MIRAGE: Model description and evaluation of aerosols and trace gases

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi: /2004jd004571, 2004 MIRAGE: Model description and evaluation of aerosols and trace gases Richard C. Easter, 1 Steven J. Ghan, 1 Yang Zhang, 2,3 Rick D. Saylor, 4,5 Elaine G. Chapman, 1 Nels S. Laulainen, 1 Hayder Abdul-Razzak, 6 L. Ruby Leung, 1 Xindi Bian, 1,7 and Rahul A. Zaveri 1 Received 25 January 2004; revised 20 May 2004; accepted 30 July 2004; published 27 October [1] The Model for Integrated Research on Atmospheric Global Exchanges (MIRAGE) modeling system, designed to study the impacts of anthropogenic aerosols on the global environment, is described. MIRAGE consists of a chemical transport model coupled online with a global climate model. The chemical transport model simulates trace gases, aerosol number, and aerosol chemical component mass (sulfate, methane sulfonic acid (MSA), organic matter, black carbon (BC), sea salt, and mineral dust) for four aerosol modes (Aitken, accumulation, coarse sea salt, and coarse mineral dust) using the modal aerosol dynamics approach. Cloud-phase and interstitial aerosol are predicted separately. The climate model, based on Community Climate Model, Version 2 (CCM2), has physically based treatments of aerosol direct and indirect forcing. Stratiform cloud water and droplet number are simulated using a bulk microphysics parameterization that includes aerosol activation. Aerosol and trace gas species simulated by MIRAGE are presented and evaluated using surface and aircraft measurements. Surface-level SO 2 in North American and European source regions is higher than observed. SO 2 above the boundary layer is in better agreement with observations, and surface-level SO 2 at marine locations is somewhat lower than observed. Comparison with other models suggests insufficient SO 2 dry deposition; increasing the deposition velocity improves simulated SO 2. Surface-level sulfate in North American and European source regions is in good agreement with observations, although the seasonal cycle in Europe is stronger than observed. Surface-level sulfate at high-latitude and marine locations, and sulfate above the boundary layer, are higher than observed. This is attributed primarily to insufficient wet removal; increasing the wet removal improves simulated sulfate at remote locations and aloft. Because of the high sulfate bias, radiative forcing estimates for anthropogenic sulfur given in 2001 by S. J. Ghan and colleagues are probably too high. Surface-level dimethyl sulfide (DMS) is 40% higher than observed, and the seasonal cycle shows too much DMS in local winter, partially caused by neglect of oxidation by NO 3. Surface-level MSA at marine locations is 80% higher than observed, also attributed to insufficient wet removal. Surface-level BC is 50% lower than observed in the United States and 40% lower than observed globally. Treating BC as initially hydrophobic would lessen this bias. Surface-level organic matter is lower than observed in the United States, similar to BC, but shows no bias in the global comparison. Surface-level sea salt concentrations are 30% lower than observed, partly caused by low temporal variance of the model s 10 m wind speeds. Submicrometer sea salt is strongly underestimated by the emissions parameterization. Dust concentrations are within a factor of 3 at most sites but tend to be lower than observed, primarily because of neglect of very large particles and underestimation of emissions and vertical transport under high-wind conditions. Accumulation and Aitken mode number concentrations and mean sizes at the surface over ocean, and condensation nuclei concentrations aloft over the Pacific, are 1 Pacific Northwest National Laboratory, Richland, Washington, USA. 2 Atmospheric and Environmental Research, San Ramon, California, USA. 3 Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina, USA. Copyright 2004 by the American Geophysical Union /04/2004JD School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA. 5 Atmospheric Research and Analysis, Inc., Snellville, Georgia, USA. 6 Department of Mechanical and Industrial Engineering, Texas A&M University-Kingsville, Kingsville, Texas, USA. 7 North Central Research Station, U.S. Department of Agriculture Forest Service, East Lansing, Michigan, USA. 1of46

2 in fair agreement with observations. Concentrations over land are generally higher than observations, with mean sizes correspondingly lower than observations, especially at some European locations. Increasing the assumed size of emitted particles produces better agreement at the surface over land, and reducing the particle nucleation rate improves the agreement aloft over land. INDEX TERMS: 0305 Atmospheric Composition and Structure: Aerosols and particles (0345, 4801); 0322 Atmospheric Composition and Structure: Constituent sources and sinks; 0365 Atmospheric Composition and Structure: Troposphere composition and chemistry; KEYWORDS: aerosols, global model Citation: Easter, R. C., S. J. Ghan, Y. Zhang, R. D. Saylor, E. G. Chapman, N. S. Laulainen, H. Abdul-Razzak, L. R. Leung, X. Bian, and R. A. Zaveri (2004), MIRAGE: Model description and evaluation of aerosols and trace gases, J. Geophys. Res., 109,, doi: /2004jd Introduction [2] Human activities are altering the composition of the atmosphere. The concentration of carbon dioxide (CO 2 )in the atmosphere has increased by 30% since 1800 as a result of biomass and fossil fuel combustion [Intergovernmental Panel on Climate Change (IPCC), 2001]. Similarly, the atmospheric concentrations of many halocarbons almost doubled between 1978 and 1995 [IPCC, 2001]. Although detection of trends in aerosol particle concentrations is much more difficult because of the short lifetime (one week) and hence large spatial and temporal variability of aerosol particles, evidence from ice cores [Mayewski et al., 1990] suggests fourfold increases over the last 150 years in remote regions. [3] The short lifetime of aerosol particles suggests that, compared to the long-lived ( years) gases, their concentrations are much lower per unit mass emitted. Moreover, the emissions of the aerosols and their precursor gases from biomass and fossil fuel combustion are much less than the emissions of CO 2 [e.g., IPCC, 2001; Andreae and Merlet, 2001]. However, the potential impact of the aerosol particles on humans and the environment may be comparable to that of the long-lived gases. These impacts include (1) modification to the Earth s radiative budget and climatic system [e.g., Kaufman et al., 1991], (2) reduction of visibility [e.g., Malm et al., 1994, 1996], (3) human health through inhalation [e.g., Dockery et al., 1993], (4) acidification of precipitation [e.g., Schwartz, 1989], and (5) indirect effects on the concentrations of other pollutants through heterogeneous chemistry on the particle surfaces [e.g., Dentener et al., 1996]. [4] A number of three-dimensional (3-D) global models have been developed to simulate the chemical and radiative/ climatic impact of aerosols since early 1990s when sulfate was first simulated on a global scale [Erickson et al., 1991; Langner and Rodhe, 1991; Langner et al., 1992; Charlson et al., 1991]. Many models developed since then focused on a single aerosol component such as sulfate [e.g., Kiehl and Briegleb, 1993; Penner et al., 1994; Pham et al., 1995; Boucher and Anderson, 1995; Chin et al., 1996; Feichter et al., 1996, 1997; Kasibhatla et al., 1997; Lelieveld et al., 1997; Restad et al., 1998; Roelofs et al., 1998; Koch et al., 1999; Kiehl et al., 2000; Barth et al., 2000; Rasch et al., 2000a; Chin et al., 2000a, 2000b; Adams and Seinfeld, 2002], mineral/soil dust [e.g., Tegen and Fung, 1994; Tegen and Lacis, 1996; Dentener et al., 1996; Tegen and Miller, 1998; Ginoux et al., 2001; Perlwitz et al., 2001; Lunt and Valdes, 2002], carbonaceous aerosols [e.g., Penner et al., 1992; Liousse et al., 1996; Cooke and Wilson, 1996; Cooke et al., 1999, 2002; Kanakidou et al., 2000; Chung and Seinfeld, 2002] and sea salt [e.g., Gong et al., 2002; Grini et al., 2002]. More recently multiple aerosol components have been simulated simultaneously in global models. Table 1 summarizes the aerosol components, size representation and microphysical processes treated in current multicomponent global aerosol models. Multiple aerosol components are treated in these models as either internally mixed [e.g., Adams et al., 1999; Metzger et al., 2002a, 2002b; Liao et al., 2003], or externally mixed [e.g., Penner et al., 1998; Collins et al., 2001; Koch, 2001; Chin et al., 2002], or both [e.g., Jacobson, 2001a, 2002; Wilson et al., 2001; Pitari et al., 2002; Iversen and Seland, 2002; Gong et al., 2003]. Most of these multicomponent models simulate sizeresolved multicomponent aerosols using various size representations, including modal [e.g., Wilson et al., 2001; Iversen and Seland, 2002], sectional [e.g., Jacobson, 2001b, 2001c; Pitari et al., 2002; Gong et al., 2003] and mixed modal-sectional approaches [e.g., Chin et al., 2002]. Another approach for size representation is the new quadrature method of moments [McGraw, 1997; Wright and Kasibhatla, 2001; Wright et al., 2002; McGraw and Wright, 2003], which can accurately and efficiently simulate sizeresolved aerosol microphysics in large-scale models, and has been used to simulate sulfate [Wright et al., 2000]. [5] Global aerosol models differ in several other important respects, including the treatments of precursor and oxidant chemistry, aerosol dynamics, water uptake, aerosol radiative properties, and cloud-aerosol interactions. Most models that simulate sulfate have relatively simple treatments of dimethyl sulfide (DMS) and SO 2 chemistry and prescribe monthly average fields for oxidants (e.g., H 2 O 2, O 3, OH, NO 3 ) based on off-line model simulations. Some of these also simulate H 2 O 2 using off-line HO 2 [e.g., Barth et al., 2000]. Full oxidant chemistry is simulated online in only a few models [Roelofs et al., 1998; Jacobson, 2001c, 2002; Tie et al., 2001; Derwent et al., 2003; Liao et al., 2003]. Most models have rather simplified treatments of aerosol microphysics (e.g., instantaneous H 2 SO 4 condensation, aqueous-phase sulfate production, and dry and wet deposition). Only a few models have detailed treatments of aerosol microphysical processes (e.g., nucleation, condensation, coagulation, and activation) and treat aerosol movement between sections/modes (or changes to moments) due to aerosol dynamic processes [Jacobson, 2001a, 2001c, 2002; Wilson et al., 2001; Wright et al., 2000; Adams and 2of46

3 Table 1. Aerosol Components, Size Representation, and Microphysical Processes Treated in Current Multicomponent Global Aerosol Models (Those Predicting Two or More Aerosol Components) a Model Name Component (Size Representation) Mixing Prognostic Variables Aerosol Microphysics References GRANTOUR S(b), b BC(b), b OC(b), b D(2s), c SS(2s) c EXT M a cond, d act, aqu, d+w dep Penner et al. [1998] and Chuang et al. [2002] GISS Caltech/CMU S(b), N(b), A(b), BC(b), e OC(b), e D(b) e,f INT M a therm (ISORROPIA), cond, d Adams et al. [1999] and Liao et al. [2003] aqu, het, e d+w dep ECHAM4 S, BC, OC, D c,f,ss c,f (all with 1m) INT and/or EXT g Ma, Nd cond, d act, aqu, d+w dep Lohmann et al. [1999a, 2000] CCSR/NIES AGCM S(b), b BC(b), b OC(b), b D(10s), SS(4s or 10s) INT+EXT Ma cond, d aqu, d+w dep Takemura et al. [2000, 2002] GISS GCM/CU S(b), BC(b), OC(b), D(8s), f SS(6s) f INT+EXT M a cond, d aqu, d+w dep Tegen et al. [2000] and Koch [2001] MATCH S(b), BC(b), OC(b), D(4s), SS(b) h EXT M a cond, d act, aqu, d+w dep Collins et al. [2001] GATORG S, N, A, BC, OC, D, SS, and 40 other species (all with 17s) INT+EXT i M a, N a, N d therm (EQUISOLV II), cond, coag, nuc, act, aqu, het, d+w dep Jacobson [2001a, 2001c, 2002, 2003] MOZART S(b), BC(b) EXT Ma cond, d act, aqu, d+w dep Tieetal.[2001] TM2-M3+ S(6m), j BC(4m), j OC(2m), j SS(1m) j INT+EXT i Ma, Na cond, coag, nuc, act, aqu, het, d+w dep Wilson et al. [2001] GOCART S (b), b BC(b), b OC(b), b D(7s), SS(4s) EXT Ma cond, d aqu, d+w dep Chin et al. [2002] CCM3/Oslo S(2m), j BC(3m), j D(44s) b,c,f, SS(44s) b,c,f INT+EXT M a cond, coag, act, aqu, d+w dep Iversen and Seland [2002] and Kristjánsson [2002] TM3-EQSAM S(b), N(b), A(b) INT M a therm (EQSAM), cond, d aqu, d+w dep Metzger et al. [2002a, 2002b] ULAQ S(15s), BC(6s), OC(6s), D(6s), SS(6s) INT+EXT i M a cond, coag, nuc, aqu, het, d+w dep Pitari et al. [2002] STOCHEM S(b), N(b), A(b), OC(b) k EXT Ma cond, d act, aqu, d+w dep Derwent et al. [2003] CAM S, N, BC, OC, D, SS (all with 12s) INT+EXT Ma, Na cond, coag, nuc, act, aqu, d+w dep Gong et al. [2003] MIRAGE S(4m), j BC(2m), j OC(2m), j D(2m), j SS(2m) j INT+EXT Ma, Na, Nd cond, coag, nuc, act, aqu, d+w dep this work a Components: S is sulfate, N is nitrate, A is ammonium, BC is black carbon, OC is organic carbon, D is mineral/soil dust, and SS is sea salt. The size representation for each (or all) aerosol components is shown in parentheses: b is bulk, xm means x modes, and ys means y sections. INT and EXT indicate internal or external mixing of species within or among size sections/modes, respectively. Ma, Na, and Nd indicate aerosol mass, aerosol number, and cloud droplet number, respectively. Aerosol microphysics: therm is thermodynamics, cond is condensation, coag is coagulation, nuc is nucleation, act is activation, aqu is aqueous-phase chemistry, het is heterogeneous chemistry, and d+w dep is dry and wet deposition. (Note that this is a rapidly developing field. The web site of the Aerosol Module Inter-Comparison in Global Models (AeroCom) project ( nansen.ipsl.jussieu.fr/aerocom) contains information on other recent multicomponent models.) b Prescribed dry size distribution is used for the calculation of aerosol radiative properties. The dependence of aerosol size on relative humidity is taken into account. c Dust and sea salt are not treated in the papers by Lohmann et al. [1999a], Penner et al. [1998], and Iversen and Seland [2002]. They are prescribed as background aerosols in the paper by Kristjánsson [2002]. d All H 2SO4 from gas-phase SO2 oxidation is assumed to condense immediately. e BC, OC, dust, and heterogeneous chemistry are not included in the paper by Adams et al. [1999]. f Monthly mean concentrations are prescribed on the basis of 3-D simulation results from an off-line model. g Mixing is either INT or EXT in the paper by Lohmann et al. [1999a], and INT+EXT in the paper by Lohmann et al. [2000]. h Sea salt is prescribed as a function of surface wind speed. i Model also simulates aging from external to internal mixing. j Modes are externally mixed; each includes either a single or multiple internally mixed components. k Treats secondary OC only. 3of46

4 Seinfeld, 2002; Pitari et al., 2002; Gong et al., 2003]. As a result of simplified aerosol treatments, most models predict only the mass for each aerosol mode or section. Only a few predict both mass and number [e.g., Jacobson, 2001a, 2001c; Wilson et al., 2001; Adams and Seinfeld, 2002; Gong et al., 2003]. The treatments of water uptake, aerosol radiative properties, and cloud-aerosol interactions in global models have been recently reviewed by Ghan et al. [2001c]. An important development since then is the online use of thermodynamic modules such as ISORROPIA [Adams et al., 1999; Liao et al., 2003] and EQUISOLV II [Jacobson, 2001a, 2001b, 2001c, 2002] or simplified equilibrium partitioning modules such as EQSAM [Metzger et al., 2002a, 2002b] to treat water uptake and aerosol thermodynamics for a complex mixture of soluble gases and aerosols. In addition, a physically based treatment for cloud-aerosol interactions becomes increasingly feasible given an increasing number of global models that treat all important aerosols with size resolution. Such a treatment enables not only a competition among different aerosol components as CCN but also a comparison between measurements and the simulated aerosol optical depth, size distribution, and cloud condensation nuclei (CCN) concentration. [6] All of the above mentioned global aerosol models have been evaluated to some extent, with the evaluations mostly focused on the predicted aerosol mass and precursor gas concentrations. In addition, global model evaluation and intercomparison results have been presented by Jacob et al. [1997], Rasch et al. [2000b], Barrie et al. [2001], Lohmann et al. [2001], Penner et al. [2001, 2002], and Kinne et al. [2003]. [7] In this paper we present a description and evaluation of a new global aerosol model, Model for Integrated Research on Atmospheric Global Exchanges (MIRAGE). Although global aerosol models have advanced considerably since the first estimates of radiative forcing for anthropogenic sulfate [Charlson et al., 1991], MIRAGE is distinguished from all other models in several important aspects. First, it predicts aerosol number, surface area, and mass for multiple components in multiple modes, and hence allows the aerosol size distribution to change in response to a variety of aerosol atmospheric and microphysical processes. We found this feature to be particularly important during the aerosol activation process, which profoundly changes the aerosol size distribution [Zhang et al., 2002], and in the estimate of the indirect forcing [Ghan et al., 2001c]. Although models that are based on a sectional size representation [e.g., Jacobson, 2001a, 2001b; Adams and Seinfeld, 2002] also allow the aerosol size distribution to vary, they do so at a much higher computational cost. Second, MIRAGE treats both external and internal mixtures of multiple aerosol components in a consistent manner for both water uptake and aerosol activation. Although far from being a comprehensive treatment of aerosol mixing [Russell and Seinfeld, 1998], the treatment in MIRAGE is more advanced than in all but a few global aerosol models [e.g., Jacobson, 2001a, 2001c]. Third, it distinguishes between interstitial and in-cloud aerosols, which is crucial for treating direct scattering and absorption of sunlight by aerosols at the high relative humidity within clouds [Ghan and Easter, 1998]. Fourth, it predicts rather than diagnoses droplet number concentration [Ghan et al., 1997b] and hence is able to treat in a physically based manner droplet loss due to collision/coalescence, collection by drizzle, and evaporation; only two other global aerosol models predict droplet number [Lohmann et al., 2000; Jacobson, 2003]. [8] The evaluation in this paper focuses primarily on the predicted mass concentrations of individual aerosol components and their gas precursors, as well as their temporal and spatial distributions, but there is a limited evaluation of predicted fine particle (accumulation and Aitken mode) number and size. The evaluation of aerosol light scattering (including aerosol optical depth, single-scatter albedo, Ångström exponent, and humidity dependence) and aerosol-cloud interactions (including CCN distributions and cloud microphysical parameters) have been presented elsewhere [Ghan et al., 2001a, 2001b; Kinne et al., 2003]. The description of MIRAGE is presented in section 2. Model results and evaluations for sulfur species (SO 2, sulfate, DMS and methane sulfonic acid (MSA)) are presented in section 3.1, followed by black carbon (BC) and organic matter (OM) in section 3.2, sea salt in section 3.3, mineral dust in section 3.4, and fine particle number and size in section 3.5. The summary and conclusions are in section Model Description [9] MIRAGE is composed of a climate model and a chemical transport model that can be run fully coupled or independently of each other. The climate model is the Pacific Northwest National Laboratory (PNNL) version of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM2). The chemical transport model is the PNNL Global Chemistry Model (GChM). When the two codes are run fully coupled, as was done for the simulations presented here, the CCM2 uses the aerosol fields from GChM when computing radiative scattering and absorption, and cloud droplet nucleation. GChM, in turn, uses transport and cloud/precipitation fields from the CCM2 as it simulates aerosol and trace gas fields Chemical Transport Model [10] Previous versions of GChM are described by Luecken et al. [1991], Benkovitz et al. [1994], and Saylor et al. [1999]. The code solves a three-dimensional continuity equation for trace gas and aerosol species, using latitude, longitude, and hybrid-pressure coordinates as in the CCM Aerosol Representation [11] MIRAGE uses the modal aerosol dynamics approach [Whitby et al., 1991; Whitby and McMurry, 1997] in which the atmospheric aerosol is treated as a set of lognormally distributed modes. The lognormal size distribution of each mode is determined by the number concentration, the mass concentration of each chemical component, and the width (i.e., geometric standard deviation, s g ) of the mode. Currently, the number and mass concentrations for each mode are simulated and the width is specified, as given by Giorgi [1986], Ackermann et al. [1999], and Wilson et al. [2001]. The main strength of the modal representation is its computational efficiency compared to the more fundamental sectional representation. Its main weakness is its inherent assumption of lognormal modes, which limits its accuracy. Observations routinely show that the atmospheric aerosol is composed of a number of size modes (e.g., ultrafine, 4of46

5 Table 2. Aerosol Mode Chemical Components, Number of Transported Variables, Geometric Standard Deviations, and Geometric Mean Diameters of Number Size Distribution a Mode Chemical Components Transported Species b sg DgN, mm for emitted sulfate, OM, and BC; for diagnosed number Aitken sulfate, MSA, OM, BC SO4-A1, MSA-A1, OM-A1, BC-A1, NUM-A1, WATER-A1; SO4-C1, MSA-C1, OM-C1, BC-C1, NUM-C for emitted sulfate, OM, and BC; 0.20 for emitted sea salt; 0.11 for emitted mineral dust or diagnosed number SO4-A2, MSA-A2, OM-A2, BC-A2, SEAS-A2, DUST-A2, NUM-A2, WATER-A2; SO4-C2, MSA-C2, OM-C2, BC-C2, SEAS-C2, DUST-C2, NUM-C2 Accumulation sulfate, MSA, OM, BC, sea salt, mineral dust for emitted sea salt or diagnosed number Coarse sea salt sea salt, sulfate, MSA SEAS-A3, SO4-A3, MSA-A3, NUM-A3, WATER-A3; SEAS-C3, SO4-C3, MSA-C3, NUM-C for emitted mineral dust or diagnosed number Coarse mineral dust mineral dust, sulfate DUST-A4, SO4-A4, NUM-A4, WATER-A4; DUST-C4, SO4-C4, NUM-C4 a Here, sg are geometric standard deviations, and D gn are geometric mean diameters of number size distributions. The emitted D gn values are used for primary emissions when both aerosol number and mass are simulated. The diagnosed number DgN values are only used when aerosol number is diagnosed from aerosol mass [Ghan et al., 2001c]. b The SO4, MSA, OM, BC, SEAS, DUST, and WATER are mass mixing ratios for sulfate, MSA, OM, BC, sea salt, mineral dust, and aerosol water. The NUM are number mixing ratios. The A denotes interstitial/clear-air particles, and the C denotes scavenged/cloud-borne particles. 5of46 Aitken, accumulation, and coarse), and there is a physical basis for the existence of these modes, as different processes dominate the production and loss of particles within the different size modes. However, fitting actual atmospheric size distributions with a few lognormal modes is approximate as best, especially when dealing with instantaneous distributions rather than temporal averages. [12] Several comparisons between the modal and sectional representations have been made, with somewhat varying results. Seigneur et al. [1986] and Zhang et al. [1999] simulated pure coagulation and pure condensation, and found the modal representation with fixed s g (as used here) to have poor accuracy under more polluted conditions, while that with variable s g had moderate accuracy. Harrington and Kreidenweis [1998b] found good agreement between a sectional and a fixed-s g modal representation (with two submicrometer modes) for simultaneous nucleation, condensation, and coagulation. Wilson et al. [2001] in a similar comparison, but with three fixed-s g submicrometer modes, found that the modal representation significantly overestimated ultrafine-mode number concentrations and moderately overestimated Aitken number concentrations. Zhang et al. [2002] simulated cloudaerosol interactions (activation, resuspension, and aqueous sulfate production) and found good agreement between sectional and both fixed- and variable-s g modal representations. These results suggest that a comparison of modal and sectional representations within a comprehensive multidimensional model is needed to better evaluate the accuracy of the modal approach. [13] Four aerosol modes are currently treated: Aitken, accumulation, coarse sea salt, and coarse mineral dust. Nanometer size particles created by homogeneous nucleation are grown into the Aitken mode rather than treated as a separate ultrafine mode. The aerosol chemical components include sulfate, MSA, OM, BC, sea salt, and mineral dust, with all of them in the accumulation mode and different subsets of them in the other modes, as shown in Table 2. Also shown are the transported aerosol species in MIRAGE, which include mass and number mixing ratios for both interstitial/clear-air and scavenged/cloud-borne paricles (see section 2.1.3), and the aerosol water mixing ratio for interstitial particles. [14] Internal mixing of chemical components is assumed within each mode, while the modes themselves are externally mixed. Field studies show evidence of both external mixing [e.g., Svenningsson et al., 1994] and internal mixing [e.g., Saxena et al., 1995]. In general, primary particles are externally mixed near their sources, but become internally mixed as they coagulate with other particles and grow by condensation. Treatment of multiple Aitken and accumulation modes, some externally and some internally mixed, is possible but has not yet been attempted in MIRAGE. Some models [e.g., Wilson et al., 2001; Iversen and Seland, 2002] have applied a simple treatment of BC aging (freshly emitted BC is externally mixed and hydrophobic, and aged BC is internally mixed and hydrophilic). This reduces activation scavenging, slows wet removal, and increases the lifetime of BC compared to a pure internal mixing approach (see section 3.2.1). A similar effect could be expected for accumulation mode mineral dust, but may be less important because of the location of major dust sources.

6 Longer lifetimes would also affect aerosol number, but the overall impact of external versus internal mixing treatments on aerosol number is not understood. OM and BC are limited to the Aitken and accumulation modes in MIRAGE, as we simulate no coarse-mode emissions for these species and their transfer into the coarse modes by coagulation is ignored. Sulfate produced from SO 2 oxidation can occur in all modes; it is assumed to be completely neutralized as ammonium sulfate as NH 3 is not currently simulated. [15] A number of observations suggest two-mode or three-mode size distributions for mineral dust [e.g., d Almeida and Schutz, 1983; d Almeida, 1987; Iwasaka et al., 1993; Lei et al., 1993; Arimoto et al., 1997], indicating that dust emitted from deserts and arid regions can contribute to fine-mode particle population. Treatment of dust particles using only a coarse mode may underestimate the number concentration and surface area of dust particles, and thus the role of dust in CCN formation and heterogeneous chemistry. In MIRAGE, we treat mineral dust in both the accumulation mode and a coarse mode, but assume that accumulation mode mineral dust is internally mixed with other accumulation mode species. [16] Table 2 also shows the widths and dry sizes (D gn is the geometric mean diameter of the number size distribution) used for each mode. Dry sizes are determined by the number and mass in each mode, and thus vary spatially and temporally, but sizes must be specified for primary emissions. (MIRAGE may also be run with aerosol number diagnosed from mass rather than predicted [Ghan et al., 2001c], in which case a fixed D gn is specified for each mode.) For the Aitken and accumulation modes, the s g and D gn values in Table 2 represent midrange values for tropospheric aerosol observations from the literature. Whitby [1978] reported D gn and s g values of mm and for Aitken mode, and mm and for accumulation mode, primarily for continental and polluted conditions. Krekov [1993] summarized a number of observations, and for continental boundary layer showed D gn and s g values of mm and for Aitken mode, and mm and for accumulation mode. Heintzenberg et al. [2000] compiled marine observations (primarily surface) and showed D gn and s g values of mm and for Aitken mode, and mm and for accumulation mode. The values for sea salt are from O Dowd et al. [1997] and are used for coarse and accumulation mode sea-salt emissions. [17] Observed size distributions for mineral dust are highly variable, depending on the source region of dust particles, the measurement period and location, and the associated meteorological conditions. Some values of D gn for single-mode fits to observed size distributions are mm for Asian dust [Arao and Ishizaka, 1986; Gomes and Gillette, 1993; Nishikawa et al., 1991; Peterson, 1968; Sviridenkov et al., 1993], mm for African dust [Gillies et al., 1996; Gomes and Gillette, 1993; Jaenicke and Junge, 1967; Savoie and Prospero, 1976], and mm for North American dust [Sverdrup et al., 1975; Patterson and Gillette, 1977]. The range of D gn values for multimode fits is even greater [d Almeida, 1987; Lei et al., 1993]. Corresponding values for s g are for Asian dust, for African dust, and for North American dust. Given such variability, it is difficult to select one size distribution that is representative of dust particles from all source regions. In MIRAGE, we use the observations of Arao and Ishizaka [1986] (D gn =1.0mm, and s g = 1.8) to represent the coarse mode dust. The model sensitivity to these values is discussed later. We note that using constant s g, and constant D gn for emissions, is a first approximation. Simulating aerosol surface area in addition to number and volume (so that s g is predicted), and accounting for spatial and process variations in emitted particles sizes, will improve this Clear Air Processes [18] Gas phase chemistry consists of CO-CH 4 -oxidant chemistry [Saylor et al., 1999] plus oxidation of DMS by OH [Chatfield and Crutzen, 1990] and SO 2 by OH [DeMore et al., 1997]. Seven trace gases are treated as transported species in MIRAGE: CO, H 2 O 2, CH 3 O 2 H, DMS, MSA vapor, SO 2 and H 2 SO 4 vapor. CH 4, NO x, and O 3 are prescribed, and other oxidant cycle species are computed assuming local steady state, as described by Saylor et al. [1999]. Currently, the CO-CH 4 -oxidant chemistry is computed only during the daytime, and nighttime NO 3 is not computed. [19] Condensation of H 2 SO 4 and MSA vapor onto aerosol particles is treated using the Fuchs and Sutugin growth law [Seinfeld and Pandis, 1998], and accommodation coefficients of 0.02 for H 2 SO 4 [Van Dingenen and Raes, 1991] and 0.09 for MSA [DeBruyn et al., 1994]. Aerosol water uptake determines particle wet size and affects gas condensation and many other processes, and is described in detail by Ghan et al. [2001a]. For each mode, Kohler theory is used to calculate the wet radius of a particle having the volume-mean dry radius of the mode, and a constant wetradius/dry-radius ratio is assumed for other sizes in the mode. A probabilistic approach is used to select between wet and dry states when the relative humidity (RH) is between the crystallization and deliquescence RH of the aerosol. At each time step, if the current aerosol water mixing ratio of mode i (WATER-Ai in Table 2) exceeds one half of the wet-state equilibrium value, then the wet state is assumed, and WATER-Ai is set to the wet-state equilibrium value. Otherwise, the dry state is assumed, and WATER-Ai is set to zero. [20] Formation of new particles through homogeneous nucleation involving H 2 SO 4 and water vapor, and possibly NH 3, is considered an important source of atmospheric aerosol particles. Recent observations of new particle formation [Weber et al., 1997] suggest that the mechanism and rate of nucleation in the troposphere is uncertain. Currently the nucleation of H 2 SO 4 and water vapor is treated using the nucleation and growth model of Harrington and Kreidenweis [1998a], which calculates the formation of nanometer sized particles during a nucleation burst and their subsequent growth to Aitken mode size (larger than 10 nm). Aerosol coagulation is calculated as given by Binkowski and Shankar [1995]. Coagulation involving the larger sized coarse modes is neglected, as it is generally much slower than coagulation involving the Aitken and accumulation modes, except under extremely high concentrations of coarse mode particles. [21] One additional aspect of the modal aerosol dynamics approach involves the transfer or renaming of Aitken mode 6of46

7 particles to the accumulation mode [Binkowski, 1999]. As a result of continuous growth processes (gas condensation and aqueous sulfate production), Aitken mode particles can grow to a size that is nominally within the accumulation mode size range. The renaming process transfers larger Aitken mode particles (those on the upper tail of its lognormal size distribution) into the accumulation mode. Harrington and Kreidenweis [1998a], Binkowski [1999], and Wilson et al. [2001] describe different approaches for this transfer process. Our approach is similar to that of Wilson et al. [2001]. Aitken mode particles that, through continuous growth processes, exceed a fixed threshold diameter are transferred to the accumulation mode. The threshold diameter (0.053 mm) is the geometric mean of the Aitken and accumulation mode nominal (diagnosednumber) D gn in Table 2. [22] Dry deposition of H 2 O 2, CH 3 O 2 H, SO 2, H 2 SO 4 vapor, and MSA vapor is computed using a series resistance approach [Wesely, 1989]. Aerodynamic resistance is calculated in the climate model (used there for surface heat flux), quasi-laminar resistance is calculated as given by Wesely and Hicks [1977], and the surface resistance parameterization of Wesely [1989] is used, with surface category calculated using the Matthews [1983] vegetation and land use data. Surface exchange of CO is described by Saylor et al. [1999]. Over oceans, surface exchange of DMS is treated as an emissions process (see below), and DMS dry deposition over land and ice is ignored. Dry deposition of aerosol number and mass follows Binkowski and Shankar [1995] and is calculated using the wet sizes of particles Cloud and Precipitation Processes [23] Stratiform (both resolved and subgrid) and convective clouds are treated in MIRAGE. Large-scale stratiform clouds are grid resolved clouds in which the relative humidity reaches 100% and cloud droplets exist throughout an entire grid cell, and these clouds are simulated in the climate model using the bulk microphysics parameterization described in section 2.2. The climate model currently does not apply its microphysical parameterization to subgrid stratiform clouds, but these are treated in GChM for the purpose of aqueous sulfate production. These clouds are assumed to be nonprecipitating, and their fractional area is diagnosed as a function of relative humidity as given by Sundqvist et al. [1989]. Convective clouds are subgrid clouds occupying a fraction of a grid cell, and they are simulated in the climate model using the Hack [1994] parameterization, which provides precipitation rate, cloud water concentration, and subgrid vertical mass flux, but no other microphysical information. [24] When cloud forms, some aerosol particles are scavenged by cloud droplets, while others remain as interstitial aerosol particles. When a cloud dissipates and cloud droplets evaporate, the scavenged particles are resuspended and become clear air particles. We simulate the scavenged (or cloud-borne ) particles separately from the interstitial and clear air particles, keeping track of number and chemical species concentrations for both interstitial/clear-air and scavenged/cloud-borne particles for each aerosol mode. Conceptually, one cloud droplet corresponds to one cloudborne aerosol particle, and the masses of various chemical components within the droplet (whether dissolved or solid) correspond to the component masses of this one aerosol particle. When additional particles are scavenged by a droplet, or sulfate is produced by aqueous reaction in a droplet, this mass is added to that of the one cloud-borne particle. [25] Large-scale stratiform clouds, and the associated scavenged particles, may exist for several hours. When subgrid clouds are present, a grid cell is partitioned into two or three subareas (clear, convective cloud, and/or subgrid stratiform cloud), depending on the cloud types present and their fractional areas. Physics and chemistry are calculated within each subarea, including convective vertical transport (updrafts and compensating subsidence) and aerosol water uptake (at ambient or 100% RH). Because the information associated with subgrid clouds (e.g., aerosol mixing ratios within each subarea) is not saved between model time steps, the subgrid clouds are formed and dissipated during each model time step (typically 1/3 hour). [26] Aerosol particle scavenging by cloud droplets through activation and Brownian diffusion is treated. For large-scale stratiform clouds the climate model computes activation scavenging rates for aerosol number and mass for each mode, using the Abdul-Razzak and Ghan [2000] activation parameterization (see section 2.2). For the subgrid clouds, the activation parameterization is applied within GChM, using the climate model s vertical velocity and variance for subgrid stratiform clouds, or an assumed 1 m s 1 cloud base updraft velocity for convective clouds. Additional in-cloud scavenging of interstitial aerosol particles by cloud droplets can occur through Brownian diffusion, thermophoresis, diffusiophoresis, turbulence, gravitational settling, and electrical effects. Most of these mechanisms are relatively slow [Pruppacher and Klett, 1997], and only Brownian diffusion scavenging of very small (Aitken mode) particles is treated. Aerosol particle scavenging by ice particles is currently ignored. [27] Aqueous-phase chemistry includes the absorption of SO 2,H 2 O 2,CH 3 O 2 H, O 3,H 2 SO 4, and MSA in cloud water (treated as an equilibrium process) and the aqueous reactions of S(IV) with H 2 O 2,CH 3 O 2 H, and O 3 to produce sulfate. Solubility and dissociation equilibrium and kinetic rate constants are taken from Pandis and Seinfeld [1989]. Because we do not currently simulate aerosol acidity and NH 3, we do not consider droplet-size-dependent ph and aqueous chemistry. A single bulk cloud water chemistry calculation is made for each grid cell and cloud type, and a cloud water ph of 4.5 is assumed. Sulfate produced by aqueous reaction is then partitioned among each aerosol mode on the basis of the amount of cloud water associated with the cloud-borne particles of each mode. The cloud water associated with each aerosol mode is computed by assuming that the largest cloud droplets are associated with the cloud-borne particles having the lowest supersaturation for activation as CCN, then mapping the cloud droplet size distribution to the multimode size distribution of cloudborne aerosol particles [Zhang et al., 2002]. [28] Precipitation scavenging occurs through transfer of cloud-borne trace gases and aerosol particles to precipitation particles, direct scavenging of aerosol particles by precipitation particles via interception and impaction, and direct scavenging of trace gases by precipitation particles. The first process includes collection of cloud droplets by precipitation particles, and collision-coalescence (or 7of46

8 autoconversion) of cloud droplets to form precipitation size drops. (The bulk microphysics parameterization treats all collision-coalescence as conversion from cloud drops to precipitation.) The removal rate of cloud-borne aerosol mass via collection and autoconversion, when expressed as a first-order loss rate, is identical to the removal rate of cloud water by precipitation. Similarly, the removal rate for cloud-borne aerosol number is identical to the removal rate for cloud droplet number. These loss rates are supplied by the climate model, although for convective clouds, we assume that the loss rate for cloud-borne aerosol number equals that for mass. [29] Direct scavenging of aerosol particles via interception and impaction is computed using expressions given by Slinn [1984], and is applied both within clouds (to interstitial particles) and below cloud. Direct scavenging of trace gases by precipitation particles is limited to the removal of SO 2,H 2 O 2,H 2 SO 4, and MSA by rain. The mass transfer of each gas to rain is computed as given by Levine and Schwartz [1982]. The uptake of H 2 SO 4 and MSA is assumed irreversible. The uptake of SO 2 and H 2 O 2 is more complicated. We currently neglect the reversible mass transfer, treating only mass transfer followed by chemical reaction, and assuming that mass transfer is the rate limiting process (i.e., the aqueous reaction is fast), so that uptake of SO 2 is equal to the smaller of the SO 2 and the H 2 O 2 mass transfer rates Emissions [30] Anthropogenic sulfur emissions are based on the seasonally and vertically resolved Global Emissions Inventory Activity (GEIA) anthropogenic SO 2 inventory, which in turn is based on the original annual GEIA inventory developed by Benkovitz et al. [1996]. This inventory represents 1985 emissions. Significant changes in emissions have occurred between 1985 and the period selected for our simulations. The Co-operative Programme for Monitoring and Evaluation of the Long Range Transmission of Air Pollutants in Europe (EMEP) estimates of annual European sulfur emissions for 1994 are 30% lower than the corresponding GEIA estimates for 1985, so we have incorporated the EMEP emissions for Shannon [1999] estimates a 12% reduction in annual sulfur emissions for the United States, Canada, and northern Mexico over this period, which we have not incorporated. More importantly, Lefohn et al. [1999] (and data on their website) estimate a 22% increase in annual sulfur emissions for east Asia (China, Korea, Japan, Southeast Asia) from 1985 to 1990, which extrapolates to almost 50% from 1985 to 1995, and these changes have not been incorporated. A small fraction (2%, T. Schultz, personal communication, 1998) of the anthropogenic sulfur is emitted as primary sulfate aerosol, with sizes based on power plant plume measurements given by Whitby [1978]. The annual volcanic SO 2 and monthly DMS emissions of the COSAM model intercomparison [Barrie et al., 2001] are used: volcanic SO 2 emissions from Graf et al. [1997] and Spiro et al. [1992]; and DMS emissions calculated with Kettle et al. [1999] seawater DMS concentrations, Liss-Merlivat parameterizations of air-ocean exchange processes, and monthly averaged ECMWF winds. Additional trace gas emissions used with the CO-CH 4 oxidant chemistry are described by Saylor et al. [1999]. [31] BC emissions from biomass burning (monthly) and anthropogenic activities (annual) are taken from the GEIA inventory developed by Cooke and Wilson [1996], which are for Monthly resolved OM emissions from biomass burning and other anthropogenic activities, which includes both primary aerosol emissions and secondary organic aerosol production, are taken from Liousse et al. [1996] and are for The secondary organic aerosol production from biogenic hydrocarbon emissions given by Liousse et al. [1996] is estimated at 7.8 Tg/yr, using an aerosol yield of 5% for monoterpenes. We have increased this to 18.7 Tg/yr using a higher yield of 13% [Pandis et al., 1992] in conjunction with the monthly biogenic hydrocarbon emissions of Guenther et al. [1995], which are for The sizes in Table 2 are used for BC and OM emissions. [32] Mineral dust emissions only occur where the vegetation cover is low and sparse. The global vegetation cover data set of Matthews [1983, 1984] is used to identify the possible dust sources such as desert, dry valley, grassland, and shrub land. Snow-covered regions are excluded on the basis of monthly mean spatial snow coverage fields extracted from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) global data assimilation system [Kalnay et al., 1996]. Mineral dust emissions are calculated using the parameterization of Gillette and Passi [1988], modified to include the dependence on soil moisture. In this parameterization, dust emissions are calculated as E ¼ C X i P i A i u 4 * 1 u * th u * ; ð1þ where E is the emission rate (g cm 2 s 1 ), C is a constant determined by calibration, A i is the fraction of the grid cell that is source area type i (desert, grassland,...), and P i is the probability that dust is emitted from the source area under current meteorological conditions. P i is unity when friction velocity (u * ) exceeds the threshold friction velocity (u * th) and the soil moisture saturation ratio (r s ) is below its threshold value (r s,th ), and is zero otherwise. The threshold friction velocities depend on the surface soil texture, surface roughness, land cover, and land use. In the MIRAGE baseline simulation, we assume different u * th values for different soil types based on the measurements of Gillette et al. [1982] and Gillette [1978, 1988], namely, 25, 35, 45, and 75 cm s 1 for desert, arid/semiarid regions, shrubland/ grassland, and other dust source regions, respectively. The saturation ratios of soil moisture are taken from the observed monthly mean NCEP/NCAR reanalysis soil moisture data [Kalnay et al., 1996]. We assume different threshold soil moisture for different soil texture based on the parameterization of Tegen and Fung [1994], namely, 20%, 35%, 40%, and 50% for sand, loamy sand/sandy loam, loam and clay loam, and clay and peat, respectively. [33] It is assumed that only dust particles with a diameter less than 10 mm can be transported a great distance. Given the same wind velocity and soil moisture, the emissions of the long-range-transported (LRT) dust from sand dunes, dry valleys, and other possible dust sources may be different. Freshly exposed soil can contain more fine material like silt 8of46

9 than aged soil surfaces where fine particles are likely to have been blown out already [e.g., Pye, 1987; Tegen and Fung, 1995]. Second, dust emissions from disturbed sources are higher than the undisturbed sources, since a lower threshold surface wind speed is sufficient to start dust deflation in soil surface disturbed by agricultural activities [e.g., Gillette, 1978, Gillette et al., 1980, 1982; Tegen and Fung, 1995]. To account for the effect of the aged surfaces, the emission fractions of the LRT dust from sand dunes, dry valleys, and other dust sources are assumed to be 0.1, 0.6, and 1.0, respectively [d Almeida and Schutz, 1983] given the same meteorological conditions. Using the observed three-mode size distribution of d Almeida [1987] for sandstorm dust, we estimate that the LRT dust in the accumulation mode accounts for 0.7% of total mass of LRT dust. Therefore we assume that 0.7% of the LRT dust emissions are associated with accumulation mode, and 99.3% are in the coarse mode in all MIRAGE simulations. The effect of human activity such as agricultural cultivation, desertification, and deforestation on the dust emissions is not considered in the base parameterization, but is investigated in a sensitivity study. [34] Sea-salt particle emissions are calculated using the conceptual approach given by Genthon [1992] and Smith et al. [1993]. Sea-salt concentrations observed near the ocean surface under nonprecipitating conditions are assumed to represent an approximate balance between emissions and dry deposition, so that E ss;m ffi V d;m c 10 ; where E ss,m is the sea-salt mass emissions flux, V d,m is the dry deposition velocity for sea-salt mass (see section 2.1.2), and c 10 is the sea-salt mass concentration at 10 m. Sea-salt mass emissions are calculated with this equation, using an empirically based c 10 that is a function of the 10 m wind speed (u 10 ), which is diagnosed from the surface stress, surface roughness, and stability using Businger-Dyer relationships. For coarse mode sea salt we use ð2þ c 10 ¼ 3:5 expð0:18u 10 Þ; ð3þ where c 10 is in mg m 3 and u 10 is in m s 1. This represents an average of several mass versus wind speed relations from the literature [cf. Gong et al., 1997a]. The sea-salt number emissions flux (E ss,n ) is calculated similarly, as the product of the deposition velocity (for number) and an empirical sea-salt number concentration (n 10 ). We compute coarse mode n 10 from c 10 assuming a lognormal distribution with D gn =2.0mmand s g = 2.0 [O Dowd et al., 1997]. For accumulation mode sea salt, we start with the number concentration versus wind speed relation of O Dowd et al. [1997] and compute c 10 from n 10 using their D gn =0.2mm and s g = Climate Model [35] The NCAR CCM2 has been modified extensively to permit estimates of the direct radiative forcing by aerosol particles and to treat the cloud-aerosol interactions necessary to estimate the indirect radiative forcing by aerosol particles. Here we describe briefly the treatments of water uptake, aerosol radiative properties, cloud-aerosol interactions, and cloud radiative properties that distinguish the PNNL version of the NCAR CCM2 from the standard version. [36] The treatment of aerosol water uptake is based upon the Kohler theory [e.g., Pruppacher and Klett, 1997] and is described by Ghan et al. [2001a]. Aerosol radiative properties (extinction coefficient s, single scattering albedo w, and asymmetry factor g) are parameterized from the Mie theory for all 18 CCM2 solar wavelengths and for the 10 mm water vapor window in the infrared, as described by Ghan et al. [2001a]. [37] Cloud-aerosol interactions are treated by using the bulk parameterization of cloud microphysics described by Ghan et al. [1997a, 1997b]. The Colorado State University Regional Atmospheric Modeling System parameterization of bulk cloud microphysics [Tripoli and Cotton, 1980; Cotton et al., 1986] has been simplified for application to stratiform clouds [Ghan and Easter, 1992], and droplet number has been introduced as a prognostic variable [Ghan et al., 1997b]. Aerosol activation/droplet nucleation is parameterized using the multimode version [Abdul-Razzak and Ghan, 2000] of the single mode parameterization developed by Abdul-Razzak et al. [1998]. The activation parameterization determines the mass and number fraction activated for each mode, which are then used to determine the droplet nucleation rate for the CCM2 and the aerosol mass and number activation rate for GChM. The activation rate for a new cloud is given by the number activated in each new cloudy layer, divided by the time step. Activation for an old cloud is assumed to occur only at cloud base, where it is expressed in terms of a flux of nucleated droplets into the lowest layer of the cloud; the activation rate is then given by the flux convergence in the lowest layer in a manner consistent with the treatment of turbulent transport of droplets [Ghan et al., 1997b]. For both new and old clouds the activation is integrated over a Gaussian subgrid frequency distribution of vertical velocity. The subgrid variance of vertical velocity is otherwise determined from the Yamada and Mellor [1979] second-order turbulence closure (level 2.5) model. In contrast to other mechanistic treatments of droplet number [e.g., Chuang et al., 1997; Lohmann et al., 1999a], no lower bound is placed on the simulated droplet number. However, a lower bound on the subgrid variance of vertical velocity of 0.04 m 2 s 2 is applied because the vertical resolution of MIRAGE is insufficient to fully resolve the cloud top radiative cooling that drives turbulence in boundary layer clouds. [38] Cloud radiative properties are parameterized as described by Ghan et al. [1997a] in terms of cloud water and cloud ice mass and number mixing ratios. The actinic flux at a wavelength of 0.3 mm is calculated for both the clear sky and for the cloudy sky, with the ratio saved for communication to GChM. [39] An elementary form of data assimilation, nudging toward analyzed winds, temperature and surface pressure [e.g., Jeuken et al., 1996; Ghan et al., 1999], can be optionally applied to the CCM2 simulation. Nudging is useful for two reasons. First, it corrects biases in the simulated circulation and hence reduces the sensitivity of the simulated winds (and dust and sea-salt emissions) to spatial resolution. Second, as noted by Jeuken et al. [1996] 9of46

10 and Feichter and Lohmann [1999], it permits comparison with observations on timescales of days and weeks rather than just months and years. This benefit is important for comparison with field campaign measurements, which typically are available only for one month or less. Note that nudging of the simulated humidity toward analyses is not applied because it can produce large errors in the simulated clouds and precipitation fields because of differences in the treatment of clouds in the CCM2 and the model used to perform the analyses. However, an option is available in MIRAGE to use the analyzed RH to determine aerosol water uptake, because the RH simulated by MIRAGE at lower levels has a high bias. (Ongoing improvements in the subgrid parameterization of clouds in MIRAGE should correct this bias.) [40] The clouds simulated by MIRAGE have been evaluated by Ghan et al. [1997b, 2001b]. Although many aspects of the clouds are simulated quite well, cloud particle phase is biased toward the ice phase at temperatures between 5 and 20 C. This bias tends to limit the wet scavenging of the aerosol in polar regions Coupling and Spatial Resolution [41] To facilitate coupling, GChM has been configured to run on the same global grid as the CCM2 and to use the same terrain-following vertical coordinate. When GChM and CCM2 are run in a coupled mode, the CCM2 provides numerous fields to GChM: temperature, pressure, water vapor, 3-D winds, vertical diffusivity, mixing ratios and tendencies for microphysical variables, precipitation rates, convective mass flux, boundary layer variables (friction velocity, 10-m wind speed, Obukov length, boundary layer height), and total sky to clear sky actinic flux ratio. GChM provides to the CCM2 the mixing ratios of aerosol number and chemical component for both the interstitial/clear-air and the scavenged/cloud-borne particles of each mode, plus the mode geometric standard deviations and physical parameters. The coupling is performed in core, with communication at every time step to adequately represent cloudaerosol interactions such as activation, aqueous chemistry, and resuspension. [42] Simulations are performed at either T42 (128 longitude and 64 latitude grids) or R15 (48 longitude and 40 latitude grids) spectral resolution. Vertical resolution is 24 levels, with level boundaries at , , 996.0, 986.9, 974.9, 960.0, 942.0, 920.7, 895.9, 867.4, 824.1, 753.9, 657.4, 558.0, 461.2, 371.0, 290.5, 221.4, 164.2, 118.5, 83.1, 49.2, 21.6, 7.9, and 2.9 hpa when surface pressure is hpa. The relatively high resolution below 850 hpa improves the simulation of low-level stratiform clouds. The base case simulations were run for 15 months (March 1994 through May 1995), with the first 3 months used as spin-up. Initial conditions for sulfur and aerosol species were low but nonzero (e.g., surface mixing ratios of 10 pptv for SO 2 and accumulation mode sulfate, and 0.01 ng/g for coarse sea salt and dust, and a 5 km scale height), while initial CO was taken from a previous 1 year simulation. Sensitivity simulations were run for 13 months, with 1 month of spin-up, and starting from the base case 1 May 1994 conditions. T42 resolution simulations now require about 1.2 days per simulation month on a Linux workstation (Athlon GHz processor), but the original simulations for Ghan et al. [2001a, 2001b, 2001c] took much longer. 3. Results and Discussion [43] The results presented are for a simulation of June 1994 through May 1995, as the MIRAGE climate model was nudged by winds and temperatures from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses for this period. Thus in the evaluation, we have attempted to use observations from this period. However, for some species and/or geographical locations where observations are limited, we have used observations from 1970 onward. While the amount of observational data available for model evaluation is extensive, the coverage of the global troposphere in a three-dimensional sense is quite limited, as other model evaluation papers have noted Sulfur Species SO 2 [44] Figure 1 shows simulated surface-level (i.e., lowest model layer) and zonally averaged SO 2 mixing ratios for January and July. The highest surface-level mixing ratios (MRs) are found over the anthropogenic source regions of eastern North America, Europe, and eastern Asia, with peak values of 22 and 11 ppbv in January and July, respectively. Strong seasonal variations are apparent in both northern and southern midlatitude anthropogenic source regions, with lower MRs during the local summer resulting from faster oxidation. Over the Southern Ocean, MRs are higher during local summer because of higher DMS emissions. A similar effect over northern oceans is masked by the anthropogenic SO 2 behavior. The slow oxidation simulated in the Northern Hemisphere in January (low oxidant MRs and, at high latitudes, low cloud water) results in high MRs in the Arctic (zonal average >100 pptv) compared to <1 pptv in July. [45] The simulated surface-level SO 2 distributions are similar to those of other models, as all are dominated by anthropogenic source regions. Compared to Koch et al. [1999] and Chin et al. [2000a], for example, the most noticeable differences are over ocean areas south of 20 N, where MIRAGE SO 2 is higher, and over major source regions (Europe, eastern North America, China) in January, where MIRAGE SO 2 is somewhat lower. The zonally averaged SO 2 distributions also show the same major features as other models [e.g., Koch et al., 1999; Lohmann et al., 1999b; Barth et al., 2000; Chin et al., 2000a], with strong and weaker surface maxima at northern and southern midlatitudes, higher MRs in the Northern Hemisphere, and low MRs in the upper troposphere, but details vary between models. The MIRAGE SO 2 MRs in the upper troposphere are generally lower than the above models. Compared to Barth et al. [2000], the MIRAGE MRs are higher at northern middle and high latitudes in the middle and lower troposphere; while compared to Lohmann et al. [1999b] and Chin et al. [2000a], the MIRAGE MRs are lower at northern high latitudes in the middle and lower troposphere. [46] Figure 2a compares observed and simulated annual SO 2 MRs over North America and Europe where extensive monitoring networks exist. The observations have been divided into regions with generally higher and lower concentrations (eastern and western North America, Europe and 10 of 46

11 Figure 1. Simulated monthly mean SO 2 for January and July, (a and b) lowest model layer and (c and d) zonal average. Scandinavia). Simulated SO 2 MRs are higher than observed values, with median ratios of simulated to observed SO 2 of 2.5 for Europe and eastern North America, and 4.3 for western North America. [47] The simulated MRs represent the center of the lowest model layer, nominally 42 m. Assuming that the lowest 42 m is a constant flux layer, a SO 2 MR at 1 m, which is more representative of observation heights, can be computed as SO 2 j 1m ¼ SO 2 j 42m ra;1m þ r b þ r c = ra;42m þ r b þ r c ; ð4þ where r a,1m and r a,42m are the aerodynamic resistances between the surface and 1 and 42 m, r b is the quasi-laminar resistance, and r c is the surface resistance. (See dry deposition in section The ratio of r a,1m to r a,42m is computed using resistance expressions given by Byun and Dennis [1995].) When the Figure 2a comparison is made using these 1 m SO 2 MRs, the model bias is lower, and median ratios of simulated to observed SO 2 are 1.8 for Europe and eastern North America, and 3.3 for western North America. [48] Figure 2b compares observed and simulated SO 2 at a number of (semi)-remote sites and from ship cruises. (Table 3 provides information on the SO 2 observations.) The majority of the simulated values are within a factor of 2 of the observations, and the simulated MRs are overall slightly higher than observed (the median ratio of simulated to observed SO 2 is 1.15). For the five Arctic and subarctic sites, simulated MRs are close to observed (except at Denali National Park). For the oceanic observations, the median ratio of simulated to observed SO 2 is 1.05, and the correlation is good (r = 0.89). The simulated SO 2 MRs at 1 m over ocean are lower by about a factor of 3, because of low surface resistance for SO 2 over water, but may not be representative of shipboard measurements. [49] Figure 3 compares observed and simulated seasonal cycles of monthly mean SO 2 MRs at a number of sites. Note that the simulated and many of the observed values are for June 1994 to May 1995, although they are plotted in a January December order. At the midlatitude North American and European sites, the model has a high bias at all seasons. The Europe, Scandinavia, and eastern North America observations have higher SO 2 in December-January- February (DJF) than in June-July-August (JJA) because of slower oxidation, and the model shows a similar trend. For western North America, however, the observations have more SO 2 in JJA, while the model shows a JJA minimum as in the other regions. At the four Arctic and subarctic sites, the observations and model both show a strong seasonal trend (higher SO 2 in DJF), and the simulated SO 2 agrees well (except at Denali National Park). At Amsterdam Island, a remote marine site where SO 2 originates primarily from DMS, observed and simulated SO 2 cycles are in close agreement. [50] High biases for SO 2 over Europe and North America have been found in a number of global models [e.g., Kasibhatla et al., 1997; Roelofs et al., 1998; Koch et al., 11 of 46

12 stronger boundary layer improved simulated SO 2. Annual average SO 2 MRs over Europe and North America were lowered by only 6 13% in a sensitivity simulation where vertical eddy diffusivities were increased by a factor of 2. [51] The SO 2 oxidation rate can be increased by increasing either OH, H 2 O 2, cloud frequency, or by adding a heterogeneous oxidation pathway [e.g., Kasibhatla et al., 1997]. The OH and H 2 O 2 fields for January and July are shown in Figure 4. The simulated OH and H 2 O 2 can be compared with Wang et al. [1998] and Hauglustaine et al. [1998]. For July OH, there is good agreement between MIRAGE and the other two models, especially for N and surface-800 hpa (or 0 2 km) where OH would influence surface-level SO 2 over northern midlatitudes. For January, the MIRAGE OH maximum is larger and shifted south compared to the other two models, but this would not affect the northern midlatitude SO 2 bias. The MIRAGE H 2 O 2 at northern midlatitudes is somewhat lower than Hauglustaine et al. [1998] in July and a factor of lower than Wang et al. [1998] in both January and July. This result can be expected from the neglect of nonmethane hydrocarbons in MIRAGE s gas-phase chemistry. Increasi Figure 2. Observed and simulated annual SO 2. (a) North America and Europe observations for June 1994 through May East North America and west North America observations are from Canadian Air and Precipitation Monitoring Network (CAPMoN) [Ro et al., 1997a, 1997b], Clear Air Status and Trends Network (CASTNet) (U.S. Environmental Protection Agency, CASTNet Database, November 1999), and Interagency Monitoring of Protected Visual Environments (IMPROVE) network [Malm et al., 1994] sites in the United States and southern Canada east and west of 97 W longitude, respectively. Scandinavia observations are from Cooperative Program for Monitoring and Evaluation of the Long Range Transmission of Air Pollutants in Europe (EMEP) [Hjellbrekke et al., 1996, 1997] sites in Iceland, Norway, Sweden, and Finland. Europe observations are from EMEP sites in other European countries. (b) High-latitude and oceanic observations at various times. A is Denali National Park, Alaska; B is Cree Lake, Canada; C is Spitsbergen, Norway; D is Bear Island, Norway; E is Jergul, Norway; F is Valentia, Ireland; G through P are Atlantic Ocean; S through Z are Pacific Ocean; a is Amsterdam Island; and b through i are Indian Ocean. A through F and a are annual means; other observations are for various time periods. Table 3 provides more information on the observations. Lines show simulated/observed ratios of 2.0 (upper dashed line), 1.0 (solid line), and 0.5 (lower dashed line). 1999; Barth et al., 2000; Chin et al., 2000b], but other global and subglobal models show little or no bias [e.g., Chin et al., 1996; Benkovitz and Schwartz, 1997; Berge and Jakobsen, 1998]. Given the differences among models, it is difficult to identify any one factor responsible for this SO 2 bias. The simulated surface-level SO 2 MRs in MIRAGE can be reduced by increasing either the vertical mixing, the SO 2 oxidation (to sulfate) rate, the SO 2 dry deposition rate, or the SO 2 wet removal rate. Dry deposition in MIRAGE uses a standard series-resistance model [Wesely, 1989]. In a sensitivity simulation (at R15 resolution) in which SO 2 deposition velocities were increased by a factor of 2, annual average SO 2 MRs were 20 25% lower over Europe and North America than in a R15 resolution base simulation. Thus the SO 2 deposition velocities would need to be increased even more to eliminate the high SO 2 bias. SO 2 MRs over ocean show similar decreases in this simulation, and are now somewhat lower than the observations in Figure 2b. The vertical mixing in MIRAGE produces well-mixed afternoon boundary layers with reasonable heights, but increasing the mixing somewhat would not be unreasonable either. Lohmann et al. [1999b] found that a 12 of 46

13 Table 3. Information on the SO 2 Observations in Figure 2b Symbol Location, Latitude-Longitude, and Time References a A Denali National Park, Alaska, 64 N, 149 W, June 1994 to May 1995 IMPROVE B Cree Lake, Canada, 58 N, 107 W, annual BA C Spitzbergen, Norway, 79 N, 12 E, June 1994 to May 1995 EMEP D Bear Is, Norway, 75 N, 19 E, annual HE E Jergul, Norway, 69 N, 25 E, June 1994 to May 1995 EMEP F Valentia, Ireland, 52 N, 10 W, June 1994 to May 1995 EMEP G Atlantic 52 N, 35 W, annual LE H Atlantic N, W, July 1981 HR I Atlantic N, W, May 1974 NG1 J Atlantic N, W, May 1974 NG1 K Atlantic 16 N, 35 W, June July 1974 NG1 L Atlantic 15 N, W, June July BO M Atlantic 1 S to4 N, 3 6 W, June 1979 DE N Atlantic 4 26 S, 10 W to10 E, March 1973 NG1 O Atlantic 4 26 S, 10 W to10 E, Dec NG1 P Atlantic S, W, March April 1986 BE Q Pacific 48 N, 129 W, April 1991 BN R Pacific 8 15 S, W, April 1975 NG1 S Pacific 0 20 N, W, Feb. March 1989 BT T Pacific 0 20 S, W, Feb. March 1989 BT U Pacific S, W, Feb. March 1989 BT V Pacific 12 S, 135 W, March 1992 YV W Pacific N, 170 W, April May 1988 QU X Pacific N, 170 W, April May 1988 QU Y Pacific 11 S to14 N, 170 W, April May 1988 QU Z Pacific 20 S to15 N, W, Feb. March 1991 HU a Amsterdam Island 38 S, 78 E, annual NG2 b Indian 5 20 S, E, May 1973 NG1 c Indian S, E, Jan. Feb NG1 d Indian S, E, April May 1973 NG1 e Indian S, E, Jan. Feb NG1 f Indian S, E, April May 1973 NG1 g Indian S, E, Dec. Jan NG1 h Indian S, E, March May 1973, 1975 NG1 i Indian S, 67 E, March 1975 NG1 a References: BA, Barrie and Bottenheim [1990]; BE, Berresheim [1987]; BN, Bandy et al. [1992]; BO, Bonsang et al. [1980]; BT, Bates et al. [1992a]; DE, Delmas and Servant [1982]; EMEP, Hjellbrekke et al. [1996, 1997]; HE, Heintzenberg and Larssen [1983]; HR, Herrmann and Jaeschke [1984]; HU, Huebert et al. [1993]; IMPROVE, Malm et al. [1994]; LE, Levy et al. [1990]; NG1, Nguyen et al. [1983]; NG2, Nguyen et al. [1992]; QU, Quinn et al. [1990]; YV, Yvon and Saltzman [1996]. ing H 2 O 2 levels, or adding a heterogeneous oxidation pathway, would reduce the SO 2 bias. However, in a R15 resolution sensitivity simulation in which the H 2 O 2 available for reaction with SO 2 was increased by a factor of 2, annual average SO 2 MRs were only 10% lower over Europe and North America than in a corresponding base simulation. [52] Figure 5 compares several observed and simulated vertical profiles of SO 2. Figures 5a and 5b over eastern Canada are discussed in detail by Lohmann et al. [2001]. (Note that the observed profiles appear different here and in the paper by Lohmann et al. [2001] because of different vertical binning of the individual observations, and the high observed values near 6 km in Figure 5a are from the Sudbury smelter plume.) In Figures 5a 5e (eastern Canada, south central United States, and northeast coast of the United States), observed and simulated SO 2 are in fairly good agreement, except for Figure 5b, where simulated SO 2 MRs below 3 km are too high. These first five profiles suggest that inadequate vertical mixing is not the cause of the model s high bias for surface SO 2 in this region, because simulated SO 2 MRs at and above 1.5 km are not low compared with the observations, and enhanced vertical mixing would increase them further. In contrast, simulated SO 2 MRs in Figures 5f (off the coast of Brazil) and 5g and 5h (Amazon Basin) appear to be vertically less well mixed than the observations, suggesting inadequate vertical mixing of SO 2 in this equatorial region. [53] Table 4 shows the simulated budgets for tropospheric SO 2 and other sulfur species. Koch et al. [1999], Lohmann et al. [1999b], and Iversen and Seland [2002] show sulfur budgets from 14 different global models. Our 0.38 Tg S burden and 1.6 day lifetime are in the midrange of these other models, which have a 0.43 Tg S median burden and a 1.9 day median lifetime. Our 5.9 Tg S yr 1 gas-phase chemistry sink is lower than the all the other models, although four of these models have gas-phase reaction sinks of Tg S yr 1. Our dry deposition sink of 20.2 Tg S yr 1 is among the lowest of the other models, for which the median dry deposition sink is 37.4 Tg S yr 1, which again suggests that insufficient dry deposition is contributing to the model s high SO 2 bias. The SO 2 wet deposition sink varies greatly among models because of different definitions of this sink. In MIRAGE it involves reactive uptake of SO 2 by raindrops (reversible uptake is not treated) Sulfate [54] Figure 6 shows simulated surface-level and zonally averaged sulfate mixing ratios for January and July. The highest surface-level MRs are found over the anthropogenic 13 of 46

14 Figure 3. Observed and simulated monthly mean SO 2 at selected sites. The Europe, Scandinavia, and eastern and western North America plots present groups of sites as in Figure 2a. For these group plots, the open symbols show medians of observed monthly means, the short vertical lines show 15th to 85th percentiles of observed monthly means (observations for June 1994 through May 1995), the jagged solid lines show medians of simulated monthly means, and the two dashed lines show 15th to 85th percentiles of simulated monthly means. For the individual site plots, the open symbols show observed monthly means for the simulation period, the solid symbols show observed monthly means over several years ( for EMEP and IMPROVE sites, otherwise shown), the short vertical lines show the range of observed monthly means (where available), and the jagged solid lines show simulated monthly means. Table 3 has references for nonnetwork sites. source regions of eastern North America, Europe, and eastern Asia, with peak values of 2.1 and 3.2 ppbv in January and July, respectively. Strong seasonal variations are apparent in northern midlatitude anthropogenic source regions, with higher MRs during July resulting from faster SO 2 oxidation. Over the Southern Ocean, MRs are higher during January because of higher SO 2 and sulfate production. In the Arctic, the sulfate MRs parallel the SO 2 MRs, with low values (<50 pptv) in July and high values (>200 pptv) in January. Note that the simulated sulfate is all non-sea-salt sulfate. Also, as described earlier, the model treats sulfate in four aerosol size modes. On a global annual basis, nearly all of the simulated sulfate mass (97.90%) is in the accumulation mode, with 1.01% in the Aitken mode, 1.05% in the coarse dust mode, and 0.04% in the coarse sea-salt mode. All simulated sulfate results presented in this section are the total for all four modes. [55] The simulated surface-level sulfate distributions show much similarity to those of other models. Compared to Koch et al. [1999] and Chin et al. [2000a], for example, the MIRAGE sulfate MRs over major source regions (Europe, eastern North America, China) show varying differences depending on location, season, and model, but the MIRAGE MRs are higher in the Arctic in January and over ocean areas south of 20 N in both January and July. Greater differences from other models are seen in the zonally averaged sulfate distributions. Compared to Koch et al. [1999], Barth et al. [2000], and Chin et al. [2000a] (which have lower sulfate burdens than MIRAGE), the MIRAGE sulfate MRs are higher in most areas, but are especially higher in the middle and lower troposphere at high latitudes (compared to Barth et al. [2000] and Chin et al. [2000a]). In contrast, the MIRAGE distribution is very similar to that of Lohmann et al. [1999b], which has a sulfate burden very close to MIRAGE. [56] Figure 7a compares observed and simulated sulfate MRs (June 1994 to May 1995 means) over North America and Europe where extensive monitoring networks exist. For eastern North America, Europe, and Scandinavia, simulated values are mostly within a factor of 2 of the observations, and median ratios for simulated to observed MRs are for the three regions. For western North America, however, simulated sulfate MRs are roughly a factor of 2 higher than observed, and as noted above, the model s SO 2 bias was also strongest here. [57] Figure 7b compares observed and simulated sulfate at a number of high-latitude and oceanic sites. (Table 5 provides information on the sulfate observations.) The simulated sulfate MRs tend to be higher than observed by roughly a factor of 2 (the median ratio of simulated to observed sulfate for these sites is 1.80). At most of the 14 of 46

15 Figure 4. Simulated monthly mean (a and b) OH (zonal average) and (c and d) H 2 O 2 (lowest model layer) for January and July. Figure 5. Observed (symbols) and simulated (dashed lines) vertical profiles of SO 2. References are as follows: (a) Isaac et al. [1998] and Lohmann et al. [2001], (b) Banic et al. [1996] and Lohmann et al. [2001], (c and d) Boatman et al. [1989], (e and f ) Thornton et al. [1993], and (g and h) Andreae et al. [1990a]. 15 of 46

16 Table 4. Annual Budgets for SO 2, Sulfate, DMS, and MSA a SO 2 Sulfate DMS MSA Sources Anthropogenic emissions 61.2 b 1.2 Natural emissions Gas-phase reaction Aqueous-phase reaction 49.2 c Sinks Dry deposition Wet deposition Gas-phase reaction Aqueous-phase reaction 49.2 c Burden Lifetime a Units are Tg S yr 1 for sources and sinks, Tg S for burdens, and days for lifetimes. b Values of 59.0 and 2.2 from fossil fuel usage and biomass burning, respectively. c Values of 37.8, 10.8, and 0.6 from reaction with H 2 O 2, O 3, and CH 3 O 2 H, respectively. northern high-latitude sites (sites A-F), the simulated sulfate is 2 3 times the observed, and it is a factor of high at the two Antarctic sites (sites W-X). At the Atlantic and Indian Ocean sites (sites G-J and T-V), the simulated sulfate is in fairly good agreement with the observations, but it is more than a factor of 2 high at most of the Pacific Ocean sites (sites L-S). At the one east Asia site (Cheju Island), the simulated sulfate is 33% low, and increasing the east Asian sulfur emissions by 50% (see section 2.1.4) would give better agreement here. The high simulated sulfate MRs suggest that too much sulfur is being transported from anthropogenic source regions to remote regions, and that sulfate wet removal in the model may be too slow. At high latitudes, this problem is accentuated by the tendency of simulated large-scale clouds to glaciate too readily [Ghan et al., 1997a]. When clouds glaciate, there is no longer any aerosol activation scavenging, and wet removal of accumulation mode particles and sulfate becomes inefficient. [58] Figure 8 compares observed and simulated seasonal cycles of monthly mean sulfate MRs at a number of sites. The observed sulfate has a strong seasonal dependence in both eastern and western North America. The simulated seasonal dependence is close to observed in eastern North America, but somewhat weaker than observed in western North America, where the simulated values are too high. Kasibhatla et al. [1997] have commented on the near absence of a seasonal cycle in sulfate MRs for Europe. Interestingly, the observations used here (for June 1994 to May 1995) show little seasonality for Scandinavia but a moderate seasonal dependency for the remainder of Europe. The simulated sulfate seasonality for Europe is too strong; however, this is a feature common in global sulfur models. At the two Arctic sites (Alert and Spitzbergen), the observed and simulated sulfate values have a strong maximum in Figure 6. Simulated monthly mean sulfate for January and July, (a and b) lowest model layer and (c and d) zonal average. 16 of 46

17 Figure 7. Observed and simulated annual sulfate. (a) For North America and Europe observations, as in Figure 2a. (b) High-latitude and oceanic observations, June 1994 to May A is Denali National Park, Alaska; B is Alert, Canada; C is Nord, Greenland; D is Spitzbergen, Norway; E is Jergul, Norway; F is Heimaey, Iceland; G is Valentia, Ireland; H is Izania, Spain; I is Bermuda; J is Barbados; K is Cheju Island, Korea; L is Midway; M is Mauna Loa, Hawaii; N is Oahu; O is American Samoa; P is Norfolk Island; Q is Invercargill, New Zealand; R is Cape Grim, Australia; S is Chatham Island, T is Reunion; U is Cape Point, South Africa; V is Falkland Islands; W is Mawson, Antarctica; and X is Palmer, Antarctica. Table 5 provides more information on the observations. Solid and dashed lines are as in Figure 2. February April, although the simulated sulfate is about a factor of 3 high then. Bermuda and Barbados have April July maxima that the model underestimates, and Cheju Island has a June July maximum that is also underestimated. At Mauna Loa (elevation 3400 m) there is an April maximum associated with transport from east Asia which the model captures, but much too strongly. At Invercargill, Falkland Islands, Reunion, and Mawson, the simulated sulfate has DJF maxima associated with biogenic sulfur production, but the simulated seasonality is stronger than observed at Invercargill and Falkland Islands while weaker at Reunion. [59] Figure 9 compares several observed and simulated vertical profiles of sulfate. In Figures 9a 9c and 9f (eastern Canada and off the northeast coast of United States), simulated sulfate MRs are much higher than observed MRs above 2 km, but either agree with (or are somewhat higher than) the observed below 2 km. In Figures 9d and 9e (south central United States), the observations suggest a more rapid decrease with height than the simulated profiles. For the northeast Pacific (off the coast of Washington state) and Tasmania profiles, neither of the observed profiles shows much decrease with height, and it is difficult to judge the simulated vertical behavior. For the two Amazon Basin profiles, simulated sulfate is a factor of greater than observed in the boundary layer (similar to the bias seen in Figure 7b), and it decreases less sharply with height than the observations. Thus most of the profiles suggest that Table 5. Information on the Sulfate Observations in Figure 7b a Symbol Location and Latitude-Longitude References b A Denali National Park, Alaska, 64 N,149 W IMPROVE B Alert, Canada, 82 N, 63 W SI C Nord, Greenland, 81 N, 18 W HE D Spitzbergen, Norway, 79 N, 12 E EMEP E Jergul, Norway, 69 N, 25 E EMEP F Heimaey, Iceland, 63 N, 20 W SA G Valentia, Ireland, 52 N, 10 W EMEP H Izania, Spain, 28 N, 16 W SA I Bermuda 32 N, 65 W SA J Barbados 13 N, 59 W SA K Cheju Island, Korea, 34 N, 126 E SA L Midway 28 N, 177 W SA M Mauna Loa, Hawaii, 20 N, 156 W HU N Oahu, Hawaii, 21 N, 158 W SA O American Samoa 14 S, 171 W SA P Norfolk Island 29 S, 168 E SA Q Invercargill, New Zealand, 46 S, 168 E SA R Cape Grim, Australia, 41 S, 145 E SA S Chatham Island 44 S, 177 W SA T Reunion 21 S, 56 E SA U Cape Point, South Africa, 34 S, 18 E SA V Falkland Islands 52 S, 60 W SA W Mawson, Antarctica, 68 S, 63 E SA X Palmer Station, Antarctica, 65 S, 64 W SA a All observations are for June 1994 to May b References: EMEP, Hjellbrekke et al. [1996, 1997]; HE, Heidam et al. [1999]; HU, B. J. Huebert (University of Hawaii, Honolulu, personal communication, 1999); IMPROVE, Malm et al. [1994]; SA, D. L. Savoie and J. M. Prospero (University of Miami, Miami, Florida, personal communication, 1999) and Chin et al. [1996]; SI, Sirois and Barrie [1999]. 17 of 46

18 Figure 8. Same as Figure 3, but for sulfate. For the individual site plots, observations are for (Alert, Barbados, Bermuda, and Mauna Loa) or (other sites). Table 5 provides more information on the observations. there is too much simulated sulfate at 2 6 km aloft, while there is better agreement with observed sulfate in the boundary layer (or below 2 km). This contrasts with the SO 2 profiles (Figure 5), where the simulated SO 2 at 2 6 km aloft was not generally higher than observed. [60] Table 6 compares observed and simulated sulfur annual wet deposition (both wet-deposition fluxes and sulfur concentrations in precipitation). Over North America and Europe, simulated wet deposition fluxes and concentrations (and annual precipitation amounts, not shown) are in fairly good agreement with observations, being somewhat high in western North America (where simulated sulfate is also high), and somewhat low in Europe (where simulated annual precipitation is 20% low). At the other locations shown there is a range of behavior, but no strong model bias: the median ratio of simulated to observed wetdeposition flux is 0.75, and the median ratio of simulated to observed wet-deposition concentration is Most simulated values are within a factor of 2 of observed, with better agreement for wet-deposition concentration, which is less affected by precipitation amount. [61] The budget for simulated tropospheric sulfate is shown in Table 4. Our 1.07 Tg S burden and 6.8 day lifetime are among the highest values of the 14 models shown by Koch et al. [1999], Lohmann et al. [1999b], and Iversen and Seland [2002], which have burdens of Tg S (median 0.68) and lifetimes of days (median 4.7). This agrees with the model-observation comparisons, which suggest that simulated sulfate MRs are too large. The sulfate burden could be reduced by either reducing sulfate sources or increasing sulfate removal rates. As noted above, SO 2 dry deposition may be low, and increasing it would reduce the sulfate sources. Wet removal is the primary sulfate sink, and our wet removal rate (wet removal sink/sulfate burden) of 0.12 d 1 is somewhat slower than the other models for which the median is 0.17 d of 46

19 Figure 9. Observed (symbols) and simulated (dashed lines) vertical profiles of sulfate. References are as follows: (a) Gorzelska et al. [1994], (b) Isaac et al. [1998] and Lohmann et al. [2001], (c) Banic et al. [1996] and Lohmann et al. [2001], (d and e) Boatman et al. [1989], (f ) Hegg et al. [1997], (g) Luria et al. [1989], (h) Andreae et al. [1988], (i) Berresheim et al. [1990], and ( j and k) Andreae et al. [1990a]. [62] A sensitivity simulation at R15 resolution was performed in which the instantaneous wet removal rates were increased by a factor of 1.5. For the remote sites in Figure 7b, simulated sulfate MRs decreased by 26% on the average, leaving a modest high bias. For the North America and Europe sites in Figure 7a, the decrease was less, with a 14% reduction at the eastern North America sites, resulting in a small low bias (except for the western North America sites). Increases in annual wet deposition were modest, with the largest changes in source regions (11 and 14% increases for the Europe and eastern North America site groups in Table 6). Simulated sulfate profiles (Figure 9) were in better agreement with observations, although the comparison still suggested too much sulfate aloft. Overall, the simulated results were in much better agreement with observations. The sulfate burden decreased by 27%, and the lifetime decreased by 25%, putting them in better agreement with other models. The greater change for remote site MRs than for source region MRs and wet deposition is explained as follows. About 70 80% of the sulfur emitted in these source regions is wet or dry deposited there, and 20 30% is exported. Increasing the local deposition fraction from 75 to 80%, thus reducing export from 25 to 20%, clearly produces greater relative changes to export and remote sites. Also, the increases to annual wet deposition in source regions are lower that the 50% increase of the instantaneous wet removal rates because increased removal during the first part of a precipitation event can result in less removal during the last part of the event. [63] In the sensitivity simulation discussed earlier where the SO 2 dry deposition velocity was increased by a factor two, there was also a general reduction in the model s sulfate bias, although the reduction was smaller. The sulfate MRs for the remote sites in Figure 7b decreased by 15%, the sulfate MRs for the sites in Figure 7a decreased by 10 18%, and the sulfate burden decreased by 12%. Increasing H 2 O 2 (also discussed earlier) made the sulfate bias worse, 19 of 46

20 Table 6. Observed and Simulated Annual Sulfur Wet Deposition Fluxes and Concentrations a Location, Latitude-Longitude, and Time Observed Flux and Concentration Simulated Flux and Concentration References b East North America (71 sites) 551 (16.3) 495 (14.9) NADP West North America (43 sites) 116 (6.3) 159 (7.5) NADP Europe (41 sites) 645 (23.1) 449 (18.8) EMEP Scandinavia (15 sites) 402 (13.8) 275 (11.2) EMEP Denali National Park, Alaska, 64 N, 149 W 20 (1.9) 125 (5.0) NADP Jergul, Norway, 69 N, 25 E 67 (7.3) 155 (9.2) EMEP Irafoss, Iceland, 64 N, 21 W 756 (16.9) 193 (6.9) EMEP Valentia, Ireland, 52 N, 10 W 249 (4.4) 243 (8.8) EMEP North Atlantic 57 N, 20 W, (4.1) (6.7) BU Bermuda 32 N, 65 W, (9.2) 242 (7.6) GA1 San Carlos, Venezuela, 7 N, 70 W, (1.4) 176 (2.8) GA1 Lake Calado, Brazil, 10 S, 50 W, 120 (1.9) 53 (0.7) GA2 Amazon Basin, Brazil, 3 S, 60 W, (1.7) 42 (2.3) AN Southern Chile 51 S, 72 W, (2.4) 82 (2.7) GA3 Kenya 2 N, 40 E, 150 (6.5) 26 (2.4) RO Katherine, Australia, 18 S, 132 E, (2.4) 26 (1.5) LI Macquarie Island 55 S, 160 E, (2.3) 67 (2.5) AY American Samoa 15 S, 170 W, 66 (1.0) 62 (0.7) PS Amsterdam Island 38 S, 78 E, (2.4) 55 (2.2) GA4 a Fluxes are given in mg S m 1 yr 1, and concentrations are given in mml 1, in parentheses. East North America and west North America are means for NADP/NTN sites east and west of 97 W longitude, respectively. Scandinavia is mean for EMEP sites in Iceland, Norway, Sweden, and Finland. Europe is mean for EMEP sites in other European countries. NADP/NTN and EMEP observations are for June 1994 through May Other observations are for various years. b References: AN, Andreae et al. [1990b]; AY, Ayers and Ramsdale [1988]; BU, Buijsman et al. [1991]; EMEP, Hjellbrekke et al. [1996, 1997]; GA1, Galloway et al. [1982]; GA2, Galloway [1985]; GA3, Galloway et al. [1996]; GA4, Galloway and Gaudry [1984]; LI, Likens et al. [1987]; NADP, National Acid Deposition Program/National Trends Network [NADP/NTN] [1995, 1996]; PS, Pszenny et al. [1982]; RO, Rodhe et al. [1981]. with a 20% increase in sulfate burden. Increasing the assumed cloud water ph from 4.5 to 5.0 (which increases aqueous oxidation by O 3 ) increased the sulfate in source regions substantially (40 50% increase for the Europe and eastern North America sites in Figure 7a), giving them a modest high bias, but there was very little increase at remote sites (Figure 7b) and to the sulfate burden. [64] The high bias for sulfate aloft could result from simulated vertical mixing being too strong, although the SO 2 and DMS profile comparisons (Figures 5 and 13) do not suggest this. Sensitivity simulations were performed in which either convective mass fluxes or vertical eddy diffusivities were reduced by 50%. In both cases there were modest reductions in sulfate aloft. However, the reductions (and improvements to the simulated profiles) were considerably less than in the sensitivity simulation were wet removal was increased. Reducing vertical mixing had very little affect on the remote site sulfate MRs and worsened the high bias for surface SO 2 over source regions. [65] Additional simulations were performed to determine sensitivity to various model assumptions. Increasing the accumulation and Aitken mode s g values to 2.25 and 2.00, respectively, decreased the sulfate burden by 13%. This is primarily due to more rapid below-cloud scavenging of accumulation mode particles. Mean sulfate MRs for the remote sites (Figure 7b) decreased a similar amount, while mean sulfate MRs for the European and North American site groupings (Figure 7a) decreased by 6 11%. Decreasing the emitted D gn for accumulation mode sulfate, OM, and BC to mm changed the sulfate burden and mean sulfate MRs for the remote sites by less than 1%. Mean sulfate MRs for the European and North American site groupings had changes between 8% and +4%. The smaller emitted size increases the dry deposition rate, but this has a small impact on sulfate. Changes to simulated sulfate were even smaller when the emitted D gn for both accumulation and Aitken mode particles were increased by factors of 2. When the aqueous sulfate partitioning is changed to be independent of particle soluble mass (section 2.1.3), the sulfate burden and sulfate MRs for the remote, European, and North American sites change by less than 1%. The amount of sulfate in the coarse and Aitken modes does change substantially (50% decrease for coarse modes and 50% increase for Aitken), but this has little impact on the total simulated sulfate, which is nearly all in the accumulation mode. [66] The simulated SO 2 and sulfate results presented here are somewhat different from earlier simulations used by Ghan et al. [2001a, 2001b, 2001c], as the result of correcting an error in the aqueous chemistry code. In the earlier simulations, the sulfate burden was 3.6% greater than here, and the SO 2 burden was 12% lower. The comparison with observations was not substantially different, in that sulfate at remote sites and aloft was too high, and surface SO 2 in source regions was also too high DMS [67] Figures 10a and 10b show simulated surface-level DMS MRs for January and July. The DMS MRs reflect the DMS emissions used in the model, although at middle and high latitudes they are strongly affected by the seasonality of the gas-phase loss rate. Peak DMS MRs exceed 650 pptv in January (both in the Southern Ocean and NE Pacific) and 450 pptv in July (NE Atlantic and south Caribbean), and minimum MRs are below 1 pptv over central Asia. [68] Figure 11 compares observed (primarily ship cruises) and simulated surface-level DMS in marine air. (Table 7 provides information on the DMS observations.) Simulated MRs are somewhat greater than observed, with median observed and simulated MRs of 94 and 130 pptv, respectively. The simulated MRs have a narrower range ( pptv versus pptv for observations), and the correlation is low (r = 0.21). The correlation between 20 of 46

21 Figure 10. Simulated monthly mean (a and b) DMS and (c and d) MSA for January and July in lowest model layer. observed DMS MRs and the model s DMS emission rates is also low (r = 0.38), while the simulated DMS MRs at the observation locations and times correlate well with the emission rates (r = 0.83). Additional simulations were made (at R15 resolution), one with DMS emissions from Spiro et al. [1992], and another with DMS emissions calculated online using seawater DMS concentrations from Kettle et al. [1999] updated according to Kettle and Andreae [2000], and air-sea exchange rates from Nightingale et al. [2000]. The simulated DMS MRs clearly differed in these simulations, particularly with the lower emissions of Spiro et al. [1992]. However, correlations in all simulations were low, both for simulated versus observed DMS MRs (r < 0.30), and for simulated DMS emissions versus observed DMS MRs (r < 0.45). Thus the low correlation between observed and simulated DMS MRs may in large be due to actual emissions at the locations and times of these observations not matching emissions used in the model. This explanation is consistent with the known high spatial variability of DMS seawater concentrations, particularly in coastal regions where concentrations have been observed to vary by an order of magnitude within a two-week period [Bates et al., 1992b]. [69] Figure 12 shows observed and simulated seasonal cycles of monthly DMS MRs at Amsterdam Island and Cape Grim. Observed DMS at both sites show strong seasonality with largest MRs in DJF. Simulated DMS at Amsterdam Island shows a similar seasonal trend, but the JJA simulated DMS is too high. At Cape Grim, the Figure 11. Observed and simulated surface-level DMS in marine air. A through P are Atlantic Ocean; Q through Z and a are Pacific Ocean; b is Cape Grim, Australia; and c is Amsterdam Island. Table 7 provides more information on the observations. Solid and dashed lines are as in Figure of 46

22 Table 7. Information on the DMS Observations in Figure 11 Symbol Location, Latitude-Longitude, and Time References a A Atlantic N, W, July Aug CO B Atlantic N, W, July Aug CO C Atlantic N, W, April May 1984 AN D Atlantic N, W, June 1984 AN E Atlantic N, W, Nov AN F Atlantic N, W, June 1985 SA G Atlantic N, W, Feb. March 1986 SA H Gulf of Mexico N, W, Sept SA I Atlantic N, W, April 1987 BU J Atlantic N, W, April 1987 BU K Atlantic 10 S to10 N, 30 W, April 1987 BU L Atlantic S, W, March 1987 BU M Atlantic S, W, March April 1986 BE N Atlantic S, W, Nov ST O Atlantic S, 4 58 W, Nov. Dec ST P Atlantic S, 4 W to17 E, Dec ST Q Pacific 48 N, 129 W, April 1991 BN R Pacific N, W, May 1987 BT1 S Pacific N, 170 W, April May 1988 QU T Pacific 11 S to14 N, 170 W, April May 1988 QU U Pacific 10 S to10 N, W, Feb. March 1989 BT2 V Pacific S, W, Feb. March 1989 BT2 W Pacific S, W, Feb. March 1989 BT2 X Pacific 15 S to10 N, W, Feb. March 1991 HU Y Pacific 0 N, W, July 1982 AN Z Pacific 12 S, 135 W, March 1992 YV a Pacific S, 160 W, Nov NG1 b Cape Grim 41 S, 145 E, annual AY c Amsterdam Island 38 S, 78 E, annual NG2 a References: AN, Andreae et al. [1985]; AY, Ayers et al. [1991]; BE, Berresheim [1987]; BN, Bandy et al. [1992]; BT1, Bates et al. [1990]; BT2, Bates et al. [1992a]; BU, Bürgermeister et al. [1990]; CO, Cooper and Saltzman [1991]; HU, Huebert et al. [1993]; NG1, Nguyen et al. [1984]; NG2, Nguyen et al. [1992]; QU, Quinn et al. [1990]; SA, Saltzman and Cooper [1988]; ST, Staubes and Georgii [1993]; YV, Yvon et al. [1996]. simulated DMS does not reproduce the observed seasonality at all. The monthly DMS emission rates at these two locations have a seasonal dependence that is much closer to that of the observed DMS MRs. The high simulated DMS MRs in JJA are likely due to neglect of the NO 3 oxidation pathway in the model (see DMS budget discussion below), although some models that treat NO 3 oxidation still overpredict DMS in JJA at these locations [Barth et al., 2000; Chin et al., 2000b]. The simulations with alternate DMS emissions do no better at reproducing the observed seasonality. [70] Figure 13 compares several observed and simulated vertical profiles of DMS at marine locations. For the Bermuda, Barbados, Tasmania, and northeast Pacific profiles, simulated DMS MRs below 1 km are a factor of 2 4 higher than observed MRs, while DMS MRs above 2 km are in agreement with observed values. For the northwest Atlantic profile ( km east of Boston), simulated DMS MRs are much too high compared to observed. Figure 10 shows that simulated surface DMS MRs in January exceed 200 pptv throughout this area. Since NO x and NO 3 MRs at this location and time could be fairly high, treating the NO 3 oxidation pathway could reduce simulated DMS to the observed range (which Chin et al. [1996] reproduced quite well). Overall, the comparisons indicate that simulated DMS MRs above 2 km are in fairly good agreement with observations, and vertical exchange between the boundary layer and lower troposphere is satisfactory. [71] The budget for simulated tropospheric DMS is shown in Table 4. Our 0.15 Tg S burden is among the highest of the 12 models shown by Koch et al. [1999], Lohmann et al. [1999b], and Iversen and Seland [2002] that simulate DMS, for which the burdens range from 0.02 to 0.22 Tg S (median 0.09). Our DMS lifetime of 2.8 days is also on the high side, compared to days (median 2.0) for the other models. This is partially due to neglect of DMS oxidation by NO 3, which Feichter et al. [1996] and Chin et al. [1996] found to be 16 28% as efficient as DMS oxidation by OH. Figure 12. Observed (symbols) and simulated (jagged solid lines) monthly mean DMS at selected sites. Otherwise, plots are as the individual site plots in Figure 3. See Table 7 for references. 22 of 46

23 simulated MSA results presented in this section are the total for all three modes. [73] Figure 14 compares observed and simulated annual mean MSA MRs. As might be expected from the low DMS correlation, the correlation between observed and simulated MSA MRs is poor (r = 0.10). The simulated MRs are larger than observed, with a median simulated/observed ratio of The high simulated values could be due to the MSA source being too large (DMS emissions and/or MSA yield from DMS oxidation too large), or the MSA sinks (primarily wet removal) being too small. As noted earlier, our DMS emissions are on the high end compared to other models. MSA MRs are about 40% lower in the simulation with DMS emissions from Spiro et al. [1992]. Also, neglect of DMS oxidation by NO 3 increases the MSA yield relative to the SO 2 yield. As discussed earlier, the too large simulated sulfate MRs at high-latitude and oceanic locations is attributed to insufficient wet removal, so MSA wet removal may also be somewhat low. MSA MRs are about 25% lower in the sensitivity simulation with enhanced wet removal. Figure 13. Observed (symbols) and simulated (dashed lines) vertical profiles of DMS. References are as follows: (a) Van Valin et al. [1987], (b) Luria et al. [1989], (c) Ferek et al. [1986], (d) Andreae et al. [1988], and (e) Berresheim et al. [1990]. Our large burden relative to other models results from the combination of a relatively high lifetime and relatively high emissions. We note that new, satellite-based estimates of oceanic DMS concentrations [Belviso and Moulin, 2001] tend to be lower, in the Indian and South Pacific Oceans during local summer, than those given by Kettle et al. [1999] and Kettle and Andreae [2000] MSA [72] Simulated surface-level MSA mixing ratios are shown in Figures 10c and 10d. There is a strong seasonality at middle and high latitudes due to faster DMS oxidation and somewhat slower wet removal in local summer. Peak MRs exceed 65 pptv in January (Southern Ocean) and 40 pptv in July (NE Atlantic), and minimum MRs are slightly below 1 pptv (Arctic in January and central Asia in July). MIRAGE treats MSA in three aerosol size modes. On a global annual basis, nearly all of the simulated MSA mass (98.1%) is in the accumulation mode, with 1.3% in the Aitken mode, and 0.6% in the coarse sea-salt mode. All Figure 14. Observed and simulated annual average MSA. A is Alert, Canada; B is Heimaey, Iceland; C is Mace Head, Ireland; D is Bermuda; E is Barbados; F is Reunion; G is Cheju Island, Korea; H is Okinawa, Japan; I is Midway, J is Oahu; K is Fanning Island; L is American Samoa; M is New Caledonia; N is Norfolk Island; O is Cape Grim, Australia; P is Palmer, Antarctica; and Q is Mawson, Antarctica. Table 5 gives latitude-longitude for most of the locations except Mace Head (53 N, 10 W), Okinawa (27 N, 128 E), Fanning Island (4 N, 159 E), and New Caledonia (22 S, 167 E). References are as follows: A is from Li and Barrie [1993]; O is from Ayers et al. [1986]; other sites are from D. L. Savoie and J. M. Prospero (University of Miami, Miami, Florida, personal communication, 1999) and Chin et al. [1996]. Solid and dashed lines are as in Figure of 46

24 Figure 15. Observed (symbols) and simulated (jagged solid lines) monthly mean MSA at selected sites. Otherwise, plots are as the individual site plots in Figure 3. See Figure 14 for references. [74] Figure 15 compares observed and simulated monthly mean MSA MRs at a number of sites. All the sites except American Samoa show a well-defined seasonal trend of more MSA during the local summer, with the trend being generally more pronounced at higher latitudes. The simulated MSA MRs capture this seasonality quite well at highlatitude and some midlatitude locations (Heimaey, Mace Head, Norfolk Island, and Palmer), but fail to capture the trend at most of the northern lower latitude locations (Bermuda and Okinawa). At higher latitudes, there are strong seasonal variations in OH concentrations and temperature, both of which affect MSA production. At lower latitudes, the seasonal variations in these parameters are much weaker, and there appear to be other factors affecting the observed MSA seasonality, which the model does not capture. MSA from the sensitivity simulations with other DMS emissions shows better agreement at some sites and poorer at others, with no overall improvement. [75] The budget for simulated tropospheric MSA is shown in Table 4. Our Tg S burden is higher than that of Pham et al. [1995], Chin et al. [1996, 2000a], and Koch et al. [1999], who had burdens of Tg S. Our lifetime of 5.6 days is somewhat shorter than these other models, which had lifetimes of days, so the larger burden is due to a larger MSA source. The MSA source can be expressed as the product of the DMS source and the MSA yield from DMS oxidation. Pham et al. [1995] and Chin et al. [1996] have DMS sources comparable to ours but their MSA yields are about 5%, whereas ours is about 11%. Koch et al. [1999] and Chin et al. [2000a] have MSA yields comparable to ours but smaller DMS sources. In any case, the comparison of our simulated MSA MRs with observations indicates that they are too high. This may be partially due to insufficient wet removal, but our combined DMS source and MSA yield are likely too high. Note that the MSA lifetime is slightly shorter than our sulfate lifetime of 6.8 days. This results from different latitudinal distributions of sulfate and MSA. The MSA distribution peaks at southern midlatitudes where wet removal is somewhat faster Carbonaceous Aerosols Black Carbon [76] Figures 16a and 16b show simulated surface-level black carbon (BC) concentrations for January and July. Highest concentrations are associated with the industrialized regions of eastern North America, Europe, and east Asia, the biomass burning regions in South America, Africa, and, to a lesser extent, northern Australia. Peak BC concentrations are 9.5 mg m 3 in January (Africa) and 7.3 mg m 3 in July (Europe). Over the industrialized regions, seasonal variations are modest. They result from variations in wet removal and vertical mixing, since the fossil fuel BC emissions used in the simulations have no seasonality. Over the biomass burning regions there are strong seasonal variations associated with the emissions, which peak in July in South America, January in northern equatorial Africa, October in southern Africa, and June in Australia. The surface BC distributions are quite similar (particularly over and near source regions) to those of Cooke and Wilson [1996], who used the same emissions in a coarser resolution model. Compared to Liousse et al. [1996] and Chuang et al. [2002], the MIRAGE surface BC concentrations are noticeably higher over North America (January and July) and North Africa (January), because of differences in BC emissions for these regions, and are somewhat higher over other biomass burning regions, because of differences in injection heights for biomass burning emissions. MIRAGE 24 of 46

25 Figure 16. Simulated monthly mean (a and b) black carbon and (c and d) organic matter concentrations for January and July in lowest model layer. treats BC in two aerosol size modes. On a global annual basis, nearly all of the simulated BC mass (99.8%) is in the accumulation mode, with 0.2% in the Aitken mode. All simulated BC results presented in this section are the total for the two modes. [77] Figure 17a compares observed and simulated annual mean BC concentrations for June 1994 to May The observations are from the IMPROVE network [Malm et al., 1994] which measures black and organic carbon twice weekly at over 40 sites in the United States. Simulated BC concentrations are mostly within a factor of 2 of observed. Simulated means and medians (over the 42 sites) are quite close, and the median ratio of simulated to observed BC is The correlation is low, however (r = 0.34). The four locations at which the simulated values are farthest from observed (simulated/observed BC 0.25) are the western locations having the highest observed BC concentrations, which suggests local emissions or local complex terrain influences which a global model cannot reproduce. Both the observed and simulated BC show little seasonal variation. The observed 42-site mean BC changes by less than 5% between JJA and DJF. [78] Figure 17b compares observed and simulated BC concentrations in surface-level air using the observations compiled by Liousse et al. [1996], which are for a variety of locations and time periods. Simulated BC concentrations are in good agreement with observations, mostly within a factor of 2. The median ratio of simulated to observed BC is The correlation (r = 0.81) is much higher than for the IMPROVE observations, which can be partially attributed to the larger geographical scale of these observations and the larger range of concentrations. [79] Table 8 compares observed and simulated black carbon in precipitation. The observations are very limited, and the majority is from high-latitude or local winter snow samples. Overall, the simulated concentrations are in moderate agreement with the observations: the median observed and simulated concentrations are 32 and 23 mg L 1,the median ratio of simulated to observed concentration is 1.50, and the correlation coefficient is The Paris and Seattle observations are in urban areas, where the simulated concentration is expected to be low because of model resolution. For Mace Head, both simulated BC in precipitation and in surface air (symbol U in Figure 17b) are close to observed. For New Mexico, simulated BC in precipitation is about a factor of 3 higher than the observations, while simulated BC in surface air is in good agreement with IMPROVE site observations in the same area. The model s overestimate may be due to the observations being all snow scavenging events and the simulated results being several month averages that include both rain and snow scavenging. [80] Table 9 shows the budgets for simulated tropospheric black carbon as well as organic matter, sea salt, and mineral dust. The simulated BC burden is 0.22 Tg, and the lifetime is 5.9 days, which is between the sulfate and MSA lifetimes. Cooke and Wilson [1996], who developed the BC emissions that we have used, simulated a BC lifetime of 5.6 days when 25 of 46

26 Figure 17. Observed and simulated black carbon concentrations. (a) Annual average at IMPROVE network sites in the western and eastern United States. (b) Observations and locations as compiled by Liousse et al. [1996]. A through E and F through T are the Atlantic and Pacific Ocean locations in Liousse et al. s Table 4a, respectively; U through e and f through i are the Northern and Southern Hemisphere rural locations in Liousse et al. s Table 4b, respectively; and j through o and p through t are the Northern and Southern Hemisphere remote locations in Liousse et al. s Table 4c, respectively. Solid and dashed lines are as in Figure 2. the BC was treated as hydrophilic upon emission, and a lifetime of 7.9 days when the BC was treated as initially hydrophobic and transformed to hydrophilic at a rate of 60% per day. In MIRAGE the BC is internally mixed and thus always hydrophilic, and our BC lifetime is close to the hydrophilic case lifetime of Cooke and Wilson. Liousse et al. [1996] simulated a BC burden of 0.13 Tg and a lifetime of 3.9 days, using somewhat lower emissions of 12.3 Tg yr 1. Dry deposition accounts for 32% of the total BC removal, in contrast to sulfate and MSA where it accounts for 17% and 16% of the total removal. This is due to BC MRs being relatively greater at the surface, because Table 8. Observed and Simulated Black Carbon Concentrations in Precipitation a Location, Latitude-Longitude, and Time Observed Simulated References b North Canada N, W, Nov. Dec c 7.8 CL Barrow, Alaska, 71 N, 157 W, April c 11.5 CL Greenland 77 N, 61 W, annual 2.4 d 5.9 CH Greenland 69 N, 38 W, annual d 15.4 CL Arctic Ocean 80 N, 2 W, July c 11.7 CL Spitzbergen, Norway, 79 N, 12 E, May c 43.5 CL North Sweden, 68 N, 18 E, March April c 72.2 CL North Sweden, 63 N, 18 E, April Aug OG South Sweden, 58 N, 15 E, April Aug OG Paris, France, 49 N, 2 E, annual DU Mace Head, Ireland, 53 N, 10 W, Oct. Nov DU Seattle, Washington, 48 N, 122 W, Dec. Jan OG Washington State, USA, 48 N, 124 W, March c 24.6 CL New Mexico, USA, N, W, March April c 51.7 CH New Mexico, USA, N, W, Dec. Feb c 21.3 CH Ivory Coast 8 N, 6 W, June Oct DU Congo 3 N, 18 E, Nov. March DU Congo 3 N, 18 E, May Oct DU Antarctica 82 S, 150 W, annual 2.5 d 0.7 CH Antarctica 90 S, annual d 1.4 WA a Concentrations are given in mg L 1. b References: CH, Chylek et al. [1987]; CL, Clarke and Noone [1985]; DU, Ducret and Cachier [1992]; OG, Ogren et al. [1984]; WA, Warren and Clarke [1990]. c Freshly fallen snow sample. d Surface snow sample. 26 of 46

27 Table 9. Annual Budgets for Black Carbon, Organic Matter, Sea Salt, and Mineral Dust a Black Organic Sea Mineral Carbon Matter Salt Dust Sources 14.0 b 91.9 c Sinks Dry deposition Wet deposition Burden Lifetime a Units are Tg yr 1 for sources and sinks, Tg for burdens, and days for lifetimes. b Values of 8.0 and 6.0 from fossil fuels and biomass burning, respectively. c Values of 73.2 and 18.7 from primary emissions and monoterpene oxidation, respectively. BC is emitted at the surface while sulfate and MSA are produced by chemical reaction throughout the troposphere. [81] The fossil fuel BC emissions of Cooke et al. [1999] are substantially lower in North America and Europe compared to the Cooke and Wilson [1996] emissions we used, and the global total is 5.1 Tg yr 1 (submicrometer fraction) versus 8.0 Tg yr 1. Using these newer emissions produces substantially lower BC concentrations in some regions. For the IMPROVE sites in Figure 17a, the median ratio of simulated to observed BC drops from 0.95 (Cooke and Wilson [1996] emissions, R15 resolution simulation) to 0.47 (Cooke et al. [1999] emissions, R15 resolution), closely following the change in North American emissions. For the widely distributed sites in Figure 17b, the median ratio changes less, from 0.73 to For BC in precipitation at the sites in Table 8 (mostly North America and Europe), the median ratio of simulated to observed concentration drops from 1.27 to These comparison results suggest that with the newer emissions the model underpredicts BC concentrations. Treating the BC as initially hydrophobic as given by Cooke and Wilson [1996] would increase BC residence time and concentration. [82] Biomass-burning emissions, which have initial buoyancy, mix into the lower troposphere and form elevated haze layers. In the base simulation, emissions were at the surface; a sensitivity simulation was performed in which biomassburning BC emissions were distributed over the lowest 2 km as given by Liousse et al. [1996]. This produced substantial changes over and downwind of major biomass-burning regions. In July, when biomass-burning emissions are high in South America and southern Africa, surface-level BC concentrations were 30 50% lower in the emissions areas. Concentrations at 3 km were a factor of 2 or more greater, and the plumes extending SE from the source regions were much stronger in the sensitivity simulation. The comparison with surface BC observations (Figure 17) changed at several locations, but the overall statistics (e.g., median ratio) changed only slightly. [83] In the sensitivity simulation in which scavenging rates were increased by 50%, the BC burden decreased by 24%, similar to sulfate. Mean BC concentrations at U.S. and other sites (Figures 17a and 71b) decreased by 8% and 11%, respectively, thus making the model s low bias for BC (with Cooke et al. [1999] emissions) somewhat worse. When the accumulation and Aitken mode s g values were increased to 2.25 and 2.00, respectively, the BC burden decreased by 8%. The BC change is lower than for sulfate, because the faster below-cloud scavenging is offset by slower dry deposition, which affects BC more than sulfate. When the emitted D gn for accumulation mode sulfate, OM, and BC were decreased to mm, the BC burden decreased by 8%, because of the more rapid dry deposition rate of smaller particles, and when the emitted D gn for both accumulation and Aitken mode particles were increased by factors of 2, the BC burden increased by 5% Organic Matter [84] Simulated surface-level OM concentrations for January and July are shown in Figures 16c and 16d. As with BC, the highest concentrations are associated with the industrialized regions of eastern North America, Europe, and east Asia, the biomass burning regions in South America, Africa, and, to a lesser extent, northern Australia. Peak OM concentrations are 34 mg m 3 in January (Africa) and 38 mg m 3 in July (South America). Over the three industrialized regions, concentrations are considerably higher in JJA than in DJF. This is due primarily to the seasonal variation of monoterpene emissions and resulting OM production, but the primary OM emissions also have modest seasonal variations. (In North America and Europe, 66 67% of the annual monoterpene emissions occur in JJA and 2 4% in DJF.) Over the biomass burning regions there are strong seasonal variations associated with primary OM emissions, which follow the seasonal behavior of BC emissions. The surface OM distributions are quite similar to those given by Liousse et al. [1996] and Chuang et al. [2002], although concentrations in biomass burning regions are higher in MIRAGE, because biomass-burning emissions were injected at the surface rather than through the lowest 200 hpa. MIRAGE treats OM in two aerosol size modes. On a global annual basis, nearly all of the simulated OM mass (99.8%) is in the accumulation mode, with 0.2% in the Aitken mode. All simulated OM results presented in this section are the total for the two modes. [85] Figure 18a compares observed and simulated annual mean organic carbon (OC) concentrations for June 1994 to May 1995 at IMPROVE network sites. (We assume OC = OM/1.4 as given by Malm et al. [1994].) Simulated OC concentrations are lower than observed: the observed and simulated means for all sites are 1180 and 710 ng m 3, and the median ratio of simulated to observed OC is The correlation is moderate (r = 0.55) and larger than for BC. The four locations at which the simulated values are farthest from observed (simulated/observed OC 0.25) are the locations at which simulated BC is farthest from observed, again suggesting local emissions or complex terrain influences. Both the observed and simulated OC concentrations are greater in JJA. The observed 42-site mean OC increases by a factor of 2.0 from DJF to JJA, while the simulated mean increases by a factor of 1.9. [86] Figure 18b compares observed and simulated OC in surface-level air at various locations, using the observations compiled by Liousse et al. [1996] plus the IMPROVE Denali, Alaska, site. The simulated OC concentrations show little bias: observed and simulated means for all sites are 470 and 740 ng m 3, medians are 380 and 190 ng m 3, and the median ratio of simulated to observed OC is However, the correlation is fairly low (r = 0.48). 27 of 46

28 Figure 18. Observed and simulated organic carbon concentrations. (a) Annual average concentrations at IMPROVE network sites in the western and eastern United States. (b) Other observations. A is Denali National Park, Alaska, annual average [Malm et al., 1994]. Other observations are as compiled in Liousse et al. s [1996] Table 5: B through N are the Northern Hemisphere, and O through V are the Southern Hemisphere. Solid and dashed lines are as in Figure 2. [87] For the IMPROVE observations, the median simulated-observed ratio for OC (0.65) is low compared to the BC median ratio (1.19) from the base simulation. In R15 resolution simulations, however, the median ratio for OC is 0.55, and is close to the BC median ratio of 0.47 in the simulation using Cooke et al. [1999] fossil fuel BC emissions. While this suggests a low bias similar to the low bias for BC, a low bias is not seen in the Figure 18b comparison. It should also be noted that there is a high uncertainty in the OM emissions [Penner et al., 2001] and that they do not include emissions from temperate and boreal forest fires. The sensitivities of simulated OM to changes in scavenging rates, s g, and emitted D gn are similar to those of BC as described above. [88] The budget for simulated tropospheric OM is shown in Table 9. The simulated OM burden is 1.38 Tg, which is about 35% of the sulfate burden, assuming sulfate present as ammonium bisulfate. The lifetime is 5.5 days, similar to that of BC and MSA. As with BC, dry deposition accounts for a larger fraction (30%) of the total removal than for sulfate and MSA Sea-Salt Aerosols [89] Figure 19 shows simulated surface-level sea-salt concentrations for January and July. Highest concentrations are generally associated with areas of strongest surface winds, and concentrations exceeding 20 mg m 3 are found in the North Pacific and North Atlantic at N in January, and in the Southern Ocean at S in both January and July. The model also simulates an area of high concentrations (>50 mg m 3 ) off the coast of Saudi Arabia in July, which results from moderately strong winds (monthly average wind speed 8 m s 1 ) combined with very little wet removal. There are strong seasonal variations at northern midlatitudes associated with seasonal variations in surface wind speed, but much weaker variations at southern midlatitudes because of weaker variations in wind speed there. MIRAGE treats sea salt in two aerosol size modes. On a global annual basis, nearly all of the simulated sea-salt mass (98.0%) is in the coarse sea-salt mode, with 2.0% in the accumulation mode. Simulated sea-salt results presented in this section are the total for the two modes, except where noted. The surface sea-salt distributions are similar to those given by Gong et al. [2002] (even the maximum near Saudi Arabia), but concentrations in the tropics and subtropics (lower wind speed areas) are higher in MIRAGE, because of the weaker wind speed dependence of sea-salt emissions. [90] Figure 20 compares observed and simulated annual mean Na concentrations at marine locations. (We use Na mass = sea-salt mass.) The simulated concentrations are somewhat lower than observed: the observed and simulated median concentrations for the 17 locations are 3.8 and 2.7 mgm 3, respectively, and the median ratio of simulated to observed Na is As described section 2.1.4, sea-salt emissions are computed using surface exchange rates and an assumed sea-salt mass concentration at 10 m that is a function of wind speed. The concentration versus wind speed relationship in equation (3) is an average of concentration versus wind speed relationships from observational studies, seven of which are shown by Gong et al. [1997a]. The low bias in our simulated Na concentrations is considerably smaller than the order-of-magnitude range in the observationally derived concentration versus wind speed relationships. 28 of 46

29 30 Pg yr 1 by Erickson and Duce [1988], 11.7 Pg yr 1 by Gong et al. [1997b], 8.9 Pg yr 1 by Tegen et al. [1997], and 10.1 Pg yr 1 by Gong et al. [2002]. Our estimated burden and lifetime are 4.3 Tg and 0.19 days. The lifetime is much shorter than for sulfate, MSA, BC, and OM, which are primarily in the accumulation mode. For the coarse-mode sea-salt particles, the geometric mean dry and wet diameters of the volume distribution are 7.8 and 18 mm, respectively, so both dry deposition and below-cloud scavenging are much faster than for accumulation mode particles. Gong et al. [1997a] calculated a residence time of 0.5 hours for 8 16 mm dry-diameter particles in a 166 m deep surface layer. For MIRAGE, the residence time for sea salt in the lowest 166 m is about 0.8 hours. Tegen et al. [1997], whose source strength was somewhat smaller than ours, simulated a larger burden of 11.4 Tg and a longer lifetime of 0.7 days. The factor of 3 differences in simulated burden and lifetime must be due to substantial differences in the treatment of removal processes. The relative contributions of dry and wet removal to the total sea-salt sink are 70% and 30%, respectively, which is quite close to that of Gong et al. [1997b]. [93] Additional simulations were performed at R15 resolution to investigate the sensitivity of simulated sea-salt concentrations to surface winds and the assumed c 10 (u 10 ) Figure 19. Simulated monthly mean sea-salt concentrations for (a) January and (b) July in lowest model layer. [91] Figure 21 compares observed and simulated seasonal cycle of monthly mean Na concentrations at a number of locations. At Heimaey and Mace Head, the observed Na concentrations in DJF are about three times greater than in JJA. Simulated concentrations are also higher in DJF, but the seasonal variation is less than observed. The simulated sea-salt emissions at these locations increase by a factor of 2.5 from JJA to DJF, but wet removal also increases. The model of Gong et al. [1997a, 1997b] gave better agreement with observations at these locations, particularly at Heimaey, where the concentration versus wind speed relation from their model had a stronger dependency on wind speed (slope = 0.26 versus our 0.18). Thus wind speed dependency of our sea-salt emissions may be too low. At Bermuda, the model better reproduces the observed seasonal variations. As discussed by Gong et al. [1997b], the seasonal variations at Oahu are opposite what would be expected from the JJA maximum in wind speed and minimum in precipitation, and the observed variations may be due to sampler orientation. At the four Southern Hemisphere locations shown, seasonal variations are generally much weaker than in the Northern Hemisphere, because surface wind speeds at southern midlatitudes have smaller seasonal variations. Simulated concentrations at the locations also show little seasonal variation. [92] The budget for simulated tropospheric sea salt is shown in Table 9. Our estimated source strength is 8.1 Pg yr 1 (1 Pg = g), which falls in the range of other recent estimates: 3.5 Pg yr 1 by Spillane et al. [1986], 10 Figure 20. Observed and simulated mean Na mass concentrations for June 1994 to May A is Heimaey, Iceland; B is Mace Head, Ireland; C is Bermuda; D is Barbados; E is Falkland Islands; F is Cape Point, South Africa; G is Reunion; H is Cheju Island, Korea; I is Midway; J is Oahu; K is American Samoa; L is Norfolk Island; M is Cape Grim, Australia; N is Chatham Island; O is Invercargill, New Zealand; P is Mawson, Antarctica; and Q is Palmer, Antarctica. All observations are from D. L. Savoie and J. M. Prospero (University of Miami, Miami, Florida, personal communication, 1999) and Gong et al. [1997b]. Solid and dashed lines are as in Figure of 46

30 Figure 21. Observed (symbols) and simulated (solid lines) monthly mean Na concentrations at selected sites. Otherwise, plots are as the individual site plots in Figure 3. References are the same as in Figure 20. relationship. The MIRAGE monthly mean u 10 are in good agreement with the ECMWF analyses used for nudging. However, the temporal variability of u 10 (expressed as the monthly variance divided by the square of the monthly mean) is low in comparison to the analysis of Pavia and O Brien [1986]. In one sensitivity simulation, sea-salt emissions were computed using the MIRAGE monthly mean u 10 values and assuming a Weibull wind speed distribution with the shape parameter values based on Pavia and O Brien [1986], who give a higher temporal variability compared to the actual MIRAGE u 10. This increased the sea-salt emissions and burden by 54% and 42% compared to an R15 resolution base simulation. The median concentration for the sites in Figure 20 increased to 3.3 mg m 3, improving the low bias. The simulated seasonal dependence (as in Figure 21) changed very little. In a second sensitivity simulation, the wind speed dependence of emissions was increased by using c 10 = 1.4 exp (1.8 u 10 ), which is the Gong et al. [1997a] fit for Heimaey. This improved the simulated seasonal dependence: the JJA to DJF variation was still somewhat low at Heimaey, but became greater than the observed variation at Mace Head and Bermuda. The total sea-salt emissions changed little (5% reduction), but the median concentration for the Figure 20 sites decreased to 2.2 mg m 3. Thus a combination of increased temporal variability in u 10 and a stronger wind speed dependence in the c 10 (u 10 ) relationship can improve the model s low bias and weak seasonal dependence. [94] In a third sensitivity study, the coarse-mode sea-salt size parameters of Erickson and Duce [1988] were used: s g = 3.0 and D gn is a function of wind speed. This gives a broader distribution with a smaller D gn and more particles per unit mass, compared to the size parameters in Table 2. In regions with high wind speed and high sea-salt emissions, however, the volume median diameter is comparable. The sea-salt burden increased by 23%, and the median concentration for the Figure 20 sites increased to 3.1 mg m 3. The simulated seasonal dependence changed very little. Thus the simulated sea-salt mass concentrations have a modest sensitivity to the size distribution parameters. [95] While most of the sea-salt mass is in the coarse mode, submicrometer sea-salt particles can be important as CCN and to light scattering. Sea salt dominated the submicrometer aerosol during the First Aerosol Characterization Experiment (ACE 1) south of Australia [Murphy et al., 1998; Quinn et al., 1998], with median submicrometer seasalt concentrations of 1 mg m 3. Our simulated submicrometer sea-salt concentrations for the same location and months (November December) are substantially lower, averaging 0.11 mg m 3. For coarse sea-salt mass, where dry-deposition is the dominant removal process, the method of calculating sea-salt emissions using empirical c 10 (u 10 ) relationships produces fairly accurate results. For accumulation-mode sea salt, where wet removal is more important, the method underestimates the emissions, even if the empirical c 10 (u 10 ) relationships reflect conditions in which wet removal is important Mineral Dust Aerosols [96] Current estimates of global dust emissions are highly variable, with values ranging from 200 to 5000 Mt yr 1 [e.g., Pye, 1987; Andreae, 1995]. The diameter of emitted dust particles (D p ) typically ranges from 0.1 mm to several hundred micrometers [Rahn et al., 1979; Nishikawa et al., 1991; Duce, 1995]. While large particles (D p >20mm) are mostly deposited by sedimentation near the source regions, smaller particles (typically with D p <20mm) can be transported a great distance. Using a best guess of 3000 Mt yr 1 30 of 46

31 Table 10. Predicted Seasonal and Annual Emissions of LRT Dust From Seven Major Dust Source Regions a Source Region Dec. Feb. Seasonal March May Emissions June Aug. Sept. Nov. Annual Emissions Percent of Continent Total b Arabian Peninsula North China NW Australia Sahara/Sahel South Africa Southern South America United States a Emissions are given in Mt. b Percentage of annual dust emissions from the corresponding continent that come from the source region. for global total dust emissions, Tegen and Fung [1994] estimated a global source of 1370 Mt yr 1 for particles with D p <20mm, of which 780 Mt yr 1 are for particles with D p < 11 mm. Other modeling estimates of global dust emissions are 1800 Mt yr 1 for particles with D p <20mm [Dentener et al., 1996], 1312 ± 97 Mt yr 1 for particles with D p <16mm [Perlwitz et al., 2001] and Mt yr 1 for particles with D p <12mm[Ginoux et al., 2001] Our base simulation predicts 2050 Mt yr 1 for global total dust emissions and 630 Mt yr 1 for particles with D p <10mm, which are in the range of previous model estimates. [97] Table 10 shows the predicted seasonal and annual emissions of long-range transport (LRT) dust (D p <10mm) in seven major source regions. Deserts and arid regions in north China and the Arabian peninsula are the two major sources in Asia, accounting for 53% and 22%, respectively, of total LRT dust emissions in Asia. The Sahara/ Sahel areas and arid regions in South Africa are the two major sources in Africa, accounting for 69% and 15%, respectively, of total LRT dust emissions in this continent. Deserts in western Australia and southern part of South America are dominant dust sources in Oceania and South America, respectively. Dust emissions from the southwestern United States accounts for about half of the total LRT dust emissions from North America. The seasonality and magnitude of dust emissions from these regions are generally consistent with available observations and previous model estimates. For example, the model predicts the highest dust emissions occur in March-April-May (MAM) in north China, the Sahara, and the United States, and in JJA in the Arabian area, which is in good agreement with observations [e.g., Sirokko and Sarntheim, 1989; Merrill et al., 1989; Swap et al., 1996]. High dust production from the Sahara can occur in all seasons except September- October-November (SON), during which the dust emission is always at the minimum [Swap et al., 1996]. MIRAGE reproduces this seasonal behavior well. The annual production of mineral dust from North Africa is estimated to be Mt [Schutz et al., 1981; d Almeida, 1987; Pye, 1987]. The Saharan dust input to the North Atlantic is estimated to be typically Mt yr 1 [Prospero, 1981; Morales, 1986] and can reach as high as 460 Mt [Swap et al., 1996]. Our predicted emissions from North Africa are 520 Mt yr 1 for total dust and 160 Mt yr 1 for LRT dust, of which 360 and 110 Mt yr 1, respectively, are emitted from Saharan/Sahel regions, and are within the range of other estimates. Gillette et al. [1992] estimated a dust source for the United States of 19 Mt yr 1 for particles smaller than 10 mm, and the modeled LRT dust source of 18 Mt for the United States is very close to their estimate. Analyses of meteorological conditions in Australia indicate that the potential for wind erosion is the greatest in January for northern and northwestern Australia and in October for southern and southwestern Australia [Kalma et al., 1988]. These parts of Australia are covered predominantly by deserts. The model predicts high dust emissions in DJF and SON in Australia, consistent with the analysis of Kalma et al. [1988]. However, the dust emissions from Australia and Sahara may be overestimated and those over regions that are strongly affected by agricultural cultivation and overgrazing (e.g., the Sahel region and north China) may be underestimated, because the influence of soil disturbance by human activities on dust emissions was not considered in the base simulation. Accounting for soil disturbance due to cultivation and overgrazing will increase dust emissions from disturbed source regions and decrease those from undisturbed deserts, as shown later in the sensitivity study. [98] Figure 22 shows simulated LRT dust concentrations (accumulation mode + coarse mode) in the lowest model layer for the four seasons. The base simulation results capture the synoptic-scale features of dust transport and distribution. The model reproduces the transport of the dust plume from central Asia to eastern China, Korea, Japan, and central North Pacific between 30 and 50 N in MAM, and that from the Sahara to the North Atlantic, Caribbean, and northeastern coast of South America between 0 and 30 N in MAM and JJA. The Saharan dust plume exhibits a seasonal latitudinal shift following the shift of the Intertropical Convergence Zone (ITCZ); that is, the plume extends about 10 farther south in DJF and MAM compared to JJA and SON [Prospero and Nees, 1976; Prospero et al., 1981; Swap et al., 1996]. This seasonal shift is well reproduced in the simulation. The model predicts the highest dust concentrations in MAM in east Asia, the Sahara, and the United States, and in JJA over the Arabian area and India. The predicted dust concentrations range from less than 0.1 to 230 mg m 3. These results are in good agreement with previous observations [e.g., Merrill et al., 1989; Hayasaka et al., 1990] and modeling studies [e.g., Wefers and Jaenicke, 1990; Tegen and Fung, 1994; Dentener et al., 1996; Ginoux et al., 2001]. [99] Figure 23 compares observed and simulated surfacelevel dust concentrations at a number of sites that are downwind of dust source regions. (Table 11 provides information on the dust observations.) The correlation coefficient between simulated and observed concentrations is 0.86, but simulated dust concentrations are generally lower than observed, with a median ratio of simulated to 31 of 46

32 Figure 22. (a d) Simulated seasonal-average dust concentrations (accumulation mode plus coarse mode) in lowest model layer. observed dust concentration of There are several possible reasons for this underestimation. The model only simulates LRT particles with D p < 10 mm, while the observations may include larger particles with 10 < D p < 50 mm, which typically account for 50 70% of total dust production [Schutz et al., 1981; Duce, 1986; d Almeida, 1987; Pye, 1987]. This would be most important for nearsource locations (e.g., east Asia) where the underestimation is greatest (factor of 7 20), and less important for remote locations (Pacific Islands, Barbados, and Bermuda) where the underestimation is less. The model may be underestimating either the LRT dust emissions themselves, or the vertical transport of emitted dust out of the boundary layer during high-wind-speed conditions, when most dust is produced, and subsequent long-range transport. Dust emissions are highly sensitive to the wind fields, being proportional to the fourth power of the wind speed (see equation (1)), and the temporal variance of the model surface winds is lower than observed (see section 3.3). These factors may account for the general underestimate, even at remote Pacific locations. The low dust concentrations at Miami and Barbados can also be attributed to the underestimate of emissions from the Sahel, which may generate more LRT dust than undisturbed areas of the Sahara. [100] Both accumulation and coarse mode dust particles are treated in MIRAGE. While the accumulation mode accounts for 0.7% of LRT dust mass emissions, it accounts for 2.6% of the LRT dust mass burden (because of slower removal of accumulation mode particles) and 81% of the LRT dust number burden. Simulated mass concentrations for accumulation mode dust of mg m 3 extend over vast regions of central Asia, central Africa, western United States, southern South America, and Australia. The corresponding number concentrations and surface areas are cm 3 and 1 15 mm 2 cm 3, respectively. Because dust measurements usually focus on total particle concentrations, observational data on mass and number concentrations of dust in accumulation mode are very sparse. The number concentrations of accumulation mode dust emitted from the Sahara desert range from 400 to 2500 cm 3 [d Almeida, 1987; Jaenicke, 1993], while those emitted from north China range from 60 to 110 [Lei et al., 1993]. Therefore the model may significantly underestimate the dust number concentrations over North Africa and off the coast of West Africa. Since dust is the primary source of accumulation mode aerosols in these regions, the underestimation of accumulation mode dust results in significantly lower aerosol optical depths (AOD) as compared to the estimation from AVHRR radiance measurements [Ghan et al., 2001b]. One cause of this underestimation is the use of a uniform mass emission fraction of 0.7% for accumulation mode dust over all dust source regions. Using a variable emission fraction for accumulation mode dust emitted from different source regions in future applications should improve the accuracy of dust and AOD calculations. A second cause is the underestimate of fine dust particles emissions from Sahel region in the southern Sahara, which 32 of 46

33 Figure 23. Observed and simulated dust concentrations at surface level downwind of the dust source regions. A is Beijing, China; B is Xian, China; C is Xiamen, China; D is Japan; E is Yaku Island, Japan; F is Shemya Island, Alaska; G is Enewetak; I is Fanning Island; J is New Zealand; K is Midway; L is Oahu; M and N are Hawaii; O is North Africa; P and Q are Mediterranean; S is Miami; T and U are Barbados; and V is Bermuda. Table 11 provides more information on the observations. Solid and dashed lines are as in Figure 2. is a major source for fine particles transported across the Atlantic. Treating accumulation mode dust as internally mixed and hydrophilic, which may overestimate its activation scavenging and wet removal, may also contribute to the model s underestimate of accumulation mode dust concentrations. [101] To study the effect of soil disturbance on dust emissions, a sensitivity simulation (R15 resolution) was conducted using soil disturbance factors as an additional constraint for dust emissions. The soil disturbance factor is defined as the degree of soil disturbance by human activities such as cultivation, deforestation, and overgrazing. Since a global database for deforestation and overgrazing is not readily available, the soil disturbance factor was estimated using the Matthews cultivation index [Matthews, 1983]. The calculated soil disturbance factor is the grid cell average of cultivation fraction times dust source strength, divided by the grid cell average dust source strength. It ranges from 0 to 1, and grid cells with a soil disturbance factor exceeding 0.1 are treated as disturbed, while others are treated as undisturbed. Different threshold wind velocities are used for disturbed and undisturbed conditions, based on the measurements of Gillette et al. [1980, 1982] and Gillette [1988]. For disturbed conditions, the threshold wind velocities are 25, 30, 35, and 75 cm s 1 for desert, arid/semiarid regions, shrubland/grassland, and other dust source regions, respectively. For undisturbed conditions, the corresponding wind velocities are 40, 55, 65, and 75 cm s 1. Dust emissions over deserts are lower in the simulation using soil disturbance factors than in the base run. Annual dust emissions from the Sahara and Australian deserts decrease by 35% and 23%, respectively. Dust emissions from areas with more human activity such as the Sahel and north China are somewhat higher than in the base run, increasing by 4% and 12%, respectively. Simulated dust concentrations at locations distant from dust sources (e.g., west Atlantic and North Pacific) are still underestimated. However, the results using soil disturbance factors are more consistent with the observed global distribution of dust emissions and concentrations near the dust sources. [102] Simulations were also performed to test model sensitivity to the assumed size distribution parameters for Table 11. Information on the Dust Observations in Figure 23 Symbol Location, Latitude-Longitude, and Time References a A Beijing, China, 40 N, 116 E, April May 1989 DU B Xian, China, 34 N, 109 E, annual DU C Xiamen, China, 25 N, 118 E, annual DU D Japan 35 N, 140 E, March May IW E Yaku Island, Japan, 30 N, 131 E, Dec. May 1988 NI F Shemya Island, Alaska, 52 N, 174 E, annual PR1 G Enewetak 11 N, 162 E, Feb. June UE H Enewetak 11 N, 162 E, July 1981 Jan UE I Fanning 4 N, 159 E, annual PR1 J New Zealand 41 S, 175 E, May Aug DU K Midway 28 N, 177 W, annual PR1 L Oahu 21 N, 158 W, annual PR1 M Hawaii 23 N, 165 W, March May PA N Hawaii 23 N, 165 W, June Feb PA O west coast North Africa 28 N, 15 W, Feb PR2 P Mediterranean N, E, Sept. Oct PR2 Q Mediterranean N, E, July Aug PR2 S Miami 26 N, 80 W, July Sept PR3 T Barbados 13 N, 59 W, July Sept PR3 U Barbados 13 N, 59 W, annual DU V Bermuda 32 N, 65 W, annual DU a References: DU, Duce [1995]; IW, Iwasaka et al. [1993]; NI, Nishikawa and Kanamori [1991]; PA, Parrington et al. [1983]; PR1, Prospero and Savoie [1989]; PR2, Prospero [1979]; PR3, Prospero et al. [1979]; UE, Uematsu et al. [1983]. 33 of 46

34 Figure 24. Simulated annual mean number concentration for (a and b) accumulation and (c and d) Aitken modes, lowest model layer and zonal average. the coarse mode dust, D gn =1.0mm and s g = 1.8, which are based on observations of Asian dust. A low-d gn simulation used D gn = 0.22 mm and s g = 2.6, and a high-d gn simulation used D gn = 1.76 mm and s g = 1.7. These D gn -s g values were selected from literature values (see section 2.1.1) as having the lowest and highest mass-to-number ratios, respectively. The simulations were at R15 resolution and for JJA only. Simulated mass concentrations for coarsemode dust are lower in both the low- and high-d gn runs than in the base run. For both these cases, more larger particles with D p >3mmare emitted than in the base run. (In the low-d gn run, this is due to the large s g.) Larger particles are removed more rapidly by dry deposition and below-cloud scavenging, so the overall removal rates are faster in both the low- and high-d gn runs than in the base run, and the global-average mass concentrations are 42% and 63% of the base run values, respectively. Dust concentrations are more affected at locations distant from sources where removal has had more effect. In west Atlantic and Caribbean regions affected by African dust, column burdens of coarse-mode dust in the low-d gn run are less than 25% of base run values. The global-average coarse-mode number concentrations in the low- and high- D gn runs are 290% and 16% of the base run values, reflecting their different mass-to-number ratios Aitken and Accumulation Mode Number and Size [103] Figure 24 shows simulated surface-level and zonalaverage values of the annual-average number concentrations for the accumulation and Aitken modes (N acc and N ait ). The surface-level concentrations are highest over continental source regions, associated with primary particle emissions from anthropogenic activities and biomass burning, with both N acc and N ait > 1000 cm 3 over large areas and peak values >10,000 cm 3. Concentrations over oceans and in polar regions are much lower, generally with cm 3 for N acc and cm 3 for N ait. The N acc generally decrease with altitude, reflecting their source at the surface, except near the poles. The N ait show a maximum in the midtroposphere resulting from H 2 SO 4 -H 2 O nucleation, and a second maximum at the surface over land areas. [104] Figure 25 shows simulated surface-level and zonalaverage values of the annual-average number distribution geometric mean diameter for the accumulation and Aitken modes (D gn,acc and D gn,ait ). The surface-level D gn,acc are lowest ( nm) over continental source regions, associated with primary particle emissions, and largest over ocean and polar regions ( nm). The surface-level D gn,ait show similar behavior, with values of nm over continental source regions and nm over ocean and polar regions (except South Pole). The D gn,acc show little variation with altitude, while the D gn,ait are smallest in the troposphere at hpa, again associated with H 2 SO 4 -H 2 O nucleation. [105] Figure 26 compares observed [from Heintzenberg et al., 2000] and simulated number concentrations and D gn for accumulation and Aitken modes at the ocean surface. The simulated N acc are in general agreement with the observations. The observations show similar concentrations in the Northern and Southern Hemispheres, while the simulated 34 of 46

35 Figure 25. Simulated annual mean D gn for (a and b) accumulation and (c and d) Aitken modes, lowest model layer and zonal average. values are 40% higher in the Northern Hemisphere. The simulated N ait are often lower than the observations, especially over S, where the observed concentrations are highest. The observed concentrations are roughly a factor of 2 higher in the Southern Hemisphere than in the Northern Hemisphere, while the simulated values are only 10% higher there. The simulated D gn,acc (zonal means of nm) are nm smaller than the observations (zonal means of nm), and this is partially caused by the underprediction of accumulation mode sea-salt particles. The simulated D gn,ait are close to the observations over 15 S to45 N, but are 5 10 nm smaller than the observations over S. Underprediction of particle nucleation over the Southern Ocean would explain both the simulated D gn,ait being high and N ait being low. [106] Table 12 compares observed and simulated number concentrations and D gn at some continental surface locations and aloft. At Melpitz, Germany (in the European source region), the simulated/observed (S/O) ratios for N acc and N ait are 37 and 5 8, while S/O ratios for D gn,acc and D gn,ait are 0.40 and The high simulated number concentrations can be mostly explained by the low simulated sizes, as the simulated accumulation mode volume (V acc ) is only 2.4 greater than observed, and the simulated Aitken mode volume (V ait ) is somewhat smaller than observed. Similar behavior is found at Hyytiälä, Finland (north of the European source region), although the number overprediction is not as great. The S/O ratios for N acc, D gn,acc, and V acc are 6.4, 0.47, and For these two locations, the observed size distributions were fitted with accumulation, Aitken, and nucleation modes, and the simulated Aitken mode parameters are in better agreement with observed parameters for a combined Aitken and nucleation mode. At the U.S. surface locations, agreement with observations is better. At the Great Smokey Mountain location, S/O ratios for N acc, D gn,acc, and V acc are 2.5, 0.54, and At the ARM SGP location (number measurements only), concentrations of particles larger than 100 and 10 nm diameter are factors of 1.8 and 0.86 times the observed. At the Bondville location, the concentration of particles larger than 14 nm diameter is a factor of 1.4 times the observed. The simulated N acc and N ait are lower than the U.S. observations of Whitby [1978], while the D gn,acc and D gn,ait are fairly close to these observations. Note that the Whitby [1978] observations, from the 1970s, could reflect significantly different emissions, and also that they are a composite of observations representing continental background conditions. Overall, these continental surface comparisons suggest that the simulated sizes (D gn,acc and D gn,ait ) are too small, while simulated numbers are correspondingly too high, particularly for the two European locations. [107] Also shown in Table 12 are comparisons of observed and simulated condensation nuclei (CN) concentrations aloft. At the Lindenberg, Germany, location (aircraft measurements during July August 1998), the S/O ratios of CN for 1 3, 3 6.5, and km MSL are 1.8, 2.2, and 3.0. Besides the general overprediction (which is smaller than the overprediction for the Melpitz, Germany location), 35 of 46