Paul ginoux noaa geophysical fluid dynamics laboratory princeton nj usa
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Paul Ginoux NOAA-GEOPHYSICAL FLUID DYNAMICS LABORATORY Princeton, NJ USA. Identification of anthropogenic and natural dust sources. US emission initiative December 4, 2009 Boulder, CO. December 4, 2009. Natural dust sources.

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Paul ginoux noaa geophysical fluid dynamics laboratory princeton nj usa

Paul Ginoux

NOAA-GEOPHYSICAL FLUID DYNAMICS LABORATORY

Princeton, NJ

USA

Identification of anthropogenic and natural dust sources

US emission initiative

December 4, 2009

Boulder, CO

December 4, 2009


Natural dust sources
Natural dust sources

bare and dry surfaces with fine soil texture (clay, silt or alluvium) = dry river, lake or sea bed

Dust from dry river bed, Alaska

Bodele depression, paleo-lake, Chad

Dust from ephemeral lakes, Mauritania


Anthropogenic dust sources
Anthropogenic dust sources

  • Disturbed soil due to agriculture, overgrazing

  • Dry sea or lake bed by excessive irrigation

  • Construction, mining, etc.

Dust from dry sea bed, Aral sea (Uzbekistan)

Cattle herds, Chad

Mining, Ohio valley


Identification of dust sources
Identification of dust sources

  • Dust size:0.1 to 10 mm radius

    Angstrom exponent: a=-log(t1/t2)/log(l1/l2)<0.5

  • Dust optical properties:

    • Absorb radiation in nUV and infrared

    • Weak absorption in visible

  • Single scattering albedo: w(l1)<w(l2)<w(l3)

Dubovik et al., JAS, 2002


Using nuv toms satellite data for global natural dust source inventory
Using nUV TOMS satellite data for global natural dust source inventory

Ginoux et al. (2001) global inventory of natural dust sources based on TOMS AI

λ: 331 or 340 nm

λ0:360 or 380 nm

DUST: w(l)<w(l0) => TOMS AI > 0 if no clouds

Dust source = maxima of distribution of Frequency Of Occurrence (FOO) TOMS AI > AIthresh

Prospero et al. RG, 2002


Identification of anthropogenic and natural dust sources
Identification of anthropogenic and natural dust sources inventory

  • Dust sources can be identified from satellite spectral data (Ginoux et al., 2009)

    • Single scattering albedo: mostly dust

    • Angstrom exponent: “freshly emitted dust”

  • There is no difference of optical properties between anthropogenic and natural dust. Need to rely on other dataset -> Land use (or land use change)


Using modis db spectral properties to retrieve dust source
Using MODIS DB inventoryspectral properties to retrieve dust source

Deep blue algorithm uses solar reflectance at 412, 470 and 670 nm to retrieve t, w, a

Advantages: retrieve over bright surface, less sensitive to aerosol height that nUV technique, good resolution (~10km)


Overlapping sources with land use change
Overlapping sources with land use change inventory

There are anthropogenic dust sources in Sahel all the way South to the Guinea Gulf but plumes less frequent than Sahara and less intense.


Validation
Validation inventory

1. Analyzing each source with independent datasets. At 10 km resolution, it is possible to use GoogleEarth to associate dust source with natural or anthropogenic features.

2. Using Dubovik et al. (JGR, 2008) adjoint model with assimilation of MODIS DEEP blue with constrain on Angstrom exponent and SSA(l)

Dust and sea-salt sources retrieved using MODIS+GOCART (Dubovik et al., 2008)


Over us
Over US inventory


Summary
Summary inventory

Dust optical properties have unique characteristics which can be used from satellite data to determine dust sources

Natural dust sources: global inventory at 1x1o and 0.25x0.25ohttp://www.gfdl.noaa.gov/atmospheric-physics-and-chemistry_data

Anthropogenic sources:

  • weaker than natural and less frequent than natural but significant and different locations.

  • Inventory 10x10 km. West Africa published. Global work in progress. Needs validation.


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