Effects of Particle-Size Distributions of Entrained Dust. on its Emission, Loading, and Oceanic Deposition

Transcription

1 Effects of Particle-Size Distributions of Entrained Dust on its Emission, Loading, and Oceanic Deposition Huiyan Yang 1,2, Yuan Gao 2, Larry Horowitz 3 1. Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey 2. Department of Earth and Environmental Sciences, Rutgers University, Newark, New Jersey 3. NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey January

2 ABSTRACT There is a large uncertainty in particle-size distribution of entrained dust. To investigate the impact of this uncertainty on dust emission, loading and deposition to the ocean, numerical simulations are performed with four different size distributions using MOZART-2. Dust emissions were scaled globally for each simulation to match the observed annual mean dust concentrations over the ocean. A factor of difference >5 was found in the required emission totals, however, variation in emission of small dust (r<3µm) is <2. The variation of global atmospheric dust loading and optical depth is <2. The variation of the total dust deposition to each oceanic basin is generally <3, and the difference in wet deposition to the ocean is <1.5. Among the particle-size distributions in dust entrainment tested in this study, the wind-carrying size distribution seems to best reproduce the dust size distribution in the atmosphere. 1. INTRODUCTION The size distribution of entrained dust (or mineral aerosol) is an indicator of the dust source characteristics, such as the wind strength, surface texture, and surface soil moisture content (e.g. Patterson and Gillette, 1977; d Almeida, 1987), and therefore it may reflect regional climate features. Calculations of direct radiative forcing of dust that plays an important role in Earth s climate system critically depends on the assumed size of dust particles (Sokolik and Toon, 1996; Kaufman et al., 2002). Dust could affect tropospheric chemistry by providing reactive sites for heterogeneous reactions involving a variety of trace gases (Dentener et al., 1996) that largely depend on the sizes of dust particles. The size distribution of entrained dust may also affect the extent of its longrange transport and deposition to the ocean (Schulz et al., 1998), an important source of 2

3 nutrient iron to phytoplankton growth in the surface waters of several large oceanic regions (e.g. Fung et al., 2000), which may consequently affect oceanic carbon cycles. Therefore, knowledge of dust size distributions in entrainment is critically important to better understand dust climate forcing and the extent of its impacts on ocean biogeochemical cycles. Over the dust source regions such as the Sahara desert, two distinct particle size distributions of dust generally exist, the so-called background and sandstorm size distributions (Schütz and Jaenicke, 1974; d Almeida and Schütz, 1983). The former occurs in fair weather and the latter in extreme weather characterized by strong and gusty winds. While there are no major changes in the size range between ~0.5 to 5µm radius (mode A), concentrations in the size range with r > 5µm (mode B) increase by more than an order of magnitude in the sandstorm size distribution (d Almeida and Schütz, 1983). It is believed that mode A is composed of disaggregated materials as a result of sandblasting, and mode B is composed of loose aggregates of parent soil (Patterson and Gillette, 1977). The general treatment of dust emission in models is to first simulate the bulk dust entrainment, and then to specify the size distributions to obtain the specific entrainment in each size bin (e.g. Schulz et al., 1998; Ginoux et al., 2001; Zender et al., 2003). d Almeida (1987) suggested three different trimodal lognormal size distributions based on observations in Sahara and the adjacent Sahel area: the sandstorm, windcarrying (Sd1) and background (Sd2) size distributions (see Table 1); the latter two most effectively contribute to the dust loading at long distances from source regions, and have been used in several global dust models. Zender et al. (2003) applied Sd2 in the Dust 3

4 Entrainment and Deposition (DEAD) model; Tegen et al. (1994) and Ginoux et al. (2001) prescribed a size distribution similar to Sd2. d Almeida et al. (1991) later suggested another two size distributions (Sd3 and Sd4 in Table 1), based on a more accurate fit to the same observations. In this paper, we assess the differences in global dust budget and oceanic deposition caused by different particle-size distributions of entrained dust (d Almeida, 1987; d Almeida et al., 1991). We evaluate the model results by comparing them with the retrieved aerosol size distributions over the desert (Dubovik et al., 2002). The results are meant to illustrate the potential impact of dust size distribution in source regions on dust loading and its oceanic deposition and to inspire more consideration on the extent of uncertainties associated with dust model simulations. 2. METHOD We use the MOZART-2 atmospheric transport model (Horowitz et al., 2003) to simulate dust transport in this study. The meteorology fields are provided by NCEP reanalysis (Kalnay et al, 1996). The model has 28 vertical layers and the horizontal resolution is 1.875º. The gravitational settling of dust particles is calculated as the Stokes velocity according to Seinfeld and Pandis (1997). The turbulent mix-out dry deposition velocity is calculated according to Giorgi (1988). Wet deposition generally follows Zender et al. (2003). The model was run for three years from 2001 to There are five dust size bins from 0.1µm to 10µm in radius (Table 1). Particles with r >10µm have atmospheric lifetimes less than ~ 1 hour due to gravitational settling, and are neglected in this study. The first size bin is further separated into four sections in the calculations of aerosol optical depth, column integrated size distribution, and 4

5 deposited surface areas as in Ginoux et al. (2001). The mass fraction m n of the assumed trimodal lognormal size distributions is mapped to obtain the mass fraction m i in each size bin by using the error function (Schulz et al., 1998): m i 3 1 ln( D i+ 1 / Dvg, n ) ln( Di / Dvg, n ) = mn erf ( ) erf ( ) n= lnσ g, n 2 lnσ g, n Where D i+ 1 and D i are the upper and lower boundary of size bin i; D, and σ g,n are the mass median diameter and geometric standard deviation of mode n. The dependence of dust entrainment on wind speed and the determination of the required parameters follow the microphysical approach developed by Marticorena and Bergametti (1995) unless noted otherwise. The threshold friction wind speed vg n u * t is set at 22 cm/s, corresponding to an optimal saltation diameter of soil particles in the range of µm. We assume that the erodible soil always contains soil particles in the range of the optimal size, and saltation is initiated whenever the minimum incremental factor of u * t is reached. The u * t produced by drag partition between erodible and nonerodible soils is set at 0.65, corresponding to a roughness length of 200 µm (Gillette et al., 1980) and a smooth roughness length of 33 µm. The horizontal to vertical flux conversion factor is set at 5.0*10-4, assuming a global uniform clay fraction of 0.2 (Zender et al., 2003). We use the topographic source function of Ginoux et al. (2001). Four experiments (Experiment1 to Experiment4) are conducted using the four entrainment size distributions (Sd1 to Sd4). The emissions are scaled globally in each experiment to match the observed annual mean dust concentrations over the ocean. Other parameters are held constant in all simulations. 5

6 3. RESULTS AND DISCUSSIONS 3.1 Model Validation and Model vs. AERONET Size Distributions Through the adjustment of the global scale factor, similar agreement in annual mean dust concentration between model and observation is achieved in each experiment. As an example, Figure 1 presents the comparison of Experiment3 (Sd3) with observation. The observational data were mostly collected in the 1980s and 1990s (Prospero, 1996). The model generally reproduces the observed dust concentrations, which range over three orders of magnitude, to within a factor of 2,. The model has a low bias over the Atlantic Ocean and a high bias in the Pacific Ocean. This might be due to the prescription of a globally uniform cloud nucleation scavenging ratio (Zender et al., 2003), while Asian dust warrants a larger one and African dust warrants a smaller one as suggested by Fan et al. (2004). A low bias is especially prominent in the model at Palmer station (9), located at the north edge of the Antarctic. The concentration at a nearby station King George (7) is ~1.5 times of that at Palmer in the observation, while it is ~3 times of that at Palmer in the model. Regional wind patterns that are not resolved in our global model may contribute to this discrepancy. The general low-biased simulated concentration over the Southern Ocean might also be attributed to the uncertainties associated with dust sources at southern latitudes, like South America and Australia, which have not been studied as intensively as in the other source regions. Figure 2 shows comparison with AERONET retrieved size distributions (column integrated) in Bahrain and Banizoumbou in dust emission regions in Africa (Dubovik and King, 2000; Dubovik et al., 2002). The observed nuclei mode (mode radius ~0.1µm) is composed of background sulfate or carbonaceous aerosols and is not of interest for 6

7 analysis here. For the observed coarse mode (mode radius ~2µm), Experiment4 (Sd4) has too much submicron mass, and much less mass in large size particles; Experiment1 (Sd1) shows the opposite trend. Experiment2 (Sd2) has a similar performance in submicron emission as Experiment3 (Sd3), while it is worse off in the large size range. Comparison in several other locations in dust emission region shows similar trends. We therefore recommend Sd3 among the four for future dust modeling research, as it seems to best reproduce the dust size distribution in the atmosphere. 3.2 Annual Mean Emission Rate and Atmospheric Loading As shown in Table 2, the total dust emission in Experiment1 is ~5 times that in Experiment2, and it is more than twice in Experiment3 compared with that in Experiment4. This is mainly because the fractional emission of particles with r > 3.0µm in Experiment1 and Experiment3 (60-80%) is much stronger than in Experiment2 and Experiment4 (25-35%). A further examination of the emission rates shows that the submicron (r = 0.1-1µm) emission rates of Experiment2 and Experiment3 are ~110Tg/yr, while Experiment1 has nearly half of that, and Experiment4 has ~1.5 times of that. The emission rate in the second size bin (r = 1-1.8µm) ranges from 175Tg/yr in Experiment1 to 228Tg/yr in Experiment2. Our best guess emission rate in the upper three size bins (r = µm) inferred from Experiment3 is ~1330 Tg/yr. The total emission in Experiment3 is close to the optimal emission of 1534 Tg/yr by Cakmur et al. (2005, submitted to J. Geophys. Res.), where more observational data were used to constrain the model. Also shown in Table 2 are the atmospheric loadings of dust from the four experiments. These loadings vary much more modestly than the total dust emission rates, as dust particles with r < 3µm dominate the atmospheric loading, accounting for between 7

8 ~62% in Experiment1 and ~97% in Experiment4. The difference of emission rate of dust particles in this size range is less than a factor of 2. The global average dust optical depths vary by less than a factor of 2 as well. Interestingly, the largest overall loading in Experiment1 produces the smallest optical depth. This is because the emission of dust particles with r = µm in Experiment4 is nearly 1.6 times of that in Experiment1, and particles in this size range contributes the most to dust optical depth. For the same reason, the optical depths in Experiment2 and 3 are almost the same. Contribution to the optical depth by dust particles with r > 3 µm is less than 3% except for Experiment1, which is ~13%. Since Sd1 in Experiment1 produces a low bias in the size range of r = 0.1-3µm as shown above, we suggest that particles with r > ~3µm make minor contributions to dust optical depth and radiative forcing globally. 3.3 Deposition to the Ocean Figure 3 shows the relative difference between Experiment3 and 4. Differences over 30% between the two are distributed within ~5000km downwind of source regions and differences within ± 20% are found in remote regions, suggesting that the impact of large size particles on oceanic deposition dilutes with distance due to their short life times. There is more wet (and thus total) deposition in remote oceanic areas in Experiment4 due to its ~1.5 times of submicron emission compared with Experiment3. The stronger emission of particles with r > 1µm in Experiment3 reduces the overall difference to within ± 20%. Table 3 lists dust deposition to the oceans and compares with previous model estimates (Ginoux et al., 2001, Zender et al., 2003). As expected, North Atlantic receives the largest amount of deposition since the tropical Atlantic is located downwind of North 8

9 Africa. The South Pacific receives the least dust deposition due to being farther away from the major dust source regions. Experiment1 produces far more oceanic deposition of dust than other experiments. Experiment3 produces ~1.5 times of that produced by Experiment4, and the difference between Experiment2 and Experiment4 is small. The variation of wet deposition to the ocean is about a factor of 1.5. Wet deposition is responsible for more than 80% of the total deposition except for Experiment1 in which wet deposition accounts for only ~50% of the total deposition. Experiments 2 and 4 agree well with DEAD (Zender et al., 2003), except in the North Atlantic. Although DEAD has a smaller size range (r = µm), Experiments 2 and 4 have relatively small emissions in the upper two size bins (r = 3-10µm). We suspect the better agreement of our simulations with DEAD than with GOCART (Ginoux et al., 2001) might largely be due to the different wet deposition schemes applied in each study. The lifetime versus wet removal is ~13 days in this study, ~11 days in Zender et al. (2003) using the same scheme, and ~56 days in Ginoux et al. (2001) using a less efficient scheme. One important aspect of dust deposition to the ocean is the dissolved fraction of iron from dust, which is suggested to be proportional to dust surface area (Meskhidze et al., 2003). Results from this work indicate that the difference in the surface area of dust particles deposited to the ocean is generally less than a factor of 1.5, smaller than the difference in deposited dust mass (Table 3). This is mainly due to the fact that smaller particles (r < 3µm) have greater surface area per unit aerosol mass. For example, in Experiment3, the contribution of particles with r < 3µm to deposited dust mass ranges from 60% (South Atlantic) to 89% (North Pacific), while the contribution of these particles to deposited dust surface area is 87-97%. In the high-nitrogen-low-chlorophyll 9

10 (HNLC) regions (Moore et al., 2004), the contribution of particles with r = 6-10µm to dust mass deposited to the Subarctic Pacific and Tropical Pacific is <1% but reaches ~9.5% in the Southern Ocean. This suggests that contributions of dust particles in this size bin to the total iron deposition to the southern ocean is more important than to other HNLC regions. 10

11 ACKNOWLEDGEMENT We would like to thank Paul Ginoux and Song-Miao Fan for their constructive comments. Paul Ginoux also kindly provided IDL codes and compilations of atmospheric dust concentrations, which greatly facilitated the comparison of model results vs. observations. We thank Didier Tanré and Brent Holben for theirs efforts in establishing and maintaining the Bahrain and Banizoumbou sites for the AERONET data from which the retrieved aerosol size distributions were made. We thank Paul Falkowski for stimulating initial motivations for this study and for valuable comments. Discussions with Hiram Levy were also helpful. NOAA/GFDL generously provided the computer resources for running MOZART-2. This work is supported by the NASA award NNG04G091G. 11

12 REFERENCES d Almeida, G. A., and L. Schütz (1983), Number mass and volume distribution of mineral aerosol and soils of the Sahara, J. Clim. Appl. Meteorol., 22, d Almeida, G. A. (1987), On the variability of desert aerosol radiative characteristics, J. Geophys. Res., 92, d Almeida, G. A., P. Koepke, and E. P. Shettle (1991), Atmospheric Aerosols, Global Climatology and Radiative Characteristics, A. Deepak Publishing. Cakmur, R. V., R. L. Miller, J. Perlwitz, I. V. Geogdzhayev, P. Ginoux, K. E. Kohfeld, D. Koch, I. Tegen, C. S. Zender (2005), Constraining the magnitude of the global dust cycle by minimizing the difference between a model and observations, submitted to J. Geophys. Res.. Dentener, F.J., G. R. Carmichael, Y. Zhang, J. Lelieveld, and P.J. Crutzen (1996), Role of mineral aerosol as a reactive surface in the global troposphere, J. Geophys. Res., 101, 22,869-22,889. Dubovik, O. and M. D. King (2000), "A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements", J. Geophys. Res., 105, 20,673-20,696. Dubovik, O., B.N.Holben, T.F.Eck, A.Smirnov, Y.J.Kaufman, M.D.King, D.Tanre, and I.Slutsker (2002), Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59, Fan, S., L. W. Horowitz, H. Levy II, and W. J. Moxim (2004), Impact of air pollution on wet deposition of mineral dust aerosols, Geophys. Res. Lett., 31, L02104, doi: /2003gl

13 Fung, I. Y., S. K. Meyn, I. Tegen, S.C. Doney, J. G. John, and J. K. B. Bishop (2000), Iron supply and demand in the upper ocean, Glob. Biogeochem. Cyc., 14(1), Gillette, D. A., J. Adams, A. Endo, D. Smith and R. Kihl (1980), Threshold velocities for input of soil particles into the air by desert soils. J. Geophys. Res., 85(C10), Ginoux, P., M. Chin, I. Tegen, J. M. Prospero, B. Holben, O. Dubovik, and S.-J. Lin (2001), Sources and distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res., 106(D10), 20,255-20,273. Giorgi, F. (1988), Dry deposition velocities of atmospheric aerosols as inferred by applying a particle dry deposition parameterization to a general circulation model, Tellus, 40B, Horowitz, L. W., et al. (2003), A global simulation of tropospheric ozone and related tracers: Description and evaluation of MOZART, version 2, J. Geophys. Res., 108(D24), 4784, doi: /2002jd Kalnay, E. et al. (1996), The NCEP/NCAR 40-year reanalysis project, Bull. Am. Meteorol. Soc., 77, Kaufman, Y., D. Tanré, O. Boucher (2002), A satellite view of aerosols in the climate system, Nature, 419, Marticorena, B., and G. Bergametti (1995), Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme, J. Geophys. Res., 100, 16,415 16,

14 Meskhidze, N., W. L. Chameides, A. Nenes, and G. Chen (2003), Iron mobilization in mineral dust: Can anthropogenic SO2 emissions affect ocean productivity?, Geophys. Res. Lett., 30(21), 2085, doi: /2003gl Moore, J. K., S. C. Doney, and K. Lindsay (2004), Upper ocean ecosystem dynamics and iron cycling in a global three-dimensional model, Global Biogeochem. Cycles, 18, GB4028, doi: /2004gb Patterson, E. and D. Gillette (1977), Commonalities in measured size distributions for aerosols having a soil derived component, J. Geophys. Res.82, Prospero, J. M. (1996), The atmospheric transport of particles to the ocean, in Particle Flux in the Ocean, edited by V. Ittekkot et al., SCOPE, 57, Schulz, M., Y. J. Balkanski, W. Guelle, and F. Dulac (1998), Role of aerosol size distribution and source location in a three-dimensional simulation of a Saharan dust episode tested against satellite-derived optical thickness, J. Geophys. Res., 103, 10,579-10,592. Sokolik, I. N. and O. B. Toon (1996), Direct radiative forcing of anthropogenic airborne mineral aerosols, Nature, 381, Tegen, I., and I. Fung (1994), Modeling of mineral dust in the atmosphere: Sources, transport, and optical thickness, J. Geophys. Res., 99, 22,897-22,914. Zender, C. S., H. Bian, and D. Newman (2003), Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology, J. Geophys. Res., 108(D14), 4416, doi: /2002jd

15 LIST OF TABLES: Table 1: Dust size distribution in entrainment Table 2: Dust annual budget and loading Table 3: Dust deposition to the major oceanic basins FIGURE CAPTIONS: Figure 1: Model vs. observed annual mean dust concentrations (Experiment3). Solid circle: North Atlantic; Triangle: South Atlantic; Square: North Pacific; Diamond: South Pacific. Figure 2: Comparison of annual mean size distributions with AERONET retrievals. Solid: AERONET; dotted: Experiment1; dashed: Experiment2: dash dot: Experiment3; gray: Experiment4. (a) Bahrain (26.3N, 50.5E), (b) Banizoumbou (13.5N, 2.7E). Figure 3: Relative difference in total deposition between Experiment3 and Experiment4 15

16 Table 1: Dust size distribution in entrainment D vg,n (µm) σ g,n m n m i Sd1 0.60, 14.68, , 2.07, , 0.957, , 0.035, 0.124, 0.421, Sd2 0.83, 4.8, , 1.9, , 0.957, , 0.238, 0.302, 0.281, Sd3 0.21, 9.75, , 2.67, , 0.599, , 0.126, 0.194, 0.350, Sd , 3.30, , 2.0, , 0.763, , 0.273, 0.227, 0.175, Sd1: wind-carrying dust in d Almeida, 1987; Sd2: background dust in d Almeida, 1987; Sd3: wind-carrying dust in d Almeida et al., 1991; Sd4: background dust in d Almeida et al., D vg : mass median diameter; σ g,n : standard deviation of the log-normal size distribution; m n : mass fraction of each mode; m i : mass fraction of each size bin in radius (µm): 0.1, 1.0, 1.8, 3.0, 6.0, Table 2: Dust annual budget and loading* Emission (Tg/yr) Dry dep (Tg/yr) Wet dep (Tg/yr) Load (Tg) Optical depth Sd1 4936(49) 4081(4) 857(45) 25(2) 0.017(0.007) Sd2 959(110) 527(9) 431(101) 16(5) 0.023(0.015) Sd3 1655(111) 1156(9) 499(101) 18(5) 0.023(0.015) Sd4 672(169) 300(14) 372(154) 15(8) 0.029(0.023) * The numbers in parentheses correspond to particles with r < 1µm Sd1 to Sd4 corresponds to Experiment1 to Experiment4 respectively. Table 3: Dust deposition to the major oceanic basins (Tg/yr) NA SA NI SI NP SP Sd1 179(94) 99(33) 111(57) 28(16) 48(36) 18(13) Sd2 83(64) 32(21) 36(28) 14(11) 35(31) 11(9) Sd3 98(68) 43(23) 48(32) 16(12) 37(32) 12(10) Sd4 69(57) 25(19) 28(23) 12(11) 33(30) 10(9) com com NA: North Atlantic; SA: South Atlantic; NI: North Indian; SI: South Indian; NP: North Pacific; SP: South Pacific. com1: Ginoux et al. (2001); com2: Zender et al. (2003). The number in parentheses is wet deposition.

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