizations in the ECHAM5-HAM model (Stieret al., 2005) are used for dry deposition and sedimentation whichare essentially based on Ganzeveld et al. (1998) and Seinfeld andPandis (1998). Wet deposition is computed according to Walcekand Taylor (1986) by integrating the product of the grid-averagedprecipitation rate and the mean cloud water concentration whichis determined from cloud base (first layer above the surface con-taining more than 0.001 g kg�1 liquid water) to cloud top (highestlevel exceeding an amount of 0.001 g kg�1 liquid water) for fairweather clouds and from the surface to cloud top for raining clouds.

The M7 aerosol dynamics module is implemented for thedetermination of aerosol dynamics and thermodynamics, which isdescribed in detail by Vignati et al. (2004) and Stier et al. (2005).The aerosol dynamical processes in M7 include nucleation, coag-ulation and condensation. The aerosol size spectrum is representedby the superposition of seven lognormal distributions subdividedinto soluble and insoluble coarse, accumulation and aitken modesand an additional soluble nucleation mode.

During the recent Eyjafjallajökull eruption an automatedvolcanic plume forecasting model was developed in order toprovide Ireland with its own capability of assessment and predic-tion. The standard domain contains 81�91�19 vertical grid boxesat 0.5� grid resolution (approx. 50 km2) with 6 h input and 3 houtput resolution. Meteorological boundary data for the modelsimulations have been obtained from the ECMWF ERA-Interimreanalysis database. The global ECMWF data are obtained ona 0.5� � 0.5� regular latitude/longitude grid with 91 vertical levels.All the data fields are extracted and interpolated to the REMOTEregional model domain. Each step of the interpolation process has

been checked and validated to ensure consistency with the originalglobal data.

Simulations performed can be broadly classified into two e (i)sensitivity experiments to determine the sensitivity of ashconcentration to changes in input eruption parameters and (ii)model simulations for key incursion events to coincide with ashcharacterization measurements at both Mace Head and Maisach.

PM10 emission rate data used for the simulations described herehave been taken from the European Monitoring and EvaluationProgramme (EMEP, 2011) estimations. These data were originallybased on tephra estimates derived from preliminary thickness dataobtained which was measured on the 17th April at two locations 20and 50 km east of the volcano. Size distribution measurements ofthese samples allow estimates of emission rates to be calculated.Whereas ash data in the EMEP study were collected independentlyof volcanic strength measurements, sources have pointed to theo-retical relationships between eruption height and volumetric flux(with the same trends as mass flux, assuming the common treat-ment of ash density as a constant value) (Sparks et al., 1997). Theinitial reported volume of 0.1 m3 of solid (unadjusted for vesicles)tephra over these 72 h was found to be in good agreement(compare with 0.09 km3) with Sparks’ H ¼ 1.67 V0.259 (Sparks et al.,1997) relationship (also unadjusted for vesicles and based on 28previous eruptions). A refinement of the sparks best fit is subject toa large degree of uncertainty (a factor of w5 in each direction ata release height of 9 km) (Mastin et al., 2009). Once the tephravolume is calculated a mass flux can be derived using an assumeddensity. A ratio from previous volcanic emissions of m63 (fraction ofparticles less than 63 mm) to total mass emissions for differenteruption types (Mastin et al., 2009) can be used to determinefeasible release rates. Given that the EMEP release rates are basedon both in-situ tephra and size distributionmeasurements they canbe treated as a refinement of the Spark’s best fit. A more completedescription of the derivation of these flux estimates can be foundelsewhere (http://www2.norvol.hi.is/Apps/WebObjects/HI.woa/swdocument/1015805/Reportþonþ14þAprilþEyjafjollþeruptionþ%25C3%259E%25C3%259EþGLþAH.pdf) whilst the temporallyvarying emissions can be found (https://wiki.met.no/emep/emep_volcano_plume#model_simulation). The uncertainty associatedwith these estimates is stated to be �50%. Other techniques havebeen deployed to determine ash emissions including an inversionscheme which couples a priori source information and the outputof a Lagrangian dispersion model with satellite which has thenbeen used to estimate the volcanic ash source strength as a functionof altitude and time (Stohl et al., 2011).

Ash size distribution is constrained by the model to a lognormaldistribution (with a fixed sigma value of 0.693) and is combinedwith a constant ash density into one input value for use in theREMOTE model. This combines the desired number median radius(1.65 mm), with an assumed volcanic ash density (2.75 g cm�3) anda conversion factor (specified by the HatcheChoate equations(Hinds, 1999)) with all the particles modeled as spheres. The choiceof number median radius is chosen so as to reflect size measure-ments from of ash grain. (http://www.earthice.hi.is/page/ies_EYJO2010_Grain) with w25% of the PM10 number concentrationsresiding in PM2.5. Given that the EMEP emissions are given as PM10

emissions and the associated model distribution contains 30% massgreater that this value and this emission flux has been normalisedto the model size distribution to account for this. LIDAR retrievaldata suggests a small contribution of particles greater than 10 mm tototal ash concentration to sites across Europe (Ansmann et al.,2010) which serves to give further justification to this approach.

Initial grain samples analysed suggest a degree of variability inthe size distribution profile (http://www.earthice.hi.is/page/ies_EYJO2010_Grain). The model size distribution is fixed (the

Fig. 1. Changes in vertical distribution by varying injection to 4.5 km (A) 5.4 km (B)and 6.5 km (C) whilst maintaining a constant mass flux.

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number median radius can be changed whilst the geometric stan-dard deviation is fixed) and therefore this will add a degree ofuncertainty. However the limited number of grain samples makesquantitative assessment of size distribution temporal variabilitydifficult and future published data may make quantification of thisuncertainty more feasible.

The selection of 2.75 g cm�3 as ash density is well within therange of previously published values: 3 g cm�3 (Stohl et al., 2011),2.6 g cm�3 (Gasteiger et al., 2010) and 2.5 g cm�3 (Schumann et al.,2010).

3. Results and discussion

The REMOTE model was initially developed to be used asa volcanic ash forecasting tool for both the public and researchcommunity. The M7 aerosol dynamics module within the REMOTEmodel was developed to include the volcanic ash parameterisationin the coarse mode taking into account the appropriate sizedistribution, density and median radius. The emission line sourcefluxes from the volcano are introduced following the six- hourlysource strength and height data from the EMEP (EMEP, 2011).

3.1. Sensitivity of ash concentration to changes in input eruptionparameters

The input parameters may be determined using directmeasurements, remote observations and previous studies oferuptions having similar features. Accurate eruption source infor-mation is crucial in initialising volcanic ash models and for reliableash forecasting. It is also critical in validating model output. One ofthe largest uncertainties in ash dispersion modelling depends onthe precise estimation of the status of the eruption during modelinitialisation. Key eruption parameters are column height, the totalerupted mass, grain-size distribution, vertical distribution anddensity. For all the sensitivity tests, the period 2nd to 8th May, 2010was arbitrarily chosen and the simulations were run with theECMWF boundary forcings. As seen during the Eyjafjallajökullvolcano eruption, there was limited accurate information availableon the source parameters which are essential for model initialisa-tion and parameterisation. Therefore, it was important to deter-mine the sensitivity of models to such arbitrary prescription ofcertain input parameterisation. A series of sensitivity tests wereperformed varying the eruption source parameters such as verticaldistribution of mass flux and plume height. Heights were chosen asthey correspond to the nearest reported volcanic ash emissionheights during the test period. The source of volcanic ash isinputted with the relevant height emission profile at each of themodel levels from volcano summit to the relevant uppermostheight level. The heights represent the top of this level with theemission taking place throughout this height level. The relevantmodel levels are 1.7, 2.2, 2.9, 3.7, 4.5, 5.4, 6.5 and 7.6 km.

3.2. Sensitivity to eruption column height data

Eruption heights from the reports of the Institute of EarthSciences (IES) at the University of Iceland and the Icelandic Mete-orological Office (IMO) include a range of sources, but mostly centeron observations of the IMO’s weather radar (time ranges often notspecified) and reports from aircraft pilots at specific times whichhave often been different from the radar data. In the first sensitivitytest, as shown in Fig.1 vertical distribution of mass flux at each levelwas made a constant, injection height was varied from 4.5 km to5.4 km and 6.5 kmwhile keeping the total mass flux constant (50%release in the top level with remainder of the emission flux evenlydistributed over the lower levels). The general distribution pattern

appears to be quite similar for the range of injection heightsconsidered. However, there is strong variability in the fine-scalestructures both in the temporal and vertical scale (Fig. 1 AeC). Tohighlight the differences distinctly, difference plots are shown inFig. 2 (AeC). The difference plots tends to suggest that volcanic ashis suspended in the atmosphere for a longer time if released froma higher injection height while higher deposition is noticed whenreleased at lower heights as can be seen around 4th and 6th May,2010 at Mace Head. These data indicate that height input param-eterization can have a significant influence on vertical structure andthat model height resolution may need to improve in order tobetter characterize volcanic ash dispersion.

3.3. Sensitivity to vertical release distribution

A source of significant uncertainty is how to most realisticallyrelease ash emissions along the REMOTE model’s vertical linesource representation of the eruptive volcano. The REMOTE modelallows a user-defined distribution of release points (heights)throughout the eruption column. These heights represent the

Fig. 2. Difference plots for variable injection heights whilst maintaining a constant mass flux. (A: 5.4e6.5 km, B: 5.4e4.5 km, C: 6.5e4.5 km).

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midpoint of that level and the release takes place throughout thecomplete grid cell. Volcanoes have been observed to releasea substantial fraction of their ash in the high-altitude “umbrellaregion” of the eruptive column (where buoyancy between risingash and the surrounding air is achieved) it would seem realistic forash to be modeled with greater emissions near the top of thecolumn. Currently, the most advanced treatment of vertical releasedistributions is in the FALL3D model, which releases much of theash in a Gaussian distribution over a daily constant height (Folchet al., 2012).

A recent study (Stohl et al., 2011) has suggested a high degree ofvariability in line source distribution emission. Given that theREMOTE model limits the equivalent region to having too fewrelease points to recognizably model a Gaussian, the top 2 or 3release points (dependent on the height of the eruption column)are fitted with an appropriate emission rates (typically 70% of themass emitted in top 30% of the eruption column). This approach isconsistent with the approach with volcanic ash dispersion studiesundertaken using the Community Multiscale Air Quality (CMAQ)model where it is assumed that volcanic ash is emitted into allheights between the altitude of the volcano and the estimatedmaximum emission height with largest emissions in the upper-most altitudes (Matthias et al., 2012).

Fig. 3 (AeC) shows the vertical distribution of volcanic ash atMace Head when they are released differently at various levels butmaintaining the source height and source strength constant: (A)50% release at uppermost level (5.4 km) and equally in the rest, (B)Equally distributed over the release column and (C) 25% eachreleased in the two uppermost height levels (5.4 km and 4.5 km)and equally in the remaining levels.

Fig. 4 (AeC) shows the corresponding difference plots. There isa clear difference in the distribution of core concentrations in bothvertical and temporal patterns. The key differences at the surfaceare in the days from 4th e 6th May where ground level concen-tration can be reduced by 0.001 mgm�3 (panel A) and increased by0.01 mg m�3 (panel B) It has to be noted that there are times whenthe descending plumes showed very little difference in theconcentrations which points to the highly non-linear impact of thevertical distribution of the source fluxes. These sensitivity studies

illustrate the importance of the injection heights and the impactthis has on the vertical and spatial dispersion of ash.

3.4. Model simulations and comparisons with measurements

Two test periods were chosen for model evaluation and verifi-cation and these corresponded to the strongest plume detections atthe Mace Head research station (53�1903600N, 9�5401400W) on theWest Coast of Ireland. The physical, chemical and optical propertiesof the volcanic plume offer a distinct opportunity to evaluate modelperformance (O’Dowd et al., 2012).

A Jenoptik CHM15k ceilometer was used to detect the verticaldistribution of the ash plume. The measuring principle is LIDAR-based with photon counting recording system and solid-stateNd:YAG laser source emitting at the 1064 nm wavelength. Thesize resolved non-refractory chemical composition of ambientsubmicron aerosol particles was measured with an Aerodyne High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToFAMS) (O’Dowd et al., 2012).

We also evaluate model performance from mass concentrationdata from Maisach, near Munich, Germany (48.209�N, 11.257�E)(Gasteiger et al., 2010). Here data from 2 LIDAR systems (MULIS andPOLIS), a CIMEL Sun photometer and a JenOptik ceilometers areused to retrieve mass concentrations of volcanic ash. Furthercomparison between model simulations were made at four Euro-pean sites which utilised a combined LIDAR/photometer methodthat allows the retrieval of vertical profiles and mass loadings(Ansmann et al., 2010).

3.4.1. Event 1: 16the23rd AprilOn the 14th April the volcano entered an explosive phase with

ash being ejected up to heights of 11 km (Langmann et al., 2012).This plume was advected into Central Europe and part of this wasthen advected over Ireland. The plume was detected at the MaceHead Ireland, in the first instance around midday on the 19th April.

3.4.1.1. Comparison with Maisach. Mass concentrations of volcanicash over Maisach (near Munich) (retrieved from lidar measure-ments using input of extinction coefficients, backscatter

Fig. 3. Changes in vertical distribution by varying vertical release distribution whilstmaintaining a constant height (5.4 km). (A) 50% release at uppermost level and equallyin the rest (B) Equally distributed over the release column (C) 25% each released in thetwo uppermost levels and equally in the rest.

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coefficients, and linear depolarization ratios) were determined onthe 17th April at approximately 02:00 (Gasteiger et al., 2010).Using this technique maximum 1 h mass concentrations of1.1 mg m�3 (0.65e1.7 mg m�3) were measured and it is estimatedthat short term maxima are close too but not above 2 mg m�3. Theaveraged concentration over the measured vertical extent of theplume (400 m) was estimated to be 0.54 mg m�3

(0.3e0.9 mg m�3) resulting in a plume loading of 216 mg m�2

(129e389 mg m�2).Model output in Fig. 5 over the relevant time period indicates

that the model plume depth is overestimated by the REMOTE. Thisis likely to be a function of the vertical resolution deployed in themodel. It is clear that REMOTE’s prediction of altitude of maximumconcentration (4 km at 00:00:00 on the 17th as opposed tow3.5 km from the measurements) is in good agreement withinstrumental data illustrating the ability of the model to replicateheights and advection times. The REMOTE plume loading wascalculated at the nearestmodel output time steps (01:00, 02:00 and03:00) resulting in values of 204, 207 and 213mgm�2. The REMOTEestimation approaches the lower limit of retrieval data and thesedifferences are likely to be attributable to grid averaged instanta-neous concentrations vs. fixed point time averaged locations aswell as inherent variability in the source emission term.

3.4.1.2. Comparison with Mace Head. Around midnight of the 19th/20th April as the plume subsided into the boundary layermeasurements of PM2.5 at Mace Head increased from baselinevalues (6.1 � 1.3 mg m�3) to 9.1 � 1.3 mg m�3 with submicronsulphate being measured at up to 2.1 � 0.2 mg m�3 (O’Dowd et al.,2012). Shown in Fig. 6 (A, B and C) is the vertical and spatialdistribution modelled over Mace Head during this period (A), thetemporal evolution of volcanic plume backscatter on the 19th April(B) and selected instrumental measurements over this period (C).The temporal evolution of volcanic plume backscatter (Fig. 6 B) onthe 19th April was detected by a Jenoptik CHM15k ceilometerwhich measures atmospheric target backscatter profiles over thenominal range 30m-15 kmwith first overlap height at around 30m(full overlap at 1500 m) (O’Dowd et al., 2012). The plume is clearlydetected at a height of 4.0 km at midday and this height reduces toboundary layer height at approximately 23:00 (O’Dowd et al.,2012). This is evidenced by the concurrent increase in PM2.5 andsubmicron sulphate (Fig. 6 C). It is also worth noting that the plumeis relatively narrow with approximately 200 m thickness. Thecorresponding model simulation (Fig. 6 A) over this period clearlyshows changes in the plume height on the 19th with the height atmaximum concentration at midday being approximately 5.0 km.This illustrates the ability of the model to accurately determineadvection time with values output every 3 h. The illustratedsensitivity to release height could be responsible for these heightdifferences.

Model predictions suggest ground level ash concentrations of17.4 mg m�3 at 00:00 and 14.6 mg m�3 at 03:00 over the relevantash incursion period. A comparison between these modelled andmeasured values is difficult given the uncertainty in size distri-bution although flight based and LIDAR measurements madeover Germany on the 19th April indicated that less than 10% ofvolcanic ash resided in particle size range below 2.5 mm(Schumann et al., 2010). This percentage would obviously bedependent on transport routes and sedimentation rates ofdifferent particle sizes and may be different at Mac Head. Offlinechemical analysis over a number of plume incursions at MaceHead suggests PM10 to PM2.5 ratios in the range of 2:1 to 5:1(O’Dowd et al., 2012). Applying this ratio to model predicted ashground level ash concentrations suggests modelled ground levelPM2.5 concentrations in the between 2.6 and 5.2 mg m�3. Theapplication of this ratio to modelled values assumes minimaltransport of ash greater than 10 mm and this is borne out byretrieved LIDAR data at a number of European sites (Ansmannet al., 2010).

These values are in reasonable agreement with the measureddata over this period given the uncertainties associated with modelinput parameters.

Fig. 4. Difference plots for variable release distribution whilst maintaining a constant mass flux and constant height. (A) 50% release at uppermost level e equally distributed overthe release height (B) 50% release at uppermost level e 25% release in each of the two uppermost levels and equally in the rest. (C) Equally distributed over the release height e 25%each released in the two uppermost levels and equally in the rest.

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3.4.1.3. Comparison with ash mass loads measured at Leipzig andHamburg. On the 16th April the 1 h mean (0530e0630 GMT) ashmass concentration over Hamburg in was estimated to reach peakvalues around 1000 mg m�3 whilst the column ash load of theroughly 4 km deep plume was estimated to 1750 mg m�2 witha likelihood that the ash mass concentrations may be under-estimated by more than 100% (Ansmann et al., 2010). The output atthe most relevant model time step (0600 GMT) gave an ash loadingof 1095 mg m�2. The ash mass load over Leipzig on the 21st Aprilbetween 2200 and 2400 GMT was determined to be 50 mg m�2 forthe free troposphere from 2 to 10 km height. The relevant modelash loading values at a representative time step (2400 GMT) was17.4 mg m�2. Given the uncertainly in estimates, (�40% for LIDARretrieval and at least�50% for all modelled values), these values arein reasonably good agreement.

Fig. 5. A: The vertical distribution modelled over Maisach during the 17th e 23rdApril.

3.4.2. Event 2: 2nde9th mayThe second evaluation period encompassed almost continuous

18 h ash characterization measurements at Mace Head over the 4thand 5th May with this being one of the strongest events encoun-tered over the whole eruption. Previous studies (O’Dowd et al.,2012) indicate that there is a degree of agreement betweenadvection times and downward transport to the surface level fora number of key events. In addition we have shown that keyinput parameters such as eruption height and line source distri-bution can make a significant impact on vertical and ground levelconcentrations.

One of the largest plume interactions at Mace Head occurredover the period from 2nd to 5th May 2010.Fig. 7 A shows themodelled vertical and spatial distribution measured over MaceHead during this period. There are a number of interesting featuresassociated with these data. The model output over this periodsuggests vertical transport to the surface on the 4th at approxi-mately 06:00. Given that the model output is for an instantaneousoutput time it is feasible that the transport time could be over-estimated within the magnitude of the model output time step(3 h). This is seen in the data on the 4thMay (Fig. 7 C) measurementdata where there is an elevation in the increase in the submicronsulphate as well as a small initial increase in PM2.5 measurementagain at approximately 08:00. This indicates reasonable agreementbetween modelled and measured advection speeds.

Fig. 7 B shows the temporal evolution of volcanic plume back-scatter coefficient over Mace Head on the 4th April clearly showsthe plume (00:00) in two layers at approximately 2500 m and3500 m which is in good agreement with the height of maximumash concentration modelled at this time (3.5 km). The fact that theplume is in two distinct layers highlights the limitations in the finestructure of the modelled vertical distribution. This is a function of

Fig. 6. A: The vertical and spatial distribution modelled over Mace Head during the 17th e 23rd April (location of Mace Head marked on map). B: Temporal evolution of volcanicplume backscatter coefficient and extinction over Mace Head on the 19th April. C: Ground level instrumental measurements at Mace Head during the 19th e 23rd April.

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the height resolution employed in the model and future iterationsof the model would employ a greater degree of height resolution.The ash cloud profile cannot be determined from 04:00 until 16:45due to cloud cover. However the attenuated backscatter coefficientsmeasured after this time suggest entrainment of the ash cloud intothe planetary boundary layer which is evidenced by the concurrentelevations in PM2.5 and submicron sulphate

3.5. Automated forecasting system

Ireland’s first automated forecast system for volcanic ashplume dispersion was developed and forecast issued twice dailyfrom early May, 2010 for the case of Eyjafjallajökull eruption.

ECMWF forecast data is first obtained at Met Eireann (IrishMeteorological Service) and transferred to ICHEC (Irish Centre

Fig. 7. A: The vertical and spatial distribution modelled over Mace Head during the 2nd e 9th may (location of Mace Head marked on map). B: Temporal evolution of volcanic plumebackscatter coefficient over Mace Head on the 4th may. C: Ground level instrumental measurements at Mace Head during the 4th e 8th may.

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for High-End Computing) where the data is processed andprepared for the initial and boundary conditions for the REMOTEmodel. The latest volcano eruption parameters are updated andmodel runs by invoking the restart file describing the prevailingash concentration. A semi-automated system is in place such thatthe model output is post-processed, graphics prepared andresults are uploaded to http://www.macehead.org and forecastissued. Forecasts include an animation showing the transport ofash cloud over the next four days and vertical distribution ofash concentration over Dublin and Mace Head over the sameperiod.

4. Conclusion

In this work we have applied a Regional Climate Model toforecast and study the dispersion of volcanic ash from the 2010eruption of the Eyjafjallajökull volcano. Themodel was driven usingECMWF ERA-Interim reanalysis data. Using prescribed ash emis-sion rates and size distribution data for selected events, the modelsensitivity to eruption column height and vertical release distri-bution was studied. We conclude from the results that quantitativedifferences in ground level concentrations and vertical ash profilescan be attributable to the description of these parameters within

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the model set up. Given that both the possible release heights andthe distribution of the ash within that height are determined by thedegree of vertical resolution this becomes an important consider-ation for future model iterations. Comparison with model outputwith aerosol and lidar measurements at Mace Head Ireland illus-trate good predictions of plume height and advection times andvery good agreement between estimates of transport to the surface.Quantitative comparison between PM2.5 concentrations and modeloutput are in reasonable agreement given the inherentuncertainties.

Column burden comparisons with retrieved LIDAR concentra-tion data obtained at a number of European sites suggest reason-able agreement (factor of w3) with the measurements. It is likelythat these differences are in the majority, attributable to uncer-tainty in the source emission term and it is hoped that futureemission constraints (including satellite measurements) will helpto improve model performance. This level of agreement isencouraging and suggests reasonable model input parameter-isation. It is hoped that as more measurement data becomesavailable the model can be better constrained.

Acknowledgements

The authors would like to thank Environmental ProtectionAgency (Ireland), Higher Education Authority (Ireland), ScienceFoundation Ireland for the financial support and Irish HighPerformance Computing Centre for the computing resources andtechnical support.

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