Introduction
Comparison between the fields of volcanology (tephrochronology), dispersion
modelling and satellite remote sensing reveals striking differences in
published distal volcanic ash grain-size data. Differences in their approaches
and frame of reference are highlighted by the terminology of each. In
volcanology, “coarse” ash refers to particles 1–2 mm in diameter and
those < 64 µm are classified as “extremely fine”
; in atmospheric science airborne particles coarser
than 2 µm diameter are defined as “coarse” aerosol
. Furthermore, volcanologists describe
particle sizes via grain lengths, whereas atmospheric scientists use the
particle radius. Scientists who extract volcanic ash grains from soils or
lakes hundreds of kilometres from their source typically report grain lengths
of 20–125 µm (Sect. ). These tephra horizons are
known as cryptotephra (hidden ashes) because they are found in deposits that
are too thin and too low in concentration to be visible to the naked eye. In
contrast, measurements of airborne volcanic ash clouds by satellite remote
sensing and direct sampling by aircraft find particle size distributions
(PSDs) with median radii of 1–4 µm in which cryptotephra-sized grains
form negligible proportions (Sect. ). Assuming
that the cryptotephra were transported to distal regions in volcanic ash
clouds, their absence from measured ash cloud PSDs, particularly those close
to the volcano (Sect. ), is intriguing. This is
the focus of this study, which integrates new results from all three
disciplines to investigate the size distributions of distal cryptotephra
deposits, volcanic ash transport models and the influence of larger particles
on satellite infrared remote sensing results.
Our results highlight the importance of considering cryptotephra-sized grains
in remote-sensing and atmospheric dispersion modelling and the need for
empirical, quantitative measurements of the optical and aerodynamic
properties of volcanic ash. They are presented in here in three sections:
Sect. covers cryptotephra size distributions, Sect.
covers transport models and Sect.
pertains to simulated satellite imagery. By presenting results from the three
fields in a single paper we aim to improve understanding and
communication between these diverse disciplines. In each section, particle
sizes are described using the dimension appropriate to that field. These are
length, diameter and radius, respectively. The findings are discussed in
Sect. .
Cryptotephra generation, transport and deposition
There is abundant evidence for distal (> 500 km in the context of
this study) volcanic ash transport provided by grains preserved in soil, peat
and lake deposits, or in snow and glacial ice, which are identified by
scientists researching these deposits
e.g..
Such distal deposits are too thin to form a visible layer, but ash grains can
be extracted in the laboratory . These “cryptotephra”
grains (also called “microtephra” or glass “shards”) are recognised by their
glassy colour (with or without the presence of crystals), their highly irregular shapes
and their often bubbly (vesicular) texture
.
Geochemical analyses by electron probe microanalysis (EPMA) or secondary ion
mass spectrometry (SIMS) can link cryptotephra to their source volcano and
possibly an eruption of known age, making tephrochronology a powerful dating
tool e.g..
The size of cryptotephra grains is described by their long axis
length, defined as the longest distance between two parallel tangents across
the grain. Cryptotephra grain sizes typically range from 20 to
> 125 µm. These grains will have been the largest within the
depositing cloud but, in reaching distal regions, they must have formed a
significant proportion of the cloud closer to the volcano. Unfortunately,
grain sizes are not routinely reported, and when they are the data are often
just exemplar, modal or maximum lengths.
The initial PSD of volcanic ejecta leaving the vent of a volcano,
collectively known as tephra, depends on the characteristics of the eruption
that produced it. Particles can range in size over 7 orders of magnitude
from microns to metres in diameter. The PSD of all ejected particles is known
as the Total Deposit Grainsize Distribution TGSD;
. The TGSD varies
significantly between eruptions and is strongly controlled by internal
factors, such as the size distribution of bubbles in the magma or the gas
content, and external factors such as particle collisions, ascent rate and
interaction with water . Magma compositions typically
range from basalt (high in Mg and Fe, dark colour, ρglass of
2.8–3.0 g cm-1) to rhyolite (high in Si and Al, light colour,
ρglass of 2.4–2.6 g cm-1). Eruptions of rhyolite composition
magma tend to produce volcanic ash grains that contain more, and smaller, bubbles
than basaltic eruptions, so rhyolite ash is normally more abundant as well as
less dense and slower settling than basalt ash. Interaction between magma and
meltwater causes increased fragmentation, however, so subglacial basaltic
eruptions can still produce extremely fine ash e.g. 20 wt % of the
Grímsvötn 2004 tephra was < 64 µm in length;
. Cryptotephra-sized grains make up a larger
proportion of the ejected mass than the particles that are most easily
identified in satellite infrared remote sensing data (less than 12 µm
diameter). Even in rhyolite eruptions, only around 1/3 of ejected material is
finer than 12 µm diameter .
The PSD evolves during transport as particles are deposited from the plume
based on their terminal velocity. For bubbly and irregularly shaped volcanic
ash particles this is typically 0.15–0.35 m s-1 100 µm
grains, which is much less than a sphere of the
same diameter. A 100 µm grain may fall at the same rate as a sphere
9–50 µm in diameter . The coarsest particles fall
out quickly and PSDs of deposits show that particles > 500 µm
in length are mostly deposited within tens of kilometres of the volcano
. In addition, a number of processes promote
early deposition of cryptotephra-sized grains and, at distances up to 500 km,
deposits contain a significant proportion of ash particles
(< 100 µm) that were deposited much earlier than would be
predicted by single-particle settling velocities. Within the first tens of
kilometres downwind, vertical gravity currents (similar to “microbursts”) can
transport particles to the ground faster than their individual terminal
velocities as “streak fallout” . Aggregation
and meteorological processes such as coating of ash particles by ice or water
and subsidence of the entire volcanic plume may also be important in the
distal evolution of the PSD .
Satellite retrievals of ash cloud mass indicate that after ∼ 24 h,
just a small proportion (< 5 %) of the erupted mass remains in the
cloud to be transported to distal locations
.
Comparisons of Numerical Atmospheric-dispersion Modelling Environment (NAME)
dispersion model predictions with measurements from aircraft during the 2010
Eyjafjallajökull eruption found similar proportions 2–6 %;
.
Satellite infrared detection of volcanic ash
The wide spatial coverage of satellite remote sensing, combined with
near-real time data for some methods, makes it a valuable tool for monitoring
volcanic ash clouds. Different techniques use different parts of the
electromagnetic spectrum. Visible and ultraviolet sensors detect scattered
or reflected sunlight. Consequently, they can only be used during daytime.
Ash clouds can be seen in satellite photographs (visible light), provided
that they are not obscured by meteorological clouds, and ultraviolet
spectrometers can be used to map sulfur dioxide, which is often transported
alongside the volcanic ash . Microwave
(mm-wavelength) radiation emitted by the Earth can be used to study volcanic
ash clouds, during both night and day. adapted
methods for calculating rainfall rates using satellite-based Special Sensor
Microwave Imager (SSM/I) data to estimate the mass of ash fallout from
volcanic clouds. As this method is sensitive to particles 0.1–1 mm in
diameter that fall out quickly, it is limited to clouds up to a few hours old
and close to the volcano. Microwave radiation is also used by ground-based
weather radar systems that can retrieve the mass and size distribution of
particles within a young volcanic plume within approximately 200 km of the
equipment . This
is an active technique, using man-made radiation. Similarly, light detection
and ranging (lidar) systems use lasers to measure the height and optical
depth of ash clouds at a single location above a measuring station
. Depolarisation
measurements can help distinguish irregularly shaped volcanic ash particles
from other aerosol. The Cloud Aerosol LiDAR and Infrared Satellite
Observations (CALIPSO) system makes space-based lidar observations of ash
cloud altitude along a narrow track beneath its orbit e.g.
, but cannot be used to map the lateral
extent of clouds.
Here, we focus on satellite infrared measurements of volcanic ash. These are
passive systems that use infrared energy radiated upwards from the Earth's
surface, so they can be used in day or night. Geostationary satellites, e.g.
Meteosat, provide wide coverage and data are updated in near-real time (every
15 min for the Spinning Enhanced Visible and Infrared Imager instrument,
SEVIRI), making them ideal for mapping ash clouds. Satellite infrared remote
sensing distinguishes volcanic ash clouds from meteorological clouds using
the different optical properties of ash and water or ice droplets
. Infrared light is absorbed
and scattered by ash, water and ice particles (as well as other aerosols) as
it passes through the cloud and this affects the signal measured by a
satellite instrument for a given pixel. The brightness temperature difference
(BTD) of a pixel between two infrared channels centred at 10.8 and
12.0 µm is often used to identify ash clouds (this is sometimes also
referred to as the reverse-absorption or split-window method). Volcanic ash
is more absorbing at 10.8 µm than at 12.0 µm and gives a negative
BTD, whilst water vapour, water droplets and ice particles have the opposite characteristics. The BTD
method has been used to identify volcanic ash for over two decades. It has
some limitations. Clouds and water vapour in the atmosphere and the ash
cloud, and temperature inversions above ice-covered land surfaces can also
reduce the strength of the BTD effect
. Ash clouds with high
concentrations are optically opaque, so have a BTD of zero
. In a volcano monitoring setting, these clouds
may still be recognised by skilled human operators but automatic detection
using the BTD method is not possible. More sophisticated ash detection
algorithms use extra tests to reduce false positives or negatives, or to take
volcanic SO2 into account, by incorporating data from a third infrared
channel . Methods that
take advantage of the many channels of hyperspectral data have also been
developed .
Lognormal number (a), and mass (b),
grain-size distributions corresponding to different effective radii, assuming that particles are dense spheres.
The mass distribution is shifted towards coarser values compared to the
number distribution. The mass median diameter and mass 95th percentile
diameter are approximately 2.5 × and 8 ×reff. For reff
> 8 µm, more than half of the mass of the distribution is
contained in cryptotephra-sized particles (> 20 µm diameter),
but only distributions with larger reff contain significant proportions
of the coarsest cryptotephra-sized particles (i.e. > 100 µm).
If the geometric standard deviation is less than 2.0, the size of the
coarsest particles is much reduced.
Once a pixel has been identified as ash contaminated, a retrieval can be made
of ash cloud properties such as height, ash column loading and particle
effective radius reff, which is a function of the PSD – see the
Appendix;.
Retrieval algorithms attempt to find the combination of parameters that best
produce the observed brightness temperatures in a satellite image. By
estimating the thickness of the ash cloud (e.g. 1 km), the ash column loading
(in kg m-2) can be converted into a concentration (in mg km-3).
These data have become more important since safety rules based on zones of
different ash concentration were introduced during the 2010
Eyjafjallajökull eruption for aircraft flying in European airspace.
Retrievals are based on the scattering of infrared light according to Mie
theory. The strength of absorption and scattering by particles is a function
of the wavelength, particle size, particle shape and the complex refractive
indices of the volcanic glass from which it is formed
.
Mie scattering occurs when particles are of a similar size to the wavelength
of the radiation, so the PSD is an important variable. Forward modelling
based on Mie theory allows the absorption and scattering coefficients for a
given reff, refractive index (ash composition) and wavelength to be
predicted, usually based on the assumption that particles are dense spheres.
Assuming a thin, homogeneous, semi-transparent, surface-parallel cloud, a
radiative transfer model uses these coefficients to calculate the BTD for
different PSDs (expressed in terms of their effective radius) and ash mass
loading (a function of optical depth, τc) for a range of cloud
heights and meteorological conditions . Some
retrieval methods assume a fixed ash cloud altitude
.
Each retrieved reff represents a PSD
containing a narrow range of particle sizes (see Fig.
for examples of different distributions and the
Appendix for equations). It has been acknowledged since the BTD method was
developed that it requires ash clouds dominated by particles
< 10 µm diameter, which corresponds to PSDs with an effective
radius less than ∼ 17 µm .
Coarser particles should have no differential absorption
effect and so should exhibit similar brightness temperatures at 10.8 and
12.0 µm. The implication of this, assuming that the BTD results from Mie
scattering by dense spheres, is that it should not be possible to detect ash
clouds close to their source, even if they are sufficiently dilute to be
semi-transparent. At the limits of detection, a lognormal distribution with a
geometric standard deviation, σ, of 2.0 and an effective radius of
17 µm has 95 % of particles < 32 µm diameter, with 95 % of the
mass within particles < 135 µm. Such a distribution would contain
cryptotephra-sized particles. Published values of retrieved reff,
however, are never this high and range from 0.5 to 9 µm
. These distributions have 95 % of particles
less than 0.9–17 µm in diameter, respectively, with 95 % of the mass
within particles less than 4–72 µm. These retrieved PSDs suggest that
the proportion of cryptotephra-sized particles within ash clouds is small to
negligible.
Comparing remote sensing PSDs with proximal deposits
In a few cases, satellite retrievals have been made of proximal
(< 200 km in the context of this study) volcanic ash clouds where
samples have also been taken from the ground. The PSDs of the deposits
contain significant cryptotephra-sized (and coarser) grains, yet the
retrieved PSDs suggest that these formed a negligible proportion of the
depositing ash cloud. For example, the deposits of the 1996 eruption of
Ruapehu, New Zealand, are exceptionally well characterised
. The total grain-size distribution of material
deposited on land between 50 and 200 km from the volcano has a mode of
125 µm, with > ∼ 80 % of deposited mass made up of grains
coarser than 64 µm. This compares to effective radius estimates derived
from AVHRR-2 and ATSR-2 data of 2–4 µm in the same region
, which would imply that 95 % of the mass is
within grains with a diameter of less than 16–32 µm.
More recently, SEVIRI retrievals were compared with ground-based sampling
during the 2010 Eyjafjallajökull summit eruption
. Samples collected on the ground 56 km downwind
from the volcano on 6 May 2010 contained grains 1–500 µm in diameter,
with a mode of 64 µm and were deposited at a sedimentation rate of
0.4 × 10-4 kg m-2 s-1. The nearest available satellite
retrieval for the same day was at a location 130 km downwind of the crater.
The mean retrieved ash radius was 4 µm corresponding to a sedimentation
rate of 0.2–0.4 × 10-6 kg m-2 s-1, which is over
100 times less. It was suggested that the 2-orders-of-magnitude discrepancy
over 50 km range is a “consequence of ash aggregation and convective
instabilities”.
Taking the Eyjafjallajökull 2010 summit eruption as a whole,
used SEVIRI data to inform the inversion of
a Lagrangian particle dispersion model, and estimated that the total mass of ash
of 2.8–28 µm diameter emitted over the entire duration of the eruption
was 8.3 ± 4.2 Tg. They included a secondary mode of coarser particles in
the input size distribution (modal size = 180 µm) in order to match the
measured size distributions on the ground. Their estimated erupted mass is
nearly an order of magnitude lower than the 70 Tg of particles finer than
28 µm calculated by mapping the thickness, mass and grain-size
distribution of tephra on the ground .
Comparing proximal deposits with satellite retrievals shows a large
discrepancy in PSDs. Here we suggest that distal deposits are likely to have
the same issue and suggest that it may result, in part, from the lack of
sensitivity of the detection and retrieval methods to large particles and to
the assumption of spherical particles used in the calculation of the
extinction coefficients.
Grain-size data for distal ash deposition. Grain sizes are long-axis
measurements made by optical or scanning electron microscopy, except values marked with “a”,
which were obtained by laser particle size analysis. GRIP, NGRIP,
GISP and GISP2 are locations of ice cores in Greenland. The largest
eruptions, with Volcano Explosivity Index (VEI) scores of 7 or 8, are rare
recurrence intervals of > 1000 and > 10 000 years,
respectively;. b Indicates data used in
Fig. .
Eruption
Deposit location
Distance (km)
Grain size (µm)
Notes
Reference
Chaitén, Chile, 2008
E Argentina
550
20–40 (mode)
Little change in mean from 300 km
Hudson, Chile, 1991
E Argentina
550
∼32 (mode)
Maximum 125–250 µm
Mount St Helens, USA, 1980
NW USA
630
38 % 32–125
64–125 µm is 10–15 % at 260–630 km
Mount Berlin, Antarctica
Siple Dome
675
20–30
Some 50 µm, 24 layers in 120 ka
Many Iceland
SE Greenland Shelf
650–700
100–250
Max 400 µm, marine sediment core
Taupo Whakamaru
Chatham Island
864
75 (mode)a
34 % >63 µm; 1500 km3 erupted; VEI 8
Vedde Ash, Katla?
Skye, Scotland
975
24–150b
Basalt and rhyolite
Hekla 1510
Scotland
860–1030
<70b
Grímsvötn 2011
Scotland
1000
20–80b
Collected in rainwater
Eyjafjallajökull 2010
UK
1000–1500
25–100
Collected in rainwater
Jan Mayen
NGRIP
1200
40–75b
Bruneau-Jarbridge, Miocene?
Nebraska
1200
77a (mode)
Some grains >250 µm; 0.4–2 m thick beds
Saksunarvatn, Grímsvötn
NGRIP
1500
<150b
Brown, cuspate
LY1877, Papua New Guinea?
Lynch's Crater, Australia
>1500
40–60
thin-walled vesicular rhyolite
Many Iceland
NGRIP
>1500
>30b
Up to 80 µm, some form visible layer
Many Iceland
GRIP
>1500
>30b
Up to 80 µm
Vedde Ash, Katla?
NGRIP
1550
<70b
Platy, cuspate, bubble walls
Many Iceland
W Sweden
1600
20–60b
Dominated by rhyolite composition
Saksunarvatn, Grímsvötn
NW Germany
2000
<110b
Colourless and brown
Campanian Ignimbrite, Italy
Various
880–2300
Median 41–50a
95th percentile 147–212a; VEI 7
Vedde Ash, Katla?
SW Germany
2400
>10
Min diameter is electron beam size
Saksunarvatn, Grímsvötn
SW Poland
2400
20–60b
Platy, curvilinear
Vedde Ash, Katla?
NW Russia
2450
>24a
Layer is 2–3 mm thick in Norway
Vedde Ash, Katla?
Switzerland
2500
>10
Min diameter is electron beam size
Vedde Ash, Katla?
Italy
2650
>10
Min diameter is electron beam size
Toba YTT
Jwalapuram, India
2669
53a (mode)
10 % >63 µm; 2500 km3 erupted; VEI 8
Vedde Ash, Katla?
Slovenia
2800
30–100b
Platy, curvilinear
Katmai, Alaska, 1912
NGRIP
4426
–
Mazama 7675±150 b2k
GISP
5300
<20
VEI 7
Multiple
Dome C, Antarctica
>6000
∼10
Geochemistry unlike local sources
Cascades and Alaska
Newfoundland
5000–7000
30–40
Rhyolite. Fluted, vesicular, platy
El Chichón, Mexico, 1982
GISP2
6450
<10
Collected in snow
Samalas 1257
GISP2
13 500
<5
Also found at South Pole
Model constraints on cryptotephra transport
Method
We carried out simple transport modelling to determine the terminal velocity
and transport range of cryptotephra particles, which depend on the size,
density and shape of the particle, and on atmospheric conditions (including
the wind velocity) and the release height. The aim was to investigate the
size of ash grains capable of being deposited in Europe following a
moderately sized Icelandic eruption. We used two different schemes to
calculate particle terminal fall velocity. The simplest possible scheme uses
Stokes' settling law and assumes spherical particles with a density of 2300 kg m-3
(rhyolitic glass) falling in a constant atmosphere. A more
realistic analysis accounts for the non-spherical shape of the particles by
using a Reynolds number dependent drag coefficient
that varies with the sphericity (ΨR) of
the particle (see Appendix for details). ΨR=0.7
was chosen for the Ganser scheme based on values from
for a rhyolitic composition (Ash Hollow
member, Nebraska; ΨR= 0.6–0.8). The variation in density with grain size
was incorporated by using the relationship presented by
, where the density decreases linearly
from that of dense glass (2300 kg m-3 for Askja 1875) to that of pumice
(440 kg m-3) as size increases from 8 to 2000 µm.
Diameter and travel distance of Icelandic cryptotephra in Europe.
Both examples from the literature (Table ) and those
measured for this study (Table ) are plotted. Horizontal
coloured bars extend from the 10th to the 90th percentiles of the PSDs.
The more realistic analysis also uses a standard, stratified, atmosphere
where the atmospheric density and viscosity decrease upwards, causing the
terminal velocity of the ash particles to increase with height above sea
level. The atmospheric effect is minor compared to corrections for the
sphericity and density distribution of the ash particles, which act to
decrease settling velocity.
The two schemes were compared to measured
terminal fall velocities (at sea level) of ash particles given by
, who report data for basaltic, andesitic and
rhyolite compositions as a function of dimensions such as long-axis length
and equivalent area diameter (Fig. ). These
dimensions correspond to the microscope measurements made by
tephrochronologists and to optical particle size measuring equipment,
respectively (Sect. ).
Modelled travel distance of ash particles as a function of particle diameter.
The grey horizontal lines represent the distances from Eyjafjallajökull
to various European locations. Measured particle sizes from the
Eyjafjallajökull 2010 eruption are plotted for comparison. Horizontal
coloured bars extend from the 10th to the 90th percentiles of the PSDs. The
shaded region indicates the 95th percentile size range implied by an
reff of 4 µm and σ of 2.0.
Scaled extinction coefficient ratio for SEVIRI channels at
10.8 and 12.0 µm for spherical andesite volcanic ash particles as a function of ash particle size.
The dotted line shows the grain size at which the reverse absorption technique
becomes insensitive to andesite volcanic ash. It is not possible to use BTD
effects to identify or automatically detect uniformly sized spherical
andesite particles with radius > 6 µm. With a geometric
standard deviation of 2.0, the BTD effect extends to mass median radius of
21 µm (black line). This corresponds to an effective radius of
16.5 µm, which is comparable to the findings of . The
sensitivity decreases rapidly with increasing mass median radius,
particularly below the single-particle detection limit of 6 µm.
Calculated minimum mean wind speeds transporting ash from
Icelandic eruptions across Europe, based on observations. It is assumed that the start
time was the onset of eruption and that the plume travelled by the most direct route to the location, as the crow flies.
Where PM10 is given, time is from eruption onset to observed peak PM10 air pollution measurement.
Eruption
Time + Date
Location of
Time + Date
Travel
Travel
Mean wind
Ref.
of eruption
observation
of observation
dist. (km)
time (h)
speed (m/s)
Hekla 1947
06:40 UTC 29/03/1974
Helsinki (Finland)
11:00 UTC 31/03/1947
2292
51
12
Eyjafjallajökull 2010
09:00 UTC 14/04/2010
Mulhouse (France) PM10
03:00 UTC 18/04/2010
2396
90
7
Eyjafjallajökull 2010
09:00 UTC 14/04/2010
Budapest (Hungary) PM10
00:15 UTC 18/04/2010
2950
87
9
Grímsvötn 2011
19:00 UTC 21/05/2011
Aberdeen (UK) PM10
01:00 UTC 24/05/2011
1856
55
9
Grímsvötn 2011
19:00 UTC 21/05/2011
Birkenes (Norway) PM10
12:00 UTC 24/05/2011
1507
65
6
Grímsvötn 2011
19:00 UTC 21/05/2011
Gothenburg (Sweden) PM10
12:00 UTC 24/05/2011
1725
65
7
Grímsvötn 2011
19:00 UTC 21/05/2011
Stockholm (Sweden) PM10
00:00 UTC 25/05/2011
1935
77
7
Askja 1875
05:30 UTC 29/03/1875
Bergen (Norway)
22:00 UTC 29/03/1875
1118
16.5
19
Askja 1875
05:30 UTC 29/03/1875
Trysil (Norway)
04:30 UTC 30/03/1875
1488
23
18
Askja 1875
05:30 UTC 29/03/1875
Stockholm (Sweden)
12:00 UTC 30/03/1875
1896
30.5
17
A mean wind speed of 10 m s-1 was chosen based on NCEP re-analysis data
of wind speeds over Iceland during the eruption of Eyjafjallajökull in
spring 2010 and timings of contemporary reports
of volcanic ash pollution in Europe following Icelandic eruptions (Askja
1875, Hekla 1947, Eyjafjallajökull 2010, Grímsvötn 2011; see Table ).
We used a release height of 10 km, the maximum plume
height of the 2010 Eyjafjallajökull eruption, which is reasonable for a
moderately sized Icelandic eruption . Atmospheric
turbulence, rising or subsiding air masses and particle aggregation are
neglected in these simple treatments.
Summary of transport model results. Ash Hollow results are based on
particles of rhyolite composition.
Scheme
Maximum
Maximum
diameter
diameter reaching
airborne for
London
24 h (µm)
(µm)
Stokes
41
29
Ganser
50
33
Ash Hollow (equiv. diam)
80
60
Ash Hollow (length)
115–135
85–105
Results
Given a horizontal wind speed of 10 m s-1, particles can be transported
850 km in 24 h. This is consistent with results of detailed
climatological analysis that found that ash from a small Hekla eruption has a
15 % probability of reaching Scotland, Northern Ireland, Norway or Sweden
within 24 h, but that transport as far as the Mediterranean was also
possible in that time . The formation of
cryptotephra deposits also depends on how long the particles remain airborne.
This was calculated using each of the particle terminal velocity schemes,
along with the distance travelled in that time. The results are shown in
Fig. and summarised in Table .
All schemes predict that cryptotephra-sized particles released by a
moderately sized Icelandic eruption can remain airborne for at least 24 h
and can travel as far as the distance to London under reasonable wind
conditions. The Stokes and Ganser schemes give similar results, with the
Ganser scheme predicting that particles can travel slightly further. Using
the Riley et al. terminal velocity data for Ash Hollow rhyolite particles
results in a significant increase in the predicted travel distance of ash
particles compared to the Stokes and Ganser schemes. It corresponds to a
3 times increase over dense spheres for 50 µm equivalent area diameter
particles (Fig. ). Ash Hollow data are
presented both in terms of particle length and particle equivalent area
diameter. For rhyolite, the particle length is 1.44–1.71 times the
equivalent area diameter of the same particle .
The measured terminal velocity of rhyolite particles was lower than basaltic particles,
which fell at the same rate as rhyolite particles 1.18–1.68 times
their equivalent area diameter. The uncertainties on measured Ash Hollow particle lengths for given terminal
velocities are not known but are likely to be significant.
Schematic of the method used to compare input ash mass
concentration and retrieved ash mass loading. The white boxes contain data and the grey boxes represent code.
These results show that in the absence of processes such as rainfall or
aggregation, we should expect even moderately sized Icelandic eruptions to
deposit cryptotephra in mainland Europe. The calculated transport distances
of particles are compatible with our cryptotephra grain-size distributions and
with measurements of maximum grain size by tephrochronologists
(Fig. ). Median cryptotephra transport
distances from our results are generally well represented by the calculated
distances using the Stokes or Ganser schemes, but calculations based on
measured Ash Hollow fall velocities are closer to maximum grain-size
measurements and the coarsest literature values.
Satellite infrared retrievals of cryptotephra-rich plumes
Method
We investigated how satellite infrared retrievals of ash characteristics
change as the particle size increases. We used a modelling approach based on
simulated satellite imagery representing data from the Spinning Enhanced
Visible and Infrared Imager (SEVIRI) instrument on the geostationary Meteosat
satellite .
Consequently, the input parameters were known and could be controlled. As the
assumptions used in generating the simulated images are the same as those
used in the retrievals, this represents a validation of the retrieval
algorithm itself and not the physics of the BTD technique. Mie theory was
used to model the absorption and scattering coefficients, which were combined
to form a scaled extinction coefficient for volcanic ash with different
refractive indices and size distributions at different wavelengths of
infrared. This quantifies the sensitivity of the BTD effect to particle
composition and size. It is an approximation for the effects of multiple
scattering and therefore a better indication of the extinction properties
than the single-scattering extinction coefficient. The refractive indices for
andesite were used, in common with other studies
e.g..
quantified the effect of using the
refractive indices of andesite, volcanic dust, obsidian and desert dust to
simulate images of volcanic ash clouds. They found that data simulated using
andesite and desert dust refractive indices gave the best agreement with
measured satellite data for the 2010 Eyjafjallajökull eruption and the
effect of varying refractive index on the simulated BTD was much smaller than
that of changing the concentration or particle size distribution. For single
particles, the geometric standard deviation (σ) was set to 1.0001 to
effectively create an infinitely narrow distribution where all the particles
are a single size, and the mass median radius (rm) of the size
distribution was varied from 0.1 to 25 µm. To simulate an ash
cloud with a range of sizes, the σ was set to 2.0, similar to
.
Radiative transfer calculations were performed using RTTOV-11, which is a
very fast radiative transfer model for nadir viewing passive infrared and
microwave satellite radiometers, spectrometers and interferometers
seefor details of the RTTOV-11 aerosol scattering and absorption
scheme and validation data.
The inputs to RTTOV-11 were Numerical Atmospheric-dispersion Modelling
Environment NAME; simulations of a volcanic ash
cloud and Numerical Prediction Weather (NWP) meteorological data from the Met
Office's Global version of the Unified Model .
RTTOV-11 was run without water and ice clouds in the simulations such that
the ash cloud was simulated in a clear sky (surface and atmospheric water
vapour and temperature variations were still present).
Simulations were performed using meteorological data and ash clouds modelled
by NAME from the Eyjafjallajökull eruption for 12:00 UTC on the following
dates: 14 and 15 April and 6–9, 11, 13–17 May. In each case, the location, altitude and concentration of
volcanic ash predicted by NAME were used. The concentration data were
converted to number density assuming the same lognormal PSD in all pixels and
interpolated onto the NWP grid for modelling. The interpolation is necessary
because the atmospheric dispersion model, NAME, is run at a finer resolution
than the NWP model. In a real ash cloud the size distribution would vary
downwind from the volcano as grains are deposited
; this is a topic for future studies of
simulated imagery. As the aim of this study was to compare a range of PSD and
weather conditions, comparisons were made on a pixel-by-pixel basis, and using
a homogeneous cloud grain size does not affect our conclusion. The geometric
standard deviation of the PSD (σ) was fixed at 2.0, following
and in line with airborne measurements of
the Eyjafjallajökull ash cloud
σ= 1.8–2.5; and the
mass median radius of the PSD was varied from 0.5–32 µm. The outputs are
simulated brightness temperatures (BTs) for SEVIRI infrared channels. High
concentrations of particles cause ash clouds to become opaque
. In the simulations presented here, the
concentration of ash was sufficiently low for the clouds to be optically
semi-transparent, even when dominated by larger particles.
Retrievals were made on the simulated images using the method of
. The primary test for volcanic ash uses the
brightness temperature difference method on the 10.8 and 12.0 µm
channels; additional pixels may be detected by tests using data from the
8.7 µm channel and simulated water-vapour-corrected, clear-sky radiances, or
removed by a test using the effective cloud emissivities and a spatial
filtering test. Once ash-contaminated pixels have been identified, a
retrieval of the physical properties is carried out using data from channels
centred at 10.8, 12.0 and 13.4 µm to obtain estimates for
the ash layer pressure (pash; a proxy for the altitude of the cloud),
the ash column mass loading (L), and the ash size distribution effective
radius (reff). The geometric standard deviation, σ, of the
retrieved ash cloud was fixed at 2.0. These values can then be compared to
the original input values (see Fig. for methodology
flowchart). The retrievals are carried out using a one-dimensional
variational (1D-Var) framework, which attempts to reach a statistically
optimal estimate of the three physical properties of ash (pash, L,
reff) consistent with the satellite data (real or simulated) and any
prior background knowledge by minimising a cost function
. The a priori effective radius used by
the Met Office in an operational setting is 3.5 µm. The total cost of the
solution describes how closely the result matches the measured radiances and
(weak) a priori constraints. The lower the total cost, the better the
fit of the modelled solution to the observations.
Results
Initial modelling using Mie theory shows that, for SEVIRI, a negative BTD can
only occur for individual (or monodisperse) spherical andesite particles with
radius less than ∼ 6 µm and that the effect is strongest for
particles with radius < 3 µm (Fig. ). Only
these particles contribute to the BTD effect, and we refer to them here as
“BTD-active”. However, volcanic ash clouds contain particles with a range of
sizes. Calculations using a lognormal PSD with geometric standard deviation
(σ) of 2.0, show that a (weak) negative BTD is produced for
distributions with mass median radius up to 21.5 µm. This corresponds to
reff= 16.5 µm, which is in good agreement with
. The sensitivity is low for mass median radii
> 6 µm.
Ash mass loading and effective radius data for 12:00 UTC on 14 May 2010.
(a) NAME ash column mass loading overlaid on the SEVIRI 10.8 µm BT image
for the corresponding time. (b) and (c) Retrieved ash column mass loading
data from simulated SEVIRI infrared data using a lognormal PSD with geometric
standard deviation of 2.0 and a mass median radius of 4 and 12 µm
respectively. The light grey line in (b) and (c) shows the extent of the NAME
ash coverage (where mass loading > 0.2 g m-2); this is overlaid on a
simulated 10.8 µm infrared image (simulated without clouds). Slightly
cooler temperatures indicate the presence of volcanic ash within the zone of
NAME ash coverage, which may be identified by a skilled forecaster. (d) and
(e) Histograms of retrieved effective radii from the same simulated
SEVIRI data as (b) and (c) respectively. The blue curves in (d) and (e) show
the input mass PSD, while the dotted line shows the corresponding theoretical
effective radius.
A comparison between the input and the retrieved ash parameters for two
example grain-size distributions, with PSD mass median radius of 4 and
12 µm, is shown in Fig. a–c. It demonstrates the
sensitivity of satellite identification of ash-containing pixels and
retrievals to grain size. In both cases, the retrieved effective radii are
scattered across a range of values (± 3–8 µm around the mean) due to
variations in atmospheric, ground and ash cloud conditions
(Fig. d, e). Fewer ash-containing pixels are detected
when the grain size is coarser and the retrieved effective radius is an
underestimate. In the case of missed pixels, a forecaster in an operational
Volcanic Ash Advisory Centre (VAAC) setting may still be able to identify a
volcanic ash cloud because single-channel infrared images can show the
presence of cooler material in the ash-filled pixels and visible imagery may
show scatter from the aerosols. However, it would not be detected by an
automatic BTD method and no retrievals are possible.
Figure a shows the relationship between the mass median radius
of the input PSD and the retrieved effective radius. There is large scatter
in the retrieved effective radii, due to variations in the atmospheric and
volcanic plume conditions. The mean value follows the theoretical line until
the mass median radius increases beyond ∼ 10 µm. At larger sizes, the
mean retrieved effective radius is lower than the theoretical effective
radius and the underestimation increases as the mass median radius increases.
The mean retrieved effective radius reaches a plateau at around 9 µm as
the infrared retrievals have reduced sensitivity to the increasing proportion
of larger particles. This may explain a lack of published retrieved effective
radii greater than this value . As the mass
median radius of the PSD increases it is increasingly difficult to find a
solution. Above a mass median radius of 21.5 µm, ash-containing pixels
are only detected by incorporating data from the 8.7 µm channel and water
vapour corrections ; these would be missed by
methods relying solely on the two-channel BTD. There are fewer ash-containing
pixels in the simulated images that have well-fitting solutions in the
retrieval (low cost values), so the density of values for these sizes is
lower. At the largest grain sizes, many retrievals result in an effective
radius closer to the a priori value set in the retrieval problem of
3.5 µm.
Retrieved effective radius for pixels where retrieved values give good fit to
simulated images (i.e. total cost < 12) and mass loading > 0.2 g m-2 against
mass median radius of a lognormal PSD with geometric standard deviation of 2.0.
The coloured contours represent the density of values from the pixels in the
12 simulated satellite images. The black diamonds are the mean retrieved
effective radius for the given mass median radius of the PSD. The vertical
dotted line shows the limit of sensitivity for the BTD method; ash-containing
pixels in coarser PSDs were identified by additional tests. (a) The mean
retrieved effective radius tracks the theoretical effective radius up to
around 10 µm. PSDs that are coarser than this still return a mean
effective radius of around 9 µm. There is a population of retrievals
clustered around the a priori effective radius of 3.5 µm. (b) As
above, but with a priori effective radius of 15 µm. This value is
much higher than is used in practice, but the plot illustrates the
sensitivity of the retrieval to the a priori estimate.
The effect of changing the a priori effective radius can be
demonstrated by running the retrievals with a value of 15 µm
(Fig. b). This is much higher than the value used in an
operational setting. Again, the mean value follows the theoretical line for
particle distributions with mass median radius of < 6 µm, but the
results are more scattered than in the 3.5 µm case and there is a
significant population of retrieved reff values around 9–14 µm. For
input mass median radii of 6–22 µm, the retrieved effective radius is
overestimated. Above this size the mean effective radius reaches a plateau at
16.7 µm, which is the theoretical maximum size at which a PSD can exhibit
the BTD effect.
The averaging kernel of a
retrieval can quantify its sensitivity to the a priori estimates. The
averaging kernel elements and the degrees of freedom of signal were
calculated for each retrieved pixel (see Supplement for plots and more
details). Theoretically, these range from 0–1 and 0–3 respectively, where 1
and 3 represent a perfect retrieval controlled only by the true state of the
system. Using the operational a priori parameters, the median
averaging kernel elements for effective radius, mass loading and ash top
pressure are 0.95, 0.97 and 0.84. The median degrees of freedom of signal
score is 2.7. This shows that retrievals are affected by the a priori
estimates to some extent and that the mass loading and effective radius are
more sensitive than the ash layer pressure to the true state of the system.
Variations in averaging kernel elements with changing input parameters show that the retrieval is most sensitive to small particles (mass median input radius
< 10 µm) and large mass loadings (> 2 g m-2).
The degrees of freedom of signal
for pixels with concentrations corresponding to low contamination of airspace
(mass loading of 0.2 g m-2 for a 1 km thick cloud)
is 2.0–2.4. Thus, the choice of a priori values is most important in
distal clouds with low mass loadings, even though they are dominated by
smaller particles.
Retrieved mass loading for pixels where retrieved values give good fit to
simulated images (i.e. total cost < 12) against mass median radius of a lognormal
PSD with a geometric standard deviation of 2.0.
Data from all 12 cases are combined.
Percentage of total mass retrieved is the sum of the retrieved total column
loadings × area, divided by the total mass input into the simulated imagery from the NAME model.
The dashed line includes only those for which volcanic ash was detected in the simulated imagery;
the solid line includes all pixels that contained ash in the input NAME data.
The error bars show the standard deviation of the data.
The percentage of the input mass retrieved for a given mass median radius of
the size distribution is shown in Fig. . The dashed line shows
data from pixels correctly identified as containing ash and represents the
accuracy of the retrieval method. The solid line compares the total ash input
from the NAME model with the total mass retrieved and is sensitive to both
the detection method and the retrieval method. Here, a cut-off mass loading
value of 0.2 g m-2 was used. This is equivalent to a
concentration of 0.2 mg m-3 for a 1 km ash cloud, which is
the minimum concentration recorded on the ash concentration charts issued as
supplementary charts by the London VAAC and has been suggested as the limit
of sensitivity of the BTD method . For PSD
with small geometric mass median radius of 1–2 µm, the detection and
retrieval steps work very well and ∼ 100 % of mass is retrieved. As the
geometric mass median radius increases, the accurate identification of
ash-contaminated pixels steadily decreases, with an approximately linear
decrease of 5 % per unit increase in geometric mass median radius. The
retrievals tend to overestimate the mass loading for PSD with geometric mass
median radii 6–10 µm by up to 60 %. At greater particle sizes the
retrieved mass loadings decrease, so the combined effect of underestimated
detection and underestimated retrievals result in the mass loadings being
increasingly underestimated. For a PSD with a mass median radius of 12 µm
only ∼ 65 % of the mass is retrieved from pixels where ash is detected.
This reduces to <25 % when considering all ash-contaminated pixels as many
pixels that contain large ash particles are no longer identified.
Discussion
Cryptotephra transport to distal regions
Icelandic cryptotephra are found across NW Europe and provide information on
the grain size of particles carried to distal regions in volcanic plumes. Our
tephrochronology results show that PSDs of cryptotephra long-axis lengths in
the UK are lognormal, with very small proportions of theoretically BTD-active
particles. The sizes are consistent with single-grain measurements from
around the world and with distal grain-size distributions from much larger
eruptions (Table ). This implies that grains
20–125 µm are present in distal ash clouds, and that they comprise a
larger fraction of the PSD closer to the volcano.
Most damaging ash–aircraft encounters occur within 24 h of the onset of
an eruption . At wind velocities observed
during recent eruptions (Table ), an ash plume could
travel 500–1600 km in this time and our model results confirm the potential
for cryptotephra-sized grains to remain airborne to these distances, even
from moderately sized eruptions. The transport models also highlight the
moderate effect of incorporating sphericity, density and atmospheric
stratification on terminal velocity calculations. The effect of using
measured fall velocities from is larger
and can result in a 3 times increase in particle travel range compared to
dense spheres (note: uncertainty on this figure may be high as error data
were not available). When comparing volcanic ash grains of different
compositions, our calculations also show that rhyolite grains are more likely
to reach the UK than basaltic ones (see Supplement), which may partly explain
the dominance of rhyolitic grains in European cryptotephra, despite explosive
basaltic eruptions being more common in Iceland .
Our modelling results show that transport of cryptotephra-sized volcanic ash
grains to distal regions should be expected, even from moderately sized
eruptions.
The PSD within ash clouds is not well constrained; this is an important
question in understanding distal transport of volcanic ash. Our results
indicate that cryptotephra-sized grains should be present in distal ash
clouds, while the assumption of Mie scattering by dense spheres implies that
any ash cloud exhibiting a BTD is dominated by grains < 10 µm in
diameter. Satellite PSDs overlap with the lower size range of cryptotephra
PSDs, so these views may be consistent in distal regions. For example,
retrieved an reff of 5.6 µm for
an ash cloud near the Faroe Islands from Eyjafjallajökull eruption on 15 April 2010.
Assuming a lognormal distribution with σ= 2.0, 50 % of the
plume mass is contained in particles < 14.3 µm in diameter (and up
to 95 % is within particles < 44.5 µm). This is compatible with
the median equivalent area diameter of particles deposited in the Faroe
Islands by the Eyjafjallajökull eruption (40 µm; see Fig. b),
but does not account for the largest particles or aggregates
> 100 µm;. This agreement is less likely
in proximal clouds.
Limitations of aircraft measurements of volcanic ash PSD
Published PSDs for airborne ash clouds are mostly limited to distal plumes,
or to areas of low ash concentration around the plume margins and may also be
limited by the sampling method. For example, the plume from the
Eyjafjallajökull 2010 eruption was sampled by the UK's Facility for
Airborne Atmospheric Measurements (FAAM) aircraft and by the Deutsches Zentrum
für Luft- und Raumfahrt (DLR) Falcon aircraft. Both aircraft used
wing-mounted sensors that estimate the grain size of particles via optical
scattering with nominal ranges of 0.6–50 µm (CAS instrument on FAAM) and
1–25 µm (FSSP-300 instrument on DLR Falcon). They also carried cloud
imaging probes (CIP-15 with size range 15–930 µm on FAAM and 2D-C with
range 25–800 µm on the DLR Falcon) that could detect much larger
particles. Neither aircraft sampled the most concentrated parts of the plume
during or immediately after the most explosive phases of the eruption
14–17 April, 5–6 May;. FAAM reported that
the most-concentrated ash (> 600 µg m-3) was measured
700 km downwind on 14 May 2010 and contained particles up to 35 µm
diameter . The DLR Falcon sampled the plume
repeatedly, recording concentrations up to 765 µg m-3 with
grain sizes up to ∼ 20 µm diameter . In
both cases, much coarser particles were detected associated with
meteorological clouds, but these were interpreted as water/ice. In another
example, volcanic ash particles were identified on the air filters of the
cabin cooling system of the NASA DC-8 aircraft that flew through ash from the
Hekla 2000 eruption at a distance of 1500 km from the volcano. Ash grains
were 1–10 µm in length , but it is not clear
if this is representative of the size in the cloud.
The lack of coarser cryptotephra-sized grains in these results may be a
consequence of sampling during weak phases of eruptions and outside the
highest concentration regions in the centre of the plume. The coarsest grains
are likely to be deposited from the climactic phases of eruptions and from
the most concentrated parts of their plumes. Alternatively, coarser ash
grains may be associated with ice as hydrometeors
, especially if an eruption was
subglacial, with large quantities of water at the vent.
The grain-size distribution within more concentrated plumes closer to the
volcanoes was measured by . A 10 km high plume
from Mount Redoubt was sampled on 8 January 1990 at a location 130 km downwind,
when the cloud was 2.5 h old. Measurements were made with a forward light-scattering particle size instrument with a stated range of 2–47 µm. The
measured distribution contains particles of all sizes from < 1 µm
and is dominated by those in the 10–30 µm size range.
However, there is evidence that this does not represent the true size
distribution within the plume. The shape of the size distribution (and those
from the Mount St Helens and St Augustine eruptions, also measured by )
shows that it has been truncated so as to
contain no particles coarser than 40 µm. This is due to the upper size
limit of the instrument and is why all emission fluxes were reported as
corresponding to particles < 48 µm diameter. In fact, it can be
expected that 50 % of the material erupted during a short-lived, subplinian
andesite eruption such as the 8 January 1990 Redoubt eruption, will have a
grain size coarser than 100 µm e.g. Mount Spurr
1992;, and that these particles will
still be airborne after just 2.5 h. This was demonstrated by the
encounter between flight KLM867 and the ash from a previous eruption of Mount
Redoubt on 15 December 1989, which took place further downwind, at a distance
of 280 km from the volcano. Analysis of the aircraft found “heavy
contamination” of the engine oil with particles up to 60 µm and a
“substantial population” of 100 µm particles on the aircraft exterior
. Thus the distributions presented in
underestimate the concentration of
cryptotephra-sized particles (and coarser) in the airborne plume. This is
important because they are commonly used by VAACs to initialise atmospheric
dispersion models e.g..
Factors affecting satellite retrievals
Analysis of simulated satellite infrared images presented here shows that the
retrieval algorithm performs best for simulated clouds with mass median
radius less than 5 µm. This corresponds to particles < 10 µm
diameter, which have the highest differential absorption between the two
infrared bands. When using the Met Office operational settings in the
retrieval algorithm with an a priori effective radius of 3.5 µm,
the retrieved effective radii are systematically underestimated in clouds
with mass median radii greater than ∼ 10 µm. This discrepancy arises
because the retrieval problem is ill posed, with many possible combinations
of reff, mass loading, cloud height and meteorological parameters that
would cause the observed (or simulated) BTD signal. Analysis of the
averaging kernel (see Supplement for details) shows that the choice of
a priori effective radius becomes more important as the ash cloud
concentration and the proportion of BTD-active particles decrease, causing a
reduction in the influence of the observations on the retrieval. Using a high a
priori effective radius of 15 µm causes overestimation of retrieved
effective radius for mass median radius above 5 µm. Our results
apply to the method of , but the higher
sensitivity of the BTD method to the finest grain sizes and the absence of
published reff values greater than 10 µm, even in proximal plumes,
indicate that it is likely to be a feature of all similar retrieval
algorithms. The results also highlight how incorporating meteorological
information and brightness temperatures from other infrared channels allows
ash-containing pixels to be identified that would otherwise be missed using
the BTD method alone. As hyperspectral infrared satellite data become more
widely available e.g., using information
from the extra bands may better constrain retrievals.
Systematic underestimation of ash cloud mass is a result of both the reduced
detection rate of ash-filled pixels containing large particles and the
underestimation of the mass loading within pixels that are correctly
identified as ash-filled but that contain large particles. This has
implications for our understanding of plume processes, as satellite data are
used to track decreasing plume mass via deposition and to estimate the
proportion transported to distal areas
, and thus our
understanding of sedimentation from volcanic plumes. Reliable ash cloud mass
data are also important for aviation safety. The London VAAC uses estimates
of the distally transported mass proportion to initialise the NAME dispersion
model . Satellite-derived mass loadings are
also increasingly used directly for advice to the aviation industry and in
inversion modelling e.g.. It is therefore
important that the bias towards small particle sizes and low mass loadings is
incorporated into any interpretation of satellite retrievals.
Meteorological factors complicate retrievals, both in simulations and
real-life clouds. The main effect is to add noise, causing the retrieved
reff from a single input distribution to have a range of values. For
this reason, we recommend that histograms of retrieved effective radius from
many pixels across the cloud should not be presented in a manner in which
they could be mistaken for the grain-size distribution in the cloud. In a real
plume, high atmospheric water vapour loading can produce positive BTDs, while
temperature inversions above ice-covered land surfaces can produce negative
BTDs . Furthermore, the presence of volcanic gases
or ice forming upon ash particles may also affect the BTD signal. Our
simulations were carried out without water and ice clouds. Including them in
the simulations is likely to decrease the number of pixels in which ash was
successfully detected. This was the finding of
, who reported that detection was difficult
when ash clouds were mixed with, or located only slightly above, water
clouds.
The simulations consider an idealised situation where ash particles are
assumed to be dense spheres that scatter infrared light according to Mie
theory. Existing methods for retrievals from volcanic ash clouds also use
this assumption, which dictates that any cloud exhibiting a BTD will be
interpreted as having a PSD dominated by particles < 10 µm in
diameter. Recently, investigations using computer models of the optical
properties of non-spherical, vesicular particles shows that irregular
particles can produce negative BTD at coarser grain sizes than dense spheres
up to 20 µm diameter;. The same study also
concludes that the assumption of dense spherical particles can underestimate
the retrieved mass by 30 % compared with porous spheres and that uncertainty
in particle shape increases the error to 50 %. This is a physical factor that
may explain why retrievals are possible from proximal clouds that should be
too coarse to exhibit a BTD effect e.g. Ruapehu 1996,
Eyjafjallajökull 2010;. Real
ash particles (such as those in Fig. ) are even more
irregular than those modelled by . It may be
possible for a platy ash grain 5 µm thick to exhibit the BTD effect,
despite having a length and width that would be reported by
tephrochronologists of 50–100 µm. Making a retrieval on an ash cloud
containing such grains on the assumption of dense spheres will lead to a
systematic, and potentially significant, underestimation of the particle
size. Current refractive index data have been measured from thin sections
e.g. or from grains sieved to less than
< 22.5 µm in size . Further
quantitative, empirical data on the optical properties of ash samples of
varied size, shape and composition are required to better-constrain this
effect. Given the large difference between fall velocities of real and
simulated ash particles, these would ideally be combined with measurements of
aerodynamic properties, thus improving dispersion modelling inputs, too.
Conclusions
We have reviewed and supplemented the evidence that volcanic ash particles
20–125 µm in length can be transported > 500 km from their
source volcanoes. We also used simple models to show that this is to be
expected, even from moderately sized eruptions. These results highlight a
discrepancy between the size of volcanic ash particles reported by
tephrochronologists and by satellite remote sensing. We suggest three reasons
for this that add to our understanding of the difference between the two
results.
The first is the way that tephrochronologists measure and report grain size.
Two factors cause reporting of slightly higher grain sizes compared to remote
sensing methods. Firstly, the long-axis length measurements made by
tephrochronologists are around 1.5 times the equivalent area diameter of
the same particles. Secondly, as manually measured cryptotephra size
distributions are lognormal, when tephrochronologists report the arithmetic
mean grain size it gives the impression that the modal grain size is larger
than it is. We recommend that the geometric mean and standard deviation are
used in future. Comparison of grain-size distributions measured by optical
microscope (lower size limit of 10–15 µm) with those measured by laser
particle size analyser (range of 0.4–2000 µm) demonstrates that modal
grain size is still captured correctly by manual measurements. Difficulty in
identifying the smallest grains is therefore not a large source of error in
reported cryptotephra sizes.
The second reason is that reff represents a size distribution extending
to much coarser grain sizes. For example, where reff=8 µm and the
geometric standard deviation σ is 2.0, 95 % of the mass is contained
in particles < 64 µm. For this reason, σ should always be
reported alongside reff values and histograms of reff should not be
presented in a way that could be misunderstood as a PSD. Cryptotephra grains
may therefore be represented by the coarse tail of the distribution, and
distal aircraft measurements of dilute ash clouds from weak eruptions are
consistent with this. It should be noted that there are no reliable published
grain-size distributions obtained by direct sampling within concentrated (e.g.
1 g m-3) ash clouds. Cryptotephra-sized grains within the coarse tail of
the distribution cannot be the whole explanation, however, as reff
values of 10–17, which are theoretically possible, are not reported in the
literature, even for proximal clouds.
Retrievals carried out on simulated satellite infrared imagery illustrate a
third reason: low reff values can result from systematic underestimation
by retrieval algorithms. This occurs because infrared data are most sensitive
to particles < 6 µm in radius. Where these represent a small
proportion of the simulated ash cloud, the solution is poorly constrained and
the a priori choice of retrieved effective radius becomes more
important. Solutions dominated by small, strongly BTD-active particles
require relatively low ash column loadings to generate the same BTD effect as
those containing large, non-BTD-active particles, so this can also lead to
underestimation in the retrieved ash cloud mass. This is an important
consideration for VAACs as the combined effect of undetected pixels and
underestimation of retrieved mass loading causes over 50 % of the mass of the
cloud to be missed.
The above reasons are still insufficient to explain why proximal clouds often produce a BTD signal, or the 10 times
discrepancy between ground- and satellite-based estimates of deposit mass in
proximal areas. We hypothesise that this results from the physics of infrared
scattering by vesicular and highly irregular volcanic ash particles. Under the dense
spheres approximation, any BTD signal is assumed to result from particles
with diameter < 12 µm. The
largest distal tephra grains have a platy morphology and can be
50–100 µm long, but < 5 µm thick; it may be possible that
they contribute to the BTD effect in certain orientations.
demonstrated that simulated spherical particles containing bubbles could
exhibit a BTD effect up to 20 µm diameter. We suggest that empirical,
quantitative studies into the optical and aerodynamic properties of volcanic
ash grains of varied composition and size are essential to address this
problem.