Trace element deposition from desert dust has important
impacts on ocean primary productivity, the quantification of which could be
useful in determining the magnitude and sign of the biogeochemical feedback
on radiative forcing. However, the impact of elemental deposition to remote
ocean regions is not well understood and is not currently included in global
climate models. In this study, emission inventories for eight elements
primarily of soil origin, Mg, P, Ca, Mn, Fe, K, Al, and Si are determined
based on a global mineral data set and a soil data set. The resulting
elemental fractions are used to drive the desert dust model in the Community
Earth System Model (CESM) in order to simulate the elemental concentrations
of atmospheric dust. Spatial variability of mineral dust elemental fractions
is evident on a global scale, particularly for Ca. Simulations of global
variations in the Ca
Desert dust aerosols are soil particles suspended in the atmosphere by
strong winds and originate primarily from regions with dry, un-vegetated
soils. Desert dust particles are thought to contain several important
chemical elements which can impact the earth system by influencing
biogeochemical cycles and, in particular, marine primary productivity (Martin et
al., 1991; Duce and Tindale, 1991; Herut et al., 1999, 2002, 2005; Okin et
al., 2004; Jickells et al., 2005). Iron (Fe) is
considered the most important element carried in dust, and low Fe supplies
combined with a low dust solubility are thought to limit phytoplankton
growth in high-nutrient low-chlorophyll (HNLC) regions. The HNLC regions
feature residual macronutrient (e.g., nitrogen, N, and phosphorus, P)
concentrations, but productivity remains limited by the low supply of Fe
(e.g., Martin et al., 1991; Boyd et al., 1998). Further studies
have linked Fe to the nitrogen cycle because of high Fe requirements of N
fixing organisms (e.g., Capone et al., 1997). While there are
internal sedimentary sources of Fe in the ocean, dust deposition is an
important source of new Fe to remote regions of the ocean (e.g., Fung et
al., 2000; Lam and Bishop, 2008; Moore and Braucher, 2008). Desert
dust also contains P, which is a limiting nutrient in some ocean and land
regions (e.g., Mills et al., 2004; Okin et al., 2004; Swap et al.,
1992), especially on longer timescales. In addition, as a dominant
constituent of mineral dust, silicon (Si) is an important nutrient for
diatoms which are central in ocean productivity (Morel et al., 2003). Other
elements released from mineral dust which may be important for ocean
biogeochemistry include manganese (Mn) as a biologically essential
nutrient and aluminum (Al) as a tracer of atmospheric inputs (e.g.,
Nozaki, 1997;
Previous studies have emphasized the importance of measuring elemental
composition of dust elements (Kreutz and Sholkovitz, 2000; Cohen et al.,
2004; Marino et al., 2004; Marteel et al., 2009), and there is a range of
studies highlighting observations of elemental distributions and ecosystem
impacts (e.g., Baker et al., 2003; Herut et al., 2002;
Buck et al., 2006; Paytan et al., 2009; Chen
and Siefert, 2004; Measures and Vink, 2000). In situ observations show
evidence of heterogeneities in elemental fractions over arid soil regions
(Svensson et al., 2000; Zhang et al., 2003; Shen et al., 2005, 2006; Li et
al., 2007). Ratios between elements including Si, Al, Mg, Ca, and in
particular Ca
Xuan (2005) has simulated the emission inventory of trace elements in the dust source regions of East Asia. However, there has not yet been a study to model the distribution of dust-associated elements on a global scale. Global dust models usually assume a fixed fraction (e.g., normalized to Al) of each element in dust to simulate global dust elemental transport and deposition. For example, Fe is thought to contribute 3.5 % and P 0.075 % to mineral dust (by mass; e.g., Luo et al., 2008; Mahowald et al., 2008). Besides spatial variations in elemental compositions, particle size distribution forms another important determinant of elemental abundance in deposited dust. Depending on the particle size distribution, trace elements may remain more or less suspended in the atmosphere and deposited by dry or wet deposition at various distances from desert regions (Seinfeld and Pandis, 1998). There have been very few studies investigating particle size distribution and elemental concentrations in soil and dust by direct measurement (Schütz and Rahn, 1982; Reid et al., 2003; Castillo et al., 2008; Engelbrecht et al., 2009), and even fewer modeling studies have included this. The ability to model the deposition of specific elements associated with dust in global simulations has been hindered by a lack of understanding of the spatial and temporal variability, as well as the particle size distribution associated with different dust sources. As noted by Lawrence and Neff (2009), it seems most appropriate to use a globally averaged value of dust composition to estimate the elemental flux from dust, given the lack of direct measurements of the spatial distribution of elements in dust. However, the use of a global mineral map (Claquin et al., 1999; Nickovic et al., 2012, 2013; Journet et al., 2014) and chemical compositions of minerals (Journet et al., 2008) allows us to simulate global elemental inventories from mineral soils, which could be used in a global dust model.
This study aims at introducing a technique to determine a size-fractionated global soil elemental emission inventory based on two different data sets, a global soil data set and a mineralogical data set. A companion paper evaluates the ability of the model to simulate mineralogy and the impact on radiation (Scanza et al., 2015). The elemental emission data set estimated for Mg, P, Ca, Fe, Mn, K Al, and Si was used as an input to a model simulation of the global dust cycle to present the elemental distributions, which were compared against available observations of concentration and deposition to different ocean regions. Our goal is to assess the variability of elemental fractions in atmospheric and deposited dust and to investigate whether the elemental emission data set can adequately predict this variability. This study focuses on desert dust particles and thus disregards other potentially important sources of the elements such as combustion processes (e.g., Guieu et al., 2005; Luo et al., 2008; Mahowald et al., 2008). We focus on total elemental concentrations but discuss two methodologies for soluble metal distributions from soil emissions. We also do not consider any atmospheric processing, which is likely to be important for some chemical components (e.g., Mahowald et al., 2005; Baker and Croot, 2010).
The soil map of the world used in this study comes from the Food and Agriculture Organization (FAO) of the United Nations soils data set and includes 136 soil units from the FAO–United Nations Educational, Scientific, and Cultural Organization (FAO-UNESCO, 1995) at a 5 min resolution. The global data set of soil clay and silt data are used in this study. Following Claquin et al. (1999) and Nickovic et al. (2012), the illite, hematite, kaolinite, smectite, quartz, feldspars, calcite and gypsum contents are specified for different clay and silt soil types, and the global mineral distribution is presented in Scanza et al. (2015). Some minerals found in dust such as dolomite were not considered by Claquin et al. (1999) and Nickovic et al. (2012) and have also been disregarded in this study due to the lack of data on their distribution.
The elemental compositions of hematite and aluminosilicate minerals used in
this study are taken from previous works (Journet et al., 2008, and
unpublished data provided by E. Journet, 2012) and were obtained by X-ray
fluorescence spectrometry (XRF; Table 1a). Most of the minerals used by Journet
et al. (2008) are reference materials from the Clay Minerals
Society's Source Clays
Repository, i.e., hematite, illite, kaolinite, and montmorillonite. The elemental
compositions obtained by XRF are in the range of published values for these
reference materials (e.g., Mermut and Cano, 2001; Gold et al., 1983),
validating the obtained composition for the unreferenced materials.
Moreover, the purity of all mineral samples is estimated by X-ray
diffraction. Note that the mineralogical maps used in this study do not
distinguish feldspar and smectite subtypes. For feldspars, the elemental
composition is mostly averaged based on two subtype minerals: orthoclase
(potassic feldspar) and oligoclase (sodium-calcium feldspar). For smectites,
the montmorillonite subtype is the most commonly identified smectite in
desert dust, particularly for Saharan dust (e.g., Goudie and Middleton, 2006).
The chemical composition of montmorillonite is used in this study as an
analog for smectite. For calcite, gypsum, and quartz, the natural minerals
could contain substitutions or impurities from clays, which are
variable depending on origin, formation, contamination, etc., of minerals.
Because regional silt samples were not available for spectroscopy, we use
the theoretical composition of elements in calcite, gypsum and quartz (Table 1a). The mass fractions of Ca in calcite (CaCO
(a) Generalized mineral compositions (%) applied in this study. (b) Elemental solubility as a percentage of the element contained in the minerals (%).
Note: Fe content came from Journet et al. (2008), the other elements were from personal communication with E. Journet, 2012.
Following the total element calculation, soluble elemental fractions are
estimated based on soluble elemental contents of minerals at pH
One drawback of our approach is that we disregard the large variability of soils included within each defined “soil type”. The range of minerals within each soil type is large (e.g., Claquin et al., 1999), and the range of elemental concentrations in each mineral is also large (Journet et al., 2008). The resolution of our model is such that despite the actual heterogeneity of soils at a particular location, we prescribe an average at each grid box which tends to reduce the variability of the elemental composition in the mineral dust in the atmosphere. This is likely to be the largest source of uncertainty in our approach.
Ten-year-averaged emission rates (Tg yr
The Community Earth System Model version 1.0.3 (CESM1.0.3) is coordinated by the
National Center for Atmospheric Research (NCAR), and has been used to
simulate elemental dust emission, transport and deposition in this study.
The bulk mineral aerosol in the Community Atmosphere Model version 4 (CAM4)
was adapted to include eight trace elements within total dust (Scanza et
al., 2015). In this model simulation, the physical scheme CAM4 is driven by
the meteorological data set MERRA (Modern Era Retrospective-Analysis) and is simulated spatially at
1.9
There has been considerable work on improving advection algorithms in atmospheric models, and here we use the finite volume advection algorithm as part of the CAM (Lin and Rood, 1997). While no advection scheme is perfectly mass conserving, monotonic, shape preserving and computationally efficient, this scheme does a good job of balancing these multiple goals and maintaining strong gradients required in modeling atmospheric constituents (e.g., Rasch et al., 2006). By splitting the dust into its different mineral elements, we may add in additional numerical errors, because the advection will not conserve the fraction of elements within dust aerosols due to small numerical errors. For the discussion of the ratios of elements, it would be better to advect the minerals themselves, and evaluate the ratio of elements later, since this would better conserve the ratios. Studies focused on elemental ratios and their distribution in ocean models have suggested there is a relatively small uncertainty associated with these types of numerical errors (e.g., Christian, 2007) and, compared with the errors in the source distribution of the minerals, errors from advection are likely to be small and are neglected here.
Comparison of modeled and observed fractions of chemical elements in TSP, and tuning ratio based on 13 site measurements. (For this table comparing the elemental ratios at the measurement sites, the percentage value at each time measured is averaged across time and space for this comparison.).
An element data set of ground-based aerosol measurements at 17 sites
(Table S3) is used to evaluate the elemental dust simulation (Sun et al.,
2004a, b; Wang et al., 2010; Chen et al., 2008; Engelbrecht et al., 2009;
Carpenter et al., 2010; Cohen et al., 2011; Guo et al., 2014; Formenti et
al., 2008; Desboeufs et al., 2010). The sites are close to major
dust-producing regions (Fig. 1), including 10 Asian sites (Central Asia:
Hetian, Tazhong; East Asia: Yulin, Duolun, Shengsi; South Asia: Hanoi, and
Manila; Middle East: Balad, Baghdad, Taji), 5 African sites (West Africa:
Cape Verde Atmospheric Observatory (CVAO); East Africa: Eilat; North Africa:
Tamanrasset, Banizoumbou, and Douz), and 2 Australian sites (Muswellbrook and
Richmond). Generally, these field aerosol samples (total suspended
particulates (TSPs), PM
In addition, the data set of dust deposition at more than 100 sites worldwide is used to evaluate modeled dust deposition fluxes (Albani et al., 2014).
Observational sites (S1-Hetian, China; S2-Tazhong, China; S3-Yulin, China; S4-Duolun, China; S5-Shengsi, China; S6-Hanoi, Vietnam; S7-Manila, Philippines; S8-Balad, Iraq; S9-Baghdad, Iraq; S10-Taji, Iraq; S11-Eilat; S12-Cape Verde Atmospheric Observatory (CVAO); S13-Muswellbrook, Australia; S14-Richmond, Australia; S15-Tamanrasset, Algeria; S16-Banizoumbou, Niger; S17-Douz, Tunisia) and dust-producing regions (WAsia: West Asia; NC-As: North Central Asia; CAsia: Central Asia; SC-As: South Central Asia; EAsia: East Asia; WN-Af: North West Africa; EN-Af: North East Africa; S-NAf: Southern North Africa; SAf: Southern Africa; MWNAm: Middle West North America; SWNAm: South West North America; SAm1: Northern South America; SAm2: Southern South America; WAus: West Australia; EAus: East Australia).
The global distributions of the elements Mg, P, Ca, Mn, Fe, K, Al, and Si in bulk soils as mass percentages in soils are presented in Fig. 2.
Global elemental distributions (in mass percentage) in a1: Clay Mg, a2: Clay P, a3: Clay Ca, a4: Clay Mn, a5: Clay Fe, a6: Clay K, a7:, Clay Al, a8: Clay Si; b1: Silt Mg, b2: Silt P, b3: Silt Ca, b4: Silt Mn, b5: Silt Fe, b6: Silt K, b7: Silt Al, b8: Silt Si.
Fractions of elements in soils vary between mineralogical clay and silt fractions. Spatial variability of soil chemistry is seen on a global scale (Fig. 2). A large range of variability for some elements within one given source region is observed (e.g., Ca, Fe, Mn, Al). The most extreme variability is observed for Ca in soil silt, which varied from 0.5 to 34.3 %, and is much higher in West and Central Asia, South Africa and Northern South America than in other parts in the world. This is ascribed to the presence of feldspar and gypsum, both being important source minerals for Ca in these regions. In Central and East Asia, the Ca content increased from east to west, showing a similar spatial trend to that reported by Xuan (2005). A south to north gradient of Ca content was also observed in the Sahara following the carbonate distribution of soils (Kandler et al., 2007; Formenti et al., 2011). In southern North Africa, South Africa and the Western Australia, clay soil and fine dust emissions have higher Al and P concentrations than elsewhere. In Eastern Australia, Patagonia, and the northern South Africa, the Fe content of soils is also higher than in other regions. Due to their high content of quartz, soils generally have 25–40 % Si. These elemental distributions are in agreement with other published data for Fe, as they are derived from similar regions (e.g., Claquin, 1999; Hand et al., 2004).
Trace elements in soils show different associations with particle size
patterns depending on the size distribution of soil minerals. For example,
Mg, P, Fe, Mn, and Al are dominant in the clay size fraction (< 2
Global mean elemental percentages in
Percentages of elements in dust concentration (mass %):
Annual elemental dust emissions over 15 dust-producing regions (shown in
Fig. 1) are determined (Table 2). The annual average of total global dust
emission is estimated to be 1582 Tg based on 2001–2010 simulations and is
within the wide range (514–5999 Tg yr
Ratio of mass fractions of elements in dust deposition to that in
atmospheric dust:
The modeled fractions of different elements in atmospheric dust have substantial spacial variability (Fig. 4). Fe content is greater than 2 % for most regions, with a global mean of 2.7 % in atmospheric dust. The maximum contributions of Fe, Al, P and Mn fractions are observed in the tropical Pacific region with values greater than 3, 10, 0.08, and 0.02 %, respectively. For Ca, Si and K, a higher fraction is evident in terrestrial environments. There are obvious land–ocean gradients existing in the distributions of elemental fractions, with higher Ca and Si fractions in terrestrial regions and higher P, Fe, and Al fractions in oceanic areas, likely due to their differences in particle size distribution (Fig. 3). There are very similar spatial patterns and magnitudes shown for the elemental fractions in deposited dust compared with those in atmospheric dust for each element (Fig. S1 in the Supplement, 5). Higher fractions of Ca and Si in deposited dust is observed in regions close to desert dust sources where the two elements occur in the coarser size fractions. Conversely, lower Mg, P, Mn, Fe and Al contents are found in dust deposits close to source regions but higher contents are found over oceans, which is consistent with the clay soil fraction dominating the finer particle size fractions. The importance of relative location of the source compared to the deposition to the elemental ratio adds complexity in applying simple percentages to dust deposition to obtain elemental deposition amounts.
Ten-year monthly variability in mean of elemental percentages in
atmospheric dust (mass %):
Ten-year monthly variability in mean of elemental percentages in dust
deposition (mass %):
As described above, the fractions of elements in dust fluctuate temporally and spatially on a global scale. There are seasonal variations in dust emissions from various desert regions showing different emission patterns (Fig. S2). The peak periods for dust emissions for various desert regions are consistent with those found by Werner et al. (2002; Fig. S2). Combining the seasonal cycles in atmospheric dust production with the element distributions in desert regions, the elemental fractions show large monthly variability but small inter-annual variability during 2001–2010 (Fig. S3). Ca and Al have clear seasonal cycles, with Ca having the largest monthly variability with peak concentrations in between July and September. This is ascribed to the higher Ca content of dust originating in West Asia, Central Asia and Southern Africa, regions that provide large global dust emissions in this period (JJAS). For the other elements, the peak concentrations usually occurred between March and May (MAM) or November through January (NDJ), corresponding to the periods when global dust emissions reach a maximum.
We modeled the seasonal variability of these elemental fractions. Elemental
percentages are calculated using the climatological monthly mean emission of
each element divided by the climatological monthly mean emission of dust. An
index describing monthly variability is calculated as
Ca
Ten-year averaged Ca
The monthly mean variation is greatest for Ca, reaching more than 30 % variability in some regions. The temporal variability of elemental percentages in deposited dust tended to be larger than those in atmospheric dust and show a greater spatial gradient from land to sea. That is similar to the trend of the elemental fractions in atmospheric and deposited dust (Sect. 3.2.1) since the temporal variation is originally induced by the spatially variable elemental fraction. In the South Indian Ocean and the South Atlantic Ocean, the monthly variability is even higher and is attributed to the combined effect of variability in dust emissions, spatial concentration of elements, and dust transport patterns.
Comparison of observed and modeled mean fractions of elements at each
site for
Of specific interest is the Ca
Mean and quartile modeled and observational fractions of elements in
Despite experiencing mixing of airborne dust from various source regions and
as a result of dust processing during transport, the Ca
The averaged modeled fractions of elements in atmospheric dust at each site
for the periods for which observations are available are comparable with
observations for most of the sites (Fig. 10a, b). It is clear most scatter
values of the model and observations are in the range of the
Percentages of elements in dust deposition (%) after tuning. It is tuned based on original percentages of elements in dust deposition in Fig. S1 by timing the observed/modeled ratios listed in Table 3. Si did not change because there are not enough observational data available.
Fractional solubility of elements (soluble element / total element)
in dust deposition (%):
For this comparison (above mentioned), we calculate the elemental fractions and average the fractions temporally for each site and compare to observations but, alternatively, we could average the elemental concentrations and divide by the elemental dust concentrations instead, and this will make a difference in our interpretations. For example, taking site 2-Tazhong, the averaged fraction is 3.5 % when we calculate the fractions of iron firstly and average those temporally. However, when we calculate the averaged iron mass and dust mass separately, their ratio is 2.3 %. For site 3-Yulin, the ratios are 3.6 and 3.1 % for the first method and the second method, respectively. This difference may be due to dust storm events. For this comparison, we use the first method, as we think it is more suitable for our goal of simulating the percentage of each element correctly.
Percentages of soluble elements in total dust deposition using
The averaged fractions of Mg and Mn in dust are underestimated by the model
at all observational sites. It should be noted that there are some
uncertainties when comparing elemental fractions. When the elemental
concentration is divided by particle mass concentration to obtain the
elemental fraction, the errors are amplified due to error propagation
associated with the combination of the error on the particle mass and that
of the element concentrations. Even though the available observational data
are chosen from source sites or dust events in non-source regions, the
contribution from other sources could be important, especially for fine-mode
particles. The modeled fraction of Mn and Al in fine particles show a larger
inconsistency than those in TSP when compared with observations. Some
of the discrepancies may be because the model only includes particles up to
10
Percentages (%) of elements in dust deposition into
different ocean basins and ice sheets
Deposition of dust elements into different
oceans and ice sheets
The daily elemental fractions across all times and sites where there is data show that while the mean of the model was similar to the mean of the observations, there are some systematic differences (Fig. 11a, b). The modeled elemental fractions are not as variable as the observations. This could be due to several issues. First there is a greater variability in the soil mineralogy and elemental composition of minerals than those included in the model (we only include the average values). Secondly, the dust model could introduce systematic errors (through advection, although this is likely to be small, as discussed in the methods Sect. 2.1), or there could be some unaccounted anthropogenic particulate sources, modifying the dust aerosol. Also, inconsistencies in the collection methods and differences in aerosol sampling periods and times could yield the observed variations in elements as concluded by Lawrence and Neff (2009).
However, the ranges of the modeled fractions of P, Ca, Fe, K and Al are
close to the dominant range of the observational fractions (Fig. 11a, b). The measured
fractions of elements in dust are reported to be 0.5–2.3 %
for Mg, 0.065–0.2 % for P, 1.0–10.2 % for Ca, 0.028–0.124 % for
Mn, 1.3–7.8 % for Fe, 1.2–4.6 % for K, 3.7–12.7 % for Al, and
22.4–35.7 % for Si (Wilke et al., 1984; Reheis and Kihl, 1995;
Stoorvogel et al., 1997; Zhang et al., 1998; Yadav and Rajamani, 2004; Goudie
and Middleton, 2006; Moreno et al., 2006; Jeong, 2008; Lawrence and Neff,
2009; Formenti et al., 2008; Desboeufs et al., 2010). The modeled elemental
fraction in dust for P, Ca, Fe, K, Al and Si were similar to observations.
However, the modeled fractions of Mg and Mn are lower (3.4 and 3.5
times, respectively; Table 3) than the observed ones for samples used in
this study or of the above-cited results. Underestimation of Mg and Mn could
be due to a deficiency of minerals containing high concentrations of Mg and
Mn in our model, as dolomite (MgCO3) or palygorskite
((Mg,Al)
For reference we show the comparison of the modeled dust deposition versus observed deposition (Fig. 12). The modeled dust deposition flux agrees well with observations. The correlation coefficient between modeled and observed dust deposition is 0.86. The median of model to observation ratio is 1.15. Overall, the model has been tuned to represent dust deposition, concentration and aerosol optical depth (AOD; Albani, et al., 2014); however, the model has difficulty matching both deposition and concentration observations, similar to other models (Huneeus et al., 2011), suggesting more work on dust emission, transport and deposition processes is needed.
Comparisons between observations and the model simulations presented here suggest some bias in the model results (Fig. 11, Table 3); subsequently the model deposition values are adjusted to better match observed measurements by the tuning ratios (Table 3; Fig. 13). Of course, improving our elemental estimates in the source region would be preferred in future studies. From the observations, we have found a wide range in fractions of elements at individual sites and at the sites together; the ratio of the maximum and minimum in measured fractions could reach more than 700 for element K, and more than 200 for Ca and Mn. Because of the limited observations, we use a global tuning factor, based on the median elemental percentage, and contrast this result with our default modeling approach (Table 3). It is noted that both the median of observed (3.10 %) and modeled (2.9 %) Fe was lower than 3.5 %, which was thought to be the fraction of Fe in dust (e.g., Luo et al., 2008; Mahowald et al., 2008).
This study suggests significant variability in the elemental fractions in dust deposition (Fig. 13, Table 4) and shows that the assumption that the fixed composition of dust being deposited over oceans is unlikely to be correct. Consistent with Mahowald et al. (2008), most dust deposition occurred downwind of dust generating regions bordering the North Atlantic, North Pacific and North Indian oceans. The Greenland ice sheet accounted for the dominant part of elemental deposition to ice sheets regions, which is equal to the total amount of elements deposited in the whole of the South Atlantic. Fe and P are key elements in the marine ecosystem, with 6.3 Tg Fe and 184 Gg P added annually to all oceans and ice sheets (Table 5).
Also, the amounts of soluble dust element deposition are determined over
different regions (see Sect. 2.1; Fig. 14). No atmospheric processing
of natural dust or other sources of particles (e.g., anthropogenic sources)
is included in this simulation. To better understand the uncertainties of
soluble element deposition, estimates from two methods are used (Sect. 2.1) in simulating soluble elemental emission, transport and deposition.
Fractional solubility of elements could not be estimated due to the lack of
total element data from Method 2 (Sillanpää, 1982). Spatial variations in
fractional solubility of elements are identified by Sol-1 (mineral method; Fig. 14). Fractional solubility of Ca increases with distance from source
regions because its solubility is higher in clay than in silt (Table 1b).
Fractional solubility of modeled P in deposition ranges from 5 to
15 %, with Saharan and Australian dust sources having solubilities
averaging
A new technique combining soil and mineralogical data sets is introduced to
estimate the global emission inventory of soil-associated elements Mg, P,
Ca, Mn, Fe, K, Al, and Si. The spatial elemental dust emissions, transport
and deposition are simulated using CESM from 2001 to 2010. Spatial variability
of soil element fractions is characterized globally (Fig. 2) and shows that
the use of a constant element fraction in dust across the globe is not
consistent with existing observational data for Ca and Al (Figs. 10, 11).
There are few observations for elemental distributions in source regions to
verify these emission, concentration and deposition simulations but, for
some elements (Ca and Al), the soil elemental distribution combined with the
transported dust flux in the model better captures the percentage of
chemical elements in dust concentrations observed (Figs. 10, 11). However,
both Mg and Mn levels are underestimated by the model using the present
mineral maps. The correlation of the percentage of elements at different
sites is not statistically significant for several elements (Mg, Mn, P and
K), suggesting that improvements in the soil inventories or simulations is
required, although these results could also be due to low numbers of
observations. The observations and model results suggest the elemental
fractions in dust varied globally and between different dust production
regions, especially for Ca with values from 1 to 30 %. The ratio of
Ca
The seasonal variability of emission, concentration and deposition of most elements is simulated in the model. Also, different soluble elemental data sets show that the fractional solubility of elements varies spatially. Mineral dust element deposition fluxes into ocean basins are updated using a variable fractional elemental inventory and could have potentially important impacts on evaluating their biogeochemical effects. This study shows that soil emission inventories do a fairly good job at predicting dust elemental concentrations during dust events, except for Mg and Mn. However, the high spatial heterogeneity in elemental distributions is not captured in the model. Several sources of uncertainties exist in the model projections, the largest of which is likely to be from the assumptions in the soil mappings of soil types to minerals to elemental distributions. In the future, these dust emission inventories can be combined with anthropogenic elemental inventories to further improve our understanding of elemental deposition to the oceans.
We would like to thank the US Department of Defense (DOD) for sharing chemical data from their Enhanced Particulate Matter Surveillance Program (EPMSP) and the anonymous reviewers for helpful comments. We acknowledge the support of NSF grants 0932946 and 1137716 and DOE-SC0006735. Simulations were conducted on the NSF National Center for Atmospheric Research supercomputers. Edited by: G. Herndl