dust deposition to the ocean - woods hole … of the ratio of dissolved to particulate a1 in rain...

GLOBAL BIOGEOCHEMIC• CYCLES, VOL. 14, NO. 1, PAGES 317-327, MARCH 2000

On the use of dissolved aluminum in surface waters to estimate

dust deposition to the ocean

C. I. Measures and S. Vink

Department of Oceanography, University of Hawaii, Honolulu.

Abstract. The concentration of dissolved A1 in surface waters from various oceanic regimes is used in a simple model to calculate the annual amount of dust deposited to the surface ocean. Cal- culated values range from 0.015 to 9.9 g dust m -2 yr -1. Comparison of these calculated dust depositions with independent dust deposition estimates obtained from direct measurements, suspended atmospheric dust loads, or sediment traps show remarkably good agreement over approximately 3orders of magnitude. In regions where the agreement between the model and other estimates is weakest, it is anticipated that local scaling of model parameters such as mixed layer depth and surface water residence time, will lead to improved agreement. Since surface water A1 concentrations appear to be driven primarily by dust deposition, the distribution of dissolved A1 in surface waters can be used to investigate the systematics of the delivery of other biologically important trace elements, for example, Fe, to the surface of the remote ocean by this route. In addition, temporal variations in surface water A1 concentrations can be used to investigate the biogeochemical consequences to the surface ocean of large-scale changes in atmospheric dust loads driven by decadal- scale climatic variations.

1. Introduction

Ice core and sedimentary accumulations [De Angelis et al., 1987; Kumar et al., 1995; Laj et al., 1997] record globally large increases in atmospheric dust fluxes during glacial periods. This information, when coupled to the discovery that a significant fraction of eolian dust dissolves releasing Fe upon entering surface waters [Zhuang et al., 1990; Zhu et al., 1997], and the observation that in certain regions of the ocean biological productivity may be limited by lack of dissolved Fe [Martin and Fitzwater, 1988] leads to the following questions.

First, to what degree does the deposition and partial dissolution of atmospheric dust contribute significant amounts of biogeochemically important trace elements to the surface waters of the remote ocean? Second, how might this input have varied over long time periods? Third, is this variation a driving factor in climatic switching mechanisms or merely a passive consequence of changed conditions? If we are to develop an adequate understanding of the synergistic and antagonistic mechanisms that sta- bilize climate modes, then we need to investigate each potential feedback loop in order to evaluate its role in the climate switching and stabilization process. To be able to determine whether dust deposition plays an active or passive role in climate feedback, it will be necessary to considerably improve our understanding of the geochemical systematics and the biological consequences of dust deposition to the surface ocean. Specifically, we will need (1) a) a method that allows us to calculate the amount of dust go- ing into the ocean in places where direct measurement of deposition is not feasible, (2) knowledge of what fraction of the deposited dust dissolves and which factors affect this process, and (3)

Copyright 2000 by the American Geophysical Union.

Paper number 1999GB001188. 0886-6236/00/1999GB001188512.00

identification of the biogeochemically active trace elements for which this process is significant and determination of what role the chemical speciation of the input plays in stimulating biological processes.

At this stag•, however, little is known about the role that dust deposition plays in modifying surface water geochemistry and even less is known about its subsequent effects on biological processes. For some trace elements, in particular A1 and Fe, it is likely that the contemporary deposition of dust and its partial dissolution in the surface waters of the ocean is an important part of their geochemical cycles. The chemical reactivity of these elements means that they are not easily transferred from their fluvial sources at the edges of continents to the interior surface waters of the remote oceans. There is good evidence that the depositional flux of Fe is important biologically, particularly in the high- nutrient low-chlorophyll (HNLC) regions of the ocean [e.g., Martin and Fitzwater, 1988]. Atmospheric deposition may also be important for trace elements which, although apparently pres- ent at sufficient concentrations, are not biologically available. In these cases the chemical speciation of the input may play an important role in moderating biological processes.

Clearly, the biogeochemical effects of dust deposition to the surface of the contemporary ocean must be understood before we can attempt to evaluate the biogeochemical consequences of enhanced dust fluxes during glacial periods. While much progress is being made in various aspects of these problems, one of the key unknowns in furthering our understanding of this phenomena is simply estimating the magnitude of contemporary dust deposition to the remote ocean and the chemical systematics of dust dissolution in surface waters.

As a preliminary step toward understanding the biogeochemical affects of dust deposition, Measures and Brown [1996] presented a model in which dissolved A1 concentrations in surface

waters were used as a proxy for dust deposition in the North At- lantic. Since the publication of this model, we have determined

317

318 MEASURES AND VINK: USING DISSOLVED A1 TO ESTIMATE OCEAN DUST DEPOSITION

surface water AI concentrations in numerous other ocean basins

spanning almost the entire range of dust depositional environments as determined by Duce et al. [1991]. In this paper, we will evaluate the Measures and Brown [1996] model using these newly collected data in order to assess the overall validity of such an indirect approach and whether future refinement of the model is warranted.

1.1. Background

The role of continental dust in transferring reactive materials to the open ocean was previously discounted since the aluminosilicate matrix of atmospheric dust was considered to be largely insoluble [Hodge et al., 1978]. This view changed when assess- ments of the ratio of dissolved to particulate A1 in rain events at Miami, Florida, indicated 3-5% of this major crustal component was in the dissolved form [Prospero et al., 1987]. Additional laboratory experiments [Maring and Duce, 1987] showed 5-10% of the A1 in mid-Pacific 'atmospheric dust dissolved in seawater over a period of 60 hours.

Despite its importance, determining the geographic distribution and magnitude of eolian deposition to the open ocean is particularly difficult. The regional and temporal variability of dust events and the short duration of research cruises make it unlikely that reliable estimates of dust deposition can ever be gained from direct shipboard monitoring. Dedicated sampling on remote is- lands, while effective, yields insufficient spatial coverage owing to lack of sites. Sediment traps are an attractive means of obtaining estimates of oceanic dust deposition since they can be deployed in the remote ocean for long periods of time, and it is relatively simple to distinguish lithogenic from biogenic material. However, limited deployment again hinders the development of significant coverage, and there are still unresolved questions about trapping efficiency in upper waters and the role of advection of hemipelagic sediments in oversupplying material to traps in deep waters [Honjo, 1982; Rea, 1994]. Satellite-based remote sensing is an extremely attractive method to assess dust deposition since it is capable of providing global coverage. However, the data products available from these instruments, such as optical density at various wavelengths, do not at this stage readily distinguish between dust and other atmospheric aerosols, e.g., soot particles, and therefore they need ground tinthing. While im- provements in sensors and algorithms can be expected to mini- mize many of these problems, the difficulty of converting atmospheric dust loads into dust depositions still remains since this conversion requires knowledge of local rainfall rates and scavenging ratios, which are usually not available.

In an inverse manner, the problem of dust deposition can be approached from the viewpoint of its effect on the surface ocean. From an oceanographic perspective this approach may even be preferable since the parameter of interest is the magnitude of chemical input rather than the dust deposition per se.

The surface ocean is, in effect, a large, uncalibrated dust col- lector that integrates the high-frequency temporal aspect of atmospheric dust input. The dissolved chemical wake that persists after the deposited particulate material (dust) has been removed from surface waters has the potential to be used to estimate the magnitude of deposition events. The relationship between the magnitude of the dissolved chemical signal and the amount of dust input is controlled by four factors: the relative abundance of the chemical element of interest in the dust, its fractional solubil-

ity in the surface ocean, the depth of the mixed layer into which it is deposited, and the element's residence time within the mixed layer. This paper evaluates the use of surface water dissolved A1 concentrations as an indicator of the magnitude of dust deposition from wide range dust deposition environments in three oceans [Measures and Brown, 1996].

A1 is an ideal tracer for indicating the input of dust to the surface ocean since it is a major and relatively invariant [Taylor, 1964; Wedepohl, 1995] component of continental materials. Its concentration in surface sea water in regions devoid of significant atmospheric input is extremely low (<1 nM= <1 x 10 '9 M) owing to its relatively short (3-6.5 years) residence time in the surface ocean [Orians and Bruland, 1986; Jickells et al., 1994]. A1 has no complicating redox chemistry and appears to be removed from surface waters with little biological recycling [Moran and Moore, 1992]. While rivers deliver significant amounts of dissolved A1 to the ocean annually [Hydes and Liss, 1977; Morris et al., 1986], like many other reactive trace elements, little of this fluvial A1 es- capes the intense scavenging of the estuarine and coastal zones [Measures et al., 1984; Kremling and Hydes, 1988; Hydes, 1989].

Qualitative evidence that surface water AI distributions are geographically similar to dust deposition maps and are also a steady state feature is provided in Plate 1; the data sources used to construct this figure are listed in Table 1. High AI concentrations stretch across the equatorial Atlantic, coincident with the region of maximal deposition of Saharan dust. To the south of this region, surface water A1 concentrations decrease rapidly, consistent with the reduced dust loads observed here as a result of the at-

tenuation of the Saharan dust load by the rainfall in the Intertropi- cal Convergence Zone (ITCZ).

To the north of the equatorial region, dissolved AI values also decrease but less dramatically. Again this observation is consistent with our current understanding of North Atlantic atmospheric dust loads. While there is good general agreement between these 7 data sets in the regions where they overlap, there are nevertheless significant differences in some areas. How much of these regional variations reflect normal annual fluctuations captured by the seasonal timing of these various expeditions and how much is the result of long-term biogeochemical changes over the 14-year span that the data set encompasses are tantalizing questions, but they are not possible to answer at this stage. However, the main point is that each data set shows spatial variations in A1 concentrations that are consistent with variations observed in atmos-

pheric dust load and that, in spite of seasonal and interannual variations that might have occurred, the data sets are broadly consistent.

1.2. Dissolved Al Model

The essence of the model proposed by Measures and Brown [ 1996] (measurement of A1 for dust calculation in oceanic waters, hereafter referred to as MADCOW) is as follows. The model postulates that the concentration of dissolved A1 in the surface mixed layer of the non-coastal ocean is in steady state and is en- tirely derived from the partial dissolution of aluminosilicate dust deposited on the surface waters. Further, this eolian input bal- ances the scavenging removal of A1 by biological processes. Several simplifying assumptions were made in order to facilitate calculati6ns.

The residence time of dissolved A1 in surface waters was as-

sumed uniformly to be 5 years [Orians and Bruland, 1986; Jick-

MEASURES AND VINK: USING DISSOLVED A1 TO ESTIMATE OCEAN DUST DEPOSITION 319

Table 1. Data Sources for Figure 1

Dates Cruise Reference

March 1982 M60 Kremling [ 1985] November 1983 EN 107 Measures et. al. [ 1984] February 1988 SAVE Measures and Edmond [ 1990] March 1990 IOC 90 Measures [ 1995] May 1990 ANT VIII/7 Hblmers et al. [1993] November 1990 ANT IX/1 Helmers et al. [1993] June 1996 IOC 96 Vink and Measures

(submitted manuscript, 1999)

ells et al., 1994]. This is clearly not truly correct since the residence time will scale with primary productivity and that is not uniform over the regions studied. In regions where primary productivity is higher than the oligotrophic Sargasso Sea and North Pacific, from which our 5-year value is drawn, our estimates of dust input will be underestimates since the increased scavenging rate will remove AI from the surface waters at a faster rate than

we use in our model [Moran and Moore, 1992]. The depth of the mixed layer was assumed to be 30 m. Should

the true mixed layer be deeper, the MADCOW modeled dust input will be underestimated. For example, a 60-m mixed layer would double the required dust input. We defend the choice of this relatively shallow mixed layer (by world average mixed layer standards) because removal of AI will be associated with the spring and summer months when insolation leads to shallow mixed layers and also by the fact that most of our data are derived from these spring/summer time periods.

The composition of aluminosilicate dust was assumed to be invariant at 8.1% AI by weight (3000 lam A1/g). This is a generally accepted value for continental materials [Taylor, 1964], and the minor deviations from this that might occur would introduce negligible error into the calculations.

The solubility of eolian dust was assumed to range between 1.5 and 5%. We have chosen this range since it reflects values obtained from laboratory studies [Maring and Duce, 1987; Pros- pero et al., 1987; Chester et al., 1993; Lim and Jickells, 1990] using atmospheric samples. However as we discuss in more detail below, there is only limited data and understanding of the factors which control the fractional solubility of dust during atmospheric transport.

Using these assumptions, it is possible to derive an equation to calculate the annual apparent dust input required to maintain the existing dissolved A1 concentrations against annual scavenging. This equation simplifies to G = A x 0.04 for 5% dust solubility, and G = A x 0.133 for 1.5% dust solubility, where G = apparent dust input in g m '2 yr -l, and A = mixed layer concentration of dissolved A1 in nM L -!.

While the values assigned to each of the variables can be de- bated and refined, the important point at this stage is to see if the model, prior to such fine-tuning, produces results that first are in the correct range for dust deposition, and second agree, within some acceptable range, with those produced by other means. Early results from the North Atlantic Ocean presented by Meas- ures and Brown [1996] indicated that there was good agreement

between the model-based estimates and other dust deposition estimates. The purpose of this manuscript is to report new data that evaluate the agreement in other ocean basins with differing productivity regimes and covering as wide a range of dust deposition environments as possible.

It should be noted that at this point exact agreement between our model-based estimates of dust deposition and those derived by other means is not necessarily expected. Surface water A1 concentrations represent an integration of dust inputs over the residence time of AI in the surface waters, here assumed to be of

the order of 5 years. In contrast, both direct dust deposition measurements, i.e., actual collections of dust deposited into collecting devices, and estimates based on the concentrations of suspended load of aerosols in the atmosphere also represent only the period of collection. In addition, as mentioned above, transforming suspended loads into deposition requires estimates of precipitation amounts, frequency and scavenging ratios, and some accounting of the relative importance of dry deposition. Given the difficulty of obtaining these parameters over much of the ocean, estimates of dust deposition from atmospheric suspended load in the global compilation of Duce et al. [1991] are considered to have uncertainties of the order of two- to three-fold. This uncertainty, however, although locally significant, is small compared to the 1000- fold range in deposition values observed globally.

2. Calibration of the MADCOW Model

2.1. Methods and Data Sources

2.1.1. AI data. The A1 data presented in this manuscript were obtained with two different analytical techniques over several years. Data from 1990 and earlier were obtained using the gas chromatographic method of Measures and Edmond [1989]. Data from 1993 and later were obtained using a flow injection analysis version of the lumogallion method of Resing and Measures [1994]. The comparability of AI data obtained by the gas chromatographic and lumogallion methodologies has been previously established [Measures et al., 1986].

Dissolved AI data have been selected from regions spanning a wide range of dust inputs (Figure 1). In many cases, these data exist as part of transects that cover larger regions. The subsets chosen for calibration were taken from regions of the transit that are close to, or coincident with, places where independent estimates of dust deposition have been made. In the case of calibration against the Duce et al. [1991] model (our main intercalibra- tion), this presents problems since the interval between contours is a factor of 10. Where possible, data that are close to one of the contours have been selected. In cases where this is not possible, we have interpolated a value from the map. Since the purpose of this manuscript is to provide a first-order validation of this tech- nique and since both the Duce et al. and this work are only claiming a factor of 2 to 3 accuracy in estimation of dust deposition, at this stage we believe the interpolation errors are insignifi- cant compared to the overall problem of estimating dust deposition in remote regions. As an additional check, where available, our dissolved A1 data are compared to values produced by other researchers.

80øW

50øN

60øW 40øW 20øW 0 ø 20øE

50øN

40øN.

30"N

20øN

10øN.

e

10øS '-i[• 20øS -

30øS -

40øS

11/83 3/82

11/90

6/96

3/90

' ..,. 2/88

40øN

30øN

20øN

- 10øN

AI (nM)

7O

6O

5O

0 ø 40

- 10øS 30

: 20øS 20

ß

'" "' ' ' '" 5/90 30os 10

40øS

80øW 60øW 40øW 20øW 0 ø 20øE

Plate 1. Contoured surface water Al concentrations compiled from the seven cruises listed in Table 1. Dates next to cruise track indicate approximate sampling period.

t

40øE 80øE 120øE 160øE 160øW 120øW 80øW 40øW 0 ø Figure 1. Location of A1 data sets used in dust deposition comparison. Dashed contour lines are from the Duce et al. [ 1991 ] model, the numbers are the dust deposition rates in g m -2 yr -]. Sample locations are SS, Sargasso Sea; C, Caribbean; 5N, Atlantic 5øN; 5S, Atlantic 5øS; GG, Gulf of Guinea; HI, Hawaii (HOTS site); NZ, New Zealand; and SO, Southern Ocean 63øS.


• 10

• 0.01

0.001

, , Gul•ofGuinca • - Caribbean •..,e•.•- -

ß Hawaii +• ;øN- 5øS Ariantic

New Zeal•• _

}01 0.01 0.1 1 10

Independent deposition estimates (g m-2 yr-•)

Figure 2. Comparison of dust deposition estimates derived from surface water A1 concentrations with those obtained by other means (see text for details). Line defines 1'1 relationship between estimates.

2.1.2. Dust deposition estimates. The difficulty of estimating dust deposition to the remote ocean also makes it difficult to vali- date the model. There are essentially three sources of independent estimates of dust deposition to the ocean: (1) direct measurement at a nearby land-based station, (2) estimates of lithogenic material collected in sediment traps, and (3) estimates based on coupled models that combine atmospheric dust loads with precipitation and scavenging models. A fourth approach using accumulations of lithogenic material in sediments [Rea, 1994], while extremely valuable in obtaining estimates of historical rates of dust deposition, integrates over long timescales which may not compare to contemporary values and therefore has only been in- voked in the absence of other estimates. As stated in section

2.1.1, we have used the Duce et al. [1991] model heavily since it is comprehensive in its coverage of the regions we are interested in. Where possible, we have favored direct deposition data since,

Table 2. Surface Water A1 Concentrations and Estimated

Dust Deposition for the Sargasso Sea

Longitude W Latitude N A1, nM Mean Dust

72.450 35.490 28.2 2.44 72.730 35.170 28.4 2.46 73.010 34.800 28.0 2.42 74.190 33.440 26.6 2.3 74.690 32.960 34.2 2.96 75.980 31.530 33.2 2.87 75.890 31.030 29.2 2.53 75.670 29.820 33.6 2.91 75.570 29.250 33.6 2.91

Mean 30.6+ 3.0 2.64+0.26

Mean dust is the average of the 1.5 and 5% estimates + standard deviation, see text for details.

as the authors point out, the Duce et al. [1991] model values contain large uncertainties, particularly in regions where a lack of mineral aerosol data necessitated large extrapolations.

Dust depositions calculated from the A1 model are compared to independent measurements in Figure 2. In the case of the A1 model, the dust estimate is plotted as a range representing the maximum and minimum estimate derived from an assumption of 1.5 and 5% dust solubility, respectively. The symbol is plotted at the mathematical mean of these two solubility estimates. The origin of the independent estimates of dust deposition plotted on the x axis are discussed in sections 2.2 and 2.3 below and are either from direct measurements, estimates from suspended load, or from sediment trap lithogenic material. In most cases these independent estimates are plotted without error bars, however where an estimate of error is available it is also plotted, as noted in the text. Each data set will be discussed in detail below.

2.2. Atlantic Data Sets

2.2.1. Sargasso Sea. The Sargasso Sea data in Figure 2 are from Measures et al. [1984] and represent the average of the A1 content seen in nine surface water samples (Table 2) collected


Dust Deposition for the Caribbean

Longitude W Latitude N AI, nM Mean Dust

75.670 16.210 38 3.29 76.590 15.560 35 3.03 76.970 14.770 40 3.46 77.360 14.070 42 3.63 77.870 13.160 39 3.37 78.420 12.420 55 4.76 78.420 11.580 45 3.89

Mean 42+ 6.5 3.63+ 0.57

Mean dust is the average of the 1.5 and 5% estimates + standard deviation, see text for details).


between 35ø29'N, 72ø27'W and 29ø15'N, 75ø34'W at the begin- ning of November 1983. The mean dissolved A1 value for this region is 30.6 nM. While seasonal variations are to be expected, these values compare remarkably well with the 30.8 nM average A1 concentration reported by Jickells et al. [1986] for a suite of surface samples collected between November 1983 and May 1985 at Bermuda's Hydrostation S. It should be noted that Jick- ells et al. [1986] report seasonal variations in surface water A1 concentrations which they ascribe to biological removal during the spring bloom and concentration in the shallow mixed layer during summer months. The dust deposition calculated from our A1 values ranges from 1.22 to 4.06 with a mean of 2.64 g m -2 yr -1 (grams dust per square meter per year). The range of dust deposition for these data and those discussed below are obtained by applying the 1.5% and 5% solubility assumptions to the mean dissolved A1 value for the region in question.

This mean dust deposition value can be compared to the directly collected atmospheric deposition of 1.9 and 1.5 g m -2 yr -1 for the years 1988/1989 and 1989/1990 respectively, as reported by Jickells et al. [1994]. Alternately, dust estimates can be obtained from the lithogenic component of material collected in the deep sediment traps deployed over the long time series at Ber- muda. Using this approach, Jickells et al. [1998] estimate an average dust flux to this region of 1.7 + 0.4 g m -2 yr -1 for the period 1980-1991. It is interesting to note that in the period 1980-1984 (which includes our A1 sampling period), Jickells et al. [1998] show a slightly higher dust deposition of 2.2 + 0.3 g m -2 yr 'l. Prospero [1996], using the GESAMP model, estimated 1.56 g m '2 yr -• in the area around Bermuda while the Duce et al. [1991] map indicates dust depositions greater than 1 g m -2 yr -• but less than 10 g m -2 yr -• In this case, since there is excellent agreement between the directly collected atmospheric determinations and the longer term mean from the sediment trap, we have used the sediment trap collections [Jickells et al., 1998] value of 1.7 g m -2 yr -l with an error of + 0.4 g m -2 yr -• as the calibration point for this data set, which is representative of a longer term average.

2.2.2. Caribbean. A1 data for the Caribbean region are from Measures et al. [1984] The data represent seven samples collected between 16ø21'N, 75ø40'W and 11ø35'N, 78ø25'W in early November 1983 (Table 3).

The tnean dissolved A1 value for this region, 42 nM, implies a dust deposition of between 1.68 and 5.59 with a mean value of 3.63 g m -2 yr 'l. At Barbados, -1800 kilometers to the east of our cruise track, J. J. Perry, Jr. and W. M. Landing (pets. comm., 1999) estimate a bulk deposition (wet + dry) of 4-6 g m -2 yr -• for 1995/6 using average crustal abundances for Fe and A1. Honjo et al. [ 1982] report a similar value of - 4 g m -2 yr -l from a 3755-m deep trap at 13ø30'N, 55ø55'W. Prospero [1996], using an average of several deposition models, predicted a deposition of 2.43 g m -2 yr -• for the 10 ø grid box 70-80øW 10-20øN, while a similar estimate for the grid element including Barbados is 4.0 g m -2 yr -•. While the location of the site at which Perry and Landing estimated dust deposition is some distance from our sample region, we have chosen it for comparison since it is the only direct measurement and is similar to the estimates of both Prospero [1996] and Honjo et al. [1982]. It is represented in Figure 2 as 5+1 g m -2 yr -•.

2.2.3. Gulf of Guinea. A1 data for the Gulf of Guinea are

from Measures [1995]. The data are four samples collected between 2 ø 20'N, 11ø29'W and 4ø19'N, 12ø50'W during early April 1990 (Table 4).


Dust Deposition for the Gulf of Guinea


11.48 2.333 65.0 5.7

11.73 2.733 82.5 7.2

12.08 3.217 86.1 7.5

12.83 4.317 61.0 5.3

Mean 73.7+12.5 6.4+ 1.1

Mean dust is the average of the 1.5% and 5% estimates + standard deviation, see text for details.

This part of the surface transect showed that the highest A1 concentrations in the equatorial Atlantic were north of the dilut- ing effects of equatorial upwelling but south of the complex convergence zone of the Canary Current and the northernmost branch of the South Equatorial Current. The mean A1 concentration in this region implies a dust deposition ranging from 2.9 to 9.8 with a mean of 6.4 g m -2 yr -1. This value is somewhat lower than the 11.2 g m '2 yr -• reported by Jahn et al. [1996], for the inorganic fraction collected in the coastal region of Benin, Africa (-2øE, 6øN). Duce et al. [1991] indicate depositions between 1 and 10 g m -2 yr '• in this region, with the main plume of Saharan dust, where fluxes are in excess of 10 g m -2 yr -• somewhat to the north of this region. Model deposition values of Prospero et al. [1996] give a value of 20.1 g m -2 yr '•, in the 10 ø box of 0ø-10øN and 10 ø- 20øW. Clearly, in this region our estimates of dust deposition are very much lower than those of either Duce et al. [1991] or Pros- pero [1996] Possible reasons for this will be discussed in section

3.3 below. In Figure 2, we have used the 11.2 g m -2 yr -• value of Jahn et al. [1996] in the plot since it is a measured value. While the mean value we predict is lower than the Jahn et al. estimate, we note that the two highest dissolved A1 values recorded in this region (average 84.3 nM) would indicate a dust deposition as high as 11.2 g m -2 yr -•, which is similar to their value.

2.2.4. Western Equatorial Atlantic. Recent data from the equatorial Atlantic at 5øS and 5øN are from S. Vink and C. I.

Measures (The role of dust deposition in determining surface water distributions of A1 and Fe in the South West Atlantic, submitted to Deep-Sea Research, 1999)(hereinafter referred to as Vink and Measures, submitted manuscript, 1999) (Tables 5 and 6). The region of 5øS is chosen since it is just south of the 1 g m -2 yr '• contour line of Duce et al. [ 1991 ].

The 5øS data were collected in May 1996, using the towed surface sampler system, by Vink et al. [1999]. Each point (except at 5.32øS) represents the average of all samples (usually 4-6) deter-


Dust Deposition f•)r the Region of 5øS


21.7 6.06 25.5 2.2

21.7 5.97 24.6 2.1

21.6 5.75 28.4 2.5

21.4 5.32 24.8 2.2

21.4 5.17 22.5 2.0

21.3 5.02 23.3 2.0

Mean 24.9 + 2.1 2.2+0.2



mined during the period between instrument standardizations (usually ~1 hour). The A1 values imply dust depositions ranging from 0.9 to 3.3 with a mean of 2.2 g m '2 yr -l. While this mean is somewhat higher than the Duce et al. [ 1991 ] implied dust deposition of 1 g m -2 yr -l, in this area, the lower bound of our estimate does encompass it.

The data from 5øN (Table 6) collected during the same cruise represent the closest approach of the ship to the 10 g m -2 yr -1 iso- pleth in the Duce et al. [ 1991 ]. map and, as such, represent an opportunity to calibrate the model at one of the highest dust inputs. The data between lø30'N and 5øN cover the region between the equatorial upwelling to the south and the confluence of the northern branch of the South Equatorial Current and the Canary Cur- rent system to the north. In this dynamic region, the data show significant small-scale variations which are reported in detail elsewhere (Vink and Measures, submitted manuscript, 1999). The mean A1 value (as with the 5øS data, each point represents an average up to 10 samples collected over ~ 1 hour between standardizations) implies a dust deposition ranging from 1.9 to 6.2 with a mean of 4.1 g m -2 yr -l. From the Duce et al. data set, we inter- polate a deposition flux in this region of 5-8 g m -2 yr -l. Prospero [1995] indicates a mineral deposition of 6.3 g m -2 yr -1 in the grid

0-10øN, 20-30øW. We have plotted the independent estimate as 6.3 + 1.3 g m -2 yr 'l in Figure 2.

2.3. Pacific Data Sets

2.3.1. Hawaii. A1 data from the central Pacific gyre have been obtained by surface samples collected during the quasi-monthly occupation of the Joint Global Ocean Flux Study (JGOFS)-World Ocean Experiment (WOCE) Hawaii Ocean Time Series (HOT) site, 100 km north of Oahu [Karl et al., 1995]. The A1 data are from 30 samples collected by pole sampler [Boyle et al., 1981] between May 1993 and March 1996. While there are seasonal and interannual variations [Tersol et al., 1996], presumably in part driven by variable dust deposition, the data yield an average surface water value of 10.8 + 2.9 nM for the period. Using this mean, the implied dust deposition ranges from 0.4 to 1.4 g m '2


Dust Deposition for the Equatorial Atlantic Region


21 1.47 48.6 4.2

21.1 1.71 46.8 4.1

21.2 1.94 45.5 3.9

21.6 2.66 39 3.9

21.7 2.9 40.8 3.6

21.8 3.14 45.3 3.9

21.8 3.32 46.1 4

21.9 3.49 47.7 4.1

22 3.72 42.7 3.7

22.2 4.08 35.8 3.1

22.4 4.28 35.1 3.1

24.2 4.78 44.4 4

25.2 4.9 52.3 4.5

25.4 4.93 54.2 4.7

25.7 4.97 52 4.5

25.9 5 58.7 5.1

26.1 5.04 56.5 4.9

Mean 46.5 + 6.8 4.1 + 0.5



Dust Deposition for the New Zealand Region


175.8

176.14

176.4

177.49

Mean

45.92 1.08 0.094

46.2 0.63 0.055

46.42 1.23 0.11

1.21 0.11

1.79 0.16

47.41 1.64 0.14

1.26 + 0.41 0.11+ 0.04


yr 'l with a mean of 0.9 g m -2 yr 'l. This value is in good agreement with published values for this region. For example, Uematsu et al. [1985] report 0.43 g m -2 yr -1 from total deposition measurements, and Honjo et al. [1982] report 0.5-0.62 g m -2 yr -I of lithogenic material from sediment trap studies conducted some 650 kilometers to the southeast. We have plotted the Uematsu et al. [1985] 0.43 g m '2 yr -I measurement in Figure 2.

2.3.2. New Zealand and 60-70øS. The last two regions selected for comparison were chosen because they encompass some of the lowest estimated dust fluxes in the world. The data used in

the comparison were obtained during the U.S. JGOFS Southern Ocean Survey 2 cruise using discrete samples obtained from the towed sampler [Vink et al., 1999].

The New Zealand data were collected between 46øS, 175ø48'E

and 47ø25'S, 177ø30'E (Table 7). This region was selected for comparison because it is north of the 0.1 g m -2 yr -1 contour in the Duce et al. [1991] presentation but is still downwind of the land mass New Zealand, which is presumably in part responsible for the southern kink in the 0.1 g m -2 yr -1 contour.

The mean A1 value, 1.26 nM, implies a dust flux range of 0.05 to 0.16 g m -2 yr -1 with a mean of 0.11 g m -2 yr -1, which is close to the value implied by the Duce et al. [1991] contour. We have plotted the Duce et al. data as 0.1 g m '2 yr 'l.

The 63øS data were selected to test the model at the lowest es-

timated flux in the Duce et al. [1991] presentation. To our knowledge there are no direct measurements in this region; although when the U.S. JGOFS Southern Ocean sediment trap data from the Ross Sea are available, the lithogenic fraction may be used to make such an estimate. Our data come from the region between 62ø53'S and 63ø53'S along 170ø6'W and were collected in mid-January 1998 (Table 8). This area was one of the farthest


Dust Deposition for the 63øS Region


170.1 62.89 0.37 0.032

170.1 63.11 0.35 0.03 170.1 63.39 0.37 0.032

170.1 63.65 0.49 0.043

170.1 63.97 0.35 0.03

170.1 64.24 0.35 0.03

170.1 64.52 0.35 0.03

170.1 64.81 0.43 0.037

Mean 0.38 + 0.05 0.033 + 0.004

Mean dust is the average of the 1.5% and 5% estimates + standard deviation, see text for details


south that Survey 2 occupied but is still north of the ice edge where slightly elevated A1 were seen on several U.S. JGOFS Southern Ocean cruises, presumably the result of ice melt (C. I. Measures and S. Vink, unpublished data, 1999).

The mean A1 value, 0.38 nM implies a dust input that ranges from 0.015 to 0.051 with a mean of 0.033 g m -2 yr -•. These values are all considerably higher than the <0.01 g m -2 yr '• implied by the Duce et al. [ 1991 ] contour for this region.

3. Discussion

3.1. Deviation of AI Model from Independent Estimates

Despite the generally good agreement between estimates, there are clearly deviations from the 1:1 line in Figure 2. The probable causes of these deviations and the associated limitations they imply for the model are discussed in sections 3.2-3.7.

3.2 Overestimates at Low Deposition Levels

One of the poorest relative agreements between the A1 model and the Duce et al. [1991] data occurs in the 63-65øS region where even our lowest estimate of dust deposition (0.015 g m -2 yr -•) is -50% higher than the value of the nearby Duce et al. contour. This remote region has few, if any, sites for direct estimation of dust deposition so it would be simple for us to suggest that this large relative disagreement may result from errors associated with the extrapolations required to produce the Duce et al. contours. However, because our model implicitly assumes that the predominant source of A1 to the surface waters is from atmospheric deposition, it is also likely that we have overestimated dust deposition in this region. Annual deepening of the mixed layer in this region, as elsewhere, will result in the entrainment of dissolved A1 into the surface waters from subsurface layers which may have A1 supplied to them advectively. In regions of sub- stantial eolian deposition, this subsurface addition will normally be quite small relative to the atmospheric input; however, in a region with very low atmospheric inputs,this is not the case. Verti- cal profiles of A1 in the Southern Ocean (C. I. Measures and S. Vink, in preparation. 1999) indicate that A1 concentrations at 200 m are virtually indistinguishable from the surface values reported in section 2.3.2, clearly indicating that the resupply of A1 to the surface waters in this region is dominated by the admixture of A1 from the deep water. Therefore if instead of using surface water values we use the difference between surface water values and

the value at the depth of the deepest mixed layer we observed in this region in October 1997 (150-160 m), we can estimate the dust input required to maintain the differential. Since the A1 values at depths to 200 m are analytically indistinguishable from the surface value, we will assume that the differential is less than or

equal to the analytical uncertainty of our determination at these concentration levels (here assumed to be -0.1 nM). With this

value, we calculate that the annual dust input required to maintain the differential would range from 0.004 g m '2 yr -1 to 0.013 g m -2 yr -• with a mean of 0.009 g m -2 yr -•. These values are much closer to the estimates of Duce et al. [1991]. Thus, in regions of extremely low deposition accurate assessment of dust deposition will require care•hl assessment of seasonal changes in mixed layer depth and the contribution that the entrainment of deep water makes to maintaining surface water concentrations.

3.3. Underestimates at High Deposition Levels

It is also clear from Figure 2 that there is a tendency for the model to underestimate dust deposition at high dust depositions. Most notable are the data from the Gulf of Guinea and at 5øN --

both in the tropical Atlantic. We recognize two possible causes for underestimation in this region.

The first possible problem may be in the lower range of dust solubility that we have used to calculate our dust deposition inputs. We have chosen a lower limit of 1.5% because that is at the lower end of the range determined for laboratory studies using atmospheric aerosols [Prospero et al., 1987; Maring and Duce, 1987; Chester et al., 1993; Lim and Jickells, 1990]. However, there is a great deal of variation in reported solubilities with some individual values as low as 0.5%. While there is only a limited amount of information concerning the relative solubility of natural aerosols, laboratory experiments utilizing aerosol material have indicated that the acidification of the aerosol material and

the amount of time it is acidified are prime factors in determining the fractional solubility [Spokes et al., 1994; Spokes and Jickells, 1996]. In the tropical Atlantic, the dust that is deposited comes from the nearby Sahara and will probably have spent only a short time in the atmosphere. In this case, it is quite possible that the solubility of the atmospheric material is lower than our lower bound estimate of 1.5%. The effect of this change is such that lowering the partial solubility from 1.5 to 1% would increase the maximum calculated dust input into the Gulf of Guinea from 9.8 g m '2 yr -1 to 14.7 g m -2 yr '•. The sensitivity of the calculation to this parameter serves to underscore the importance of determining the factors which control the magnitude and natural variability of dust solubility in the natural environment. The accuracy of the model will be improved considerably by the determination and use of dust solubilities that are region specific.

The other phenomena that may be contributing to underestimates in the tropical Atlantic is that of advection. Since the model merely evaluates the A1 input required to maintain an existing dissolved A1 concentration against scavenging removal (i.e., steady state), it implicitly assumes that the surface water mass is stationary and that inputs per square meter of ocean surface are similar if averaged over the residence time of the A1 in the surface waters. Thus advection of surface waters from re-

gions of high dust deposition to regions of low deposition will result in overestimates of dust input in the former and underestimates in the latter. In the equatorial Atlantic we have an extremely steep gradient in dust deposition as well as an extremely dynamic current system. Thus it is likely that much of the over- estimate of dust deposition that we see in the 5øS A1 data is the result of entrainment of Al-rich Gulf of Guinea water (described in more detail by Vink and Measures (submitted manuscript, 1999) into the westward flowing South Equatorial Current. The apparent underestimates at 5øN and the Gulf of Guinea are likely to be the inverse problem, i.e., the dilution of the deposited A1 signal through its advection out of these regions. In regions with high advection rates, improving the accuracy of the model will require incorporation of local advection rates.

3.4. The Arabian Sea: A Region of Strong Seasonal Input

The Arabian Sea presents an interesting area in which to ex- plore the applicability and limits of the current model. This is a region that experiences a large dust input over a relatively short


period [Measures and Vink, 1999], coupled with extremely high biological production and export from the surface ocean fueled by nutrients derived from wind-driven upwelling. A1 data collected during several of the U.S. JGOFS Arabian Sea cruises provide an opportunity to directly observe the geochemical effects of dust deposition. Measures and Vink [1999] observed a significant increase in dissolved A1 in the surface waters of the north-

eastern part of the Arabian Sea during the SW Monsoon. They interpreted this increase to be the result of the deposition of eolian material carried to the region by the Findlater Jet from NE Africa. By comparing pre-SW Monsoon surface water values with those during the monsoon, they estimated that between 2.0 and 6.8 g m '2 of dust was deposited to the surface of the northeast part of the Arabian Sea during the period between January and July 1995. This value was calculated by determining how much A1 was needed to raise the 52-m deep mixed layer concentration by the concentration change observed in this region between January and July (5.6 nM). These calculated deposition values are somewhat lower than estimates for the long-term mean for this region determined from sediment accumulation rates of ~ 10- 13 g m -2 yr' 1 [Prell et al., 1980; Goldberg and Griffin, 1970]. The comparison between Measures and Vink's [1999] January- July data with annual averages was justified by the assumption that the SW Monsoon was the main contributor to annual dust

deposition. Measures and Vink's [1999] calculations were made assuming

that A1 inputs were into an effectively static water body. How- ever, if the lower A1 surface waters, seen to the west, were ad-

vected into this region (the predominant direction of flow during the SW Monsoon [Shetye et al., 1994], then significantly higher dust inputs would be calculated. For example, if the range of A1 surface water values of 3-5 nM (average 4 nM) seen to the west prior to the onset of the monsoon better represents the base A1 values, then the surface water A1 increase in the northeast region would be ~ 10.4 nM, and the calculated dust input needed to sup- port this increase would rise to between 3.6 and 12 g m -2.

In addition to the direct observation of a dust induced change in surface water A1 chemistry, Measures and Vink [1999] also observed the inverse process, i.e., the removal of the A1 dust signal by export production. During the late SW Monsoon, A1 concentrations in the noncoastal regions dropped dramatically (by 5- 9 nM) coincident with the export of biological material. Such a decrease indicated that the residence time of A1 under these ex-

port production conditions was closer to 2 years rather than the 5 years assumed by the MADCOW model. The Arabian Sea results indicate that although the simple steady state model is capable of predicting dust inputs that are within a factor of 2 to3 of other observations, in regions with such dramatic seasonal inputs and removal, attention needs to be paid to both the timing of sampling as well as to scaling the residence time of A1 with known or modeled values of export production [Falkowski et al., 1998].

3.5. Role of Dust Solubility and its Geographic Variations

One of the parameters of greatest importance to the prediction of dust input by this method is understanding the mechanism and natural variability of the fractional solubility of aluminosilicate dusts. We have used a range of solubilities based on experimental work from 1.5 to 5%. Since the processes that cause dust to

solubilize are likely to be in cloud acidification cycles associated with the condensation and oxidation of atmospheric sulphur species, it is reasonable to expect significant variations in partial, solubilities as a result of the time that the dust remains airborne

(i.e., increased number of condensation/evaporation cycles lead- ing to increased partial dissolution) and the sulfur chemistry associated with the transporting air masses, i.e., presence of natural and anthropogenic sulfur compounds. In this regard, it is interesting to note that in the regions of highest dust input, closest to the dust sources, the A1 model tends to underestimate inputs, per- haps as a result of lower partial solubilities of the dust. These parameters need to be investigated since they will affect any extrapolations that can be made on the basis of glacial-interglacial dust flux changes. We note, however, that Zhu et al. [1997] discounted the possibility that repeated photochemical cycling during extended atmospheric transport had any effect on the magnitude of Fe dissolution.

3.6. Validity and Applicability of the Model

Despite the model shortcomings outlined in sections 3.2 and 3.3, over the approximately 3 orders of magnitude of comparison, the agreement between dust deposition estimates based on dissolved A1 and those from other sources are, when the mutual un-

certainties of the approaches are considered, remarkably good. We take this as strong evidence that the underlying assumption that dissolved A1 in surface ocean water is acting as a proxy for dust deposition is correct, and, that the A1 distribution can be used as a tool in evaluating the biogeochemical effects of dust deposition to the surface ocean.

Regional variations in depths of the mixed layer can be used to improve future model predictions. Similarly, it is likely that estimates of export production can also be incorporated to vary A1 residence time which will also improve model predictions. These values either exist as measured parameters or may be available from other model output for incorporation into a future genera- tion of the A1 model. Incorporation of the factors that control the magnitude of dust dissolution into the model will also lead to major gains in model accuracy. Incorporation of local advection rates can also be used to mitigate the problem of advection.

However, it should be noted that while advection may introduce errors in estimating the amount of dust deposited to each square meter of Earth's surface, the A1 content of surface water is a recrder of the amount of dust that has been deposited into that body of water, and this, from an oceanographic perspective, is the relevant biogeochemical parameter. However, extrapolation of dust deposition estimated from A1 to the magnitude and biological impact of Fe addition to the surface ocean will require a better understanding of the residence time of Fe in surface waters as well as the systematics controlling its dissolution from atmospheric dusts.

3.7. Use of AI to Estimate Variations of Dust Input on Decadal Timescales

Since the surface water dissolved A1 concentration appears to be reflecting the process of dust deposition to the surface ocean, it can be used as a tool to investigate the biogeochemical response of the surface ocean to changes in atmospheric dust deposition. Long-term records of dust concentrations at Barbados have shown significant changes in suspended atmospheric dust loads

_

326 MEASURES AND VINK: USING DISSOLVED AI TO ESTIMATE OCEAN DUST DEPOSITION

during the period 1965-1992 [Prospero and Nees, 1986; Pros- pero, 1995]. Records of rainfall over the Sahel indicate that these changes may be related to greater mobilization of desert soils under abnormally dry conditions [Prospero and Nees, 1986; Pros- pero, 1995].

However, it is also possible that the correlation may be the result of changes in circulation patterns associated with large-scale meteorological changes that lead to the drought conditions in North Africa. Nevertheless, large-scale changes in the atmospheric suspended dust load over decadal timescales furnish an opportunity to observe the biogeochemical response of the surface ocean to changes in dust deposition. Such observations will provide important insights into the nature of the coupling of atmospheric-oceanic processes that will be invaluable as we seek to understand the likely biogeochemical consequences of enhanced dust fluxes during glacial periods.

Acknowledgments. We would like to thank Stefano Guerzoni for •nany fruitful discussions on the role of dust deposition to geochemical cycles and its role in the surface ocean as well as his hospitality during CIM's sabbatical in Bologna, Italy. We would also like to thank Joe Prospero and one anonymous reviewer whose detailed comments helped improve the manuscript. The data reported in this work was supported by several grants: OCE 93-10943, OCE 95-31847, OCE 95-30961 and Of- fice of Naval Research grant N00014-92-J-1485 to CIM. This is contribution 4959 of the School of Ocean Earth Science and Technology, Uni- versity of Hawaii.

References

Boyle., E. A., S.S. Huested, and S. P. Jones, On the distribution of cop- per, nickel, and cadmium in the surface waters of the North Atlantic and North Pacific Ocean, J. Geophys. Res., 86, 8048-8066, 1981.

Chester, R., K .J.T. Murphy, F. J. Lin, A. S. Berry, G. A. Bradshaw, and P. A. Corcoran, Factors controlling the solubilities of trace metals from non-remote aerosols deposited to the sea surface by the dry deposition mode, Mar. Chem., 42, 107-126, 1993.

De Angelis, M., N. I. Barkov, and V. N. Petroy, Aerosol concentrations over the last climatic cycle (160 kyr) from an Antarctic ice core, Na- ture, 325, 318-321, 1987.

Duce, R. A., et al., The atmospheric input of trace species to the World Ocean, Global Biogeochem. Cycles, 5, 193-259, 1991.

Falkowski, P. G., R. T. Barber, and V. Smetacek, Biogeochemical con- trols and feedbacks on ocean primary production, Science, 281, 200- 206, 1998.

Goldberg, E. D., and J. J.Griffin, The sediments of the northern Indian Ocean, Deep-Sea Res., 17, 513-537, 1970.

Helmers, E., and M. Rutgers van der Loeff, Lead and aluminum in Atlan- tic surface waters (50øN to 50øS) reflecting anthropogenic and natural sources in the eolian transport, J. Geophys. Res., 98, 20,261-20,274, 1993.

Hodge, V., S. R. Johnson, and E. D. Goldberg, Influence of atmospheri- cally transported aerosols on surface water composition, Geochem. J., 12, 7-20, 1978.

Honjo, S., Seasonality and interaction of biogenic and lithogenic particulate flux at the Panama Basin, Science, 218, 883-884, 1982.

Honjo, S., S. J. Manganini, and L. J. Poppe, Sedimentation of lithogenic particles in the deep ocean, Mar. Geol., 50, 199-220, 1982.

Hydes, D. J., Seasonal variation in dissolved aluminum concentrations in coastal waters and biological limitation of the export of the riverine input of aluminum to the deep sea, Cont. ShelfRes. 9, 919-929, 1989.

Hydes, D. J., and P.S. Liss, The behaviour of dissolved aluminum in estuarine and coastal waters, Estuarine Coastal Mar. Sci., 5, 755-769, 1977.

Jahn, R., L. Herrmann, and K. Stahr, Bedeutung Aolischer Eintrage fur Bodenbildung und Standortseigenschafien im circumsaharischen, Raum. Zbl. Geol. Palaont. Teil I, 3/4: 421-432, 1996.

Jickells, T. D., T. Church, A. Veron, and R. Afimoto, Atmospheric inputs

of manganese and aluminum to the Sargasso Sea and their relation to surface water concentrations, Mar. Chem., 46, 283-292, 1994.

Jickells, T. D., S. Dorling, W. G. Deuser, T. M. Church, R. Aftmoro, and J. M. Prospero, Airborne dust fluxes to a deep water sediment trap in the Sargasso Sea, Global Biogeochem. Cycles, 12, 311-320, 1998.

Karl, D., R. Letelier, D. Hebel, L. Tupas, J. Dore, J. Christian, and C. Winn, Ecosyste•n changes in the North Pacific subtropical gyre attrib- uted to the 1991-92 El Nino, Nature, 373, 230-234, 1995.

Kre•nling, K. The distribution of cadmimn, nickel, manganese, and aluminum in surface waters of the open Atlantic and European shelf area, Deep Sea Res. 32, 531-555, 1985.

Kremling, K., and D. Hydes, Summer distribution of dissolved AI, Cd, Co, Cu, Mn and Ni in surface waters around the British Isles, Cont. ShelfRes., 8, 89-105, 1988.

Kumar, N., R. F. Anderson, R. A. Mortlock, P. N. Froelich, P. Kubik, B. Dittrich-Hannen and M. Surer, Increased biological productivity and export production in the glacial Southern Ocean, Nature, 378, 675- 680, 1995.

Laj, P., G. Ghermandi, R. Cecchi, V. Maggi, C. Riontino, S. Hong, J. Candelone, and C. Boutron, Distribution of Ca, Fe, K and S between soluble and insoluble •naterial in the GRIP ice core, J. Geophys. Res., 102, 26,615-26,626, 1997.

Li•n, B., and T. D. Jickells, Dissolved, particulate and acid-leachable trace •netal concentrations in North Atlantic precipitation collected on the global change expedition, Global Biogeochem. Cycles, 4, 445-458, 1990.

Maring, H. B., and R. A. Duce, The i•npact of atmospheric aerosols on trace metal chemistry in open ocean surface seawater, 1, Aluminum., Earth Planet. Sci. Lett., 84, 381-392, 1987.

Martin, J. H., and S. E. Fitzwater, Iron deficiency limits phytoplankton growth in the north-east subarctic Pacific, Nature, 331,341-343, 1988.

Measures, C. I., The distribution of A1 in the IOC stations of the South and Central Atlantic, Mar. Chem., 49, 267-281, 1995.

Measures, C. I., B. Grant, M. Khadem, D. S. Lee, and J. M Edmond, Dis- tribution of Be, A1, Se and Bi in the Surface Waters of the Western North Atlantic and Caribbean, Earth Planet. Sci. Lett., 71, 1-12, 1984.

Measures, C. I., J. M. Edmond, and T. D. Jickells, Aluminum in the North West Atlantic, Geochim. et Cosmochim. Acta, 50, 1423-1429, 1986.

Measures, C. I., and J .M. Edmond, Shipboard determination of aluminum in seawater at the nanomolar level by electron capture detection gas chromatography, Anal. Chem., 61,544-547, 1989.

Measures, C. I., and J. M. Edmond, Aluminum in the South Atlantic: steady state distribution of a short residence time element, J. Geophys. Res., 95, 5331-5340, 1990

Measures, C. I., and E. T. Brown, Estimating dust input to the Atlantic Ocean using surface water AI concentrations, in The bnpact of Desert Dust across the Mediterranean, edited by S. Guerzoni and R. Chester, pp. 301-311, Kluwer, Dordrecht., 1996.

Measures, C. I., and S. Vink, Seasonal variations in the distribution of Fe and AI in the surface waters of the Arabian Sea, Deep-Sea Res., 46, 1597-1622, 1999.

Moran, S. B., and R. M. Moore, Kinetics of the removal of dissolved aluminum by diatoms in seawater: A comparison with thorium, Geo- chim. Cosmochim. Acta, 56, 3365-3374, 1992.

Morris, A. W., R. J. M. Howland, and A. J. Bale, Dissolved aluminum in the Tamar estuary, southwest England, Geochim. Cosmochim. Acta, 50, 189-197, 1986.

Orians, K. J., and K. W. Braiand, The biogeochemistry of aluminum in the Pacific Ocean, Earth Planet. Sci. Lett. 78, 397-410, 1986.

Prell, W. L., W. H. Hutson, D. F. Williams, A. W. H. Be, K. Geitzenauer, and B. Molfino, Surface circulation of the Indian Ocean during the last glacial maximum, approximately 10,000 yr. B.P, Quat. Res., 14, 309- 336, 1980.

Prospero, J. M, The atmospheric transport of particles to the ocean, in Particle flux in the ocean, edited by V. Ittekot, S. Honjo, and P. De- petris, pp. 19-52, John Wiley, New York, 1995.

Prospero, J. M., Saharan dust transport over the North Atlantic Ocean and Mediterranean: an overview, in The Impact of Desert Dust across the Mediterranean, edited by S. Guerzoni and R. Chester, pp. 131-151, Kluwer, Dordrecht., 1996.

Prospero, J. M., and R. T. Nees, Impact of the North African drought and El Nino on mineral dust in the Barbados trade wind, Nature, 320, 735- 738, 1986.


Prospero, J. M., R. T. Nees, and M. Uematsu, Deposition rate of particulate and dissolved aluminum derived from Saharan dust in precipitation at Miami, Florida, J. Geophys. Res., 92, 14,723-14,731, 1987.

Ratmeyer, V., G. Fischer, and G. Wefer, Lithogenic particle fluxes and grain size distributions in the deep ocean off northwest Africa: Impli- cations for seasonal changes of aeolian dust input and downward transport, Deep-Sea Res., 46, 1289-1337, 1999.

Rea, D. K., The paleoclimatic record provided by eolian deposition in the deep sea: The geologic history of wind, Rev. Geophys., 32, 159-195, 1994.

Resing, J., and C.I. Measures, Fluorimetric determination of AI in seawater by FIA with in-line preconcentration, Anal. Chetn., 66, 4105- 4111, 1994.

Shetye, S. R., A.D. Gouveia, and S. S. C. Shenoi, Circulation and water masses of the Arabian Sea, in Biogeochemistry of the Arabian Sea, edited by D. Lal, pp. 9-25, Indian Academy of Sciences, pp 318, 1994..

Spokes, L. J., and T. D. Jickells, Factors controlling the solubility of aerosol trace metals in the atmosphere and on mixing into seawater, Aquat. Geochem., 1,355-374, 1996.

Spokes, L. J., T. D. Jickells, and B. Lim, Solubilization of aerosol trace metals by cloud processing: A laboratory study, Geochim. Cosmo- chim. Acta, 58, 3281-3287, 1994.

Taylor, S. R., Abundance of chemical elements in the continental crust: A new table, Geochim. Cosmochim. Acta, 28, 1273-1285, 1964.

Tersol., V., S. Vink, J. Yuan and C. I. Measures, Variations in iron, aluminum and beryllium concentrations in surface waters at Station Aloha, Eos Trans. AGU, 76(3), Ocean Sci. Meet. Suppl., 65, 1996.

Uematsu, M., R. A. Duce, and J. M. Prospero, Deposition of atmospheric particles in the North Pacific Ocean, J. Atmos. Chem., 3, 123-138, 1985.

Vink, S., E.A. Boyle, C.I. Measures, and J. Yuan, Automated high reso- lution determination of the trace elements iron and aluminum in the

surface ocean using a towed fish coupled to flow injection analysis, Deep-Sea Res., in press, 1999.

Wedepohl, K. H., The composition of the continental crust, Geochim. Costm•chitn. Acta, 59, 1217-1232, 1995.

Zhu, X. R., J. Prosper,o and F. J. Millero, Diel variability of soluble Fe(II) and soluble total Fe in North African dust in the trade winds at Barba-

dos, J. Geophys. Res., 102, 21,297-21,305, 1997. Zhuang, G., R. A. Duce, and D. R. Kester, The dissolution of atmospheric

iron in surface seawater of the open ocean, J. Geophys. Res., 95, 16,207-16,216, 1990.

C. J. Measures and S. Vink, Department of Oceanography, University of Hawaii, 1000 Pope Road, Honolulu, HI, 96822. ([email protected])

(Received June 14, 1999; revised October 4, 1999; accepted October 11, 1999)

dust deposition to the ocean - woods hole … of the ratio of dissolved to particulate a1 in rain...

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