Modelled dust cycle of the
largest desert dust source in
the world, the Bodélé
depression (Chad)
Christel BOUET(1), Guy CAUTENET(1), Benoit
LAURENT(2), Béatrice MARTICORENA(3), Gilles
BERGAMETTI(3), and Frédéric LASSERRE(1)
Laboratoire de Météorologie Physique, Clermont-Ferrand, FRANCE
Institut für Troposphärenforschung, Leipzig, GERMANY
(3) Laboratoire Inter-universitaire des Systèmes Atmosphériques, Créteil, FRANCE
(1)
(2)
July, 9 2007
IUGG 2007 Perugia Italy
Outline
1. Scientific context
2.Numerical tool
3.Validation of the numerical tool
4.Presentation of the simulation
5.Results
6.Conclusions
Scientific context
IPCC 2001
Æ Mineral dust radiative effect poorly understood
Scientific context
Mineral dust global
emissions (Mt)
500
60-360
100-500
60-360
130-200
1800-2000
2000
1500-2000
3000
1500
1000-2000
1000-3000
1000-3000
Reference
Peterson and Junge [1971]
Hidy and Brock [1971]
SMIC [1971]
Judson [1968]
Joseph et al. [1973]
D’Almeida [1986]
Schütz [1897]
Jaenicke and Matthias-Maser [1992]
Tegen and Fung [1995]
Andreae [1994]
Duce [1994]
Houghton et al. [2001]
Zender et al. [2004]
From Duce [1994] completed
Æ Great uncertainty on the emitted quantities
Scientific context
High intra-annual variability of dust emission and transport
Dust cycle difficult to model
Why
focusing
on
Bodélé depression?
NDJ
the
¾The Bodélé depression is
the greatest source of
mineral dust in the world
¾Particularly difficult to
model this emissive area
(complex topography)
MJJ
Engelstaedter et al. [2006]
Numerical tool
Regional Atmospheric Modeling System (RAMS, Cotton et
al. [2003]) coupled online with the Dust Production Model
(DPM, Marticorena and Bergametti [1995], Laurent [2005])
Initialization and nudging data:
P, T, wind (u, v, w), relative humidity
from ECMWF
Model outputs:
Mesoscale model:
Concentrations
RAMS
Deposit
Version paralleled 4.3.0
wind at 10 m
Transport
emission flux
Model of mineral dust production:
Dust Production Model [Marticorena and Bergametti, 1995]
Size distribution
Optical depth
Validation of the numerical tool:
the case of BoDEx 2005 (Washington et al. [2006]; Todd et al. [2007])
Horizontal resolution: 10 x 10 km²
11 mars, 10TU
Tibesti
Tibesti
MODI
S
(Terra)
RAMS
Ennedi
Ennedi
15
Dust optical thickness
e AOT at 440 nm
+ AOT at 670 nm
10
Daily mean flux (10-12 mars 2005) :
Todd et al. (2007) : 1.2 Mt/jour
Bouet et al. (GRL, 2007) : 1 Mt/jour
CEPMMT : 0.4 Mt/jour
Model able to reproduce the
main features of the event
(plume, concentrations)
5
0
March, 9
March, 10
March, 11
March, 12
March, 13
(Bouet et al., GRL, 2007)
Description of the simulation
One year of simulation (2001)
∆x = ∆y = 50 km
nx = 55
ny = 39
nz = 30
Validation data:
-OMM stations
-TOMS AI
∆x = ∆y = 20 km
nx = 139
ny = 99
nz = 30
27N
N
Hoggar
Algeria
25
E
Hoggar
Faya
Mali
Tibesti
Aïr
The Bodélé depression
15
Aïr
20
Tibesti
Ennedi
Ennedi
10
N
Bodélé
N’Guigmi
Mao
Sudan
N’Djamena
12N
0E
Benin
Nigeria
26.5E
0E
5
10
15
20
25
Results (1): dust mass flux density
Whole simulated area
Mass flux density (µg/m²/s)
Mass flux density (µg/m²/s)
Bodélé
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Calculations
agree
with
observations of great dust
events over the Bodélé
depression
Washington et al. [JGR, 2006]
Results (2): regional dust mass budget
and a comparison with a GCM approach
The same DPM uses either RAMS or ECMWF surface wind field
ECMWF
RAMS
30
5
4.5
4
Dust mass flux (Tg/day)
Dust mass flux (Tg/day)
25
20
15
10
3.5
3
2.5
2
1.5
1
5
0.5
0
0
0
50
100
150
200
Julian day
250
300
350
0
50
100
150
200
250
300
350
Julian day
ÆDifference in annual seasonality
ÆDifference in intensity
Annual regional mass budget: - RAMS: 390 Tg (170 Tg for Bodélé only)
- ECMWF: 150 Tg
Results (3): surface wind velocity
RAMS vs. observations at Faya
ECMWF vs. observations at Faya
RAMS
+ Observations
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Monthly averaged surface wind velocity (m/s)
Solid line:
monthly average
Surface wind velocity (m/s)
Surface wind velocity (m/s)
ECMWF
+ Observations
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
RAMS vs. ECMWF vs. observations at Faya
Vertical bar:
monthly standard deviation
of observations
RAMS
ECMWF
Obs.
Seasonality better retrieved with
RAMS than with GCM in agreement
with previous results (previous slide)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Results (4a): surface wind field
27N
January, 3
27N
July, 19
Σ
Σ
Tibesti
Bodélé
12N
0E
Tibesti
Ennedi
26.5E
Bodélé
Ennedi
12N
0E
ÆDifferent wind field pattern according to the season
What effect on dust regime?
•Wind flux WF is calculated at the North of the defile between the Tibesti
and the Ennedi across control surface Σ (about 250km x 50m)
•Froude number relative to the Tibesti is calculated at the green point
(overpassing or bypassing the mountain?)
26.5E
Results (4b): surface wind field
Hourly and monthly averaged surface wind flux entering
the defile
6E11
(Σ)
4E11
Surface wind flux (m3/s)
Wind flux:
rr
WF = ∫∫V.ndσ
Froude number:
Fr= U
Nh
2E11
g ∂θ
N
=
with
θ ∂z
0
-2E11
the Brünt-Vaïsälä
frequency
-4E11
-6E11
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Froude number
Monthly averaged surface wind
velocity (m/s) at Bodélé
Froude number*50
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Results (5): link between dust
uptake and wind field
Wind flux < 0
Wind flux > 0
SWÆNE
Dust mass flux (kg/m2/s)
NEÆSW
0
-6E11
-4E11
-2E11
0
2E11
4E11
6E11
Surface wind flux (m3/s)
ÆStrong dust uptake is generally associated with
northeasterly circulation (WF<0: wind bypasses the
Tibesti and the Ennedi and is accelerated in the defile)
Conclusions
A one-year simulation of the dust cycle over the Bodélé
depression and its surroundings:
-Annual dust cycle well retrieved by the model
-Annual wind velocity cycle well retrieved by the model
-Seasonality of these cycles explained by the regional
atmospheric circulation
-Strong dust uptake associated with northeasterly
(Harmattan) circulation