Introduction
Atmospheric mineral dust is the dominant aerosol species in terms of mass
, yet it is one of the major sources of
uncertainty in the climate system despite recent
efforts to reduce these uncertainties from a remote sensing , physico-chemical , or modelling point of view . Numerical models are a key tool for predicting weather and climate.
Given the interaction between mineral dust and the climate system, e.g.
radiation , clouds , and weather systems
such as tropical cyclones (), it is important for models to
simulate the dust cycle well. Key elements of model dust emission schemes are
largely based on empirical data from wind tunnel experiments. Their emitted
dust loadings have often been tuned to match global or regional satellite or
in situ dust data rather than attending to
the efficacy of the emissions in key regions. None of the currently existing
schemes has been thoroughly assessed with field data at the scale of
a numerical model grid box.
Prompted by this apparent gap in appropriate data with which to evaluate
numerical model dust emission schemes, DO4Models aims to provide dust
source-area processed data tailored to regional climate model grid-box
resolution (12 km × 12 km) in order to test the
performance of three dust emission schemes. These data have been obtained
from a remote source area, Sua Pan, Botswana, undisturbed by background dust
aerosol. In this paper we report on the characteristics of three emission
schemes and quantitatively evaluate their performance at process level.
Using a box model approach and DO4Models field campaign data from 2011, we
first quantify the magnitude and frequency of the simulated dust emission
fluxes by comparing them with observed fluxes at the field sites. Three
state-of-the-art schemes are employed: (hereinafter
MB95), the scheme of (AG01), and that of
(SH04). Secondly, we examine the impact of three sand transport formulations
upon the simulated dust fluxes: the models of (OW64),
(LL78), and (which itself is based on
). These formulations predict a range of sand transport
rates that vary by an order of magnitude and eventually control the dust
production of the model as discussed and illustrated in (their
Fig. 6.9) and (their Fig. 4). Thirdly, we test the impact
the input parameters have on the sandblasting mass efficiency α
(vertical-to-horizontal mass-flux ratio) and the threshold friction velocity
u∗thr. The analysis is associated with an assessment of the
box model performance as a function of surface roughness length, soil
moisture content, and soil particle size distribution. The sensitivity of the
simulated emission fluxes to observed soil and surface properties is
discussed in the context of apparent model mismatches. Critical model
components responsible for the discrepancies are identified.
The background to state-of-the-art dust emission schemes and an introduction
of the observational data obtained during the field campaign are given in
Sect. . The parameterisations used in the newly developed box
model are introduced in Sects. –, including
the model evaluation strategy (Sect. ). We
analyse the model performance in
Sects. – and
discuss their implications in Sect. . Our
findings are summarised in Sect. .
Background
The dust emission process is commonly described by three major mechanisms:
dust emission by (1) aerodynamic lift, by (2) saltation bombardment
(sandblasting), and by (3) disintegration of aggregates (auto-abrasion) as
illustrated in . Several parameterisation schemes have been
developed to describe these mechanisms e.g.. See for a comprehensive review.
Auto-abrasion is considered only by . Typically, each scheme
parameterises the following quantities in separate steps or modules: (a) the
threshold friction velocity for particle movement, (b) the horizontal
saltation flux (defined as the vertical integral of the streamwise particle
flux density) which describes the motion of saltating particles, and (c) the
vertically emitted dust flux (defined as the emitted dust mass concentration
per unit area and time) which determines the dust loading in the first model
layer.
The threshold friction velocity, defined as the minimum friction velocity
required to initiate the motion of soil grains, is specified over a smooth
and dry surface (u∗dry), requiring a drag partition
correction to account for roughness elements at the surface, and a moisture
correction to reflect moisture content in the soil which acts to inhibit the
emissions. The saltation flux is proportional to the shear velocity,
represented by a large array of parameterisation options .
The smooth threshold friction velocity, the saltation flux as well as the
vertical emission flux are also functions of the size distribution and
chemical composition of the soil particles .
Field data against which to test model output were gathered by the Dust
Observations for Models (DO4Models) field campaign between 24 July and
14 October 2011. This campaign was focused on a 12 km2 measurement grid
at Sua Pan in Botswana (20.55∘ S and 25.95∘ E). Sua Pan is
one of southern Africa's most important aeolian dust source areas
and, as part of the 3400 km2 Makgadikgadi pan complex,
it experiences ephemeral flooding of its surface . This
flooding results in the development of a highly uneven polygonal salt crust
of varying morphology and in various states of formation and degradation. As
such the crust presents a surface which is highly variable and dynamic in
both roughness and erodibility , with subsequent
impact on the distribution of sites of aeolian dust emission. Such a surface
presents a significant challenge for dust emission schemes as most are not
explicitly developed for crusted surfaces as they can be found in many dust
source regions worldwide . Sua Pan has been chosen for this field campaign because of its
remote situation from other major sources, which allows for an undisturbed
characterisation of the emitted dust, which is particularly relevant for the
estimation of the vertical dust flux.
The Sua Pan 12 × 12 km grid with three AWS sites (orange
dots) and another eight MET/MET+ sites (yellow dots). Through combined use
of a range of remote sensing data, three zones which allowed for a distinct
interpretation in terms of crust types and potential for erodibility were
selected. The colours indicate different soil conditions present throughout
the campaign. Red: well-developed salt crust which would not be easily
erodible (A/B/G); green: intermediary salt crusts that were either not as
well developed as in A, B and G or significantly less moist than in E, F and
I; blue: relatively moist surfaces that were most likely to have been either
re-set (dissolved/reworked) or degraded (partially dissolved/reworked) by
recent flooding and dilute inflow. The relatively high moisture content of
these surfaces would render them relatively non-erodible.
![]()
Minimally and fully disturbed soil size distribution for each field
site at Sua Pan. The mass fraction (in percent) for each parent soil type is
given. FMS is fine/medium sand and CS is coarse sand. (m) refers to minimally
disturbed and (f) to fully disturbed soil. The non-emissive crust sample is
used instead. The two right-hand columns are the average surface roughness
(∅z0 in cm) and soil moisture content (∅w in
m3 m-3) at each site and averaged over the grid.
Site
Type
Clay (m)
Silt (m)
FMS (m)
CS (m)
Clay (f)
Silt (f)
FMS (f)
CS (f)
∅z0
∅w
B3
MET+
0.0
22.3
52.4
25.3
10.7
63.5
25.7
0.1
0.236
0.060
B7
AWS
0.0
8.8
36.7
54.5
10.1
72.7
17.1
0.1
0.200
0.151
D2
MET
0.0
4.6
24.6
70.7
13.9
74.3
11.7
0.1
NA
NA
D5
MET+
0.0
13.2
51.2
35.6
10.2
68.2
21.4
0.1
0.291
0.147
D10
MET+
0.0
14.4
48.0
37.5
11.6
68.6
19.1
0.7
0.292
0.040
G2
MET+
0.0
20.8
67.0
12.1
6.6
60.5
32.9
0.0
0.293
0.077
G6
AWS
0.0
14.7
55.7
29.6
11.4
76.9
10.7
0.1
0.391
0.113
I4
MET+
0.0
21.0
72.6
6.5
8.6
60.6
29.4
1.5
0.230
0.072
I8
MET
0.0
6.2
45.6
48.2
10.8
79.7
9.3
0.2
NA
NA
J11∗
MET+
0.0
3.6
32.0
64.4
9.3
74.1
15.7
0.1
0.108
0.166
L5∗
AWS
0.0
4.9
20.9
74.2
9.4
63.4
27.2
0.0
0.006
0.168
ALL
∅
0.0
12.8
48.0
39.1
10.1
67.8
21.7
0.4
0.175
0.096
* In a few cases the fluff
material could not be sampled.
Our field measurement arrays consisted of 11 meteorological stations
distributed throughout the grid located within zones of differing surface
characteristics, as interpreted from remote sensing imagery
(Fig. ). Each station is identified by a label representing
its relative horizontal (A–L) and vertical (1–12) position within the
12 km2 grid. Each site was equipped with an anemometer mast measuring
wind velocity at heights of 0.25, 0.47, 0.89, 1.68, 3.18, and 6.0 m (AWS,
MET/MET+ sites). Wind velocity data were averaged over a 1 min period to
allow calculation of shear velocity (u*) and aerodynamic roughness
(z0). A Sensit mass erosion monitor was installed on the surface at each
site to provide 1 min resolution data on sand saltation activity (within
5 cm above the surface) and BSNE (Big Spring Number Eight) dust traps
were positioned at heights of 0.25, 0.47, 0.89, and 1.89 m
to determine the average horizontal sediment flux over periods of 14 days.
Data from the BSNEs allowed for the estimation of the integrated vertical
flux and are used to convert the Sensit frequency data into a horizontal mass
flux. At nine of the meteorological stations (AWS, MET+ sites), DustTrak
DRX aerosol monitors were installed at a height of 3.18 m to record
concentrations of PM1, PM2.5, PM10, and PMtot
particles at 2 min temporal resolution. Thetaprobe moisture sensors were
installed in the pan surface at each site to measure moisture content
integrated across depths of 0–3 and 9–12 cm. Automatic weather stations
were deployed at three AWS sites. Two CIMEL sun photometers were deployed
inside (at the centre) and outside (upwind) of the grid in order to obtain
the atmospheric aerosol optical depth (AOD) and the Ångström
exponent. Finally, the threshold shear velocity for dust emission was
assessed at 98 locations across the measurement grid using a Pi-SWERL wind
tunnel , providing a potential dust source map for the grid.
Surface sediment at each site was sampled and returned to Oxford for grain
size analysis using a Malvern laser granulometer. This was used in “wet”
fully dispersed mode (assumed to represent the dust in suspension), and also
in “dry” minimally dispersed mode using an air dispersion unit (which
maintains and measures any particle agglomerates which might be assumed to
comprise the saltation flux). The sediment sampled included the surface crust
(0–0.5 cm thick, where present), a dry “fluff” layer often present
beneath the crust (1–3 cm thick), and a deeper clay soil unit beneath (see
Table ).
To drive the box model, we are using roughness length data (z0) which
were assumed to be constant in each direction for three consecutive days,
derived from 10 min wind observations. Observed gravimetric soil moisture
content at 0–3 cm depth (w) which closely matches the soil moisture
provided by atmospheric models in their uppermost soil layer is used. For the
purpose of grid-wide box model comparison, we take the arithmetic mean values
of z0 and w in 2011 (Table ). Also, the
minimally and the fully disturbed soil size distributions are used
(Table ). For the direct model comparison, the
shear velocity (u∗) is used. It is obtained using the measured wind
profile data and the surface roughness data. The saltation flux
QOBS is assumed to be proportional to the Sensit counts,
calibrated using the BSNE data. The vertical distribution of the dust mass
collected in the BSNEs follows an exponential function which is in good
agreement with empirical considerations. The total vertical dust flux
(FOBS) is estimated following the procedure of
from the DustTrak concentration data in the following way: FOBS=(PMtot-PM2.5)⋅u∗′.
PMtot is the total and PM2.5 is the particulate
matter smaller than 2.5 µm in diameter. The fluctuating component
of the shear velocity is calculated as u∗′=u∗-u‾∗, with u‾∗ as the mean shear velocity
at each site during the campaign period. As we are interested in the positive
dust flux, FOBS is considered as contributing emission flux only
if FOBS,t-FOBS,t-1>σ, with σ as
the standard deviation of FOBS. The time interval Δt is 2
min for all parameters.
The deduced fluxes are not a direct flux measurement. Both QOBS
and FOBS are subject to uncertainties. The uncertainty associated
with QOBS is likely quite high relative to the value for most of
the site measurements due to the very limited quantity measured by the BSNE
during each collection interval. However, for the sites that experienced
relatively higher amounts of QOBS, this uncertainty is greatly
reduced because more mass was collected at each collection interval to
calibrate the Sensit record. The FOBS uncertainty is rooted in
the error of the DustTrak and the flux calculation methodology. The DustTrak
used in this study has shown to have very small errors (∼10 %) for
PM2.5 values when compared with a TEOM and reasonable error
(∼15 %) for PMtot when compared with a condensation
particle counter . When combined with the high measurement
frequency capabilities of the DustTrak, this instrument outperforms most
other nephelometers for PM10 measurements and far exceeds the
performance of aerosol optical particle counters . The
vertical dust flux calculation methodology will underestimate the total dust
flux when compared to theoretical estimates from a removal of both the
PM2.5 mass fraction and the very high-frequency wind
fluctuations. This bias then minimises the likelihood of including dust
emission fluxes that are only entraining PM2.5 particles (at
3.18 m in height) or that are associated with smaller fluctuations in
u∗. The former is of minimal concern, as most dust emission mass
fluxes from crusted surfaces contain a larger portion of PM10
than PM2.5 . The latter bias does increase the
uncertainty in the calculated FOBS as it omits mechanisms such as
dust devils that aerodynamically entrain dust particles through thermal
instabilities. Although this is an important mechanism for dust uplift from
crusted sources, it is not a process captured by the dust emission schemes
tested in this paper and therefore introduces minimal uncertainty in the
comparative results.
Since no severe dust event could be observed in the course of the 2011
campaign period, difficulties arise in establishing a relationship between
u∗ and the fluxes over a wider range of values. We therefore cannot
rule out an unexpected increase in the emission flux which deviates from
theoretical considerations. We have however high confidence in the
identification of the emission signal resulting from specific wind events.
Box model development
This paper investigates a newly constructed set of box models which can
either be run with synthetic data to test the range of potential changes in
dust emission due to individual model parameters, or which can be driven with
observational data. Input parameters are the shear velocity (u*), the
surface roughness (z0), the soil moisture content (w) and the mass
size distribution of the soil (ΔDp). Four parent particle size
populations are considered for all simulations (diameter range in
parentheses): clay (0–2 µm), silt (2–50 µm),
fine/medium sand (FMS; 50–500 µm), and coarse sand (CS;
500–1000 µm). They cover the typical size range and chemical
composition of dust particles in desert regions. In regional and global
numerical dust models, these four populations are converted into soil texture
classes in order to match the information provided by the
global soil data sets e.g..
The Marticorena scheme
The MB95 emission scheme as implemented in the box model starts with the
calculation of the semi-empirically derived threshold friction velocity over
smooth surfaces (u*dry) . Required
input parameters are the air density (ρair), the soil particle
density (ρp=2.5 g m-3 for clay; 2.65 g m-3 for the
rest), and the median particle diameter (Dp). The exact empirical
formulation for u*dry(Dp) is given in box 1a in Fig. 1 in
.
The calculation of the threshold velocity u*thr over a rough
surface with potentially wet soil conditions requires the application of a
moisture (H) and roughness correction (R) for u*dry:
u*thr(Dp,z0,w)=u*dry(Dp)R(z0)⋅H(w),
with
R(z0)=1-lnz0z0sln0.7⋅cMB95/McK04z0s0.8
and
H(w)=1+1.21⋅(w-w′)0.680.5w>w′1w<w′.
The roughness correction after (McK04) was originally
developed for vegetated terrain, but has the advantage of spanning a wider
range of roughness values, which turns out to be important in our case, as
discussed in Sect. . The constant
cMcK04 is assumed to be 122.5 m and the constant
cMB95 is set to 0.1 m . Either
cMB95 or cMcK04 can be used in
Eq. (). Both corrections follow the concept of a
drag partition between mobile sand particles at the ground (smooth roughness
z0s) and larger non-erodible roughness elements (aeolian roughness
z0). For a more detailed discussion on the concept of the characteristic
roughness length scales, we refer the reader
to . We treat the local-scale roughness (smooth roughness) as
1/30 of the median diameter Dp of the undisturbed coarse mode particles
. The moisture correction applies in cases when the soil
moisture w exceeds the threshold w′=0.0014⋅(%clay)2+0.17⋅(%clay). The higher the clay content in the soil, the
less likely dust production will occur under a given soil moisture content.
The sand transport model after is used to obtain the
streamwise horizontal saltation flux QMB95(Dp). g is the
gravitational constant and ρair the air density as before:
QMB95(Dp)=CMB95⋅ρairg⋅u*3⋅1+u*thr(Dp)u*⋅1-u*thr2(Dp)u*2.
The correction factor CMB95 (used to adjust the saltation flux
according to experimental results) was originally set to 2.61
but later revised to 1.0 which
is why we adopted CMB95=1.0 in our box model set-up.
Alternatively, the sand transport formulations after (OW64)
and (LL78) are applied for sensitivity test purposes.
OW64 considers the concentration and vertical distribution of saltating
grains in the saltation layer above the ground, making use of the grain size
terminal velocity ws. ws is determined as a function
of particle mass, diameter and the drag coefficient in consideration of
different possible Reynolds regimes . The momentum flux is
derived by relating upward and downward moving particles in the saltation
layer. C1 and C2 (empirical constants to specify the ratio between
ws and u∗) have values of 0.25 and 0.33, respectively
:
QOW64(Dp)=ρairg⋅u*3⋅1-u*thr(Dp)2u*2⋅C1+C2⋅wsu*.
LL78 accounts for excess shear velocity relative to u*thr. We
use a factor of 6.7 for CLL78, and Dref is the
reference grain size with a diameter of 250 µm as used in wind
tunnel experiments . ρp is the soil particle
density:
QLL78(Dp)=CLL78⋅DpDrefρpg⋅u*-u*thr(Dp)⋅u*2.
The integrated horizontal flux G relates QMB95/OW64/LL78 to the
relative surface area fraction Srel, which is the percentage of
soil grains with diameter Dp relative to the total surface covered by
soil particles. The minimally disturbed field soil sample size distribution
is used in our case.
The integrated vertical mass flux FMB95(Dp) in the case of the
MB95 scheme is obtained by means of an empirical approach which assumes a
constant sandblasting (mass) efficiency α for each size bin. We use
values between 1×10-5 and 1×10-7 cm-1 for the
four corresponding parent soil types as suggested by . While
this approach reflects aggregate disintegration to some extent as the emitted
particle size spectra shift towards smaller particles compared to the
horizontal mass flux, only mobilised particles (expressed in terms of G)
will eventually be emitted. We try to minimise this problem by weighing each
of the four bins according to its fraction in the fully disturbed field soil
sample (see Table ). The resulting sum of the four
bins then determines the total α.
The Shao scheme
The SH04 emission scheme is a more physical approach. relate
the binding energy of the dust particles to the threshold shear velocity.
Over smooth surfaces, derived u*dry by adjusting
the empirical expression of :
u*dry(Dp)=AN⋅ρp⋅g⋅Dpρair+Γρair⋅Dp.
The interparticle cohesion force is considered as the combined effect of the
van der Waals force and electrostatic force. It is assumed to be proportional
to the soil particle size . The parameter Γ accounts for
the magnitude of the cohesive force and has values between 1.65×104
and 5.0×104 kg s-2. We use the smallest value which seems to
fit best for the applied particle size range . The parameter
AN is a dimensionless threshold friction velocity which is expressed as
a function of the particle Reynolds number Ret. The weak
dependence upon Ret for dust particles led to a recommended
factor of 0.0123 .
For R(z0) in Eq. (), a double drag partition scheme is proposed which treats bare and vegetated surfaces
independently . In fact, it introduces a
roughness density in terms of the frontal area covered by the non-erodible
roughness elements present at the surface. As there is no vegetation present,
we simplify the scheme such that it only depends on β (ratio of the
shear stress threshold of the bare erodible surface to the total shear stress
threshold), σ (ratio of the basal to frontal area of the roughness
elements), m (spatio-temporal variations of the underlying surface stress),
and λ(z0) (roughness density of the non-erodible elements):
R(z0)=11-m⋅σ⋅λ(z0)⋅11+m⋅β⋅λ(z0).
Although a wide range of β values has been measured depending on
surface type , we adopt values from for
β as well as σ and m (β=90; σ=1; m=0.5). For
λ(z0), we take the values (based on field measurements;
) given in Table 2 in according to
our observed z0 values at each field site. For H(w), a straightforward
formulation based on wind tunnel experiments as proposed by
is applied in the SH04 scheme as one choice:
H(w)=e22.7⋅ww<0.03e95.3⋅w-2.03w>0.03
The sand transport formulation based on the OW64 model is used
in the SH04 horizontal flux parameterisation. The dimensionless constant
CSH04 can vary between 1.8 and 3.1 and is set to 2.45 in our
experiments :
QSH04(Dp)=CSH04⋅ρair⋅u*3g⋅1-u*thr(Dp)u*.
The integrated horizontal flux G relates QSH04 to the relative
surface area fraction of each bin (denoted here as pA(Dp) instead of
Srel). As for MB95, we use the size distribution of the minimally
disturbed soil sample.
For the integrated vertical mass flux, proposed a scheme that
accounts for saltation bombardment and aggregate disintegration. We use the
simplified version introduced by . The size range of particles
emitted by saltation bombardment differs from that of saltating particles
(those in the horizontal saltation flux). While SH04 specifies a certain size
range, we keep the original size range of the four parent soil types for
saltating as well as sandblasted particles. However, we account for the
changing size range by applying the prescribed (i.e. observed) minimally
(pm(Dpm))
and fully disturbed (pf(Dpf)) volume size
distributions. It is assumed that the undisturbed soil sample represents the
saltating particles, while the fully disturbed soil sample represents the
smaller particles which control the vertical emission dust mass flux (and
hence account for aggregate disintegration). If strong erosion occurs, the
scheme acts to shift the soil particle size distribution towards the fully
disturbed sample. Furthermore, the ratio of auto-abrasion is parameterised by
the free-dust-to-aggregated-dust mass ratio σp=pm(Dpm)/pf(Dpf). The
corresponding vertical flux formulation is the following:
FSH04(Dpm,Dpf)=cγ⋅ηf(Dpf)⋅((1-γ)+(γ⋅σp))⋅(1+σm)⋅QSH04(Dpm)⋅gu*2.
Here, γ is specified as γ=e-(u*-u*thr)3,
while ηf(Dpf) refers to the mass fraction of the
dust particles having diameters less than 20 µm. We assume the mass
fractions of the fully disturbed soil sample to be representative of that (it
contains only clay- and silt-sized particles in most cases, as shown in
Table ). The parameter σm depends on
u*, the plastic pressure p of the soil surface and the bulk soil
density ρb. Together with cγ, the latter two values
are taken from assuming sandy loamy soil conditions on average
at the field site. The flux of the individual bins is finally integrated over
the entire particle size range.
The Alfaro scheme
Similar to , offer a more sophisticated scheme
for the conversion of the horizontal flux into the vertical mass flux
compared to MB95. However, AG01 requires the calculation of the saltation
mass flux as a prior condition. While AG01 has been combined with the MB95
horizontal flux scheme before , in our
experiments we use the SH04 horizontal flux as input parameter. It enables us
to evaluate the performance of two complex vertical flux schemes which both
attempt to describe the physical processes involved. Instead of four size
bins, we use a discretised full-resolution soil size distribution in order to
calculate the SH04 horizontal flux as it is required for the AG01 scheme. The
size distribution is assumed to follow a multimodal lognormal shape with
geometric mean diameters identical to the parent soil size bins (2, 15, 160,
710 µm) . Accordingly, the relative surface area
fraction Srel is recalculated for the discretised particle size
spectra, with Dpk referring to the diameter of the discretised
full-resolution soil size distribution in the range of Dpmin and
Dpmax with number Nclass.
The AG01 scheme takes the individual kinetic energy Ekin of
saltating soil grains required to separate dust particles entirely from each
other by overcoming the interparticle cohesion
forces into account. The dust emitted by sandblasting is characterised by
three modes i which are considered to be independent of the soil grain type
. As soil aggregate size or model wind speed
increases, first a coarse mode particle with the lowest cohesion energy
ei becomes released by Ekin, followed by intermediate and
fine mode particles. The vertical dust flux in this case becomes
FAG01(Dpi,Dmk)=∑k=1Nclassπ6⋅ρ⋅βAG01⋅pi(Dpk)⋅Dmi3eidG(Dpk).
Here, Dmi is the mean mass diameter of the three soil grain modes (1.5,
6.7, 14.2 µm), βAG01 is an empirically derived
parameter (163 m s-2), and
pi(Dpk) are the fractions of Ekin required for the
release of the dust particles in the respective mode . Note
that the AG01 scheme does not provide a size-resolved dust emission flux as
the discretised particle size spectrum in which the interparticle energy
exchange forces act comprises a distinctively different size range than that
of the emission flux. One could redistribute the accumulated dust over the
four parent soil classes according to the observed disturbed size sample, but
this would not be an actual prediction of this particular emission scheme. As
noted by , it is unlikely that interparticle cohesion can
ever be predicted with the desired accuracy in order to resolve this problem
in a satisfactory manner.
Box model experiments
To test the box model, we run the model with observational data as well as
academic data (full range of possible shear velocities). This enables us to
(1) estimate the sensitivity of the model to simulate dust emission, and
(2) attribute the discrepancies to specific components of the emission
schemes, or the choice of the emission scheme itself. We also test the
critical parameter α as a function of u*. The set of experiments
used in these exercises is schematically shown in
Table . Each experiment uses a specific model set-up
based on the schemes introduced in Sect. : the sand transport
model, the saltation flux and the vertical dust flux scheme.
Individual model set-ups (1–5) and the conducted
experiments (a–d). The sand transport models (STM) used for the two
principal horizontal flux (HFlux) models (MB95, SH04) and the selected
vertical flux (VFlux) schemes with the number of the corresponding set-up are
given. The lower-case letters refer to the sensitivity experiments with the
correction schemes.
Exp
HFlux
STM
VFlux
dragC
moistC
1
MB95
MB95
MB95
2
MB95
OW64
MB95
3
MB95
LL78
MB95
4
SH04
SH04
SH04
5
SH04
SH04
AG01
a
*
*
*
ON
ON
b
*
*
*
ON
OFF
c
*
*
*
OFF
ON
d
*
*
*
OFF
OFF
* Experiments are carried out for each model set-up (1–5).
For the first runs, we only use experiments 1a, 4a and 5a, i.e. all
correction schemes switched on, using the MB95, SH04 and AG01 schemes for the
vertical emission flux. We focus on the most emissive period during the 2011
campaign, selecting a 30 day interval with three major dust events
(17 September–17 October 2011). The field campaign begins with the end of
the dry season in March/April. Conditions become increasingly dry, with
average daytime maximum temperatures typically reaching > 35 ∘C.
Note that the rate of decrease in soil moisture varies between each
individual field site and throughout time. Higher surface temperatures are
accompanied by increasing boundary layer turbulence. Both the increased
availability of momentum and deflatable dust explain the more active
late season during the first part of the DO4Models campaign. The
dust emission season ended with the first rains in mid-October.
For the second and third set of model runs, the box model is configured to
represent a single atmospheric model grid cell. We use the temporally
resolved average roughness, soil moisture, and particle size distribution to
drive the model. For each experiment set-up, the model is manipulated with
(a) all corrections schemes switched on, (b) the soil moisture correction
scheme (Eq. ) switched off, (c) the drag partition
correction scheme (Eq. ) switched off, and
(d) both correction schemes switched off.
pointed out that the soil moisture correction after
(see Eq. ) might be excessively
sensitive to changes in the soil moisture content. This will be tested using
the MB95 formulation given in Eq. (). The same
will be done with Eq. () for roughness. In addition,
the corresponding sensitivity of the simulated fluxes is discussed in the
context of the observed fluxes.
Results and discussion
We start with an overview of observed dust emissions from the field site and
compare them with the box model results in Sect. .
We then test the emission schemes over a range of shear velocities and
quantify the differences with observations
(Sect. ). This is followed by an exploration of
separate box model components (soil moisture and drag partition correction
scheme; sand transport formulation) in an attempt to diagnose
model–observation differences in emission
(Sect. ). The examination of the box model
results is accompanied by a discussion of the errors and uncertainties
involved. The applicability of the existing emission schemes is discussed on
the basis of our model results and implications for regional and global dust
modelling are highlighted in Sect. .
Model performance during the field campaign
During our chosen period of highest emission activity, three major dust
events were recorded: 25 September (DOY 268), 2 October (DOY 275), and
3 October (DOY 276), as evident in the observational data at 2 min temporal
resolution (Figs. –). Peak
wind speeds at 6 m height reached up to 18 m s-1. Corresponding
maximum u* values as high as 0.9 m s-1 were observed (with regard
to Δt=2 min). Two smaller events were recorded on 17 September
(DOY 260) and on 6 October 2011 (DOY 279), though u* did not reach a
threshold of 0.4 m s-1 at all sites. Simultaneously during these wind
events, decreasing Ångström exponents obtained from CIMEL data
indicated dust loadings rather than biomass burning as the dominant aerosol
type. The comparison between observed and simulated horizontal and vertical
fluxes is shown in Figs. , ,
and , corresponding to the baseline Exps. 1a (MB95),
4a (SH04) and 5a (AG01), respectively. In order to provide a representative
view of dust emissions, the most emissive site I4 (red border), the least
emissive site L5 (blue border), and three average sites, B3, D10, and J11
were evaluated to provide perspectives on the role of surface type and
emissivity.
Horizontal and vertical flux for Exp. 1a (MB95 scheme) at five field
sites: B3 (a, b), I4 (c, d), L5 (e, f),
D10 (g, h), and J11 (i, j). The observed (modelled)
saltation and vertical fluxes are shown in grey (blue) and black (dark red)
dots. The period between DOY 260 (17 September) and DOY 290 (17 October 2011)
is shown. The box model is driven with observed u* values. On the
left-hand side, the shear velocity is shown (orange; values on the right
ordinate). On the right-hand side, the soil moisture content below
0.3 m3 m-3 is shown (dark yellow; values on the right ordinate).
Site I4 is referred to as a dusty site (c, d). Site L5
emitted least throughout the 2011 campaign (e, f). I4 and L5 are
marked with red and blue borders throughout the manuscript.
![]()
Same as Fig. but for Exp. 4a (SH04
scheme).
![]()
Same as Fig. but for Exp. 5a (AG01
scheme).
![]()
Site I4 shows a pronounced flux signal during the three major dust events
(Fig. c). Another small event was recorded on
6 October 2011 (DOY 279). The temporal agreement between the modelled fluxes
and the observed peak shear velocities over the 17 September–17 October
period (2 min temporal resolution) is highest at site I4, particularly for
MB95. However, the modelled horizontal flux – associated with the saltation
flux – overestimates the observed horizontal flux by 3 to 4 orders of
magnitude. This discrepancy exists regardless of the strength of the dust
event. The modelled vertical emission flux – associated with the
sandblasting process – overestimates the observed vertical flux
approximately by an order of magnitude. While the model performance is
ultimately measured in terms of vertical emission flux (arguably with a much
smaller model vs. observation mismatch), the sandblasting efficiency α
differs by 2 to 3 orders of magnitude between model and observation (see
Fig. and the discussion in
Sect. ).
At sites B3 and D10, only one major saltation event was recorded
(Fig. a, g). Likewise, vertical dust flux was
calculated from concentration measurements for only one time interval at B3
(Fig. b). D10 did not emit at all, despite favourable
observed soil moisture conditions (Fig. h). Due to
the low soil moisture at both sites (a considerable drop for B3 after DOY
270), the emission threshold in the MB95 model is frequently exceeded,
leading to substantially more frequent dust emissions. As at site I4, the
modelled saltation flux during the event on 2 October (DOY 275) at sites B3
and D10 is strongly overestimated by up to 4 orders of magnitude. The
vertical dust flux at B3 during the same event is overestimated by 1 to 2
orders of magnitude. A few Sensit hits were recorded (expressed in terms of
QOBS in Fig. e) at L5, associated with a
rare number of events where vertical dust emission flux was measured
(Fig. f). Both observed fluxes and the low shear
velocity at L5 are a result of very smooth surface conditions in combination
with very wet sub-surface conditions. Equally wet soil conditions at J11 lead
to the suppression of dust emissions in the model
(Fig. i, j) altogether. As a consequence, the model
does not simulate dust emission during the event on 25 September (DOY 268).
There are more frequent dust emissions with higher concentrations simulated
with SH04 compared with MB95 (Fig. ). The saltation
flux is also strongly overestimated by approx. 4 orders of magnitude, whereas
the vertical dust emission flux is overestimated by 1 to 2 orders of
magnitude. Sites B3 and D10 showcase the effect low soil moisture conditions
will have upon the modelled emission fluxes (Fig. a,
b, g, h). Unambiguously, the emission threshold is exceeded far more often in
the model at sites I4 and D10 (Fig. c, d, g, h). Site
D10 reveals a potential advantage of the more complex SH04 scheme: the
modelled saltation flux does not necessarily result in an equally
overestimated vertical dust mass flux due to the variable sandblasting
efficiency. In contrast, the saltation flux is more strongly overestimated in
SH04 compared to MB95. Fluxes with SH04 at site L5 are similar to fluxes
simulated with MB95 (Fig. e, f). The temporal
agreement between observed and modelled fluxes at site J11 is better with
SH04 than with MB95 (Fig. i, j).
There is close agreement in the case of the saltation fluxes between AG01 and
SH04. This is to be expected given that both experiments differ from one
another only in the way the size bins are partitioned (see
Sect. ). At the same time, the good agreement between both
saltation flux estimates is indicative of a limited impact of the size bin
resolution on the resulting dust flux estimate. The modelled vertical fluxes
in both schemes are different to those in MB95, LL78, and OW64 in two ways
though: (1) vertical fluxes are more frequent due to substantially higher
saltation fluxes in the first place, and (2) the magnitude of the vertical
fluxes with AG01 is on average the lowest of all schemes used in our
experiments. The observed dust emission flux is overestimated by less than an
order of magnitude in the model with AG01. While modelled fluxes at B3, I4
and D10 occur much more frequently than observed fluxes
(Fig. b, d, h), L5 and J11
(Fig. f, j) agree very well in that regard.
In essence, both the frequency and strength of the dust emission flux are
poorly reproduced in the three emission schemes. The emission threshold is
least underestimated in MB95. The vertical emission flux is least
overestimated in AG01.
Examination of dust transport/emission schemes
Horizontal and vertical emission fluxes for Exps. 1a–5a (a, b) and Exps. 1d–5d (c, d). Bold lines are the sum of the flux over
all four size bins. Thin lines are individual model particle size categories
(fine/medium sand is emitted first). Coloured circles are the field
observations.
![]()
Before we elaborate on the potential causes of this mismatch between observed
and modelled fluxes as well as for the substantial differences between the
emission schemes, we explore the impact of the emission and sand transport
schemes upon the simulated saltation and vertical flux in a wider context. We
focus on Exps. 1a–5a and Exps. 1d–5d as shown in
Fig. a, b and c, d, respectively. The simulated
horizontal (Fig. a, c) and vertical fluxes
(Fig. b, d) represent the sum of the individual
fluxes for each parent soil type. Note that the AG01 scheme (Exp. 5a) uses a
sub-bin size distribution of which only the total sum is shown, whereas the
clay, silt, fine/medium and coarse sand fractions are shown individually
(thin lines) in addition to the sum of all four bins (bold lines) for the
MB95, LL78, OW64, and SH04 schemes (Exp. 1a–4a). Note also that the emission
threshold is exceeded only for the silt, fine/medium and coarse sand
fractions (u*thr(clay)>1.4 m s-1). Box model
fluxes are computed using observed data as before, averaged over the entire
time period of the field campaign and all grid points (see
Table ).
Model Exps. 1a–5a (Fig. a and b) reconfirm the
results of the preceding section. The saltation flux in model schemes is
overestimated by 3 to 4 orders of magnitude, whereas the simulated vertical
flux is overestimated by 1 to 2 orders of magnitude in all schemes, with AG01
and OW64 showing the smallest mismatch regarding the vertical flux (cyan line
in Fig. b). SH04 has a 0.2 m s-1 lower
threshold velocity than AG01 and MB95. As our observed u* never exceeds
0.85 m s-1, we can only speculate whether we would have observed
disproportionally increasing saltation flux rates with higher surface shear
stress.
Model Exps. 1d–5d (Fig. c and d) reveal a
surprisingly close range of threshold shear values for all schemes. They
start to emit at u*∼0.2 m s-1 with no exception. While the
simulated emission fluxes are still too high, the underlying sand transport
concept is robust in all schemes with respect to the emission threshold.
Beyond the minimum erosion threshold, soil moisture content and surface
roughness fundamentally control the frequency of occurrence of dust
emissions.
Summarising the key aspects of the two sections, we find that the model
(1) strongly overestimates the saltation flux and moderately overestimates
the vertical emission flux, and (2) tends to be very sensitive to changes in
moisture and roughness, leading to inconsistent or inaccurate emission
thresholds for individual field sites. The general discrepancy between model
results and observations indicates that the emission schemes have problems in
representing key physical processes over crusted soil surfaces properly.
Potential reasons for the model discrepancies
In this section, we aim to understand the causes of the box
model–observation discrepancies. Specifically, we aim to identify the
parameters that contribute the largest to the model–observation differences.
Considering the empirical basis of the emission schemes, it is worth noting
that MB95 (mainly based on the formulation after ) as well
as SH04 (based on the formulation after ) rely on the
theoretical concept of equilibrium between forces acting on a spherical loose
particle at rest and under the influence of an air stream. As cautioned by
, this theoretical assumption is bound to break down if
loose particles are hidden under a resistant crust. The same is true for the
concept of equilibrium between gravitational and interparticle cohesion
forces which is the basis of SH04 as it was developed in .
While SH04 allows adjustment to the magnitude of the cohesive force
(parameter Γ), MB95 is limited in this regard. Deficiencies arising
from the MB95 saltation flux formulation are directly passed to the vertical
flux estimate. In turn, the explicit formulation of α in SH04 could
potentially reduce intrinsic weaknesses of the saltation flux formulation.
Problems in the simulated fluxes
Given that the model overestimates the saltation flux much more than the
vertical flux – irrespective of the emission scheme – evaluation of the
vertical-to-horizontal flux ratio α is necessary. In
Fig. , the discrepancy between the observed and
modelled ratio is represented by the distance between the filled coloured
dots (αOBS) and the open coloured dots
(αMB95; Exp. 1a) or triangles (αSH04; Exp. 4a),
respectively. The temporal resolution between two flux measurements in our
data is 2 min, which requires coincident observations of FOBS>0.0 mg m-1 s-1 and
QOBS>0.0 µg m-2 s-1 to determine
αOBS. This condition is only met at site I4 for two dozens
of 2 min measurement intervals, mainly referring to DOY 275
(Fig. c). B3 provides sparse additional values
(Fig. a). L5 (Fig. g) is
discussed later in this section. The remaining sites are plotted in order to
show the variability of the modelled α(MB95/SH04).
The temporal evolution of the simulated vertical-to-horizontal flux
ratio α for Exp. 1a (open circles) and 4a (open triangles) is shown in
comparison to the observed values (closed circles). The colour refers to
10 day time intervals during the field season, with the start DOY given for
each period. Nine out of 11 field sites are shown. In cases of
FOBS without simultaneous QOBS, α is zero.
Note that there are situations in which vertical emission flux was measured
without saltating particles.
![]()
With the simple MB95 scheme in place, α is strictly constant at each
site. The more complex SH04 scheme allows for a varying α in response
to changes in soil composition, surface roughness and soil moisture content.
The observed changes in z0 and w over the 3 month field interval have
a profound impact on the modelled α, as can be seen in
Fig. a (B3) and i (D10). The SH04 ratio varies by up to
1 order of magnitude (as a function of soil moisture which varies over time
as reflected in the associated 10 day time interval) and can either be
smaller or larger than the constant MB95 ratio. Despite the model
variability, what is really striking is the mismatch of 2 to 4 orders of
magnitude between observed and modelled α at I4
(Fig. c) as initially outlined in
Sect. . The weak observed saltation flux causes
α to be unprecedentedly high. The majority of the α values lie
between 1×10-1 and 1×10-3 cm-1.
On the basis of the surface conditions at our most emissive site I4, which
features a thin crust with open cells filled with very fine deflatable
particles, we hypothesise that saltating particles are likely to be trapped
by the salt-containing fluff in these open cells which then absorbs the
saltation momentum. Under the assumption that I4 is not a source of larger
saltating particles itself, it represents a net sink for creeping and
saltating particles, which leads to a cessation in the saltation flux. While
the horizontal flux ceases, the comparably high shear stress maintains the
vertical flux of smaller particles, though at a less efficient rate. Hence,
direct entrainment (production of vertical flux without saltating particles)
has a larger share in the total emission flux. Whether the shape of the cells
or the chemical properties of the fluff material are the major cause of I4
being a saltation sink remains to be explored. In contrast to I4, sustained
particle motion (hitting the Sensit counter persistently) was observed at
site L5 during the wind events, without ever recording actual vertical
emission of finer particles. Wet sub-surface conditions led to the
development of a fresh but very smooth and resistant crust at L5.
Counter-intuitively, the smooth surface allowed coarser particles (advected
from contiguous pan surfaces with broken crusts) to move easily. Presumably,
the observed saltation flux at L5 is a result of the very exceptional surface
conditions due to L5's situation on the grid.
Neither the shape of a partly crusted and rippled surface nor the crust
itself is represented in our schemes, and this is likely the main cause of
the large gap between observed and modelled fluxes. While the theoretical
basis of the sand transport and dust emission schemes is well established and
often successfully reproduced e.g., the observed
crust puts a considerable limit on their applicability in our case. One might
argue that it is of lesser relevance to reproduce the saltation flux
quantitatively correctly in the model as long as the vertical emission flux
is correctly balanced, but this inevitably implies the acceptance of
fundamental errors in the parameterisation of the nature of the dust emission
process. While the initial emission threshold is very sensitive to z0,
w, and particle size, these factors become less important at higher wind
speeds, as the sand transport scheme controls the bulk of the vertical dust
emission flux.
This study is not the first to report on diverging α values. Based on
measurements with a sand particle counter (saltation flux) and an optical
particle counter, obtained similar values to ours for
α over bare soil during the Japan Australia Dust Experiment (JADE)
. On the basis of their findings, they proposed
that convective turbulent dust emission might play an important role. We
concur with this proposition as we have indeed been observing frequent dust
devils over the pan, indicative of large eddies generated by localised
momentum fluxes to the surface which intermittently receives a surge of
strong shear stress leading to direct dust entrainment .
also highlight the size dependency of the emission flux,
as evident in their field data. Other studies matched empirical expectations
quite well. For example, using test soils,
in Mali, and at
Owens Lake, USA, in Queensland, Australia,
in Niger, or in Spain, all found α values in good
agreement with theory. These studies have in common that wind tunnels were
used to determine the fluxes experimentally, a fact that might well be key to
understanding the difference between their reported results and our field
data.
Problems in the correction schemes
The remaining variability of the calculated dust fluxes is determined by the
correction schemes for surface roughness and soil moisture content – both
known to have a large impact on modelled mineral dust emission fluxes
. The full range of sensitivities for the baseline
experiments (1a, 4a) is shown in Fig. . For
z0, the observed range is 0.001 cm < z0 < 1.0 cm. The
minimum and maximum value for w has also been chosen according to the
respective range of observed values:
0.01 < w < 0.16 m3 m-3. It is expressed in equivalent
terms of percent water per soil volume. For Exp. 1a, the range of
u*thr varies between 0.25 and 0.8 m s-1. The threshold
shear velocity is equally sensitive to both, z0 and w, yielding a
corresponding inhibition of the simulated fluxes. The higher the
u*thr, the lower the simulated fluxes once the threshold is
exceeded. Exp. 4a is similarly sensitive to z0. In turn, for increasing
w, it tends to increase the emission threshold exponentially rather than
linearly. As noted in Sect. , it is the scheme after
as used in MB95. The scheme proposed by
(Eq. ) would span twice the range of potential
u*thr values, which cannot be reconciled with the observed
sensitivity (not shown).
Horizontal and vertical emission fluxes for the baseline
Exp. 1a (a, b) and Exp. 4a (c, d, e, f). The entire range
of observed surface roughness and soil moisture is plotted as a function of
u*. Likewise, the observational data are split into groups of different
roughnesses and moisture. Lowest observed z0 are indicated by red and
dark red dots, and highest observed z0 by orange and yellow dots (see
legend). Lowest observed w are indicated by black and dark grey open
circles around the dots, and higher observed w by brown and light grey open
circles (see legend). Modelled z0 are set to two groups of 0.001 and
1 cm, whereas modelled w are set to three groups of 6, 11, and 16 %,
respectively.
![]()
In Fig. , the observed fluxes are divided into the
same sub-categories. The results show that sites with the highest observed
saltation fluxes have a very limited range of z0 (0.1–1 cm). Likewise,
the range of w is confined to lower values (<0.11 m3 m-3) for
those sites. The stronger fluxes at higher u* are tied to lower w
values. Lower z0 (smoother surface) corresponds well to emission at
lower u* values. Emission flux for u*>0.6 is observed only for
w<0.06 m3 m-3 (with very few exceptions). At the lower end,
medium roughness dominates. Occasionally, we measured vertical dust flux at
sites with w>0.06 m3 m-3 despite u*<0.4 m s-1 (high
saltation flux at L5 under these conditions, though). The fact that the
sample size is small and the inherent measurement uncertainties are large (as
discussed in Sect. ) is suggestive of an artefactual
behaviour. However, observed local
dust devils can pick up substantial amounts of dust which the dust tracks at
3 m height would easily record. The fraction of the emitted mass flux at low
u* with respect to the total mass flux might not be significant during
dust events with a high saltation flux, but the omission of frequent low dust
emission below the saltation threshold can lead to measurable systematic
underestimation of the dust emission flux.
Vertical emission flux for Exps. 1a, b (a, c), and 4a,
b (b, d). Coloured circles are the observed fluxes. The simulated
grid average flux is shown in black. The fluxes of the individual field sites
are complementarily given by the dotted coloured lines. The dashed grey lines
refer to the model particle size categories as specified on the top left,
with fine/medium sand being emitted first (compare
Fig. ).
![]()
In Fig. e and f, the roughness scheme proposed by
(Eq. ) is applied. Lesser sensitivity of
u*thr to changes in z0 is found with this scheme. Although
it spans a range of u*thr values which is in good agreement
with the observations, it is rather insensitive to variations in aerodynamic
surface roughnesses >0.5 cm. Given that the majority of our observed
z0 values is <0.5 cm, the applicability of the SH04 roughness
correction scheme seems questionable, despite having selected the remaining
parameters such that they fit the category for bare surfaces with dense solid
obstacles.
In Fig. b and d, Exps. 4a and 4b are compared with
observations as a function of u*. It can be seen that
u*thr of the vertical flux is basically insensitive to changes
in roughness in the case of SH04. Rather, u*thr is controlled
by the soil moisture alone. Replacing it with the McK04 drag partition scheme
leads to more variability and eventually better agreement with observations
(results not shown). In the case of MB95, u*thr is equally
controlled by surface roughness (Fig. c) and soil
moisture (not shown).
The MB95 drag partition scheme relates z0 to roughness densities of
solid obstacles. A major limitation is its non-applicability for larger
obstacles. At the pan surface, large crustal plates got lifted by compressive
stress due to drying of the crust material. These vertically displaced plates
reached 10–20 cm in height, stretching over several 100 m in a wavelike
pattern with high lateral cover. High surface roughnesses were also reported
by from space-borne observations in Death Valley, USA, or
by from ground-based observations in Tunisia. The
ridge-induced change in roughness has been studied and shown to be important
in reducing the saltation flux . To account for higher
roughnesses, (McK04) corrected the MB95 scheme such that
it is applicable for rougher surface conditions. In their case, the higher
roughness is caused by vegetation (central Mojave Desert, USA). Hence, doubts
remain as to whether the assumptions made are perfectly valid for our
purposes, despite the fact that the scheme performs better than the SH04.
With regard to the soil moisture correction, both the parameterisations
developed by (MB95) and by (SH04) require the
exact knowledge of the moisture in the top 1–2 cm soil layer. We consider
our 0–3 cm moisture measurement to be representative of this layer. The key
aspects regarding the sensitivity of the threshold shear velocity outlined in
Sect. are reconfirmed in
Fig. a, b. In Exps. 1c and 4c, the sole application of
the soil moisture correction tends to improve agreement between simulated and
observed u*thr as well as the vertical emission flux (not
shown). Note that both formulations (MB95 and SH04) are empirically derived
and hence not universally applicable for all soil moisture conditions. As
pointed out by , they fail to be reproducible in data sets
other than those from which the formulation was initially derived.
The fact that none of the evaluated model correction schemes can be used
without limitations as they struggle to reproduce the observed range of
u*thr is attributable to two principal shortcomings. (1) The
roughness correction does parameterise unevenness of the terrain, but is not
designed to account for different shapes such as open cells. (2) The moisture
correction does parameterise the wetness of the soil, but does not
incorporate moisture-dependent chemical properties of the soil which may lead
to crust formation.
Implications for dust modelling
Sua Pan is observed to be a major Southern Hemisphere dust source. It is
therefore crucial to ensure that we not only understand the physics of the
dust emission process better, but are also able to represent it in
state-of-the-art model dust emission schemes. Our results suggest that there
is a critical problem with the current generation of dust emission schemes,
as they tend vastly to overestimate the observed fluxes. Reasons are
primarily related to the fact that existing schemes cannot represent all the
relevant physical processes. As stated in Sect. ,
observed small-scale surface features such as large ripples or small open
cells within an otherwise crusted surface are not described in the existing
schemes. Failing to include a crust leads to a higher availability of
sediment in the model as, in the field, deflatable fluff material is either
trapped in open cells of the crust (absorbing saltation momentum), or is
buried under a thick crust. Also, the availability of coarse material is
limited due to the surface characteristics. Our findings may imply that most
of the modelled global dust emissions are based on partly invalid
assumptions.
Why – despite these limitations – are current emission schemes able to
reproduce the global dust cycle fairly well? Apart from the potential
counterbalancing effect of equally erroneous dry and wet deposition
assumptions, the fact that global emissions are controlled by a few very
productive sources which are driven by frequent and excessive exceedance of
the threshold wind speeds tends to eradicate problems which occur at wind
speeds just above u*thr. For example, neither the drag
partition nor the soil moisture correction will have a
sizeable effect once u*thr is
exceeded. Furthermore, the signal-to-noise-ratio increases with higher wind
speeds, acting to minimise biases introduced by inaccurate representations of
the surface conditions. Instead, invariable parameters such as the soil size
distribution become the dominant source of error.
Another – and perhaps the most important – reason for the acceptably good
reproduction of the global dust budget is the fact that many models assume an
empirical background size distribution rather than
modelling it explicitly. Equally important, the concept of preferential dust
sources acts to nudge the models towards the
observed dust emission patterns by relaxing back the threshold emission and,
in essence, removing the crusting issue from the modelling process. The fact
that none of the current model emission schemes is able to reproduce the
spatial distribution of the major dust sources correctly without applying
either of these auxiliary steps reinforces our concerns regarding the
validity of the emission schemes.
Given the important role that surface crust seems to play, we recommend that
these features be represented in the models. A crustiness parameter
to correct u*thr could be defined as the aggregated state of
the dry ground surface for resistant crusts as proposed by
. Using available maps of aerodynamic surface roughness
length , an adjusted version which takes crust
cover into account may be possible. In addition, the spatio-temporal
considerations can help to find an appropriate tuning constant to constrain
the spatial heterogeneity. This is particularly true as only a small portion
of the grid (I4 in our case) controls the bulk of the emissions. The
incorporation of sub-grid scale emission schemes into climate or NWP models
could be a worthwhile effort in that regard. What remains elusive so far is
whether the small range of roughness and soil moisture values for which we
measured dust fluxes at the grid is indicative of a systematic relation
between z0, w and the properties of the crust.
The aspect of spatial heterogeneity is also related to model resolution. A
typical grid box in a regional climate or NWP model corresponds to the size
of our grid in the field (12 km2). One such single grid box is treated as
a homogeneous surface, with soil moisture, soil size distribution and surface
roughness being equal everywhere. In an ideal modelling world, not only do
the grid box average values have to provide a balanced portrait of the
emissive area fraction, but they also have to fit the observations of soil
available for emission adequately. In the real world, most models make use of
the soil texture classes after . In our box model experiments,
the soil texture class which comes closest to our grid average size
distribution is the loamy sand category. Comparing the emission flux
obtained with the size distribution given by this fixed category and the
observed size distribution, we find that the resulting model saltation flux
is significantly reduced in the case of the fixed category. A recently
published new data set of soil mineralogy for dust productive soils could
alleviate the problem . Ideally, a correction
which aims at splitting the dictated size distribution into a minimally and
fully disturbed subset of data could be introduced. As it is a difficult goal
to achieve, the SH04 scheme should preferentially be used as it tries to
account for the shift in the size distribution, at least to some extent.
In this context, it should be noted, though, that using the fully disturbed
rather than the minimally disturbed size distribution for the saltation flux
calculation in our box model experiments actually reduces the resulting
vertical emission flux by almost an order of magnitude, which in turn reduces
the gap between model and field results considerably. Unfortunately, it
happens for the wrong reason, as saltating particles do indeed consist of
soil aggregates with larger particle diameters compared to what is used in
NWP models. This is in accordance with other studies that have shown the size
dependency of the emission flux to be important. As a result,
proposed a size-dependent power law
relation and developed an emission parameterisation based on
the brittle fragmentation theory . Both options offer another
route for improvement with regard to current schemes.
Finally, our results indicate that direct entrainment of dust particles plays
a moderate role in the emission process. This assumption is based on the low
correlation between simulated and observed fluxes with the tested emission
schemes, particularly for the saltation flux. Although the impact of this
emission mechanism is thought to be small as far as global climate
simulations are concerned (since it is confined to low shear stress
conditions), there is increasing evidence that sediment erosion and transport
may respond effectively to wind turbulence . Indeed,
have noted that surface gustiness at dust hotspots
exerts a much stronger temporal control on the timing of emissions than
large-scale winds. If they are correct, direct entrainment during such gusts
will very likely play a role, with concomitant effects on the global scale.
Undoubtedly, direct entrainment matters for regional short-term applications
(e.g. local dust storm warnings). As current schemes do not capture these
aspects well, those that take stochastic effects into account could alleviate the problem to some extent.
Conclusions
The performance of current state-of-the-art dust emission schemes has been
tested against observational data retrieved during the 2011
DO4Models field campaign in Botswana. The capabilities of these
schemes to describe the physical processes which are thought to play a role
in the dust emission process have been explored. We have found that all
models fail to reproduce the observed dust fluxes in all experiments,
regardless of their level of complexity. In particular, the horizontal
saltation flux is overestimated by several orders of magnitude, causing the
commonly used concept of an approximately constant sandblasting mass
efficiency (vertical-to-horizontal flux ratio) to break down. The main reason
is that the field site is characterised by a crust of varying thickness and
extension.
The current results suggest that the observed saltation flux is several
orders of magnitude lower than anticipated from theoretical considerations,
even at our most emissive field site. Yet the measured vertical dust emission
flux is closer to theoretical expectations. We therefore infer that
saltation, sandblasting and aggregate disintegration are not the only
emission processes at play. Rather, these results indicate that direct dust
entrainment plays a vital role too. Since none of the tested schemes accounts
for direct entrainment as explicitly mentioned in , the
discrepancy in the sandblasting efficiency is explicable. Stochastic schemes
such as the one recently proposed by might help to overcome
this problem. We believe that our results provide a fairly robust starting
point to test these emerging new schemes.
Furthermore, we have found that the most sensitive parameter for the
determination of the emission threshold in the model, the soil moisture, does
not always relate to the potential emissivity of the site. Some sites with
low enough soil moisture values to allow for dust emission did in fact not
emit owing to a thick and continuous crust. As a result, spatio-temporal
variations of the emission flux are large, both in the observations and in
the box model. The agreement for individual field sites is often poor, which
is indeed indicative of a rather loose relationship between soil and surface
properties and the resulting dust flux. The agreement between model and field
data is, however, acceptable in the baseline experiments at the most emissive
site. Encouragingly, the wettest site (with a smooth and thick crust) was
essentially non-emissive during the 2011 field campaign.
The sensitivity experiment also taught us that even the least sensitive soil
moisture correction for u*thr still tends to be
too sensitive. The drag partition correction for u*thr is less
sensitive, but only the scheme proposed by is applicable
over the entire range of observed aerodynamic surface roughnesses, despite
the fact that it was originally proposed for vegetated desert surfaces. Using
a minimally and a fully disturbed soil size distribution data set at each
site for the model calculation of the horizontal and the vertical dust mass
flux, respectively, the observed particle size range could be realistically
represented by virtue of the availability of soil aggregate and soil
individual particle size information.
Having systematically examined the impacts of the major emission model
components, we highlight the following key findings and implications.
Strong overestimation of saltation flux in all schemes
Moderate overestimation of vertical flux in all schemes
The OW64 transport scheme reduces the quantitative bias.
Soil moisture sensitivity is too high in the Fecan scheme.
McK04 drag partition correction outperforms MB95.
The SH04 scheme captures observed spatial variability better.
Vertical emission flux sensitive to soil size distribution
Crust properties have a large impact on emitted dust mass.
Spatio-temporal crust variability needs to be parameterised.
The stochastic approach for direct entrainment is desirable.
In this context, we note that an atmospheric model's meteorological fields
are another key factor which may well outweigh the impact of spatio-temporal
variability or measurement uncertainty e.g.. We address this aspect in an upcoming study using a
state-of-the-art climate model.
We would like to emphasise that it is certainly necessary to include missing
processes in dust emission schemes if one wants to move forward towards a
more realistic description of the emission process. This is particularly true
if one is aiming to provide regional or local dust emission forecasts,
bearing also in mind that surface gustiness is a controlling factor for dust
emission . A better constrained dust emission flux
inherently helps to reduce uncertainties in other parts of the dust cycle,
preferentially in the deposition flux. As many of the most emissive dust
spots worldwide share common soil and surface properties, we argue that the
incorporation of parameterisations which reflect mechanisms that are
characteristic of crusted soils can potentially improve the overall accuracy
of the models, particularly over regions which feature frequent changes
between dry and wet conditions, as most monsoon regions do.