Directory UMM :Data Elmu:jurnal:A:Atmospheric Research:Vol52.Issue3.Sept1999:
Atmospheric Research 52 Ž1999. 195–220
www.elsevier.comrlocateratmos
Some effects of cloud–aerosol interaction on cloud
microphysics structure and precipitation formation:
numerical experiments with a spectral microphysics
cloud ensemble model
A. Khain ) , A. Pokrovsky, I. Sednev
The Hebrew UniÕersity of Jerusalem, The Institute of the Earth Sciences, Department of Atmospheric Sciences,
Jerusalem, Israel
Received 17 December 1998; received in revised form 20 May 1999; accepted 20 May 1999
Abstract
A spectral microphysics Hebrew University Cloud Model ŽHUCM. is used to evaluate some
effects of cloud–aerosol interaction on mixed-phase cloud microphysics and aerosol particle size
distribution in the region of the Eastern Mediterranean coastal circulation. In case of a high
concentration of aerosol particles ŽAPs., the rate of warm rain formation is several times lower, a
significant fraction of droplets ascends above the freezing level. These drops produce a large
amount of comparably small graupel particles and ice crystals. The warm rain from these clouds is
less intense as compared to clouds with low drop concentration. At the same time, melted rain
from clouds with high droplet concentration is more intense than from low drop concentration
clouds. Melted rain can take place downwind at a distance of several tens of kilometers from the
convective zone. It is shown that APs entering clouds above the cloud base influence the evolution
of the drop size spectrum and the rate of rain formation. The chemical composition of APs
influences the concentration of nucleated droplets and, therefore, changes accumulated rain
significantly Žin our experiments these changes are of 25–30%.. Clouds in a coastal circulation
influence significantly the concentration and size distribution of APs. First, they decrease the
concentration of largest APs by nucleation scavenging. In our experiments, about 40% of APs
were nucleated within clouds. The remaining APs are transported to middle levels by cloud
updrafts and then enter the land at the levels of 3 to 7 km. In our experiments, the concentration of
small APs increased several times at these levels. The cut off APs spectrum with an increased
concentration of small APs remains downwind of the convective zone for several of tens and even
hundreds of kilometers. The schemes of drop nucleation Žbased on the dependence of nucleated
)
Corresponding author. Tel.: q972-2-658-5822; fax: q972-2-566-2581; E-mail: [email protected]
0169-8095r99r$ - see front matter q 1999 Published by Elsevier Science B.V. All rights reserved.
PII: S 0 1 6 9 - 8 0 9 5 Ž 9 9 . 0 0 0 2 7 - 7
196
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
drop concentration on supersaturation in a certain power. and autoconversion Žbased on the
Kessler formula. are unsuitable for an adequate description of cloud–aerosol interaction. The
Kessler formula predicts an incorrect tendency in the rate of raindrop formation while increasing
APs’ concentration. Prediction errors concerning the rate of raindrop formation can easily result in
a 10-fold increase. It indicates that the spectral Žbin. microphysics scheme Žor parameterizations
based on the bin schemes. can be used for an adequate description of cloud–aerosol interaction.
q 1999 Published by Elsevier Science B.V. All rights reserved.
Keywords: Spectral cloud microphysics; Cloud–aerosol interaction; Coastal circulation
1. Introduction
As is well known, clouds forming over continents Žcontinental air masses. and over
the sea Žmaritime air masses. have different regimes of rain formation. These differences
are attributed to both different thermal conditions Žlapse rates and humidity. and to
different concentration of atmospheric aerosol serving as cloud condensation nuclei
ŽCCN.. To reveal the role of atmospheric aerosol, cloud development should be
compared under similar thermal conditions. Recent observations of cloud development
within different types of air masses Žmaritime or continental type. reveal crucially
different cloud development and rain formation. So, according to Rosenfeld and Lensky
Ž1998., Lensky and Rosenfeld Ž1998. observed that in polluted areas over Thailand and
Indonesia some clouds do not precipitate at all having narrow spectra of small droplets.
At the same time, similar clouds start precipitating in clear air in about 15 min after their
formation.
Different regimes of precipitation formation indicate significant differences in cloud
microphysics, rates of latent heat release, as well as possible differences in cloud
dynamics. The role of cloud–aerosol interaction appears to be significant in those
atmospheric phenomena, in which latent heat release is an important energy source.
Some indirect evidence of the potentially important effects of atmospheric aerosols on
the tropical cyclone ŽTC. intensity can be inferred from a recent statistical analysis of
weekly fluctuations of intensity of landfalling hurricanes in the North Atlantic ŽCerveny
and Balling, 1998.. The TC statistics indicate that the intensity of landfalling storms is
usually lower during weekends Žwhen human-induced aerosol generation is minimal. as
compared to weekdays, while associated precipitation reaches its maximum during the
same days. This effect cannot be explained by conventional convective parameterizations. It is because these parameterizations imply that precipitation rate is proportional to
the rate of latent heat release. Different microphysical structures of clouds, in their turn,
cause different radiative cloud properties.
Thus, allowing for cloud–aerosol interaction is potentially very important for the
understanding of the dynamics and microphysics of a great number of atmospheric
phenomena, as well as the climate and possible climatic changes.
Adequate description of cloud–aerosol interaction turns out to be an actual problem
for different numerical models.
In some advanced mesoscale models with explicit microphysics such as RAMS
ŽPielke et al., 1992; Walko et al., 1995., GCEM ŽSimpson and Tao, 1993. and MM5
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
197
ŽReisner et al., 1998., the so-called ‘‘bulk parameterization’’ microphysical schemes are
used. In these schemes, all microphysical processes are described in terms of integral
parameters, such as mass content and number concentration. The comparably small
number of integral parameters makes the bulk parameterization schemes computationally efficient. However, the ‘‘bulk parameterization’’ microphysical schemes used in the
RAMS and MM5 mesoscale models also have limited capabilities in describing the
effects of atmospheric aerosols on clouds. For example, in studies by Pielke et al.
Ž1992., Reisner et al. Ž1998. and many others, the autoconversion rate Žthe rate of rain
water production by coagulation of small cloud droplets. is described by the following
formula ŽKessler, 1969.:
A u s max K Ž CWC y CWC cr . ,0 4 ,
Ž 1.
where CWC is cloud water content, CCWcr is an empirical threshold value, K is an
empirical constant. It is known, however Že.g., Beheng, 1994., that formulation Ž1.
appears highly intuitive without taking into account any microphysical considerations.
For instance, formula Ž1. ignores any dependence of raindrop production on the cloud
droplet size and width of the droplet size spectrum. The latter is the major controlling
factor of rain formation.
The two-dimensional cloud ensemble model HUCM Žthe Hebrew University Cloud
Model. with detailed description of cloud microphysics ŽKhain and Sednev, 1995, 1996;
Khain et al., 1996. has been designed to take into account the effects of cloud–aerosol
interaction on latent heat release and precipitation formation. The HUCM microphysics
is based on the spectral Žor bin. approach according to which each of the seven types of
cloud hydrometeors Žwater drops, three types of ice crystals, snow, graupel and hail. are
described using size-distribution functions containing several tens of bins of masses. To
make it possible to describe cloud–aerosol interaction and drop formation by nucleation
processes, a special size distribution for aerosol particles ŽAPs. is incorporated into the
model. This function contains several tens of mass bins as well. Contrary to the bulk
parameterization schemes, the size distributions in the model are not determined a priori,
but are the result of model integration.
Cloud microphysics models based on spectral microphysics proved to be effective
when simulating precipitation formation and cloud–aerosol interactions Že.g.,
Khvorostyanov et al., 1989; Khain et al., 1996; Reisin et al., 1996a,b; Levin et al.,
1998..
In the present paper, we use the HUCM to illustrate the effects of cloud–aerosol
interaction on cloud microphysical parameters and rain formation in the Eastern
Mediterranean coastal zone during the cold season.
2. Model description
The HUMC ŽKhain and Sednev, 1995, 1996; Khain et al., 1996, 1998. is a
two-dimensional non-hydrostatic model based on the deep convection equation system.
198
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
The computational area of the model in the present study extends 192 km horizontally
and 16 km vertically Ž129 = 41 grid points.. The coastline is located at x s 90 km.
The variables of the model include wind velocity Žvorticity and stream function.,
virtual potential temperature, mixing ratio, the number density size distribution functions
of water drops, ice crystals Žplates, dendrites and columns., snowflakes, graupel and
hailrfrozen drops.
The non-linear advection terms were approximated using the Arakawa method, which
conserves vorticity, the square of the vorticity and kinetic energy. The stream function
was found by solving the Poisson equation. Each size distribution function is described
using the same mass grid containing 33 categories Žbins.. The maximum mass in the
mass grids corresponds to a water drop with the radius of 3250 mm. The model provides
calculation of values of different types: precipitation amount, precipitation rates, fluxes
of different hydrometeors, mass contents, radar reflectivity from water and ice, the mean
and effective radii of droplets and ice particles, fluxes from the underlying surface and
so on.
The model takes into account the following microphysical processes: nucleation of
CCN; formation Žnucleation. of ice crystals, condensational growthrevaporation of
droplets; diffusion growthrsublimation of ice particles; freezing of drops; melting of ice
Žsimplified description., drop–drop, drop–ice and ice–ice collisions; spontaneous
breakup of rain drops and snowflakes.
In order to describe cloud–aerosol interaction and to reveal the influence of aerosols
on the cloud microstructure and cloud thermodynamics, a special size distribution
function of APs is used. It also contains 33 mass bins.
The HUCM applies an explicit analytical method for the calculation of supersaturations with respect to water and ice. These values of supersaturation are used to calculate
drop growth by diffusion and drop nucleation processes. Using the values of supersaturation, the critical sizes of APs are determined. The APs of different chemical composition
greater than these critical values are activated. The corresponding sizes of cloud droplets
are calculated.
Actually, CCN start growing in a water vapor field long before they enter the cloud,
even at supersaturations less than critical. These size changes provide the initial value of
the wet nuclei radius for subsequent condensation calculations. As was indicated by
Mordy Ž1959., Ivanova et al. Ž1977., Kogan Ž1991. as well as from our supplemental
experiments, the smallest nuclei Žwith the dry fraction under about 0.3 mm. reach their
equilibrium radii in a reasonably short time Žseconds of fraction of seconds.. We
assumed that these small particles were in the equilibrium and the size of corresponding
droplets was calculated from the Kohler equation.
Large particles require significant time Žsometimes, even days. to come to their
equilibrium radii at 100% relative humidity.
To calculate the initial size of droplets arising on CCN with the dry fraction over 0.3
mm, we applied the results of detailed calculations performed by Ivanova et al. Ž1977..
According to these results, the initial sizes of droplets at zero supersaturation level are
about five times as large as the radii of corresponding dry CCN. This assumption
excludes the formation of very large droplets at the cloud base just after nucleation.
Similar approach has been used by Kogan Ž1991. and Yin et al. Ž1999..
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
199
Activation of new crystals is simulated following Meyers et al. Ž1992.. The type of
fresh nucleated crystals depends on temperature. The probability of drop freezing was
assumed proportional to the drop mass Žprobability freezing. ŽKhain and Sednev, 1996..
Size spectra evolution by drop–drop, drop–ice and ice–ice collisions is described by
solving a system of stochastic collision equations for size distribution functions. The
stochastic coalescence equations were solved using the method of Berry and Reinhardt
Ž1974.. Collision kernels for ice–ice collisions depend on temperature and supersaturation with respect to ice. A stochastic nature of terminal fall velocity of non-spherical ice
particles is taken into account by the corresponding increase of the swept volume. The
values of terminal fall velocities of different hydrometeors as functions of their mass
were taken mainly from Pruppacher and Klett Ž1978..
3. Description of numerical experiments
During the cold season, the sea surface temperature is higher than the temperature of
the land by about 3 to 108C. In our experiments, the sea–land temperature difference is
set equal to 58C. The SST is set equal to 198C, which is a value typical of December.
The initial profile of the background wind was chosen as follows: a linear increase was
set in the lowest 4 km from 4 to 12 mrs with a further increase toward 30 mrs at
z s 16 km. Vertical profiles of temperature and humidity were chosen typical of rain
events in the Mediterranean region ŽKhain and Sednev, 1996.. Initial relative humidity
over the sea was taken equal to 90%, 5% greater than that over the land. The surface
temperature was not changed during the model integration.
The purposes of numerical experiments are to investigate Ž1. the influence of
concentration and chemical composition of APs on cloud microphysics and rain amount
and distribution and Ž2. the influence of clouds on the concentration and size distribution
of APs. Only nucleation scavenging will be taken into account in the study.
In the control experiment E500, a modified gamma distribution function was used to
describe the size distribution function of APs subject to nucleation in clouds:
fa s Araaq1 exp Ž yBrag . .
Ž 2.
The parameters in Eq. Ž2. were set so as to be typical of the Eastern Mediterranean
conditions ŽLevin, 1994. with the concentration of APs near the surface of 500 cmy3 .
The spectrum is similar to that observed in the maritime air. The spectrum contains a
significant number of particles whose is ranged from 0.01 to 0.1 mm. The maximum size
of dry APs in the spectrum is about 1 mm. There are no ultra-giant coalescence nuclei in
the simulations.
The exponential decrease of APs concentration was assumed as the initial condition
at t s 0, when there was no convection and no clouds:
A Ž z . s A 0 exp Ž yzrzU . , where A 0 s 8.55 P 10 4 cmy3 mm, B s 17.89 mmy0 .5 ,
a s 1, g s 0.5.
200
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
When convection develops convective motions determine the spatial distribution of
APs leading to homogenization of APs concentration in the vertical. The homogenization of AP’s concentration with time takes place in the atmospheric boundary layer
beyond the convective zone due to vertical turbulent mixing as well.
Under a dominating contribution of the western background flow, the coastal region
even over the land should have a distribution of APs similar to that over the sea. In the
paper, we discuss 2-h simulations of the coastal circulation. During this time period, the
contribution of such maritime air aerosols must be dominating, and the assumption that
spatial distribution of APs is horizontally homogeneous seems to be acceptable.
APs in E500 were assumed to consist of NaCl. The values of APs’ concentration near
the surface remain constant during the simulations.
To reveal the influence of APs’ concentration above the cloud base, exp. E500C was
conducted, in which APs’ concentration was set equal to 500 cmy3 throughout the
whole computational area.
To investigate the influence of APs’ concentration on the rain amount and distribution, experiments E100 and E1000 were conducted. These experiments are similar to the
control experiment E500, with the exception that the APs’ concentration at the surface
was set close to 100 cmy3 and 1000 cmy3 , respectively.
Experiment E500 wŽNH 4 . 2 SO4 x was similar to the control experiment E500, with the
only exception: APs were assumed to consist of ŽNH 4 . 2 SO4 . This experiment was
conducted to reveal the sensitivity of model precipitation to the chemical composition of
APs.
4. Results of the experiments
4.1. Control experiment E500
Low-level wind convergence between the dominating westerly wind and coastal
breeze-like circulation creates favorable conditions for the development of convective
clouds over the sea, 15–18 km from the shore line. The clouds are then transported by
the background flow toward the land. Vertical velocities in clouds reached up to 9 mrs.
The height of the cloud top is 6 to 6.5 km at different time instances.
At the time when vertical velocities reach their maximum Ž t s 3000 s., the maximum
drop concentration was 170 cmy3 . The maximum drop concentration varies with time
within the range 150 to 250 cmy3 . The main feature of the simulation is the alteration of
cloud microstructure inland. Convective rain Žwater drops with a diameter greater than
64 mm. falls downwind of the zone of convective updraft. A significant fraction of the
rain falls over the sea. The existence of the convective rain maximum in the vicinity of
the shoreline is an observed climatic feature of precipitation in Israel ŽKutiel and Sharon,
1980.. A significant amount of suppercooled water can be seen above the freezing level
Ž2 km. ŽFig. 1b, Fig. 2b.. The maximum of cloud water content ŽCWC. of 1.65 g my3 is
located at z s 2 km, while the height of the maximum of rain water content ŽRWC. of 1
g my3 is located at 3 km, 1 km above the freezing level. The height of the maximum
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 1. CWC Žg my3 . at t s 3000 s in Ža. E100, Žb. E500 and Žc. E1000.
201
202
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 2. RWC Žg my3 . at t s 3000 s in Ža. E100, Žb. E500 and Žc. E1000.
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
203
drop content descends with the decrease of the maximum velocity to 2.5 km at t s 1 h.
Then, the height of the maximum RWC remains actually unchanged.
Typical drop size spectra in the area of the maximum convective updraft at 1 km
Ž x s 73.5 km. and 1.5 downwind Ž x s 75 km. are shown in Fig. 3. One can see that at
the level z s 0.8 km Žabout 400 m above the cloud base., the size distribution of cloud
drops is centered at 14 mm. The maximum of the spectrum shifts with the height to
greater sizes: 17 mm at 2 km, and 22 mm at 3.6 km due to diffusion growth. One can
see the formation of raindrops at x s 75 km, whose size increases downward reaching
the radius of about 1 mm at z s 0.8 km.
As Khain and Sednev Ž1996. reported, high-density ice particles, such as frozen drops
Žwith the density of 0.9 g my3 . are located in the area of comparably strong updrafts
and start precipitating over the land as soon as vertical updrafts decrease. Then, graupel
particles begin to contribute to precipitation. Ice crystals and snowflakes are able to
enter the land by several tens and even hundreds of kilometers. Therefore, it is mainly
melted drops that cause precipitation over the land.
In Fig. 4, the field of APs concentration at t s 2 h is presented. One can see two
main effects of cloud influence on the APs’ concentration: Ž1. transport of APs by
convective motions and Ž2. decrease of APs concentration due to the activation of CCN
and formation of new droplets.
Convective motions transport APs from the boundary layer Žwhere a significant
decrease of APs’ concentration can be seen. to the middle atmosphere. These particles
are transported then by the background flow inland at the levels of 3 to 7 km.
Fig. 3. Typical drop size spectra in the area of the maximum convective updraft at 1 km Ž x s 73.5 km. and 1.5
km Ž x s 75 km. downwind of maximum low level updraft. As can be seen from Ža., process of collisions
begins below z s800 m, so that at z s800 m the largest drops reach the 100 mm radius. Symbol lnR denotes
the logarithm of drop radius with the base es 2.7 Žaccording to Berry and Reinhardt, 1974. method. Symbol
LogŽR. denotes a logarithm with the base of 10, suitable for plotting the drop radii values.
204
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 4. Fields of APs’ concentration at t s 2 h in E500.
Comparison of APs’ concentration within a cloud with that at the cloud base shows
that 35 to 40% of APs experience activation and turn into drops. A decrease of APs’
concentration is caused by activation of the largest particles in the APs’ size spectrum.
Fig. 5a–c shows size distributions of APs at t s 230 min at different heights Ža. in the
undisturbed area at the upwind side of the convective activity zone x s 69 km, Žb. in the
area of maximum convective updraft, x s 73.5 km, and Žc. about 60 km inland from the
zone of coastal convection. Comparison of figures Ža. and Žb. indicates a sharp size
spectrum cut off at 0.4 mm in the area of cloud activity. APs transported by clouds into
the middle Žand upper. troposphere have small sizes. Ten kilometers downwind Žnot
shown. the size spectrum of APs at z s 3.6 km indicates the concentration of small APs
with the radii below 0.4 mm three times as high Žas compared to the concentration in the
undisturbed area. and the decrease of the concentration of APs’ with the radii greater
than 0.4 mm by a factor of two. A significant influence of convection on the APs’ size
spectrum is pronounced at several tens of kilometers Žfigure c. and even 100 km
downwind of convective zone Žnot shown..
4.2. Experiment E500C
In this experiment, initial APs’ concentration was set 500 cmy3 over the whole
computational region. The main question we are addressing in the experiment is the
influence of APs located at levels above the cloud base on cloud microphysics.
Drop concentration in E500C was about 25% higher than in E500. To reveal the
cause of the difference, we compared the maximums of drop concentration during cloud
development at 20, 30, 40, and 50 min, when cloud tops were 2, 4, 6, and 6.4 km,
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
205
Fig. 5. Size distributions of APs at t s 200 min at different heights Ža. in the unperturbed area at the upwind
side of the convective activity zone, x s69 km, Žb. in the area of the maximum convective updraft, x s 73.5
km, and Žc. 60 km inland from the zone of coastal convection.
respectively. The difference in drop concentration maximums at these time instances
was 11, 25, 26, and 75 cmy3 . In 50 min, the difference in drop concentration maximums
slightly oscillated around 70 cmy3 . We interpret the results as follows. The difference in
drop concentration at an early stage is caused by the difference of APs’ concentrations at
the cloud base. This difference was comparably small in exps. E500 and E500C. Then,
during cloud growth, the entrainment APs into the cloud becomes substantial. Some of
the APs are activated. Because in E500C APs’ concentration above the cloud base is
higher, the entrainment increases the drop concentration difference between E500C and
E500. Thus, a significant fraction of the drop concentration difference can be attributed
206
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
to fresh drop nucleation above the cloud base due to the entrainment of APs through
cloud boundaries. The analysis of the drop size spectra evolution in E500 and E500C
seems to support the conclusion. There is actually no difference in the droplet spectra at
z s 0.4 and 0.8 km Žnot shown.. At z s 2 km, the difference in the size spectra becomes
noticeable: in E500C, the cloud spectrum contains drops about 1 mm smaller than in
E500. The difference in the spectra increases with height, so that at 3.6 km, the drop
spectrum in E500C contains cloud drops that are by about 2 mm smaller than in E500.
The increase in drop concentration in E500C and a corresponding decrease of the
droplet size led to some delay in rain formation and to a corresponding shift of the rain
maximum 6 to 10 km downwind. Accumulated rain decreased by about 15%. Thus, the
effects of APs located above the cloud base cannot be neglected.
Fig. 6 presents the APs’ concentration field in E500C at 160 min. The minimum
value of APs’ concentration was 227 cmy3 as compared to the maximum value of 500
cmy3 . This again shows that in the zone of active convection nucleation scavenging can
eliminate from the atmosphere up to 40% of APs Žin case of typical size distribution..
4.3. Experiments E100 and E1000
These experiments were similar to the control experiment E500, except for the
maximum concentrations of APs at the surface, which were 100 cmy3 Žexp. E100. and
1000 cmy3 Žexp. E1000.. Fig. 1 shows CWCs in these two experiments, as well as in
the control experiment E500 at t s 3000 s. The maximums of CWC in E100, E500 and
Fig. 6. Concentration of APs in E500C at 160 min.
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
207
E1000 are 1.1 g my3 , 1.65 g my3 and 2.0 g my3 , respectively. The difference of the
CWC fields in exps. E100 and E1000 is shown in Fig. 7a. From Figs. 2 and 7a one can
see that the CWC in E100 is significantly lower than in E1000. According to the Kessler
formula Ž1., the rate of rainwater production, and, consequently, RWC must also be
lower than in E500 and E1000. However, this is not the case in the spectral micro-
Fig. 7. The fields of Ža. the CWC difference and Žb. RWC difference between experiments E100 and E1000.
208
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
physics model, as shown in Fig. 2a,c, Fig. 7b. The maximums of RWC in E100 Žclear
maritime air., E500 and E1000 Žrelatively dusty air. were 1.3 g my3 , 1.1 g my3 and
0.85 g my3 , respectively. Thus, as we can see in Fig. 7b, the RWC in E100 is nearly
twice as large as in E1000. This difference remains in the convective region all the time
of model integration. We attribute the difference in the RWCs to the fact that drop
concentration in E1000 Žwith maximum of 260 cmy3 . is several times higher than in
E100 Žwith the maximum of 58 cmy3 .ŽFig. 8a–c. and, therefore, cloud droplets in
E1000 are significantly smaller than in E100.
Thus, the rate of raindrop formation in E100 was twice as large as that in E1000,
while according to the Kessler formula Ž1. it should be half as low. It means that
neglecting the effect of drop size in Ž1. leads to an error by a factor of 4. Note that in
E1000 drop concentration is not as large, as in very continental clouds, where drop
concentrations can exceed 1000 cmy3 . In case cloud development is simulated in a
highly dusty air, formula Ž1. can easily overestimate the rate of rain drop formation by
the factor of 10.
Differences in the CWC and RWC between E100 and E1000 lead to differences in
cloud ice structure. During the period of active ice formation Ž t s 3000 s. graupel forms
at the levels 3 to 6 km ŽFig. 9a,b, Fig. 10a,b. by collisions of ice crystals with droplets
of greater mass. Because in E1000 the mean drop size is smaller Ždroplets have a lower
terminal fall velocity. than in E100, droplets ascend to higher levels before forming
graupel. Thus, graupel particles in E1000 are located at higher levels than in E100 Žsee
the fields of mass and concentration differences in Fig. 9c, Fig. 10c.. Graupel particles,
located at higher levels, where the velocity of the background flow is higher, are
transported inland faster. Over the land, the largest of them descend because of a fast
decrease of the air vertical velocity inland. The maximum of graupel mass content
descends from 4 km at 3000 s to 2.5 km at t s 4200 s. ŽFig. 11a–c, Fig. 12a–c.. At the
same time, graupel particles in E100 are larger, they grow owing to intensive accretion
of small droplets and the largest of them precipitate within 10 km downwind of the
maximum convective updraft. As a result, at t s 4200 s the graupel mass content in
E1000 turns out to be greater than in E100 in the region with x ) 82 km.
Concentrations of columnar crystals and dendrites are similar in both experiments
Ž10–15 ly1 .. Plate-like crystals form within the layer of 6 to 7 km Žnot shown..
Concentration of plate-like ice crystals in E1000 Žup to 500 ly1 . is much higher than
that in E100 Žup to 100 ly1 .. The majority of plate-like crystals forms in the model by
freezing of small droplets at low temperatures. As it was discussed above, the concentration of small cloud droplets in E1000 is much higher than that in E100. It explains the
difference in plate crystal concentration. At the same time, the crystals in E1000 are
smaller than in E100. Their size is usually less than 15 mm. Most particles cannot be
detected by the equipment available. At the same time, small ice crystals influence
significantly the radiation properties of clouds. This indicates the importance of adequate
Fig. 8. Cloud drop concentrations in Ža. E100 and Žb. E1000 and Žc. the difference between these fields. The
drop concentration in E1000 Žwith the maximum of 260 cmy3 . is several times higher than in E100 Žwith the
maximum of 58 cmy3 ..
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
209
210
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
description of cloud–aerosol interaction to provide the correct determination of cloud
radiation properties.
Snowflakes form from the very beginning at the heights of 5 to 6 km Žnot shown..
The mass of snowflakes is smaller than that of graupel by a factor of 1.5 to 2. The mass
of snowflakes in E100 is greater than in E1000. We explain the result as follows.
Snowflakes in our model are formed, first, as a result of collisions of ice crystals. Then,
the mass of snowflakes grows by riming. In spite of a higher ice crystal concentration in
E1000, many crystals Žmainly plate-like crystals. are too small to collide.
The height of graupel and snow generation decreases with time from 5 km to 6 km at
t s 3000 s to 3–4 km at t s 6000 s. We attribute this effect, first of all, to the descent of
ice particles within downdrafts on the downwind side of the convective zone and
sequential entrainment into the areas of suppercooled rain. Ovtchinnikov Ž1998. discussed the important role of ice recirculation for graupel formation.
As it was noted above, precipitation over the land Ž x ) 90 km. is related to the
melting of ice particles Žmainly graupel in the present experiments.. Graupel mass
content over the land is greater in E1000 than in E100. Thus, over the land melted rain
in E1000 is greater than in E100. We illustrate this result by Fig. 13a–c, Fig. 14a–c,
where radar reflectivity of water and total ice at t s 5400 s are presented, respectively.
Radar reflectivity in the spectral HUCM is calculated directly using the size spectra of
cloud particles according to its definition Žsee Khain and Sednev, 1996 for more detail..
The radar reflectivity of supercooled water in E100 is concentrated in the zone of high
updraft velocity, from 75 to 84 km. In E1000, the zone of supercooled water reflectivity
extends further inland to 87 km. It is related to a smaller mass of raindrops in E1000, as
it was shown above. The radar reflectivity of liquid water in E100 exceeds 40 DBz at
x s 84 to 89 km. The zone of the maximum radar reflectivity begins from the melting
level, which indicates a dominating contribution of large rain droplets formed as a result
of graupel and frozen drops melting. In E1000 Žfigure b., the zone with radar reflectivity
exceeding 40 DBz is located further inland: from 86 to 93 km. The maximum is reached
below the 1-km level. This indicates a significant contribution of warm rain at seashore
in this case.
In the convective zone below 2.5 km the radar reflectivity of liquid water is greater in
exp. E100 Žlocation of large raindrops.. Above 2.5 km, the reflectivity in E100 is
smaller than in E1000 because in E100, large drops either remain below the level or turn
into graupel Žor hail.. At the same time, the radar reflectivity of total ice in the
convective zone is greater in E100, because of larger size of graupel. Further inland at
x ) 90 km, radar reflectivity of ice in E1000 can be 12 DBz higher than in E100.
Respectively, in E1000 radar reflectivity of liquid water is 12–14 DBz higher than in
E100, which shows clearly the dominating contribution of melted raindrops.
Thus, we see that difference in AP concentration leads to redistribution of precipitation in time and space.
Fig. 9. Graupel mass content in experiments Ža. E100 and Žb. E1000 and Žc. the difference between the mass
contents during the period of active ice formation Ž t s 3000 s..
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
211
212
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 10. The same as in Fig. 9, but for graupel concentrations.
213
Fig. 11. The same as in Fig. 9, but at t s 4200 s.
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 12. The same as in Fig. 11, but at t s 4200 s.
214
215
Fig. 13. Radar reflectivity of liquid water at t s 5400 s.
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 14. Radar reflectivity of total ice at t s 5400 s.
216
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
217
Analysis of vertical velocities in the convective zone shows that in E1000 vertical
updraft velocities as well as compensation downdrafts are higher by about 1 mrs Žor by
12%. than those in E100 Žnot shown.. This increase in updraft is reached in spite of
larger loading in exp. E1000. We attribute this effect to a greater contribution of latent
heat of fusion in exp. E1000, because in this experiment a larger fraction of drops
ascends above 2-km level and experiences freezing.
4.4. Experiment E500 [(NH4 )2 SO4 ]
This experiment was similar to the control experiment E500, with only one exception: APs were assumed to consist of ŽNH 4 . 2 SO4 . Size distributions of APs were
assumed to be the same as in the control experiment. Because the critical size of APs
Žand corresponding concentration of nucleated drops. depends on their chemical composition, precipitation also depends on the chemical composition of APs. The purpose of
the experiment is to evaluate the sensitivity of precipitation to the variation of the
chemical composition of APs.
In Fig. 15, the fields of APs’ concentration in experiments E500 and wŽNH 4 . 2 SO4 x
are shown at 180 min. The concentration of NaCl APs is smaller than that of
ŽNH 4 . 2 SO4 particles, indicating more effective nucleation and higher concentration in
Fig. 15. APs’ concentration in experiments E500 and E500 wŽNH 4 . 2 SO4 x at 180 min.
218
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
the first case. As a result, the accumulated rain and precipitation rate in the case of
ŽNH 4 . 2 SO4 APs turned out to be 20 to 30% higher than in the case of NaCl APs.
Thus, accumulated rain and rain rates are sensitive to the APs chemical composition.
5. Discussion and conclusions
A spectral microphysics cloud ensemble model is used to evaluate the effect of
cloud–aerosol interaction on the microphysics of mixed-phase clouds and on the APs
size distribution in the region of coastal circulation. A situation typical of the Eastern
Mediterranean is simulated. The sea surface is warmer than the land surface. The
background wind is directed inland. As a result, the coastal circulation at low levels
turned out to be opposite to the direction of the background flow and a zone of air
convergence arises over the sea at about 20 km offshore.
It was shown that concentration, size distribution, spatial distribution and chemical
composition of atmospheric APs crucially influence cloud microphysics, processes of
rain and ice formation. Natural or human-induced increase of atmospheric AP concentration can lead to a change of the cloud type Žfrom, say, maritime to continental type. with
corresponding decrease Žby several times. of the rate of warm rain formation and
increase in the ice crystal formation. In case of a background wind, it leads to a
significant spatial redistribution of precipitation. In case of a low APs’ concentration
warm rain prevails in the vicinity of the region of cloud formation. Graupel and frozen
drops in these clouds are large and also fall in the vicinity of cloud formation.
In case of a high APs’ concentration, the rate of warm rain formation can be several
times lower, a significant fraction of droplets ascends above the freezing level. These
drops produce large amounts of comparably small graupel particles and ice crystals.
Warm rain from these clouds is less intense than from clouds of low drop concentration.
At the same time, melted rain from these clouds is more intense than from low drop
concentration clouds. Melted rain can take place downwind at a distance of several tens
of kilometers from the convective zone. Because of very different ice particle concentrations, radiation properties of clouds must be also different. This problem needs a special
consideration.
It is shown that APs entering clouds above the cloud base influence the evolution of
drop size spectrum and the rate of rain formation.
The chemical composition of APs influences the concentration of nucleated droplets
and, therefore, changes accumulated rain significantly Žin our experiments by 25–30%..
Clouds in a coastal circulation influence significantly the concentration and size
distribution of APs. First, they decrease of the concentration of largest APs by
nucleation scavenging. In our experiments, about 40% of APs were nucleated within
clouds. The remaining APs are transported to middle levels by cloud updrafts and then
enter the land at the levels of 3 to 6 km. In our experiments, the concentration of small
APs increased several times at these levels. The cut off APs spectrum with a greater
concentration of small APs remains downwind of the convective zone for several tens
and even hundreds of kilometers.
The schemes of drop nucleation Žbased on the dependence of nucleated drop
concentration on supersaturation at a certain power. and autoconversion Žbased on the
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
219
Kessler formula. are unsuitable for an adequate description of cloud–aerosol interaction.
The Kessler formula predicts incorrect tendency as to the rate of raindrop formation
while increasing APs’ concentration. Prediction errors of the rate of raindrop formation
can easily give a 10-fold increase. This indicates that the spectral Žbin. microphysics
schemes Žor parameterizations based on the bin schemes. can be used for an adequate
description of cloud–aerosol interaction.
Finally, it must be stressed that despite the fact that the method of Berry and
Reinhardt Ž1974. used in this study has no numerical spreading Žin contrast to the
Kovetz and Olund, 1969 scheme., it has, however, another deficiency: the method does
not conserve the mass of hydrometeors, especially large ice particles. We have conducted a supplemental experiment similar to E500, except with a mass grid containing
43 mass bins. The results showed an increase of the precipitation rate Žformed from
melted particles. of about 20%. No significant difference in the results at t - 1 h were
observed. The difference in content of large ice particles became pronounced in 1.5 h,
when these particles formed downwind of the zone of active convection. Note, however,
that no qualitative differences in the results were observed when the number of grid
points in the mass mesh had been increased.
Activities connected with updating the model by introducing the new collision
scheme developed by Bott Ž1998. is under way. This scheme has no numerical
spreading, conserves the mass and is effective from the computational point of view. We
are going to present the results of the utilization of the scheme in future publications.
Acknowledgements
The authors are grateful to Christiane Textor Žthe Max-Plank Institute for Meteorology, Hamburg. for her assistance in calculations and useful discussions, as well as to Dr.
Danny Rosenfeld ŽThe Hebrew University of Jerusalem. for useful discussions and for
providing us with observational data. The Israel Ministry of Science Žgrant 6767-95. and
the Israel Academy of Science Žgrant 572r97. supported the study.
References
Beheng, K.D., 1994. A parameterization of warm cloud microphysical conversion processes. Atmos. Res. 33,
193–206.
Berry, E.X., Reinhardt, R.J., 1974. An analysis of cloud drop growth by collection: Part 1. Double
distributions. J. Atmos. Sci. 31, 1814–1824.
Bott, A., 1998. A flux method for the numerical solution of the stochastic collection equation. J. Atmos. Sci.
55, 2284–2293.
Cerveny, R.S., Balling, R.C., 1998. Weekly cycles of air pollutants, precipitation and tropical cyclones in the
coastal NW Atlantic region Žletter to Nature.. Nature 394, 561–563.
Ivanova, E.T., Kogan, Y., Mazin, I., Permyakov, M.S., 1977. The ways of parameterization of condensation
drop growth in numerical models. Izvestia, Atmospheric and Oceanic Physics 13 ŽN11., 1193–1201.
Kessler, E., 1969. On the distribution and continuity of water substance in atmospheric circulation. Met.
Monographs 10, 84.
Khain, A.P., Sednev, I., 1995. Simulation of hydrometeor size spectra evolution by water–water, ice–water
and ice–ice interactions. Atmos. Res. 36, 107–138.
220
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Khain, A.P., Sednev, I., 1996. Simulation of precipitation formation in the Eastern Mediterranean coastal zone
using a spectral microphysics cloud ensemble model. Atmos. Res. 43, 77–110.
Khain, A.P., Sednev, I., Khvorostyanov, V., 1996. Simulation of deep convection–breeze interaction in the
Eastern Mediterranean using a cloud ensemble model with an explicit description of warm and ice
microphysical processes. J. Clim. 9, 3298–3315.
Khain, A., Pokrovsky, A., Sednev, I., 1998. Simulation of cloud–aerosol interaction with a spectral
microphysics cloud ensemble model. First Israel–Germany Bi-National Workshop on trends in Aerosol
Research, Atmospheric and Industrial Aerosols. Israel Ministry of Science, Deutsche Forschungsgemeinschaft, Israel, June 2–3.
Khvorostyanov, V., Khain, A., Kogteva, E., 1989. A two-dimensional non-stationary microphysical model of a
three-phase convective cloud and evaluation of the effects of seeding by crystallizing reagent. Soviet
Meteorology and Hydrology 5, 33–45.
Kogan, Y., 1991. The simulation of a convective cloud in a 3-D model with explicit microphysics: Part 1.
Model description and sensitivity experiments. J. Atmos. Sci. 48, 1160–1189.
Kovetz, A., Olund, B., 1969. The effect of coalescence and condensation on rain formation in a cloud of finite
vertical extent. J. Atmos. Sci. 26, 1060–1065.
Kutiel, H., Sharon, D., 1980. Diurnal variation of rainfall in Israel. Arch. Meteorol., Geophys. Bioklimatol.,
Ser. A 29, 387–395.
Lensky, I., Rosenfeld, D., 1998. Space-borne insights into precipitation formation processes in continental and
maritime convective clouds: Part 1. Case studies. Conference on Cloud Physics, Everett, 17–21 Aug., pp.
J32–J35.
Levin, Z., 1994. Aerosol composition and its effect on cloud growth and cloud seeding. Sixth WMO Scientific
Conference on Weather Modification and Applied Cloud Physics, Paestum, Italy, 28 May–4 June, pp.
367–395.
Levin, Z., Wurzler, S., Reisin, T., 1998. Modification of mineral dust particles by cloud processing and
subsequent effects on drop size distributions. Conference on Cloud Physics, Everett, 17–21 Aug., pp.
504–505.
Meyers, M.P., DeMott, P.J., Cotton, W.R., 1992. New primary ice-nucleation parameterizations in an explicit
cloud model. J. Appl. Meteorol. 31, 708–721.
Mordy, W., 1959. Computations of the growth by condensation of a population of cloud droplets. Tellus 11,
16–44.
Ovtchinnikov, M., 1998. An investigation of ice production mechanisms using a 3-D cloud model with explicit
microphysics. Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK 73019, Report
107, 128 pp.
Pielke, R.A., Cotton, W.R., Walko, R.L., Tremback, C.J., Lyons, W.A., Grasso, L.D., Nichols, M.E., Moran,
M.D., Wesley, D.A., Lee, T.J., Copeland, J.H., 1992. A comprehensive meteorological modeling systemRAMS. Meteorol. Atmos. Phys. 49, 69–91.
Pruppacher, H.R., Klett, J.D., 1978. Microphysics of Clouds and Precipitation. Kluwer Academic, 941 pp.
Reisin, T., Levin, Z., Tzivion, S., 1996a. Rain production in convective clouds as simulated in an axisymmetric model with detailed microphysics: Part 1. Description of model. J. Atmos. Sci. 53, 497–519.
Reisin, T., Levin, Z., Tzivion, S., 1996b. Rain production in convective clouds as simulated in an
axisymmetric model with detailed microphysics: Part 2. Effects of varying drops and ice initiation. J.
Atmos. Sci. 53, 1815–1837.
Reisner, J., Rasmussen, R.M., Bruintjes, R.T., 1998. Explicit forecasting of supercooled liquid water in winter
storms using the MM5 mesoscale model. Q. J. R. Meteorol. Soc. 124, 1071–1107.
Rosenfeld, D., Lensky, I., 1998. Space-borne insights into precipitation formation processes in continental and
maritime convective clouds: Part 1. Methodology. Conference on Cloud Physics, Everett, 17–21 Aug., pp.
J28–J31.
Simpson, J., Tao, W.-K., 1993. Goddard cumulus ensemble model: Part 2. Applications for studying cloud
precipitating process and for NASA TRMM. TAO 4 Ž1., 73–116.
Yin, Y., Levin, Z., Reisin, T., Tzivion, S., 1999. The effect of giant cloud condensational nuclei on the
development of precipitation in convective clouds — a numerical study. Atmos. Res., in press.
Walko, R.L., Cotton, W.R., Meyers, M.P., Harrington, J.Y., 1995. New RAMS cloud microphysics parameterization: Part 1. The single-moment scheme. Atmos. Res. 38, 29–62.
www.elsevier.comrlocateratmos
Some effects of cloud–aerosol interaction on cloud
microphysics structure and precipitation formation:
numerical experiments with a spectral microphysics
cloud ensemble model
A. Khain ) , A. Pokrovsky, I. Sednev
The Hebrew UniÕersity of Jerusalem, The Institute of the Earth Sciences, Department of Atmospheric Sciences,
Jerusalem, Israel
Received 17 December 1998; received in revised form 20 May 1999; accepted 20 May 1999
Abstract
A spectral microphysics Hebrew University Cloud Model ŽHUCM. is used to evaluate some
effects of cloud–aerosol interaction on mixed-phase cloud microphysics and aerosol particle size
distribution in the region of the Eastern Mediterranean coastal circulation. In case of a high
concentration of aerosol particles ŽAPs., the rate of warm rain formation is several times lower, a
significant fraction of droplets ascends above the freezing level. These drops produce a large
amount of comparably small graupel particles and ice crystals. The warm rain from these clouds is
less intense as compared to clouds with low drop concentration. At the same time, melted rain
from clouds with high droplet concentration is more intense than from low drop concentration
clouds. Melted rain can take place downwind at a distance of several tens of kilometers from the
convective zone. It is shown that APs entering clouds above the cloud base influence the evolution
of the drop size spectrum and the rate of rain formation. The chemical composition of APs
influences the concentration of nucleated droplets and, therefore, changes accumulated rain
significantly Žin our experiments these changes are of 25–30%.. Clouds in a coastal circulation
influence significantly the concentration and size distribution of APs. First, they decrease the
concentration of largest APs by nucleation scavenging. In our experiments, about 40% of APs
were nucleated within clouds. The remaining APs are transported to middle levels by cloud
updrafts and then enter the land at the levels of 3 to 7 km. In our experiments, the concentration of
small APs increased several times at these levels. The cut off APs spectrum with an increased
concentration of small APs remains downwind of the convective zone for several of tens and even
hundreds of kilometers. The schemes of drop nucleation Žbased on the dependence of nucleated
)
Corresponding author. Tel.: q972-2-658-5822; fax: q972-2-566-2581; E-mail: [email protected]
0169-8095r99r$ - see front matter q 1999 Published by Elsevier Science B.V. All rights reserved.
PII: S 0 1 6 9 - 8 0 9 5 Ž 9 9 . 0 0 0 2 7 - 7
196
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
drop concentration on supersaturation in a certain power. and autoconversion Žbased on the
Kessler formula. are unsuitable for an adequate description of cloud–aerosol interaction. The
Kessler formula predicts an incorrect tendency in the rate of raindrop formation while increasing
APs’ concentration. Prediction errors concerning the rate of raindrop formation can easily result in
a 10-fold increase. It indicates that the spectral Žbin. microphysics scheme Žor parameterizations
based on the bin schemes. can be used for an adequate description of cloud–aerosol interaction.
q 1999 Published by Elsevier Science B.V. All rights reserved.
Keywords: Spectral cloud microphysics; Cloud–aerosol interaction; Coastal circulation
1. Introduction
As is well known, clouds forming over continents Žcontinental air masses. and over
the sea Žmaritime air masses. have different regimes of rain formation. These differences
are attributed to both different thermal conditions Žlapse rates and humidity. and to
different concentration of atmospheric aerosol serving as cloud condensation nuclei
ŽCCN.. To reveal the role of atmospheric aerosol, cloud development should be
compared under similar thermal conditions. Recent observations of cloud development
within different types of air masses Žmaritime or continental type. reveal crucially
different cloud development and rain formation. So, according to Rosenfeld and Lensky
Ž1998., Lensky and Rosenfeld Ž1998. observed that in polluted areas over Thailand and
Indonesia some clouds do not precipitate at all having narrow spectra of small droplets.
At the same time, similar clouds start precipitating in clear air in about 15 min after their
formation.
Different regimes of precipitation formation indicate significant differences in cloud
microphysics, rates of latent heat release, as well as possible differences in cloud
dynamics. The role of cloud–aerosol interaction appears to be significant in those
atmospheric phenomena, in which latent heat release is an important energy source.
Some indirect evidence of the potentially important effects of atmospheric aerosols on
the tropical cyclone ŽTC. intensity can be inferred from a recent statistical analysis of
weekly fluctuations of intensity of landfalling hurricanes in the North Atlantic ŽCerveny
and Balling, 1998.. The TC statistics indicate that the intensity of landfalling storms is
usually lower during weekends Žwhen human-induced aerosol generation is minimal. as
compared to weekdays, while associated precipitation reaches its maximum during the
same days. This effect cannot be explained by conventional convective parameterizations. It is because these parameterizations imply that precipitation rate is proportional to
the rate of latent heat release. Different microphysical structures of clouds, in their turn,
cause different radiative cloud properties.
Thus, allowing for cloud–aerosol interaction is potentially very important for the
understanding of the dynamics and microphysics of a great number of atmospheric
phenomena, as well as the climate and possible climatic changes.
Adequate description of cloud–aerosol interaction turns out to be an actual problem
for different numerical models.
In some advanced mesoscale models with explicit microphysics such as RAMS
ŽPielke et al., 1992; Walko et al., 1995., GCEM ŽSimpson and Tao, 1993. and MM5
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
197
ŽReisner et al., 1998., the so-called ‘‘bulk parameterization’’ microphysical schemes are
used. In these schemes, all microphysical processes are described in terms of integral
parameters, such as mass content and number concentration. The comparably small
number of integral parameters makes the bulk parameterization schemes computationally efficient. However, the ‘‘bulk parameterization’’ microphysical schemes used in the
RAMS and MM5 mesoscale models also have limited capabilities in describing the
effects of atmospheric aerosols on clouds. For example, in studies by Pielke et al.
Ž1992., Reisner et al. Ž1998. and many others, the autoconversion rate Žthe rate of rain
water production by coagulation of small cloud droplets. is described by the following
formula ŽKessler, 1969.:
A u s max K Ž CWC y CWC cr . ,0 4 ,
Ž 1.
where CWC is cloud water content, CCWcr is an empirical threshold value, K is an
empirical constant. It is known, however Že.g., Beheng, 1994., that formulation Ž1.
appears highly intuitive without taking into account any microphysical considerations.
For instance, formula Ž1. ignores any dependence of raindrop production on the cloud
droplet size and width of the droplet size spectrum. The latter is the major controlling
factor of rain formation.
The two-dimensional cloud ensemble model HUCM Žthe Hebrew University Cloud
Model. with detailed description of cloud microphysics ŽKhain and Sednev, 1995, 1996;
Khain et al., 1996. has been designed to take into account the effects of cloud–aerosol
interaction on latent heat release and precipitation formation. The HUCM microphysics
is based on the spectral Žor bin. approach according to which each of the seven types of
cloud hydrometeors Žwater drops, three types of ice crystals, snow, graupel and hail. are
described using size-distribution functions containing several tens of bins of masses. To
make it possible to describe cloud–aerosol interaction and drop formation by nucleation
processes, a special size distribution for aerosol particles ŽAPs. is incorporated into the
model. This function contains several tens of mass bins as well. Contrary to the bulk
parameterization schemes, the size distributions in the model are not determined a priori,
but are the result of model integration.
Cloud microphysics models based on spectral microphysics proved to be effective
when simulating precipitation formation and cloud–aerosol interactions Že.g.,
Khvorostyanov et al., 1989; Khain et al., 1996; Reisin et al., 1996a,b; Levin et al.,
1998..
In the present paper, we use the HUCM to illustrate the effects of cloud–aerosol
interaction on cloud microphysical parameters and rain formation in the Eastern
Mediterranean coastal zone during the cold season.
2. Model description
The HUMC ŽKhain and Sednev, 1995, 1996; Khain et al., 1996, 1998. is a
two-dimensional non-hydrostatic model based on the deep convection equation system.
198
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
The computational area of the model in the present study extends 192 km horizontally
and 16 km vertically Ž129 = 41 grid points.. The coastline is located at x s 90 km.
The variables of the model include wind velocity Žvorticity and stream function.,
virtual potential temperature, mixing ratio, the number density size distribution functions
of water drops, ice crystals Žplates, dendrites and columns., snowflakes, graupel and
hailrfrozen drops.
The non-linear advection terms were approximated using the Arakawa method, which
conserves vorticity, the square of the vorticity and kinetic energy. The stream function
was found by solving the Poisson equation. Each size distribution function is described
using the same mass grid containing 33 categories Žbins.. The maximum mass in the
mass grids corresponds to a water drop with the radius of 3250 mm. The model provides
calculation of values of different types: precipitation amount, precipitation rates, fluxes
of different hydrometeors, mass contents, radar reflectivity from water and ice, the mean
and effective radii of droplets and ice particles, fluxes from the underlying surface and
so on.
The model takes into account the following microphysical processes: nucleation of
CCN; formation Žnucleation. of ice crystals, condensational growthrevaporation of
droplets; diffusion growthrsublimation of ice particles; freezing of drops; melting of ice
Žsimplified description., drop–drop, drop–ice and ice–ice collisions; spontaneous
breakup of rain drops and snowflakes.
In order to describe cloud–aerosol interaction and to reveal the influence of aerosols
on the cloud microstructure and cloud thermodynamics, a special size distribution
function of APs is used. It also contains 33 mass bins.
The HUCM applies an explicit analytical method for the calculation of supersaturations with respect to water and ice. These values of supersaturation are used to calculate
drop growth by diffusion and drop nucleation processes. Using the values of supersaturation, the critical sizes of APs are determined. The APs of different chemical composition
greater than these critical values are activated. The corresponding sizes of cloud droplets
are calculated.
Actually, CCN start growing in a water vapor field long before they enter the cloud,
even at supersaturations less than critical. These size changes provide the initial value of
the wet nuclei radius for subsequent condensation calculations. As was indicated by
Mordy Ž1959., Ivanova et al. Ž1977., Kogan Ž1991. as well as from our supplemental
experiments, the smallest nuclei Žwith the dry fraction under about 0.3 mm. reach their
equilibrium radii in a reasonably short time Žseconds of fraction of seconds.. We
assumed that these small particles were in the equilibrium and the size of corresponding
droplets was calculated from the Kohler equation.
Large particles require significant time Žsometimes, even days. to come to their
equilibrium radii at 100% relative humidity.
To calculate the initial size of droplets arising on CCN with the dry fraction over 0.3
mm, we applied the results of detailed calculations performed by Ivanova et al. Ž1977..
According to these results, the initial sizes of droplets at zero supersaturation level are
about five times as large as the radii of corresponding dry CCN. This assumption
excludes the formation of very large droplets at the cloud base just after nucleation.
Similar approach has been used by Kogan Ž1991. and Yin et al. Ž1999..
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
199
Activation of new crystals is simulated following Meyers et al. Ž1992.. The type of
fresh nucleated crystals depends on temperature. The probability of drop freezing was
assumed proportional to the drop mass Žprobability freezing. ŽKhain and Sednev, 1996..
Size spectra evolution by drop–drop, drop–ice and ice–ice collisions is described by
solving a system of stochastic collision equations for size distribution functions. The
stochastic coalescence equations were solved using the method of Berry and Reinhardt
Ž1974.. Collision kernels for ice–ice collisions depend on temperature and supersaturation with respect to ice. A stochastic nature of terminal fall velocity of non-spherical ice
particles is taken into account by the corresponding increase of the swept volume. The
values of terminal fall velocities of different hydrometeors as functions of their mass
were taken mainly from Pruppacher and Klett Ž1978..
3. Description of numerical experiments
During the cold season, the sea surface temperature is higher than the temperature of
the land by about 3 to 108C. In our experiments, the sea–land temperature difference is
set equal to 58C. The SST is set equal to 198C, which is a value typical of December.
The initial profile of the background wind was chosen as follows: a linear increase was
set in the lowest 4 km from 4 to 12 mrs with a further increase toward 30 mrs at
z s 16 km. Vertical profiles of temperature and humidity were chosen typical of rain
events in the Mediterranean region ŽKhain and Sednev, 1996.. Initial relative humidity
over the sea was taken equal to 90%, 5% greater than that over the land. The surface
temperature was not changed during the model integration.
The purposes of numerical experiments are to investigate Ž1. the influence of
concentration and chemical composition of APs on cloud microphysics and rain amount
and distribution and Ž2. the influence of clouds on the concentration and size distribution
of APs. Only nucleation scavenging will be taken into account in the study.
In the control experiment E500, a modified gamma distribution function was used to
describe the size distribution function of APs subject to nucleation in clouds:
fa s Araaq1 exp Ž yBrag . .
Ž 2.
The parameters in Eq. Ž2. were set so as to be typical of the Eastern Mediterranean
conditions ŽLevin, 1994. with the concentration of APs near the surface of 500 cmy3 .
The spectrum is similar to that observed in the maritime air. The spectrum contains a
significant number of particles whose is ranged from 0.01 to 0.1 mm. The maximum size
of dry APs in the spectrum is about 1 mm. There are no ultra-giant coalescence nuclei in
the simulations.
The exponential decrease of APs concentration was assumed as the initial condition
at t s 0, when there was no convection and no clouds:
A Ž z . s A 0 exp Ž yzrzU . , where A 0 s 8.55 P 10 4 cmy3 mm, B s 17.89 mmy0 .5 ,
a s 1, g s 0.5.
200
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
When convection develops convective motions determine the spatial distribution of
APs leading to homogenization of APs concentration in the vertical. The homogenization of AP’s concentration with time takes place in the atmospheric boundary layer
beyond the convective zone due to vertical turbulent mixing as well.
Under a dominating contribution of the western background flow, the coastal region
even over the land should have a distribution of APs similar to that over the sea. In the
paper, we discuss 2-h simulations of the coastal circulation. During this time period, the
contribution of such maritime air aerosols must be dominating, and the assumption that
spatial distribution of APs is horizontally homogeneous seems to be acceptable.
APs in E500 were assumed to consist of NaCl. The values of APs’ concentration near
the surface remain constant during the simulations.
To reveal the influence of APs’ concentration above the cloud base, exp. E500C was
conducted, in which APs’ concentration was set equal to 500 cmy3 throughout the
whole computational area.
To investigate the influence of APs’ concentration on the rain amount and distribution, experiments E100 and E1000 were conducted. These experiments are similar to the
control experiment E500, with the exception that the APs’ concentration at the surface
was set close to 100 cmy3 and 1000 cmy3 , respectively.
Experiment E500 wŽNH 4 . 2 SO4 x was similar to the control experiment E500, with the
only exception: APs were assumed to consist of ŽNH 4 . 2 SO4 . This experiment was
conducted to reveal the sensitivity of model precipitation to the chemical composition of
APs.
4. Results of the experiments
4.1. Control experiment E500
Low-level wind convergence between the dominating westerly wind and coastal
breeze-like circulation creates favorable conditions for the development of convective
clouds over the sea, 15–18 km from the shore line. The clouds are then transported by
the background flow toward the land. Vertical velocities in clouds reached up to 9 mrs.
The height of the cloud top is 6 to 6.5 km at different time instances.
At the time when vertical velocities reach their maximum Ž t s 3000 s., the maximum
drop concentration was 170 cmy3 . The maximum drop concentration varies with time
within the range 150 to 250 cmy3 . The main feature of the simulation is the alteration of
cloud microstructure inland. Convective rain Žwater drops with a diameter greater than
64 mm. falls downwind of the zone of convective updraft. A significant fraction of the
rain falls over the sea. The existence of the convective rain maximum in the vicinity of
the shoreline is an observed climatic feature of precipitation in Israel ŽKutiel and Sharon,
1980.. A significant amount of suppercooled water can be seen above the freezing level
Ž2 km. ŽFig. 1b, Fig. 2b.. The maximum of cloud water content ŽCWC. of 1.65 g my3 is
located at z s 2 km, while the height of the maximum of rain water content ŽRWC. of 1
g my3 is located at 3 km, 1 km above the freezing level. The height of the maximum
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 1. CWC Žg my3 . at t s 3000 s in Ža. E100, Žb. E500 and Žc. E1000.
201
202
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 2. RWC Žg my3 . at t s 3000 s in Ža. E100, Žb. E500 and Žc. E1000.
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
203
drop content descends with the decrease of the maximum velocity to 2.5 km at t s 1 h.
Then, the height of the maximum RWC remains actually unchanged.
Typical drop size spectra in the area of the maximum convective updraft at 1 km
Ž x s 73.5 km. and 1.5 downwind Ž x s 75 km. are shown in Fig. 3. One can see that at
the level z s 0.8 km Žabout 400 m above the cloud base., the size distribution of cloud
drops is centered at 14 mm. The maximum of the spectrum shifts with the height to
greater sizes: 17 mm at 2 km, and 22 mm at 3.6 km due to diffusion growth. One can
see the formation of raindrops at x s 75 km, whose size increases downward reaching
the radius of about 1 mm at z s 0.8 km.
As Khain and Sednev Ž1996. reported, high-density ice particles, such as frozen drops
Žwith the density of 0.9 g my3 . are located in the area of comparably strong updrafts
and start precipitating over the land as soon as vertical updrafts decrease. Then, graupel
particles begin to contribute to precipitation. Ice crystals and snowflakes are able to
enter the land by several tens and even hundreds of kilometers. Therefore, it is mainly
melted drops that cause precipitation over the land.
In Fig. 4, the field of APs concentration at t s 2 h is presented. One can see two
main effects of cloud influence on the APs’ concentration: Ž1. transport of APs by
convective motions and Ž2. decrease of APs concentration due to the activation of CCN
and formation of new droplets.
Convective motions transport APs from the boundary layer Žwhere a significant
decrease of APs’ concentration can be seen. to the middle atmosphere. These particles
are transported then by the background flow inland at the levels of 3 to 7 km.
Fig. 3. Typical drop size spectra in the area of the maximum convective updraft at 1 km Ž x s 73.5 km. and 1.5
km Ž x s 75 km. downwind of maximum low level updraft. As can be seen from Ža., process of collisions
begins below z s800 m, so that at z s800 m the largest drops reach the 100 mm radius. Symbol lnR denotes
the logarithm of drop radius with the base es 2.7 Žaccording to Berry and Reinhardt, 1974. method. Symbol
LogŽR. denotes a logarithm with the base of 10, suitable for plotting the drop radii values.
204
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 4. Fields of APs’ concentration at t s 2 h in E500.
Comparison of APs’ concentration within a cloud with that at the cloud base shows
that 35 to 40% of APs experience activation and turn into drops. A decrease of APs’
concentration is caused by activation of the largest particles in the APs’ size spectrum.
Fig. 5a–c shows size distributions of APs at t s 230 min at different heights Ža. in the
undisturbed area at the upwind side of the convective activity zone x s 69 km, Žb. in the
area of maximum convective updraft, x s 73.5 km, and Žc. about 60 km inland from the
zone of coastal convection. Comparison of figures Ža. and Žb. indicates a sharp size
spectrum cut off at 0.4 mm in the area of cloud activity. APs transported by clouds into
the middle Žand upper. troposphere have small sizes. Ten kilometers downwind Žnot
shown. the size spectrum of APs at z s 3.6 km indicates the concentration of small APs
with the radii below 0.4 mm three times as high Žas compared to the concentration in the
undisturbed area. and the decrease of the concentration of APs’ with the radii greater
than 0.4 mm by a factor of two. A significant influence of convection on the APs’ size
spectrum is pronounced at several tens of kilometers Žfigure c. and even 100 km
downwind of convective zone Žnot shown..
4.2. Experiment E500C
In this experiment, initial APs’ concentration was set 500 cmy3 over the whole
computational region. The main question we are addressing in the experiment is the
influence of APs located at levels above the cloud base on cloud microphysics.
Drop concentration in E500C was about 25% higher than in E500. To reveal the
cause of the difference, we compared the maximums of drop concentration during cloud
development at 20, 30, 40, and 50 min, when cloud tops were 2, 4, 6, and 6.4 km,
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
205
Fig. 5. Size distributions of APs at t s 200 min at different heights Ža. in the unperturbed area at the upwind
side of the convective activity zone, x s69 km, Žb. in the area of the maximum convective updraft, x s 73.5
km, and Žc. 60 km inland from the zone of coastal convection.
respectively. The difference in drop concentration maximums at these time instances
was 11, 25, 26, and 75 cmy3 . In 50 min, the difference in drop concentration maximums
slightly oscillated around 70 cmy3 . We interpret the results as follows. The difference in
drop concentration at an early stage is caused by the difference of APs’ concentrations at
the cloud base. This difference was comparably small in exps. E500 and E500C. Then,
during cloud growth, the entrainment APs into the cloud becomes substantial. Some of
the APs are activated. Because in E500C APs’ concentration above the cloud base is
higher, the entrainment increases the drop concentration difference between E500C and
E500. Thus, a significant fraction of the drop concentration difference can be attributed
206
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
to fresh drop nucleation above the cloud base due to the entrainment of APs through
cloud boundaries. The analysis of the drop size spectra evolution in E500 and E500C
seems to support the conclusion. There is actually no difference in the droplet spectra at
z s 0.4 and 0.8 km Žnot shown.. At z s 2 km, the difference in the size spectra becomes
noticeable: in E500C, the cloud spectrum contains drops about 1 mm smaller than in
E500. The difference in the spectra increases with height, so that at 3.6 km, the drop
spectrum in E500C contains cloud drops that are by about 2 mm smaller than in E500.
The increase in drop concentration in E500C and a corresponding decrease of the
droplet size led to some delay in rain formation and to a corresponding shift of the rain
maximum 6 to 10 km downwind. Accumulated rain decreased by about 15%. Thus, the
effects of APs located above the cloud base cannot be neglected.
Fig. 6 presents the APs’ concentration field in E500C at 160 min. The minimum
value of APs’ concentration was 227 cmy3 as compared to the maximum value of 500
cmy3 . This again shows that in the zone of active convection nucleation scavenging can
eliminate from the atmosphere up to 40% of APs Žin case of typical size distribution..
4.3. Experiments E100 and E1000
These experiments were similar to the control experiment E500, except for the
maximum concentrations of APs at the surface, which were 100 cmy3 Žexp. E100. and
1000 cmy3 Žexp. E1000.. Fig. 1 shows CWCs in these two experiments, as well as in
the control experiment E500 at t s 3000 s. The maximums of CWC in E100, E500 and
Fig. 6. Concentration of APs in E500C at 160 min.
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
207
E1000 are 1.1 g my3 , 1.65 g my3 and 2.0 g my3 , respectively. The difference of the
CWC fields in exps. E100 and E1000 is shown in Fig. 7a. From Figs. 2 and 7a one can
see that the CWC in E100 is significantly lower than in E1000. According to the Kessler
formula Ž1., the rate of rainwater production, and, consequently, RWC must also be
lower than in E500 and E1000. However, this is not the case in the spectral micro-
Fig. 7. The fields of Ža. the CWC difference and Žb. RWC difference between experiments E100 and E1000.
208
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
physics model, as shown in Fig. 2a,c, Fig. 7b. The maximums of RWC in E100 Žclear
maritime air., E500 and E1000 Žrelatively dusty air. were 1.3 g my3 , 1.1 g my3 and
0.85 g my3 , respectively. Thus, as we can see in Fig. 7b, the RWC in E100 is nearly
twice as large as in E1000. This difference remains in the convective region all the time
of model integration. We attribute the difference in the RWCs to the fact that drop
concentration in E1000 Žwith maximum of 260 cmy3 . is several times higher than in
E100 Žwith the maximum of 58 cmy3 .ŽFig. 8a–c. and, therefore, cloud droplets in
E1000 are significantly smaller than in E100.
Thus, the rate of raindrop formation in E100 was twice as large as that in E1000,
while according to the Kessler formula Ž1. it should be half as low. It means that
neglecting the effect of drop size in Ž1. leads to an error by a factor of 4. Note that in
E1000 drop concentration is not as large, as in very continental clouds, where drop
concentrations can exceed 1000 cmy3 . In case cloud development is simulated in a
highly dusty air, formula Ž1. can easily overestimate the rate of rain drop formation by
the factor of 10.
Differences in the CWC and RWC between E100 and E1000 lead to differences in
cloud ice structure. During the period of active ice formation Ž t s 3000 s. graupel forms
at the levels 3 to 6 km ŽFig. 9a,b, Fig. 10a,b. by collisions of ice crystals with droplets
of greater mass. Because in E1000 the mean drop size is smaller Ždroplets have a lower
terminal fall velocity. than in E100, droplets ascend to higher levels before forming
graupel. Thus, graupel particles in E1000 are located at higher levels than in E100 Žsee
the fields of mass and concentration differences in Fig. 9c, Fig. 10c.. Graupel particles,
located at higher levels, where the velocity of the background flow is higher, are
transported inland faster. Over the land, the largest of them descend because of a fast
decrease of the air vertical velocity inland. The maximum of graupel mass content
descends from 4 km at 3000 s to 2.5 km at t s 4200 s. ŽFig. 11a–c, Fig. 12a–c.. At the
same time, graupel particles in E100 are larger, they grow owing to intensive accretion
of small droplets and the largest of them precipitate within 10 km downwind of the
maximum convective updraft. As a result, at t s 4200 s the graupel mass content in
E1000 turns out to be greater than in E100 in the region with x ) 82 km.
Concentrations of columnar crystals and dendrites are similar in both experiments
Ž10–15 ly1 .. Plate-like crystals form within the layer of 6 to 7 km Žnot shown..
Concentration of plate-like ice crystals in E1000 Žup to 500 ly1 . is much higher than
that in E100 Žup to 100 ly1 .. The majority of plate-like crystals forms in the model by
freezing of small droplets at low temperatures. As it was discussed above, the concentration of small cloud droplets in E1000 is much higher than that in E100. It explains the
difference in plate crystal concentration. At the same time, the crystals in E1000 are
smaller than in E100. Their size is usually less than 15 mm. Most particles cannot be
detected by the equipment available. At the same time, small ice crystals influence
significantly the radiation properties of clouds. This indicates the importance of adequate
Fig. 8. Cloud drop concentrations in Ža. E100 and Žb. E1000 and Žc. the difference between these fields. The
drop concentration in E1000 Žwith the maximum of 260 cmy3 . is several times higher than in E100 Žwith the
maximum of 58 cmy3 ..
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
209
210
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
description of cloud–aerosol interaction to provide the correct determination of cloud
radiation properties.
Snowflakes form from the very beginning at the heights of 5 to 6 km Žnot shown..
The mass of snowflakes is smaller than that of graupel by a factor of 1.5 to 2. The mass
of snowflakes in E100 is greater than in E1000. We explain the result as follows.
Snowflakes in our model are formed, first, as a result of collisions of ice crystals. Then,
the mass of snowflakes grows by riming. In spite of a higher ice crystal concentration in
E1000, many crystals Žmainly plate-like crystals. are too small to collide.
The height of graupel and snow generation decreases with time from 5 km to 6 km at
t s 3000 s to 3–4 km at t s 6000 s. We attribute this effect, first of all, to the descent of
ice particles within downdrafts on the downwind side of the convective zone and
sequential entrainment into the areas of suppercooled rain. Ovtchinnikov Ž1998. discussed the important role of ice recirculation for graupel formation.
As it was noted above, precipitation over the land Ž x ) 90 km. is related to the
melting of ice particles Žmainly graupel in the present experiments.. Graupel mass
content over the land is greater in E1000 than in E100. Thus, over the land melted rain
in E1000 is greater than in E100. We illustrate this result by Fig. 13a–c, Fig. 14a–c,
where radar reflectivity of water and total ice at t s 5400 s are presented, respectively.
Radar reflectivity in the spectral HUCM is calculated directly using the size spectra of
cloud particles according to its definition Žsee Khain and Sednev, 1996 for more detail..
The radar reflectivity of supercooled water in E100 is concentrated in the zone of high
updraft velocity, from 75 to 84 km. In E1000, the zone of supercooled water reflectivity
extends further inland to 87 km. It is related to a smaller mass of raindrops in E1000, as
it was shown above. The radar reflectivity of liquid water in E100 exceeds 40 DBz at
x s 84 to 89 km. The zone of the maximum radar reflectivity begins from the melting
level, which indicates a dominating contribution of large rain droplets formed as a result
of graupel and frozen drops melting. In E1000 Žfigure b., the zone with radar reflectivity
exceeding 40 DBz is located further inland: from 86 to 93 km. The maximum is reached
below the 1-km level. This indicates a significant contribution of warm rain at seashore
in this case.
In the convective zone below 2.5 km the radar reflectivity of liquid water is greater in
exp. E100 Žlocation of large raindrops.. Above 2.5 km, the reflectivity in E100 is
smaller than in E1000 because in E100, large drops either remain below the level or turn
into graupel Žor hail.. At the same time, the radar reflectivity of total ice in the
convective zone is greater in E100, because of larger size of graupel. Further inland at
x ) 90 km, radar reflectivity of ice in E1000 can be 12 DBz higher than in E100.
Respectively, in E1000 radar reflectivity of liquid water is 12–14 DBz higher than in
E100, which shows clearly the dominating contribution of melted raindrops.
Thus, we see that difference in AP concentration leads to redistribution of precipitation in time and space.
Fig. 9. Graupel mass content in experiments Ža. E100 and Žb. E1000 and Žc. the difference between the mass
contents during the period of active ice formation Ž t s 3000 s..
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
211
212
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 10. The same as in Fig. 9, but for graupel concentrations.
213
Fig. 11. The same as in Fig. 9, but at t s 4200 s.
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 12. The same as in Fig. 11, but at t s 4200 s.
214
215
Fig. 13. Radar reflectivity of liquid water at t s 5400 s.
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Fig. 14. Radar reflectivity of total ice at t s 5400 s.
216
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
217
Analysis of vertical velocities in the convective zone shows that in E1000 vertical
updraft velocities as well as compensation downdrafts are higher by about 1 mrs Žor by
12%. than those in E100 Žnot shown.. This increase in updraft is reached in spite of
larger loading in exp. E1000. We attribute this effect to a greater contribution of latent
heat of fusion in exp. E1000, because in this experiment a larger fraction of drops
ascends above 2-km level and experiences freezing.
4.4. Experiment E500 [(NH4 )2 SO4 ]
This experiment was similar to the control experiment E500, with only one exception: APs were assumed to consist of ŽNH 4 . 2 SO4 . Size distributions of APs were
assumed to be the same as in the control experiment. Because the critical size of APs
Žand corresponding concentration of nucleated drops. depends on their chemical composition, precipitation also depends on the chemical composition of APs. The purpose of
the experiment is to evaluate the sensitivity of precipitation to the variation of the
chemical composition of APs.
In Fig. 15, the fields of APs’ concentration in experiments E500 and wŽNH 4 . 2 SO4 x
are shown at 180 min. The concentration of NaCl APs is smaller than that of
ŽNH 4 . 2 SO4 particles, indicating more effective nucleation and higher concentration in
Fig. 15. APs’ concentration in experiments E500 and E500 wŽNH 4 . 2 SO4 x at 180 min.
218
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
the first case. As a result, the accumulated rain and precipitation rate in the case of
ŽNH 4 . 2 SO4 APs turned out to be 20 to 30% higher than in the case of NaCl APs.
Thus, accumulated rain and rain rates are sensitive to the APs chemical composition.
5. Discussion and conclusions
A spectral microphysics cloud ensemble model is used to evaluate the effect of
cloud–aerosol interaction on the microphysics of mixed-phase clouds and on the APs
size distribution in the region of coastal circulation. A situation typical of the Eastern
Mediterranean is simulated. The sea surface is warmer than the land surface. The
background wind is directed inland. As a result, the coastal circulation at low levels
turned out to be opposite to the direction of the background flow and a zone of air
convergence arises over the sea at about 20 km offshore.
It was shown that concentration, size distribution, spatial distribution and chemical
composition of atmospheric APs crucially influence cloud microphysics, processes of
rain and ice formation. Natural or human-induced increase of atmospheric AP concentration can lead to a change of the cloud type Žfrom, say, maritime to continental type. with
corresponding decrease Žby several times. of the rate of warm rain formation and
increase in the ice crystal formation. In case of a background wind, it leads to a
significant spatial redistribution of precipitation. In case of a low APs’ concentration
warm rain prevails in the vicinity of the region of cloud formation. Graupel and frozen
drops in these clouds are large and also fall in the vicinity of cloud formation.
In case of a high APs’ concentration, the rate of warm rain formation can be several
times lower, a significant fraction of droplets ascends above the freezing level. These
drops produce large amounts of comparably small graupel particles and ice crystals.
Warm rain from these clouds is less intense than from clouds of low drop concentration.
At the same time, melted rain from these clouds is more intense than from low drop
concentration clouds. Melted rain can take place downwind at a distance of several tens
of kilometers from the convective zone. Because of very different ice particle concentrations, radiation properties of clouds must be also different. This problem needs a special
consideration.
It is shown that APs entering clouds above the cloud base influence the evolution of
drop size spectrum and the rate of rain formation.
The chemical composition of APs influences the concentration of nucleated droplets
and, therefore, changes accumulated rain significantly Žin our experiments by 25–30%..
Clouds in a coastal circulation influence significantly the concentration and size
distribution of APs. First, they decrease of the concentration of largest APs by
nucleation scavenging. In our experiments, about 40% of APs were nucleated within
clouds. The remaining APs are transported to middle levels by cloud updrafts and then
enter the land at the levels of 3 to 6 km. In our experiments, the concentration of small
APs increased several times at these levels. The cut off APs spectrum with a greater
concentration of small APs remains downwind of the convective zone for several tens
and even hundreds of kilometers.
The schemes of drop nucleation Žbased on the dependence of nucleated drop
concentration on supersaturation at a certain power. and autoconversion Žbased on the
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
219
Kessler formula. are unsuitable for an adequate description of cloud–aerosol interaction.
The Kessler formula predicts incorrect tendency as to the rate of raindrop formation
while increasing APs’ concentration. Prediction errors of the rate of raindrop formation
can easily give a 10-fold increase. This indicates that the spectral Žbin. microphysics
schemes Žor parameterizations based on the bin schemes. can be used for an adequate
description of cloud–aerosol interaction.
Finally, it must be stressed that despite the fact that the method of Berry and
Reinhardt Ž1974. used in this study has no numerical spreading Žin contrast to the
Kovetz and Olund, 1969 scheme., it has, however, another deficiency: the method does
not conserve the mass of hydrometeors, especially large ice particles. We have conducted a supplemental experiment similar to E500, except with a mass grid containing
43 mass bins. The results showed an increase of the precipitation rate Žformed from
melted particles. of about 20%. No significant difference in the results at t - 1 h were
observed. The difference in content of large ice particles became pronounced in 1.5 h,
when these particles formed downwind of the zone of active convection. Note, however,
that no qualitative differences in the results were observed when the number of grid
points in the mass mesh had been increased.
Activities connected with updating the model by introducing the new collision
scheme developed by Bott Ž1998. is under way. This scheme has no numerical
spreading, conserves the mass and is effective from the computational point of view. We
are going to present the results of the utilization of the scheme in future publications.
Acknowledgements
The authors are grateful to Christiane Textor Žthe Max-Plank Institute for Meteorology, Hamburg. for her assistance in calculations and useful discussions, as well as to Dr.
Danny Rosenfeld ŽThe Hebrew University of Jerusalem. for useful discussions and for
providing us with observational data. The Israel Ministry of Science Žgrant 6767-95. and
the Israel Academy of Science Žgrant 572r97. supported the study.
References
Beheng, K.D., 1994. A parameterization of warm cloud microphysical conversion processes. Atmos. Res. 33,
193–206.
Berry, E.X., Reinhardt, R.J., 1974. An analysis of cloud drop growth by collection: Part 1. Double
distributions. J. Atmos. Sci. 31, 1814–1824.
Bott, A., 1998. A flux method for the numerical solution of the stochastic collection equation. J. Atmos. Sci.
55, 2284–2293.
Cerveny, R.S., Balling, R.C., 1998. Weekly cycles of air pollutants, precipitation and tropical cyclones in the
coastal NW Atlantic region Žletter to Nature.. Nature 394, 561–563.
Ivanova, E.T., Kogan, Y., Mazin, I., Permyakov, M.S., 1977. The ways of parameterization of condensation
drop growth in numerical models. Izvestia, Atmospheric and Oceanic Physics 13 ŽN11., 1193–1201.
Kessler, E., 1969. On the distribution and continuity of water substance in atmospheric circulation. Met.
Monographs 10, 84.
Khain, A.P., Sednev, I., 1995. Simulation of hydrometeor size spectra evolution by water–water, ice–water
and ice–ice interactions. Atmos. Res. 36, 107–138.
220
A. Khain et al.r Atmospheric Research 52 (1999) 195–220
Khain, A.P., Sednev, I., 1996. Simulation of precipitation formation in the Eastern Mediterranean coastal zone
using a spectral microphysics cloud ensemble model. Atmos. Res. 43, 77–110.
Khain, A.P., Sednev, I., Khvorostyanov, V., 1996. Simulation of deep convection–breeze interaction in the
Eastern Mediterranean using a cloud ensemble model with an explicit description of warm and ice
microphysical processes. J. Clim. 9, 3298–3315.
Khain, A., Pokrovsky, A., Sednev, I., 1998. Simulation of cloud–aerosol interaction with a spectral
microphysics cloud ensemble model. First Israel–Germany Bi-National Workshop on trends in Aerosol
Research, Atmospheric and Industrial Aerosols. Israel Ministry of Science, Deutsche Forschungsgemeinschaft, Israel, June 2–3.
Khvorostyanov, V., Khain, A., Kogteva, E., 1989. A two-dimensional non-stationary microphysical model of a
three-phase convective cloud and evaluation of the effects of seeding by crystallizing reagent. Soviet
Meteorology and Hydrology 5, 33–45.
Kogan, Y., 1991. The simulation of a convective cloud in a 3-D model with explicit microphysics: Part 1.
Model description and sensitivity experiments. J. Atmos. Sci. 48, 1160–1189.
Kovetz, A., Olund, B., 1969. The effect of coalescence and condensation on rain formation in a cloud of finite
vertical extent. J. Atmos. Sci. 26, 1060–1065.
Kutiel, H., Sharon, D., 1980. Diurnal variation of rainfall in Israel. Arch. Meteorol., Geophys. Bioklimatol.,
Ser. A 29, 387–395.
Lensky, I., Rosenfeld, D., 1998. Space-borne insights into precipitation formation processes in continental and
maritime convective clouds: Part 1. Case studies. Conference on Cloud Physics, Everett, 17–21 Aug., pp.
J32–J35.
Levin, Z., 1994. Aerosol composition and its effect on cloud growth and cloud seeding. Sixth WMO Scientific
Conference on Weather Modification and Applied Cloud Physics, Paestum, Italy, 28 May–4 June, pp.
367–395.
Levin, Z., Wurzler, S., Reisin, T., 1998. Modification of mineral dust particles by cloud processing and
subsequent effects on drop size distributions. Conference on Cloud Physics, Everett, 17–21 Aug., pp.
504–505.
Meyers, M.P., DeMott, P.J., Cotton, W.R., 1992. New primary ice-nucleation parameterizations in an explicit
cloud model. J. Appl. Meteorol. 31, 708–721.
Mordy, W., 1959. Computations of the growth by condensation of a population of cloud droplets. Tellus 11,
16–44.
Ovtchinnikov, M., 1998. An investigation of ice production mechanisms using a 3-D cloud model with explicit
microphysics. Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK 73019, Report
107, 128 pp.
Pielke, R.A., Cotton, W.R., Walko, R.L., Tremback, C.J., Lyons, W.A., Grasso, L.D., Nichols, M.E., Moran,
M.D., Wesley, D.A., Lee, T.J., Copeland, J.H., 1992. A comprehensive meteorological modeling systemRAMS. Meteorol. Atmos. Phys. 49, 69–91.
Pruppacher, H.R., Klett, J.D., 1978. Microphysics of Clouds and Precipitation. Kluwer Academic, 941 pp.
Reisin, T., Levin, Z., Tzivion, S., 1996a. Rain production in convective clouds as simulated in an axisymmetric model with detailed microphysics: Part 1. Description of model. J. Atmos. Sci. 53, 497–519.
Reisin, T., Levin, Z., Tzivion, S., 1996b. Rain production in convective clouds as simulated in an
axisymmetric model with detailed microphysics: Part 2. Effects of varying drops and ice initiation. J.
Atmos. Sci. 53, 1815–1837.
Reisner, J., Rasmussen, R.M., Bruintjes, R.T., 1998. Explicit forecasting of supercooled liquid water in winter
storms using the MM5 mesoscale model. Q. J. R. Meteorol. Soc. 124, 1071–1107.
Rosenfeld, D., Lensky, I., 1998. Space-borne insights into precipitation formation processes in continental and
maritime convective clouds: Part 1. Methodology. Conference on Cloud Physics, Everett, 17–21 Aug., pp.
J28–J31.
Simpson, J., Tao, W.-K., 1993. Goddard cumulus ensemble model: Part 2. Applications for studying cloud
precipitating process and for NASA TRMM. TAO 4 Ž1., 73–116.
Yin, Y., Levin, Z., Reisin, T., Tzivion, S., 1999. The effect of giant cloud condensational nuclei on the
development of precipitation in convective clouds — a numerical study. Atmos. Res., in press.
Walko, R.L., Cotton, W.R., Meyers, M.P., Harrington, J.Y., 1995. New RAMS cloud microphysics parameterization: Part 1. The single-moment scheme. Atmos. Res. 38, 29–62.