computational modelling of aerosol cycle an integrated environmental modelling system and its...

Computational Modelling of Aerosol Cycle

An Integrated Environmental Modelling System and Its Applications

Dr. Yaping Shao

CEMAP, School of MathematicsThe University of New South Wales

Sydney, AustraliaTel: 61 2 9385 5746; Fax: 61 2 9386 7123

email: y.shao@unsw.edu.au

Aerosols

– Aerosols are small particles suspended in air. The sizes of aerosols range between 0.1 - 20 microns;

– Aerosol sources include natural and human induced ones.

Aerosol Research: Climate and Weather

• Directly, aerosols affect atmospheric radiation budget through scattering and absorbing;

• Indirectly, aerosols modify the optical properties and lifetimes of clouds;

• Dust (global emission): ~ 3000 Mt/yr.• Sea salt: ~ 1300

Mt/yr.• Dust (mean column load): ~ 65 mg m-2

• Sea salt: ~ 7 mg m-2

Dust storm in Africa: 27 July 1998, Algeria and Mali

A severe dust storm over China (16 April 1998)

Dust clouds seen from satellite picture (14 April 1998)

• Aerosols cause air-quality hazards in populated areas, e.g., Beijing;

• Many contaminants which pose significant risks to human health and the environment are found or associated with dust, including metal, pesticides, dioxins and radionuclides.

Aerosol Research: Air quality

A severe dust storm (acknowledgement)

• In agricultural areas, soil erosion depletes fine particles which are rich in organic matters and soil nutrients. This leads to land degradation;

• Wind erosion also reduces water, resulting in desertification.

Land-Use Sustainability

A dust storm in an agricultural area

Soil Erosion

Melbourne 08-02-1983 dust storm: Nutrient content in soil particles < 44 microns

SiteSoil Type

WamberraSand

Box CreekClay Loam

Montarna SandyLoam

Total N (%) 0.226 0.16 0.153

Totol P (%) 0.038 0.029 0.034

N enrichmentratio

19 2 2

P enrichmentratio

5.7 1.9 2.4

Mass fraction 0.003 0.11 0.06

Melbourne 08-02-1983 dust storm

Total loss of top soil M=2 million tonnes

Total loss of N M*0.0017=3400 tonnes

Total loss of P M*0.000055=110 tonnes

Cost of fertilizer(N:P:K=32:10:0)

0.37 dollars

Cost of N 3.9 million dollars

Cost of P 0.4 million dollars

Mineral Aerosol Cycle

– Entrainment: atmosphere and land-surface interactions; multi-disciplinary;

– Transport: atmospheric circulation; atmospheric boundary layers; turbulence; two phase flow problem

– Deposition: turbulent diffusion; clouds and precipitation.

Integrated Environmental Modelling

– How can such complex environmental problems be simulated and predicted?

– Computational environmental modelling: the integration of dynamic models with spatially distributed data

– Atmosphere-land surface interactions– Air quality– Aerosol cycle– Land surface hydrology and salinity

Framework I

Computational Environmental Modelling System (CEMSYS_3)

– Atmospheric prediction model (HIRES): high-resolution limited-area; nested in GCM, self-nested; 3rd order upwinding and semi-lagrangian schemes; clouds and radiation.

– Land surface (ALSIS): Soil moisture, temperature; fluxes of energy, mass and momentum;

– Aerosol cycle: entrainment, transport and deposition.

– Air quality, etc

Framework of CEMSYS_3 (partial)

Physical processes involved in wind erosion

Particle Motion

– Saltation: hop motion of sand particles;

– Suspension: small particles can remain suspended once airborne.

• The capability of wind to cause erosion is quantified by surface friction velocity, u*,depending on wind speed and surface roughness

• The ability of the surface to resist erosion is quantified by threshold friction velocity u*t, depending on soil texture, compactness, moisture content and surface coverage

• Modeling u*t is difficult

Friction velocity & threshold friction velocity

Entrainment of Coarse Particles• Balance of aerodynamic, gravity and cohesive forces,

fa, fg and fi, determines the entrainment;• For coarse particles, fa overcome fg and fi;• Friction velocity u* measures aerodynamic forces;• Threshold friction velocity u*t measures retarding

forces. • Shao-Lu model for u*t is

) )( (Re* *d

gd f up t

Entrainment of Fine Particles

• The Entrainment mechanisms for coarse and fine particles differ as the importance of forces change.

• fg d3, fa d2 and fi d; fi

dominates.

Dust Emission Mechanisms• Fa, aerodynamic lift. Particles can be lifted

directly by fa, but emission is weak;

• Fb, saltation bombardment. Striking particles cause local impacts, overcome fi, result in strong emission;

• Fc, aggregates disintegration. Fine particles exist as aggregates. Weak events, they behave as grains. Strong events, they disintegrate.

• Dust-emission rate:

F = Fa + Fb + Fc

• Soil particle size ranges: 0.1 m - 2 m• Gravel: 2000 m < d 2m• Sand: 63 < d 2000 m• Silt: 4 < d 63 m• Clay: d 4 m• Silt and clay particles are dust.

Particle-size Distribution

Particle-size Distributions

• ps(d): sediment particle-size distribution (psd);

• pm(d): in-situ soil psd; minimally dispersed analysis;

• pf(d): fully-disturbed soil psd; fully-dispersed analysis.

Model for ps(d)• Limiting cases

]n)t*

u*

u(kexp[Weight

)d(f

p)1()d(mp)d(spModel

t*u

*u)d(

fp)d(sp

t*u

*u)d(mp)d(sp

Example of ps(d)

Fractions of Fine Particles

dd

0d)d(mp)d(

fpc

dd

0d)d(sps

dd

0d)d(

fp

f

dd

0d)d(mpm

m: free dust, lower limit for dust emission from unit soil mass;f: not free dust, released through saltation impact and aggregates disintegration, upper limit for dust emission from unit soil mass;s: aerosol in suspension

Theory of Saltation• Saltation plays a critical role in the process of

dust emission. • Two quantities are of particular importance,

namely, the streamwise saltation flux, Q, and the number flux of striking particles, ns

)cosUcosU(umc

Qgn

)u

u1(u

gcQ

2211*0s

2*

2t*3

*0

Volume Based Model for Fb

Particle trajectory is (XT, YT) in soil, forms a crater of volume

Volume Based Model for Fb

•Trajectory from equation of particle motion;•cbf: fraction released;• (1-cb)f : fraction retained;

ssfbsb

pT

pT

yyp

x xp

Tt

0 T

n c )(dF

0 v- dY

0; u - dX

0; p a dv

m 0; pa dt

dum

dt dt

dXYb

c

dtdtdt

Volume Based Model for Fb

•Particle trajectory is (XT, YT) in soil, forms a crater of volume ;•Trajectory from equation of particle motion;•cbf: fraction released; (1-cb)f : fraction retained;

ssfbsb

pT

pT

yyp

x xp

Tt

0 T

n c )(dF

0 v- dY

0; u - dX

0; p a dv

m 0; pa dt

dum

dt dt

dXYb

c

dtdtdt

Aggregates Disintegration: Fc

• Aggregates disintegration occurs as they strike surface.

• Corresponding to ns, the mass flux of particles striking surface is mns.

Fc(ds) = cc fc m ns

• cc: a coefficient

Total Dust Emission: F• Divide particles into I size groups, mean di,

increment di; Consider emission of i group

)i(dI

iF F

d(d) s, d) pi(ddd F ) i(dF

m )ci b

fi

( Qgcc

. ) s, di(dFid

E cccbc

sm nci cc) s, di(dcFsnbfib

c ) s, di(db

F

1ˆ :Total

ˆ :group ith of Emission

056

~)(

~~

~~;~~

2

1

Model of Particle Size Distribution• Emission model requires pm(d) and pf(d).

• Express as sum of J log-normal pdfs with parameters wj, Dj and j; both for pm(d) and

pf(d) for sand, loam and silty clay.

22

2)ln(lnexp

121

j

jDd - •

J

j j

jw

d p(d)

c

• Model requires ;

• cE fi /: fraction of release;

• cY = 1/7 co, order 0.1.

m)cifib(

muQg]

idf

pidmp

)-[(Yc)s,di(dF

idf

pidsp

fisiEc

sm)n b

(fi

Ec)s, di(dF

2*

)()(

1~

)()(

~

Quantities Required• u*: friction velocity;

• u*t: threshold friction velocity for surface;

• pm(d): minimally-dispersed psd;

• pf(d): fully-dispersed psd;

b, p: bulk soil and particle density;

• s: soil drag coefficient;

• pys:vertical component of plastic pressure.

Results

Conclusions for Emission Model• Concept: F is related to Q;• Mechanisms: saltation bombardment and aggregates

disintegration;

• Models for Fb and Fc;

• Soft soils, Fb dominates;Hard soils, Fc dominates;

• psds are used to eliminate empirical parameters;• psds modeled using log-normal pdfs;• Emission rates compare well with observations.

Transport: Lagrangian• Particles are individuals; Trajectories are

determined by integrating equations of motion;• Isentropic trajectories on surface of constant

potential temperature;

• Fluid parcel and particle are at height zft-1 = zp

t-1 at t-1, fluid moves to zf

t, particle to zpt=zf

t+wtt.

Transport: Eulerian• Particulate phase is a continuum;• Particle concentration obeys advection-diffusion type

of conservation equation;

• Kpx: particle eddy diffusivity; Sr: wet and dry removal; Sc: dry and wet convection; F0: dust flux at surface

00

zc pzK

Fzc

pzK)c - tw(w

c S rSzc

pzKz

yc

pyKyx

cpxK

xzc)tw(w

ycv

xcu

tc

Inertial and Trajectory Crossing

Particle Eddy Diffusivity

Deposition• Dry-deposition flux

Fd = -wd[c(z)-c(0)]

• c(0), c(z): concentration at surface and reference level; wd: dry-deposition velocity.

• Single-layer dry-deposition model

wd=-wt+gbb+gbm

• gbb: molecular conductance; gbm: impaction conductance; fr: ratio of pressure drag to total drag;

wd=-wt+ga[fr ap em+(1-fr)avSc-2/3]

Wet Deposition

Wet deposition is the removal of aerosols by precipitation. The processes is extremely complicated, but is commonly calculated using

Fw=w pr0s0c0

s0: scavenging ratio is a function of many parameters, but ranges from 100 to 2000.pr0: rain received at the surface;c0: concentration in rain water .

Example 1: How does the Scheme Work

Comparison with Field Measurements

Land Surface Data

Weather pattern

Feb. 1996

Soil Erosion

Threshold Friction Velocity

Friction Velocity

Concentration Cross Section

Total Suspended Dust Time Series

Comparison with Satellite Image

Aerosol Concentration

Surface Concentration: Birdsvill, Feb. 1996

HigherResolution

Higher Resolution

• A comprehensively integrated system has been developed for the simulation and prediction of the entire mineral dust cycle, from entrainment, transport to deposition. CEMSYS_3 has a much wider range of applications;

• I have illustrated how the entire cycle can be modeled. Each of the modeling components constitutes an interesting research area. I have concentrated on dust emission in this talk;

• Coupling dynamic models with spatially distributed data has enabled the predictions of dust storm events.

Summary