Simulation of dust aerosol radiative feedback using the Global Transport Model of Dust: 1. Dust cycle and validation

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114,, doi: /2008jd010995, 2009 Simulation of dust aerosol radiative feedback using the Global Transport Model of Dust: 1. Dust cycle and validation Xu Yue, 1,2,3 Huijun Wang, 1,2 Zifa Wang, 1 and Ke Fan 1 Received 17 August 2008; revised 4 February 2009; accepted 18 February 2009; published 22 May [1] We have developed a Global Transport Model of Dust (GMOD) within a general circulation model, using comprehensive parameterizations of the emission and deposition processes from Wang et al. (2000). These parameterizations are modified to match the surface conditions and meteorological fields of the climate model. A 20-year simulation from the dust model predicts an average dust emission of 1935 ± 51 Tg a 1 and a global dust burden of 27.8 ± 0.8 Tg for particles whose radii are smaller than 10 mm. Comparisons with observations show that the GMOD reproduces reasonably well dust concentrations (mean bias MB of 0.67 mg m 3, normalized mean bias NMB of 8.0%, correlation coefficient of 0.96 at 18 sites), logarithmic total deposition ( 0.62 g m 2 a 1, 36.0%, 0.84 at 251 sites), and aerosol optical thickness ( 0.04, 26.7%, 0.80 at 16 sites). The simulated dust particle size distribution is consistent with observations; both have a volume median radius in the range mm. We examine the temporal variation of dust transport on different timescales. The simulated interannual variability is small, but the seasonal variation is quite large in the Sahara Desert and central Asia. We pay special attention to the diurnal variation of dust; both observations and simulations show that dust mobilization is more active during the local daytime than nighttime. On a global and annual mean basis, the simulated ratio of the daytime maximum uplift to the nighttime minimum is 75. Both the dust burden and dry deposition of dust show a similar diurnal cycle peaking in the late afternoon. Citation: Yue, X., H. Wang, Z. Wang, and K. Fan (2009), Simulation of dust aerosol radiative feedback using the Global Transport Model of Dust: 1. Dust cycle and validation, J. Geophys. Res., 114,, doi: /2008jd Introduction [2] The transport and climatic impact of mineral dust aerosol are major concerns in climate research today. Every year, about 200 to 5000 trillion grams (Tg) of dust mass are entrained into the air from arid and semiarid areas [Goudie, 1983]. The total mass of the suspended dust particles in the troposphere is about 20 Tg, which is nearly half of the total aerosol mass loading [Qian et al., 1999]. The haze and dust storms caused by these particles influence air quality and reduce atmospheric visibility. In addition to the environmental impacts, mineral dust aerosol has both direct and indirect effects on climate. Suspended dust particles can absorb and scatter shortwave and longwave radiation [Carlson and Benjamin, 1980; Miller and Tegen, 1998; Dufresne et al., 2002; Markowicz et al., 2003]. They can also influence cloud formation and modify the optical properties of clouds by acting as cloud condensation nuclei [Levin et al., 1996]. 1 Nansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. 2 Climate Change Research Center, Chinese Academy of Sciences, Beijing, China. 3 Graduate School of the Chinese Academy of Sciences, Beijing, China. Copyright 2009 by the American Geophysical Union /09/2008JD [3] The loading of dust aerosol is highly variable in space and time [Tegen and Fung, 1994], but global observational data are quite limited [Sokolik and Toon, 1996]. As a result, numerical simulations have become an effective way to study the climatic impact of dust aerosols [d Almeida, 1986; Tegen and Fung, 1994; Ničković and Dobričić, 1996; Marticorena et al., 1997a; Wang et al., 2000; Ginoux et al., 2001; Woodward, 2001; Lunt and Valdes, 2002; Gong et al., 2003; Zender et al., 2003; Easter et al., 2004]. Tegen and Fung [1994] simulated the global distribution of mineral dust with the three-dimensional tracer transport model of the Goddard Institute for Space Studies (GISS). The simulated seasonal variations of dust concentrations generally show reasonable agreement with the observed patterns. Wang et al. [2000] developed a deflation module in the study of long-range transport of yellow sand over east Asia; the results of this study are in good agreement with observational records. Ginoux et al. [2001] used assimilated data to drive the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model and obtained a simulated dust climatological distribution, which includes dust concentration, deposition, optical depth, and size distribution. Zender et al. [2003] designed a detailed Dust Entrainment and Deposition (DEAD) module to simulate the dust climatology in the 1990s. The module is embedded in a chemical transport model and driven by the National Center for Environmental Prediction/National 1of24

2 Center for Atmospheric Research (NCEP/NCAR) reanalyses [Kalnay et al., 1996]. These simulations mainly focused on annual and global mean emissions, burden, and seasonal variation of dust; few studies examined the diurnal variation of dust. [4] In this study, a new dust model, designated the Global Transport Model of Dust (GMOD), is developed within a climate model. The model predictions are extensively compared with observations, including dust concentrations at low levels, deposition, dust optical thickness (DOT), and particle size distribution. We further investigate the temporal characteristics of dust transport on interannual, seasonal, and diurnal scales. In addition, this work is the first step of our studies on dust-climate interactions for paleoclimate and present-day climate, which will be reported subsequently. [5] In the next section, a detailed description of the GMOD is presented. Section 3 presents the simulated dust climatology, including dust uplift, deposition, and optical thickness. In section 4, station-based observations are used for further evaluation. The observed data include low-level dust concentrations from the University of Miami Ocean Aerosol Network [Prospero, 1996a], dust deposition from the Dust Indicators and Records of Terrestrial and Marine Paleoenvironments (DIRTMAP) database [Mahowald et al., 1999; Kohfeld and Harrison, 2001], and aerosol optical depth and particle size distribution from the Aerosol Robotic Network (AERONET) [Holben et al., 1998; Dubovik et al., 2000]. Section 5 analyzes the temporal characteristics of simulated dust aerosols. 2. Model Description 2.1. Dynamical Core [6] The GMOD is driven by the meteorological conditions from the IAP grid-point nine-layer atmospheric general circulation model (IAP9L-AGCM), which is a GCM developed by Zeng et al. [1989], Zhang [1990], and Liang [1996]. Bi [1993] modified the codes of the GCM to improve its performance. Since then, the climate model has been widely used in many research studies [Chidiezie et al., 1997; Wang, 1999; Xue et al., 2001; Jiang et al., 2005; Ju et al., 2007; Zhang et al., 2007]. Recently, Yue and Wang [2009] replaced the previous radiation scheme in the GCM with that from the NCAR/CCM3 [Kiehl et al., 1998], which further improves the model s performance. [7] The transport process of the GMOD is simulated with the tracer transport model in the IAP9L-AGCM. Different dust bins are transported independently in the model. The GCM is a C-grid point model with 4 5 horizontal resolution. It uses s p vertical coordinates (s = p p t p ¼ p p t p s p t ) to divide the atmosphere into nine levels, the top of which is set to 10 hpa. The dynamical framework for the model is a set of primitive equations discretized with the conservative format proposed by Zeng [1979]. Additionally, the model uses the Robert [1966] filter to dampen the highfrequency noise caused by the leapfrog scheme used in the time integration [Zhang, 1990]. Using this method, the GCM is very stable in long-term simulations Dust Emission Scheme [8] A large part of the uncertainties in dust simulation comes from the empirical parameterizations of the uplifting and removal processes. In this section, we will discuss the dust emission scheme used in the GMOD. [9] Dust emission is associated with many environmental factors. Generally, dust storms are caused by strong, gusty winds associated with multiscale disturbances or convective activity. Dust mobilization is inhibited by surface-covering elements such as vegetation, snow cover, and rocks. It is also constrained by soil-binding conditions such as high soil moisture and high salinity. With these conditions, active dust-producing areas are confined to bare ground or sparsely vegetated ground in arid and semiarid places and to regions with strong winds (T. Tanaka, Global dust budget, in Encyclopedia of Earth, edited by H. Hanson, 2007). [10] The parameterizations of dust uplift in models always take into consideration the above mentioned factors, though the combinations in formulation and weights of each parameter are diverse. Zender et al. [2003] provided a review of the mobilization schemes that appear in contemporary dust models. There are generally two distinct classes. One group uses concise parameterizations that take into consideration the influence of surface wind velocity (and soil moisture content) with a macroscopical view, and the other group uses more complicated schemes to represent related microphysical processes. Although the latter group is more physically correct, the lack of a global distribution of the needed input factors restricts its accuracy. [11] We use the empirical formulation proposed by Wang et al. [2000] for the dust emission, which can be grouped in the first class as defined above, 8 < Q p ¼ C 1C 2 s p u 2 * 1 u * t u 1 RH RH * t u * u *t and RH RH t : 0 otherwise: ð1þ where Q p is the uplift flux of particle size class p in units of kg m 2 s 1, C 1 is the potential emission coefficient of the soil, C 2 is an empirical constant set to [Hu and Qu, 1997], u* is the surface wind friction speed calculated by the land-surface model in the GCM [Liang, 1996], RH is the relative humidity of the surface air as a fraction, which is closely related to soil wetness, and s p is the mass fraction of each size bin; this parameter will be defined later. Equation (1) indicates that there will be dust emission only when the surface wind friction speed exceeds the threshold u* t and the relative humidity of the surface air is below RH t. Faster winds and/or drier air induce more dust emission. Soils that are especially vulnerable to dust uplift or deflation could be incorporated through variations in C 1, but the preferential soil region treatment of Ginoux et al. [2001] has not been incorporated here. [12] The threshold wind friction velocity u* t is an important factor for dust mobilization and is closely related to soil texture, surface atmospheric conditions, and the size of disturbed crusts [Marticorena et al., 1997b]. Some studies measure u* t directly in field experiments or wind tunnels [Musick and Gillette, 1990; Gillette et al., 1980; Batt and Peabody, 1999]. Based on these observations, several empirical parameterizations are proposed [Iversen and White, 1982; Marticorena and Bergametti, 1995; Shao and Lu, 2000]. However, most of these schemes are only appropri- 2of24

3 Table 1. Ratio Factor of Potential Emission Coefficient (C 1 0 )in the GMOD a Index in AGCM Vegetation Type C Mixed farming, tall grassland 0 2 Tall/medium grassland, evergreen scrubland Short grassland, meadow and scrubland Evergreen forest (conifer) 0 5 Mixed deciduous, evergreen forest 0 6 Deciduous forest 0 7 Tropical evergreen broadleaved forest 0 8 Medium/tall grassland, woodlands 0 9 Tundra 0 10 Desert 1.0 a GMOD, Global Transport Model of Dust; AGCM, atmospheric general circulation model. ate for particles exceeding 50 mm in diameter because a lack of reliable experimental data restricts such validations for smaller particles [Shao and Lu, 2000]. Easter et al. [2004] set the thresholds of surface wind friction speed to 25, 35, 45, and 75 cm s 1 for desert, arid/semiarid, scrubland/ grassland, and other dust source regions. For simplicity, we employ a similar method used by Lunt and Valdes [2002], by setting u* t to a uniform constant of 40 cm s 1 for all the model grids, although different soil textures and dust size distributions may correspond to distinct threshold values. The latter influences are reflected by the potential emission coefficient C 1 and mass percentage s p in equation (1), respectively. [13] The emission scheme in the GMOD is similar to that proposed by Gillette and Passi [1988], which is also used in GOCART [Ginoux et al., 2001] except that, as noted above, we have not included their preferential source region treatment. Their formulation uses a wind speed of 10 m rather than the surface wind friction velocity. The threshold wind is determined by an empirical function, which includes the effect of surface wetness. The GMOD independently considers the soil moisture content by employing a linear relationship between dust emission and relative humidity of surface air, as shown in equation (1). The threshold RH t is set to 0.4, which is smaller than the value of 0.5 used by Ginoux et al. [2001]. [14] The potential emission coefficient C 1 represents the uplifting capability of the land surface. As is known, dust storms usually take place in arid and semiarid regions. However, the uplift dust mass is constrained by geographic conditions, such as vegetation, water, and snow cover [Lunt and Valdes, 2002; Zender et al., 2003]. In the GMOD, we consider such influences by relating C 1 to the vegetation types of the GCM. Wang et al. [2000] set this factor according to thirteen kinds of land conditions [see Wang et al., 2000, Table 1]. In the GMOD, we make some modifications by dividing C 1 into two parts: [15] Table 1 gives a description of the vegetation types in IAP9L-AGCM [Liang, 1996]. The global distribution of these plants is determined from a Koppen climate classification [Bi, 1993]. In the GMOD, there are only three kinds of soil that yield mineral dust. The emission capability is weighted as 0.1, 0.3, and 1.0 for tall grassland, meadow, and desert, respectively. A spatial nine-point smoothing is conducted to avoid the computational instability caused by abrupt changes in C 0 1. In this way, we obtained a global distribution of the potential emission coefficients, as shown in Figure 1. [16] Figure 1 reflects the location and intensity of dust sources in the GMOD. The largest dust sources are located in the Northern Hemisphere (NH), mainly in a broad dust belt that extends from North Africa, over the Middle East and central Asia, to China; this is consistent with observational data [Prospero et al., 2002]. The dust source in the Southern Hemisphere (SH) is located at approximately 30 S, with the largest center in the Australian Desert. There are remarkably few large-scale dust source regions outside of these regions. The source distribution shown in Figure 1 is in agreement with previous studies [Ginoux et al., 2001; Lunt and Valdes, 2002]. [17] The last factor in equation (1) that needs to be determined is s p, which is the mass percentage of each size class. This is potentially one of the most uncertain parameters in the GMOD and in other dust models. The cause of the uncertainty is not only due to lack of observations but also the fact that the dust particles change in size through agglomeration or interaction with other gases/aerosols during transport [Maring et al., 2003; Bauer and Koch, 2005; Zhang and Iwasaka, 2006]. At present, most models simplify the size distribution of mineral dust by dividing the total size spectra into several bins that do not vary. Similarly, the GMOD considers four bins of dust with idealized spherical shapes whose radii span from 0.1 to 10 mm. The mobilization of particles smaller than 0.1 mm by wind is limited because soil adhesion tends to form larger particles. On the other hand, those larger than 10 mm are always C 1 ¼ a C 0 1 ð2þ where a is a constant determined a posteriori. C 0 1 represents the emission potential in ratio to that of desert soils, which is listed in Table 1. Figure 1. The global distribution and strength of dust sources in the Global Transport Model of Dust (GMOD) denoted by the ratio factor of potential emission coefficients C 1 0 as defined in Table 1. 3of24

4 removed from the air in a few hours by their large gravitational settling velocity; this settling restricts their long-range transport and possible climate effects [Tegen and Fung, 1994]. [18] The dust spectra in the GMOD obey a power law distribution of n(r) / r 3 as described by Mishchenko et al. [1999], so that the mass content of each dust bin varies almost linearly with respect to the radius span. The four dust bins in the GMOD are mm, mm, mm, and mm, with corresponding effective radii of 0.39, 1.44, 3.27, and 7.21 mm. According to the definition proposed by the U.S. Department of Agriculture (USDA), the particles in the GMOD are clay (r 1.0 mm) and small silt (1.0 mm <r 10 mm), whose mass densities are 2.5 and 2.65 g cm 3, respectively [Hillel, 1982] Dust Removal Processes [19] Generally, there are two processes that remove dust from the atmosphere. One is dry deposition which is caused by gravity and turbulence, and the other is wet scavenging in and under clouds (i.e., wet deposition). [20] Dry deposition is the most effective mechanism for removing large particles from the atmosphere [Tegen and Fung, 1994]. The efficiency of such sedimentation is measured by the deposition velocity, which is defined as follows [Wang et al., 2000]: 8 < V d ¼ V g þ : V g u 2 * k^uðsc 0:6 þ10 3=St Þ lowest level other levels: where V g is the gravitational speed of the particle given by V g ¼ 2 r p r re 2gC c where r p and r are the densities of mineral dust and dry air, respectively. r e is the effective radius of the particles, g is the gravitational acceleration, g is the dynamical viscosity of air, and C c is the Cunningham correction factor defined as [Seinfeld and Pandis, 1997] C c ¼ 1 þ l 1:257 þ 0:4 exp 1:1r e ð5þ r e l where l is the mean free path of air. [21] Dust at all model levels is affected by gravity. For the particles at the lowest model level, however, turbulent mixing also has an effect in the deposition process. For equation (3), ^u is the wind speed at the lowest level of the GCM, k is the von Karman constant (0.4), and S c and S t are the particle Schmidt number and Stokes number, respectively [Zhang et al., 2001]. [22] The formulation of the settling velocity in equation (4) indicates that V g is quite sensitive to variation in the effective radius, which explains why larger particles are affected by a greater settling velocity. However, for the small particles (r <1.0mm), the V d is so small that they remain suspended in the atmosphere for nearly 1 year if there is no other removal mechanism [Tegen and Fung, 1994]. In this case, wet deposition becomes an effective 9g ð3þ ð4þ scavenging process. The wet deposition scheme in the GMOD can be described by the following equation: dc dt ¼ mc where m is a scavenging coefficient, which is different among the four dust classes. They are empirically set to p, p, p 0.83, and p 0.83, respectively, where p is the total precipitation rate (mm h 1 ) including both convective and large-scale rainfall. The coefficients for wet deposition are comparable to observations [Volken and Schumann, 1993] and other simulation settings [Lunt and Valdes, 2002; Woodward, 2001]. [23] The GMOD also considers the possible influence of precipitation height. It is well known that most precipitation occurs below the tropopause, which means that there will be no wet removal when aerosols are suspended in the stratosphere. Tegen and Fung [1994] give an empirical equation of H = cos (2q) (km) to define the vertical range for rain scavenging, where q is the latitude. In their definition, wet scavenging takes place only below H. As a result, wet deposition could occur as high as 11 km for the equator region, where q = 0. In contrast, the limit height is only 3 km in the polar regions. Similarly, wet deposition in the GMOD is restricted below the 4th model level which is about 300 mb, above which there are only high clouds that do not precipitate. 3. Model Climatology [24] The performance of the GMOD is analyzed in this section. The model is driven by the meteorological fields from a GCM every 1 h. A sensitivity analysis shows that the model reaches equilibrium in several months (not shown). However, to obtain a more reliable result, we ran the model for 25 years, and the average of the last 20 years is considered to be the climatology. In reference to seasonality, four boreal seasons from spring to winter are denoted by MAM (March May), JJA (June August), SON (September November), and DJF (December February) Global Dust Distribution and Budget [25] The total emission of mineral dust is first estimated to determine the constant a in equation (2). Figure 2 shows a comparison of the climatological dust emission flux from different studies. Though different simulations generally have different size ranges of particles, the estimated dust uplift fluxes are within 1000 to 3000 Tg a 1. A similar error span in dust emission has been reported by the third assessment report of the Intergovernmental Panel on Climate Change (IPCC) [Penner et al., 2001]. In the GMOD, we set the a posteriori coefficient a to Kg s m 4.In this way, we obtain an average emission of 1935 Tg a 1 for the 20-year simulation. [26] Given this definition of a, the spatial distribution of dust emission and deposition is studied and is shown in Figure 3. The uplift domain is generally consistent with the distribution of the ratio factor C 0 1, as shown in Figure 1. The source intensity is strengthened in the desert regions because the surface air over these regions is drier than the air ð6þ 4of24

5 Figure 2. Estimated dust emission flux in different literature. Units: Tg a 1. The different letters denote different references listed as given in Table 8. elsewhere. About 94% of the total dust uplift takes place in the NH, most of which originates from the Sahara and the Taklimakan Desert. The largest source in the SH is the Australian Desert. [27] The deposition domain is far larger than the uplift area. Since most of the dust sources are located in the NH, the sedimentation amount is correspondingly greater in this hemisphere. Smaller particles can be transported to high latitudes and contribute to the deposition over those regions. The total deposition is divided into the dry and wet contributions, as shown in Figures 3 (bottom). Most dry deposition occurs near the source regions because large particles easily settle, due to their large settling velocity. The dry deposition over the ocean regions is mainly composed of small aerosols. The westerlies near 30 N transport a great number of particles into the open sea, making a ring of deposition around the world (Figure 3, top right). The ring indicates that dust aerosols are transported around the globe, including across the Pacific [VanCuren and Cahill, 2002; Uematsu et al., 2003; Zhao et al., 2003] and the Atlantic [Lee, 1983; Prospero, 1996b] propagation. [28] The predicted dust budget is listed in Table 2. It includes the total uplift, deposition, burden, and lifetime of dust. Table 2 shows that dry deposition accounts for a high percentage of the total deposition, especially for particles whose radii are larger than 5 mm. Wet deposition, which is the primary scavenging process for particles smaller than 2 mm, comprises 33% of the total deposition. The percentage is smaller than the value of 41% given by Zender et al. [2003], but larger than the value of 13% given by Ginoux et al. [2001]. The differences among the three studies are probably caused by the application of different scavenging coefficients [Zender et al., 2003]. In addition, the difference in particle size distribution may also contribute much to such inconsistency. For example, Table 2 shows that large Figure 3. Simulated annual mean dust mobilization and deposition. The total deposition is the sum of the dry and wet deposition. Units: g m 2 a 1. 5of24

6 Table 2. Global Dust Budget Radius (mm) Effective R (mm) Uplift (Tg a 1 ) Dry (Tg a 1 ) Wet (Tg a 1 ) Burden (Tg) Lifetime (days) particles (r > 5 mm) contribute 74% of the total dry deposition. However, Zender et al. [2003] only considered particles smaller than 5 mm. These differences show the great uncertainty encountered in understanding the role of wet deposition in global dust transport. Observations of the ratio of the dry to wet deposition are quite limited on a global scale [Duce et al., 1991], which makes the validation of this aspect of the model difficult. As a result, more observations of dust dry and wet deposition are required to reduce model biases. [29] The small mass of small particles keeps them suspended in the air for long periods of time. In contrast, large particles are removed from the atmosphere in about 1 day. This feature helps to increase the percentage of uplifted clay (r <1.0mm) partition in all. As a result, the particles in the four size bins make up 20%, 30%, 39%, and 11% of the total dust mass. The average lifetime of dust aerosols in the atmosphere is about 5.2 days, which is close to the result given by Luo et al. [2003] and is consistent with other studies [Tegen and Lacis, 1996; Chin et al., 2002; Zender et al., 2003]. [30] A detailed investigation of the dust budget for different continents is shown in Table 3. Dust emissions in DEAD are as follows (units: Tg): Africa, 980; Asia, 415; Australia, 37; South America, 35; North America, 8 [Zender et al., 2003]. The GMOD has a stronger dust flux in source regions but a weaker intensity elsewhere, indicating differences in the dust source intensity in the two studies. The effects of dry and wet deposition are different for each continent. In Africa, Asia, and Australia, where there are large deserts, more large particles are available and, therefore, dry deposition comprises a high percentage of the total deposition. In Europe, where there is little dust emission, wet deposition is more than 5 times the dry deposition because most of the aerosols are small clay particles transported from Africa or Asia [d Almeida, 1986; Prospero, 1996b]. In North and South America, dry and wet deposition are comparable. [31] The dust budget in Table 3 reveals the main dust sources and continental sinks. The largest desert, the Sahara Desert, makes Africa the biggest source of dust in the world. Table 3. Continental Dust Budget Regions Up (Tg a 1 ) Dry (Tg a 1 ) Wet (Tg a 1 ) Budget a (Tg a 1 ) Burden (Tg) Africa Asia Australia North America South America Europe a Budget = Up Dry Wet. Table 4. Oceanic Dust Deposition Regions Dry (Tg a 1 ) Wet (Tg a 1 ) Total (Tg) DJF MAM JJA SON North Pacific South Pacific North Atlantic South Atlantic North Indian South Indian Arctic Total Extensive arid and semiarid regions in Asia contribute large amounts of mineral dust aerosols to the atmosphere. In contrast, Europe is the primary continental dust sink, as many particles are brought to the ground by rain. A further comparison between the budget and deposition in Table 3 shows that large sinks always correspond to a high percentage of wet deposition. For example, the wet deposition accounts for 51%, 64%, and 86% of the total deposition in North America, South America, and Europe, respectively. This feature is a result of the combined effects of the geographic conditions and particle size distribution. [32] Table 4 shows the simulated dust deposition over the oceans. The main characteristic of oceanic deposition is that the wet deposition is greater than the dry deposition. The waters near the source regions, such as the North Pacific and the North Atlantic, receive more dust deposition than elsewhere. As a result, the deposition in the NH is generally larger than that in the SH. The seasonal variations of oceanic dust deposition are also shown in Table 4. The global oceanic deposition reaches a maximum in the boreal summer and a minimum in the boreal winter. The seasonal variation of deposition in the Atlantic is consistent with observations [Gao et al., 2001], both of which show peaks in JJA. The simulated dust deposition in the Pacific shows comparable intensities in MAM and JJA. These results differ from observations. For example, Holzer et al. [2005] documented that the maximum transpacific transport occurs in the boreal spring. The underestimation of the dust deposition to the North Pacific in MAM indicates that the GMOD simulates weaker springtime dust sources in central Asia than observations. Such deviation has also been reported in other dust model evaluations [Ginoux et al., 2001; Woodward, 2001; Grini et al., 2005]. This underestimation may be induced by the underestimation of the anthropogenic sources in northern China and Mongolia. Tegen and Fung [1995] considered the influence of the large domain of cultivated soil in the above regions and obtained a reasonable springtime aerosol optical thickness in east Asia. [33] A comparison of the oceanic deposition with estimates from observations [Duce et al., 1991; Prospero, 1996a] and other models [Ginoux et al., 2001; Zender et al., 2003] is listed in Table 5. The largest oceanic deposition in the GMOD is in the North Pacific, which is consistent with Duce et al. [1991], but different from the results of Prospero [1996a], Ginoux et al. [2001], and Zender et al. [2003], where the North Atlantic is the largest oceanic sink. The disagreement among models is probably caused by the differences in dust size distribution, simulated meteorolog- 6of24

7 Table 5. Comparison of Oceanic Deposition a Regions Duce et al. [1991] Prospero [1996a] GOCART DEAD GMOD North Pacific South Pacific North Atlantic South Atlantic North Indian South Indian a Units: Tg a 1. Sources: Duce et al. [1991], Prospero [1996a], Ginoux et al. [2001], and Zender et al. [2003]. GOCART, Global Ozone Chemistry Aerosol Radiation and Transport; DEAD, Dust Entrainment and Deposition. ical conditions, and the efficiency of the wet scavenging processes. However, the inconsistency between observations indicates that more in situ measurements are required to reduce the currently large biases in estimating dust oceanic deposition. Except for this disagreement, most of the results predicted by the GMOD are comparable to previous studies. For example, the simulated intensity of the dust deposition in the Atlantic is about 147 Tg a 1, close to the estimation of 140 Tg a 1 by Kaufman et al. [2005]. The deposition in the South Pacific, the South Atlantic, and the South Indian Ocean are close to that estimated by Duce et al. [1991] Dust Optical Thickness [34] The radiative forcing by mineral dust aerosols is sensitive to many factors, including the refractive index, height of the dust layer, dust particle size, and the dust optical depth [Liao and Seinfeld, 1998]. In this section, we will evaluate the predicted dust optical thickness (DOT) in the GMOD, which can be used as an indication of the predicted dust radiative effects. [35] Generally, the DOT (denoted as t in this paper) at a specific wavelength l at one grid point can be calculated using the following equation [Tegen et al., 1997; Ginoux et al., 2001]: t i;j ðlþ ¼ X4 K e ðlþ n M i;j;n n¼1 where n denotes the sequence of the four dust bins in the GMOD, M i, j, n is the dust burden of the nth bin at the (i, j) grid point, and K e is the mass extinction coefficient, which is determined by Shi et al. [2005]: K e ðlþ ¼ 3Q eðlþ 4r e r In this equation, r e and r are the effective radius and mass density of dust, respectively. Q e (l) is the extinction coefficient of a spherical particle, which can be obtained from Mie theory [de Rooij and van der Stap, 1984; Mishchenko et al., 1999]. This depends on the dust size distribution and refractive index. The GMOD adopts a power law size spectrum [Mishchenko et al., 1999] for all dust classes (see section 2.2). For the refractive index, we set k = i at 0.67 mm, which is interpolated from Woodward [2001]. ð7þ ð8þ [36] Figure 4 shows the DOT at 0.67 mm in different seasons. Clearly, the Northern Hemisphere has the largest optical depth in the boreal summer, with the maximum located in the Sahara Desert. The dust column density in central Asia also becomes the highest in this season, allowing the global mean of the DOT to reach as high as However, as noted above, most observed dust events in Asia take place in the spring rather than in the summer. In the boreal winter, most intense sources in the NH become moderate and, as a result, the total global DOT drops to The desert activity in Australia shows opposite seasonality, and, as a result, the DOT in this region is highest in DJF and lowest in JJA. [37] The global averages of the DOT for the four different bins are 0.019, 0.007, 0.004, and The largest percentage of the DOT comes from the smallest particles, though they comprise only 20% of the total dust mass. The second and the third dust bins show comparable optical depths. The DOT of the large particles is very small. The extinction coefficient K e in equation (7), calculated by Mie theory, is inversely proportional to the particle size. This explains why small particles are responsible for a large DOT. For the same reason, the shortwave radiative effect of dust is mainly determined by small particles [Yoshioka et al., 2007]. [38] The annual global mean DOT at 0.67 mm is about ± in the GMOD. This value is consistent with the value of ± predicted by Zender et al. [2003] and the value of by Tegen et al. [1997] at 0.63 mm. The optical thickness of mineral dust aerosols is comparable to the optical depth of anthropogenic sulfate, which is estimated in the fourth assessment report of the IPCC to be approximately [Forster et al., 2007]. This result indicates that both anthropogenic and natural sources need to be considered when determining the total aerosol optical depth. 4. Station-Based Evaluation [39] The simulated dust climatology is validated by observed data in this section. When discussing the model performance, we use the mean bias (MB), normalized mean bias (NMB), and correlation coefficient to quantify the results. MB and NMB are defined as follows [Liao et al., 2007] MB ¼ 1 n X n i¼1 NMB ¼ 100% Xn i¼1 ðp i O i Þ ð9þ ðp i O i Þ= Xn O i i¼1 ð10þ where P i and O i are the modeled and the observed result at site i, respectively. n is the number of model-observed pairs for all qualified data Concentration [40] In this section, the dust concentration at the lowest level of the GMOD is evaluated with the observational data from the 18 sites operated by the University of Miami 7of24

8 Figure 4. brackets. Dust optical thickness at 0.67 mm in different seasons. The global mean of DOTs is shown in Figure 5. (a) Locations of 18 sites in the University of Miami Ocean Aerosol Network. Circles denote sites in the North Atlantic, triangles denote sites in the southern oceans, asterisks denote sites in the Indian Ocean, squares denote sites in the North Pacific, and diamonds denote sites in the South Pacific. (b) Comparison of annual dust concentration at low level between observations and simulation results of the GMOD. Solid diagonal means simulations and observations are in perfect agreement. Two dashed lines indicate a factor of 2.0 or 0.5, respectively, between simulations and observations. The scales of the coordinates are logarithmic. Units: mg m 3. 8of24

9 [Prospero, 1996a]. Figure 5a shows the locations of these sites. It should be noted that not all the stations record dust concentrations continuously, and some do not have complete year-round records. Most of the sites have been operating for less than 10 years, which indicates that the natural temporal variability may reduce the accuracy of the data set to a certain degree. However, these observations provide an objective means of evaluating dust models and have been widely used in past studies [Ginoux et al., 2001; Woodward, 2001; Lunt and Valdes, 2002; Luo et al., 2003; Zender et al., 2003; Liao et al., 2004; Tanaka and Chiba, 2006; Li et al., 2008]. [41] The comparisons between simulated and observed dust concentrations at low levels at all 18 sites are shown in Figure 5b. The MB of the simulation is 0.67 mg m 3, and the NMB is equal to 8%. The correlation between the modeled and observed results is Generally, simulations in the North Atlantic and the Indian Ocean are in better agreement with observations than those at the other sites. This is probably due to the adoption of a reasonable source intensity in North Africa and the Arabian Peninsula in the model. The GMOD overestimates the dust density at sites by a factor of These stations are all located in the central Pacific. This overestimation is caused by an overestimation of the westerly wind and an underestimation of precipitation in this area as simulated by the GCM (not shown), both of which help to blow more dust over the Pacific. On the other hand, the modeled dust concentration at King George Island (site 10) is underestimated by a factor of 40. This site, located on the tail of the Antarctic Peninsula, is affected by dust particles that originate from South America [Li et al., 2008]. However, the GCM underestimates the source strength by setting the land as scrubland instead of desert. [42] The monthly dust concentrations at each site are shown in Figure 6. Sites 1 7 are located in the North Atlantic and are generally influenced by the dust from the Sahara Desert. At Barbados (site 1), the observations indicate a maximum in May July, but the GMOD predicts an active dust period from June to August. The simulated amplitude of the peak is about one half of that observed. As the nearest site to Barbados (site 1), Cayenne (site 2) has a similar dust amount but different seasonality, which peaks in the spring. The simulation at this site does not capture the springtime maximum but obtains a reasonable dust density in the other seasons. The simulated maximum dust concentration in Miami (site 3) appears in May, while the observed concentration peaks in July. The dust activity in Miami (site 3) is strongly affected by the dust from the Sahara Desert, which peaks in May in the GMOD. As a result, the simulated dust concentration shows a high value in May in Miami (site 3). In Bermuda (site 4), the model results and observations are in good agreement in terms of both seasonality and magnitude. Sites Izania (site 5) and Sal Island (site 6) are located along the west coast of North Africa. These sites contain the highest dust concentrations among the 18 sites. Izania (site 5) is located at an altitude of 2360 m above sea level, which is about 800 hpa in the free air. We use the mean value of the 7th and 8th model levels dust density to denote the concentration at this site. The model results at both Izania (site 5) and Sal Island (site 6) do not capture the dust plume in early spring. Except for this disparity, the GMOD generally reproduces the seasonal variations and magnitudes at these sites. At Mace Head (site 7), which is located at a high latitude in the North Atlantic, the simulated seasonality is consistent with observations. [43] Sites 8 10 are all in the southern oceans. Cape Point (site 8) is located at the southern tip of Africa. The observations at this site are incomplete. However, the range of results from the simulation is in reasonable agreement with the sparse record. Cape Grim (site 9) is close to Australia; the correlation coefficient between the observation and simulation at this site is about This indicates that the GMOD reproduces both the magnitude and variability at this site well. The underestimation of the dust concentration at King George Island (site 10) was shown in Figure 5b and is caused by the application of an inconsistent surface type in South America. [44] Kaashidhoo (site 11) is the only site located in the Indian Ocean. The GMOD reproduces a reasonable magnitude and seasonality of the dust concentration at this site, except for an overestimation of the springtime dust concentration. Okinawa (site 12) and Cheju (site 13) are in east Asia. The observed dust concentrations in these regions peak in the spring due to frequent dust storms [Zhao and Yu, 1990; Zhou et al., 2002]. Since the intensity and frequency of dust events vary from year to year, the observations in April at Okinawa (site 12) and Cheju (site 13) show large variability. The GMOD underestimates the dust maximum in the spring for both sites, which causes an underestimation of the springtime transpacific transport, as discussed in section 3.1. [45] Sites are in the central Pacific. As the results in Figure 5b show, the dust concentrations at these sites are overestimated. This is very similar to the result of Ginoux et al. [2001]. However, Lunt and Valdes [2002] obtained a good agreement between model and observations in the Pacific but less agreement at the Atlantic sites. The difference between the results of Lunt and Valdes [2002] and that of the GMOD is probably caused by the difference in the particle size span. As an ocean near dust sources, the Atlantic receives more large particles than the Pacific. However, the dust model discussed by Lunt and Valdes [2002] only simulated submicron particles. This is reasonable for the dust simulation over the Pacific but causes large biases for the dust over the Atlantic. [46] Generally speaking, the GMOD reproduces the dust concentrations at most sites to within a reasonable range. The major deficiency is that the simulated dust density over the central Pacific is higher than in observations, which is probably caused by the biases of the meteorological fields in the GCM Deposition [47] Dust deposition records from the DIRTMAP database are used for model validation in this section. This database is comprised of geologic dust records obtained from ice cores, marine sediments, and terrestrial (loess) deposits [Kohfeld and Harrison, 2001]. Figure 7 shows the distribution of the sampling sites in DIRTMAP. There are a total of 251 sites, most of which are located in the oceans between the middle latitudes of the two hemispheres. The largest land records are from the Chinese loess plateau, which has the highest deposition rate in the whole data set. 9of24

10 Figure 6. Comparison of the monthly time series of dust concentrations in 18 sites between observations and simulations. Solid line represents simulated monthly means with one standard deviation denoted by shadings. Dots are observed monthly means. Error bars indicate one standard deviation. Units of vertical coordinate: mg m 3. The time span of observed record at each site is shown in the upper righthand corner of each box. 10 of 24

11 Figure 7. Distribution of the sampling spots in the Dust Indicators and Records of Terrestrial and Marine Paleoenvironments (DIRTMAP) database. Solid circles denote spots on the Atlantic, triangles denote spots on Greenland, asterisks denote spots on the Indian Ocean, diamonds denote spots in China, empty circles denote spots on the Pacific, and squares denote spots on the Antarctic. [48] The comparison of dust deposition between the DIRTMAP records and the simulation of the GMOD is shown in Figure 8. As it shows, the observed deposition varies greatly among different sites from about to 1000 g m 2 a 1. For such a large data span, it is more reasonable to calculate the MB and NMB of logarithmic deposition values. In this way, the calculated MB is 0.62 g m 2 a 1 and the NMB is 36.0%. The log correlation coefficient is 0.84, higher than the result of 0.76 given by Mahowald et al. [1999] and 0.82 given by Lunt and Valdes [2002] for the same data. [49] The simulated deposition in the Atlantic shows the best agreement, most of which is a factor of 0.5 to 2.0 times that of the observations. The simulation for sites in the Pacific and the Indian Ocean are also reasonable. The deposition near the polar region is small. The GMOD captures this feature and obtains reasonable deposition in both Greenland and Antarctica. The largest deviation between the model and observations appears in China, where the GMOD underestimates the deposition by a factor of 22. The depositions at these sites are mainly obtained from the loess records, which include many large particles. However, the GMOD only considers the transport of particles smaller than 10 mm. The difference in the dust size range may explain the great deviation between the model and observations over that region. This underestimation accounts for a large portion of the disparity between simulation and observations. After eliminating the deposition records in China, the MB and NMB reduce to 0.07 g m 2 a 1 and 8.0%, respectively Dust Optical Thickness [50] In section 3.2, the simulated DOT is analyzed and compared with other model results. In this section, we evaluate the simulated DOT with station-based observations. [51] The observed aerosol optical thickness (AOT) that we used for validation comes from AERONET [Holben et al., 1998; Dubovik et al., 2000], which is a federation of ground-based remote sensing aerosol networks. However, the observed AOT usually includes information about sulfate, black carbon, organic carbon, sea salt, and dust, and it is hard to distinguish the contribution of each Figure 8. Comparison of dust deposition between observations and simulations of the GMOD. Bold solid diagonal means simulations and observations are in perfect agreement. Dotted lines indicate a ratio of 2.0 or 0.5 between simulations and observations. Thin solid lines indicate a ratio of 5.0 or 0.2 between simulations and observations. The designations for spots are explained in the Figure 7 caption. The scales of the coordinates are logarithmic. Units: g m 2 a of 24

12 Figure 9. (a) Distribution of the chosen stations in Aerosol Robotic Network (AERONET). Empty circles denote sites in North Africa, solid circles denote sites in Arabian Peninsula, triangles denote sites in central Asia, squares denote sites in Australia, diamond denotes site in South Africa, cross denotes site in North America, and asterisks denote sites in South America. (b) Comparison of aerosol optical thickness (AOT) at 0.67 mm between observations and simulations. Solid diagonal means simulations and observations are in perfect agreement. Dashed lines indicate a factor of 2.0 and 0.5, respectively, between simulations and observations. component. To solve this problem, we set up a number of criteria in choosing observational stations. First, the sites are located on land and, therefore, the influence of sea salt is excluded. Second, the sites are located in uncultivated land, where there is little or no human activity such that the influence of anthropogenic aerosols such as sulfate and black carbon are excluded. Third, the sites are located in or around desert regions and, therefore, the signal of dust is dominant. With these criteria, we selected the 16 AERONET sites shown in Figure 9a. [52] The chosen sites shown in Figure 9a are generally in arid or semiarid regions. The comparison between model and observations at these sites is shown in Figure 9b. Overall, most of the simulated results are reasonable; the MB, NMB, and R are 0.04, 26.7%, and 0.80, respectively. The largest deviation is that the model underestimates the DOT in sites by over 50%; these sites are located in South Africa, North America, and South America, respectively. We checked the vegetation types in those regions and found that the GCM underestimates the source strength by setting the vegetation type as scrubland instead of desert. This deficiency also explains the discrepancy between the GMOD and observations at high southern latitudes that is shown in Figure 5. [53] A detailed investigation of the validation at each site is shown in Figure 10. Sites 1 4 are located in or near the Sahara Desert. The GMOD reproduces the seasonality and magnitude of DOT at these sites well, where the average correlation coefficient is up to The model underestimates the JJA peak in Dahkla (site 1) and Ras El Ain (site 4) and slightly overestimates the magnitude in Izana (site 3). [54] Sites 5 7 are located in the Arabian Peninsula. The simulation in SEDE BOKER (site 5) agrees with observations, except that the model overpredicts the DOT in JJA by a factor of 2. The simulation in Solar Village (site 6) is in good agreement with station records including seasonality, magnitude, and variability. The modeled DOT at Hamim (site 7) is underestimated in JJA, which is opposite to the situation at SEDE BOKER (site 5). [55] Sites 8 10 are located in central Asia. The GMOD shows a DOT peak during JAS (July September) in these areas. This trend is consistent with observations in Irkutsk (site 9) as opposed to the case at Tomsk (site 8) and Dalanzadgad (site 10). The failure in differentiating the seasonality of the DOT at the above three sites is attributed to the low resolution of the GCM, which makes it difficult to discern the subgrid surface conditions and meteorological fields in central Asia. [56] Lake Argyle (site 11) and Birdsville (site 12) are located in Australia. The DOT values at these sites show seasonality opposite to those of the sites in the NH, such as Izana (site 3) and Solar Village (site 6). This trend has been discussed in section 3.2. The simulated seasonality at sites generally agrees with observations, especially at Birdsville (site 12), with a correlation coefficient of 0.8. [57] The underestimation of the DOT at sites was shown in Figure 9b. The probable cause is the inconsistent surface vegetation types used in the GCM Size Distribution [58] The particle size distribution is very important for studying the radiative effect of dust aerosols. Small particles are more efficient in scattering solar radiation, but large particles are more absorbing of longwave radiation [Yoshioka et al., 2007]. The observed particle size distribution used in our validation is from AERONET [Dubovik et al., 2000]. Six stations, shown in Figure 9a, are chosen because relatively longer and more complete records are found at these sites. We compare the simulated seasonal 12 of 24