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

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3/20

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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)