NATURAL EMISSION: Mineral Dust Aerosols

Transcription

1 NATURAL EMISSION: Mineral Dust Aerosols Paul Ginoux NOAA-GFDL Princeton, NJ Land-surface processes Dust emission modeling Evolution of global inventories and satellite instruments Present state of global inventories Future directions

2 Mineral Dust Aerosols: Physical Processes: Particle motions! "#$ "% &' % $$( "! $(" "#$ )* + ',$""% ( "! )+ ',% -!"( "! $" #$. )'&+ /,01,2,,1

3 Mineral Dust Aerosols: Physical Processes: Dust uplift 134" - ""% &',$""( ( 5#4" 4 ##" $! "#$!# )/# " $' &66 &667+' "! " "$ $# 8#" " "!$-' ' 9$ $3". " - "" " ##"" # 3.! (" #! $""' "" $"" #"!#! " "#$ 3$. "3$ ):($ &6&+',01

4 Mineral Dust Aerosols: Physical Processes: Sand blasting. #! $!"( "! "!# "( $"" 33 " 3. "#$ 3# " 3"- " "#$ " "( " $$- #" ' ',! " :0, 0!" 3. $"",$""! 0!" 3. -.#" ;( ""#$!# 4". 4".

5 Mineral Dust Aerosols: Physical Processes: Saltation flux "-!$5!$5 < (4 3. :($ )&6&+% = ) + = ρ = Φ) = = + > - =? =" >& - = =" (( "!". 9#" 4$#". $!#" 4$#".

6 Mineral Dust Aerosols: Physical Processes: Dust emission $! " % &+ $$" )&6AA+% +, )&66+% 5$#"$. ##"! "!!#"! "#$ B = ) :(" )&66+% ##"! 3$"( 3. $ # - )'A &'A+ +, )+% $ 4!,)&66+ $" 4 = ) = = + = '& ) ) + + ) + + ) +δ δ $.

7 03".!$ " - "" )&6AC &66DC' &666C C C E +% &' 2=" $. F "#!#"! "#$ " -"! ;> & " & ' 2=" #"$$ $. 3. " $#$ $ ( B ;" # > 3!#!"$$.!$" $ 3 ' 1" "" " % &' "% =" # 3. G! " &# ""! 0@ $" ',$ "% =")@+>()@+=").+ - ( #$ 5"$!#" $..!# # " " ' "( 3. $"% =" 3$ -" &G #! $" #"" $. "3 $ -$ 3 5#" " 3 3$ $ - "

8 Mineral Dust Aerosols: Global models: Dust ($3$ " $ $$" )&6AA+!$" -"!"! " " $ B "3" #$ " "!!##.!#" α' Horizontal flux of Sand Q=C s p u *2 (u*-u *t ) Vertical Flux of clay and silt "# :(" )&66+% )& α = & &+ The threshold velocity u *t is a function of particle radius, surface roughness height, temperature, and surface wetness

9 Mineral Dust Aerosols: Global models: Input datasets " ""! ($3$ " $% % = % ($3$ 4! -!$ $"!% G#$. G$" G % ($3$ " )'(' 91+ " ($".% ($3$ " )'(' 91+ #% B % ($ )'(' "!# "#+ 3" ($3$ " $% G$" G1 % ($3$ " % ω % $!# $ :#. "" )'(' B+ ( " " ($3$ #$ " "$ "3"! " # 4 " 3 #! ' 4$"! " # 4" 4 #$$.!$$- " (! "$$" ""'

10 Dust Source Inventories following Progress of Satellite Instruments HI// Dec. Jan. Feb. 90N 60N 30N S 60S 90S Mar. Apr. May N 60N 30N 1@, S 60S 90S Jun. July Aug N 60N 30N S 60S 90S Sep. Oct. Nov N 1;,

11 Satellite Instruments for Sources study AVHRR (.63/.83µm) 1984 Mishchenko/Geogdzhayev (.63µm) 1979 Stowe TOMS (.34/.38µm) 1979 Torres/Herman/Bhartia MODIS (.44/.67/1.6/2.2µm) 2000 Kaufman/Tanre/Remer/Chu Instrument Limitations AVHRR model, ocean-limited, calibration, clouds TOMS model, height, spatial resolution (50km at nadir) MODIS model, aerosol shape, limited land-coverage

12 What can we learn from AVHRRs data? 90N 60N 30N 0 30S 60S 90S 1 Dec. Jan. Feb. Dec-Feb Long range transport of dust from African deserts to Caribbean and SE USA (Prospero and Carlson, 1972) 90N 60N 30N 0 30S 60S 90S 90N 60N 30N 0 30S Mar. Apr. May Mar-May Jun. July Aug Latitudinal variation of African plume over the Atlantic: minimum in January (5N) Maximum in August (20N) 60S 90S 90N 60N Jun-Aug Sep. Oct. Nov N 0 30S 1 60S 90S W 60W 0 60E 120E 180 Sep-Nov

13 Mineral Dust Aerosols: Global models: Natural dust sources (Tegen and Fung, 1994) 91% "- H("" #4 ( 9( )&66+% " # > G$. G,$" 4 -$. 4(""!# $!" HI//

14 Mineral Dust Aerosols: Global models: Anthropogenic dust sources (Tegen and Fung, 1995) :. ( G "(# # ( 9( )&66+ $ #" $!"! $ HI//

15 Mineral Dust Aerosols: TOMS Aerosol Index TOMS Aerosol Index (Herman et al., 1997)! "! = &K$()! λ λ +! $()! L " -4$(" )& + L $( -4$(" )7 A + λ λ + J "%"'(!#''(4

16 What can we learn from TOMS aerosol index? The maxima of TOMS AI are persistently (over 20 years of observations) at the same locations (Prospero et al., RG, 2002). ( 4 3 )@ $+ : $ ) + "$ )+ 03. Prospero, Ginoux, Torres, Nicholson, and Gill, Rev. Geophys., 40(1), 2002.

17 Mineral Dust Aerosols: TOMS Aerosol 5 #" -" "(#. $ 4 3 ) " $' +

18 Mineral Dust Aerosols: Global dust sources 5 " $' )&+ #3 " " # 3 "(. &#& 5 = &#& &#& 5 4("" )3!#! HI// &5& (+ 1/ 9;0 $ M/@ 1,I@ 2,. 4$ ($ $ & /11@'

19 Mineral Dust Aerosols: GOCART dust model GOCART Aerosol Model Sulfate, Dust, Black Carbon, Organic, Sea-salt Chin et al. (2000); Ginoux et al. (2001); Chin et al. (2002) Global, 2x2.5, 21 levels GEOS DAS Assimilated Meteorology Source Function S Topography & Vegetation Emission F=C.s p.s.u 2.(u-u t ) Transport Advection Eddy Diffusion Moist Convection Removal Gravitational Settling Wet deposition (rainout/washout) Surface dry deposition Size distribution 7 bins: m

20 Mineral Dust Aerosols: Global dust emission 1/ $ ($3$! &6A& " &667 )5 " $' + $" 1/ -" );9+ -" 1 5 )5 " $' +

21 Mineral Dust Aerosols: Other global dust inventories & '' (# #" -" $ $ "(# # $ " &G' )& ''"($(#$ # N O " $' )3+ # -" 5" $' )&+ ( " $' )+' ' 0)+% "(# # #$ 3 ( G $"( " #"" #" -" -!#" 4$#". " $$- #$ ##$

22 Mineral Dust Aerosols: Validation using TOMS AI The dust emission can be evaluated by calculating the TOMS AI, either by using radiative calculation (Ginoux et al., EMS, 2004); or by using an empirical formulation (Ginoux and Torres, JGR, 2003): Explicit function of physical quantities: plume altitude h, optical thickness τ, "! = K + $ )& ω + J) τ+ ω ω For 0.75 < ω TOMS (331/360): a=0.65 b=4.25 TOMS (340/380): a=1.25 b=5 % 1$. "" -# $$- #" # $. ($3$$ "PP +0- "4". "!# ($ #"( $3 " 3 4 '

23 What can we learn from MODIS 500m RGB? : " " $ #" -" $ $

24 Mineral Dust Aerosols: TOMS AI frequencies and ephemeral lakes, $ $ #"4 " "' 1@, # $ " -# #"4 3"! -" " $" "!" #$"! ($ " - " " -.'

25 Mineral Dust Aerosols: Anthropogenic sources, " $ " #" -" $ $ " 3! "(# (% 4#$. $ " 5#4 $ 0.,

26 Present state At the present state there are available: three global inventories (G-2001; Z-2003; T-2004) which reproduce essential global characteristics of dust observations (correct seasonal variations everywhere, concentrations within a factor 2, size distribution, AOT, etc.). These global inventories offer the advantage to by-pass complex physical models which needs highly variable surface soil characteristics; two high resolution inventories: Western Africa (B. Marticorena), and Australia and China (Y. Shao). The anthropogenic contribution to dust emission seems to be limited to about 10% but it is mostly a guess. With actual dust inventories, the most important parameter are the soil moisture, vegetation cover, ( and surface roughness for physical process models).

27 Future directions Comparison of source inventories using satellite data: TOMS AI is global, daily and 25 years. Could also be used for dust CTM inversion. OMI launch (July 2004) could be helpful because it will provide much better resolution than TOMS. On-going work of dust retrieval with 1.5 km vertical resolution globally and daily from AIRS satellite instrument could be very helpful. Do we need to get better characterization of dust sources and include ephemeral lakes considering that it is a painstaking job using MODIS Terra or Aqua level1b data and will it be possible to understand why some are active and others not? Anyway, several groups are attempting it. Prospero and Lamb (2004) found a good correlation between Sahel drought index of previous year and dust concentration at Barbados over 40 years of data. What is the physical explanation? No one knows.

28 References 1. Bagnold, R. A., The physics of Blown Sand and Desert Dunes, Methuen&Co., Ltd., London, Fecan, F., Marticorena, B., and G. Bergametti, Parameterization of the increase of the aeolian erosion threshold wind friction velocity due to soil moisture for arid or semi-arid areas, Annal. Geophysic., 17, , Gillette, D. A., and R. Passi, Modeling dust emission caused by wind erosion, J. Geophys. Res., 93, , Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and S-J. Lin, Sources and distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res., 1006, , Ginoux, P. and O. Torres, Empirical TOMS index for dust aerosol: Applications to model validation and source characetrization, J. Geophys. Res., 108(D17), 4534, doi: /2003jd003470, Ginoux, P., Prospero, J. M., O. Torres, and M. Chin, Long-term simulation of global dust distribution with the GOCART model: correlation with North Atlantic Oscillation, Environmtal Modeling & Soft., 19, , Herman, J. R., Bhartia, P. K., Torres, O., Hsu, C., Seftor, C., and E. Celarier, Global distribution of UVabsorbing aeorosols from NIMBUS-7/TOMS data, J. Geophys. Res., 102, , Mahowald,N. and C. Luo, A less dusty future?, Geophys. Res. Letters, 30, no 17, 1903 doi: /2003gl017880, Marticorena, B, and G. Bergametti, Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme, J. Geophys. Res., 100(D8), , Marticorena, B., Bergametti, G., Aumont, B., Callot, Y., N Doume, C., and M. Legrand, Modelling the atmospheric dust cycle: 2. Simulation of Saharan dust sources, J. Geophys. Res., 102, , McKenna-Neuman, Effects of temperature and humidity upon the entrainment of sedimentary particles by wind, Boundary Layer Meteorol., 108, 61-89, Nickling, W.G.., The stabilizing role of bonding agents on the entrainment of sediment by wind, Sedimentology, 31, , 1984.

29 References 13. Prospero, J. M. and T. N. Carlson, Vertical and areal distribution of Saharan dust over the western equatorial North Atlantic ocean, J. Geophys. Res., 77, , Prospero, J. M., Ginoux, P., Torres, O.,Nicholson, S. E., and T. E. Gill, Environmental characetrization of global sources of atmospheric soil dust identified with the NIMBUS-7 TOMS absorbing aerosol product, Rev. Geophys., 40, 1-31, Rice, M., Willetts, B., and I. McEwan,An experimental study of multiple grain-size ejecta produced by collisions of saltating grains with a flat bed, Sedimentology, 42, , Rice, M., Willetts, B., and I. McEwan, Wind erosion of crusted soil sediments, Earth Surf. Process. Landform, 21, , Shao, Y., Raupach, M. R., Findlater, P. A., The effect of saltation bombardment on the entrainment of dust by wind, J. Geophys. Res., 98(D7), , Shao, Y., Physics and Modelling of Wind Erosion, Kluwer Academic Publishers, Dordrecht, Shao, Y., Simplification of a dust emission scheme and comparison with data, J. Geophys. Res., 109, D10202, doi: /2003jd004372, Tegen, I. And I. Fung, Modeling of mineral dust in the atmosphere: Sources, transport, and optical thickness, J. Geophys. Res., 99(D11), , Tegen, I., and I. Fung, Contribution to the atmospheric mineral aerosol load from land surface modification, J. Geophys. Res., 100(D9), , Tegen, I., S. P. Harrison, K. E. Kohfeld, I. C. Prentice, M. C. Coe, and M. Heimann. The impact of vegetation and preferential source areas on global dust aerosol: Results from a model study. J. Geophys. Res.,107, doi: /2001jd000963, Zender, C. S., H. Bian, and D. Newman, Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology, J. Geophys. Res., 108(D14), 4416, doi: /2002jd002775, 2003a. 24. Zender, C. S., D. J. Newman and O. Torres, Spatial heteogeneity in aeolian erodibility: Uniform, topographic, geomorphic, and hydrologic hypotheses, J. Geophys. Res., 18, 4416, doi: /2002jd003039, 2003b.