journal of geophysical research, vol. ddd, addddd, …dust.ess.uci.edu/ppr/ppr_btj07.pdf · 118 119...

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. ddd, Addddd, doi:10.1029/2007JAdddddd, 2007.

DRAFT 29 May 2007 1 of 54

Numerical simulations of the three-dimensional distribution of meteoric dust in the 1

mesosphere and upper stratosphere 2

3

C. G. Bardeen1, O. B. Toon1, E. J. Jensen2, D. R. Marsh3, and V. L. Harvey4 4

5

1 Department of Atmospheric and Oceanic Sciences, Laboratory for Atmospheric and 6

Space Physics, University of Colorado, Boulder, Colorado, USA. 7

2 NASA Ames Research Center, Moffet Field, California, USA. 8

3 National Center for Atmospheric Research, Boulder, CO, USA 9

4 Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, 10

Colorado, USA. 11

12

13



Micrometeorites that ablate in the lower thermosphere and upper mesosphere are thought 13

to recondense into nanometer-sized smoke particles and then coagulate into larger dust 14

particles. Previous studies with one-dimensional models have determined that the 15

meteoric dust size distribution is sensitive to the background vertical velocity and have 16

speculated on the importance of the mesospheric meridional circulation to the dust spatial 17

distribution. We use a three-dimensional general circulation model with sectional 18

microphysics to study the distribution and characteristics of meteoric dust in the 19

mesosphere and upper stratosphere. We find that the mesospheric meridional circulation 20

causes a strong seasonal pattern in micrometeorite concentration in which the summer 21

pole is depleted and the winter pole is enhanced. This summer pole depletion of dust 22

particles results in fewer dust condensation nuclei (CN) than has traditionally been 23

assumed in numerical simulations of polar mesospheric clouds (PMCs). However, the 24

total number of dust particles present is still sufficient to account for PMCs if smaller 25

particles can nucleate to form ice than is conventionally assumed. During winter, dust is 26

quickly transported down to the stratosphere in the polar vortex where it may participate 27

in the nucleation of sulfate aerosols, the formation of the polar CN layer, and the 28

formation of polar stratospheric clouds (PSCs). Dust distributions in our model are 29

compared to results from the one-dimensional models, to observations of charged dust in 30

the mesosphere and to observations of meteoric dust content in stratospheric aerosols. 31

32

INDEX TERMS: 0305 (Aerosols and particles), 0340 (Middle atmosphere: composition 33

and chemistry), 0341 (Middle atmosphere: constituent transport and chemistry), 0545 34

(Modeling), 3332 (Mesospheric dynamics) 35



36

KEYWORDS: meteoric, dust, smoke, mesosphere, microphysics, model 37

38

39



1. Introduction 39

Estimates of the flux of meteoric material into the Earth’s atmosphere vary considerably 40

[Gabrielli, et al., 2004], but commonly used values are 16±5 kt yr-1 [Hughes, 1978] and 41

40±20 kt yr-1 [Love and Brownlee, 1993]. Most of these micrometeorites are thought to 42

ablate in the lower thermosphere and upper mesosphere [Hunten, et al., 1980; 43

Kalashnikova, et al., 2000], resulting in atomic metal layers observed by lidar between 80 44

and 110 km [Kane and Gardner, 1993; Plane, 2003]. The ablation products are likely to 45

recondense into nanometer-sized smoke particles [Rosinski and Snow, 1961], which are 46

extremely small and difficult to detect [Gelinas, et al., 1998; Havnes, et al., 2001; Lynch, 47

et al., 2005; Rapp, et al., 2005]. These small particles can coagulate into larger particles 48

[Hunten, et al., 1980] which may act as heterogeneous ice nuclei for polar mesospheric 49

clouds (PMC) [Rapp and Thomas, 2006; Turco, et al., 1982] and polar mesospheric 50

summer echoes (PMSE) [Rapp and Lubken, 2001]. 51

52

Murphy et al. [1998] and Cziczo et al. [2001] have observed meteoric dust in ~50% of 53

their samples of stratospheric aerosols. Meteoric dust may affect the size distribution and 54

composition of the sulfate aerosol layer and subsequently affect the nucleation of polar 55

stratospheric cloud (PSC) particles [Prather and Rodriguez, 1988]. Curtius et al. [2005] 56

observed an increased fraction of stratospheric aerosols with a non-volatile content inside 57

the Arctic polar vortex (67%) compared to outside the vortex (24%). They concluded that 58

meteoric dust is transported downward from the mesosphere inside the vortex and the 59

increased fraction is therefore an indicator of mesospheric air inside the polar vortex. 60

Meteoric dust descending in the polar vortex may also contribute to enhancements in the 61



polar condensation nuclei layer observed in the winter around 30 km [Hofmann, et al., 62

1988]. 63

64

Previous numerical simulations of meteoric dust formation and transport have been one-65

dimensional [Gabrielli, et al., 2004; Hunten, et al., 1980; Megner, et al., 2006]. Megner 66

et al. [2006] found that the size distribution of dust particles is sensitive to vertical 67

velocity; however, the overall impact of transport is difficult to evaluate from a one-68

dimensional simulation. Three-dimensional simulations of meteoric metals [Prather and 69

Rodriguez, 1988] and chemical tracers [Fischer and O'Neill, 1993; Plumb, et al., 2002] 70

have shown that the meridional mesospheric circulation will transport material from the 71

summer pole to the winter pole where it will be transported downward inside the polar 72

vortex. For this study, we created a three-dimensional general circulation model with 73

sectional microphysics (WACCM/CARMA) to investigate the combined impact of 74

transport and coagulation on the distribution of meteoric dust in the mesosphere and 75

upper stratosphere. 76

77

2. Model Description 78

WACCM/CARMA is a combination of the Whole-Atmosphere Community Climate 79

Model (WACCM) general circulation model [Garcia, et al., 2007] and a sectional 80

microphysics model based upon the Community Aerosol and Radiation Model for 81

Atmospheres (CARMA) [Toon, et al., 1988; Turco, et al., 1979]. As is shown in Figure 82

1, a single column version of CARMA is implemented as a WACCM physics package. 83

This means that the model’s state is passed to CARMA one column at a time, that 84



CARMA calculates tendencies on constituents within these columns, and that these 85

tendencies are then passed back to WACCM where they are used to adjust the model’s 86

state. Each CARMA size bin is added as a unique WACCM constituent. WACCM is 87

responsible for advection, vertical diffusion and wet deposition of all constituents, while 88

CARMA is responsible for the production, sedimentation and coagulation of the meteoric 89

dust constituents. 90

91

2.1 WACCM 92

We used WACCM3 version 3.1.9 tag 9 at 4˚x5˚ resolution with 66 vertical levels and a 93

model top near 150 km, which provides a vertical spacing of ~3.5 km in the mesosphere. 94

For one simulation, we used a vertical grid with 125 levels and ~0.5 km spacing near the 95

mesopause. WACCM’s vertical diffusion algorithm handles turbulence, gravity waves, 96

and molecular diffusion. The molecular diffusion code was designed for gases and is 97

unstable for the larger mass of the dust particles. Therefore it was disabled for the dust 98

particles. The default tuning of the gravity wave algorithm [Beres, et al., 2005; Garcia, et 99

al., 2007; Sassi, et al., 2002] is a compromise between generating a good climatology in 100

the stratosphere and the mesosphere. We conducted sensitivity tests with parameter 101

choices for the gravity wave parameterization that create a more realistic mesopause. 102

WACCM has water vapor and prescribed sulfate aerosols; however, in these experiments 103

we did not let the dust particles nucleate ice particles or interact with the sulfate aerosols. 104

Dust particles are removed in the troposphere via wet deposition using the existing 105

scheme [Barth, et al., 2000]. 106

107



2.2 CARMA 108

We used CARMA 2.3 configured for one-dimensional columns using the same vertical 109

grid as WACCM. Most runs used 21 size bins for dust particles with a mean radius from 110

1 to 100 nm and a doubling of particle volume between adjacent bins. Some runs used 28 111

size bins with radii from 0.2 to 100 nm. The initial particle size is a function of the 112

coagulation [Hunten, et al., 1980; Rosinski and Snow, 1961] and polymerization [Plane, 113

2003] that may occur in the region of the meteor trail. We base our cases on the 114

approximate size of an iron or silicon oxide molecule of 0.2 nm and a slightly larger size 115

of 1 nm that assumes more initial recombination. The density of the dust particles is 116

assumed to be 2.0 g cm-3 [Hunten, et al., 1980]. 117

118

The dust source function uses a production rate from Kalashnikova et al. [2000], which 119

has emissions from 75 to 110 km with a peak around 83 km (figure 2). All particles are 120

placed into the smallest size bin and the production rate is scaled over the area of the 121

Earth and the production altitude range to preserve the global mean mass influx at a rate 122

of ~16 kt yr-1. This global mass influx is based on the Kalashnikova et al. [2000] 123

production rate for particles with a radius of 1.3 nm and a density of 2.0 g cm-3 and is 124

similar to the rates measured by Hughes et al. [1978] and Gabrielli et al. [2004]. If 125

instead, the mass influx from Love and Brownlee [1993] were used, approximately twice 126

as many particles would be created. This would have a significant effect on the 127

coagulation rates and the resulting particle distributions. The dust source is assumed to be 128

temporally and horizontally uniform. Meteoric particles that do not ablate because of 129

their size are not included in the model. These particles represent about 20% of the total 130



mass [Kalashnikova, et al., 2000], are larger (5-50 µm radius) [Kalashnikova, et al., 131

2000] and are expected to sediment rapidly [Hunten, et al., 1980; Turco, et al., 1981]. 132

133

CARMA includes routines for sedimentation and coagulation of particles. The fall 134

velocities are calculated by assuming a Stokes flow with size corrections from Fuchs 135

[1964]. The vertical transport is solved using the piecewise parabolic method of Colella 136

and Woodward [1984]. No additional diffusion of aerosol particles is added by CARMA. 137

Coagulation coefficients are calculated to include Brownian, convective and gravitational 138

effects. A sticking coefficient of 1 is used, which means that particles are assumed to 139

stick together once they collide. The sticking coefficient and coagulation coefficients 140

could be affected by particle charging [Hunten, et al., 1980; Jensen and Thomas, 1991; 141

Reid, 1997], intermolecular forces like Van der Wals forces [Hunten, et al., 1980] and 142

magnetic dipole interactions [Saunders and Plane, 2006], which could either reduce or 143

enhance coagulation. Megner et al. [2006] identified coagulation rates as a significant 144

source of uncertainty in their meteoric dust model and it remains so in these simulations. 145

CARMA calculates the effect of coagulation using the numerical approach described in 146

Jacobsen et al. [1994]. 147

148

3. Results 149

We conducted 7 simulations including a control run with a 10-year duration and 6 150

sensitivity tests with 4-year durations. The definition of each simulation is shown in 151

Table 1. All simulations except simulation 7, the 125 level case, start with the same initial 152

condition and none of them start with any meteoric dust. The 125 level case has an initial 153



condition interpolated from a slightly different reference simulation; however, these 154

differences should not have an influence on the results. Figure 3 shows the total mass of 155

dust in the atmosphere as a function of height and time for the control run. A semiannual 156

pattern in dust mass exists in the mesosphere, while an annual pattern exists in the 157

stratosphere and troposphere. The model nearly reaches steady state in the entire 158

atmosphere after 10 years and reaches steady state above 30 km within 3 years (figure 4). 159

Since we are not modeling the interaction of dust with sulfate aerosols and water vapor, 160

we do not expect the model to properly represent the state of the dust particles in the 161

troposphere or lower stratosphere. Therefore, the analysis that follows is restricted to 162

heights above 30 km. In the control run, we have averaged the last 7 years of data, but in 163

the sensitivity tests, we only use the last year of the 4-year run. Comparisons between the 164

control run and the sensitivity tests are made using the fourth year of each simulation. 165

166

3.1 Control 167

Our control simulation uses the default WACCM configuration and an initial dust particle 168

radius of 1 nm. The simulation was run for 10 years and the average of the last 7 years for 169

January and July is shown in Figure 5. There is a large seasonal variation in dust 170

concentrations caused by the mesospheric circulation that goes from the summer pole to 171

the winter pole. These two seasons are the extremes cases, with a more uniform 172

distribution occurring in-between. It can also be seen that the mass density is highest in 173

the winter pole and increases toward the stratosphere. Figure 6 shows polar plots at 174

several altitudes for the month with the peak in the dust mass mixing ratio at that altitude. 175

Other than at 90 km, the dust is largely confined to the vortex. The descent through the 176


DRAFT 29 May 2007 10 of 54

mesosphere takes about a month from April to May; however, it takes an additional four 177

months, out to September, to get the peak to 30 km. 178

179

To study the vortex in more detail, the vortex identification technique of Harvey et al. 180

[2002] was used to locate the vortex in the last year of the data. This technique calculates 181

a scalar quantity Q that is an indicator of rotation (negative) and strain (positive) in the 182

wind field and attempts to find streamlines around areas of cyclonic rotation where the 183

contour integral of Q is zero. Figure 7 shows the mass mixing ratio of all the meteoric 184

dust inside and outside of the vortex for both hemispheres. The seasonal transport of dust 185

between hemispheres because of the meridional circulation can be seen clearly both 186

inside and outside of the vortex between potential temperatures of 4000 and 5000 K. At 187

lower potential temperatures, dust can be seen descending into the stratosphere mostly 188

inside the vortex starting in the fall. Figure 8 shows the ratio of the dust mass mixing 189

ratio inside the vortex to that outside the vortex. This figure highlights that the dust is 190

well confined while it descends in the vortex, particularly in the southern hemisphere. In 191

the southern hemisphere, a vortex with enhanced dust concentrations persists throughout 192

the year between 500 and 1000 K. Curtius et al. [2005] measured the fraction of 193

stratospheric aerosols with a non-volatile component and found a ratio of 3:1 between the 194

inside and the outside of the vortex. Their measurements were made between 10 January 195

2003 and 19 March 2003 at potential temperatures of 300 to 500 K near Kiruna, Sweden. 196

While not measuring exactly the same thing, in Figure 8 we get a ratio ~3:1 at a similar 197

time and location. Figure 8 indicates that much larger dust gradients should be 198

measurable at other times and locations. 199


DRAFT 29 May 2007 11 of 54

200

Hunten et al. [1980], did some of the fundamental modeling work on meteoric dust using 201

one-dimensional simulations with an earlier version of CARMA using conditions from 202

mid-latitude. In Figure 9, we compare our results for the 1 nm case to theirs presented in 203

their Figures 4 and 5. Hunten et al. [1980] significantly overestimates the concentration 204

and surface area density below ~75 km during the summer at mid to high latitudes 205

relative to our results. Our results also show an enhancement during the winter at high 206

latitudes. In Figure 10, we compare our size distributions at 30, 60 and 90 km to those of 207

Hunten et al. [1980]. At 90 km, we have fewer large particles than did Hunten et al. 208

[1980] in the summer because of the mesospheric circulation, but overall the distributions 209

are similar. At 60 km, the summer size distributions show a large depletion in small 210

particles, but a slight enhancement in large particles relative to Hunten et al. [1980] 211

because of ascending air in the region during the summer. At 30 km, the distribution is 212

shifted towards larger particles than Hunten et al. [1980] found because of the enhanced 213

dust concentrations from the mesospheric circulation and the confinement within the 214

polar vortex. 215

216

It is very difficult to measure meteoric dust particles, because of their small sizes and 217

their remote location. Several groups have attempted to detect charged dust particles with 218

rocket soundings. Figure 11 shows a comparison of our data from the control run and 219

simulation 7 with the dust observations from Lynch et al. [2005] made on 07 March 2002 220

and 15 March 2002 from Poker Flats, Alaska. Simulation 7 has a higher vertical 221

resolution than the control run and also has a smaller initial particle radius. To compare 222


DRAFT 29 May 2007 12 of 54

their charged particle measurements to our particle concentrations, we have scaled up 223

their results assuming that only 5% of the total particles were charged and that the 224

particles they detected were ~1 nm in radius. We see good agreement in the overall shape 225

and approximate quantities, with the higher vertical resolution simulation showing a 226

better fit to the sharp gradients in the observations. Our results show more particles than 227

observed below 80 km, however the amount of free electrons is dropping in this region 228

making charging less efficient and the instrument’s measurements below 77 km are 229

suspect [Lynch, et al., 2005]. 230

231

3.2 Sedimentation 232

Figure 12 shows the results of simulation 2, which does not include the sedimentation of 233

dust particles. Sedimentation plays an important role in defining the sharp drop off in 234

particle concentration above 90 km, since particle fall velocities (Figure 13) are larger 235

than the vertical wind (Figure 14) in this region. Sedimentation also plays a role in the 236

descent of particles in the mesosphere and stratosphere. Without sedimentation, the 237

particles take longer to descend in the polar vortex, which leads to increased mass 238

densities in the mesosphere and decreased mass densities in the upper stratosphere. The 239

differences between the simulations are about 20% of the total mass density, so 240

sedimentation is not as important as advection for the transport of meteoric dust to the 241

stratosphere. The model assumes that the dust particles are spherical. If the larger 242

particles have a fluffier shape [Saunders and Plane, 2006], then the fall velocities will be 243

reduced and sedimentation may play a lesser role in the lower mesosphere and 244

stratosphere than indicated by these results. 245


DRAFT 29 May 2007 13 of 54

246

3.3 Coagulation 247

The results of simulation 3, which does not include coagulation, are shown in Figure 15 248

and are similar to the results from simulation 2 (Figure 12) in the mesosphere and 249

stratosphere. This result indicates that it is the sedimentation of large particles formed by 250

coagulation that is important in this region, while it is the sedimentation of small particles 251

that is important for defining the sharp gradient in number concentration near 90 km 252

(Figure 5). Coagulation is enhanced in the Polar Regions because of the increased 253

concentration of dust from the meridional circulation and the confinement of dust in the 254

polar vortex. We assume a sticking coefficient of 1.0 in the model; however, particle 255

charging in the lower thermosphere and upper mesosphere [Jensen and Thomas, 1991; 256

Reid, 1997] and dipole-dipole interactions [Saunders and Plane, 2006] could impact the 257

choice of the sticking coefficient and the coagulation kernels. Further study is needed to 258

reduce the uncertainty in the effect of the coagulation process upon the distribution of 259

meteoric dust particles. 260

261

3.4 Smaller Initial Particle Size 262

Lacking observations, there is uncertainty as to what the particle size will be after the 263

ablation and initial recombination in the meteor trail. Hunten et al. [1980] used sizes 264

ranging from 0.2 nm to 10 nm, with the smallest size being the approximate radius of an 265

iron oxide molecule [Rosinski and Snow, 1961]. Figure 16 shows the results of simulation 266

4, which assumes an initial particle radius of 0.2 nm and a concentration production rate 267

that scales with radius to conserve the total mass being produced at ~16 kt yr-1. The 268


DRAFT 29 May 2007 14 of 54

increase in the small particle production rate causes an increase in particle concentration 269

near the source region. Because the transport of dust away from the summer pole by the 270

meridional circulation is relatively rapid, coagulation cannot quickly create 1 nm particles 271

so there is a reduction in the number of particles 1 nm or larger in the summer mesopause 272

region relative to the 1 nm case. The reduction in the effective radius causes a slower 273

descent of mass into the stratosphere leaving increased mass densities in the mesosphere. 274

The size distribution and number concentrations are sensitive to the initial size of the 275

particles as they emerge from the meteor trail. Comparisons of vertical profiles of dust 276

concentration and surface area density and size distributions to Hunten et al. [1980] are 277

shown in Figure 17 and Figure 18, with differences similar to those seen in the control 278

run. Attempts to measure dust particles in the mesosphere suggest that initial sizes less 279

than 1 nm and closer to 0.2 nm are most likely (John Plane, personal communication, 280

2007); however, additional measurements would help constrain the model. 281

282

3.5 Gravity Waves 283

Gravity waves generated in the troposphere are filtered by the mesosphere depositing 284

momentum, which affects the circulation in the mesopause region and alters the 285

mesopause height and temperature [Fritts and Alexander, 2003; Lindzen, 1981]. 286

WACCM does not resolve these gravity waves, so a gravity wave parameterization is 287

used that is tuned to provide a reasonable climatology in both the stratosphere and 288

mesosphere [Beres, et al., 2005; Garcia, et al., 2007; Sassi, et al., 2002]. The summer 289

mesopause created in WACCM by the default tuning is lower and warmer than 290

observations [Lubken, 1999; Lubken, et al., 2004]. In simulations 5 and 6, we used a 291


DRAFT 29 May 2007 15 of 54

smaller shear stress for the background gravity waves τb*, which results in the waves 292

breaking at a higher altitude. When the waves break at a higher altitude, the mesopause 293

height increases, the mesopause temperature decreases and the winds intensify (Figure 294

14). Intensified winds result in a decrease in dust concentration near the summer 295

mesopause, but cause an increase in dust concentration in the mesosphere inside the polar 296

vortex (Figure 19). With increased shear stress, the pattern of vertical winds in the winter 297

pole is shifted to a higher altitude, resulting in an increased descent of dust above 60 km 298

and a decreased descent of dust below 60 km at the winter pole relative to the normal 299

gravity wave tuning. The impact of changes to the gravity wave parameterization upon 300

the overall climatology of the model has not been studied; however, the distribution of 301

meteoric dust is sensitive to the gravity wave parameterization. 302

303

3.6 Dust Near The Summer Mesopause 304

Meteoric dust is one of the leading candidates to be the source of condensation nuclei 305

(CN) needed for the formation of PMCs [Rapp and Thomas, 2006; Turco, et al., 1982]. 306

Turco et al. [1982] indicate that concentrations of several hundred particles cm-3 with a 307

radius of at least 1 to 1.5 nm are necessary to form PMCs under typical summer 308

mesopause conditions. However, this requirement rests on numerous assumptions and is 309

not based upon observations. Large temperature variations driven by gravity waves could 310

permit ice nucleation on the more numerous smaller dust particles. A sufficient 311

concentration of large particles are present in the control run to nucleate under the 312

traditional assumptions, but reductions in the number concentration of large particles are 313

seen when starting with a smaller initial radius and when the gravity wave 314


DRAFT 29 May 2007 16 of 54

parameterization is tuned to make a more realistic mesopause. In simulation 7, we 315

investigate the availability of meteoric dust CN by using a higher vertical resolution that 316

provides ~0.5 km spacing near the mesopause, an initial dust particle radius of 0.2 nm, 317

and a background shear stress of 0.002 N m-2. Our results from simulation 7 show that the 318

dust concentrations of particles 1 nm and larger are 15 to 30 cm-3 in the summer 319

mesopause region with maximum values above the temperature minimum and very few 320

particles below the mesopause (Figure 20). Figure 21 shows that these patterns are fairly 321

symmetric and centered near the pole during the summer in the northern hemisphere. The 322

dust particle size distribution at several levels near the summer polar mesopause from 323

simulation 7 is compared to Hunten et al. [1980] in Figure 22. There is a significant 324

depletion of particles at high latitude in the summer relative to Hunten et al. [1980]. 325

However, the ascending winds do appear to loft a small amount of larger particles. The 326

meteoric dust concentration, mass density and size distribution are compared to Megner 327

et al. [2006] in Figure 23 and Figure 24. The summer depletion and winter enhancement 328

in concentration and mass density are not as large as those seen in Megner et al. [2006], 329

particularly for particles that are 1 nm and larger in the summer. This simulation shows 330

that size distributions like Hunten et al. [1980] often used in PMC models, significantly 331

overstate the number of 1 nm and larger particles available and that particle 332

concentrations are extremely depleted below the mesopause. However, summer 333

conditions are not as depleted as indicated by Megner et al. [2006]. If meteoric dust is a 334

CN for PMC particles, nucleation is more likely to occur at or above the mesopause and 335

nucleation may require higher supersaturations than is typically assumed. It is also 336


DRAFT 29 May 2007 17 of 54

possible that the dust may interact with other gases and aerosols to create larger particles 337

[Mills, et al., 2005]. 338

339

4. Summary and Future Work 340

All simulations showed a strong seasonal pattern with meteoric dust particles being 341

transported horizontally from the summer pole to the winter pole where they are quickly 342

transported down into the stratosphere within the polar vortex. The increased 343

concentration of dust particles in the vortex creates larger particles in the stratosphere 344

than previously suggested by one-dimensional simulations and may make meteoric dust 345

important to the polar CN layer and the sulfate aerosol layer. Any impact on the 346

characteristics of sulfate aerosols in the winter stratosphere could also affect PSC 347

nucleation. 348

349

The calculated depletion of particles in the summer mesopause region may reduce the 350

concentration of large particles near the mesopause below the number traditionally 351

thought to be needed for PMC nucleation; although, the amount and location of the 352

depletion depends on the initial particle radius and the gravity wave parameterization. 353

Uncertainties in the coagulation efficiency could also have an impact on the number of 354

larger particles available. Additional dust observations and laboratory studies would be 355

useful to constrain the model. For studies of the summer mesopause, additional tuning 356

and validation of the WACCM gravity wave parameterization is necessary. We have 357

constructed a PMC model using WACCM/CARMA and the dust source described here, 358

which we plan to use to evaluate the potential for meteoric dust particles to be a 359


DRAFT 29 May 2007 18 of 54

significant source of condensation nuclei for PMCs. We intend to compare the model 360

results to observations from the recently launched Aeronomy of Ice in the Mesosphere 361

satellite mission, as well as other PMC climatologies. 362

363

Based upon radar observations, Janches et al. [2006] have suggested that there are 364

temporal and spatial variations in the meteoric influx. Some of this variation may get 365

averaged out as the dust is accumulated and transported; however, it may be important in 366

areas where dust is depleted such as the summer mesopause. We plan to use a dust source 367

function derived from Janches et al. [2006] and Fentzke and Janches (manuscript in 368

preparation, 2007) to investigate the sensitivity of the dust distribution to temporal and 369

spatial variations in the source function. 370

371


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Acknowledgements 371

The authors thank Guy Brasseur, Rolando Garcia, Doug Kinnison, Francis Vitt and Stacy 372

Walters of the National Center for Atmospheric Research for providing us with and 373

supporting our use of the WACCM model. 374

375

Support for Charles Bardeen was partially provided by a NASA Earth System Science 376

Fellowship, grant No. NNG05GQ75J. Additional support for this project came from 377

Aura grant No. NNG06GE80G and NSF grant No. ATM0435713. 378

379


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Hofmann, D. J., J. M. Rosen, and J. W. Harder (1988), Aerosol Measurements in the 415 Winter/Spring Antarctic Stratosphere: 1. Correlative Measurements with Ozone, J. 416 Geophys. Res., 93, 665-676. 417


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Hughes, D. W. (1978), Meteors, in Cosmic Dust, edited by J. A. M. McDonnel, pp. 123-418 185, Wiley, London. 419

Hunten, D. M., R. P. Turco, and O. B. Toon (1980), Smoke and Dust Particles of 420 Meteoric Origin in the Mesosphere and Stratosphere, J. Atmos. Sci., 37, 1342-1357. 421

Jacobson, M. Z., R. P. Turco, E. J. Jensen, and O. B. Toon (1994), Modeling Coagulation 422 among Particles of Different Composition and Size, Atmos. Environ., 28, 1327-1338. 423

Janches, D., C. J. Heinselman, J. L. Chau, A. Chandran, and R. Woodman (2006), 424 Modeling the global micrometeor input function in the upper atmosphere observed by 425 high power and large aperture radars, J. Geophys. Res., DOI: 10.1029/2006JA011628. 426 Jensen, E. J., and G. E. Thomas (1991), Charging of Mesospheric Particles: Implications 427 for Electron Density and Particle Coagulation, J. Geophys. Res., 96, 18,603-618,615. 428 Kalashnikova, O., M. Horanyi, G. E. Thomas, and O. B. Toon (2000), Meteoric smoke 429 production in the atmosphere, Geophys. Res. Lett., 27, 3293-3296. 430 Kane, T. J., and C. S. Gardner (1993), Lidar Observations of the Meteoric Deposition of 431 Mesospheric Metals, Science, 259, 1297-1300. 432 Lindzen, R. S. (1981), Turbulence and Stress Owing to Gravity-Wave and Tidal 433 Breakdown, J. Geophys. Res., 86, 9707-9714. 434 Love, S. G., and D. E. Brownlee (1993), A Direct Measurement of the Terrestrial Mass 435 Accretion Rate of Cosmic Dust, Science, 262, 550-553. 436 Lubken, F. J. (1999), Thermal structure of the Arctic summer mesosphere, J. Geophys. 437 Res., 104, 9135-9149. 438 Lubken, F. J., A. Mullemann, and M. J. Jarvis (2004), Temperatures and horizontal winds 439 in the Antarctic summer mesosphere, J. Geophys. Res., DOI: 10.1029/2004JD005133. 440 Lynch, K. A., et al. (2005), Multiple sounding rocket observations of charged dust in the 441 polar winter mesosphere, J. Geophys. Res., DOI: 10.1029/2004JA010502. 442 Megner, L., M. Rapp, and J. Gumbel (2006), Distribution of meteoric smoke - sensitivity 443 to microphysical properties and atmospheric conditions, Atmos. Chem. Phys., 6, 4415-444 4426. 445

Mills, M. J., O. B. Toon, and G. E. Thomas (2005), Mesospheric sulfate aerosol layer, J. 446 Geophys. Res., DOI: 10.1029/2005JD006242. 447

Murphy, D. M., D. S. Thomson, and T. M. J. Mahoney (1998), In situ measurements of 448 organics, meteoritic material, mercury, and other elements in aerosols at 5 to 19 449 kilometers, Science, 282, 1664-1669. 450 Plane, J. M. C. (2003), Atmospheric chemistry of meteoric metals, Chem. Rev., 103, 451 4963-4984. 452 Plumb, R. A., et al. (2002), Global tracer modeling during SOLVE: High-latitude descent 453 and mixing, J. Geophys. Res., DOI: 10.1029/2001JD001023. 454 Prather, M. J., and J. M. Rodriguez (1988), Antarctic Ozone - Meteoric Control of Hno3, 455 Geophys. Res. Lett., 15, 1-4. 456


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Rapp, M., et al. (2005), Observations of positively charged nanoparticles in the nighttime 457 polar mesosphere, Geophys. Res. Lett., DOI: 10.1029/2005GL024676. 458

Rapp, M., and F. J. Lubken (2001), Modeling of particle charging in the polar summer 459 mesosphere: Part 1 - General results, J. Atmos. Sol. Terr. Phys., 63, 759-770. 460

Rapp, M., and G. E. Thomas (2006), Modeling the microphysics of mesospheric ice 461 particles: Assessment of current capabilities and basic sensitivities, J. Atmos. Sol. Terr. 462 Phys., DOI: 10.1016/j.jastp.2005.10.015. 463 Reid, G. C. (1997), On the influence of electrostatic charging on coagulation of dust and 464 ice particles in the upper mesosphere, Geophys. Res. Lett., 24, 1095-1098. 465 Rosinski, J., and R. H. Snow (1961), Secondary Particulate Matter from Meteor Vapors, 466 J. Met., 18, 736-745. 467 Sassi, F., R. R. Garcia, B. A. Boville, and H. Liu (2002), On temperature inversions and 468 the mesospheric surf zone, J. Geophys. Res., DOI: 10.1029/2001JD001525. 469 Saunders, R. W., and J. M. C. Plane (2006), A laboratory study of meteor smoke 470 analogues: Composition, optical properties and growth kinetics, J. Atmos. Sol. Terr. 471 Phys., 68, 2182-2202. 472

Toon, O. B., R. P. Turco, D. Westphal, R. Malone, and M. S. Liu (1988), A 473 Multidimensional Model for Aerosols - Description of Computational Analogs, J. Atmos. 474 Sci., 45, 2123-2143. 475 Turco, R. P., P. Hamill, O. B. Toon, R. C. Whitten, and C. S. Kiang (1979), One-476 Dimensional Model Describing Aerosol Formation and Evolution in the Stratosphere .1. 477 Physical Processes and Mathematical Analogs, J. Atmos. Sci., 36, 699-717. 478

Turco, R. P., O. B. Toon, P. Hamill, and R. C. Whitten (1981), Effects of Meteoric 479 Debris on Stratospheric Aerosols and Gases, J. Geophys. Res., 86, 1113-1128. 480

Turco, R. P., O. B. Toon, R. C. Whitten, R. G. Keesee, and D. Hollenbach (1982), 481 Noctilucent Clouds - Simulation Studies of Their Genesis, Properties and Global 482 Influences, Planet. Space Sci., 30, 1147-1181. 483 484

485


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Figure Captions 485 Figure 1. CARMA sectional microphysics is implemented as an interactive “physics 486

package” in WACCM. This means that CARMA processes the state for one WACCM 487

column at a time, providing tendencies on dust particles back to the parent model. The 488

standard run defines 21 dust size bins from 1 to 100 nm, which are all added as 489

constituent in WACCM. WACCM is responsible for the advection, diffusion and wet 490

deposition of these particles, while CARMA is responsible for the source function, 491

sedimentation and coagulation. 492

Figure 2. The production rates of meteoric dust particles from Kalashnikova et al. [2000] 493

(red) and Hunten et al. [1980] (green) as a function of pressure. Both of these curves 494

assume all the particles have a 1.3 nm radius. The black lines indicate that the source 495

algorithm reproduces the Kalashnikova et al. [2000] production rate (dotted) and indicate 496

how the rate is scaled to preserve mass for the 1 nm radius (dashed) and 0.2 nm radius 497

(solid) cases. 498

Figure 3. Evolution of the vertical distribution of the mass of meteoric dust in the 499

atmosphere during the 10 years of the control simulation (left) and larger view of the last 500

2 years (right). At high altitudes, dust descending in the winter pole creates a semiannual 501

pattern in dust distribution, while at low altitudes there is an annual pattern with 502

enhanced loss during the southern hemisphere winter and spring. Steady state takes the 503

longest time to attain in the lower stratosphere. 504

Figure 4. The atmosphere does not quite reach steady state during the 10 years of the 505

control simulation as indicated by the total mass of meteoric dust (solid line). The 506

atmosphere above 30 km (dotted line) reaches steady state within 3 years, while after 10 507

years the model is only just reaching steady state below 30 km (dashed line). There is a 508


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seasonal cycle in the total mass, which is likely caused by enhanced loss below 30 km in 509

the polar vortex during the southern hemisphere winter. 510

Figure 5. Zonal average plots for January (top) and July (bottom) of the meteoric dust 511

particle concentration, mass density, and effective radius for the control simulation. The 512

data is an average of the last 7 years of the 10-year simulation. A strong seasonal 513

variation is evident with particles being advected from the summer to winter pole by the 514

mesospheric circulation. There is also rapid descent through the mesosphere at the winter 515

pole. The peak in effective radius at low latitudes and altitudes is because the circulation 516

ascends from the summer pole to the low latitudes in the winter hemisphere and thus only 517

large particles with higher sedimentation velocities will be able to descend in the tropics. 518

Figure 6. Polar plots of the mass mixing ratio of meteoric dust in the southern polar 519

region showing the months of peak concentrations at the selected altitudes as the dust 520

descends in the polar vortex. The white arrows are the monthly averaged horizontal wind 521

vectors. The monthly averaged dust mass mixing ratio contours have been scaled 522

differently for each month to emphasize the structure. 523

Figure 7. The annual variation in the vertical profile of the meteoric dust mass mixing 524

ratio inside (left) and outside (right) the polar vortex for the northern (top) and southern 525

(bottom) hemispheres. The dust descends in the polar vortex rapidly through the 526

mesosphere and more gradually in the stratosphere. The peaks near the mesopause in the 527

winter to spring time frame are because of the mesospheric meridional circulation and are 528

not confined to the vortex. 529

Figure 8. The ratio of the average mass mixing ratio of meteoric dust inside the polar 530

vortex to that outside the vortex for the Northern Hemisphere (left) and Southern 531


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Hemisphere (right). The descent of dust within the vortex is clearly visible. The vortex is 532

particularly isolated in the Southern Hemisphere winter from 700 to 2000K. 533

Figure 9. Comparison of the average vertical profile of concentration and surface area 534

density of meteoric dust between Hunten et al. [1980] (thick green line) and the control 535

simulation (thin lines). The color indicates the latitude range and the line style indicates 536

the time period being averaged. The left panels are the annual averages and the right 537

panels are the JJA (dotted) and DJF (dashed) seasonal averages. Hunten et al. [1980] is a 538

mid-latitude model; however, the mid-latitude annual averages from the control run (red 539

and blue lines) are significantly reduced below ~75 km in both concentration and surface 540

area density relative to Hunten et al. [1980]. The summer season is significantly depleted 541

and winter season is enhanced in concentration and surface area density at mid to high 542

latitudes. 543

Figure 10. Comparison of the average size distribution of meteoric dust at selected 544

altitudes between Hunten et al. [1980] (thick green line) and the control simulation (thin 545

lines). The color indicates the latitude range and the line style indicates the time period 546

being averaged. The top panels are the annual averages and the bottom panels are the JJA 547

(dotted) and DJF (dashed) seasonal averages. The small sizes are depleted during the mid 548

and high latitude summer at 60 km and 30 km. The large sizes are enhanced at 30 km 549

because of increased coagulation in the polar vortex. 550

Figure 11. Comparison of particle concentrations between the control simulation and 551

rocket soundings by Lynch et al. [2005] from Poker Flats, Alaska. Their observation of 552

charged particles has been converted into dust particles by assuming that 5% of the dust 553

particles are charged and that the particle radius is ~1 nm [Gelinas, et al., 2005; Lynch, et 554


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al., 2005]. The black line is the average of the last 7 years of the control run taken from 555

the 4˚x5˚model grid point containing Poker Flats using a daily average for the date 556

indicated assuming 1 nm particles. The blue line is sampled in a similar way from 557

simulation 7, and assumes the particle radius is 0.8 nm to 1.3 nm. The vertical resolution 558

in the control run near the mesopause is ~3.5 km, while simulation 7 has an increased 559

resolution of ~0.5 km. The points on the solid lines indicate the midpoint altitudes of the 560

model levels. The dashed lines are the observations. Both simulations do a good job of 561

matching the observations; however, simulation 7 with a higher vertical resolution that 562

supports sharper gradients is a better match at lower and higher altitudes. 563

Figure 12. Zonal average plots for July of the concentration and mass density for 564

simulation 2, the no sedimentation case. The difference between these results and the 565

control simulation are shown in the right panels. Sedimentation is important in 566

establishing the boundary near 90 km of high gradients in dust concentrations. It plays a 567

lesser role in transporting dust into the stratosphere. 568

Figure 13. Meteoric dust fall velocities from CARMA as a function of altitude for each 569

particle bin from 0.2 nm (left) to 100 nm (right). Velocities for mean radii of 1 nm and 10 570

nm are highlighted (bold lines). 571

Figure 14. Zonal average plots for July of the fourth year of the simulation for the 572

temperature and the vertical wind in the control simulation and simulations 5 and 6, 573

which use different values for the gravity wave parameterization. The black lines are 574

temperature contours from 130 to 160 K. A smaller background shear stress (τb*) causes 575

the waves to break at higher altitudes. Waves breaking at higher altitudes raise and cool 576

the summer mesopause and increase the downdrafts in the winter polar vortex. 577


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Figure 15. Zonal average plots for July of the mass density for simulation 3, the no 578

coagulation case. The difference between this result and the control simulation are shown 579

in the right panel. Below 80 km, this simulation is similar to the no sedimentation case 580

shown in Figure 12, since there are no large particles created and thus sedimentation 581

velocities are low. 582

Figure 16. Zonal average plots for July of the concentration of particles with 1 nm or 583

larger radius, mass density, and effective radius for simulation 4, the 0.2 nm initial radius 584

case. The difference between these results and the control simulation are shown in the 585

right panels. Starting with a smaller initial radius reduces the number of larger particles 586

formed by coagulation and therefore slightly reduces the sedimentation into the 587

stratosphere. The effective radius in the summer near 80 km is also significantly reduced 588

relative to the control run. 589

Figure 17. Comparison of the average vertical profile of concentration and surface area 590

density of meteoric dust between Hunten et al. [1980] (thick green line) and simulation 4, 591

the 0.2 nm initial radius case (thin lines). The color indicates the latitude range and the 592

line style indicates the time period being averaged. The left panels are the annual 593

averages and the right panels are the JJA (dotted) and DJF (dashed) seasonal averages. 594

Hunten et al. [1980] is a mid-latitude model; however, the mid-latitude annual averages 595

from the control run (red and blue lines) below ~75 km are significantly reduced in both 596

concentration and surface area density relative to Hunten et al. [1980]. The summer 597

season is significantly depleted and winter season is enhanced in concentration and 598

surface area density at mid to high latitudes. 599


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Figure 18. Comparison of the average size distribution of meteoric dust at selected 600

altitudes between Hunten et al. [1980] (thick green line) and simulation 4, the 0.2 nm 601

initial radius case (thin lines). The color indicates the latitude range and the line style 602

indicates the time period being averaged. The top panels are the annual averages and the 603

bottom panels are the JJA (dotted) and DJF (dashed) seasonal averages. The small sizes 604

are depleted during the mid and high latitude summer at 30 km and 60 km because of the 605

mesospheric circulation. The large sizes are enhanced at 30 km because of increased 606

coagulation in the polar vortex. 607

Figure 19. Zonal average plots for July of the fourth year of the simulation for the 608

concentration and mass density of dust particles in the control simulation and simulations 609

5 and 6, which use different values for the gravity wave parameterization. The black lines 610

are temperature contours from 130 to 160 K. The increased meridional wind relative to 611

the control run causes a reduced concentration near the summer mesopause. Increased 612

vertical winds relative to the control run cause more rapid transport in the winter polar 613

vortex above 60 km. Below 60 km, decreased vertical winds relative to the control run 614

slow transport into the stratosphere. 615

Figure 20. Zonal average plots for January and July of the concentration of particles with 616

1 nm or larger radius for simulation 7, which has enhanced vertical resolution and a more 617

realistic mesopause thermal structure than the control run. The black lines are 618

temperature contours from 130 to 160 K. Particle concentrations near the summer 619

mesopause are reduced compared to other simulations. 620

Figure 21. Polar plots of the concentration of dust particles with a radius of 1 nm or 621

larger during the month of July in the northern hemisphere for simulation 7 at various 622


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altitudes. There are widespread areas with large depletions relative to Hunten et al. 623

[1980] and to our other simulations, particularly below the mesopause. 624

Figure 22. Comparison of the average size distribution of meteoric dust during the 625

summer at high latitude between Hunten et al. [1980] (green line) at 90 km and 626

simulation 7 (black lines) at various altitudes near the mesopause. There is a significant 627

depletion in the concentration of smaller particles at all altitudes relative to Hunten et al. 628

[1980]. There is a small increase in the concentration of large particles due to particles 629

that are transported up from lower altitudes. 630

Figure 23. Comparison of the average vertical profile of concentration and mass density 631

of meteoric dust during the summer (solid) and winter (dashed) between Megner et al. 632

[2006] (green) and simulation 7 at high (purple) and mid (blue) latitude in the Northern 633

Hemisphere. Our model shows significantly less depletion in the summer and slightly 634

less enhancement in the winter relative to Megner et al. [2006], who used extreme 635

vertical velocities from CHEM2D for their winter and summer wind profiles. The 636

concentration of particles 1 nm and larger and the mass density in the summer in our 637

results are also influenced by the uplift of residual particles from the previous winter. 638

Figure 24. Comparison of the average size distribution of meteoric dust between 80 km 639

and 90 km during the summer (solid) and winter (dashed) between Megner et al. [2006] 640

(green) and simulation 7 at high (purple) and mid (blue) latitude in the Northern 641

Hemisphere. There are more large particles relative to Megner et al. [2006], particularly 642

in the summer. 643

644


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Tables 644

Simulation Duration

(years)

Particle

Radius

(nm)

Levels τb*

(Pa)

Microphysical Processes

1 (Control) 10 1-100 66 0.006 all

2 4 1-100 66 0.006 coagulation, wet deposition

3 4 1-100 66 0.006 sedimentation, wet deposition

4 4 0.2-100 66 0.006 all

5 4 1-100 66 0.003 all

6 4 1-100 66 0.002 all

7 4 0.2-100 125 0.002 all

Table 1. Definitions of the control run and sensitivity tests. All of the simulations include 645

the Kalashnikova et al. [2000] source function applied to the smallest size bin and wet 646

deposition based on Barth et al. [2000]. Most simulations include the microphysical 647

processes of coagulation, sedimentation and wet deposition (all). The background gravity 648

wave shear stress is controlled by the τb* parameter in the WACCM gravity wave 649

parameterization [Garcia, et al., 2007; Sassi, et al., 2002]. The default for τb* is 0.006 Pa. 650

Smaller values cause the gravity waves to break at higher altitudes resulting in a higher 651

and colder mesopause. 652

653


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Figures 653

654 Figure 2. CARMA sectional microphysics is implemented as an interactive “physics 655 package” in WACCM. This means that CARMA processes the state for one WACCM 656 column at a time, providing tendencies on dust particles back to the parent model. The 657 standard run defines 21 dust size bins from 1 to 100 nm, which are all added as 658 constituent in WACCM. WACCM is responsible for the advection, diffusion and wet 659 deposition of these particles, while CARMA is responsible for the source function, 660 sedimentation and coagulation. 661


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662 Figure 2. The production rates of meteoric dust particles from Kalashnikova et al. [2000] 663 (red) and Hunten et al. [1980] (green) as a function of pressure. Both of these curves 664 assume all the particles have a 1.3 nm radius. The black lines indicate that the source 665 algorithm reproduces the Kalashnikova et al. [2000] production rate (dotted) and indicate 666 how the rate is scaled to preserve mass for the 1 nm radius (dashed) and 0.2 nm radius 667 (solid) cases. 668

669


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669

670 Figure 3. Evolution of the vertical distribution of the mass of meteoric dust in the 671 atmosphere during the 10 years of the control simulation (left) and larger view of the last 672 2 years (right). At high altitudes, dust descending in the winter pole creates a semiannual 673 pattern in dust distribution, while at low altitudes there is an annual pattern with 674 enhanced loss during the southern hemisphere winter and spring. Steady state takes the 675 longest time to attain in the lower stratosphere. 676

677


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677

678 Figure 4. The atmosphere does not quite reach steady state during the 10 years of the 679 control simulation as indicated by the total mass of meteoric dust (solid line). The 680 atmosphere above 30 km (dotted line) reaches steady state within 3 years, while after 10 681 years the model is only just reaching steady state below 30 km (dashed line). There is a 682 seasonal cycle in the total mass, which is likely caused by enhanced loss below 30 km in 683 the polar vortex during the southern hemisphere winter. 684

685


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685

686 Figure 5. Zonal average plots for January (top) and July (bottom) of the meteoric dust 687 particle concentration, mass density, and effective radius for the control simulation. The 688 data is an average of the last 7 years of the 10-year simulation. A strong seasonal 689 variation is evident with particles being advected from the summer to winter pole by the 690 mesospheric circulation. There is also rapid descent through the mesosphere at the winter 691 pole. The peak in effective radius at low latitudes and altitudes is because the circulation 692 ascends from the summer pole to the low latitudes in the winter hemisphere and thus only 693 large particles with higher sedimentation velocities will be able to descend in the tropics. 694

695


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695

696 Figure 6. Polar plots of the mass mixing ratio of meteoric dust in the southern polar 697 region showing the months of peak concentrations at the selected altitudes as the dust 698 descends in the polar vortex. The white arrows are the monthly averaged horizontal wind 699 vectors. The monthly averaged dust mass mixing ratio contours have been scaled 700 differently for each month to emphasize the structure. 701

702


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702

703 Figure 7. The annual variation in the vertical profile of the meteoric dust mass mixing 704 ratio inside (left) and outside (right) the polar vortex for the northern (top) and southern 705 (bottom) hemispheres. The dust descends in the polar vortex rapidly through the 706 mesosphere and more gradually in the stratosphere. The peaks near the mesopause in the 707 winter to spring time frame are because of the mesospheric meridional circulation and are 708 not confined to the vortex. 709


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710 Figure 8. The ratio of the average mass mixing ratio of meteoric dust inside the polar 711 vortex to that outside the vortex for the Northern Hemisphere (left) and Southern 712 Hemisphere (right). The descent of dust within the vortex is clearly visible. The vortex is 713 particularly isolated in the Southern Hemisphere winter from 700 to 2000K. 714 715


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716 Figure 9. Comparison of the average vertical profile of concentration and surface area 717 density of meteoric dust between Hunten et al. [1980] (thick green line) and the control 718 simulation (thin lines). The color indicates the latitude range and the line style indicates 719 the time period being averaged. The left panels are the annual averages and the right 720 panels are the JJA (dotted) and DJF (dashed) seasonal averages. Hunten et al. [1980] is a 721 mid-latitude model; however, the mid-latitude annual averages from the control run (red 722 and blue lines) are significantly reduced below ~75 km in both concentration and surface 723 area density relative to Hunten et al. [1980]. The summer season is significantly depleted 724 and winter season is enhanced in concentration and surface area density at mid to high 725 latitudes. 726

727


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727

728 Figure 10. Comparison of the average size distribution of meteoric dust at selected 729 altitudes between Hunten et al. [1980] (thick green line) and the control simulation (thin 730 lines). The color indicates the latitude range and the line style indicates the time period 731 being averaged. The top panels are the annual averages and the bottom panels are the JJA 732 (dotted) and DJF (dashed) seasonal averages. The small sizes are depleted during the mid 733 and high latitude summer at 60 km and 30 km. The large sizes are enhanced at 30 km 734 because of increased coagulation in the polar vortex. 735

736


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736

737 Figure 11. Comparison of particle concentrations between the control simulation and 738 rocket soundings by Lynch et al. [2005] from Poker Flats, Alaska. Their observation of 739 charged particles has been converted into dust particles by assuming that 5% of the dust 740 particles are charged and that the particle radius is ~1 nm [Gelinas, et al., 2005; Lynch, et 741 al., 2005]. The black line is the average of the last 7 years of the control run taken from 742 the 4˚x5˚model grid point containing Poker Flats using a daily average for the date 743 indicated assuming 1 nm particles. The blue line is sampled in a similar way from 744 simulation 7, and assumes the particle radius is 0.8 nm to 1.3 nm. The vertical resolution 745 of the control run near the mesopause is ~3.5 km, while simulation 7 has an increased 746 resolution of ~0.5 km. The points on the solid lines indicate the midpoint altitudes of the 747 model levels. The dashed lines are the observations. Both simulations do a good job of 748 matching the observations; however, simulation 7 with a higher vertical resolution that 749 supports sharper gradients is a better match at lower and higher altitudes. 750

751


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751

752 Figure 12. Zonal average plots for July of the concentration and mass density for 753 simulation 2, the no sedimentation case. The difference between these results and the 754 control simulation are shown in the right panels. Sedimentation is important in 755 establishing the boundary near 90 km of high gradients in dust concentrations. It plays a 756 lesser role in transporting dust into the stratosphere. 757


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758 Figure 13. Meteoric dust fall velocities from CARMA as a function of altitude for each 759 particle bin from 0.2 nm (left) to 100 nm (right). Velocities for mean radii of 1 nm and 10 760 nm are highlighted (bold lines). 761

762


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762

763 Figure 14. Zonal average plots for July of the fourth year of the simulation for the 764 temperature and the vertical wind in the control simulation and simulations 5 and 6, 765 which use different values for the gravity wave parameterization. The black lines are 766 temperature contours from 130 to 160 K. A smaller background shear stress (τb

*) causes 767 the waves to break at higher altitudes. Waves breaking at higher altitudes raise and cool 768 the summer mesopause and increase the downdrafts in the winter polar vortex. 769

770


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770

771 Figure 15. Zonal average plots for July of the mass density for simulation 3, the no 772 coagulation case. The difference between this result and the control simulation are shown 773 in the right panel. Below 80 km, this simulation is similar to the no sedimentation case 774 shown in Figure 12, since there are no large particles created and thus sedimentation 775 velocities are low. 776

777


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778 Figure 16. Zonal average plots for July of the concentration of particles with 1 nm or 779 larger radius, mass density, and effective radius for simulation 4, the 0.2 nm initial radius 780 case. The difference between these results and the control simulation are shown in the 781 right panels. Starting with a smaller initial radius reduces the number of larger particles 782 formed by coagulation and therefore slightly reduces the sedimentation into the 783 stratosphere. The effective radius in the summer near 80 km is also significantly reduced 784 relative to the control run. 785


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786 Figure 17. Comparison of the average vertical profile of concentration and surface area 787 density of meteoric dust between Hunten et al. [1980] (thick green line) and simulation 4, 788 the 0.2 nm initial radius case (thin lines). The color indicates the latitude range and the 789 line style indicates the time period being averaged. The left panels are the annual 790 averages and the right panels are the JJA (dotted) and DJF (dashed) seasonal averages. 791 Hunten et al. [1980] is a mid-latitude model; however, the mid-latitude annual averages 792 from the control run (red and blue lines) below ~75 km are significantly reduced in both 793 concentration and surface area density relative to Hunten et al. [1980]. The summer 794 season is significantly depleted and winter season is enhanced in concentration and 795 surface area density at mid to high latitudes. 796


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797 Figure 18. Comparison of the average size distribution of meteoric dust at selected 798 altitudes between Hunten et al. [1980] (thick green line) and simulation 4, the 0.2 nm 799 initial radius case (thin lines). The color indicates the latitude range and the line style 800 indicates the time period being averaged. The top panels are the annual averages and the 801 bottom panels are the JJA (dotted) and DJF (dashed) seasonal averages. The small sizes 802 are depleted during the mid and high latitude summer at 30 km and 60 km because of the 803 mesospheric circulation. The large sizes are enhanced at 30 km because of increased 804 coagulation in the polar vortex. 805


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806 Figure 19. Zonal average plots for July of the fourth year of the simulation for the 807 concentration and mass density of dust particles in the control simulation and simulations 808 5 and 6, which use different values for the gravity wave parameterization. The black lines 809 are temperature contours from 130 to 160 K. The increased meridional wind relative to 810 the control run causes a reduced concentration near the summer mesopause. Increased 811 vertical winds relative to the control run cause more rapid transport in the winter polar 812 vortex above 60 km. Below 60 km, decreased vertical winds relative to the control run 813 slow transport into the stratosphere. 814


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815 Figure 20. Zonal average plots for January and July of the concentration of particles with 816 1 nm or larger radius for simulation 7, which has enhanced vertical resolution and a more 817 realistic mesopause thermal structure than the control run. The black lines are 818 temperature contours from 130 to 160 K. Particle concentrations near the summer 819 mesopause are reduced compared to other simulations. 820

821


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821

822 Figure 21. Polar plots of the concentration of dust particles with a radius of 1 nm or 823 larger during the month of July in the northern hemisphere for simulation 7 at various 824 altitudes. There are widespread areas with large depletions relative to Hunten et al. 825 [1980] and to our other simulations, particularly below the mesopause. 826

827


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827 Figure 22. Comparison of the average size distribution of meteoric dust during the 828 summer at high latitude between Hunten et al. [1980] (green line) at 90 km and 829 simulation 7 (black lines) at various altitudes near the mesopause. There is a significant 830 depletion in the concentration of smaller particles at all altitudes relative to Hunten et al. 831 [1980]. There is a small increase in the concentration of large particles due to particles 832 that are transported up from lower altitudes. 833


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834 Figure 23. Comparison of the average vertical profile of concentration and mass density 835 of meteoric dust during the summer (solid) and winter (dashed) between Megner et al. 836 [2006] (green) and simulation 7 at high (purple) and mid (blue) latitude in the Northern 837 Hemisphere. Our model shows significantly less depletion in the summer and slightly 838 less enhancement in the winter relative to Megner et al. [2006], who used extreme 839 vertical velocities from CHEM2D for their winter and summer wind profiles. The 840 concentration of particles 1 nm and larger and the mass density in the summer in our 841 results are also influenced by the uplift of residual particles from the previous winter. 842


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843 Figure 24. Comparison of the average size distribution of meteoric dust between 80 km 844 and 90 km during the summer (solid) and winter (dashed) between Megner et al. [2006] 845 (green) and simulation 7 at high (purple) and mid (blue) latitude in the Northern 846 Hemisphere. There are more large particles relative to Megner et al. [2006], particularly 847 in the summer. 848

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