JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 104, NO. D18, PAGES 22,243-22,256, SEPTEMBER 27, 1999

Modeling the mineralogy of atmospheric dust sources

T. Claquin, • M. Schulz, • Institut ffir Anorganische und Angewandte Chemie, Universit•it Hamburg, Hamburg, Germany

Y. J. Balkanski

Laboratoire des Sciences du Climat et de l'Environnement, Direction des Sciences de la Mati•re, Commissariat k l'Energie Atomique, Gif-sur-Yvette, France

Abstract. The variability of atmospheric dust mineralogy influences the impact of desert dust on the Earth's radiative budget and biogeochemical cycles. Until now, atmospheric transport models have assumed that dust was a constant •--• •].u•mmu•,• m•uii. S

mixture, hence neglecting this variability. The lack of mineralogical data in arid areas prevented a better description of the atmospheric dust composition, and we propose here a new formulation to estimate the mineral content of arid surfaces on a global scale. First, we collect a Database of Arid Soil Surface Mineralogy for eight major minerals: quartz, feldspar, calcite, gypsum, illite, kaolinite, smectite, and hematite, both for the clay and silt fraction. On the basis of this, we formulate a Mean Mineralogical Table that relates classical soil types to surface mineralogy. We use this table and the geographical distribution of soil types given in the Food and Agriculture Organization Soil Map of the World to obtain the mineralogy of arid surfaces globally. In order to validate these results, we present a compilation of measured mineralogical composition of dust samples with identified sources. The correlation between observed dust mineralogy and those inferred from soil types in corresponding areas is between 0.70 and 0.94. We then calculate the maps of the single scattering albedo and of the ratio of infrared extinction to visible extinction for the erodible fraction of arid areas. Mineralogical maps presented here will be used in future studies with an emission scheme in a global transport model.

1. Introduction

Wind erosion in arid areas is responsible for an atmo- spheric dust veil that influences the Earth-atmosphere radiative budget [Tegen and Lacis, 1996; $okolik and Toon, 1996], the atmospheric chemistry [Dentenet et al., 1996], ocean biogeochemical cycles [LoUie-Pilot et al., 1986; Swap et al., 1992; Martin et al., 1994] and soil formation [ Tiessen et al., 1991; Simonson, 1995; $toor- vogel et al., 1997]. Reflecting the variability of Earth surface materials, dust particles can have very differ- ent mineralogical compositions that affect their physi- cal and chemical properties. For instance, $okolik et al. [1998] and Claquin et al. [1998] have shown that vari- ations in the dust mineralogy can drastically modify the amplitude of the dust radiative forcing in the in-

1now at Laboratoire des Sciences du Climat et de

l'Environnement, Direction des Sciences de la Mati•re, Commissariat k l'Energie Atomique, Gif-sur-Yvette, France.

Copyright 1999 by the American Geophysical Union.

Paper number 1999JD900416.

0148-0227 / 99 / 99 JD 900416509.00

frared and in the visible range. However, recent works on numerical models [Tegen and Fung, 1994; Schulz et al., 1998] and on satellite retrieval algorithms [Moulin et al., 1997; Husar et al., 1997] consider dust as a constant homogeneous mixture of minerals (e.g., in radiative cal- culations, with a single refractive index), mainly owing to lack of a better description. The quantification and the understanding of the influence of dust require there- fore the development of tools to understand the global repartition of different mineral species at the surface of arid areas, especially that fraction of the soil subject to uplift by wind.

The mineralogy of dust sources can be assessed with two methods: the study of the mineralogy of atmo- spheric dust samples or the direct study of the miner- alogy of potential source regions. For the first method, the source of the dust outbreak has first to be identified, and the mineralogy of the dust sample is then attributed '" the region. Of course, the identification of the remote source area is the difficult step in these studies, and early works were therefore limited to establishing rough geographical links [e.g., Glaccum and Prospero, 1980; Chester et al., 1972]. The recent use of back trajectories has considerably improved the precision of source iden- tification [Merrill et al., 1994; Caquineau, 1997; Avila

22,243

22,244 CLAQUIN ET AL.: MODELING DUST MINERALOGY

et al., 1997]. Unfortunately, these studies are too geo- graphically limited to provide a global perspective. The second method, that is, studies made directly on soils, is complicated by the necessary definition of an erodible fraction. Some systematic studies, which included dif- ferent classifications of soils erodibility and mineralogy, have been done in the southern United States [Gillette et al., 1980; Breuniger et al., 1989] and the northern Sahara [Gomes, 1990; Rognon et al., 1989; Chatenet et al., 1996], but this method has never been applied to Asia, the Sahel, or Australia, and no global description of the mineralogy of soil erodible fraction is currently available.

In the present study, we develop an original method, based on the Food and Agriculture Organization (FAO) Soil Map of the World (hereafter FSMW) [FA O-United Nations Educati.onal, Scientific, and Cultural Organi- zation ('FAO-UNE$CO), 1995] and on a literature sur- vey of the mineral composition of soils of arid areas, to establish the mineralogy of atmospheric dust sources globally. After selection of the most important miner- alogical species (section 2), we establish a correspon- dence between the FSMW and the mineralogical com- position of erodible soils in arid areas (section 3). Our results are then presented and compared to observed dust mineralogy, and the maps of dust radiative prop- erties (single scattering albedo, infrared absorption) are evaluated (section 4).

2. Choice of a Mineralogical Set

The mineralogical diversity in dust samples is as large as in rocks of the Earth's surface: feldspar, quartz, phyl- losilicates in various crystalline forms, carbonates, sul- fates, phosphates, salts, and heavy minerals like pyrox- enes or amphiboles are all present in dust samples [Pye, 1987]. The numerical study of the dust cycle requires the selection of a set of minerals species which are, in the present paper, quartz, feldspars, illite, kaolinite, smec- tite, carbonates, gypsum, and iron oxides. The selection (or the nonselection) of minerals is justified in terms of abundance, optical properties, and chemical proper- ties, except in the case of apatite (a phosphate mineral important for biogeochemistry), which was not studied because too few measurements are available.

In the family of silicates, we decided to treat sepa- rately quartz, feldspars, illite, kaolinite, and smectite. First, illite, kaolinite, and smectite are interesting trac- ers of the climate since their content in soils depends upon humidity and temperature at the time of forma- tion [Millot, 1964; Biscaye, 1965]. Second, phyllosili- cates crystallize in small plate minerals, while tectosili- cates are larger and more spherical [Glaccum and Pros- pero, 1980]. Hence the study of both groups of minerals implicitly requires the description of the size distribu- tion of dust particles. Third, because of A1 substitu- tions in the SiO2 network, phyllosilicates and feldspars absorb less infrared radiations than quartz [Hunt et al.,

1950; Patterson, 1981] and may therefore affect the ra- diative budget differently. Finally, phyllosilicates have a large surface area, which implies considerable chem- ical reactivity and the possibility of binding ions and trace elements [Norfish and Pickering, 1983]. For all these reasons, the determination of the relative abun- dance of quartz, feldspars, illite, kaolinite, and smectite in the erodible fraction of arid areas would help geo- chemical and climate modeling. Two important phyl- losilicates are not considered in this list: chlorite and

palygorskite, the first being present in low contents [Chester et al., 1972] and the second one being geo- graphically limited to calcareous areas [Coud•-Gaussen et al., 1987; Caquineau, 1997] and generally more abun- dant in soils than in airborne dust samples [Schlitz and $ebert, 1987].

Carbonates and gypsum are also studied separately in this study. Mainly present over carbonaceous base- ments, carbonates (calcite, dolomite) absorb few in- flared radiations between 8. and 12. /•m [Long et al., 1993] and have a high solubility, which influences the pH of rains [Loije-Pilot et al., 1986] and favors them to act as cloud condensation nuclei (CCN), thereby contribut- ing to an indirect radiative effect. In turn, gypsum par- ticles come from a lacustrine (or paleolacustrine) en- vironment, and though they might be abundant, their occurrence is usually local (e.g., in the near east) [Levin and Lindberg, 1979; Khalaf et al., 1985]. Gypsum par- ticles have a moderate absorption in the infrared range. The main interest of studying them comes from the combination of calcium and sulfates, which may impact atmospheric oxidative pathways and the pH of droplets.

Moreover, in addition to the minerals discussed above, iron oxides (hematite and goethite) are also constituents worth studying. These oxides account for the "free" part of the iron present in dust (the remaining iron con- tent being trapped in insoluble minerals). Iron oxides supply a limiting nutrient for the development of phy- toplankton in iron-limited regions [Duce, 1986; Martin and Gordon, 1988] and in parts of the Amazonian rain forest [Swap et al., 1992]. Iron oxides also control the amplitude of light absorption of dust in the visible range and therefore its climatic effect [$okolik et al., 1993]. In soil studies and in dust analysis, the most common forms of iron oxides, hematite and goethite, are often considered together because they usually have a limited abundance. As far as mineral aerosols are concerned, hematite and goethite have close radiative properties, and, in this work, we consider them together.

Finally, different minerals do not appear in the same particle size range, which implies that the mineralogy of a dust outbreak also depends on the particle size distribution. In turn, this mineralogy is a function of those processes which control the particle size distri- bution, namely, the wind energy involved in erosion [Alfaro et al., 1997] and the subsequent removal dur- ing atmospheric transport. Since there are not enough measurements to attribute a size distribution (e.g., log-

CLAQUIN ET AL.: MODELING DUST MINERALOGY 22,245

normal or gamma) to each mineral, we decided to treat the size dependence of the mineralogy by distinguishing the mineralogy of the clay and silt fraction. However, two points need to be clarified. First, the clay (or silt) mineralogy of a dust sample is not directly related to the clay (or silt) mineralogy of the soil, because of a release of small particles by sand blasting during the emission of dust [Alfaro, 1997]. Second, soil analyses are usually done after wet sieving, which separates small particles from aggregates, and, to our knowledge, relationships between the mineralogy of the "dry" soil, the mineral- ogy of the "wet sieved" soil, and the mineralogy of the suspended dust have not been studied. We will there- fore assume that the clay and silt mineralogy obtained after a wet sieving is a reasonable estimate of the rela- tionship of dust mineralogy to the size of particles.

3. Methodology

a.1. Hypothesis

In this work, we would like to map the amount of quartz, feldspars, illire, kaolinire, smectite, calcite, gyp- sum, and iron oxides in soils surface of arid areas. We use for this purpose the geographical distribution of soil types, particle size distribution, and chemical proper- ties compiled in the FAO Soil Map of the World (here- after FSMW)[FAO-UNESCO, 1995], which is probably the best data set for this first approximation of desert mineralogies. FAO soil types are defined on the ba- sis of present knowledge of the formation, physical and chemical characteristics, and distribution of the soils covering the Earth surface [FAO-UNESCO, 1974]. The mineralogical content of the soil surface is not used as a diagnostic criterion or property in the FAO map. Our hypothesis is, however, that the mineralogy of the sur- face depends on the size distribution, the chemistry, and the color of the soil and that an average surface miner- alogy can be inferred for each soil unit of the arid zone and topsoil textural class shown on FSMW. The largest limitation is due to the importance of aeolian deposits at the surface of desertic and peridesertic areas [Gomes, 1990; Jahn, 1995], which implicitly are not taken into account in the FAO legend. The mineralogy of such deposits cannot be linked to the properties of the soil, and we are therefore prone to neglect it.

3.2. Database of Arid Soil Surfaces Mineralogy

In order to establish a correspondence between soil types and surface mineralogies, we first compiled avail- able literature into a Database on Arid Soil Surfaces

Mineralogy (DASSM) (Table 1) that we use to establish a relationship between each soil type and a mineralogy: the Mean Mineralogical Table (MMT) (Table 2). This approach was necessary because existing soil databases do not contain information on the mineralogy, as this information is not collected during routine soil surveys [Scholes et al., 1995; Batjes et al., 1995]. References used in the DASSM were selected using four eriteria:

the soil analyses had to (1) be done on a sample com- ing from an arid area, (2) contain quantitative mea- surements of all relevant minerals in the first 20 cm

of the soils, (3) be relatable to a FAO soil type, and (4) treat separately the clay mineralogy and the silt mineralogy. When the aridity of the sampling site was not explicitly specified by authors, we used the crite- rion that local climatological precipitation should be lower than 200 mm/yr, which also corresponds to a Normalized Difference Vegetation Index (NDVI) lower than 0.07 [Tegen and Fung, 1994]. The database indi- cates the content of quartz, feldspars, illire, kaolinire, smectite, hematite, and calcite; the relative amount of clay, silt, and sand; and the color [Munsell Color Com- pany, 1995] at the soil surface. A limitation of the use of the DASSM is the lack of any probabilistic scheme in the selection of soil descriptions, since the DASSM arises from the published literature. As a result, there is no guarantee that the natural variability of soil types is well represented. This limitation is inherent to our method. The DASSM includes 239 descriptions of soils from the United States, Israel, Australia, north Africa, China, Iran, Iraq, and northern India, which are the main sources of atmospheric dust. For illire, kaolinire, smectite, and calcite, the DASSM has numerous exam- ples for each soil type, but for quartz, feldspars, and hematite, some soil types are not represented. In the DASSM, calcite often represents the whole soil fraction (clay, silt, and sand), since the calcite content is usu- ally determined destructively with HC1 before the de- termination of the particle size distribution and of the mineralogy.

3.3. Mean Mineralogical Table

For each soil type, we evaluate the mean, standard de- viation, and distribution (normal or lognormal) of the mineral content. This method is sufficient in 80% of the

cases (a case relating the amount of one mineral to a soil type). For soil types for which the DASSM is not complete (20% of the cases), we had to use other empir- ical relationships. The use of such an analytical method to determine the soil mineralogy is a classical technique [Nettleton and Brasher, 1983]. In this study, we use four relationships: we establish three of them from the DASSM (for quartz, feldspars, and calcite), and one was taken from the literature. First, we evaluate the appor- tionment of the total CaCO3 in the clay (or silt) frac- tion and the clay (or silt) content of the soil (Figure 1). The apportionment of CaCO3 in the clay fraction (or silt) increases with the amount of clay (or silt) in the soil (Figure 1). This relationship is steeper for the silt fraction than for the clay fraction (Figure 1, top ), as predicted by Gomes [19901 and Khalaf et al. [1985]. Second, we linked the portion of quartz and feldspars in the silt fraction to the silt/sand ratio of the soil (Fig- ure 2). This relationship stems from the larger size of quartz grains. Finally, the last relationship to deduce

22,246 CLAQUIN ET AL.- MODELING DUST MINERALOGY

Table 1. The 25 Soil Types Mapped in This Study Together With References Used to Retrieve an Average Surface Mineralogy for Each of Them

Soil Classes Soil Types Mineralogical References

Lithosols I I

Fluvisols J Calcic Jc

Eutric Je

Arenosols Q Cambic Qc

Regosols R

Solonetz S

Vertisols V

Xerosols X and Yermosols

Solontchaks Z

Sand dunes SD

Salt fiats ST

Ferralic Qf Luvic Q1 Calcaric Rc

Dystric Rd Eutric Re

Orthic So

Chromic Vc

Haplic Yh,Xh

Calcic Yk,Xk

Luvic Y1,X1

Takyric Yt

Gypsic Yy, Xy

Gleyic Zg Orthic Zo

Takyric Zt SD

ST

Dan et al. [1981], Alaily [1987], Jahn [1988] Hseung and Jackson [1952].

at. [X81], at. [XO6O], Abtahi [1980], Shadfan and Mashady [1985] FA O-UNESCO [1974]

EI-Attar and Jackson [1973], USDA* [1998], FA O-UNESCO [1974].

$tace et al. [1968], Shadfan and Mashady [1985], FA O -UNESCO [1974]

FAO-UNESCO [1974], MOberg and Esu [1991]. FAO-UNESCO [1974], Moberg and Esu [1991]. Dan et al. [1981], Stace et al. [1968],

Shadfan and Mashady [1985], USDA* [1998], FA O-UNESCO [1974]

Jahn [1995], FA O-UNESCO [1974]. Stace et al. [1968], Alaily [1987], Abtahi [1977]

USDA* [1998], FAO-UNESCO [1974]. Stace et al. [1968], FAO-UNESCO [1974],

dzkan and Ross [1979], Bearington [1978] USDA* [1998], Stace et al. [1968], Alaily [1987],

Sourhard and Sourhard [1985], FAO-UNESCO [1974]. FAO-UNESCO (1974), Stace et al. (1968), Dan et al. [1981],

Sourhard and Sourhard [1985], Gharaee and Mahjoory [1984], VSDA' [1998].

Stace et al. [1968], Dan et al. [1981], AI-Rawi et al. [1969] USDA* [1998], FAO-UNESCO [1974].

FAO-UNESCO [1974], Blume and Petermann [1979]. Shadfan and Mashad• [1985], FAO-UNESCO [1974]. Dan et al. [1981]. Dan et al. [1981], Abtahi [1977], FAO-UNESCO [1974]. Dan et al. [1981], FAO-UNESCO [1974]. Rognon et al. [1989], Gomes [1990], Lanchaster [1989]. Rognon et al. [1989], Gomes [1990],

Breumger et al. [1989], Coud•-Gaussen [1989].

* United States Department of Agriculture- Natural Resources Conservation Service, National Soil Characteri- zation Database, 1998. Available at http://www.statlab.iastate.edu/soils/ssl/natl_char.html

hematite was taken from the literature. This quantita- tive relationship links the soil hematite content to the soil redness [Torrent et al., 1983, Figures 3 and 5]. Soil redness is determined from the color of the soil [Munsell Color Company, 1995, Torrent et al., 1983] and is due to the dark red color of hematite grains compared to other soil minerals whose color tends to be white [see Torrent et al., 1983]. The relationship is based on the color of dry soil, and we assume that it holds for arid surfaces.

3.4. From the FAO Soil Map of the World to Mineralogical Maps

The 1:5,000,000 FAO Soil Map of the World (FSMW) gives the distribution of soil types on a 5' x 5' grid, and we convert this information into maps of arid surface mineralogy with the help of the MMT. Since these maps are intended for global models, the results have been regridded to 1 ø x 1 ø. The amount of each clay (or silt) mineral in a 5' x 5' grid is weighted with the clay (or

CLAQUIN ET AL.' MODELING DUST MINERALOGY

Table 2. Mean Mineralogy Table

22,247

Clay Fraction

Soil Types Ill Kao Sme Cal Qua

Silt Fraction

Qua Fel Cal Hem Gyp N A

I

Average 40 20 29 4 7 s.d. 13 18 17 4 3

Jc

Average 22 9 46 11 12 s.d. 4 3 13 2 3

Je

Average 18 23 55 I 3 s.d. 10 11 19 1 2

Qa Average 21 53 21 0 4 s.d. 8 11 5 0 2

Qc Average 16 66 5 1 11 s.d. 3 34 4 1 7

Qf Average 23 48 23 1 5 s.d. 8 11 5 I 2

Average 10 78 3 I 9 s.d. I 5 2 1 2

Rc

Average 42 39 9 4 7 s.d. 2 2 5 2 3

Re

Average 32 53 10 1 5 s.d. 6 21 4 1 1

So

Average 37 32 17 6 7 s.d. 2 6 5 4 2

Vc

Average 16 27 48 4 5 s.d. 2 14 4 2 3

Xh

Average 20 54 22 1 3 s.d. 3 23 9 I 2

Xk, Yk Average 57 13 16 11 3 s.d. 24 4 4 3 2

X1, Y1 Average 45 20 20 7 7 s.d. 14 5 5 4 2

Xt

Average 21 51 21 3 5 s.d. 6 9 4 2 2

Xy, Yy Average 27 18 40 8 7 s.d. 6 3 3 4 3

53 40 6 1 1 6 7 14 12 6 1 1

31 39 30 0 2 10 2 2 4 3 0 1

86 10 2 I I 12 1 13 5 2 I 1

84 15 0 I I I 6 25 14 0 0 1

82 14 I 4 I 5 10 25 14 I 2 1

84 15 I I I 2 3 25 15 I I 1

70 22 I 7 I 3 1 8 13 I 2 1

75 19 3 3 I 19 8 2 4 3 0 1

59 38 I 2 I 6 4 15 12 I I 1

71 23 4 2 I 19 3 15 6 4 2 1

31 62 3 4 I 24 7 7 12 3 4 1

73 24 I 2 I 30 14 13 10 I 2 1

78 7 14 2 I 34 11 18 4 4 2 2

70 23 5 2 I 39 3 15 7 5 2 1

16 80 4 I I 2 1 3 13 4 I 0

57 26 16 0 6 11 1 8 7 4 0 6

silt) content of the soil types, and nondesert soil types are omitted in the averaging procedure.

After the combination of the FSMW with the MMT, we obtain maps of surface mineralogies, based on a nec- essarily small, yet currently the best available, data set. A contradictory point appears between our choice of soil types on a criterion of aridity and the fact that cli- matic characteristics are not included in the definition

of FAO soil types [FAO-UNESCO, 1974]. Historically, with the preparation of the FSMW, it was recognized that a separation should be made between soils which were morphologically similar but occurred in different climatic environments, but it was decided not to do this separation. As a result, the combination of the FSMW with the MMT also gives mineralogies in some nonarid regions.

22,248 CLAQUIN ET AL.: MODELING DUST MINERALOGY

Table 2. (continued)

Clay Fraction Silt Fraction

Soil Types Ill Kao Sme Cal Qua Qua Fel Cal Hem Gyp N A

Zg Average 16 32 24 21 5 51 28 20 0 15 2 0 s.d. 4 6 3 13 2 16 11 8 0 10

Zo

Average 31 6 46 11 7 34 43 22 I 6 8 1 s.d. 5 4 9 6 I 10 12 17 0 6

Zt

Average 25 33 25 10 6 22 66 12 0 I 3 0 s.d. 6 9 4 4 2 5 12 7 0 1

SD

Average 50 9 26 I 14 92 6 I I I 8 12 s.d. 12 5 6 I 4 3 3 I I 1

ST

Average 39 4 26 29 I 5 I 93 I 26 14 3 s.d. 12 3 18 3 I 4 I 27 I 10

The mean composition in terms of illite (Ill), kaolinite (Kao), smectite (Sme), calcite (Cal), quartz (Qua), felspath (Fel), gypsum (Gyp), and hematite (Hem) are given for the clay fraction (normalized to 100 %) and the silt fraction (normalized to 100 %) for each soil type. For each soil type, the number of soil studies averaged (N) and the area covered (A) (in % of all potential arid areas) are indicated.

4. Results and Discussion

4.1. Mean Mineralogical Table

The MMT is itself an interesting result and is there- fore worth discussion. As presented in Table 2, 21 soil types were found to be potentially arid. Soil specialists should notice that yermosols are classified in the MMT together with xerosols, since these two soil classes have similar mineralogical properties. Since we cannot dis- cuss here all 21 examples, we chose to present in depth two examples of abundant soils that are also representa- tive of the problems encountered establishing the MMT: calcic xerosols (Xk), which are typical desert soils de- veloping over limestones and are well represented in the DASSM, and sand dunes (SD), which are an impor- tant component of desert areas but whose mineralogical composition has seldom been studied because of little agricultural interest.

For both Xk and SD, illite is the dominant mineral in the clay fraction (57 and 50%; see Table 2), but it is noticeable that the standard deviations are very differ- ent: 24% for Xk and 12% for SD. These large standard deviations have two explanations: first, the fact that mineralogical studies are often done in a "semiquanti- tative" way (i.e., mineral contents are often given with large uncertainties) and, second, the natural variability of soil type mineralogies. This latter observation ques- tions our basic hypothesis that each FSMW soil type can be linked to a typical mineralogy. In this study, the standard deviations of mineral content predicted using this method represent between 27 and 33% of the aver- age observed value. This shows that our results cannot

be used to make an "exact" prediction of a soil miner- alogy, but rather to have a reasonable estimate of the relative portion of the eight minerals we consider.

4.2. Maps of mineralogies of erodible fractions

Global maps are presented in Plate 1. The evaluation of these maps is not straightforward, since there are no other global scale studies of the mineralogy of the erodi- ble fraction of soils. Hence we followed two evaluation

strategies: (1) a qualitative evaluation that considers the validity of the global trends of mineral distributions for which we do not have any quantitative information and, (2) a quantitative evaluation that checks locally the order of magnitude of our results.

For example, illite is known as a ubiquitous mineral in soils [Millot, 1964; Singer, 1988], and this feature also appears in Plate la, where the amount of illite in the clay size fraction is always over 25% over all the considered regions. Some areas also show illite contents close to 50%, especially in the Sahara and in the south of Saudi Arabia, which is considered to be sand dunes. However, dust samples often are over 50% illite [Chester et al., 1972; Caquineau, 1997]. This discrepancy be- tween our maps and the observations may be due to the mineral fractionation during the emission process, as discussed below. A second important mineral, kaolinite, forms at lower latitudes, where temperature and humid- ity lead to high hydrolyse conditions. The zonal distri- bution of the kaolinite (Plate lc) shows low amounts in the Sahara and high amounts in the Sahel, as described by Millot [1964]. Smectite/montmorillonite occur in lower fraction (Plate le), except in vertisols (Ethiopia,

CLAQUIN ET AL' MODELING DUST MINERALOGY 22,249

80.0

70.0

60.0

50.0

40.0

30.0

20.0

10.0

0.0 0.0

, i

10.0

y = 1.4 x - 17.5, 22 points, r=0.87

ß ß

20.0 30.0 40.0 50.0

Silt (%)

i ,

60.0 70.0

30.0

20.0

10.0

0.0 0.0

y = 0.5 x - 4.5, 23 points, r= 0.72

C• ,• •.. , I , i i i 1 .0 20.0 30.0 40.0 50.0 60.0

Clay(%) 70.0

Figure 1. Empirical relationships established to link (top) soil surface apportionment of total CaCO3 in the silt fraction to the silt content and (bottom) the appor- tionment of the total CaCO3 in the clay fraction to the clay content. These relationships are used for soil stud- ies where the total CaCOs amount is evaluated before the separation of silt and clay fractions.

India), which do not contribute much to dust emissions because of vegetation cover (FAO-UNESCO, 1974). We find high amounts of calcite (both in the clay and the silt fraction) in the north Sahara and in the Euphrates val- ley, in agreement with measurements on dust samples [Coud•-Gaussen et al., 1987; Schlitz and $ebert, 1987; Khala/et al., 1985]. The position of gypsum sources (Plate lh) is also in agreement with the large amount of gypsum found in Israel [Levin and Lindberg, 1979; Khala/et al., 1985] and the small amount found over Africa and the Mediterranean Sea [Gomes, 1990; LoUie- Pilot et al., 1986].

A quantitative comparison of the estimated mineral maps with soil analyses would be useless, since all avail-

able data were used to generate this estimation. We thus compare our maps to dust sample measurements. Mineralogical measurements of dust samples need to fulfill two conditions: first, be quantitative for the eight minerals of this study and, second, be associated with a precise determination of the geographical source re- gion. In fact, the determination of the origin is diffi- cult when the dust is sampled far away from arid areas. Early studies used qualitative observations of the wind patterns, but the estimation of the source position is then too rough to be used for this evaluation except if the concerned arid area is small enough (e.g., Atacama Desert [Prospero and Bonatti, 1969]). The use of back trajectories in recent studies has improved the precision on the localization of the source though there are still intrinsic limitations to this method [Caquineau, 1997; Merrill et al., 1994; Avila et al., 1997]. Finally, areas available for the evaluation represent 10% of the arid areas (Figure 3). In these studies, there are no mea- surements of the associated particle size distribution, which influences the dust mineralogy. Hence it would be meaningless to compare the mineral content of dust samples of unknown size distributions. To reduce this difficulty, we use mass ratios of mineral species of sim- ilar sizes, these ratios being supposed not to vary with the size of the particles. We chose one ratio in the clay fraction, illite / kaolinite (I/K), and three ratios in the silt fraction, calcite / quartz (C/Q), feldspars / quartz (F/Q), and hematite / quartz (H/Q). The values found for these ratios in the articles cited previously are then compared to our results (Figure 4). Note that gypsum is not studied in this evaluation since the few measure-

ments do not fulfill the conditions of this validation and

since it is difficult to analyze because of neoformations [Leinen et al., 1994].

1.0

0.52. x ** (0.717), 31 points, r=0.95

,,ol , , , ...........

;'.o o.o Silt / Sand

Figure 2. Empirical relationship between the silt/sand ratio of the soil surface and the feldspar/quartz ratio in the silt fraction. This relationship is used to evaluate the tectosilicates composition for soil types whose silt mineralogy is unknown.

22,250 CLAQUIN ET AL.- MODELING DUST MINERALOGY

a- Illire (clay fraction) b- Quartz (silt fraction)

. ,. ..

0 t0 20 .• •1) ., 0 0 30 48 O0 70

IM,,

c- Kaolinire (clay fraction) d- Feldspar (silt fraction)

e- Smectite (clay fraction)

)- IZ•I iM 0 I• $•11 i!

f- Hematite (silt fraction) '..• :x_ -•,'--e• - -• '" ß "7 •. 'i• •.t•ø

"'

Ioi1•

IM

4C8

g- Calcite (silt fraction)

'• • ,}• - ,....

h- Gypsum (silt fraction)

it,, '• .-•.• .......... • '. :"•' '•i-•r".'

, 10 15 20 20 4 8 8 10

Plate 1. Amount (in % by weight) of (a) illite, (c) kaolinite, and (e) smectite in the clay fraction (<2. pm), and of (b) quartz, (d) feldspar, (f) hematite, (g) calcite, and (h) gypsum in the silt fraction (2.-50. pro) at the surface of soils that are potentially arid.

CLAQUIN ET AL.- MODELING DUST MINERALOGY 22,251

60N

50N

40N

30N

20N

1ON

EQ

lOS.

20S-

30S 1

60S/ 180

t•" )

Figure 3. Areas in light gray are the evaluation points, that is, regions that were precisely related to a dust sample whose mineralogical content has been measured. These areas are taken from Caquineau [1997] and Avila et al. [1997] (Africa), $idhu [1977] (India), Prospero and Bonatti [1969] (West coast of America), Khalaf et al. [1985] (Saudi Arabia), Merrill et al. [1994] (China, California), Ganor and Foner [1996] (Near East) and Kielerr et al. [1996] (Australia). Dust mineralogies found in these areas are compared to our results.

10.0

1.0

0.1 0.1

17 points, r = 0.82

1.0

Illite/Kaolinite in dust (measurements)

1 lO.O

•: 1.0 .•_

._

o

.=_ N ß c:: 0.1

0.1 1.0

Calcite/Quartz in dust (measurements)

• 1.0 o

.=_ N

.o_

a. 0.1 o.1 1 .o

Plagioclase/Quartz in dust (measurements)

0.10

o

._•

o

.•- 0.01

E

2 points '

O.Ol O. lO

Hematite/Quartz in dust (measurements)

Figure 4. Correlation between mineralogical properties of desert soil surfaces as described in. this study and mineralogical properties in dust samples whose geographical origin is known. Note that particle size distributions in dust samples are not known. Therefore, to avoid the effect of size on the mineralogy, we compare ratios of mineralogical species that have "roughly" the same size. The correlation coefficient is not indicated for the H/Q ratio because of lack of enough data.

22,252 CLAQUIN ET AL.- MODELING DUST MINERALOGY

The first ratio, illite/kaolinite (I/K), characterizes the composition of the clay fraction and has been used by Caquineau et al. [1998] as a tracer for Saharan sources. Illite and kaolinite are often measured (17 points), the ratio I/K being between 0.1 and 10 (Figure 4a). The comparison of I/K measured in dust samples (x axis) with I/K in our maps (y axis) shows that extreme val- ues of I/K (over 5 or under 0.2) are not well captured in our maps (Figure 4a). This result means that the large predominance of illite or kaolinite in some areas is un- derestimated in our maps. This limitation can be due to two separate reasons. First, our method is mainly based on an overall average of limited samples, and ex- treme values can thus be biasing the results. Second, $chiitz and $ebert [1987] and Gomes [1990] found that I/K can decrease during dust emission, but it is unclear whether these results were due to different aerodynamic behavior of illite and kaolinite (size, layering, or ability to form aggregates) or artifacts in the data.

The second ratio, calcite/quartz (C/Q), is often used as a tracer of the northern Saharan sources but has been

adequately evaluated in only five of the selected studies. These five measurements have a large variability (C/Q between 0.04 and 2.). Our results are in good agreement with them (Figure 4b). However, this good agreement between C/Q in soils and C/Q in dust samples could be questioned since C/Q is thought to increase during dust emission [Schlitz and $ebert, 1987] and to decrease during transport as calcite is slowly dissolved. More measurements and a better understanding of the pro- cesses that control the evolution of calcite during the dust transport are therefore necessary. With these lim- itations, we think that our maps satisfyingly reproduce the gross variations of the C/Q ratio at the surface of arid areas.

The third ratio, feldspars/quartz (F/Q), is seldom measured in dust samples (seven points) and is less ad- equately represented in our maps (r=0.70) (Figure 4c).

60N

SON-

40N -

30N-

20N-

1ON

EO

10S

20S

5OS

40S

50S

60S 180

Single Scattering Albedo at 550 nm

]2bW 66W 6 6•)E I•OE 180

60N

SON-

40N -

30N-

2ON'

1ON'

10S,

20S.

3OS.

40S*

50S.

60S180

0.92 0.94 0.96 0.98

Extinction ratio Qext-550.,,/Qext-•r.•

-' ' ...!:....,e•. :•.,.:•:• -

-'•:•c•.•. . .?•, . ,...:• •

-., - . ...... , ß :• ....... :•.'::• ..?: ..... •.:•..'.' :•:.•: --.•: ::• •'•:•:• '-•4. • .

12bw 65w 5 • 1•oE

0.26 0.27 0.28 0.29

Figure 5. (top) Single scattering albedo at 550 nm and (bottom) ratio of visible extinction to infrared extinction at the surface of potential arid areas. The calculations are-done using an external mixture of quartz, clays, calcite, and feldspar, and a coating of hematite on these particles. We used the Mie calculator for a two-layer sphere designed by Toon and Ackermann [1981].

CLAQUIN ET AL.: MODELING DUST MINERALOGY 22,253

Quartz and feldspars are difficult to relate to the soil chemistry and to the soil type, and thus F/Q is the most difficult ratio to assess. In addition, to our knowl- edge, the distribution of feldspars in soils has never been assessed, and it is therefore difficult to judge the qual- ity of our maps. Hence the use of our map of feldspars distribution can be only qualitative.

The fourth ratio, hematite/quartz (H/Q), has been measured in only two dust samples (Figure 4d) ham- pering the evaluation of our map of the soil surface hematite content. The lack of data on dust hematite

content is due to its small content, its little crystallinity, and the difficulty in measuring it. The importance of hematite for radiative calculations implies, however, that new mineralogical measurements should be made on dust samples or that the development of another method (satellites) may be necessary to assess the iron oxide content at the surface of arid areas.

4.3. Application to the Evaluation of Dust Radiative Properties

A straightforward application of these maps is the evaluation of the dust radiative properties at the sur- face of arid areas. These properties concern only the di- rect radiative effect of dust, but an indirect forcing, by the enhancement of droplet formation, also could occur because of the dissolution of carbonates or the coating of sulfates on mineral particles [Levin et al., 1996]. The direct radiative effect of dust is controlled by the size distribution, the composition (given by our maps), and the state of mixing of the particles. For the particle size distribution, we use a mean mass diameter of 2.0/•m, a geometric standard deviation of 2.0, and a lognor- mal distribution. These parameters are representative of the erodible fraction of the soil that might travel far from the source [Schlitz, 1979; Patterson and Gillette, 1977; Levin et al., 1980; McTainsh et al., 1997]. Deter- mining the amount of mixing of dust particles is more complicated since measurements are sparse and unsyn- thesized. The most intuitive approximation is then to consider that all species are externally mixed except the hematite, which is often coated on the larger parti- cles [e.g., Bain and Tait, 1977]. We then evaluate the radiative properties of dust particles with Mie calcula- tions adapted to coated spheres [Toon and Ackermann, 1981]. As far as radiative calculations are concerned, differences between the refractive indices of aluminosil-

icates (illite, kaolinite, smectite, and feldspars) are not significant compared to the natural variability of each specie, neither in the visible [Lindberg and Smith, 1974] nor in the infrared [Hunt et al., 1950, Toon et al., 1977]. The variability of the refractive indices of dust species is then represented with quartz [Peterson and Weinman, 1969; Steyer et al., 1974], clay and feldspars [Toon et al., 1977; Lindberg and Smith, 1974], calcite [Long et al., 1993; Querry et al., 1978], and hematite [Bedidi and Ce•'velle, 1993; Ivlev and Popova, 1973].

Results are presented in Figure 5 for the single scat- tering albedo • at 550 nm, and for the ratio of visi- ble extinction to extinction in the atmospheric windows (8 to 12 •m), Qext-55onm/Qext-IR, hereafter k. The variations of these two parameters with the mineral- ogy are significant in respect to radiative calculations. For instance, in northern Sahara, dust has a large con- tent in carbonates (small absorption in the infrared) and a small amount of hematite (large absorption in the visible) and has therefore a high • (0.96) and a low k (0.262). In the Sahelian zone, dust has a large quartz content (large infrared absorption) and a large hematite content and has therefore a low • (0.91) and a high k (0.287). Dust outbreaks outpouring from the southern Sahara, usually during winter, should there- fore have a larger impact on the radiative budget than dust plumes coming from the northern Sahara in the summer. Of course, this latter conclusion is tentative since our knowledge on the mixing state of dust particles is still poor and since the assessment of the global effect would require the use of a global model. Nonetheless, this result is interesting and underlines that the mod- elling of dust as a mixture of different mineral species should significantly improve our understanding of the dust radiative forcing.

5. Conclusion

For the first time, the mineralogical composition of the erodible fraction of the soils of arid areas has been

estimated on a global scale on the basis of a neces- sarily small data set. The results reproduce the geo- graphical distribution of calcite grains and phyllosilicate species. Quantitative comparisons with available mea- surements show satisfactory results for these mineralog- ical families. The determination of the feldspars/quartz ratio has been less successful, essentially because our method is probably less adapted to these species, and the amount of hematite has been difficult to evaluate

because this mineral is seldom measured in dust. Since

the quality of these results relies on the DASSM, we will continue expanding the considered soil studies. In par- ticular, more references on Asian sources might signifi- cantly improve the results. Taking into account regional variations of the mineralogy of soil types would also im- prove the precision of the maps. We believe that our method will greatly benefit from the current effort of soil scientists to update the information on the world soil re- sources in a World Soils and Terrain Digital Database [Scholes et al., 1995, Batjes et al., 1995, Nachtergaele, 1998].

The combination of our results with other methods

should also be very fruitful. First, the use of back tra- jectories is promising. Until now, they have had a di- agnostic value on the timescale of at most a week and they neglect, close to the source, critical subgrid-scale events such as local convection. Hence they have had to be used together with other methods in order de-

22,254 CLAQUIN ET AL.: MODELING DUST MINERALOGY

termine the source (statistical methods or infrared evi- dence of a dust uplift on satellite images). Improvement of the quality and of the geographical coverage of these methods would have the invaluable advantage of giving information on the mineralogy of the effective soil erodi- ble fraction. Nevertheless, these studies are also limited by the few recent quantitative evaluations of the dust mineralogy, and this latter effort should to encouraged. Also, the use of satellites may be an interesting alter- native to infer the mineralogy of arid surfaces for those mineral species which have a recognizable spectral sig- nature (e.g., hematite in the visible spectrum).

Our maps give the first approximation of the miner- alogy of the erodible fraction of arid areas. They will be refined as the amount of basic data increases in our

DASSM database. Meanwhile, being the best sets avail- able, these maps will be included in a global transport model to obtain the global-scale partition of different mineral species. The main application of such a distri- bution will clearly be the evaluation of dust radiative impacts, but it could also be useful in other scientific fields. For instance, the evaluation of the deposition of different mineral species onto soils or oceans will per- mit the more precise quantification of the role of mineral dust in biogeochemical cycles. Also, our results could be used to understand sedimentary patterns of different minerals in oceans. Such a result could lead to paleore- constitutions of continental climates, since climate has a significant effect of the formation of different miner- als, and of the extent of arid areas. Finally, a discrim- ination between the mineral species suspended in the atmosphere could lead to an improved understanding of heterogeneous chemistry since distinct minerals have different abilities to catalyze reactions.

Acknowledgments. We would like to thank Olivier Flores for help on compu-

tations and work on iron oxides and Natalie Mahowald for improving the quality of the manuscript. We also acknowl- edge Francois Dulac, Laurent Gomes, Annie Gaudichet, Beatrice Marticorena, and Freddy Nachtergaele for helpful discussions and the two reviewers for very valuable com- ments. Our work was funded under the Aerosol research

programme BMBF FSrderkennzeichen 07 AF 312 B/7 and under the Environmental and Climate Project SINDICATE of the European Community (contract ENV4-CT47-0483). Travel funding between Paris and Hamburg was kindly pro- vided by the German-French exchange project PROCOPE. This LCSE contribution number 285.

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(Received January 29, 1999; revised May 21, 1999; accepted May 25, 1999.)