Yu et al., J. Geophy. Res in press,...

Yu et al., J. Geophy. Res., 2009JD013473, in press, 2010

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Spatial distributions of particle number concentrations in the global troposphere: 1 Simulations, observations, and implications for nucleation mechanisms 2

3 Fangqun Yu1, Gan Luo1, Timothy S. Bates2, Bruce Anderson3, Antony Clarke4, Vladimir 4

Kapustin4, Robert M. Yantosca5, YuxuanWang6, and Shiliang Wu7 5 6

1Atmospheric Sciences Research Center, State University of New York, Albany, NY 7 2NOAA/Pacific Marine Environmental Laboratory, Seattle, WA 8

3Chemistry and Dynamics Branch/Science Directorate, NASA LaRC, Hampton, VA 9 4Department of Oceanography, University of Hawaii, Honolulu, HI 10

5School of Engineering and Applied Sciences, Harvard University, Boston, MA 11

6Department of Environmental Science and Engineering, Tsinghua University, Beijing, China 12 7Dept. of Civil and Environmental Engineering, Michigan Technological Univ., Houghton, MI 13

14 Correspondence to F. Yu ([email protected]) 15

16 17 Abstract. Particle number concentration in the troposphere is an important parameter controlling 18 the climate and health impacts of atmospheric aerosols. We show that nucleation rates and total 19 particle number concentrations in the troposphere, predicted by different nucleation schemes, 20 differ significantly. Our extensive comparisons of simulated results with land-, ship-, and 21 aircraft- based measurements indicate that, among six widely used nucleation schemes involving 22 sulfuric acid, only the ion-mediated nucleation scheme can reasonably account for both absolute 23 values (within a factor of ~ 2) and spatial distributions of particle number concentrations in the 24 whole troposphere. Binary homogeneous nucleation (BHN) schemes significantly under-predict 25 particle number concentration in the lower troposphere (below ~ 500 mb), especially in the 26 boundary layer over major continents (by a factor of up to ~ 10). BHN is also insignificant in the 27 upper troposphere based on a recent kinetically self-consistent nucleation model constrained by 28 multiple independent laboratory data. Previous conclusions about the importance of BHN in the 29 upper troposphere should be revisited. Empirical activation and kinetic nucleation formulas 30 significantly over-predict the particle number concentrations over tropical and sub-tropical 31 oceans (by a factor of up to ~ 10 in the boundary layer), and the over-predictions extend from 32 ocean surface to around ~ 400 mb. This study represents the first comprehensive comparison of 33 global particle number simulations with relevant measurements that have a 3-D global spatial 34 coverage. Our results suggest that ion-mediated H2SO4-H2O nucleation appears to dominate over 35 neutral H2SO4-H2O nucleation, not only in the lower troposphere but also in the middle and 36 upper troposphere. 37

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39 1. Introduction 40

Aerosol particles appear in the troposphere due to either in-situ nucleation (i.e, secondary 41 particles) or direct emissions (i.e., primary particles). In addition to the mass concentration, the 42 particle number concentration affects the climate and health impacts of atmospheric aerosols. 43 Field measurements show clearly that the number concentrations of cloud drops increase with 44 increasing total aerosol number concentrations [e.g., Ramanathan et al., 2001]. High number 45


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concentrations of ultrafine particles may also lead to adverse health effects [e.g., Penttinen et al., 46 2001; Oberdörster et al., 2004]. 47

Because of the importance of knowing accurately the aerosol number concentrations and 48 their spatiotemporal variations, more and more global models are beginning to include explicit 49 calculations of the new particle formation and size-resolved aerosol microphysics [e.g., Bauer et 50 al., 2008; Spracklen et al., 2008; Makkonen et al., 2009; Wang and Penner, 2009; Pierce and 51 Adams, 2009; Yu and Luo, 2009]. Nucleation, which has been frequently observed in many parts 52 of the troposphere [e.g., Clarke and Kapustin, 2002; Kulmala et al., 2004; Yu et al., 2008], is an 53 important source of global particles and appears to dominate the total particle number abundance 54 in most part of troposphere [Yu and Luo, 2009]. While in situ measurements have long 55 established the involvement of sulfuric acid gas in atmospheric nucleation [e.g., Weber et al., 56 1996; Clarke et al., 1998], a consensus in the scientific community with regard to the dominant 57 nucleation mechanism(s) remains to be achieved despite decades of investigation on the 58 atmospheric nucleation phenomena. Nucleation theories of various kinds and versions have been 59 continuously developed and updated by different research groups. As a result of the uncertainties 60 in the thermodynamic data and various approximations employed in theoretical derivations, the 61 nucleation rates predicted by different nucleation theories/parameterizations differ by many 62 orders of magnitude [Yu and Turco, 2008]. More importantly, different kinds of nucleation 63 theories have different sensitivities to key parameters. A recent global simulation indicates that 64 aerosol indirect forcing is sensitive to the nucleation process [Wang and Penner, 2009]. In order 65 to assess the aerosol indirect radiative forcing and confidently project the future climate change, 66 identification of the dominant particle formation mechanism(s) and key controlling parameters is 67 imperative. 68

In this study, we employ a recently developed global size-resolved aerosol microphysics 69 model [Yu and Luo, 2009] to study the difference in the number concentrations of atmospheric 70 aerosols and their spatial distributions predicted by different nucleation schemes. The modeling 71 results are compared with an extensive set of land-, ship-, and aircraft- based measurements, 72 aiming to assess the ability of different nucleation theories in capturing the spatial distributions 73 of global particle number concentrations and to identify the nucleation scheme(s) that can 74 reasonably account for the observations. 75 76 2. Overview of nucleation theories/schemes and their differences 77

There exist three relatively well-developed atmospheric nucleation theories: (a) binary 78 homogeneous nucleation (BHN) of H2SO4 and H2O [e.g., Noppel et al., 2002; Vehkamäki et al., 79 2002; Yu, 2007, 2008]; (b) ternary homogeneous nucleation (THN) of H2SO4-H2O-NH3 [e.g., 80 Napari et al., 2002; Yu, 2006a, Merikanto et al., 2007]; and (c) ion-mediated nucleation (IMN) 81 [Yu and Turco, 2000; Yu, 2006b, 2009] or ion-induced nucleation (IIN) [e.g., Lovejoy et al., 82 2004; Sorokin et al., 2006] of H2SO4-H2O-Ion. In addition, a number of regression analyses 83 indicate that the particle formation rates derived from observations have a linear or square 84 dependence on H2SO4 vapor concentration ([H2SO4]) [e.g., Riipinen et al., 2007; Kuang et al., 85 2008], and empirical activation and kinetic nucleation formulas have been used in several studies 86 to represent boundary layer nucleation. Based on the kinetic THN model constrained by 87 laboratory studies [Yu, 2006a] and revised classical THN model [Merikanto et al., 2007], THN is 88 likely to be negligible in both the boundary layer because of high temperature [Yu and Turco, 89 2008] and the upper troposphere because of lower ammonia concentration. In the present study, 90 we focus on binary homogeneous, ion, and empirical nucleation, with two different schemes 91 selected for each category. Details of these nucleation schemes are given in Table 1. These six 92


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nucleation schemes represent the most recently updated version of corresponding nucleation 93 schemes currently available from several research groups. 94

Figure 1 shows the predicted nucleation rates based on six different nucleation schemes (JX, 95 X=IMN, IIN, KBHN, CBHN, EAN, EKN, see Table 1) as a function of [H2SO4], temperature 96 (T), relative humidity (RH), and surface area of pre-existing particles (S). Nucleation rates under 97 the same conditions given by two different parameterizations of the same nucleation kind differ 98 by up to many orders of magnitude. JIMN is generally several orders of magnitude higher than 99 JIIN. The possible cause of the difference has been discussed in Yu [2006b] and Yu and Turco 100 [2008]. It should be noted that an increase in JIIN as T increases when T > 290 K (Fig.1d) and as 101 [H2SO4] decreases when [H2SO4]< ~ 2×106 cm-3 (Fig.1b) is physically unrealistic and likely to 102 be caused by the error in the parameterization of Modgil et al. [2005]. Thus, care must be taken 103 in using the parameterization of Modgil et al. [2005] in regional and global simulations as it may 104 give erroneously high nucleation rates when T > ~ 290 K and [H2SO4]< ~ 106 cm-3. In the 105 present study, JIIN is set to zero when T>290 K, or when [H2SO4]<106/cm3 if 290 K>T>270 K. 106 The application of this filter will not affect results because under such conditions, nucleation rate 107 is generally negligible even based on IMN. The BHN is negligible in the warm lower 108 troposphere but could be significant in the cold upper troposphere. At colder T, JCBHN is several 109 orders of magnitude higher than JKBHN. Laboratory studies [Hanson and Lovejoy, 2006] have 110 shown that the Vehkamäki et al. [2002] model over-predicted the BHN rates by ~ 3 orders of 111 magnitude at low T. A significant over-prediction may mislead one to think that BHN can 112 account for the observed new particle formation rates in the upper troposphere while in reality 113 the observed nucleation events might be dominated by other mechanisms. The KBHN model 114 [Yu, 2007, 2008], which is constrained by multiple independent laboratory data sets (including 115 those of Hanson and Lovejoy [2006]), appears to be more accurate. JEAN and JEKN with the 116 assumed values of pre-factors A and K are generally higher than JIMN, especially at high T. The 117 pre-factors A and K, derived based on different measurements, vary by up to ~ four orders of 118 magnitude [Riipinen et al., 2007; Kuang et al., 2008]. It remains to be studied what controls the 119 variations of A and K. In the present study, the mean values of A and K derived from Hyytiälä 120 measurements (Riipinen et al., [2007]) are assumed. 121

[H2SO4] and T appear to be the two most important parameters controlling the formation 122 rates of particles in the troposphere [Yu, 2010]. It is clear from Fig. 1 that the dependence of J on 123 [H2SO4] and T differs significantly, with important implications to how future climate and 124 emission changes may impact new particle formation, aerosol indirect radiative forcing, and 125 climate feedback mechanisms [Yu, 2010]. Compared to JIMN, JCBHN and JKBHN are generally more 126 sensitive to the changes in T and [H2SO4]. Under the conditions shown in Fig. 1e, JIMN is very 127 sensitive to RH when RH is low (<~20%) but the sensitivity decreases as RH increases. JEAN and 128 JEKN have a weaker dependence on [H2SO4] and no dependence on T, RH, and S. It is interesting 129 to note that JIMN and JEAN, within certain parameter spaces, are close in terms of absolute values 130 and their sensitivities to [H2SO4], T, and RH. The dependence of JIMN on T, [H2SO4], and RH is 131 non-linear because of limitations in other parameters, especially ionization rates. Future climate 132 and emission changes are expected to substantially change T and [H2SO4] in different parts of 133 the atmosphere, and different nucleation theories may predict quite different nucleation rates 134 which would impact aerosol indirect radiative forcing and thus climate feedback processes. In 135 addition, ionization rate variations associated with solar activities will affect the ion nucleation 136 rates [Yu, 2010], but will have no influence on neutral nucleation. Therefore, it is critically 137 important to identify the dominant nucleation mechanism(s) in the atmosphere. 138


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Because of large spatial and temporal variations of T, [H2SO4], and other parameters in the 139 atmosphere, nucleation rates and total particle number concentrations in the global troposphere 140 predicted by different nucleation schemes are expected to differ significantly. With a global 141 aerosol microphysics model, different nucleation schemes for new particle formation can be 142 evaluated quantitatively by comparing the predicted particle number concentrations with the 143 field data taken in different parts of atmosphere during the past two decades. 144

145 3. Global size-resolved aerosol microphysics model 146

GEOS-Chem, a global 3-D model of atmospheric composition driven by assimilated 147 meteorological data from the NASA Goddard Earth Observing System 5 (GEOS-5), is employed 148 for this study. The GEOS-Chem model has been developed and used by research groups 149 worldwide and contains many state-of-the-art modules treating various chemical and aerosol 150 processes [e.g., Bey et al., 2001; Martin et al., 2003; Park et al., 2004; Evans and Jacob, 2005; 151 Nenes et al., 1998; Liao et al., 2007] with up-to-date key emission inventories [e.g., Bond et al., 152 2007; Zhang et al., 2009]. A detailed description of the model (including the treatment of 153 various emission sources, chemistry and aerosol schemes) can be found in the model webpage 154 (http://acmg.seas.harvard.edu/geos). 155

Recently, Yu and Luo [2009] incorporated an advanced particle microphysics (APM) model 156 into GEOS-Chem to treat size-resolved microphysics, dry deposition, and wet scavenging for 157 aerosols. In the present version of the aerosol module, size-resolved microphysics for secondary 158 particles (i.e., those formed from gaseous species) and sea salt has been treated with 40 sectional 159 bins to represent sulfate (or secondary) particles and 20 sectional bins to represent sea salt 160 particles. The bin structure is chosen to have relatively high resolution for the size range 161 important to the growth of nucleated particles (a few nanometers) to CCN. The growth of 162 nucleated particles through the condensation of sulfuric acid vapor and equilibrium uptake of 163 nitrate, ammonium, and secondary organic aerosol is explicitly simulated, along with the 164 scavenging of secondary particles by primary particles (dust, black carbon, organic carbon, and 165 sea salt). More details of the aerosol model can be found in Yu and Luo [2009]. The APM 166 module, after some further improvement and proper validation, will be added to the standard 167 version of GEOS-Chem in the near future. 168

Here, we extend the studies presented in Yu and Luo [2009] by simulating the same period 169 (year 2006) based on the six different nucleation schemes given in Table 1, with a horizontal 170 resolution of 4ox5o and 47 vertical layers up to 0.01hpa (GEOS-5 meteorological fields). The 171 GEOS-Chem v8-01-03 used in Yu and Luo [2009] has been updated to v8-02-02 for the present 172 simulation. To clearly compare the differences, the fraction of anthropogenic sulfur emitted as 173 primary sulfate (used to represent sub-grid nucleation process) has been set to zero. The oceanic 174 α-pinene emission indicated by ship measurements is not considered in the present study due to 175 the large unresolved difference between the total fluxes derived from “top-down” and “bottom-176 up” approaches [Luo and Yu, 2010]. The global ionization rates due to cosmic rays are calculated 177 based on the schemes given in Usoskin and Kovaltsov [2006] and the contribution of radioactive 178 materials from soil to ionization rates is parameterized based on the profiles given in Reiter 179 [1992]. Figure 2 gives the calculated zonally-averaged annual mean ionization rates (Q, in ion-180 pairs cm-3s-1). In the lower troposphere, Q is generally below 10 ion-pairs cm-3s-1. In the upper 181 troposphere, Q is in the range of 10-20 ion-pairs cm-3s-1 at the low latitudes (30oS-30oN) and 20-182 40 ion-pairs cm-3s-1 at the high latitudes. 183

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4. Global simulations and comparisons with measurements 185 Figures 3 and 4 present the horizontal (averaged within lower boundary layer 0-0.4 km, 186

annual mean) and vertical (zonal-averaged, annual mean) spatial distributions of nucleation rates 187 based on the six different nucleation schemes described in Table 1. As expected, nucleation rates 188 predicted based on different schemes vary significantly both in absolute values and in their 189 spatial distributions. Throughout the troposphere, the annual mean JIIN is a factor of ~ 10-100 190 smaller than JIMN, while JKBHN is a factor of ~ 10-100 lower than JCBHN. CBHN theory predicts 191 largest nucleation rates in the upper troposphere. CBHN theory also predicts large nucleation 192 rates in polar upper troposphere, differing substantially from predictions based on other 193 nucleation schemes. Large differences in annual mean nucleation rates predicted based on the ion 194 nucleation and BHN models has significant implications with regard to the relative importance 195 of ion versus neutral nucleation as a source of new particles in the troposphere. Based on CBHN, 196 neutral binary homogeneous nucleation dominates over ion nucleation in the upper troposphere. 197 However, based on KBHN, ion nucleation (IMN or IIN) dominates throughout the troposphere. 198 BHN has long been suggested as a major source of new particles in the upper troposphere. As we 199 have pointed out in section 2, the KBHN model is consistent with recent laboratory data and its 200 predictions should be considered to be more robust than CBHN. Therefore, it is reasonable to 201 conclude that ion nucleation dominates neutral nucleation, not only in the lower troposphere but 202 also in the middle and upper troposphere. This appears to be physically sound since ions are 203 known to enhance the nucleation as a result of charge effects on cluster stability and growth rates 204 [Yu, 2010]. There is no physical reason that the homogeneous nucleation rate exceeds ion 205 nucleation rates except under the conditions when the nucleation rate is larger than the ionization 206 rate (i.e., when JIMN is limited by ionization rate, see Fig.1). Our simulations indicate that the 207 conditions in the ambient troposphere generally do not reach the level that nucleation rates (Figs. 208 3-4) exceed ion production rates (Fig. 2). 209

Both EAN and EKN predict very large nucleation rates in most parts of the troposphere and 210 give nucleation rates spatial distributions quite different from those based on ion and neutral 211 nucleation theories. The empirical formulas (EAN and EKN) predict wide-spread nucleation 212 over tropical and sub-tropical oceans, in dramatic difference with those based on theoretical 213 models (IMN, IIN, KBHN, and CBHN). The main reason is the effect of temperature which is 214 not considered in empirical formulas. Another significant difference is that theoretical models 215 predict a high nucleation rate zone in the tropical upper troposphere while empirical formulas do 216 not predict such a zone. The reason again is associated with the effect of temperature. [H2SO4] 217 generally decreases with altitude, and this is why JEAN and JEKN decrease with altitude. The 218 theoretical models give a higher nucleation rate in the upper troposphere because of the lower T 219 which favors the nucleation based on the nucleation thermodynamics. 220

In the last two decades, particle number concentrations have been measured in different parts 221 of troposphere with land-, ship-, and aircraft-based instruments. A number of surface-based 222 stations have long term (multiple-year) continuous measurements of total number concentration 223 of particles larger than ~ 10 nm (CN10). The ship- and aircraft-based measurements of particle 224 number concentrations have limitations in the duration of the observations, but provide a much 225 wider spatial coverage, which is necessary for global 3-D comparisons. The particle number 226 concentration data from these measurement campaigns and monitoring stations can be used to 227 assess the ability of various nucleation schemes to capture the absolute values as well as the 228 spatial distributions of particle number concentrations. 229

Figure 5 shows the horizontal spatial distributions of annual mean number concentrations of 230 CN10 averaged within the lower boundary layer (0-0.4 km), simulated with the six nucleation 231


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schemes described in Table 1. Overlaid on each panel for comparison (symbols) are the nineteen 232 observed annual or multiple-year averaged CN10 values compiled in Yu and Luo [2009] plus two 233 additional data points from recent literature: 2730 cm-3 in Mukteshwar, India [Komppula et al., 234 2009] and 2030 cm-3 in Mount Waliguan, China [Kivekäs et al., 2009]. A more specific 235 comparison of simulated CN10 with observed values is presented in Figure 6 where the 236 simulated values based on different nucleation schemes are plotted against corresponding 237 observed values. It is clear from Figs. 5-6 that, under the present model assumptions regarding 238 primary emissions and other factors that affect CN concentrations, CN10 values simulated based 239 on IMN agree reasonable well with observations (within a factor of 2 for most sites), while those 240 based on CBHN, KBHN, and IIN underestimate the CN10 values by a factor of 2-10 for most 241 sites. The CN10 values based on empirical formulas (EAN, EKN) are generally higher than 242 observed ones (i.e., above 1:1 line) but are within a factor of ~ 2 for most of sites. IMN, EAN, 243 and EKN over-predict CN10 by a factor of more than 2 for two, five, and nine sites, respectively. 244 Although the annual mean nucleation rates predicted by different nucleation schemes differ by 245 several orders of magnitude (Fig. 3), the differences in the simulated CN10 are generally within 246 one order of magnitude due to the emission of primary particles, coagulation, and transport. We 247 would like to acknowledge that the present comparisons are subject to uncertainty associated 248 with the parameterizations of primary particle emissions [e.g., Pierce and Adams, 2006] and 249 inter-annual variations in the observed CN10 [e.g., Dal Maso et al., 2008]. While secondary 250 particles dominate CN concentration in most part of troposphere, primary particles can have 251 substantial contribution to particle numbers near the source regions [Yu and Luo, 2009] and they 252 can influence secondary particles formation via acting as condensation and coagulation sinks. 253 Further measurements to characterize the size-resolved primary particle emissions are needed to 254 reduce the uncertainty. 255

The comparisons shown in Figs. 5-6 are limited to 21 sites over the continents. Fig. 5 also 256 indicates large differences in the predicted particle number concentrations over oceans, 257 especially between theoretical models (IMN, IIN, CBHN and KBHN) and empirical formulas 258 (EAN and EKN). The ship-based data archived on the NOAA PMEL Atmospheric Chemistry 259 Data Server provide an extensive set of aerosol number concentration data with large spatial 260 coverage over the oceans and can be used to assess the modeled predictions. Figure 7 shows the 261 total number concentrations of condensation nuclei larger than 4 nm (CN4) simulated with six 262 different nucleation schemes. The symbols overlaid on each panel are the measured values from 263 13 ship-cruise campaigns taken by the PMEL Atmospheric chemistry group (MAGE-92, RITS-264 93, RITS-94, ACE-1, ACE-2, INDOEX99, NAURU99, ACE-Asia, NEAQS-2002, NEAQS-265 2004, TexAQS-GoMACCS, ICEALOT, and VOCALS; see http://saga.pmel.noaa.gov/data for 266 more information) and another ship-cruise from the English Channel to the coast of Antarctica 267 reported in Koponen et al. [2002]. It should be noted that the model results in Fig. 7 represent 268 annual mean values (for year 2006), while the observations for a given ship-cruise represent 269 average number concentrations of particles detected during a certain period of time in a specific 270 month of a certain year. This kind of comparison (including the one discussed below for aircraft 271 data) is necessary to establish the global 3-D pictures of particle number concentrations, and is 272 justified by the relatively small seasonal and annual variations of total particle number 273 concentrations over remote oceans and in the middle and upper troposphere. The number 274 concentrations measured during some cruises are for particles larger than ~ 3 nm while others are 275 for particles larger than ~ 5 nm. For comparison, the simulated results shown are for particles 276 larger than 4 nm. The difference caused by different cut-off sizes is less than ~ 10-20%. 277


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The ship data show clearly that total aerosol number concentrations over the ocean are 278 generally below 1000 cm-3 except near coastal regions. The EAN and EKN schemes over-predict 279 the CN4 over tropical and subtropical oceans by a factor of up to ~ 10. Such a significant over-280 prediction clearly indicates that the empirical formulas, derived empirically from the 281 measurements taken in several land-based stations [Riipinen et al., 2007; Kuang et al., 2008], is 282 likely to be invalid over the tropical and subtropical oceans. Under the present model 283 assumptions regarding primary emissions and other factors that affect CN concentrations, the 284 simulations based on the IMN scheme capture the observed CN4 gradients near the coast regions 285 of Asia, India, Europe, and the Eastern US. The simulated CN4 over remote oceans based on the 286 IMN scheme are generally within a factor of 2 when compared to ship-data. The factors 287 contributing to the difference at a specific location include the uncertainties in the model 288 (emission, chemistry, microphysics, transport and scavenging) and observations (counting 289 efficiency for small particles, etc.), seasonal variations (annual mean versus daily mean), and 290 spatial inhomogenuity (grid-box average versus value at a specific location). Overall, the IMN 291 prediction agrees well with ship-data. The low CN4 over remote oceans predicted by BHN and 292 IIN schemes are generally consistent with the ship data, indicating that in situ nucleation is not 293 necessary to explain the observed CN4 over remote oceans. Nevertheless, BHN and IIN 294 significantly under predict the CN4 near the coastal regions, associated with the under-295 predictions of nucleation and particles over major continents (Figs. 5-6). 296

So far, our comparisons with measurements have been limited to surface-based 297 measurements of particle number concentrations in the lower boundary layer. To assess the 298 vertical structure of global particle number concentrations, we use the total number 299 concentrations of ultrafine condensation nuclei (UCN) measured with a CN counter (low cut-off 300 size ~ 3-4 nm, high cut-off size ~ 3000 nm) during a number of aircraft-based field campaigns 301 (GLOBE, ACE-1, PEM-Tropics A and B, TRACE-P, INTEX-A, NAMMA, TC-4, and 302 ARCTAS). Information about these field measurements can be found in Clarke and Kapustin 303 [2002], NASA’s Global Tropospheric Experiment (GTE) database website, and corresponding 304 field campaign websites. Figure 8 gives 3-D and 2-D spatial distributions of the observed UCN 305 concentrations. Figure 8a contains about 63,000 one-minute-average data points, plotted in the 3-306 D coordinate system based on the measurement locations (longitude, latitude, and pressure or 307 sigma). In Fig. 8b, to avoid overlap owing to the large number of data points, the sigma-latitude 308 cross section is divided into a 0.01 x 1o grid, wherein all data, at all longitudes, are averaged. The 309 data shown in Fig. 8, obtained during nine different airborne field campaigns, covers a wide 310 range of areas and seasons. The high UCN regions in the tropical upper troposphere and northern 311 mid-latitude lower troposphere, and low UCN regions in tropical lower troposphere and the 312 Arctic can be clearly seen from Fig. 8. The large amount of UCN data given in Fig. 8, along with 313 a number of other airborne measurements reported in the literature [e.g., Brock et al., 1995; 314 Heintzenberg et al., 2003; Lee et al., 2003], consistently show that the average UCN 315 concentration in the tropical upper troposphere is generally in the range of ~ 5000 – 10000 cm-3 316 at standard temperature and pressure (STP). The UCN concentrations in the tropical lower 317 troposphere and the Arctic are generally below ~ 500-1000 cm-3 at STP. 318

To compare the simulated UCN concentrations with aircraft-based measurements, we select 319 modeling output data points at the locations (longitude, latitude, and altitude) corresponding to 320 those observed. Figure 9 shows the 2-D distributions of these selected modeling data points 321 (averaged in the same way as for the observed data shown in Fig. 8b) based on six different 322 nucleation schemes. Similar to Fig. 7, the model results in Fig.9 represent annual mean values 323 (for year 2006), while the observations in Fig. 8 represent average number concentrations of 324


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particles detected during a certain period of time on a specific day of a year. The low cut-off size 325 of CN counter is ~ 3 nm in some measurements and is ~ 4 nm in others. The simulated results are 326 for all particles > 4 nm. Both observed values in Fig. 8 and modeled values in Fig. 9 have been 327 normalized to standard temperature and pressure (STP: 1000 mb and 298 K). 328

Figures 8-9 show that observed UCN concentrations have clear vertical structures and 329 different nucleation schemes predict quite different vertical features of CN4. The CN4 values in 330 the middle troposphere (~ 500-600 mb) predicted by IMN are somehow higher than the observed 331 ones while those in the lower troposphere at latitudes of ~ 25 oN-50 oN are somehow lower than 332 the observed values. The uncertainties in the model processes and observations, seasonal 333 variations, and spatial inhomogenuity may all contribute to the difference. CBHN, KBHN, and 334 IIN significantly under-predict UCN concentration in the lower troposphere (below ~ 500 mb). 335 CN4 vales based on CBHN are in reasonable agreement with observed values at higher altitudes 336 (~ 300-500 mb). However, CBHN over-predict the particle number concentration in the upper 337 troposphere (~ 200 mb), likely as a result of the over-prediction in the nucleation rates (Fig. 4d) 338 associated with the capillarity approximation implied in CBHN [Yu, 2007]. KBHN and IIN lead 339 to the formation of new particles in the upper troposphere, but the predicted concentrations are 340 smaller than IMN and are not able to account for the observed values. The over-prediction of 341 particle number concentrations over tropical oceans by EAN and EKN extends from the surface 342 (also see Figs. 7e-f) to around ~ 400 mb. All nucleation schemes predict relatively low CN4 343 values over the Arctic region, in agreement with the data. 344

The IMN prediction captures the low CN concentrations over tropical oceans and the 345 observed minimum particle number concentration in the middle troposphere over middle 346 latitudes in the northern hemisphere. In agreement with observations, IMN predictions indicate 347 two peak particle number concentration zones: one located in the boundary layer over continents 348 at middle latitudes in the Northern Hemisphere and the other in the tropical upper troposphere. 349 Such a two-peak distribution can also be seen from IIN, CBHN, and KBHN simulations. The 350 zone in the boundary layer is a result of high anthropogenic sulfur emissions. In contrast, the 351 zone in the tropical upper troposphere appears to be largely associated with naturally emitted 352 sulfur species (DMS and oxidation products) and clean marine air lofted by convection and 353 scavenged of most pre-existing particles. The downward transport of particles formed aloft is a 354 major source of sulfate particles in the tropical regions (especially over oceans) which can be 355 clearly seen from the vertical profile of sulfate particle size distributions in the region [Yu and 356 Luo, 2009]. The downward transport of particles in the tropical regions has been suggested in a 357 number of previous studies [e.g., Raes, 1995; Clarke et al., 1999a, b], but these particles have 358 generally been considered to nucleate via binary homogeneous nucleation. By comparing 359 measured [H2SO4] with critical [H2SO4] required for a nucleation rate of 1 cm-3s-1 (calculated 360 with the parameterization of Wexler et al. [1994]), Clarke et al. [1999a, b] suggested that the 361 classical BHN theory of natural sources of sulfuric acid could account for the observed 362 nucleation in the near-cloud environment. The present study shows that the formation of these 363 particles in the tropical upper troposphere, based on thermodynamically and kinetically most up-364 to-date ion and neutral nucleation mechanisms, appears to be dominated by ion nucleation 365 instead of neutral nucleation (Figs. 4 and 9). In a separate detailed study on the mechanism of 366 particle production near marine clouds (manuscript in preparation), we show that the observed 367 nucleation in the near-cloud environment is consistent with IMN mechanism while BHN (based 368 on KBHN) is totally negligible. It appears that the BHN rates based on the parameterization of 369 Wexler et al. [1994] are even higher than those of CBHN [Vehkamäki et al., 2002] which are 370


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known to have already over-predicted BHN rates by around three orders of magnitude [Hanson 371 and Lovejoy, 2006; Yu, 2007]. 372

373 4. Summary and discussion 374

The main objective of this study was to identify the nucleation scheme(s) that can account for 375 both the absolute values and spatial distributions of particle number concentrations in the 376 troposphere, through comparing global aerosol simulations based on six different nucleation 377 schemes with an extensive set of land-, ship-, and aircraft- based particle number concentration 378 measurements. The nucleation schemes considered in the present study included: ion-mediated 379 nucleation (IMN) [Yu and Turco, 2000; Yu, 2010], ion-induced nucleation (IIN) [Lovejoy et al., 380 2004; Modgil et al., 2005], kinetic binary homogeneous nucleation (KBHN) [Yu, 2007, 2008], 381 classical binary homogeneous nucleation (CBHN) [Noppel et al., 2002; Vehkamäki et al., 2002], 382 empirical activation nucleation (EAN) and empirical kinetic nucleation (EKN) [Riipinen et al., 383 2007; Kuang et al., 2008]. 384

We showed that nucleation rates predicted based on different schemes vary significantly both 385 in absolute values and in their spatial distributions. The comparisons of simulated global particle 386 number concentrations with land-, ship-, and aircraft- based measurements indicated that, under 387 the present model assumptions regarding primary emissions and other factors that affect CN 388 concentrations, only the IMN scheme could reasonably account for the absolute values (within a 389 factor of ~ 2) and spatial distributions of total particle number concentrations in the whole 390 troposphere. The IMN prediction captured the low concentrations in the lower troposphere over 391 tropical oceans and the observed minimum in particle number concentration in the middle 392 troposphere over middle latitudes in the Northern Hemisphere. Consistent with observations, 393 IMN simulations indicated two peak particle number concentration zones: one located in the 394 boundary layer over continents at middle latitudes in the Northern Hemisphere due to high 395 anthropogenic sulfur emissions, and the other in the tropical upper troposphere associated with 396 naturally emitted sulfur species. The downward transport of particles formed in the upper 397 troposphere is a major source of sulfate particles in the tropical regions. CBHN, KBHN, and IIN 398 significantly under-predicted particle number concentration in the lower troposphere (below ~ 399 500 mb), especially in the boundary layer over major continents (by a factor of up to ~ 10). In 400 the upper troposphere, KBHN and IIN lead to the formation of new particles, but the predicted 401 concentrations were much smaller than the observed values. In contrast, CBHN over-predicted 402 the particle number concentration in the upper troposphere (~ 200 mb). EAN and EKN 403 significantly over-predicted the particle number concentrations over tropical and sub-tropical 404 oceans (by a factor of up to ~ 10 in the boundary layer), and such over-predictions extended from 405 the ocean surface to around ~ 400 mb. It should be noted that the current EAN and EKN 406 parameterizations assume constant pre-factors and the performance of EAN and EKN 407 parameterizations could be improved if the dependence of pre-factors on other parameters (such 408 as temperature, etc.) can be identified and incorporated. 409

Our results suggest that ion mediated H2SO4-H2O nucleation appears to dominate over 410 neutral H2SO4-H2O nucleation, not only in the lower troposphere but also in the middle and 411 upper troposphere. According to the recently developed KBHN model constrained by multiple 412 independent laboratory measurements, H2SO4-H2O binary homogeneous nucleation is negligible 413 when compared to IMN, even in the cold upper troposphere. Previous conclusions about the 414 importance of BHN in the upper troposphere based on the CBHN model should be revisited, as 415 the CBHN model has been shown recently in laboratory and theoretical studies to over-predict 416 the nucleation rates under upper tropospheric conditions by three orders of magnitude. The 417


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present global scale study, combined with previous well constrained case studies of nucleation 418 events observed in boreal forests [Yu and Turco, 2008], and the fact that the IMN model is based 419 on state-of-the-art thermodynamic and laboratory data [Yu, 2010], make a strong case for the 420 importance of ion nucleation in the troposphere. This may have important implications because 421 ion nucleation rates and hence global particle number abundance are subject to the influence of 422 ionization rate variations associated with solar activities, which may amplify the impact of solar 423 variations on Earth’s climate. The magnitude of solar variations on the abundance of climate 424 effective particles remains to be investigated. We would like to point out that there exists 425 controversy over the importance of ion-mediated nucleation versus neutral nucleation based on 426 the multiple-instrument characterization of nucleation events in boreal forests [Laakso et al., 427 2007; Kulmala et al., 2007; Manninen et al., 2009; Yu and Turco, 2008; Yu, 2010]. The possible 428 reasons of difference in the interpretation of same measurements have been discussed in detail in 429 Yu [2010]. It should also be noted that nucleation has been observed to be enhanced in recent 430 laboratory studies when sulfuric acid is produced via SO2 + OH reaction (rather than from liquid 431 samples) [e.g., Berndt et al., 2006; Benson et al., 2007]. However, the level of enhancement 432 differs significantly based on the studies of the two different groups [Du and Yu, 2009] and a 433 theory considering the enhancement remains to be developed. Some yet to be identified 434 nucleation mechanisms (both neutral and ion-mediated, involving species in addition to H2SO4 435 and H2O) may occur in the atmosphere as well. Such new nucleation mechanisms, when 436 available, should also be assessed against aerosol measurements with globe coverage. 437

As far as we know, this study represents the first comprehensive comparison of global 438 particle number simulations with land-, ship-, and aircraft-based measurements, which 439 establishes a global 3-D picture of particle number abundance in the troposphere. Nevertheless, 440 some of these comparisons were between simulated annual mean values and short-term ship and 441 aircraft measurements. The present global aerosol microphysical simulations are also subject to 442 uncertainties associated with parameterizations of primary particles, contributions of secondary 443 organic species to particle growth, and cloud scavenging and transport processes. For example, 444 Pierce and Adams [2006] showed that sea-salt particle concentration simulated by different 445 emission schemes differ substantially. Our analyses indicate that the uncertainties in primary 446 particle emission parameterizations do not affect the conclusions of this paper, mainly because of 447 the dominance of total particle number concentrations by secondary particles in most part of the 448 troposphere [Yu and Luo, 2009]. Currently it is hard to evaluate how the uncertainties in the 449 secondary organic condensation and sub-grid cloud processing may affect the simulated particle 450 number distributions, largely associated with the poor understanding of the formation and 451 properties of secondary organics and low spatial resolution of global models. Comparison with 452 simulated particle properties with various detailed aerosol measurements, such as the one 453 presented in this study, provides a mean to evaluate the performance of models and identify areas 454 for further improvement. Simulations with higher horizontal spatial resolution (for example, with 455 nested domain) and more detailed comparisons with field measurements (for example the diurnal 456 and seasonal variations of particle size distribution evolutions) should be carried out to improve 457 our understanding of key processes controlling particle properties in the atmosphere. 458 459 Acknowledgments. This study is supported by the NSF under grant AGS-0942106 and NASA 460 under grant NNX08AK48G. The GEOS-Chem model is managed by the Atmospheric Chemistry 461 Modeling Group at Harvard University with support from the NASA Atmospheric Chemistry 462 Modeling and Analysis Program. 463 464


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618 619 620 621 622 623 624 625

Table 1. Six nucleation schemes (IMN, IIN, KBHN, CBHN, EAN, EKN) used in the present 626 study and corresponding variables controlling the predicted nucleation rates (J). JIMN and JIIN 627 depend on sulfuric acid vapor concentration ([H2SO4]), temperature (T), relative humidity (RH), 628 ionization rate (Q), and surface area of pre-existing particles (S). JKBHN and JCBHN depend on 629 [H2SO4], T, and RH. JEAN and JEKN depend on [H2SO4] only. 630

Nucleation Model /Parameterization

Controlling variables References

Ion-mediated nucleation (IMN)

JIMN=f ([H2SO4], T, RH, Q, S) Yu and Turco [2000]; Yu [2006b, 2009].

Ion-induced nucleation (IIN)

JIIN=f ([H2SO4], T, RH, Q, S) Lovejoy et al. [2004]; Modgil et al. [2005]

Kinetic binary homogeneous nucleation (KBHN)

JKBHN=f ([H2SO4], T, RH) Yu [2007, 2008]

Classical binary homogeneous nucleation (CBHN)

JCBHN=f ([H2SO4], T, RH) Noppel et al. [2002]; Vehkamäki et al. [2002]

Empirical activation nucleation (EAN)

JEAN=A [H2SO4], A=3.5×10-7 used in this study

Riipinen et al. [2007]; Kuang et al. [2008]

Empirical kinetic nucleation (EKN)

JEAN=K [H2SO4]2, K=5.5×10-14 used in this study

Riipinen et al. [2007]; Kuang et al. [2008]

631 632 633 634


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Figure Captions: 635 Figure 1. The dependence of nucleation rates on (a, b) [H2SO4], (c, d) T, (e) RH, and (f) S, based 636 on six nucleation schemes described in Table 1 under selected conditions. Ionization rate (Q) is 637 assumed to be 10 ion-pairs cm-3s-1 for all IMN and IIN cases, and the values of other parameters 638 are specified in the legend. 639 640 Figure 2. Vertical (sigma=pressure/surface pressure) structure of annual-mean, zonally-averaged 641 ionization rates due to galactic cosmic rays and radioactive materials from soil. 642 643 Figure 3. Horizontal spatial distributions of annual mean nucleation rates in the lower boundary 644 layer (0-0.4 km) simulated with six different nucleation schemes described in Table 1. 645 646 Figure 4. Vertical (sigma=pressure/surface pressure) structure of annual-mean, zonally-averaged 647 nucleation rates predicted with six different nucleation schemes described in Table 1. 648 649 Figure 5. Horizontal distributions of annual mean number concentrations of condensation nuclei 650 larger than 10 nm (CN10) in the lower boundary layer (0-0.4 km) simulated with six different 651 nucleation schemes described in Table 1.The observed annual or multiple year averaged CN10 652 values from 21 sites are also overlapped on the plots for comparison. 653 654 Figure 6. Comparison of annually averaged number concentrations of CN10 observed at 21 sites 655 shown in Figure 5 with those simulated based on six different nucleation schemes described in 656 Table 1. The solid line shows a 1:1 ratio, the dashed lines show ratios of 2:1 and 1:2, and dot-657 dashed lines show ratios of 5:1 and 1:5. 658 659 Figure 7. Horizontal spatial distributions of annual mean number concentrations of particles 660 larger than 4 nm (CN4) in the lower boundary layer (0-0.4 km) simulated with six different 661 nucleation schemes described in Table 1. Overlapped on the plots for comparison are ship-based 662 measurements of CN4 during 13 ship-cruise campaigns taken by the PMEL Atmospheric 663 chemistry group and another ship-cruise from the English Channel to the coast of Antarctica 664 reported in Koponen et al. [2002]. 665 666 Figure 8. (a) 3-D and (b) 2-D spatial distributions of ultrafine condensation nuclei (UCN) 667 measured with CN counter (with low cut-off size of ~ 3-4 nm) during a number of aircraft-based 668 field campaigns (GLOBE, ACE-1, PEM-Tropics A and B, TRACE-P, INTEX-A, NAMMA, TC-669 4, and ARCTAS). Panel (a) contains about 63,000 one-minute-average data points. In panel (b), 670 the sigma-latitude cross section is divided into 0.01 x 1o grids, wherein all data within a grid (at 671 all longitudes) are averaged. The values have been normalized to standard temperature and 672 pressure (STP: 1000 mb and 298 K). 673 674 Figure 9. 2-D distributions of these selected modeling data points (averaged in the same way as 675 for the observed data shown in Fig. 7b) based on six different nucleation schemes. The values 676 have been normalized to standard temperature and pressure (STP: 1000 mb and 298 K). 677 678 679 680 681


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106 107 108

[H2SO4]

10-610-510-410-310-210-1100101102103

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leat

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m-3

s-1)

(a)

JIMN JIIN JKBHN JCBHN JEAN JEKN

(b)

200 220 240 260 280T (K)

(c) (d)

10-610-510-410-310-210-1100101102103

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[H2SO4]

10-610-510-410-310-210-1100101102103

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200 220 240 260 280 300T (K)

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0 20 40 60 80RH (%)

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10-2

100

102

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leat

ion

Rat

e (c

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s-1)

(e)

0 20 40 60 80 100S (μm2/cm3)

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100

101

102

103

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leat

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Rat

e (c

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(f)

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e (c

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s-1)

Nuc

leat

ion

Rat

e (c

m-3

s-1)T=270 K, RH=70%

S= 95 m /cmμ 2 3T=290 K, RH=90%S= 95 m /cmμ 2 3

RH=50%S= 50 m /cmμ 2 3

[H SO ]=3x10 /cm 2 46 3

RH=70%S= 95 m /cmμ 2 3

[H SO ]=2x10 /cm 2 47 3

S= 30 m /cmμ 2 3[H SO ]=1x10 /cm 2 4

7 3T=270 K

[H SO ]=5x10 /cm 2 46 3

T=240 K, RH=50%

682 683

Figure 1 684 685


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686 687

Figure 2 688 689

690 691

Figure 3 692


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693 694

695 696

Figure 4 697 698


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699 Figure 5 700

701


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cm3 )

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702 703

Figure 6 704 705


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Figure 8 712


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Figure 9 716 717

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