Introductory essay - Centrum för naturkatastrofslära

Transcription

An introduction essay
Dispersion of ash and gases
from volcanic eruptions
Adam Dingwell
Uppsala universitet
Institutionen för geovetenskaper
Centrum för naturkatastrofslära
Uppsala University
Department of Earth Sciences
Centre for Natural Disaster Science
Abstract
Volcanic eruptions emit huge quantities of gas and ash into the atmosphere, some
of which have negative impact on human health, animal and plant life and/or important infrastructure. These emissions are transported by air currents and can travel
around the world in a matter of weeks. Whether an eruption is of explosive or passive
character, the atmosphere plays an important role in determining where exposure to
the emissions occur.
The atmospheric circulation and the development of weather systems determine
how volcanic plumes extend over time. Substances will efficiently be removed from
portions of the plume exposed to cloud formation and precipitation. Turbulent motions in the air results in mixing of plume content and ambient air and aids in removal
of plume matter.
In order to fully understand the risks related to volcanic eruptions, we must
understand how emissions are transported in the atmosphere. The tools available
for this purpose include a range of measurement techniques and modelling tools,
which together give us the best view of the situation. By studying historic events
and their outcome, we are able to improve the models and thereby better describe
future eruptions when they happen. Different scenarios can be modelled to make
risk assessments of areas which might be affected by eruptions; studies which can be
used as a basis for mitigation planning.
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Referat
Vulkanutbrott släpper ut stora mängder gas och aska i atmosfären som kan ha
negativ inverkan på människors hälsa, växt- och djurliv samt slå ut viktig infrastruktur. Vindar kan transportera dessa utsläpp runt jorden inom loppet av några veckor.
Vare sig ett utbrott är av explosiv eller lugn karaktär, har atmosfären en betydande
inverkan på vilkan områden som utsätts för utsläppen.
Den atmosfäriska cirkulationen tillsammans med utvecklingen av vädersystem
avgör hur vulkaniska plymer breder ut sig över tiden. De delar av plymen som utsätts
för molnbildning och nederbörd rensas effektivt från vulkaniskt material. Turbulens
i atmosfären blandar ut plymen med omgivande luft och bidrar till depositionen av
partiklar och gaser.
För att helt förstå riskerna vid vulkanutbrott måste vi känna till hur utsläppen
transporteras i atmosfären. De verktyg som finns till hands innefattar diverse mätmetoder och modeller som tillsammans ger oss den mest kompletta bilden av händelsen.
Genom att studera historiska utbrott och deras utfall kan vi bättra på modellerna i
syfte att bättre beskriva framtida utbrott. Olika scenarier kan simuleras för att göra
riskbedömningar över områden som riskerar att utsättas; studier som kan vara till
grund för skadeförebyggande projekt.
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Contents
1 Introduction
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2 Volcanoes and society
2.1 Quiescent plumes and passive degassing . . . . . . . . . . . . . . . . . . . .
2.2 Explosive eruptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3 Centre for Natural Disaster Science
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4 Volcanic eruptions and the atmosphere
4.1 Meteorological concepts . . . . . . . . . . . .
4.1.1 Atmospheric stability . . . . . . . . . .
4.1.2 Deposition processes . . . . . . . . . .
4.2 Eruption column dynamics . . . . . . . . . . .
4.2.1 Estimating sources and eruption rates .
4.2.2 Particle characteristics and formation .
4.2.3 Fine ash fraction . . . . . . . . . . . .
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6 Measurements
6.1 Ground based observations . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Satellite observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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7 Early results
7.1 WRF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Dispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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8 Research Plan
8.1 Climatology of ashfall over Scandinavia . . . . . . . . . . . . . . . . . . . .
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5 Modelling tools
5.1 Meteorological data . . . . . . . . . . . . . . .
5.1.1 Reanalysis . . . . . . . . . . . . . . . .
5.1.2 Weather Research and Forecast model
5.2 Atmospheric Dispersion Models . . . . . . . .
5.2.1 HYSPLIT . . . . . . . . . . . . . . . .
5.2.2 NAME . . . . . . . . . . . . . . . . . .
5.2.3 FLEXPART . . . . . . . . . . . . . . .
5.2.4 PILT – FLEXPART-WRF . . . . . . .
5.2.5 Online modelling . . . . . . . . . . . .
5.3 Application of models . . . . . . . . . . . . . .
5.3.1 Ash climatology . . . . . . . . . . . . .
5.3.2 Exposure . . . . . . . . . . . . . . . .
5.4 Best fit modelling of eruptions . . . . . . . . .
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8.2
8.3
8.4
High resolution local/regional climatology . . . . . . . . . . . . . . . . . .
Fluoride mapping – mid range study of toxicity . . . . . . . . . . . . . . .
Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9 Summary
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v
1
Introduction
Volcanic eruptions inject huge quantities of ash and gases of different properties into the
atmosphere. These pollutants can have severe impact on human health and result in serious
costs to society. Several of these pollutants can remain in the atmosphere for days or even
weeks before being removed or diluted to harmless concentrations.
In April 2010, Europe was made aware of the hazards posed by volcanic eruptions as
the Icelandic volcano Eyjafjallajökull erupted. Ash was transported over 1,000 km before
reaching the European continent, resulting in the closing of large portions of European air
space. On the 15-22 April 104,000 flights were cancelled, flights supposed to carry goods
and a total of 10 Million passengers (EUROCONTROL, 2010).
The reason why this eruption had such a wide-spread effect was not due to the magnitude of the eruption itself. This eruption was small compared to the 2011 eruption of
Grı̀msvötn, which had less impact over Europe. The main difference between the two eruptions were the prevailing weather conditions. For the eruption in 2010, ash had injected
into the jet stream, which during one of the explosive phases was directed toward central
Europe. This was not the case in 2011, showing the important role of the weather for the
transport of emissions.
The impact of such extreme events is also highly dependent on the ability of society to
prepare. Increased knowledge and better predictions allows society to prepare before the
disaster strikes as well as to make better decisions during the event. This project aims to
increase our understanding of the transport of volcanic emissions and apply this knowledge
to improve methods and existing models.
The purpose of this essay is twofold. First, it gives a literature review on the subject
of dispersion modelling with focus on volcanic emissions. Second, it aims to introduce
peers within natural disaster science to the discipline of atmospheric dispersion. Section 2
introduces some of the hazards volcanoes pose to society, Section 3 presents the research
centre behind this project. An introduction to the dynamics of volcanic eruptions is given
in Section 4 followed by a description of available modelling tools and observational data
in Sections 5 and 6, respectively. Some early results are presented in Section 7. Finally, a
proposed research plan is presented in Section 8.
2
Volcanoes and society
Depending on the type of eruption, society faces different threats. Quiescent active volcanoes emit toxic gases, which affect human and animal health as well as vegetation.
Explosive eruptions emit gases as well as ash, which also has the potential of health and
vegetative damage. In addition, ash presents an additional threat to infrastructure and
aviation.
1
2.1
Quiescent plumes and passive degassing
Gas emissions from geologically active areas are not only from venting at the volcano.
Surrounding areas can be subject to degassing through soil layers. The main gases from
a hazards perspective are CO2 , SO2 , HCl and HF. CO2 is not a toxic gas, however high
concentrations can be hazardous as it replaces the oxygen in the air, causing asphyxiation.
Since CO2 is heavier than air it can build up to harmful levels in trenches and basements,
especially in areas with soil degassing (Annunziatellis et al., 2003; Baxter et al., 1999).
This makes it important to inform the public of dangers and warn when CO2 -surges might
occur.
Acidic compounds (e.g. SO2 , H2 S, HCl) can lead to acidification of water supplies and
the corrosive effect on metals make constructions such as roofs, antennas and machinery
wear down faster. The reactive species will irritate airways when inhaled, presenting a respiratory health risk (Delmelle et al., 2002). Fluoride (F) is water soluble and can thus affect
drinking water quality. It is toxic at high concentrations and long term exposure to moderate levels (such as through fluoridated drinking water) can disturb dental development
for children (Baxter et al., 1999; Delmelle et al., 2002).
2.2
Explosive eruptions
Solid particles (tephra) of varying sizes and composition are formed during explosive eruptions. Fine ash can have direct impact on human health, both acute and chronic respiratory problems have been connected to ash exposure. Acute respiratory affects are common
among people with chronic health conditions (e.g. asthma) or during extreme exposure.
Larger particles can settle on roofs and gutters, unattended accumulation can lead
to heavy loads with risk of damaging the structure. Fallen tephra can interfere with
infrastructure, such as roads, water ways or power stations, and has an abrasive effect on
machinery.
Particle surfaces can adsorb gases and carry toxic substances, which irritate the airways,
long term exposure can lead to damage on the respiratory system such as scarring or
inflammation (Martin et al., 2012; Horwell and Baxter, 2006). The most common issue
however, is the presence of Fluoride (F) on particles. Fluoride is emitted mainly as HF
which adheres to the surface of particles in the plume (Thorarinsson and Sigvaldason,
1972). Since the ratio between particle surface area and mass increases with smaller size,
the highest F-content, relative to particle mass, is found among finer ash. This makes
fluoride poisoning a mid-range problem as the concentrations are not necessarily closest to
the volcano. Grazing animals are especially vulnerable to such spread of F since finer ash
particles adhere well to the surface of vegetation, which is ingested in large amount by the
animals (Cronin et al., 2000).
The risk from airborne ash is not necessarily over once the ash has settled after an
eruption. Small particles can remain in the environment for months or even years, and
be resuspended by wind storms, or anthropogenic activity, such as traffic or agriculture
(Horwell and Baxter, 2006). These ash storms can produce similar effects as the initial
2
eruptions and can affect previously unexposed areas, Wilson et al. (2011).
Airborne ash present a hazard for air travel, especially in the upper troposphere, where
most commercial jets navigate. Due to the abrasive properties and the low melting temperature of ash, it can cause severe damage to jet engines. Ash entering the engine will
melt in the hotter parts and solidify in the rear end of the engine where temperatures are
lower. This can result in complete shutdown of the engines. Over the period 1953–2009
there have been 94 confirmed encounters of aircraft with volcanic ash clouds, 79 of which
resulting in damage to the aircraft (Guffanti et al., 2010).
The first well documented encounter resulting in damage to the aircraft was in 1982,
when a Brittish Airways jet passed through the ash plume from Galunggung volcano in
Indonesia. The aircraft lost power to all four engines and descended steeply from 11 000 m
down to below 4 500 m before power was restored to three of the engines. The aircraft was
able to make an emergency landing at Jakarta airport. Later examination of the aircraft
showed that it had encountered the ash cloud. The aircraft had suffered severe abrasion on
leading edges, oil and air systems were contaminated by ash. The wind shields and three
of the engines had to be replaced before the aircraft could be taken back into service.
In 1989 similar event took place, an aircraft destined for Anchorage Alaska lost power
to all engines while descending for landing. Once again power was restored in time and no
one was injured. The repair costs for the damages to the aircraft, was estimated to $80
million, Casadevall (1994).
Following these events, the International Civil Aviation Organization (ICAO) set up a
global network of Volcanic Ash Advisory Centres (VAAC). The monitoring areas of each
centre is shown in Figure 1. The centres gather information from volcanic observatories
and monitor ash clouds from erupting volcanoes. Forecasts are made and advisories are
given to air traffic control to warn pilots and reroute flights. However, the area of ash
advisory is developing and guide-lines as to what concentrations of ash should be tolerated
are not yet well determined.
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Centre for Natural Disaster Science
The Centre for Natural Disaster Science, CNDS, aims to increase our understanding of extreme natural events and their impact on society. The centre is based around the three universities: University of Karlstad (Climate & safety), Uppsala University (natural-, social-,
and engineering sciences) and the Swedish National Defence College. The centre has four
main objectives:
• Develop tools for conducting negotiations and formulating treaties, which can effectively tackle and help prevent natural disasters.
• Improve management of natural disasters by an increased understanding of their
reasons (including socio-cultural and human behaviour).
• Improve information handling and know-how on warning, decision, and other support
systems in natural-disaster management.
3
VAAC
Montreal
VAAC
Anchorage
VAAC
London
VAAC
Tokyo
VAAC
Washington
VAAC
Wellington
VAAC
Darwin
VAAC
Buenos Aires
VAAC
Toulouse
Figure 1: Volcanic ash advisory centres and areas of resposnsibility as issued by the International Civil Aviation Organization.
• Develop infrastructure that is robust in case of natural disasters, e.g., autonomous,
secure, and robust energy generation, and information and communication technology.
The Swedish Natural Disaster Mitigation Research School (SENDIM), constitutes the central activity of CNDS, where PhD students and researchers from different disciplines collaborate toward these objectives.
This project is connected to the seismology and geochemistry groups at the department
of Earth sciences, studying the Katla volcano. Together, these projects cover the history
and present state of the volcano in addition to the dispersion of emissions, which is the
main focus of this project. The joint effort aims to improve predictability and hazard
assessment of volcanic eruptions.
4
Volcanic eruptions and the atmosphere
The strength of eruptions can vary strongly over time, a variation related to the species
emitted. It is therefore important to understand some of the dynamics of the eruptions.
This is used to determine several source parameters and is needed before covering the
atmospheric transport processes. The behaviour of eruptions is also affected by the atmosphere to some extent, to facilitate the understanding of these effects, we should first
establish some meteorological concepts.
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4.1
Meteorological concepts
This section covers some concepts which are used later in this essay. It is mainly aimed
toward those who have little experience with the subject. Therefore, focus is put on
explaining the concepts with words rather than mathematics.
4.1.1
Atmospheric stability
The vertical temperature profile of the Earth’s atmosphere is commonly used to divide the
atmosphere into different layers, as shown in Figure 2. The vertical temperature profile
together with humidity determines how stable the atmosphere is. The rate by which
temperature decreases by altitude is commonly referred to as the lapse rate:
Γ=−
dT
dz
(1)
where T is the temperature and z is the altitude. In the lowest portion of the atmosphere,
the troposphere (Fig. 2), temperature decreases with altitude. This is due to vertical
mixing of the air. The surface of the Earth is heated by the sun, which in turn warms the
overlying air, making it lighter. The air starts rising and is replaced by heavier surrounding
air. As the air parcel rises, the environmental pressure decreases, the parcels expands and
its cools off (internal heat energy is used to displace the surrounding air, i.e. adiabatic
cooling).
For dry air, i.e. with the absence of water vapour, this cooling is Γd = 9.8 K km−1 .
However, if water vapour is present and the air becomes saturated while rising, latent heat
is released as water condenses, giving a lower temperature decrease. This lapse rate for
saturated air depends on both temperature and pressure and is typically around Γs ≈
6 K km−1 . For the standard atmosphere, the lapse rate of the troposphere is assumed to
be near moist adiabatic at 6.5 K km−1 .
For simplifying the coming arguments, we define the general adiabatic lapse rate as:
Γd for sub-saturated conditions
Γx =
Γs for saturated conditions
The lapse rate is used as an indication of the stability of the atmosphere, which is important
when determining the buoyancy of displaced air. If the temperature decrease with altitude
is weaker than the general adiabatic lapse rate (Γ < Γx ) the conditions are stable, i.e.
if an air parcel is displaced vertically it will be forced back toward its initial position by
the surrounding air. Therefore, vertical mixing of air is low, which is the case in the
stratosphere and the mesosphere (Fig. 2).
When the temperature profile closely resembles the general adiabatic lapse (Γ ≈ Γx ),
the atmosphere is in a near neutral condition. I.e. an air parcel can be displaced vertically
and will still have the same buoyancy as the surrounding air; it will not be forced back
toward its original position. This state is generally the result of vertical mixing of the air,
and is also a state where mixing easily occurs.
5
.
.
PRESSURE (hPa)
ALTITUDE (km)
Mesosphere
Stratopause
Stratosphere
Tropopause
Figure 2: Temperature profile of the
atmosphere from sea level to 85 km
altitude, based on the US 1976 standard atmosphere. The atmosphere
is divided into vertical layers, based
on the lapse rate (Γ). Shown in
the figure are the three lowermost
layers, the troposphere, stratosphere
and mesosphere. Each of these vertical layers are separated by layers
of constant temperature (Γ = 0),
the tropopause and stratopause, each
named after their corresponding underlying layer.
Troposphere
TEMPERATURE (C)
The last possible case is the unstable atmosphere, when temperature decrease by altitude is stronger than the general adiabatic lapse rate (Γ > Γx ). In this case a vertically
displaced air parcel will be forced further away from its initial position by the ambient air.
E.g. an air parcel moving upward will become lighter than the surrounding air, forcing
it to move even further; this will continue until the air parcel reaches a stably stratified
layer. In order to maintain an unstable stratification, a continuous forcing must be applied,
otherwise the atmosphere will soon reach a neutral state due to mixing.
The stability of the atmosphere affects the development of volcanic plumes. Using
the standard atmosphere (Fig. 2), consider a plume rising from ground level. Since the
troposphere is in a state of near neutral stratification, little forcing is required for the
plume to rise throughout this layer. As the plume reaches the tropopause the conditions
become stable and a higher forcing is required to reach higher. In the stratosphere, the
conditions are even more stable, and stability increases with altitude, requiring an even
stronger forcing.
4.1.2
Deposition processes
Another concept which is of importance for atmospheric dispersion is deposition. Deposition is the general name for numerous processes which remove solids, liquids or gases,
generally referred to as species, from the atmosphere.
Dry deposition is group of processes which transfer species to the surface of the Earth
without the presence water. A special case of dry deposition is gravitational settling which
mainly affect particles (solid or liquid) which are larger than 10 µm in diameter and depends
on particle size and density.
6
For smaller particles, gravitational forces become negligible compared to dynamic forces
in the air. The rate at which fine particles, as well as gases, are removed from the atmosphere is determined by the dynamic properties of the air (turbulence and viscosity) as well
as properties specific to the surface of the Earth and the species being deposited. Each of
these properties are important for specific parts of the deposition process.
Figure 3 illustrates how the different parts of dry deposition relate. Consider a cloud
of dust close to a surface, as the cloud moves closer the flow becomes increasingly affected
by the surface. This is first noticed by an increase in turbulence when entering the surface
layer (A). The dust grains are randomly moved about by turbulent eddies, by chance, this
will move some of the grains closer to the surface. Before reaching the surface however,
the grains must pass through a thin layer of air where turbulence is negligible, the laminar
sublayer (B). The grains will move about at random throughout this layer as well but due
to collisions with molecules in the air (Brownian motion) rather than by larger turbulent
eddies. This type of motion is less efficient than the turbulent flux, but will cause some
grains to collide with the surface (C). The surface will not bind all grains that come into
contact with it. The efficiency at which species adhere to the surface depends on both the
surface and the species.
In models the rate of deposition is often defined as the mass flux density:
FC = −Vd C
(2)
where C and Vd are the concentration and deposition velocity of specific species. The
deposition velocity is a measure of the efficiency of the deposition, or the inverse of the
system’s “resistance” against deposition (Vd = 1/rd ). With the second definition, Eq (2)
becomes analogous to electric circuits and Ohm’s law. The deposition velocity can thereby
be determined based on the resistances of each sub-process:
Vd =
1
1
=
rd
rA + rB + rC
(3)
where rA is the aerodynamic resistance of the surface layer, rB is the viscous resistance of
the laminar sublayer and rC is surface’s resistance against sorption of a specific species.
Wet deposition covers removal processes where water is involved. There are three
main categories: nucleation scavenging, in-cloud scavenging and below cloud scavenging.
Nucleation scavenging is where particles are activated to form cloud droplets. Water can
exist on a particle’s surface without it being an activate cloud condensation nucleus. The
particle will behave in a similar way to any other dry particle of the same size. However,
when enough water accumulates around a particle it is activated, meaning that it will
continue to grow as a cloud droplet as long as there is available water vapour in the air.
Eventually the droplet reaches a size where gravitational forces start to dominate and it
falls out as precipitation, efficiently deposition its contents to the ground.
In-cloud scavenging represents how particles within a cloud collide with, and adhere
to, cloud droplets or ice crystals. Once bound in this way the particles follow the same
deposition processes as in the nucleation scavenging case. Below-cloud scavenging occurs
7
net flux
B
A
C
Figure 3: Illustration of the different sub processes of dry deposition. (A) represents the
surface layer, where turbulent fluxes dominate, (B) is the laminar sublayer where turbulence
is negligible and viscous forces dominate and (C) is represent surface properties.
when falling hydrometeors (e.g. rain, snow or hail) collide with particles before reaching
the ground.
In general, wet deposition is a more efficient process than dry deposition. However,
wet deposition only occurs where liquid water or ice is present, while dry deposition is
a continually ongoing process. Thus wet deposition is also harder to predict than dry
deposition.
4.2
Eruption column dynamics
Volcanic eruption columns can typically be divided into three components (Sparks and
Wilson, 1976; Sparks, 1986), as is shown in Figure 4. Closest to the vent of an explosive
eruption is the gas thrust region. As magma is pushed up toward the surface, gas bubbles in
the magma undergo rapid expansion as pressure decreases. The expanding gas accelerates
ejected material to speeds over 100 ms−1 , possibly up to 600 ms−1 for stronger eruptions
(Blackburn et al., 1976; Wilson et al., 1980). However, this forcing rapidly decreases with
distance from the vent as the gas pressure approaches that of the ambient atmosphere. If
no other forces would act on the column, the column would come to a halt several hundreds
of meters above the vent for weaker eruption, up to about a thousand metres for larger
ones.
The effective density of column is initially greater than the surrounding air, but decreases as air is entrained into the column and due to the fallout of larger solids. Hot
particles will warm the entrained air and increase the overall buoyancy of the column.
If this entrainment and heating is sufficient, the effective density of the column will fall
below that of the ambient air and the column will continue to rise, forming a convective
region. This region will make up the largest portion of the column, up to 90 % of the
total column height (Sparks and Wilson, 1976). For quiescent gas plumes, this is the only
upward forcing.
The convective region ends where the effective density (buoyancy) of the column equals
8
that of the ambient air. However, the column continues to rise due to inertia, becomes
heavier than the surrounding air and spreads out horizontally due to gravity. This is called
the umbrella region.
gas thrust
4.2.1
velocity
height
Figure 4: Qualitative illustration of an
eruption column, showing variations in density and velocity by height. Original image:
Sparks (1986)
re
sphe
atmo
e
plum
convective
height
umbrella
density
Estimating sources and eruption rates
When estimating the dispersion from an eruption it is crucial to correctly describe the
eruptive plume. For stable eruption columns, the major part of the emitted ash and gases
should be expected to reach the top of the eruption column (Sparks et al., 1997). This
part of the column would in those cases make out the major source of fine ash and gases
in a dispersion model, as the updraft velocity decreases below that of the horizontal wind.
For weaker columns the vertical velocity will be smaller compared to the horizontal wind
throughout the column. In these cases it might be more suitable to assign the entire
column as effective source for the atmospheric dispersion (H. F. Dacre et al., 2011; Mastin
et al., 2009). For unstable columns, the entrainment of air might not be sufficient enough
to support a stable column, which can lead to column collapse. During such eruptions the
effective source will be harder to define.
Methods for determining eruption rates have been adopted from engineering formulae
describing emissions from smoke stacks. Settle (1978) discussed the relation between mass
flux through the vent and eruption column height, analogous to relations describing plume
rise from industrial chimneys. Applying a least square fit to six historical eruptions yielded
the relation:
∆H = 0.117 (dM/dt)0.22
(4)
where ∆H is the plume height above the vent and dM/dt the mass eruption rate. The
mass flux was calculated based on deposit volume and solid particle (pyroclast) density and
the eruption heights were based on eye-witness accounts. However, the relation does not
account for atmospheric stability or turbulence. Within engineering sciences it is possible
to study the plume rise in more detail, due to the well determined systems being described.
Morton et al. (1956) derived an expression based on vertical stability in a fluid, and a stable
heat release (e.g. fires, factory chimneys).
1/4
∆H = 5.52 (1 + η)−3/8 QH
9
(5)
where η = ΓΓd is the fraction of environmental lapse rate, Eq. (1), and the adiabatic lapse
rate of dry air, Γd , QH is the heat flux and the constant (5.52) is based on laboratory
experiments. Eq. (5) is valid for stable (η > −1), low wind conditions as it does not
account for horizontal entrainment of the plume. During windy conditions stability is
of less concern as entrainment becomes the important factor. Based on observations at
Tilbury power station (England), Lucas et al. (1963) and Lucas (1967) suggested the
following relation for windy, near neutral (η ' −1) conditions:
1/4
Q
∆H = [12.9 + 0.0211(hc − 100)] H
u
(6)
where u is the horizontal wind speed at height 1.5hc , hc is the chimney height in the case
of industrial plumes. The chimney height is sometimes important to account for since
turbulent mixing decreases with height, raising the chimney height can thus decrease entrainment, giving a higher plume rise. In the case of volcanic plumes, hc is harder to define
but could represent the height above average terrain surrounding the vent. Combining
Eq. (4)-(6), Settle (1978) proposed a linear relation between dM/dt and QH which when
compared to the heat content of pyroclasts, showed that the industrial relations based on
well determined heat fluxes can be used for volcanic eruptions as well.
While Eq. (5) and Eq. (6) are suitable for factory plumes, applying them to the more
vivid case of volcanic eruptions is not without complications. First, Eq. (5) requires near
zero wind speeds, which is not applicable for eruption columns spanning several kilometres
altitude. Second, Eq. (6) assumes a constant ambient wind speed and will therefore not
be able to account for variability in wind speed by altitude. An extreme case would be
an eruption breaching the polar jet stream, which was the case in the 2010 eruption of
Eyjafjallajökull.
Further work in describing the eruption columns has taken the direction of more elaborate numerical models. Wilson (1976) described a simple 1-D eruption column model
describing the lower portion of the column. The model was used by Sparks and Wilson
(1976) to study the structure of explosive eruptions and the formation of a convective
plume.
Bursik (2001) used a quantitative integral model to explain the impact of wind conditions on plume rise. The model handles pyroclast fallout and entrainment of air (momentum and density) and uses a set of plume following diagnostic equations of motion.
The ATHAM model was used by Graf et al. (1999) to study the effects of environmental
temperature, humidity and wind on weaker eruptions. The model has a full set of NavierStokes equations and supports active tracer feedback. Both studies show a decrease in
plume height with increasing wind speeds. However, Graf et al. (1999) shows that high
wind speeds in the lower troposphere can also result in a higher gas plume than compared
to low wind conditions as solids are more efficiently separated from the rest of the plume.
10
4.2.2
Particle characteristics and formation
The characteristics of particles in a volcanic cloud depend on formation processes and
magma composition. The four main formation processes recognized are:
• Explosive vesiculation – pressurized gas bubbles burst as pressure in ascending magma
drops.
• Hydrothermal explosions – magma in contact with water results in thermal shock,
as the water is instantly vapourized.
• Milling – solid particles grind against each other, forming smaller particles in and
out of the vent.
• Atmospheric processes – gas to particles formation, surface chemical reactions, condensations or sublimation of water vapour and aggregation of particles.
The first three determine the composition of the initial emissions and have to be defined
at model initialization. The last represent processes in the atmosphere which have to be
treated in the dispersion model if they are to be represented.
The presence of water will affect the size distribution of the formed particles. Depending
on the proportions of magma and water this has a different affect. Increased volumes of
water will give a higher abundance of fine ash up to a maximum at approximately equal
volumes of water and magma (Wohletz, 1983). After which the explosivity of the mixture
decreases.
4.2.3
Fine ash fraction
Depending on the area of interest, the fraction of fine ash is of varying importance. When
studying the long range dispersion and influence on aviation, the fine ash is the biggest
contribution. Studies focusing on the short distance deposition are less influenced by fine
ash since the mass will be dominated by larger particles. However, air quality during
an eruption could still be highly dependent on the fine ash content. The methods for
estimating mass eruption rates are less reliable for fine ash since total erupted mass and
grain size distribution is often estimated based on tephra deposit volume. Most of the fine
ash will not be found in the deposits since it is blown away, either before settling or after
being resuspended from the ground, as noted by Bursik et al. (1992). The fine ash can
potentially be underestimated as most of it will be transported elsewhere and is not found
in the deposited layers.
Mastin et al. (2009) showed the high variability in fine ash fraction by comparing the
results of 10 different studies covering 16 eruptions. The fraction of particles smaller than
63 µm (1/16 mm) varied between 1-70 % of the total mass. The variability is a result of
magma type, the presence or absence of pyroclastic flows, the completeness of sampling
attempts and different estimation techniques. This shows the importance of choosing
data with the right treatment of fine ash fraction when used with dispersion models. An
11
alternative way of determining the fine ash fraction is the crystal concentration method
described by Walker (1980). In the example used by Walker the fraction of particles finer
than 1/8 mm constituted 77% of the total mass.
5
Modelling tools
While measurements give us information about the historic and current state of nature,
there are several reasons why measurements alone are not sufficient for many studies.
First, measurements can never give us an overall view of nature. Whether we make point
measurements or remote sensing cover larger areas there will be large gaps in our data,
either spacial or temporal. Second, measurements alone are seldom enough to make a
proper forecast of the future. Third, measurements can contain significant errors, either
through technical limitations, environmental interference.
In order to get a better picture of nature we need the models, by modelling we are able
to fill in the gaps between measurements and make forecasts. The models are not perfect
but neither are the measurements, when they are combined we can get the most complete
picture of reality.
A multitude of tools exist for modelling eruption plumes. In this project, a system
of two models will be used. The Weather Research and Forecast (WRF) model will be
used in conjunction with the Pacific Northwest National Laboratory (PNNL) Integrated
Lagrangian Transport (PILT) model, a revised version of the FLEXPART model.
5.1
Meteorological data
Meteorological measurements of the atmosphere are made globally at ground stations, by
balloon soundings and by satellites. This can be used directly but often requires some processing before it can be applied. This is due to the uneven placing of the observation sites,
and the fact that different stations make different measurements. What is generally desired
is gridded data, where each grid point contains the same set of atmospheric parameters.
5.1.1
Reanalysis
One way of achieving gridded data is by reanalysis. Atmospheric reanalysis is the process
of running forecast systems with historic observations in order to provide consistent data
sets of atmospheric parameters, typically spanning several decades. Data is gathered from
different observations, (e.g. ground observations, balloon soundings, satellite imagery, etc.).
The observations are checked for bad values (observation errors) and assimilated with
previous forecasts to give a current state of the atmosphere from which to make the next
forecast. This is the same process as is used in most operational forecast systems.
An alternative to reanalysis data when studiying historical weather is to use archived
weather forecasts. However, operational systems undergo development, improvements will
be included over time and eventually models are replaced by newer, state of the art systems.
12
This makes archived weather data less reliable when comparing data from different periods
since the method used changes over time. Reanalysis solves this by running the new models
with old data, creating historic data with the same up to date systems through the entire
archive.
There are several centres around the world which produce reanalysis data sets with
global coverage, two of the most well known being the European Centre for Medium
Range Weather Forecasts (ECMWF) and the (US) National Centres for Environmental
Prediction (NCEP). In this project, ECMWF’s latest set ERA-Interim will be the main
source for meteorological data. The Interim project was meant as an intermediate system
in preparation for a more ambitious future project covering the entire 20th century. Before
such a project is available ERA-Interim provides a modern data set spanning from 1979
to present day. A thorough description of the data assimilation system including some
performance testing was provided by Dee et al. (2011).
The ECMWF provide continuous global meteorological data using the ECMWF Integrated Forecast System (IFS). The latest dataset available is the ERA-Interim set which
provides 6-hourly data on a 0.75 degree grid.
5.1.2
Weather Research and Forecast model
While reanalysis data such as ERA-Interim is useful for getting consistent data, the resolution is not sufficient for regional use. For some applications this is solved by interpolating
to a finer grid. Another approach is to feed the low resolution data into a high resolution
regional model (dynamical downscaling), such as the WRF model. This is not the same
as “filling the gaps” by interpolation, rather additional physically consistent information
is created and we get a better picture of the state of the atmosphere.
An example of this is shown in Figure 5, where the results are shown side-by-side. Data
from ERA-Interim is first interpolated to a finer grid as shown to the left. The same data
is then used for model initialization and subsequent boundary conditions in WRF, the
final result of which is shown to the right. The result from the WRF model shows higher
details, especially over Iceland, compared to the original (ERA-Interim) data. This is due
to improved topographic information and the ability of WRF model to describe dynamics
on a finer scale.
It is possible go further by setting up smaller domains with higher resolution inside the
main domain (i.e. nesting). This can be repeated for the inner domain until the desired
resolution is achieved, an example of such a setup is shown in Figure 6. There are two main
reasons why this method is preferred over simply setting up a small high resolution domain
over the area one is interested in. First, there could be obstacles (e.g. mountains or islands)
outside the domain disturbing the flow over a larger region. Such disturbances would be
completely missed unless they are large enough to be resolved by the global model. Second,
numerical problems may arise if there is a large difference in resolution between the global
and regional model, it is thus advised to downscale in several steps. These and several
other issues regarding downscaling are discussed further by Warner (2011).
The modular approach of WRF makes it a versatile tool which can be used for many
13
Figure 5: Direct interpolation of ERA Interim temperature field (left) versus downscaling
of the same data using the WRF model (right). Both figures show temperature at the
altitude defined by the lowest model layer in WRF.
Figure 6: Example of a nested domain
setup, boundary conditions are given on
a 0.75 degree resolution, the regional domains have resolutions of 13.5 km, 4.5 km
and 1.5 km, with the coarsest resolution
corresponding to the largest domain.
14
purposes. There is a wide range of parameterizations and physics schemes officially supported. However, choosing the right settings requires some consideration. While there are
several studies addressing the performance of different schemes, they can at most be used
as recommendations unless the exact same setup is used as in the studies.
Some focus is here put on microphysics schemes as these determine the formation
of clouds and precipitation. This plays an important role for atmospheric dispersion as it
affects deposition processes as well as chemistry and particle coalescence. Four microphysics
schemes are presented briefly here (see Table 1), a comparison of them is made in Section
7.1.
WRF Single Moment 3-class, WSM3, (Hong et al., 2004; Hong et al., 1998) is a basic
scheme which treats three classes of water, i.e. vapour, cloud water and rain, the corresponding ice classes are included by switching the liquid classes to cloud ice and snow at
below zero temperatures. A schematic of the processes is shown in Figure 7b.
(a)
(b)
ion
orat
evap
cloud ice
T<0
Switch at T=0
accretion
accretion
T>0
autoconversion
deposition
tion
lima n
sub
io
osit
dep
cloud
water
sublimation
water
vapour
autoconversion
evaporation
condensation
snow
rain
sedimentation
Figure 7: a) Illustration of microphysical processes in the ATHAM-model, each category
(except vapour) can include any mixture of water/ice and ash. Image source: Textor et al.
(2006). b) microphysics in the basic WRF Single Moment class 3 (WSM3) scheme, using
a “clean” atmosphere – no particles are considered.
WSM5 has the added support of mixed phases (i.e. clouds can contain both ice and
liquid water). WSM6 includes a sixth class, graupel, which is important in deep convective
clouds. Each of these schemes are “single moment”, meaning that mass mixing ratio of
the cloud content is calculated using prognostic equations, the number concentration is the
derived diagnostically from the mixing ratio based on a predetermined size distribution.
15
Table 1: List of microphysics schemes tested in the WRF model and some technical differences.
Scheme
WSM3
Moment
single
WSM5
single
WSM6
Thompson
single
double
Classes Comment
3
Classes: vapour, cloud water, rain (switches to
cloud ice and snow at T < 0 ◦ C)
5
Handles mixed phases i.e. liquid water and ice are
separate classes
6
Includes graupel class
6
Mixing ratio predicted for each class, prognostic
number concentration added for cloud ice.
An alternative is to include a second moment, with prognostic number concentration. The
double moment approach allows for more flexibility but requires more computing power,
making it less suitable for operation use.
The Thompson et al. (2004, 2008) scheme was developed as a compromise between
performance and accuracy. The scheme is 6-class with single moment for all classes except
cloud ice which has double moment. The snow parameterization has been improved to
support non-spherical shape and size distribution depends on both temperature and ice
water content. This structure makes the scheme behave more like double moment schemes
with less computation time than a full double moment scheme.
5.2
Atmospheric Dispersion Models
Atmospheric dispersion models simulate transport of contaminants by solving transport
schemes. This can be achieved with different approaches, typically Eulerian, Lagrangian
and Gaussian. In the Eulerian approach, the model domain is divided into fixed grid.
Bulk transport (advection) and random mixing (diffusion) are typically treated by the
same equation which is solved for each grid cell. Eulerian models are useful when complex
emission scenarios are considered with many sources spread over large areas, i.e. the emissions must be represented in each grid point. The gridded approach makes these models
suitable for treating chemistry, with reactions between the released species possible. Emissions from a source within a certain grid box will immediately mix with the air in that box,
when air flows from one box to an adjacent that air will similarly mix with the contents of
the target box.
Lagrangian models use artificial “particles” instead of grids. Each model particle represents a bundle of “true particles” (determined by e.g. a mass and size distribution) in
an infinitesimally small air parcel. Advection and diffusion are treated separately and is
solved for each “particle”, rather than solving the motion on a grid. Turbulent mixing
is usually treated by adding an extra “random walk” step to each particle’s trajectory.
The number of particles combined with the resolution of the input data determines the
resolution of the Lagrangian model. The number of particles released is typically in the
16
Table 2: Atmospheric dispersion model in use by VAAC’s and SMHI, the second column
states model type (E – Eulerian, L – Lagrangian, H – Hybrid).
Model
Type Operational use
FALL3D
E
Buenos Aires VAAC
FLEXPART
L
HYSPLIT
H
Anchorage VAAC
Darwin VAAC
Washington VAAC
Wellington VAAC
JMA-model
L
Tokyo VAAC
MATCH
E
SMHI
MLDP0
L
Montreal VAAC
MOCAGE
E
Toulouse VAAC
NAME
L
London VAAC
PUFF
L
Anchorage VAAC
Buenos Aires VAAC
Darwin VAAC
Wellington
order of 10 000. Lagrangian models are generally suitable for scenarios with a few well
defined point sources.
The third approach is the Gaussian setup. These models are generally purely analytical
and applicable for smaller domains, where the plumes from a number of point sources are
represented by trajectories. The concentration over a cross section of each plume is assumed
to follow a 2D or 3D Gaussian distribution. The concentration of any down-wind point can
then be calculated by adding the contribution from each trajectory. The main advantage
of Gaussian models is their computation efficiency, however, since they lack prognostic
equations their application is limited.
Some Lagrangian-Gaussian hybrid models exist however, which utilize the computationally efficient approach from Gaussian models in an otherwise Lagrangian system. The
following sections (5.2.1–5.2.4) present several dispersion models and some of their different
features. A list of models used for operational forecasts and research is shown in Table 2;
in addition to the seven models used by VAACs, FLEXPART and MATCH are listed due
to their interest to this project.
5.2.1
HYSPLIT
The HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was developed by NOAA and Australia’s Bureau of Meteorology, and is used by several VAAC’s
(Anchorage, Darwin, Washington and Wellington). The model manages both dispersion
and deposition. Advection is calculated by the mean of the velocity in the initial position
17
and a first guess position (the mid–point method without solving for V̄ ). The first guess
position is given by:
r̄0 (t + ∆t) = r̄(t) + V̄ (r̄, t)∆t
(7)
where r̄(t) is the position at time, t, and V̄ (r̄, t) is the velocity in the initial position. The
final position is given by:
r̄(t + ∆t) = r̄(t) + 0.5 V̄ (r̄, t) + V̄ (r̄0 , t + ∆t) ∆t
(8)
HYSPLIT is able to represent turbulence in two ways, either by traditional Lagrangian
particle cluster dispersion or by letting each particle represent a Gaussian puff. A puff
will spread out over time, representing subgrid turbulence, and split into smaller puffs at
a certain threshold, representing grid scale dispersion. In this way the model can simulate
turbulent mixing with fewer particles than in a pure Lagrangian approach. HYSPLIT is
a pure transport model and does not treat any deposition processes, particles reaching
ground level will be reflected back into the atmosphere. A description of the HYSPLIT
model is available by Draxler and Hess (1998).
5.2.2
NAME
Developed at MetOffice UK, the Numeric Atmospheric-dispersion Model Environment
(NAME) is a Lagrangian particle dispersion model. The model is designed to work on
scales ranging from a few metres to global, given the appropriate input. It’s approach is
similar to that of HYSPLIT with both particles and Gaussian puffs. The model can be
set up to use Gaussian puffs for young particles, where the plume dimensions are generally
small compared to boundary layer depth. Single particle-puffs will spread out and split
into smaller ones at a flow-dependent threshold. Since the benefit of particle-puff representation grows less as the plume spreads, the particle-puffs can switch to point size particles
at a certain age threshold. In this way NAME is able to simulate short scale dispersion
with better accuracy without major performance trade-off at larger scales.
The Advection scheme in NAME is based on the Euler step-wise method:
r̄t+∆t = r̄t + V̄ (r̄t ) + V̄ 0 (r̄t ) + V̄l0 (r̄t ) ∆t
(9)
where the next position (r̄t+∆t ) is derived from the current position (r̄t ) and wind velocity
(V̄ ), as well as small scale turbulence (V̄ 0 ) and low frequency meandering of the wind (V̄l0 ).
The meandering term represents variations in the wind field between input intervals (i.e.
between advective and turbulent scales). Variances are based on a three year time series
at Cardington, England, and could differ depending on location.
The modular approach allows for flexibility when choosing input data, with support
for nested domains ranked by priority. Data from MetOffice’s NWP “Unified Model”, or
single site met observations are supported by the model’s flow modules.
Chemistry is treated for about 30 atmospheric species, including OH, HO2 , H2 O2 and
O3 in the ambient air. Reactions between species in different particles is possible and is
18
calculated on a fixed chemical grid. A single model particle can contain multiple chemical
species.
A description of the latest version (NAME III) was put together by Jones et al. (2007),
5.2.3
FLEXPART
The FLEXPART model is an offline Lagrangian particle dispersion model developed at
the Norwegian Institute for Air Research (NILU). The model simulates long-range and
mesoscale transport, diffusion, wet and dry deposition and radioactive decay. It is designed
to run with input from meteorological models on the format provided by the ECMWF and
GFS.
The advection is calculated using a first order Euler method:
r̄(t + ∆t) = r̄t + V̄ (r̄, t)∆t
(10)
Diffusion is assumed to be stochastic and only depends on the current state of the atmosphere (Markov process). Different formulation of the diffusion is applied depending on
the time-step. FLEXPART can either run with a fixed time-step for fast computations or
it can calculate the time-step based on the Lagrangian time-scale for better representation
of turbulence, which is suitable for shorter simulations.
The user has the option to use particle splitting, which will take place at a given age.
This is done by setting a time constant, ∆ts . Particles will then split in two after it reaches
the ages ∆ts , 2∆ts , 4∆ts , and so on. In this way, it is possible to achieve fast performance
for the early stages of dispersion, when the plume has yet to spread over larger areas.
However, since the splitting is determined by a time constant, it is up to the user to tune
the splitting for the best results.
The model treats chemistry, but only as a removal process through reaction with OH.
Reactivity with OH should be set for species, and reaction rates are based on climatological
OH-data. Dry deposition includes gravitational settling but cover other processes as well.
The settling is also important for particle trajectories, which will only be treated properly
in single species runs. This is due to the implementation of mixed species particles.
Wet deposition is based on precipitation rate and prior to version 8.0, the same scavenging rate was applied to entire vertical columns. The current scheme uses temperature
and humidity to determine the position of clouds, this allows for different treatment of inand below-cloud scavenging.
A technical description of FLEXPART version 6.2 was written by Stohl et al. (2005),
updated model descriptions and the complete model source code is publicly available at
the NILU atmospheric transport web page1 .
5.2.4
PILT – FLEXPART-WRF
The Pacific Northwest National Lab (PNNL) Integrated Lagrangian Transport (PILT)
model is a modified version of FLEXPART, designed to run on mesoscale input from
1
http://transport.nilu.no/flexpart
19
the WRF model. The model is based on FLEXPART V6.4 and was initially named
FLEXPART-WRF by the developers (Fast and Easter, 2006b). Later modifications to the
model changed the code extensively and the model was renamed PILT (Fast and Easter,
2006a). The most important changes from the FLEXPART code are:
• Runs are made on the same grid as used in WRF
• Support for reading from NetCDF-format (used by WRF)
• Support for re-griding of GFS-data to WRF grid
• New parameterizations suitable for mesoscale rather than global (e.g. convection,
turbulence)
Changing the map projection required changes to most class-files in PILT making the
code practically incompatible with the original FLEXPART code. Due to this, PILT is
currently being developed independently of FLEXPART by several groups in different
countries. Several improvements made in FLEXPART since the branching of the model
are therefore lacking in PILT. This includes a new wet deposition scheme and OH-reaction.
As of today there are several desired additions which could improve FLEXPART-WRF.
The cloud cover is one of them, while WRF calculates cloud water and ice content on a
3D-grid, FLEXPART requires a 2D cloud cover. The cloud cover determines the wet
deposition and since there is no information on cloud height, the same deposition rate
is applied to entire vertical columns. In addition, the scavenging rates for cloud water
and ice are assumed the same (realistic? would it be useful to split up?). A new wet
deposition was implemented in version 8 of FLEXPART but since FLEXPART-WRF is
developed independently it has yet to include this. (still 2d cc?). Simulating ash dispersion
on a smaller scale requires higher resolution This project aims to better understand the
processes involved in atmospheric dispersion of volcanic emissions.
5.2.5
Online modelling
All of the above models require input from some meteorological model (however NAME
can optionally run on observational data on smaller scales). There is no backward coupling
from these dispersion models to the meteorological models (i.e. the dispersion is simulated
“off line”).
Many studies have shown that large volcanic eruptions can affect global climate (e.g.
Rampino and Self, 1984; Sigurdsson, 1990; Robock, 2000; Grainger and Highwood, 2003).
While, so far, there is a lack of studies on how eruptions affect local weather, there are some
which address the effect of ash plumes on the microphysics in the atmosphere, which is
important for different deposition processes. This is done by including dispersion processes
in meteorological models or by backward coupling the results from a dispersion model to
the governing meteorological model.
The impact of volcanic emissions on clouds was studied by Yarker et al. (2010) using
the WRF-CHEM model. The CHEM module is part of the WRF modelling system and
20
couples meteorological processes with dispersion and chemistry between a set of species.
The Thompson cloud physics scheme was modified to include aerosol effects. Four different
aspects of the emissions were considered: emission of water vapour, heat, light scattering
aerosols and aerosols acting as cloud condensation nuclei.
The study showed that eruptions have a notable impact on the weather in the vicinity of the volcano. Affecting temperature, water content (vapour as well as cloud water
and ice) and precipitation. Precipitation is especially interesting since it washes out ash.
However, since each aspect of the emission affects the atmosphere in different ways, the
results are non-trivial. E.g. radiative heating from the volcano can increase cloud temperatures, thereby increasing the vapour saturation level, reducing cloud droplet and ice
crystal growth. However, an increased amount of aerosols in the atmosphere will increase
light scattering which could lead to a net cooling of lower levels, counteracting the heating
effect.
A similar study by Textor et al. (2006) focused on particle aggregation and accretion,
which plays an important role for transport since larger particles will fall out faster. The
study used the Active Tracer High Resolution Atmospheric Model (ATHAM), Oberhuber
et al. (1998). The aggregation processes were included in a microphysics scheme with
support for volcanic ash. An illustration of the processes involved is shown in Figure 7a,
which also shows a schematic of the WSM3-scheme available in WRF for comparison. The
main difference between the schemes, besides ash inclusion, is support for mixed phase
clouds in the ATHAM scheme (both cold (ice) and warm (water) classes can exist at the
same time) while WSM3 switches between cold and warm classes at T = 0.
Aggregation is expected to increase in wet conditions as the wet surfaces make the
particles stick together. Mineral salts on the surface of particles affect the freezing point
of water, which could reduce formation of ice in clouds.
5.3
5.3.1
Application of models
Ash climatology
Climatological studies conducted by MetOffice, UK, have shown that the disruption of
the European air space in 2010, was a rare occurrence (Leadbetter and Hort, 2011). In
the study, the NAME model was used to describe an eruption of Hekla (Iceland). The
eruption characteristics where fixed but meteorological conditions varied. An eruption was
modelled every third hour between 1 January 2003 and 31 December 2008, and the output
was combined to describe the probability of ash concentrations exceeding a threshold value
at specific times after an eruption. The threshold chosen represents the concentration at
which air travel should be avoided. An example of the results from the study is presented
in Figure 8.
The setup uses a rather abstract representation of the source, the emission rate is
fixed at 1 g/6h, which is the same method used by London VAAC. The concentration
is determined using a lookup table and the eruption column height. This allows for a
quick deployment of the model when an eruption occurs before detailed information of the
21
eruption strength is available. Once additional information is available it can be used to
interpret the model output.
Figure 8: Probability of ash concentration exceeding threshold values between 24-48 hours
after an eruption. (A) shows regions where the probability of concentrations exceeding the
threshold is 10 % and (B) shows regions where the probability is 20 %. Three different
threshold values are used, the Medium value represents the no-flight threshold and Low
and High levels are within a factor ten of this value, which represents the uncertainty of
the study. Source: Leadbetter and Hort (2011)
The study showed that in most cases westerly winds would transport the ash cloud
eastwards over the North sea and Scandinavia. While the total costs to society would be
lower in such cases, they are still of interest especially to countries directly affected. In
addition, the high vulnerability toward the rare events with northerly winds, makes these
cases high-risk events as well.
5.3.2
Exposure
Peterson and Dean (2008) presented a simple way for calculating exposure of moving or
stationary receptors. A common way to measure exposure is through concentration data.
This is useful for showing peak values and time series, however, it does not represent
accumulated exposure over time. In order to include the time dependence, the exposure
for moving targets (e.g. aircrafts) can be calculated by integrating over the flight trajectory.
Using the setup from Figure 9, the exposure can be calculated by:
Z
Z
E = Cds = CV dt
(11)
In the first form we integrate over concentration along the flight trajectory, s. The second
form, integrating over time, is useful when both velocity and concentration changes with
22
time. The exposure, E, is then given in mass per unit area. As an example, exposure can
be used to calculate the total mass of ash passing through a jet engine by M = E · A,
where A corresponds to the area of air intake.
Figure 9: Diagram illustrating the air
volume swept through by an air plane.
The volume is stretched out along the
flight trajectory with a constants cross
sectional area, A. Both the velocity of
→
−
the aircraft, V , and the concentration
of the cloud, c, are functions of position,
→
−
x ,and time, t. Exposure calculations are
the integrated cloud concentration along
this trajectory. Image source: Peterson
and Dean (2008)
Two case studies were made to demonstrate the method, each based on information
available during a real-time event. The first test used the February 2000 eruption of Hekla,
where ash was encountered by a NASA DC-8 aircraft on a research mission. Dispersion was
calculated using the PUFF model, and using flight trajectories the exposure was calculated
as in Eq. (11). The results were compared to data from instruments on the aircraft. The
calculated exposure was 10 times greater than the observed, however, this is currently
within reasonable limits for a real-time analysis.
To further show the applicability of this method, the second test used flight tracking
data from 40 000 flights after the 2001 eruption of Mt. Cleveland. Flight tracking data is
available in near real time since it is required by air traffic control. The data can be used
to identify exposed aircrafts which should undergo thorough inspection to prevent future
damage.
A measure of exposure was also proposed for stationary targets (Estat ) by integrating
concentration over time:
Z
Estat = Cdt
(12)
which has a different unit than E and thus must be applied differently. This measure can
be applied to estimate the relative exposure of specific sites or applied to an entire model
domain to get the spacial variation of hazard. Both of which were done as examples by
Peterson and Dean (2008).
5.4
Best fit modelling of eruptions
A recent study by Stohl et al. (2011), based on the eruption of Eyjafjallajökull in 2010,
demonstrated how a dispersion model coupled to satellite data can be used to determine
the eruption strength. The method includes several steps, first an eruption column model
23
is run to determine the a priori emission strength based on eruption column height and
meteorological data from the ECMWF. The model was run iteratively to estimate the
vertical profile of the mass flux for every 3-hour interval of the eruption, 328 intervals
in total. The eruption column was split into 19 levels, yielding 6232 emission grid cells
(328 × 19).
The FLEXPART model was run once for each emission cell and the output from each
run was given on a horizontal grid as the vertically integrated concentration. The data
was compared with satellite observations using a fitting algorithm which determines the
influence of each emissions cell upon the output, which was used to determine the posteriori
eruption rate for each eruption cell. In this way, the eruption rate can be determined more
accurately than by using statistical relations as presented in Section 4.2.1.
6
Measurements
This section will cover some of the observation techniques available. The goal is to give an
overview of available data, not to describe observation techniques in detail. Observational
data is necessary during eruptions to initialize the models and can also be used for runtime
nudging or validation of the models. The most important information required revolves
around the source strength and emission rate. In real time, observations are in shortage
and models will be used to fill in the gaps. Over time, more data will become available and
preliminary data will undergo verification. These records after eruptions are important
when building and calibrating the models, and preparing for future events.
A range of observation techniques exist, equipment can be ground based, airborne or
from satellites. Tupper and Wunderman (2009) showed differences between ground- and
satellite based observation of eruption columns in Indonesia. Depending on the eruption
height, different methods are recommended. High plumes (near tropopause) are better
observed from satellites, while lower plumes are generally better observed by ground based
measures. This is partly due to the influence of meteorological clouds. Low sampling rates
combined with frequently obscured vision gives high uncertainties in observations. Since
plume height can change in the order of kilometres over several minutes, a high sampling
rate is important.
6.1
Ground based observations
Ground based measurements include weather radars, mounted (optical) cameras, in cloud
measurements of particles and trace gases and post-eruption mapping of the ash deposits.
Cameras and webcams are relatively cheap and easily deployed in field, they are often
placed for reasons other than research, (e.g. broadcasting images of the eruption to the
public) and are therefore often available. This makes them an important tool in keeping
track of plume height during eruptions. However, they are restricted to day-time use and
have limited performance during cloudy conditions.
Radars complement camera images, and can be used for plume tracking. They are
24
useful even in low light conditions (night-time) and have longer range, typically 100-200
km. Their maximum range is limited by topography and the curvature of the Earth,
and the increased probability of the signal being obstructed by clouds at longer range.
Permanent weather radars provide important data, when eruptions occur within detection
range, since they are constantly deployed for operational use. However, while mobile
units exist, they are not as cost efficient and are harder to deploy in field than cameras.
A compilation of ground based observations of the Eyjafjallajökull eruption in 2010 was
made available by Arason et al. (2011). While radar data was available more often than
camera images, the radar observations had difficulties determining the column height at
certain altitudes. During periods with lower plume height, the radar observations where
obstructed by topography.
Since the eruption was unusually long lasting, Petersen et al. (2012), was able to study
the diurnal variations of the plume. An increase in plume height in the afternoon coincides
well with the weakening of (nocturnal) inversion. This shows the important role which
atmospheric conditions plays on the plume rise, which is rarely included in relations describing eruption rates. Important to note is also that observed eruption columns can be
misleading, as an example, the Eyjafjallajökull eruption had an effusive period (18 April–
22 May) where ice melt and evaporation due to lava flow beneath the glacier resulted in
a steam plume occasionally reaching 4 km in height. The plume was mostly below radar
coverage but was seen clearly by the two cameras deployed at that time, giving a false
impression of the state of the eruption.
6.2
Satellite observations
There is a range of satellite borne sensors which can be used to detect and quantify volcanic gases and particles in the atmosphere. The main sensing techniques detect SO2 or
ice crystals due to their unique spectral signatures making them distinguishable from the
background atmosphere. Many of the instruments used, were originally launched for other
purposes, such as monitoring stratospheric ozone or meteorological clouds. However, different combinations of the retrieval bands and other radiation models enable the detection of
additional species, such as SO2 or silicate particles. Thomas and Watson (2010) provided
a current state of the science for remote sensing volcanic clouds from space.
Due to physical limitations of instruments and orbit mechanics, no instrument can have
a high spacial, temporal, spectral and radiometric resolution simultaneously. Resolution is
rather a trade off between the four of them. Systems in use today have resolutions ranging
from metres to kilometres and hours to days.
Silicate ash and other mineral rich particles (1-15 µm) can be detected due to their
scattering properties on micro-waves.
Common for most space borne retrieval techniques is the impact of H2 O, either by its
radiative properties in gas phase or the obstructive effect of clouds. This makes remote
sensing difficult in the tropics and the lower troposphere where atmospheric water content
is high. Therefore, detection from space tends to be more reliable for high volcanic plumes
(above tropospheric clouds). Ground based observations are for the same reason more
25
reliable for low plumes below cloud level.
7
Early results
This section provides some early data, the aim is to give a general idea of what can be
produced.
7.1
WRF
The WRF model was tested with the different microphysics schemes described in Section 5.1.2. The model was run over Iceland with three nested domains as shown in Figure 6.
The runs all used the ERA-Interim data for April 2010. A comparison of cloud water and
ice content was made over 14–21 April, the results of low clouds are shown in Figure 10.
The low cloud water content for the WSM3 case indicates that the scheme is not able
to represent topographic clouds. The other schemes seem to cover these clouds, with some
occurrence in the WSM5 and WSM6 cases and a higher occurrence for the Thompson case.
The reason why WSM5 and WSM6 give such similar result is probably due to the lack of
deep convection. It is only in clouds with strong convection that graupel can form, and
since the inclusion of graupel in WSM6 is the only difference between the schemes, there is
no notable difference in the results. However, differences between the schemes could arise
during the summer months when insolation is sufficient to form convective clouds.
The difference of cloud water in low clouds between the advanced schemes (WSM5,
WSM6, Thompson) and the simpler WSM3 scheme is shown in Figure 11. In all three cases
there is an increase in cloud water content compared to WSM3. High clouds (500 hPa) are
not as dependent on the physics schemes, except for the proportions between cloud water
and ice. However, the Thompson scheme gave a generally lower cloud water/ice content for
high clouds, which could be due to the enhanced formation of lower clouds and orographic
precipitation resulting in a lack of water at higher altitude.
Since computing power becomes important for larger studies, each run was timed and
the ratio between real and simulated time was calculated. The result is shown in Table 3.
The Thompson scheme used up the most processing time, which shows the penalty of using
a double moment scheme. Surprisingly, WSM3 and WSM6 used about the same cpu-time
to complete the runs. Even more remarkable is that the WSM5 scheme required even less
computing time to finish. This could be due to the adaptive time stepping used in the runs.
If WSM5 and WSM6 are able to represent the atmosphere in a way that is more stable
numerically, the time step will increase, requiring fewer steps to complete a model run.
I.e. while each time step takes longer to finish when using an advanced physics scheme,
this can be made up for by taking fewer steps. The benefit is less for WSM6, probably
since the inclusion of the graupel class in this scheme does not seem to contribute much
to the results. The time steps should therefore be the same as for WSM5 and the extra
computations required only serve to increase computation time.
26
(a)
(b)
(c)
(d)
Figure 10: Cloud water content from different microphysics schemes in WRF, both liquid
water and ice is included. The figures show the two inner domains of the run. The different
schemes are WSM3 (a), WSM5 (b), WSM6 (c) and Thompson (d). The values are averaged
over 14-21 April 2010.
Table 3: Computing time required to complete one modelled hour for each tested microphysics scheme.
cpu-time (min)
WSM3
2.83
WSM5
2.55
27
WSM6
2.80
Thompson
3.21
(a)
Figure 11: Difference in averaged (1421 April 2010) cloud water content (both
liquid and frozen) between microphysics
schemes in WRF. The figures show the
difference, relative WSM3, of WSM5 (a),
WSM6 (b) and Thompson (c).
(b)
(c)
28
Table 4: Ash particle size distribution used in PILT
Particle diameter (µm)
0.20
0.65
2.00
6.50
20.0
65.0
7.2
σ (%)
25
42
25
42
25
42
Fraction of total mass (%)
0.1
0.5
5.0
20.0
70.0
4.4
Dispersion
A simulation of the 2010 eruption of Eyjafjallajökull was made. Dispersion of ash was
simulated within the innermost domain shown in Figure 6. Meteorological data from the
WRF model was used to run PILT, the model settings were the same as in the previous
section, using the WSM3 microphysics scheme. Since the wet deposition scheme in PILT
only supports 2D-cloud cover in the current version, using a more detailed representation is
not expected to improve the results. Especially when considering that PILT does not take
cloud base or cloud top into account. Instead precipitation rate at ground level is used to
calculate wet scavenging throughout the entire plume. This is inherited from FLEXPART,
which does not have detailed cloud data as input. Since most of the plume is expected to
stay above the low clouds seen in Figure 10, this method is not realistic.
Ash size distribution was based on averaged plume measurements of the explosive
eruptions of Mount Redoubt (8th January 1990), Mount St Helens (18th May 1980) and
St Augustine (8th February) (Hobbs et al., 1991; Leadbetter and Hort, 2011). The size
distribution used is shown in Table 4. The emissions were represented by a line source
approximately following the observed plume heights, presented by Arason et al. (2011)
and shown in Figure 12. The emission rate was determined by Eq. (4), using the same
column height as for the spatial extent of the source. The simulation starts at the beginning
of the eruption (14 April) and ends at the end of the first explosive phase (end of 17 April).
Figure 13 shows the accumulated deposition of ash over the simulation period. The
dry deposition shows a branch stretching out westward against the average flow. This
consists mainly of coarser particles, which settle faster and are affected by the flow along
the ground. The eastward flow is due to a katabatic (down-hill) flow forming in the evening
and breaking up at sunrise. This is due to cold air forming over the glacier due to radiative
cooling. Heavy cold air flows down into the valley north of the volcano, larger particles
settling into this flow east of the volcano are transported downhill by south-easterly winds.
Most of this ash is deposited before reaching the coast.
Whether or not this flow is realistic can be discussed. One might argue that the eruption
should disrupt the flow by preventing cold air from forming in its vicinity. The heat release
should form a convergence zone around the eruption unless the gas release prevents this
by breaking continuity.
29
10
Radar scans
Assumed height
Radar det. limit
Summit level
z (km a.s.l)
8
6
4
2
0
15
16
Day of April
17
Figure 12: Plume height of the 2010 eruption of Eyjafjallajökull, during 14-18 April. Dots
show individual radar scans from the weather radar at Keflavik airport. The solid line
shows the plume height used in PILT, the dashed line shows the radar’s lowest detection
limit and the dash-dotted line shows the summit height of the volcano.
These are some of the question that arise when trying to resolve the flow on smaller
scales. As model resolution increases the influence of the eruption on the flow grows
larger. Until some point where it can no longer be ignored. This could either be included
by allowing the dispersion model to modify the flow or by coupling the models together. An
option in WRF would be to create a fake station at the site of the volcano and nudge the
model toward warmer and wetter values at the site. However, to get the correct nudging,
testing and comparing with plume models is required.
8
Research Plan
The preliminary plan for the continued research includes four studies.
• Climatology of ash over Scandinavia.
• High resolution local/regional climatology
• Fluoride mapping – mid range study of toxicity
• Exposure
30
2
Wet Deposition (g/m ) h1
Dry Deposition (g/m2) h1
2000
48’
48’
1800
1600
36’
36’
1400
1200
24’
24’
1000
800
12’
12’
600
400
63oN
o
63 N
200
30’
o
20 W
30’
o
19 W
30’
30’
o
20 W
30’
o
19 W
30’
Figure 13: Accumulated Wet (left) and Dry (right) deposition over 14-17 April. The
coloured field show ash load in g/m2 , thin contours show ground elevation in 100 m increments as used in WRF. Bold contours show the coast lines.
8.1
Climatology of ashfall over Scandinavia
This study will focus on one or multiple eruption scenarios, the same eruption will be
simulated based on historic weather. E.g. an eruption could be modelled several times per
day over several years. This would give us some insight in the probability of ash reaching
European (or more specifically Scandinavian) airspace for typical eruptions and also give us
realistic worst case scenarios. Similar to the study by Leadbetter and Hort (2011), Section
5.3.1, our study would focus more on Scandinavia and could consider different eruption
types (source strength) or different volcanoes (locations). In order to set up realistic
eruption parameters, aid from the volcanology researchers in CNDS is encouraged. The
interest of long range dispersion is mainly its impact on Aviation, health hazards should
be less of a concern as concentrations are lower further from the source.
This study requires accurate description of deposition processes as the amount of ash
reaching overseas will be highly dependent on the losses during transport. A new wet deposition scheme will have to be developed for FLEXPART-WRF, or studies will be conducted
with FLEXPART. Using FLEXPART-WRF will allow more flexibility with the model resolution as the WRF model lets us downscale the meteorological data to a suitable resolution.
FLEXPART on the other hand already has updated wet deposition and also includes chemical removal processes (to some degree). Using this model however, will restrict us to using
ECMWF or NCEP global reanalysis data which comes on a fixed latitude–longitude grid of
0.75 and 0.5 degrees resolution respectively. Using a latitude/longitude grid is not optimal
for mid-high latitudes due to the difference in resolution depending on direction.
31
8.2
High resolution local/regional climatology
This is similar to the above but will only include the early dispersion, however, the smaller
domains allow us to run at a higher resolution, giving more detailed data. Such a study
is interesting for short- to mid-range hazards, e.g. health concerns due to bad air quality, infrastructure and river obstruction, ground and water toxicity as well as damage to
buildings from tephra fallout.
Meteorological data will be downscaled to a horizontal resolution of several kilometres using the WRF model. The coverage of the model runs will be in the order of 100
kilometres. Accurate deposition is important as with the long range study. The study
could either focus on explosive eruptions or persistent degassing. In the case of explosive
eruptions, dry deposition will be dominated by gravitational settling. Wet deposition will
be of interest but also precipitation in itself, since rain will affect the deposited mass by
increasing density of ash layers and washing out soluble materials.
Persistent degassing will require focusing more on gas emissions than particulates. Concentrations of toxic gases will be of interest, especially peak values. Accurate wet deposition
is necessary since it might affect water quality. The development of a chemical module
should be considered since it will likely have a notable impact on the results.
A possible case study would be the Nyiragongo volcano in eastern Congo which is one of
the strongest sources of volcanic gases in the world. Chalmers University of Technology is
involved in the observation of emissions from the volcano and are deploying the same model
system as will be used in this project. This provides and opportunity for collaborative
development, application and validation of the model system.
8.3
Fluoride mapping – mid range study of toxicity
Based on the concept that gases adhere to the surface of particles, it is possible to estimate
where the highest deposits of some toxic species are located. This requires some understanding of at what rate gases adhere and/or nucleate. The adhesion process could either
be applied to the model output as a function of particle age, surface area and concentration of gases in the initial emissions or it could be parameterized in the model based on
concentrations of particulates and gases as well as the presence of water. Both wet and dry
conditions need to be considered since many of these gases are soluble and will be affected
by the presence of liquid water.
8.4
Exposure
Exposure studies as discussed in Section 5.3.2 can be made using the same model setup
as presented in the previous Section. It can be included as part of a climatology study,
focusing on stationary targets or historic case studies. An operational exposure model will
be of interest to the aviation industry and could be integrated into flight routing software.
It could also be used on a local scale, relating to health hazards in a specific region.
32
9
Summary
Different processes important for the determining the hazards of volcanic eruption have
been discussed, covering the dynamics behind the eruption source, atmospheric transport
and deposition processes. Several methods for estimating source strength have been presented and difficulties in determining the size distribution of emitted particles has been
discussed briefly. Both eruption strength and size distribution are important factors of uncertainty. Different data sets and methods for preprocessing meteorological data have been
discussed, with focus on the ERA-Interim reanalysis data and the WRF model. Different
dispersion models have been presented briefly with focus on special properties specific to
each model, some of these properties might be implemented in the PILT model as part of
this project.
Example application areas of dispersion modelling was presented, two of which were
ash climatology and exposure studies; these can be used for disaster preparedness and
mitigation. Observation techniques for tracking volcanic plumes were discussed, which
provide data useful both during eruption events for real time tracking and for testing and
calibrating models during model development. Some early results were presented to give
an overview of the current state of the modelling system. Finally, a number of planned
studies were listed.
The continued work with this project will begin with the first of the suggested studies.
The first step of which will be to improve some aspects of PILT, desired improvements
include a new wet deposition scheme and stable support for nested input. This will be of
use in the first two studies suggested (ash and gas climatology on different scales). The two
subsequent studies (fluoride mapping and exposure) can either build on the results from
the first two studies or be implemented in the model as well and simulated in separate
studies.
Further improvements of the model system could include a built-in description of the
eruption column or a coupled eruption column model. This would allow easier and more
accurate setup of the eruption source, making the model more suitable for operational use.
Being able to follow the variations in eruption column height at a higher frequency has an
important impact on the results of the model as was shown by H. F. Dacre et al. (2011).
The knowledge gained from the studies can be used for planning against future events
and the tools developed could be further set to use in operational systems, in addition to
further research.
33
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