ACTAUNIVERSITATIS

UPSALIENSISUPPSALA

2016

Digital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Science and Technology 1433

Dispersion modelling of volcanicemissions

ADAM DINGWELL

ISSN 1651-6214ISBN 978-91-554-9704-0urn:nbn:se:uu:diva-303959

Dissertation presented at Uppsala University to be publicly examined in Axel Hambergssalen,Villavägen 16, Uppsala, Thursday, 17 November 2016 at 10:00 for the degree of Doctorof Philosophy. The examination will be conducted in English. Faculty examiner: ProfessorDavid Simpson (Chalmers University of Technology, Department of Earth and SpaceSciences).

AbstractDingwell, A. 2016. Dispersion modelling of volcanic emissions. (Spridningsmodellering avutsläpp från vulkaner). Digital Comprehensive Summaries of Uppsala Dissertations fromthe Faculty of Science and Technology 1433. 53 pp. Uppsala: Acta Universitatis Upsaliensis.ISBN 978-91-554-9704-0.

Gases and particles released by volcanoes pose a serious hazard to humans and society.Emissions can be transported over long distances before being reduced to harmlessconcentrations. Knowing which areas are, or will be, exposed to volcanic emissions is animportant part inreducing the impact on human health and society. In this thesis, the dispersionof volcanic emissions is studied using a set of atmospheric models.

The work includes contribution to the development of the Lagrangian Particle DispersionModel FLEXPART-WRF. Three case studies have been performed, one studying potential ashemissions from potential future eruptions on Iceland, a second covering SO2 emissions fromMt. Nyiragongo in D.R. Congo, and a third studying the SO2 emission rate of the Holuhrauneruption (Iceland) in 2014–2015.

The first study covers volcanic ash hazard for air traffic over Europe. Three years ofmeteorological data are used to repeatedly simulate dispersion from different eruption scenarios.The simulations are used to study the probability of hazardous concentrations in ash in Europeanairspace. The ash hazard shows a seasonal variation with a higher probability of efficienteastward transport in winter, while summer eruptions pose a more persistent hazard.

In the second study, regional gas exposure around Mt. Nyiragongo is modelled using fluxmeasurements to improve the description of the emission source. Gases are generally transportedto the north-west in June–August and to the south-west in December–January. A diurnalvariation due to land breeze around lake Kivu contributes to high concentrations of SO2 along thenorthern shore during the night. Potentially hazardous concentrations are occasionally reachedin populated areas in the region, but mainly during the nights.

The third study uses inverse dispersion modelling to determine the height and emission ratesbased on traverse measurements of the plume at 80–240 km from the source. The calculatedsource term yields better agreement with satellite observations compared to commonly usedcolumn sources.

The work in this thesis presents improvements in dispersion modelling of volcanic emissionsthrough improved models, more accurate representation of the source terms, and throughincorporating new types of measurements into the modelling systems.

Keywords: dispersion modelling, atmospheric, volcano, gas emissions, volcanic ash,FLEXPART, FLEXPART-WRF

Adam Dingwell, Department of Earth Sciences, LUVAL, Villav. 16, Uppsala University,SE-75236 Uppsala, Sweden. ,

© Adam Dingwell 2016

ISSN 1651-6214ISBN 978-91-554-9704-0urn:nbn:se:uu:diva-303959 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-303959)

Akademisk avhandling som för avläggande av teknologie doktorsexamen vid Uppsala univer-sitet kommer att offentligt försvaras i Axel Hambergssalen, Villavägen 16, Uppsala, torsdag 17 november 2016 kl. 10:00. Disputationen sker på engelska. Opponent: Professor Simpson David (Chalmers Tekniska Högskola, Institutionen för Rymd och geovetenskap). Sammanfattning Dingwell, A. 2016. Dispersion modelling of volcanic emissions. (Spridningsmodellering av utsläpp från vulkaner). Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1433. 53 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-554-9704-0. Gas- och partikelutsläpp från vulkaner utgör en fara för människor och för vårt samhälle. Ut-släppen kan transporteras över långa avstånd innan de reduceras till oskadliga halter. Att känna till vilka områden som utsätts för, eller kommer utsättas för, utsläppen är ett viktigt verktyg föratt minska påverkan på folkhälsa och samhälle. I avhandlingen studeras spridningen av ut-släpp från vulkanutbrott med hjälp av en uppsättning numeriska atmosfärsmodeller.

Den Lagrangiska Partikelspridningsmodellen FLEXPART-WRF har förbättrats och applice-rats för spridningsmodellering av vulkanutbrott. Tre studier har utförts, en fokuserar på vulka-naska från potentiella framtida utbrott på Island, den andra studerar SO2-ustläpp från vulkanen Nyiragongo i Demokratiska Republiken Kongo, och den tredje studerar SO2-ustläpp från ut-brottet i Holuhraun (Island) 2014–2015.

Den första studien uppskattar sannolikheten för att vulkanaska från framtida vulkanutbrott på Island ska överskrida de gränsvärden som tillämpas för flygtrafik. Tre år av meteorologisk data används för att simulera spridningen från olika utbrottsscenarier. Sannolikheten för skad-liga halter aska varierar med årstid, med en högre sannolikhet för effektiv transport österut un-der vintermånaderna, sommarutbrott är istället mer benägna att orsaka långvariga problem överspecifika områden.

In den andra studien undersöks spridningen av SO2 från Nyiragongo över en ettårsperiod. Flödesmätningar av plymen används för att förbättra källtermen i modellen. Gaserna transpor-teras i regel mot nordväst i juni–augusti och mot sydväst i december–februari En dygnsvariat-ion, kopplad till mesoskaliga processer runt Kivusjön, bidrar till förhöjda halter av SO2 nattetid längs Kivusjöns norra kust. Potentiellt skadliga halter av SO2 uppnås av och till i befolkade områden men huvudsakligen nattetid.

Den tredje studien utnyttjar inversmodellering för att avgöra plymhöjd och gasutsläpp base-rat på traversmätningar av plymen runt 80–240 km från utsläppskällan. Den beräknade källter-men resulterar i bättre överensstämmelse mellan modell- och satellitdata jämfört med enklare källtermer.

Arbetet i den här avhandlingen presenterar flertalet förbättringar för spridningsmodellering av vulkanutbrott genom bättre modeller, nogrannare beskrivning av källtermer, och genom nya metoder för tillämpning av olika typer av mätdata. Nyckelord: Spridningsmodellering, atmosfär, vulkan, gasutsläpp, vulkanaska, FLEXPART, FLEXPART-WRF Adam Dingwell, Department of Earth Sciences, LUVAL, Villav. 16, Uppsala University, SE-75236 Uppsala, Sweden. © Adam Dingwell 2016 ISSN 1651-6214 ISBN 978-91-554-9704-0 urn:nbn:se:uu:diva-303959 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-303959)

The beginning of knowledgeis the discovery of something we do not understand

Frank Herbert

List of papers

This thesis is based on the following papers, which are referred to in the text

by their Roman numerals.

I Brioude, J., Arnold, D., Stohl, A., Cassiani, M., Morton, D., Seibert, P.,

Angevine, W., Evan, S., Dingwell, A., Fast, J. D., Easter, R. C., Pisso,

I., Burkhart, J. and Wotawa, G. (2013). The Lagrangian particle

dispersion model FLEXPART-WRF version 3.1. Geoscientific ModelDevelopment, 6(6):1889–1904.

II Dingwell, A. and Rutgersson, A. (2014). Estimating volcanic ash

hazard in European airspace. Journal of Volcanology and GeothermalResearch, 286:55–66.

III Dingwell, A., Rutgersson, A., Claremar, B., Arellano, A., Mapendano,

Y. and Galle, B. (2016). Seasonal and diurnal patterns in the dispersion

of SO2 from Mt. Nyiragongo. Atmospheric Environment, 132:19–29

IV Dingwell, A., Rutgersson, A., Arellano, S., Galle, B. Using DOAS

traverses and atmospheric modelling to determine plume height and

eruption rate of the 2015 Holuhraun eruption. Manuscript.

Reprints were made with permission from the publishers.

In Paper I, I participated in development of the model, including the design

and implemention of a new output format; I further improved on the model in

Papers II and III.

I came up with the initial ideas for Papers II, III and IV and drafted and

implemented the methods. I configured and executed all model simulations.

All co-authors have contributed through advice and suggestions for both data

analysis and writing of the papers.

In Paper II, I had the main responsibility for designing and implementing the

method and data analysis and for the writing of the paper. In Paper III, I

was responsible for most of the data analysis — except for the processing of

the flux-data — and also implemented 2 new land-use options in FLEXPART-

WRF. I had the main responsibility for writing the paper. In Paper IV, I

designed the study and carried out the short- and long-range modelling. I had

the main responsibility for the analysis and for writing the paper.

The following related publication is not included in the thesis:Arellano, S.; Yalire, M.; Galle, B.; Bobrowski, N.; Dingwell, A.;

Johansson, M. and Norman, P. (2016). Long-term monitoring of

SO2 quiescent degassing from Nyiragongo’s lava lake. Journalof African Earth Sciences.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.1 Aim of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2 Modelling tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1 Meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1.1 Downscaling meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.1.2 Meteorological model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.2 Dispersion modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.2.1 Advection of particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2.2 Deposition processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.2.3 Aggregation and Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.2.4 Plume rise from volcanic eruptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.2.5 Mass eruption rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.2.6 Inverse modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3 Model application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.1 Ash hazard climatology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.2 Regional exposure to passive degassing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.3 Inverse modelling of SO2 emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.4 Model enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.1 Ash hazard climatology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.2 Regional exposure to volcanic degassing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.3 Inverse modelling of SO2 emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.1 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

6 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

7 Sammanfattning pa svenska (Summary in Swedish) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

1. Introduction

Planet Earth is host to many volcanic systems, about 1,300 of these have had

eruptions within the past 10,000 years. Some eruptions are short lasting, such

as the frequent eruptions at Sakurajima (Japan) or Vulcano (Italy); others can

last for several decades or millennia, such as Stromboli volcano in Italy. Volca-

noes in general are natural emitters of aerosols and numerous hazardous gases.

These emissions can have a negative impact on human health, infrastructure

and/or the environment. Emission plumes from large eruptions can maintain

harmful concentrations for days or even weeks, before being reduced through

removal processes and dispersion. Gas emissions can occur either as part of

eruptions or through passive degassing over longer periods. The emissions can

affect cloud chemistry and microphysics and cause acid rain (e.g. Delmelle

et al., 2001; Rose et al., 2006). Exposure to gas plumes can increase wear

on man-made structures (e.g. antennas, machinery, roofs). Acidic compounds

(e.g. SO2, H2S, HCl, HF) can also have an irritating effect on airways when

inhaled (Delmelle et al., 2002). Fluoride (F) — which is mainly emitted as

HF — is toxic at high concentrations and can disturb dental development for

children (Baxter et al., 1999; Delmelle et al., 2002); it can even prove fatal for

grazing animals, which might ingest large quantities of fluoride coated plants

(Cronin et al., 2000; Thorarinsson and Sigvaldason, 1972).

Volcanic emissions have been dispersed over Europe many times through-

out history, one of the most notable cases was the Laki fissure eruption in

Iceland in 1783–1784. The eruption had severe impacts on Iceland; most of

Iceland’s livestock died within a year from the eruption by ingestion of ash

coated grass. During the this period, the human population suffered from

famine and exposure to the toxic emissions; the population was reduced by

19–22 %, or approximately 22,000 people (Demaree and Ogilvie, 2001). The

eruption also affected the European continent, where a persistent haze was re-

ported in mid to late June 1783, lasting for about two months and eventually

extending as far as Moscow, Baghdad and northern Africa (Stothers, 1996).

The eruption received attention recently, when a fissure eruption started in

Holuhraun (Iceland) on the 29th of August, 2014; the eruption lasted until late

February 2015. The Holuhraun eruption was one of the largest emissions of

SO2 since the Laki eruption.

Gas emissions from volcanoes are not uncommon, the main contribution

being from passive degassing (Andres and Kasgnoc, 1998). Individual cases

of passive degassing generally have weaker emission rates than events like

the Laki eruption, however, they can still pose a regional hazard. Passively

11

degassing volcanoes often entails a negative impact on human health and the

environment in the downwind areas (Hansell and Oppenheimer, 2004).

Explosive eruptions pose a different hazard by releasing ash at high alti-

tudes; the ash can be transported long distances and cause serious damage to

jet engines. The eruption of Eyjafjallajokull in April 2010 resulted in almost

complete closing of the European airspace through pre-cautionary decisions.

Fine ash was injected in the jet stream and transported over 1000 km, reaching

continental Europe within a matter of days. Around 104,000 flights were can-

celled form the 15th to the 22nd of April (EUROCONTROL, 2010). While

the total ash emissions of the eruption were not exceptional, the fraction of

fine ash produced was unusually high. Persistent northerly winds resulted in

a large portion of the fine ash to reach continental Europe (Stohl et al., 2011).

In 2010, the European countries were not sufficiently prepared to efficiently

manage such a situation. As a response, new guide-lines for volcanic ash were

decided upon and training programmes were started.

The impact of extreme natural events is highly dependent on the informa-

tion available to decision makers. Increased knowledge and better predictions

enable our society to make preparations and better manage the situation when

eruptions do occur.

1.1 Aim of this thesis

In this thesis, exposure to volcanic ash and gases are studied and techniques

for estimating hazards for different types of volcanoes are further developed.

• Paper I presents the first stable release of the multi-scale Lagrangian

particle dispersion model FLEXPART-WRF.

• The probability of encountering volcanic ash in European airspace as the

result of potential future eruptions on Iceland is investigated in Paper II.

• Regional exposure to gases emitted from Mt. Nyiragongo (eastern Demo-

cratic Republic of Congo) is covered in Paper III.

• Different methods for calculating fluxes and plume height are presented

and evaluated in Paper IV.

12

2. Modelling tools

The transport of pollutants in the atmosphere can be affected by a variety of

processes (advection, turbulent mixing, fallout, impaction, rain-out, wash-out,

radioactive decay, and/or chemical reactions); a full understanding of the com-

bined effect of these processes is hard to achieve even if each process in itself

is well understood. Atmospheric models in general are used in order to study

the combined effect of various physical and/or chemical process.

A combination of two types of models have been used throughout this the-

sis: a meteorological model, mainly used to produce wind and precipitation

data, and a dispersion model, which uses the meteorological data to simulate

transport and removal of the emissions. In Papers II and III the Weather Re-

search and Forecasting (WRF) model was used together with the Lagrangian

Particle Dispersion Model (LPDM) FLEXPART-WRF. In Paper IV simula-

tions made with FLEXPART-WRF were compared to results from the similar

FLEXPART model.

2.1 Meteorological data

Dispersion modelling requires certain meteorological data, mainly winds and

precipitation. For short scales, single meteorological measurements can be

used directly to force a dispersion model. For application over larger areas

gridded data is required, which best achieved through meteorological mod-

els. One option is to use a combination of analysis and forecast data, which

is continuously produced by a number of meteorological centres around the

world. The European Centre for Medium-range Weather Forecasts (ECMWF)

produce global, hourly forecasts using their Integrated Forecast System (IFS).

Today, the global forecasts can be retrieved by member states down to a res-

olution of 0.1 degrees (∼ 10 km), however, this is a fairly new product and

older data have a coarser spatial resolution. The global forecasts are suitable

for use in atmospheric dispersion model as long as the temporal resolution is

sufficient.

If higher resolution is required it might be a better option to use reanaly-

sis data, e.g. when studying short range dispersion or during a period when

high resolution forecast data is not available. Reanalysis data is produced in a

similar fashion as forecast data but is to a large extent produced retroactively

(Warner, 2011). A reanalysis product is produced using a large set of observa-

tions and a meteorological model; data is typically produced covering several

13

decades to provide a long time series of consistent data. This means that re-

analysis data seldom uses the latest versions of models and usually run at a

coarser resolution compared to the most recent forecasts.

ERA-Interim (Dee et al., 2011) is a reanalysis product produced by ECMWF

which has been used extensively in this thesis. The data comes on a horizontal

resolution of 0.75 degrees with an output interval of 6 hours. The large gaps

between output times can be problematic in dispersion modelling, e.g. due to

meandering of winds or propagation of frontal systems which might appear

to skip over grid cells if the temporal resolution is too coarse. Furthermore,

a higher spatial resolution is sometimes desired, in which case an additional

step is necessary to increase the resolution. An efficient way to solve most of

these problems is dynamical downscaling.

2.1.1 Downscaling meteorological data

In order to improve the resolution of a meteorological dataset, one can either

interpolate (which only smooths the data without adding much information),

use a statistical downscaling (which is more suitable for climatology) or one

can use a limited-area meteorological model (LAM). The coarse data is used

to initiate the LAM, which itself runs at a higher resolution. Forcing is applied

along the boundaries of the model domain, throughout the simulation, using

data from the global model. In this way, the large-scale dynamics are used

to feed smaller scale dynamics in the LAM which cannot be resolved by the

global model. The LAM is able to use more detailed land-use and topography

data than a global model; both topography and land-use data will influence the

development of the smaller-scale dynamics.

The method of increasing resolution by the use of a LAM, is called dynam-ical downscaling. The result is a more detailed description of the meteoro-

logical situation than what can be achieved by interpolation. The downside

is that dynamical downscaling requires far more computational resources than

other downscaling techniques. A comparison between interpolated and dy-

namically downscaled meteorological data is shown in Figure 2.1; both cases

produce smooth solutions but the interpolated data lacks the additional infor-

mation which is added with dynamical downscaling.

The goal of dynamical downscaling is to add information about smaller-

scale processes while retaining the information from the large scale processes

in the global model. Downscaling should be made incrementally if there is

a large difference between the initial resolution of the global model and the

final resolution of the LAM; this is due to how forcing is applied during a

simulation. If there is a large resolution-gap between parent (low resolution)

and child (high resolution) domains, then processes from intermediate scales

will never be resolved. This leaves the child domain without proper forcing,

allowing it to develop features which, in reality, do not exist.

14

245 250 255 260 270265 275 280

Temperature (K)

66°N

64°N

62°N

60°N

58°N40°W 35°W 30°W 25°W 20°W 15°W

66°N

64°N

62°N

60°N

58°N40°W 35°W 30°W 25°W 20°W 15°W

Figure 2.1. Comparison of near-surface temperature from basic interpolation (left)

versus dynamical downscaling (right) from Dingwell (2012). The left image was

produced by interpolating data from ERA-Interim at 0.75 degree resolution (≈ 35×85 km) down to 13.5× 13.5 km. The right image has the same resolution, but was

produced by the WRF-model, using the interpolated data from ERA-Interim as forc-

ing.

Incremental nesting is especially important if there are e.g. mountains, lakes

or islands nearby which are too small to be properly resolved by the global

model but which have an important influence on the dynamics in the region.

With nesting, these influences can be accounted for in an intermediate domain

which might not require much computational time compared to expanding

the innermost domain. Furthermore, numerical problems may arise if there

is a too large difference between the input data and the computational grid

(Warner, 2011). There are however cases where direct nesting can be just as

accurate as incremental nesting, e.g. climatology studies (Beck et al., 2004).

We have used incremental downscaling with resolutions increasing by a factor

of 3 between nests; this is the most common approach with the meteorological

model we used.

2.1.2 Meteorological model

When downscaling meteorological data, we have used the Advanced Weather

Research and Forecasting (WRF) model (Skamarock et al., 2008). The WRF-

model is a 3-dimensional, non-hydrostatic, mesoscale meteorological model.

It is designed with a modular approach, which enables wide use-cases from

operational forecasting to idealized scenarios. Multiple options are available

for most physics schemes in the model, several of which were tested for Pa-pers II, III and IV.

For the purpose of this thesis, WRF has been set up to downscale ERA-

Interim reanalysis data. First, the model is initiated in each grid-cell using the

interpolated reanalysis data. The model then requires 6–12 simulation hours to

15

relax the dynamics and precipitation fields (spin-up) before producing reliable

results. This type of initialization is called a cold start. During a simulation,

the reanalysis data is used to force the model at the boundaries of the outermost

model domain. No forcing is applied within the domains; this might cause the

model to deviate from the forcing data during longer simulations in a similar

way as when a long-term forecast deviates from the actual weather. Sequential

initiations were used in order to prevent the model from deviating too far from

its forcing data. A restart interval of 6 + 24 hours (spin-up + production) was

used for the mid-latitude simulations in Paper II. A longer spin-up time was

required in Paper III; therefore, the restart interval was extended to 12 + 48

hours. There are several possible reasons why a longer spin-up was required.

Paper III covered the inland tropical region of eastern D.R. Congo. Numer-

ical weather prediction is more difficult in the tropics compared to the mid-

latitudes, where predictable synoptic systems mostly dominate. In the tropics,

mesoscale processes have a larger influence on the weather (Laing and Evans,

2011) but the forcing data are too coarse to fully resolve them. Furthermore,

the model resolution was increased in Paper III; with a large difference be-

tween the forcing data and the final product, more work could be required by

WRF to “fill in the gaps”.

The WRF model simulates soil moisture and temperature down to 200 cm,

divided into 4 layers. Soil moisture has a strong influence on surface fluxes

in mesoscale models (Angevine et al., 2014). Soil layers need more time for

spin-up than the atmosphere; the topmost soil layer can spin up in a couple

of days but the deeper layers take longer. Therefore, in Paper III, the soil

layers were retained between restarts allowing them to develop throughout

the entire simulation period. Similarly, accumulated fields (e.g. precipitation)

were retained between restarts (in Papers II and III), producing continuously

increasing values; this was not necessary in Paper IV since the simulation

periods were short enough to cover in a single model run.

2.2 Dispersion modelling

Atmospheric dispersion models are used to simulate the transport of emissions

in the atmosphere; some models are designed to run in parallel with a meteo-

rological model (online) while others run separately using prepared meteoro-

logical data (offline). Online models have the benefit of allowing emissions to

affect the dynamics in the meteorological model (active trace feedback); how-

ever, these models are more complicated to design and tend to require more

computational resources than offline models.

The two most common types of dispersion models are Eulerian grid models

and Lagrangian Particle Dispersion Models (LPDM). Eulerian models have a

fixed grid through which emissions are transported by calculating fluxes be-

tween adjacent grid cells. Calculations for emissions, removal processes and

16

chemistry are applied to each grid cell. A Lagrangian model works differently,

emissions can be assigned anywhere within the model domain and are not lim-

ited by a superimposed grid. The emissions are assigned to computational par-

ticles which are released within a point-, line-, area-, or volume source. The

computational particles represent air parcels containing a mixture of chemical

species or particulate matter. The computational particles are moved within

the model domain through advection and turbulent mixing. Computational

particles simulating clouds of aerosols are also affected by gravitational set-

tling. Calculations are made on a per-particle basis rather than for a set of

predetermined grid cells; this allows LPDMs to efficiently simulate emissions

from a single source in a large model domain without wasting calculations on

empty grid cells. Another benefit from LPDMs is their ability to generate re-

alistic thin plumes from point sources; Eulerian models suffer from numerical

diffusion which causes plumes to “fan out” excessively unless compensated.

LPDMs are not perfect, like Eulerian models they have certain limitations.

An LPDM requires a large number of computational particles to be released;

if the distance between particles become to large, the output becomes patchy

and harder to interpret reliably. The distance between particles will increase

further from the source as the plume fans out due to mixing processes. For

large domains, one must either release an excessive amount of particles at the

source or add particles along the way as the plume fans out. It is also harder to

implement chemistry and aggregation of particulates in a Lagrangian model;

chemical reactions are typically limited to reactions between species within a

particle or between emitted species and background species.

In this thesis, two LPDMs were used, FLEXPART-WRF (Papers II, IIIand IV) and FLEXPART (Paper IV). The main difference between the mod-

els is the meteorological data they require; FLEXPART-WRF uses meteoro-

logical data from WRF, while FLEXPART can run on different data products

from ECMWF. FLEXPART-WRF was developed based on the FLEXPART

model, technical aspects of its first stable release is described in Paper I.

2.2.1 Advection of particles

The core of any LPDM is the advection of computational particles. Particle

positions are updated by moving particles according to the wind field, i.e. by

numerically solving a differential equation for each particle’s position. The

simplest advection scheme is to use the Euler method — not to be confused

with Eulerian models — as in FLEXPART and its derivatives:

r(t +Δt) = r(t)+V (r, t)Δt (2.1)

where r(t) is the particle’s position at any given time, t; Δt is the time step of

the simulation and r(t +Δt) is the particle’s position at the next step. Some

models use more elaborate methods, e.g. the HYSPLIT model which uses

17

the improved Euler’s method (also called Heun’s method) for calculating the

advection (Draxler and Hess, 1998). Additional terms are typically added to

(2.1), representing horizontal and vertical turbulent perturbations and/or sub-

grid convection, which displace particles from the mean flow.

When modelling aerosols, the dispersion model needs to account for gravi-

tational settling by adding a term for terminal velocity, vg, to (2.1). For spher-

ical particles larger than ∼10 μm, vg can be calculated as a balance between

downward gravitational pull and the upward drag force:

vg =

√4

3

dpgCD

(ρ −ρair

ρair

)≈√

4

3

dpgCD

(ρ

ρair

)(2.2)

where dp is the particle’s diameter, g is acceleration due gravity, ρ is the par-

ticle density and ρair is the density of air. CD is the drag coefficient which,

in FLEXPART and its derivatives, is calculated using the method by Naslund

and Thaning (1991). The drag coefficient varies with Reynolds number which

in turn depends on the velocity, therefore, (2.2) and CD must be determined

iteratively.

Smaller particles (∼1 μm) are affected by the mean free path between mole-

cules in the air. In this case, the air behaves less like a continuum and there-

fore, the drag force exerted on a small particle will be weaker than what is

assumed in (2.2). In order to accurately describe terminal velocities of parti-

cles down to micrometre size, (2.2) can be adjusted by dividing CD with the

Cunningham correction factor as proposed by Cunningham (1910); Knudsen

and Weber (1911):

Ccun = 1+2λdp

(A0 +Q · e−C

dp2λ

)(2.3)

where λ is the mean free path of gas molecules in air. A0, Q and C are di-

mensionless constants, which in FLEXPART-WRF are set to 1.257, 0.400 and

1.10, respectively. While (2.2) and (2.3) enable the calculation of settling

velocities of a wide size-range of particles, there are still limitations. The ex-

pression (2.3) assumes spherical particles, which is rarely the case. Correcting

for non-spherical particles is usually done by defining particles by their aero-

dynamic diameter, i.e. the diameter which a spherical particle should have in

order to have the same terminal velocity as the studied particle.

2.2.2 Deposition processes

Deposition of aerosols or gases from the atmosphere can take two paths: dry

or wet deposition. Dry deposition includes gravitational settling to the surface

(large particles), impaction with the surface (small particles), and adsorption

or absorption of gases by various surface elements. Wet deposition includes

18

the nucleation of rain droplets within a cloud (rain-out) and the collection of

particles or gases through collision with existing rain drops (wash-out).

Dry deposition can be described in terms of a deposition velocity, Vd , de-

fined as the flux, F divided by the concentration, C, of a given species. Dif-

ferent processes apply to particles and gases, so the two cases need to be de-

scribed separately.

Dry deposition of gases can be determined using resistances similar to an

electric circuit (McMahon and Denison, 1979; Wesely and Hicks, 2000):

Vd =1

rA + rB + rC(2.4)

where rA is the aerodynamic resistance of the surface layer which can be esti-

mated from Monin-Obukhov similarity theroy (Stull, 1988). rB is the viscous

resistance of the quasi-laminar sublayer, and rC is the bulk surface resistance

which collects a number o off surface-specific properties. An illustration of

the processes described in (2.4) is shown in Figure 2.2. The most common ap-

proach to determining rC is by using a number of land-use categories with

pre-determined resistances for a number of known chemical species. We-

sely (1989) proposed a parameterization for 11 land-use types and 2 chemical

species (O3 and SO2); it was suggested that dry deposition for other species

could be parameterized as well by comparing their oxidizing properties with

O3 and their solubility with SO2.

A

B

C

net flux

Figure 2.2. Illustration of the three sub-regions for dry deposition. (A) represents the

surface layer, where the resistance is determined by turbulent eddies. (B) is the laminar

sublayer, a thin layer of air in contact with surfaces, where turbulence is negligible and

viscous forces dominate. Transport through this layer depend on Brownian motion

and inertia of particles or molecular diffusion of gases. (C) represents the bulk surface

properties, which combines numerous processes for absorption of gases by the surface.

Dry deposition of particles require a different expression than what is used

for gases. Surface resistances are negligible for particles, which tend to “stick”

to surfaces due to their own weight; instead, settling velocities need to be taken

into account as a parallel process to dynamic and diffusive fluxes. This can be

19

described by (e.g. Seinfeld and Pandis, 2016):

vd =1

rA + rB + rArBvg+ vg (2.5)

which is analogous to (2.4). The third term in the denominator is not a physical

resistance, but appears when deriving the resistances from fluxes and concen-

trations. In LPDMs the dry deposition is typically implemented as a mass loss

per computational particle, using a decay function:

m(t +Δt) = m(t)exp

(−Vd(z)Δt2z

)(2.6)

where z is the altitude above ground level and m(t) is the particle mass at a

given time. (2.6) is typically applied to particles below a given altitude, e.g.within an assumed constant flux layer.

Wet deposition can be expressed similarly, as was suggested by McMahon

and Denison (1979):

m(t +Δt) = m(t)exp(−ΛΔt) (2.7)

where Λ is a scavenging coefficient which depends on the precipitation inten-

sity. Empirical tests by McMahon and Denison (1979) suggested the following

from for the scavenging coefficient:

Λ = AIB (2.8)

where A and B are coefficients which need to be determined for each modelled

species (i.e. for each particle type and for each gas). Equation (2.8) is designed

for below cloud scavenging, i.e. for the collection of particles/gases by falling

rain drops.

Scavenging can also be the result of nucleation of cloud droplets on parti-

cles or the dissolution of gases in existing cloud droplets; both of these pro-

cesses contribute to in-cloud scavenging. In-cloud scavenging can occur either

prior to or simultaneously as precipitation. The actual removal of species from

the atmosphere is through precipitation, regardless of the scavenging process.

Hertel et al. (1995) described a parameterization which includes in-cloud scav-

enging according to:

Λi =SiIHi

(2.9)

where Si is the scavenging ratio and Hi is the height over which scavenging

takes place. The scavenging ratio is the ratio between concentrations in pre-

cipitation and concentrations in air; it is determined differently for particles

and gases. For particles, Si is determined by the fraction of particles activated

as condensation nuclei. For gases, Si is determined from the solubility of a

given gas (based on Henry’s law). For details, see Hertel et al. (1995).

20

2.2.3 Aggregation and Chemistry

Chemistry and particle aggregation might affect the removal rate and aero-

dynamic properties of emissions; neither of the two are easy to include in

Lagrangian models. The most common solutions are to superimpose an Eu-

lerian grid for chemistry and aggregation calculations (as in NAME) or re-

strict chemistry to reaction between primary species and climatological back-

ground species (as in FLEXPART). Another example is the GRAL-C model,

which combines Lagrangian and Eulerian frameworks in a way where primary

species follow Lagrangian dispersion while secondary species are dispersed

through an Eulerian grid (Oettl and Uhrner, 2011). Businger et al. (2015)

demonstrated a fully Lagrangian model where SO2 is converted to sulphate

particles over time; both species were represented in each computational parti-

cle, therefore, no gravitational settling could be applied. The fully Lagrangian

approach to chemistry works as long as there is only a single (or a few) simple

reactions involved.

Volcanic plumes contain numerous reactive compounds in gas, liquid, and

solid phases; they contain large amounts of sulphuric gases which, given time,

will form droplets of sulphuric acid. Furthermore, particles in a volcanic

plume might adhere to one-another and form aggregate particles with differ-

ent aerodynamic properties. Particles released directly into the atmosphere are

called primary particles. The term secondary particles is used to refer to par-

ticles formed from gas-to-particle formation, aggregation of other particles, or

condensation of gases on existing particles; “secondary” since they form from

the primary emissions. The formation of secondary particles will alter wet and

dry deposition rates over time.

One option for handling secondary particles is to adjust the primary emis-

sions (at the source) to emulate secondary particle formation which, in reality,

occurs further downwind. This approach is not perfect since the physical and

chemical properties of the emulated secondary particles will apply directly at

the source, instead of gradually building up further downwind.

Furthermore, current parameterizations for atmospheric chemistry are mainly

designed for industrial emissions. Volcanic plumes contain different mixtures

of emissions than emissions from industries, therefore the parameterizations

used in atmospheric modelling might not be a good representation of the pro-

cesses within volcanic plumes.

2.2.4 Plume rise from volcanic eruptions

Volcanic eruptions can be intense events; large eruptions can transport mate-

rials far into the stratosphere, through a combination of plume rise processes.

Sparks and Wilson (1976) presented a general model for plume rise from vol-

canoes, where the eruption column was divided into two parts: the gas thrust

and convective regions. In the vent and conduit of an erupting volcano, ma-

21

terials can be accelerated to speeds greater than 100 ms−1 due to the rapid

expansion of gases (Blackburn et al., 1976; Wilson et al., 1980). As the ma-

terials are released into the atmosphere, they continue to flow upward due to

inertia.

The lowermost part of the eruption column is non-buoyant so the ascent

velocity decreases with altitude. This part of the eruption columns makes up

the gas-thrust region. Within the gas-thrust region, heavier particles fall out,

effectively decreasing the bulk density of the column. Ambient air is entrained

into the eruption column which is heated by the erupted material; the heated air

expands, which further decreases bulk density and increases buoyancy (Sparks

and Wilson, 1976; Sparks, 1986).

If enough air is entrained into the eruption column, it becomes buoyant

and continues to rise, forming the convective region. The convective region

typically makes up the major contribution to the plume rise when present.

The extent of the convective region has been shown to depend on how much

thermal energy is released at the vent, ambient air temperature and lapse rate

(Wilson et al., 1978; Settle, 1978) as well as ambient wind conditions (Graf

et al., 1999).

Eventually, the eruption column will reach an altitude where its density

equals that of the ambient air, buoyancy becomes neutral but the material con-

tinues to rise due to inertia. This led Sparks (1986) to introduce a third compo-

nent of the eruption column: the umbrella region. In the umbrella region, the

upward flux is inertia driven; the column reaches beyond the level of neutral

buoyancy and begins to expand laterally due to gravity flow (i.e. by displacing

the now lighter ambient air). Figure 2.3 shows an illustration of an eruption

column with three components, with corresponding temperature and velocity

profiles.

gas thrust

convective

umbrella

velocity

height

atmosphere

density

plumeheight

Figure 2.3. Qualitative illustration of an eruption column. The column is divided into

three regions with different velocity and buoyancy characteristics. (Original image:Sparks (1986))

Not all eruptions will produce a three-component eruption column, e.g.Wilson et al. (1978) found that a too large eruption rate could prevent a con-

vective component from forming. For weak eruptions, or passive degassing,

the column can consist solely of a convective component.

22

Volcanic eruption columns are small scale processes when compared to

meteorological modelling, including eruption dynamics in a meteorological

model requires a high resolution which is computationally expensive. There

are models designed to bridge the two areas, such as the Active Tracer High

resolution Atmospheric Model, ATHAM, (Textor et al., 2006). However, in

broader application of dispersion modelling it is usually not possible to include

a fluid-dynamic representation of the source due to the added computational

demand.

2.2.5 Mass eruption rates

While the plume rise from volcanic eruptions can often be observed directly,

determining the mass eruption rate (MER) is usually more difficult. Building

upon the model of a convective eruption column, there should be a relation

between the heat flux at the vent and the total plume rise. Assuming an even

distribution of heat, the MER should be proportional to the heat release. Sev-

eral empirical relations have been proposed to describe the relation between

eruption rate and plume rise. Analogous to plume-rise from smoke stacks,

Settle (1978) proposed the following relation based on data from 6 historical

eruptions:

ΔH = 0.117

(dMdt

)0.22

(2.10)

where dM/dt is the MER and ΔH is the plume height above the vent. The

MER was calculated based on deposit volumes and pyroclast densities, the

eruption heights were based on eye-witness accounts. A more recent attempt

was made by Mastin et al. (2009), who proposed the following relation based

on data from 34 historic eruptions:

ΔH = 2.00

(dVdt

)0.241

(2.11)

where dV/dt is the volume eruption rate of solid and liquid material. Since

the mass release should be dominated by liquid and solid particles, dV/dtshould be proportional to dM/dt. In reality, eruption dynamics are far more

complicated, with possibly rapid changes in MER and the composition of the

emissions (e.g. shapes and sizes of particles or mixture of gases) and with both

temporal and spatial variations in atmospheric properties (e.g. stability, wind

shear and/or humidity). Modellers rarely have the possibility to include but a

few of these processes.

2.2.6 Inverse modelling

Designing eruption parameters based on empirical relations introduces large

uncertainties to the dispersion modelling. An alternative method is to use

23

inverse modelling to determine the MER and emission height. Eckhardt et al.

(2008) demonstrated how inversion schemes could be used to improve results

from dispersion modelling when combined with satellite observations of the

plume. The method involves setting up a number of potential sources. A

dispersion simulation made with the potential sources yields an a-priori plume

which can then be compared with measurements.

The aim of the a-priori plume is not to replicate the true plume but to present

the modeller with the relation between gridded concentration data and emis-

sion rates from each of the potential sources. By comparing the a-priori results

with e.g. satellite observations, it is possible to optimize the source in order to

create a posteriori plume which better resembles the observation. This is done

using a regression algorithm which yields new emission rates for the potential

sources. This is called an inversion scheme, since the model output is used to

determine the source parameters.

Inverse modelling is a type of regression problem, i.e. the goal is to find a

solution for:

y≈Mx (2.12)

where x is a n× 1 matrix of source elements, y is a m× 1 matrix of observa-

tions (e.g. column densities of a SO2), and M is an m× n matrix calculated

using the dispersion model, determining the relation between x and y. In other

words, (2.12) is an equation system with m equations and n unknown variables

(x). If n > m, the problem is overdetermined and needs to be solved through

regression. One method of solving (2.12) for x is to use a linear-least squares

solution, which changes the problem to:

x =(MTM

)−1 MTy (2.13)

The least-squares solution cannot be directly applied to dispersion modelling

for several reasons. First, the solution allows negative emission sources which

is unphysical. Second, a least-squares solution can provide a reliable result as

long as M is well conditioned and correlated errors are small; this is often not

the case for dispersion modelling. For these reasons, a more robust method is

needed.

Eckhardt et al. (2008) developed an inversion scheme suitable for use with

volcanic emissions and satellite observations. The potential emission sources

(x) were set to represent different levels in a column source. A-priori emission

strengths (xa) were assumed to have an influence on the final result. Including

a-priori emission in the inversion required a modified form of (2.12) which

included both a-priori and posteriori source terms:

Mx ≈ y (2.14)

where x = x−xa is the difference between posteriori and a-priori source terms,

and y = y−Mxa is the difference between observations and a-priori modelled

24

values. The least-squares solution (2.13) was updated with the new terms (J1)

and expanded to include two terms, J2 and J3, representing the deviation from

a-priori source terms and the smoothness of the source terms, respectively:

J1 = σ−2o (Mx− y)T (Mx− y)

J2 = xTdiag(σx−2)x (2.15)

J3 = ε (Dx)T Dx

where σo is the observation error (assumed to be the same for all observations,

diag(σ−2x ) is a diagonal matrix with the main diagonal consisting of squared

inverse errors of the a-priori emissions, D is the discrete representation of the

second derivative and ε is a regularization parameter which is used to adjust

the weight of the smoothness term. The inversion was completed by solving

(2.14) for x. Negative values in x were avoided by decreasing the values of

corresponding elements in σx (nudging toward a-priori values) and iterating

until only negligible negative values remain.

Throughout the rest of this thesis, this method will be referred to as the

Eckhardt inversion scheme. The Eckhardt inversion scheme is specifically

designed to work with dispersion modelling of volcanic emissions and is more

robust than simpler methods such as ordinary least-squares or non-negative

least squares. However, the method requires some tuning before each use

case. It is possible to apply (2.15) to different configurations, e.g. Stohl et al.

(2009) used it to determine the contribution to global emission of greenhouse

gases from different regions around the world.

25

3. Model application

During the development of the stable release of FLEXPART-WRF (Paper I),

several validation studies were made (e.g. Fast and Easter, 2006; Brioude

et al., 2012; Angevine et al., 2013). The model has since been used in various

cases. For the purpose of this thesis the model has been applied to study

dispersion from volcanic emissions for three different cases.

3.1 Ash hazard climatology

In Paper II the hazard from various eruption scenarios was studied. The prob-

ability of exposure to airborne ash was estimated over the north Atlantic Ocean

and Europe by modelling the dispersion of emissions from different eruption

types. A total number of 90,000 dispersion simulations were performed cov-

ering various weather conditions over a period of 3 years. The study required

realistic representation of the eruption parameters in each scenario. Basic

eruption parameters — such as plume height, eruption rate, and grain-size

distribution — were retrieved from records of historic eruptions (Table 3.1).

The goal was not to completely replicate any of the eruptions, but rather to

create a set of realistic parameters for each scenario. FLEXPART-WRF was

used for the dispersion modelling; the model can only simulate particles of a

single size at a time so separate simulations were performed for the different

particle sizes. The particle-specific simulations were then combined and the

full plume was analyzed.

The evolution of the plume from each simulated eruption was studied for

96 hours from the onset of the eruption. Hourly averages of the modelled

concentrations were produced and compared with threshold values used to

determine flying conditions for commercial aircrafts. Maximum hourly con-

centrations were calculated for three different altitude ranges (flight levels in

units of 100 ft above sea level: 0–FL200, FL200–FL350, FL350–FL550). The

probability of exceeding each threshold value was calculated for each altitude

range on a 15×15 km grid over northern Europe (see Figure 3.1A).

An important part in setting up the study was assigning the vertical dis-

tribution of emissions. As was discussed in Chapter 2, plume height is of-

ten governed by the convective updraft in the eruption column. In dispersion

modelling, a common approach has been to assign a uniform column source

representing the whole eruption column (e.g. Witham et al., 2007). Using

a uniform column source introduces some errors into the model. First there

26

Table 3.1. Eruption parameters used for the scenarios simulated in Paper II. Eachscenario is based on a historic eruption, some of which with multiple explosive phases;for those cases, all phases were included in each simulation. The columns presenteruption column height (ΔH), duration (dt), mass eruption rate (ΔM

Δt ) as well as thefraction of the mass (×10−2) allocated to the different particle classes (6Φ− 11Φ).Table from: Paper II.

Case Phase ΔH Δt ΔMΔt 6.0Φ 7.5Φ 9.0Φ 11.0Φ VEI

[km] [h] [kgs−1] 16 μm 5.5 μm 2.0 μm 0.49 μm

Mt.Spurr 1992 1 14.0 3.5 3×106 13 5.3 1.1 0.05 3

1 8.0 6.5 2×106 20 11 13 1.1 4

Eyjafjallajokull 2010 2 5.5 3.5 7×104

3 6.0 5.4 6×105

1 8.0 1.0 3×106 20 11 13 1.1 5

Askja 1875 2 22.8 1.0 1×108

3 26.0 6.0 2×107

Pinatubo 1991 1 35.0 3.0 4×108 9.7 2.6 0.39 0.26 6

2 30.0 6.0 2×108

Tambora 1815 1 35.0 28.0 5×108 30 9.5 3.5 0.90 7

is no accounting for adiabatic expansion of the column as the gas mixture is

de-pressurized; the concentration of emissions should actually decrease with

altitude. Second, the majority of the fine ash should be released at the upper

portion of the eruption column due to it’s lower terminal velocity (Carey and

Sparks, 1986; Koyaguchi and Ohno, 2001).

~

Lake

Kivu

60°N

50°N

40°N

30°N

(A) (B)KitchangaKaheMasisiSakeGoma EGoma WNyamulagira (3058 m)Nyiragongo (3470 m)

Figure 3.1. Geographical coverage of the two studies included in this thesis. (A)

shows three nested domain, all of which were used in the dispersion simulations in

Paper II; a similar setup was used in Paper IV but focusing mainly on Iceland. (B)

shows the single domain (third order nest) used in the dispersion simulations in Pa-per III.

Some problems from uniform column sources can be overcome by dis-

tributing mass differently within the column, making an non-uniform column

source. This was done by e.g. Peterson and Dean (2008); Steensen et al.

(2013), who used top-weighted functions to determine the particle release

27

in the source column. While this method reduces the error of how mass is

distributed vertically, it increases the error in the initial concentrations. Top-

weighted column sources will result in higher concentrations at higher altitude,

which is the opposite of what one would expect from a convective eruption

column. This problem was addressed in Paper II, where the source volume

was designed to maintain a fixed emission rate per unit-volume, while still re-

leasing most of the ash at high altitudes. A Poisson distribution was used to

determine the emission rate at different altitudes. The mass release rate was

then used to determine the horizontal extent of the source volume at each level

according to:

d =M(zi)

U(zi)CΔz(3.1)

where M(zi) is the mass release rate in a given source segment (zi), U(zi) is

the average wind speed within a segment, Δz is the segment thickness, and

C is a constant proportional to the concentration within each segment. This

method reduces the overestimation of concentration in the upper portions of

the plume. The method could be further improved by including atmospheric

density profiles.

3.2 Regional exposure to passive degassingExposure to gases emitted from Mt. Nyiragongo (eastern D.R. Congo) was

studied in Paper III. Mt. Nyiragongo is an active volcano which has been

emitting large amounts of SO2 since its most recent eruption in 2002. Ground

based monitoring of SO2 in the plume has been in place since 2007 as part

of the global Network for Observation of Volcanic and Atmospheric Change

(NOVAC) since 2007 (Galle et al., 2010). The measurements are made by

four scanning Differential Optical Absoroption Spectroscopy (DOAS) sta-

tions. The stations are located 10–15 km south-west of the summit of Mt.

Nyiragongo. By combining DOAS measurements with wind profiles above

the summit, it is possible to calculate the SO2-flux and altitude of the plume;

this was done by Arellano et al. (2016), who used meteorological data pro-

duced by WRF-simulations from Paper III.

The main focus of Paper III was the regional distribution SO2. The WRF-

model and FLEXPART-WRF were used to simulate the dispersion of emis-

sions from Mt. Nyiragongo. A map over the studied area is shown in Fig-

ure 3.1B; note the mountain-range extending from north to south (the Alber-

tine rift) and lake Kivu which have an important influence on the dynamics of

the region. Also note the close proximity of Nyiragongo to the city of Goma.

Meteorological data was produced by dynamically downscaling ERA-Inter-

im, using the WRF-model in four steps to a final resolution of 2×2 km. The

data was then used in dispersion simulations using FLEXPART-WRF. Emis-

sion rates and altitude were determined based on the DOAS-observations;

28

however, since the observations can only be made during daylight, there were

regular nightly gaps between observations. Due to various reasons there were

also occasional longer gaps in the time series. Since the dispersion model re-

quired more than 12 hours to spin-up properly, a technique for filling the gaps

was required in order to study the long-term exposure.

A technique was developed where fluxes and emission heights were ran-

domly sampled from the rest of the time-series whenever a gap longer than

1 hour was encountered; each sample was used to cover a period of ∼30 min.

In order to estimate the error introduced when using this technique, the process

was repeated 30 times, creating 30 times series of emission rates and heights.

Each time series was used in a separate dispersion simulation, which — when

combined — formed an ensemble of 30 members.

Dispersion simulations were made covering one year and seasonal changes

were identified. Near-surface concentrations were calculated for a number of

populated areas downwind of the volcano.

3.3 Inverse modelling of SO2 emissions

Determining the emission rate and plume rise is crucial for accurate mod-

elling of atmospheric dispersion from volcanic eruptions. The methods de-

scribed in Chapter 2, i.e. (2.10) and (2.11), attempt to counter this but involve

large uncertainties. An alternative method was developed based on (2.15) as

part of Paper IV, where inverse modelling was used to determine the plume

height and eruption rate of the Holuhraun eruption in 2014–2015. With the

then available high-resolution (down to 0.1 degrees) global analysis data from

ECMWF, it was also possible to compare results from FLEXPART-WRF and

FLEXPART.

Dispersion simulations were configured with a number of potential emis-

sion sources as illustrated in Figure 3.2. The potential emission sources were

used to simulate a-priori plumes without any detailed information of plume

rise or emission rate. The a-priori results were used in an inversion together

with traverse measurements of the plume. The measurements were conducted

using a mobile-DOAS instrument and were presented by Gıslason et al. (2015).

Each traverse consisted of a series of around 1,000 measurements of the col-

umn densities of SO2, made by a mini-DOAS instrument mounted on a car.

The car traversed the plume along the ringroad on Iceland at a distance of 80–

240 km from the eruption site, depending on the current wind direction. The

dispersion models (FLEXPART and FLEXPART-WRF) were used to calculate

modelled vertical columns along the same transects of the plume as covered by

the car traverses. Each individual measurment in each traverse was matched

with a corresponding modelled value.

Two different inversion algorithms were tested: a non-negative least-squares

(NNLS) regression and modified version of the Eckhardt scheme. The main

29

2 4 6 8 10 12

(km)6

5

4

3

2

1

(33)

(14)

columnsources

surfacesources

Figure 3.2. Illustration of potential emissions sources used for inverse modelling of

emissions from the Holuhraun eruption (Paper IV). The setup consists of 33 stacked

column sources located above the main fissure vent and 14 surface sources covering

the lava field.

modification made was to the smoothing term, i.e. J3 in (2.15), which was

adjusted to only apply to the column sources. Inversions were compared

for time-averaged and time-interpolated model results. Finally, the posteri-

ori emissions were used in extended simulations and compared with satellite

instruments from the Ozone Monitoring Instrument (OMI) on NASA’s Earth

Observing System, the AURA satellite (Levelt et al., 2006).

3.4 Model enhancementsIn Paper III, we discovered problems with the dry-deposition of gases in

FLEXPART-WRF. First, the land-use data supplied with the model was at a

horizontal resolution of 0.3◦, while the output grid used a resolution of 2 km.

High-resolution land-use data was made available in FLEXPART-WRF as part

of Paper III by adding support for reading this data from WRF. This so-

lution did not completely solve the problem until it was discovered that an

ill-formated table prevented roughness lengths, z0, less than 1 m from being

properly initialized. This in turn resulted in the aerodynamic resistance (rA)

being undefined for all land-use types except water (where Charnock’s rela-

tionship is used). Furthermore, a missing update of the table (which should

have been included in version 3.0) caused the Charnock relation to be applied

to a non-water land-use type.

Neither of the land-use problems were obvious in Paper II since the study

focused on particulate matter which can still be deposited through the settling

velocity according to (2.5). However, in Paper III the focus was on gases

which only have one deposition pathway through the surface layer according

to (2.4). After correcting z0, a comparison was made between one simula-

tion using the old land-use data and one using the new. Using the set-up

from Paper III, two simulations were made, each covering a one-year pe-

30

riod. The accumulated dry deposition from each of the land-use data sets were

compared (Figure 3.3A). The dry deposition using the new land-use deviated

locally from the original by ±50 %. Differences were also seen in the aver-

age concentration of SO2 below 500 m altitude (Figure 3.3B), mainly due to

different land types directly west of the emission source.

0.75

1.25

0.951.05

0.85

1.15

1.5

1.3

1.10.9

0.7

0.5

(B)(A)

g/m3

g/m3g/m2

g/m2

Figure 3.3. Difference between FLEXPART-WRF simulations using different land-

use data (figure from Paper III). Both figures show average values from a simulation

with land-use data from WRF divided by results from a simulation using the old built-

in data. (A) shows differences in accumulated dry-deposition of SO2 over one year.

(B) shows difference in average SO2-concentration 0-500 m a.g.l. during September–

November 2010.

31

4. Results

4.1 Ash hazard climatology

The probability of exposure to ash as a result of future volcanic eruptions

on Iceland were studied for different eruption scenarios in Paper II. Results

from the Askja scenario (see Table 3.1 for details) are shown in Figure 4.1;

the development over time is shown from top to bottom (four 24-hour pe-

riods). Figure 4.1A–D shows the probability of exposure for eruptions oc-

curring any time of the year, while E–H and I–L represent wintertime and

summertime eruption, respectively. The area of highest probability of expo-

sure is seen to propagate eastward over time. The hazard varies with seasons,

with the strongest difference seen between summer (June–August) and winter

(December–January). Wintertime eruptions have a higher probability of af-

fecting most of the Scandinavian peninsula and areas around the Baltic Sea.

Since the polar front is weaker during summer, the westerly winds are weaker

and less persistent, resulting in a higher probability of ash being transported

in other directions. In winter, however, the strong polar front, results in strong

westerly winds most of the time, transporting ash eastward in most of the

cases. This transport is both more frequent as well as more efficient, resulting

in high concentrations of ash at greater distances from the source.

A comparison was made between different eruption scenarios, an example

of this is shown in Figure 4.2A-E, using a threshold value of 0.2 mg/m3; this

threshold corresponds to the no-flight condition in use prior to the eruption of

Eyafjallajokull in 2010 (Webster et al., 2012). Note that the Eyafjallajokull

eruption case (covering the most intense phases of the eruption) showed low

probabilities (below 10 %) of exceeding the threshold values for most land

areas beyond Iceland. The weakest scenario (based on Mt. Spurr) shows no

exceedances for this period, partly due to the short duration of the eruption.

In general, ash at higher altitude is more consistently transported eastwards

compared to lower levels. However, the longest transport is seen in the mid-

level (∼6–11 km) where the jet stream is expected.

A comparison is also made with earlier results by Leadbetter and Hort

(2011) (Figure 4.2F), who used a similar approach. The scenario set up by

Leadbetter and Hort (2011) should, in terms of emission strength, correspond

to the weakest scenario in Paper II (i.e. Figure 4.2A). However, their results

are more similar to the Askja-1875 scenario (Figure 4.2C), suggesting that the

ash hazard is lower than previously estimated.

32

Figure 4.1. Probability of ash concentration exceeding the no-flight threshold (2.0

mg/m3) for an eruption similar to Askja-1875 (from Paper II). This scenario is de-

signed to be roughly 10 times stronger than the Eyjafjallajokull eruption in 2010. The

probability is calculated for three different periods: yearly (A–D), winter (E–H) and

summer (I–L). Different time periods are given, specifically (top to bottom) 0–24, 24–

48, 48–72 and 72–96 hours relative the onset of the eruption. All cases are for flight

levels FL200–FL350 (∼ 6–11 km a.s.l.).

33

Figure 4.2. Areas where the probability to of exceeding hourly average concentrations

of 0.2 mg/m3 is at least 10 % within 24–48 h after the onset of an eruption (from

Paper II). Five different eruption scenarios, based on historic events, are presented

(A-E), as well as results from Leadbetter and Hort (2011) (F). Contours are given for

three different flight levels (given in units of 100 ft.) as indicated by the grey-scale.

4.2 Regional exposure to volcanic degassing

The geographical distribution of gases emitted from Mt. Nyiragongo was stud-

ied in Paper III. At first only the meteorological influence on the dispersion

was studied by using a constant emission source. The mean dispersion direc-

tion over the whole year was to the west (as expected due to trade winds).

However, a seasonal shift was seen with the higher portion of the plume be-

ing transported further north in December–January compared to the annual

mean. This can be seen in the plume cross section shown in Figure 4.3, which

stretches along the line marked in Figure 3.1B. Lower portions of the plume

was instead transported to the south. In June–August, the pattern was re-

versed with northward transport of the lower and southward transport of the

higher parts of the plume. This skewness corresponds with the migration of

the Inter Tropical Convergence Zone (ITCZ), which is usually located to the

north in July, and to the southwest in January. Surface winds converge at the

ITCZ, which is why lower portions of the plume vary with season. At higher

altitudes, air flows away from the ITCZ which causes the skewness seen in

Figure 4.3.

Figure 4.3 also shows a diurnal variation. In daytime, emissions are more

likely to remain over land (i.e. north of 1.6◦S in Figure 4.3). In nighttime,

emissions tend to form a shallow layer with high concentration of SO2 over

34

DJF

SON

10

8

6

4

2

Hei

ght a

bove

sea

leve

l [km

]JJA

MAM

Local Time06:00 13:00

Season

105103102 104 ng/m3

10

8

6

4

2

10

8

6

4

2

10

8

6

4

2

-1.8° -1.6° -1.4° -1.2° -1.8° -1.6° -1.4° -1.2°Position along cross section (degress latitude)

10

8

6

4

2

10

8

6

4

2

10

8

6

4

2

10

8

6

4

2

Daily

-1.8° -1.6° -1.4° -1.2°

Figure 4.3. Modelled seasonal average diurnal variation of SO2 along the cross section

marked in Figure 3.1B. The leftmost column shows average concentrations for each 3-

month period of a full year. The middle middle column shows average concentrations

between 05:30–06:30, local time (i.e. around sunrise) while the rightmost columns

shows average concentrations between 12:30–13:30 (i.e. at maximum insolation).

The two leftmost columns represent the diurnal extremes regarding the concentrations

over lake Kivu. The data covers one year (April 2010 through March 2011) and uses

a constant emission source. Figure from Paper III

35

lake Kivu. This is related to a lake-/land breeze cycle forming over lake Kivu,

especially evident around the equinoxes, i.e. when the ITCZ has the least

meridional influence on the flow. As can be seen from Figure 3.1B, lake Kivu

is located in a rift valley with steep slopes (escarpments) on its east and west

sides, with a valley located about halfway on the eastern side. The escarpments

create a channel effect, preventing air exchange other than at the northern and

southern shores (or through the eastern valley). This concentrates the lake-

/land breeze flow at the northern and southern shores.

Using the flux data from the NOVAC network, an estimate of past SO2 con-

centrations in populated areas (marked in Figure 3.1B) was made. Results

from this are shown in Figure 4.4, which shows the development of daily av-

eraged SO2-concentrations over one year. There is a clear difference between

southern (A–C) and northern (E–F) communities, related to the annual migra-

tion of the ITCZ. The highest exposure to SO2 in Sake (A) and Goma (B,C)

was reached in November–March, with daily averaged concentrations exceed-

ing the background levels ∼ 80 % of the days. The lowest concentrations were

seen in April–August when concentrations were below the background levels

∼ 80 % of the days (∼ 70 % in Sake).

The seasonal variations in exposure were reversed for the northern commu-

nities (Figure 4.4E–F); the lowest exposure in Kahe and Kitchanga was seen

in November–March and the highest in April–September. Masisi — located

further south — showed a bimodal exposure as the averaged plume passed

over the community twice during the year (around the equinoxes).

4.3 Inverse modelling of SO2 emissions

An example inversion is shown in Figure 4.5. The a-priori plume is shown in

Figure 4.5A, this was generated from assigning the same emission rate to all

potential sources in FLEXPART. The model predicts an additional branch of

the plume east of the performed measurements. The corresponding posteriori

plume from a successful inversion (using the modified Eckhardt scheme) is

shown in Figure 4.5B; the additional branch is gone and the main plume has

shifted a bit to the east. Note that the column densities in Figure 4.5 are nor-

malized for comparison; the total emission rates were 43 kg/s for the a-priori

sources (1 kg/s per source element) and 260 kg/s for the posteriori sources.

The average emission rate of the three inversion techniques (Eckhardt and

NNLS with temporally averaged or interpolated data) was 490± 180 kg/s on

September 21 and decreased over time to 21± 25 kg/s on February 4. This

trend applied to inversions where directional wind shear was present at 0–

4 km a.g.l. Two dates had low wind shear and produced higher emission rates:

October 6 and November 21; since the technique relies on variations in wind

direction by altitude, the results for these two dates were unreliable.

36

10 μg/m3

100 μg/m31000 μg/m3

10000 μg/m3 Background (~2.4 μg/m3)

1.0

0.8

0.6

0.4

0.2

1.0

0.8

0.6

0.4

0.2

Frac

tion

of h

ours

abo

ve s

elec

ted

refe

renc

e va

lues

(A) (B) (C)

0.8

0.6

0.4

0.2

0.0

0.8

0.6

0.4

0.2

0.0

(D) (E) (F)

4 6 8 10 12 2 4

4 6 8 10 12 2 42010 2011

4 6 8 10 12 2 4

4 6 8 10 12 2 42010 2011

4 6 8 10 12 2 4

4 6 8 10 12 2 42010 2011

Date (month and year)

Figure 4.4. Monthly variation of SO2 exposure in populated areas near Mt. Nyi-

ragongo (from Paper III). The figures show the fraction of days during which the

24-hour average SO2-concentration exceeded different threshold values (arbitrarily

chosen). Each subfigure corresponds to a location marked in Figure 3.1B, as indicated

by the titles. Values are presented for 12 months, from April 2010 to February 2011.

The lines represent the average result from an ensemble of 30 dispersion simulations,

the shaded areas show the 25–75 percentiles of ensemble members.

1.0

0.018°W 14°W

64°N

66°N

18°W 14°W

(A)(B)

Figure 4.5. Normalized vertical column densities of the Holuhraun plume for a-priori

(A) and posteriori (B) emission profiles. Simulations were made using the FLEX-

PART model and mobile-DOAS measurements made in the afternoon on September

21st. The measurements used for the inversion are marked by the path between the red

lines; the inversion was further constrained by samples taken along the circle below

the red lines, where zero-values were assumed.

37

A validation of the inverse modelling was made against OMI satellite data.

The results from the three inversion techniques are shown in Figure 4.6; a

simulation using a simple uniform column source is shown for comparison,

the simulation used the emission rate from Gıslason et al. (2015) and column

height from Michele et al. (2016). All three inversion techniques improve

the results compared to the a-priori emission profile. The posteriori emis-

sion profiles all produce column densities similar to OMI. The best agreement

between FLEXPART and OMI is seen when the Eckhardt inversion is used;

the NNLS-method with time-interpolated data showed similar agreement but

the Eckhardt method was less sensitive to the type of data used. Using time-

interpolated data with NNLS was consistently better than time-averaged data

for all types of meteorological data tested (ECMWF analysis or different con-

figurations of WRF).

0 1 2 3 4 5 6

(A) (B) (C) (D) R=0.37R=0.36R=0.31R=0.19

Figure 4.6. Comparison between modelled and measured SO2 column densities. The

filled contours show satellite observations by OMI, the solid black lines mark the 2 DU

contour from model simulations. Correlation between model and OMI are shown at

the top of each figure. (A) used a uniform column source (see text for details); (B),(C)

and (D) are produced using posteriori emission profiles. (B) used NNLS with 3-hour

average column densities, (C) used NNLS with time-interpolated column densities,

and (D) used the Eckhardt method with 3-hour average column densities.

38

5. Concluding remarks

This thesis demonstrates the potential of developing and improving modelling

tools for more accurate estimates of climatology, risk assessment and short-

term dispersion forecasting for volcanic eruptions.

Since the stable release of FLEXPART-WRF (Paper I), the model has

been applied in various cases including Papers II, III and IV. Over the past

three years since the release of version 3.0 in 2013, the number of publica-

tions listed by Google scholar using FLEXPART-WRF has grown from 25

to nearly 100. Later improvements developed during the work in this the-

sis have been included in version 3.3 of FLEXPART-WRF which was pub-

lished online1 in September 2016. Long term plans for the FLEXPART-WRF

model are to adopt a similar modularity as is seen in the WRF-model and unify

FLEXPART-WRF with FLEXPART.

In Paper II we developed a system for applying the dispersion model to

volcanic eruptions. We studied seasonal variations in dispersion for different

eruption scenarios on Iceland. The more persistent westerly winds in winter,

make it more likely for ash to be transported westward, typically reaching fur-

ther than if the same eruption would occur in summer. Summertime eruptions

are less predictable in the long term, since the otherwise dominating westerly

winds are less frequent. However, we also found that winter conditions vary

more between the years studied compared to summer. In order to further im-

prove the accuracy of the probability analysis, it might be more important to

study additional winters than full years.

Paper III demonstrated the first attempt at a regional dispersion study

around Mt. Nyiragongo. The combination of observational data from DOAS

retrieval with a regional modelling system is an important step in studying

health problems in the region. The study shows the importance of including

both daytime and nighttime conditions as well as seasonal variations. Past air-

quality measurements have only been made over shorter periods (Baxter et al.,

2003). If air quality is to be reliably assessed, long term measurements are

needed. Several options for this have been proposed. A network of passive dif-

fusion samplers could be used to study exposure on monthly time scales over

larger areas; diffusion samplers were delivered to the observatory in Goma in

2013, but rebel activity has prevented deployment. Another possibility is the

installation of active in-situ measurements in Goma.

1https://www.flexpart.eu

39

Different inversion algorithms for dispersion modelling were compared in

Paper IV. We demonstrated the first attempt of using ground-based mobile-

DOAS in inverse modelling of atmospheric dispersion of emissions. It was

shown that both NNLS and Eckhardt inversions would improve the dispersion

simulations compared the a-priori simulation. The Eckhardt technique was de-

signed to create smooth emission profiles which is suitable when the modelled

source represents an temporal average but the true source varies over time.

However, results from Paper IV show that even simpler techniques (without

smoothing) can produce emission profiles which manage to reproduce the ob-

served plume over longer distances.

The overall usefulness of mobile-DOAS measurements in inverse mod-

elling of volcanic emissions depends on the future development of the tech-

nique and the possibility for in-field measurements. Alternative methods have

other limitations. Satellite measurements (e.g. OMI) require good viewing

conditions, especially for low plumes, and data-quality is generally poor at

low solar zenith angles (i.e. near twilight or at high latitudes during winter).

These are similar limitations as for the ground-based DOAS measurements;

however, most of the DOAS-observations used in Paper IV were made during

conditions when good quality OMI-data was not available. Aircraft measure-

ments can be used to find the plume height, but also require measurements in

the field. Another option is LIDAR measurements, LIDAR stations in Europe

have detected both plumes from Holuhraun (Boichu et al., 2016) and Eyjafjal-

lajokull (Dacre et al., 2011); however, this requires that the plume passes over

the observation site, which limits the availability of data.

Due to the different limitations of the various types of measurements, fur-

ther development of operational “dispersion forecasts” will likely require in-

corporating different types of measurements. Modellers need to develop sys-

tems which can utilize many types of data but that do not critically depend on

any single observation.

5.1 Outlook

Atmospheric dispersion modelling of volcanic emissions combines elements

from many scientific fields. As is demonstrated in this thesis, improved model

accuracy can be achieved through different techniques. Inversion techniques

are currently efficient tools for improving model accuracy when suitable ob-

servations are available (Paper IV, Eckhardt et al., 2008; Stohl et al., 2011;

Fu et al., 2015; Zidikheri et al., 2016).

Many cases are not suitable for inverse modelling; Paper III demonstrated

such a case, the emission rates were too low to be reliably detected by single

satellite observations, the insecurity in the region limits the possibilities for

fieldwork, and the persistence of the emissions requires less costly monitoring

techniques in order to maintain measurements over longer periods of time. An-

40

other example where inverse modelling cannot be used was shown in Paper II,

which studied potential future eruptions. Situations where inverse modelling

cannot be applied are more dependent on accurate model physics since it more

is difficult to improve model results retroactively. Therefore, further improve-

ments in model accuracy can likely be made through better understanding of

the physical and chemical processes specific to volcanic plumes; parameteri-

zations of these processes, specialized for volcanic emissions, could improve

the accuracy dispersion models. Such improvements would also benefit cases

where inversion techniques can be used.

41

6. Acknowledgments

This PhD-position was announced back when I was working as a statistics

investigator for the city of Stockholm; I jumped at the opportunity and was

happy to get accepted. I’ve truly enjoyed these five years at Uppsala Uni-

versity. I would like to thank my supervisors Anna Rutgersson and Bjorn

Claremar, for giving me this chance in the first place and for your support

throughout this project. Through questions, suggestions and criticism, you’ve

helped me develop my skills as a researcher.

Before taking this position, I hardly knew anything about volcanoes. I owe

a debt of gratitude to the volcanologists at the department, especially Valentin

Troll, David Budd and Steffi Burchardt for your input on my early work and

for informative discussions. Thank you, Bo Galle, for introducing me to your

work at Mt. Nyiragongo and for giving me a chance to travel to Rwanda (oh,

and thank you, Anna, for letting me take that chance). I really wish I could

have gone to Iceland to help you with the measurements at Holuhraun, too bad

I didn’t have a driver’s license. But hey, I’m working on that now! Both Bo and

Santiago Arellano have been very helpful, you shared your data and helped

me understand it; through the later part of my project you have both been

very helpful. I should also thank the staff at “Observatoire Volcanologique de

Goma” who keep the equipment around Mt. Nyiragongo running, despite the

many difficulties faced.

Magnus Baltscheffsky, you helped me get started with FLEXPART-WRF

and WRF, thank you for your patience. Getting FLEXPART-WRF ready for

application was a bit tedious; initially, I worked with Saji Hameed, whom I

thank for letting me work on his version of the model, we had some common

goals and most of them were reached. However, when I learned of the work

Jerome Brioude and others were doing, I jumped ship. Saji, I hope you find

some use for my work and that your “grand plan” for your fork of the model

proves successful. Jerome, thank you for helping me push my changes to the

main release. I would also like to thank the rest of the FLEXPART people

for helping me join the team and for your assistance with both FLEXPART

and FLEXPART-WRF; especially Henrique Barbosa for your unexpected, but

much appreciated bug-fix on roughness lengths in FLEXPART-WRF (I hope

you found the receptor output useful!).

To the CNDS PhD-students (of course including those of you recently grad-

uated): for discussions at assemblies, conferences and academies, and for the

after-works, parties and other social events. You’re a great bunch of people.

Keep seeking out the disasters you can learn from and avoid the rest (if any

42

remain), I wish you the best of luck. To the senior CNDS-staff: Some of you

I know better than the others, many of you have provided helpful input on my

work and you should know it’s been appreciated. Therese Huldtgren, our local

CNDS fix-it-all, not only did you shoulder a great load of administrative work

for CNDS, you did it with a big smile! And thank you for discovering those

missing 10 percent.

The meteorologists at the department, thank you for all useful input and

support, for informative seminars, and for the fun facts and stories from field

work, which I myself have very little of. Also, let’s not forget the Christmas

dinners and summer barbecues.

My office-buddies Marc and Jean-Marc, you have so confusingly similar

names that I sometimes feel a bit left out being the only non-Marc in the

room. We’ve shared our frustrations on buggy models, crashing computers,

tough reviews and general thesis panic. I hope you’ve enjoyed the company

as much as I have.

To the activity groups: the Lunch runners, the Wednesday dancers, the

Christmas band(s) and the cross-country skiers; those days were good days!

Nina, you made me realize that regency parties are actually quite fun. You

know the rest of that story, I’m not the only one who should thank you. Agnes,

my unofficial mentor and a good friend from the start; you basically introduced

me to Geo, and the people at Geo, and parties with people form Geo, and

parties vaguely related to Geo. Diana, your musical taste (not to be confused

with Tom Jones concerts) is truly inspiring and has made many evenings on my

side of the wall more enjoyable. Desk hopping! Thank you Anna, for letting

me use yours after having to part with my own, and thank you for not being

upset when you came back and I was still there. The hammer-guy, who pounds

in the basement, thank you for reminding me when it’s past office hours and

time to go home (I do not read Morse, but I think I got your message). An

ingeniously genius man with a pen, who created elated words now and then.

A doctor by name, his books quite insane, why words wound like screws, by

the great Dr. Suess.

Sarah, jag hoppas att du tycker om den har avhandlingen, den har ju tagit

lite tid att skriva och det har blivit en del sena kvallar pa kontoret. For tre

ar sedan hade jag inte kunnat forstalla vad vi har idag. . . eller jo, kanske, jag

forestaller mig alla mojliga knappa saker – hela tiden – det ar bara inte sa

mycket som faktiskt intraffar.

Our daughter Eleonore, I was half-way through writing this thesis when

you were born, you are currently downstairs with you mother as I write this,

sleeping (and sometimes screaming, but mostly sleeping) and you are 30 days

old. When your daddy defends his thesis you will be 92 12 days; when you read

this — I don’t know if you ever will — you’ll have to be old enough to read.

There will be many more days before that and I hope most of them will be

happy, cherishable and worth remembering.

43

Finally, a big thank you to the rest of my friends and family, for putting up

with my weird working hours and all modelling jargon which I keep blabbing

on about. Thank you all for being here, under the same blue sky.

For those of you not specifically mentioned, the rest of this page is dedicated

to you (look at all that paper!).

44

7. Sammanfattning pa svenska (Summary inSwedish)

Spridningsmodellering av utslapp fran vulkaner

Vid kraftfulla vulkanutbrott frigors stora mangder halsovadliga gaser och

vulkanaska i atmosfaren; utslappen kan transporteras over stora avstand in-

nan halterna minskar till oskadliga nivaer genom omblandning, deposition-

sprocesser och/eller kemiska reaktioner. Utslappen medfor en naturlig miljo-

paverkan som kan ha negativa konsekvenser for vart samhalle. Vulkanaska

ar speciellt farlig for flygtrafik eftersom askan leder till kraftigt slitage pa ex-

ponerade delar, igensattning av ventilationsfilter och i varsta fall motorhaveri;

detta uppmarksammades inte minst i samband med Eyjafjallajokulls utbrott i

april 2010, da en stor del av flygtrafiken over Europa stoppades for att fore-

bygga olyckor.

For att minimera skador och omkostnader, orsakade av utslapp fran vulka-

nutbrott, ar det viktigt att kunna gora tillforlitliga prognoser over vart utslappen

tar vagen. For detta kravs sakra vaderprognoser, goda kunskaper om egen-

skaperna hos utslappet (kalltermen), samt spridningsmodeller som beskriver

transporten av utslappen.

Att bestamma kalltermen ar en stor utmaning eftersom det ofta saknas till-

forlitlig matdata. Storleksfordelningen pa askpartiklar ar svar att uppskatta

men ar viktig att kanna till for att avgora partiklarnas livslangd i atmosfaren.

Gasplymens sammansattning behovs for att uppskatta skadligheten pa halsa

och miljo. Det totala utslappet av partiklar och gaser behovs for att bedoma

hur stora omraden som kommer exponeras; aven totalutslappet ar svart att

direkt mata.

Spridningsmodeller kombinerar information om kalltermen med kemiska

och aerodynamiska egenskaper hos utslappen samt meteorologisk data fran

vadermodeller; pa sa satt kan langvaga transport av utslappen bestammas. For

att en spridningsmodell ska producera tillforlitliga resultat ar det viktigt att

den meteorologiska drivdatan noga efterliknar atmosfarens tillstand, att kall-

termen ar noggrant uppskattad, samt att fysiska och kemiska processer i sprid-

ningsmodellen nara efterliknar verkliga processer.

Denna avhandling beror flera metoder for att forbattra spridningsmodeller-

ing av utslapp fran vulkaner. Det inledande arbetet (Artikel I) fokuserar pa

45

vidareutveckling av en atmosfarisk spridningsmodell, avsedd for att anvandas

tillsammans med en mesoskalig meteorologisk modell. Ett vatdepositions-

schema, som tidigare saknats, anpassades for modellen och flertalet praktiska

forbattringar infordes (t ex utdata pa netCDF-format och inbyggt stod for par-

allellberakningar). Modellen anvands i samtliga studier i avhandlingen.

Sannolikheten for att framtida vulkanutbrott pa Island ska medfora prob-

lem for flygtrafiken undersoks i Artikel II genom ett antal modellscenar-

ier. Flera utslappscenarier studeras, samtliga ar baserade pa historiska utbrott

av vulkaner; detta for att kunna basera kalltermerna pa historisk data. Ut-

brotten kan rangordnas efter intensitet, de svagare utbrotten ar baserade pa

vulkaner fran hoga breddgrader (framst fran Island) for att aterspegla om-

givningens egenskaper. Eftersom data fran storre utbrott pa Island ar mycket

begransad anvands aven data fran andra vulkaner (Pinatubo och Tambora) for

att beskriva de kraftigaste utbrotten. Totalt fem scenarier anvands i spridnings-

berakningarna.

Meteorologisk ateranalysdata, hamtad fran Europeiska Centret for Medel-

langa vaderprognoser (ECMWF), anvands for att driva en mesoskalig vader-

modell (WRF). Den mesoskaliga modellen producerar hogupplost vaderdata,

timme for timme, over norra Atlanten och Europa. Totalt tackts en period

pa tre ar in i studien. Vaderdatan anvands for att gora spridningsberakningar

utifran samtliga utslappsscenarier. En simulering for varje scenario startas

var 6e timme och varje simulering kords for fem dygn. Resultatet blir drygt

4000 korningar for varje scenario. Resultaten anvands for att uppskatta sanno-

likheten for att askkoncentrationen i atmosfaren ska overskrida de gransvarden

som idag galler for civilflyg.

Resultaten visar att sannolikheten for liknande storningar i flygtrafiken som

under Eyjafjallajokulls utbrott 2010 ar laga; enligt modellkorningarna kom-

mer liknande utbrott overskrida aktuella gransvarden for vulkanaska i 1–5 %

av fallen for norska kusten, Skottland och Irland och under 1 % av fallen for

ovriga Europa. Den laga sannolikheten beror till stor del pa hogre gransvarden

for vad som ar acceptabla askkoncentrationer for flygtrafiken. Daremot sa har

det forekommit betydligt kraftigare vulkanutbrott pa Island, exempelvis Ask-

jas utbrott 1875; liknande utbrott skulle idag kunna medfora stora storningar

flygtrafiken.

Artikel II pavisar aven olika monster for spridning under olika arstider.

Utbrott under vintern ger en annorlunda riskbild an utbrott som sker under

sommaren. Under vintern ar vastvindarna over norra Atlanten mer domi-

nanta; detta innebar en storre sannolikhet for att utslappen ska transporteras

direkt mot Skandinavien eller Centraleuropa. Rackvidden for vinterutbrott

blir darmed langre medan sannolikheten for att en given plats exponeras blir

mer kortvarig.

I en andra studie appliceras modellsystemet for gasutslapp fran vulkanen

Nyiragongo i ostra Demokratiska Republiken Kongo. Nyiragongo ar en av de

mest aktiva vulkanerna i Afrika. Nyiragongos senaste utbrott var 2002 och

46

sedan 2004 har man gjort kontinuerliga matningar av svaveldioxid (SO2) som

slapps ut fran vulkanen. Matningarna ar gjorda med markbaserade DOAS-

system (differentiell optisk absorptionsspektroskopi). I Artikel III anvands

dessa matningar for att studera exponering av SO2 i olika omraden runt vulka-

nen.

Sasongsvariationer i exponeringen identifieras och kopplas till variationer

i det lokala klimatet, speciellt laget av den intertropiska konvergenszonen

(ITCZ). Omraden sydvast om vulkanen ar framst exponerade under norra

halvklotets (NH) vinterhalvar medan omraden nordvast om vulkanen framst

exponeras under NH sommar.

Mesoskaliga processer fran topografi (katabatisk vind och kanalisering)

samt kontraster mellan land och vatten (sjo- och landbris) bidrar till dygnsvari-

ationer i luftkvaliteten, bland annat i provinshuvudstaden Goma (ca 20 km

soder om Nyiragongo). Tillsammans med variationer i det atmosfariska grans-

skiktet medfor detta hogre halter SO2 nattetid jamfort med dagtid. Endast ett

fatal luftkvalitetsmatningar har gjorts i markniva i regionen, det finns planer

pa att utfora fler matningar men radande oroligheter har an sa lange forhin-

drat detta. Modellresultaten kan bland annat anvandas for att avgora lampliga

platser for markmatningar.

I den sista studien (Artikel IV) anvands inversmodellering for att bestamma

kalltermen for Bardarbungas utbrott i Holhraun (Island) 2014–2015. Mobila

DOAS-matningar har gjorts langs Islands ringvag (80–240 km fran utbrottet);

matningarna ger en uppskattning av den totala mangden SO2 i vertikalkolum-

nen ovanfor instrumentet. I studien anvands spridningsmodeller for att fa

fram beraknade varden for samma vertikalkolumner som for matningarna.

Modellresultaten anpassas genom olika regressionsverktyg for att efterlikna

matningarna; I praktiken innebar detta att man justerar kalltermen i modellen

sa att skillnaden mellan modellerade och uppmatta varden minimeras. Slutre-

sultatet blir en uppskattning av kalltermen (plymhojd och utslappstakt).

Den anpassade kalltermen anvands aven for langre modellkorningar vars

resultat jamfors med satellitmatningar. Spridningsberakningar baserade pa

olika meteorologisk data jamfors: hogupplost (1.5 km) meteorologisk data

fran WRF-modellen och lagupplost analys- och prognosdata fran ECMWF

(0.2◦). De spridningsberakningar som baserats pa data fran ECMWF stamde

battre overens (R = 0.37) med satellitmatningar, jamfort med korningar baser-

ade pa data fran WRF (R = 0.29). Det totala SO2-utslappen fran Holuhraun

uppskattades till 490± 180 kg/s i September 2014 och avtog med tiden till

21±25 kg/s i borjan av Februari 2015.

Avhandlingen presenterar olika metoder for hur spridningsmodeller kan

anvandas for att studera utslapp fran vulkaner. Metoder for att inkludera

matningar i spridningsmodellering har utvecklats som del av Artikel III och

Artikel IV. For att forbattra prognosverksamheten inom omradet ar det vik-

tigt att ta till vara pa den matdata som finns tillganglig i varje given situation.

Eftersom olika matmetoder har olika svagheter sa ar det viktigt att modellerare

47

inte forlitar sig pa en enskild metod; istallet bor man utveckla modellsystem

som kan styras med flera olika typer av matningar, beroende pa vilken data

som finns tillganglig for varje specifikt fall.

48

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