AN ONLINE MINERAL DUST MODEL WITHIN THE GLOBAL/REGIONAL NMMB: CURRENT PROGRESS AND PLANS
Carlos Pérez1, Karsten Haustein1, Zavisa Janjic2, Oriol Jorba1, José María Baldasano1, Tom Black3, Slobodan Nickovic4
1. Barcelona Supercomputing Center. Earth Sciences Department. Barcelona, Spain.2. University Corporation for Atmospheric Research, Boulder, Colorado.3. NOAA/NWS/ National Centers for Environmental Prediction. Camp Springs, Maryland.4. World Meteorological Organization. Geneva. Switzerland.
AGU FALL MEETING, 14-19 December, San FranciscoAirborne Mineral Dust: Sources, Emissions, Destinations I
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North Africa and Mediterranean- 1/3 x 1/3 degree resolution- 24 vertical levels - 72 - h daily forecasts - Initialized at 12UTC
Full Asia domain-1/2 x 1/2 degree resolution - 24 vertical levels - 72 - h daily forecasts - Initialized at 00UTC
http://www.bsc.es/projects/earthscience/DREAM/Current daily forecast domains
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Model predictions (72-h):Horizontal distribution
PM2.5, PM10 at surface Total column mass (dust load)Dust aerosol optical depthWet, dry, total depositionMeteorological variables
Vertical distributionConcentration / extinctionCross sectionsFixed point/time profiles
Fixed point (selected sites/cities)DustgramsMeteograms
Request-only basis:Numerical dataClimatology
New operational domain with new BSC/DREAM (Nickovic et al., 2001) model versionhttp://www.bsc.es/projects/earthscience/DREAM/
BSC/DREAM version features • Dust production scheme with introduced viscous sub-layer(Shao et al., 1993; Janjic, 1994)• USGS 1km vegetation and FAO 4km soil texture data• 8 particle size bin distribution. Sub-bin log normal distribution (Zender et al, 2003, D’Almeida 1987)• Soil wetness effects on dust production (Fecan et al., 1999)• Dry deposition (Georgi, 1986) and simple below cloud scavenging (corrected)• Horizontal and vertical advection, turbulent and lateral diffusion (Janjic, 1994; 1997) represented as for other scalars in the Eta/NCEP model• Dust radiative feedbacks on meteorology (Pérez et al., 2006)
Embeded on-line intothe old NCEP/Eta model
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MeteosatMeteosat Second GenerationSecond Generation SeaWIFSSeaWIFSLidarsLidars -- EARLINETEARLINET AERONET AERONET -- ONLINEONLINE
Dust forecast and daily evaluation
Model has shown good agreement with observations in a number of studies of single events (e.g., Ansmann et al., 2003, Papayannis et al., 2005; Balis et al., 2006; Pérez et al., 2006a;b; Jiménez et al,
2006 ….)
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AERONET: Full year 2004 validation
Spain
North Africa coast
Very good behaviour duringthe whole yearly cycle in theMediterranean
West/Central Mediterranean
Eastern mediterranean
Courtesy of Sara Basart
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AERONET: Full year 2004 validation
Banizoumbou
- Good behaviour in the Middle EastAnd Atlantic (Izaña)
Dust mass is missing in the Sahel during the dry season: (meteorology? dust emission scheme?Soil database?)
Dakar
Izaña
Middle East
Courtesy of Sara Basart
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Meteosat Second Generation: Large scale dust storms
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EXPERIMENTAL CAMPAIGNSBSC/DREAM-SAMUM I (Haustein et al. 2008; GRL accepted)Problems with moist convective events
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EXPERIMENTAL CAMPAIGNSBSC/DREAM-BODEX 2005 (Todd et al 2008, JGR)
First regional model intercomparison in theBodélé hot spot
RegCM3LM-MUSCATMeso-NHRAMS-DPMBSC/DREAM
0
2
4
6
8
10
12
14
16
18
20
10/0
3/20
05
03 U
TC
06U
TC
09U
TC
12U
TC
15U
TC
18U
TC
21U
TC
11/0
3/20
05
03 U
TC
06U
TC
09U
TC
12U
TC
15U
TC
18U
TC
21U
TC
12/0
3/20
05
03 U
TC
06U
TC
09U
TC
12U
TC
15U
TC
18U
TC
21U
TC
10m
win
d sp
eed
RegCM3LM-MUSCATMNHRAMSDREAMObserved
- BSC/DREAM (Eta) consistenly simulates low level jet- Strong differences between models!!!!
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CURRENT ACTIVITIES AND PROGRESS
GOALS- Replace the ETA weather forecast model with a state-of-the art model regional/global model withimproved dynamics and physics
- Implement a common on-line dust module for regional and global domains
- Global dust forecasts up to 7 days at mesoscale resolutions. Nested regional domains at very highresolution.
- Feedbacks between dust and meteorology
- Upgrade the current dust emission scheme to a physically based scheme explicitly accounting forsandblasting
- Include new high resolution databases for soil textures and vegetation fraction
- Upgrade depostion schemes
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Unified nonhydrostatic dynamical core (list of features is not exhaustive)Wide range of spatial and temporal scales (from meso to global)
Regional and global domains (just a simple switch)
Evolutionary approach,built on NWP experience by relaxing hydrostatic approximation (instead ofextending cloud models to large scales; Janjicet al., 2001, MWR; Janjic, 2003, MAP)
Applicability of the model extended to nonhydrostatic motionsFavorable features of the hydrostatic formulation preserved
The nonhydrostatic option as an add–on nonhydrostatic module
No problems with weak stability on mesoscales
Conservation of important properties of the continuous system (Arakawa, 1966, 1972, …; Janjic, 1977, …; Sadourny, 1968; aka “mimetic”approach in Comp. Math)
Arakawa B grid (in contrast to the WRF-NMM E grid)
Pressure-sigma hybrid (Arakawa and Lamb, 1977)
NMMb or Unified Model (UMO)Under development at NCEP (Janjic, 2007)
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NMMb or Unified Model (UMO)Under development at NCEP (Janjic, 2007)
Adams-Bashforthfor horizontal advection of u, v, T and Coriolis force
Crank-Nicholson for vertical advection of u, v, T (implicit)
Forward-Backward (Ames, 1968; Janjicand Wiin-Nielsen, 1977; Janjic1979, Beitrage) for fast waves
Implicit for vertically propagating sound waves (Janjicet al., 2001, MWR; Janjic, 2003, MAP)
Improved tracer advection: Eulerian, positive definite, mass conservative and monotonic
A variety of physical parameterization schemes from WRF modeling system will be available
NMMb regional will become the next-generation NCEP mesoscale model for operational weather forecasting in 2010
15 min clock time per simulated day at 0.3 º x 0.45 º x 64 levels global with 88 CPU
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RUN DYNAMICS RUN PHYSICS
Coupler DYN-PHYSDynamics export
T, U, V, Q, CW, Q2, OMGALF
Coupler DYN-PHYSPhysics Export
T, U, V, Q, CW, Q2
ATM
NMMb usingEarth System Modeling Framework (ESMF)
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RUN DYN + dust hadv, vadv and hdiff)
RUN PHYS + (dust emission, vdiff,Conv. transp, sedim, dry dep, wet scav)
Coupler DYN-PHYSDynamics export
T, U, V, Q, CW, Q2, OMGALF, DUST
Coupler DYN-PHYSPhysics Export
T, U, V, Q, CW, Q2, DUST
ATM
NMMb/BSC-DUST usingEarth System Modeling Framework (ESMF)
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Model developmentsFrom BSC/DREAM to NMMb/BSC-DUSTEMISSION SCHEME
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛−⋅⋅=
2
*
*3* 1
uuucF t
S δtuu ** ≥ Shao et al. [1993]
c tunning parameterδ source function
BSC/DREAM8 dust transport particle sizes
Vertical flux GcFS
NMMb/BSC-DUST4 soil particle size populations and 8 dust transport particle sizes
⋅⋅⋅= δα
∑⎥⎥⎦
⎤
⎢⎢⎣
⎡⋅⎟
⎟⎠
⎞⎜⎜⎝
⎛−⎟⎟
⎠
⎞⎜⎜⎝
⎛+⋅=
ii
tta suu
uu
ug
G 2*
2*
*
*3* 11
ρVertical flux
Horizontal fluxWhite [1979]
α vertical to horizontal flux ratioc tunning parametersi relative surface area of each soil particle fraction
γβδ ⋅⋅= AΑ landuse fraction (desert mask) [0-1]
γ mass available for uptake [0-1]
β fractions of clay (bins 1 to 4)and silt (bins 5 to 8)
Source function
)1)(1( SNVFPA −−⋅⋅=δA landuse fraction (desert mask) [0-1]P preferential source probability [0-1]VF Vegetation fraction [0-1]SN Snow cover relative area [0-1]
Source function
68.0'** )(21.11 wwuu tdryt −+=
( ) ClayClayw %17.0%0014.0' 2 ⋅+⋅= Fecan et al. [1999]
Soil moistureeffects
68.0'** )(21.11 wwuu tdryt −+=
( ) ClayClayw %17.0%0014.0' 2 ⋅+⋅= Fecan et al. [1999
Soil moistureeffects
a
aptdry gRAu
ρρρ −
= 2*
Bagnold [1941] Threshold ustar
a
pt
ptdry
DgDg
uρ
ρρ
⋅⋅⋅−⋅⋅⋅
⋅⋅⋅
+= −−
2/10922.0*5.2
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* )1Re928.1(0.12911061(
10Re03.0 * ≤≤ t
a
p
pt
Dge
Dgu t
ρρ
ρ⋅⋅
⋅⋅−⋅⋅⋅⋅
⋅+= −−
−
)085.01(0.1201061( )10(Re0617.05.2
7
**
Threshold ustar
10Re* ≥t
Iversen and White [1982]Marticorena and Bergametti [1995]
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Low sparse grassland (2)Bare desert (8)XXX Sand desert (50)Semi-desert shrubs (51)Semi-desert sage (52)
Based on NAAPS model approach
• Low sparse grassland only in China and Mongolia. Areas of the Steppes, Turkey, New Zealand, and N. America excluded
(2) Bare desert and Semi-desert shrubs north of 60N are excluded.
Erodible Land Use type plotsUSGS dataset 1km (94 Land Use Types)
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Preferential Source AreasGinoux et al. (2001) in GOCART model (topographic approach)
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minmax
max⎟⎟⎠
⎞⎜⎜⎝
⎛−−
=zzzzS i
S: probability to have accumulated sediments in the grid cell i of altitude zi
zmax and zmin: maximum and minimum elevations in the surrounding 10ºx10º topography
best fit with the sources identified by Prospero et al. 2000
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Vegetationfraction
NESDIS 0.144-deg monthly 5-year climatology Time interpolated to daily between successive months
USGS
Source Function [0-1]Ginoux et al. (2001) in GOCART model (topographic approach)
USGS [0-1] * (1-VEGFRAC)
PreferentialSourcesTopo approach USGS [0-1] * (1-VEGFRAC) * PREF [0-1]
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Top soil texture classesNew STASGO-FAO database 1km
Soil Texture triangle to determine the fractions of sand, silt, and clay
Class No. Soil texture class % Sand % Silt % Clay1 Sand 92 5 32 Loamy Sand 82 12 63 Sandy Loam 58 32 104 Silt Loam 17 70 135 Silt 10 85 56 Loam 43 39 187 Sandy Clay Loam 58 15 278 Silty Clay Loam 10 56 349 Clay Loam 32 34 3410 Sandy Clay 52 6 4211 Silty Clay 6 47 4712 Clay 22 20 58
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Modified following Tegen et al. (2002) approach:
Clay loams highly unlikely to contain coarse sandwhile sandy clay loams could containboth coarse and medium/fine sand.Loamy sands tend to contain aggregated clay particles
Top soil texture classesNew STASGO-FAO database 1km
Class No. Soil texture class % Coarse Sand % Fine Medium Sand % Silt % Clay1 Sand 46
4126000
2900000
46 5 32 Loamy Sand 41 18 03 Sandy Loam 26 32 104 Silt Loam 17 70 135 Silt 10 85 56 Loam 43 39 187 Sandy Clay Loam 29 15 278 Silty Clay Loam 10 56 349 Clay Loam 32 34 34
10 Sandy Clay 52 6 4211 Silty Clay 6 47 4712 Clay 22 20 58
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Clay (0-2 μm)
Parent soil size distribution (4 categories) Following Tegen et al. (2002)
Silt (2-50 μm)
Fine/medium sand (50-500 μm) Coarse sand (500-1000 μm)
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BSC/DREAM
zCCKzF LM
SS Δ−
⋅= 00 [Janjic, 1994]Viscous sub-layer for dust injection near the surface
zCCKzF LM
SS Δ−
⋅= 00 [Janjic, 1994]Viscous sub-layer for dust injection near the surface
Model developmentsFrom BSC/DREAM to NMMb/BSC-DUSTEMISSION SCHEME
SEDIMENTATION and turbulent mixout
Settling velocity calculated fromStokes Formula υ
ρρ⋅
⋅−⋅⋅=
18)(2 Ccgdv kk
gk
Cc: Cunningham correction factor accounting for the reduced resistance of viscosity as particle size approaches the mean free path of the air molecules
⎟⎟⎠
⎞⎜⎜⎝
⎛++=
−λλ kd
k
ed
Cc55.0
4.0257.121
Giorgi [1986] for dry deposition Giorgi [1986] for dry deposition
DYNAMICS and MIXING
Dust horizontal advection (Janjic, 1997; Lagrangian)Dust vertical advection (Janjic, 1997 Stepwise linear; Eulerian)Dust horizontal difussion (Janjic, 1990)Dust vertical diffusion (MYJ; Janjic 1994)
NO DUST CONVECTIVE TRANSPORT CONVECTIVE TRANSPORT PLANNED
Improved dust dynamics: Eulerian, positive definite, mass conservative and monotonic
NMMb/BSC-DUST
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BSC/DREAM
Model developmentsFrom BSC/DREAM to NMMb/BSC-DUSTWET SCAVENGING
LMS tP
zC
tC
⎟⎠⎞
⎜⎝⎛
∂∂
Δ⋅−=⎟
⎠⎞
⎜⎝⎛
∂∂ φ
Simple below cloud wasout ratiofor grid-scale and convectiveprecipitation
Grid-scale precipitation: Zhao microphysics Grid-scale precipitation: Ferrier microphysics
)()()()( LC
LCWLP
dtLdC
kCR
kliqin
k ⋅⋅−=−
ε
In-cloud scavenging from grid-scale clouds
Proportional to autoconversion of cloud water to rain and accretion of cloud water by rain and the fraction of dust contained in cloud water, which can eventually be precipitated.
)()(
))(,()()( LCLD
LDdELPcdt
LdCk
kliqkL
liqsub
k ⋅⋅⋅
−= −
−
(Slinn, 1984)
Directional Interception (Eint), Inertial Impaction (Eimp) and Brownian Diffusion (EBD)
Below-cloud scavenging from grid-scale precipitation(snow and rain)
( ) ⎥⎦
⎤⎢⎣
⎡⋅⋅++⋅⋅=− Dd
Dd
E k
w
akk
2/1int Re214
μμ
3/2
* 13/2 −
− ⎥⎦⎤
⎢⎣⎡ +
−=
StStE kimp
)Re16.0Re4.01(Re
4 2/12/13/12/1 ScScSc
E kBD ⋅⋅+⋅⋅+⋅
=−
(Slinn, 1983)
NMMb/BSC-DUST
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BSC/DREAMWET SCAVENGING
Model developmentsFrom BSC/DREAM to NMMb/BSC-DUST
Convective precipitation: Betts-Miller-Janjic Convective precipitation: Betts-Miller-Janjic
LMS tP
zC
tC
⎟⎠⎞
⎜⎝⎛
∂∂
Δ⋅−=⎟
⎠⎞
⎜⎝⎛
∂∂ φ
Simple below cloud wasout ratiofor grid-scale and convectiveprecipitation
In-cloud and below-cloud scavengingparameterized together as suggested by Loosmore and Cederwall, (2004):
In-cloud and sub-cloud scavenging with the Slinn’sexpression for below cloud scavenging:
)()(
))(,()()( LCLD
LDdELPcdt
LdCk
kliqkL
liqsub
k ⋅⋅⋅
−= −
−
(Slinn, 1984)
considering that dust particles with less than 10 μm act as particles of 10 μm
DUST-RADIATION FEEDBACK
NASAShort wave and longave radiationpackage with aerosol capability (Pérez et al., 2006) PLANNED to couple RRTM LW and SW with aerosol capability
NMMb/BSC-DUST
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Simulation set-up:Global60 hours forecast160 CPU’s
769x541 (0.3º x 0.46º)
64 sigma layers
FIRST RESULTS
MARENOSTRUMSupercomputer
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Cycle 20080124 12 UTC54h forecast
FIRST RESULTS
Kg/m3
Dust conc bin 4 Sigma layer 52
Sorry for such a rudimentary visualization
BSC/DREAM
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Kg/m3
Dust conc bin 4 Sigma layer 52
Cycle 20080124 12 UTC60h forecast
FIRST RESULTS
BSC/DREAM
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NMMb/BSC-Dust
Chimere-Dust
Eta/BSC-DREAM
NMMb/BSC-Dust
NAAPS
Cycle 20081201 00 UTC (Cold START)48h forecast
Chimere-dust: courtesy of Laurent Menut
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NMMb/BSC-Dust
Chimere-Dust
Eta/BSC-DREAM
NMMb/BSC-Dust
NAAPS
Cycle 20081201 00 UTC (Cold START)60h forecast
Chimere-dust: courtesy of Laurent Menut
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NMMb/BSC-Dust
Chimere-Dust
Eta/BSC-DREAM
NMMb/BSC-Dust
NAAPS
Cycle 20081201 00 UTC (Cold START)72h forecast
Chimere-dust: courtesy of Laurent Menut
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NMMb/BSC-Dust
Chimere-Dust
Eta/BSC-DREAM
NMMb/BSC-Dust
NAAPS
Cycle 20081201 00 UTC (Cold START)84h forecast
Chimere-dust: courtesy of Laurent Menut
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NEXT STEPS (2009)
- Convective transport and radiative feedback (1st half 2009)
- Specifically check sources in America, Autralia and South Africa
- Cal / val activites and intercomparison with other regional and global dust models
- High resolution global/regional simulations in Marenostrum Supercomputer
- First global operational forecasts (end 2009)
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