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Ž .Atmospheric Research 52 1999 195–220www.elsevier.comrlocateratmos

Some effects of cloud–aerosol interaction on cloudmicrophysics structure and precipitation formation:numerical experiments with a spectral microphysics

cloud ensemble model

A. Khain ), A. Pokrovsky, I. SednevThe 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

Ž .A spectral microphysics Hebrew University Cloud Model HUCM is used to evaluate someeffects of cloud–aerosol interaction on mixed-phase cloud microphysics and aerosol particle sizedistribution in the region of the Eastern Mediterranean coastal circulation. In case of a high

Ž .concentration of aerosol particles APs , the rate of warm rain formation is several times lower, asignificant fraction of droplets ascends above the freezing level. These drops produce a largeamount of comparably small graupel particles and ice crystals. The warm rain from these clouds isless intense as compared to clouds with low drop concentration. At the same time, melted rainfrom clouds with high droplet concentration is more intense than from low drop concentrationclouds. Melted rain can take place downwind at a distance of several tens of kilometers from theconvective zone. It is shown that APs entering clouds above the cloud base influence the evolutionof the drop size spectrum and the rate of rain formation. The chemical composition of APsinfluences the concentration of nucleated droplets and, therefore, changes accumulated rain

Ž .significantly in our experiments these changes are of 25–30% . Clouds in a coastal circulationinfluence significantly the concentration and size distribution of APs. First, they decrease theconcentration of largest APs by nucleation scavenging. In our experiments, about 40% of APswere nucleated within clouds. The remaining APs are transported to middle levels by cloudupdrafts and then enter the land at the levels of 3 to 7 km. In our experiments, the concentration ofsmall APs increased several times at these levels. The cut off APs spectrum with an increasedconcentration of small APs remains downwind of the convective zone for several of tens and even

Žhundreds of kilometers. The schemes of drop nucleation based 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.Ž .PII: S0169-8095 99 00027-7

( )A. Khain et al.rAtmospheric Research 52 1999 195–220196

. Ždrop concentration on supersaturation in a certain power and autoconversion based 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 increasingAPs’ concentration. Prediction errors concerning the rate of raindrop formation can easily result in

Ž . Ža 10-fold increase. It indicates that the spectral bin microphysics scheme or 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

Ž .As is well known, clouds forming over continents continental air masses and overŽ .the sea maritime air masses have different regimes of rain formation. These differences

Ž .are attributed to both different thermal conditions lapse rates and humidity and todifferent concentration of atmospheric aerosol serving as cloud condensation nucleiŽ .CCN . To reveal the role of atmospheric aerosol, cloud development should becompared under similar thermal conditions. Recent observations of cloud development

Ž .within different types of air masses maritime or continental type reveal cruciallydifferent cloud development and rain formation. So, according to Rosenfeld and LenskyŽ . Ž .1998 , Lensky and Rosenfeld 1998 observed that in polluted areas over Thailand andIndonesia 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 theirformation.

Different regimes of precipitation formation indicate significant differences in cloudmicrophysics, rates of latent heat release, as well as possible differences in clouddynamics. The role of cloud–aerosol interaction appears to be significant in thoseatmospheric phenomena, in which latent heat release is an important energy source.Some indirect evidence of the potentially important effects of atmospheric aerosols on

Ž .the tropical cyclone TC intensity can be inferred from a recent statistical analysis ofŽweekly fluctuations of intensity of landfalling hurricanes in the North Atlantic Cerveny

.and Balling, 1998 . The TC statistics indicate that the intensity of landfalling storms isŽ .usually lower during weekends when human-induced aerosol generation is minimal as

compared to weekdays, while associated precipitation reaches its maximum during thesame days. This effect cannot be explained by conventional convective parameteriza-tions. It is because these parameterizations imply that precipitation rate is proportional tothe 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 theunderstanding of the dynamics and microphysics of a great number of atmosphericphenomena, as well as the climate and possible climatic changes.

Adequate description of cloud–aerosol interaction turns out to be an actual problemfor different numerical models.

In some advanced mesoscale models with explicit microphysics such as RAMSŽ . Ž .Pielke et al., 1992; Walko et al., 1995 , GCEM Simpson and Tao, 1993 and MM5

( )A. Khain et al.rAtmospheric Research 52 1999 195–220 197

Ž .Reisner et al., 1998 , the so-called ‘‘bulk parameterization’’ microphysical schemes areused. In these schemes, all microphysical processes are described in terms of integralparameters, such as mass content and number concentration. The comparably smallnumber of integral parameters makes the bulk parameterization schemes computation-ally efficient. However, the ‘‘bulk parameterization’’ microphysical schemes used in theRAMS and MM5 mesoscale models also have limited capabilities in describing theeffects of atmospheric aerosols on clouds. For example, in studies by Pielke et al.Ž . Ž . Ž1992 , Reisner et al. 1998 and many others, the autoconversion rate the rate of rain

.water production by coagulation of small cloud droplets is described by the followingŽ .formula Kessler, 1969 :

A smax K CWCyCWC ,0 , 1� 4Ž . Ž .u cr

where CWC is cloud water content, CCW is an empirical threshold value, K is ancrŽ . Ž .empirical constant. It is known, however e.g., Beheng, 1994 , that formulation 1

appears highly intuitive without taking into account any microphysical considerations.Ž .For instance, formula 1 ignores any dependence of raindrop production on the cloud

droplet size and width of the droplet size spectrum. The latter is the major controllingfactor of rain formation.

ŽThe two-dimensional cloud ensemble model HUCM the Hebrew University Cloud. ŽModel with detailed description of cloud microphysics Khain and Sednev, 1995, 1996;

.Khain et al., 1996 has been designed to take into account the effects of cloud–aerosolinteraction on latent heat release and precipitation formation. The HUCM microphysics

Ž .is based on the spectral or bin approach according to which each of the seven types ofŽ .cloud hydrometeors water drops, three types of ice crystals, snow, graupel and hail are

described using size-distribution functions containing several tens of bins of masses. Tomake it possible to describe cloud–aerosol interaction and drop formation by nucleation

Ž .processes, a special size distribution for aerosol particles APs is incorporated into themodel. This function contains several tens of mass bins as well. Contrary to the bulkparameterization 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 effectiveŽwhen simulating precipitation formation and cloud–aerosol interactions e.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–aerosolinteraction on cloud microphysical parameters and rain formation in the EasternMediterranean coastal zone during the cold season.

2. Model description

Ž .The HUMC Khain and Sednev, 1995, 1996; Khain et al., 1996, 1998 is atwo-dimensional non-hydrostatic model based on the deep convection equation system.


The computational area of the model in the present study extends 192 km horizontallyŽ .and 16 km vertically 129=41 grid points . The coastline is located at xs90 km.

Ž .The variables of the model include wind velocity vorticity and stream function ,virtual potential temperature, mixing ratio, the number density size distribution functions

Ž .of water drops, ice crystals plates, dendrites and columns , snowflakes, graupel andhailrfrozen drops.

The non-linear advection terms were approximated using the Arakawa method, whichconserves vorticity, the square of the vorticity and kinetic energy. The stream functionwas found by solving the Poisson equation. Each size distribution function is described

Ž .using the same mass grid containing 33 categories bins . The maximum mass in themass grids corresponds to a water drop with the radius of 3250 mm. The model providescalculation of values of different types: precipitation amount, precipitation rates, fluxesof different hydrometeors, mass contents, radar reflectivity from water and ice, the meanand effective radii of droplets and ice particles, fluxes from the underlying surface andso on.

The model takes into account the following microphysical processes: nucleation ofŽ .CCN; formation nucleation of ice crystals, condensational growthrevaporation of

droplets; diffusion growthrsublimation of ice particles; freezing of drops; melting of iceŽ .simplified description , drop–drop, drop–ice and ice–ice collisions; spontaneousbreakup of rain drops and snowflakes.

In order to describe cloud–aerosol interaction and to reveal the influence of aerosolson the cloud microstructure and cloud thermodynamics, a special size distributionfunction of APs is used. It also contains 33 mass bins.

The HUCM applies an explicit analytical method for the calculation of supersatura-tions with respect to water and ice. These values of supersaturation are used to calculatedrop growth by diffusion and drop nucleation processes. Using the values of supersatura-tion, the critical sizes of APs are determined. The APs of different chemical compositiongreater than these critical values are activated. The corresponding sizes of cloud dropletsare 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 ofthe wet nuclei radius for subsequent condensation calculations. As was indicated by

Ž . Ž . Ž .Mordy 1959 , Ivanova et al. 1977 , Kogan 1991 as well as from our supplementalŽ .experiments, the smallest nuclei with the dry fraction under about 0.3 mm reach their

Ž .equilibrium radii in a reasonably short time seconds of fraction of seconds . Weassumed that these small particles were in the equilibrium and the size of correspondingdroplets was calculated from the Kohler equation.

Ž .Large particles require significant time sometimes, even days to come to theirequilibrium radii at 100% relative humidity.

To calculate the initial size of droplets arising on CCN with the dry fraction over 0.3Ž .mm, we applied the results of detailed calculations performed by Ivanova et al. 1977 .

According to these results, the initial sizes of droplets at zero supersaturation level areabout five times as large as the radii of corresponding dry CCN. This assumptionexcludes the formation of very large droplets at the cloud base just after nucleation.

Ž . Ž .Similar approach has been used by Kogan 1991 and Yin et al. 1999 .


Ž .Activation of new crystals is simulated following Meyers et al. 1992 . The type offresh nucleated crystals depends on temperature. The probability of drop freezing was

Ž . Ž .assumed proportional to the drop mass probability freezing Khain 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. Thestochastic coalescence equations were solved using the method of Berry and ReinhardtŽ .1974 . Collision kernels for ice–ice collisions depend on temperature and supersatura-tion with respect to ice. A stochastic nature of terminal fall velocity of non-spherical iceparticles is taken into account by the corresponding increase of the swept volume. Thevalues of terminal fall velocities of different hydrometeors as functions of their mass

Ž .were taken mainly from Pruppacher and Klett 1978 .

3. Description of numerical experiments

During the cold season, the sea surface temperature is higher than the temperature ofthe land by about 3 to 108C. In our experiments, the sea–land temperature difference isset 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 wasset in the lowest 4 km from 4 to 12 mrs with a further increase toward 30 mrs atzs16 km. Vertical profiles of temperature and humidity were chosen typical of rain

Ž .events in the Mediterranean region Khain and Sednev, 1996 . Initial relative humidityover the sea was taken equal to 90%, 5% greater than that over the land. The surfacetemperature was not changed during the model integration.

Ž .The purposes of numerical experiments are to investigate 1 the influence ofconcentration and chemical composition of APs on cloud microphysics and rain amount

Ž .and distribution and 2 the influence of clouds on the concentration and size distributionof APs. Only nucleation scavenging will be taken into account in the study.

In the control experiment E500, a modified gamma distribution function was used todescribe the size distribution function of APs subject to nucleation in clouds:

f sAr aq1exp yBrg . 2Ž .Ž .a a a

Ž .The parameters in Eq. 2 were set so as to be typical of the Eastern MediterraneanŽ . y3conditions Levin, 1994 with the concentration of APs near the surface of 500 cm .

The spectrum is similar to that observed in the maritime air. The spectrum contains asignificant number of particles whose is ranged from 0.01 to 0.1 mm. The maximum sizeof dry APs in the spectrum is about 1 mm. There are no ultra-giant coalescence nuclei inthe simulations.

The exponential decrease of APs concentration was assumed as the initial conditionat ts0, when there was no convection and no clouds:

A z sA exp yzrzU , where A s8.55P104 cmy3mm, Bs17.89 mmy0 .5 ,Ž . Ž .0 0

as1, gs0.5.


When convection develops convective motions determine the spatial distribution ofAPs leading to homogenization of APs concentration in the vertical. The homogeniza-tion of AP’s concentration with time takes place in the atmospheric boundary layerbeyond the convective zone due to vertical turbulent mixing as well.

Under a dominating contribution of the western background flow, the coastal regioneven over the land should have a distribution of APs similar to that over the sea. In thepaper, we discuss 2-h simulations of the coastal circulation. During this time period, thecontribution of such maritime air aerosols must be dominating, and the assumption thatspatial distribution of APs is horizontally homogeneous seems to be acceptable.

APs in E500 were assumed to consist of NaCl. The values of APs’ concentration nearthe surface remain constant during the simulations.

To reveal the influence of APs’ concentration above the cloud base, exp. E500C wasconducted, in which APs’ concentration was set equal to 500 cmy3 throughout thewhole computational area.

To investigate the influence of APs’ concentration on the rain amount and distribu-tion, experiments E100 and E1000 were conducted. These experiments are similar to thecontrol experiment E500, with the exception that the APs’ concentration at the surfacewas set close to 100 cmy3 and 1000 cmy3, respectively.

wŽ . xExperiment E500 NH SO was similar to the control experiment E500, with the4 2 4Ž .only exception: APs were assumed to consist of NH SO . This experiment was4 2 4

conducted to reveal the sensitivity of model precipitation to the chemical composition ofAPs.

4. Results of the experiments

4.1. Control experiment E500

Low-level wind convergence between the dominating westerly wind and coastalbreeze-like circulation creates favorable conditions for the development of convectiveclouds over the sea, 15–18 km from the shore line. The clouds are then transported bythe 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.

Ž .At the time when vertical velocities reach their maximum ts3000 s , the maximumdrop concentration was 170 cmy3. The maximum drop concentration varies with timewithin the range 150 to 250 cmy3. The main feature of the simulation is the alteration of

Žcloud microstructure inland. Convective rain water 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 ofŽthe shoreline is an observed climatic feature of precipitation in Israel Kutiel and Sharon,

.1980 . A significant amount of suppercooled water can be seen above the freezing levelŽ . Ž . Ž . y32 km Fig. 1b, Fig. 2b . The maximum of cloud water content CWC of 1.65 g m is

Ž .located at zs2 km, while the height of the maximum of rain water content RWC of 1g my3 is located at 3 km, 1 km above the freezing level. The height of the maximum


Ž y3 . Ž . Ž . Ž .Fig. 1. CWC g m at ts3000 s in a E100, b E500 and c E1000.


Ž y3 . Ž . Ž . Ž .Fig. 2. RWC g m at ts3000 s in a E100, b E500 and c E1000.


drop content descends with the decrease of the maximum velocity to 2.5 km at ts1 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 kmŽ . Ž .xs73.5 km and 1.5 downwind xs75 km are shown in Fig. 3. One can see that at

Ž .the level zs0.8 km about 400 m above the cloud base , the size distribution of clouddrops is centered at 14 mm. The maximum of the spectrum shifts with the height togreater sizes: 17 mm at 2 km, and 22 mm at 3.6 km due to diffusion growth. One cansee the formation of raindrops at xs75 km, whose size increases downward reachingthe radius of about 1 mm at zs0.8 km.

Ž .As Khain and Sednev 1996 reported, high-density ice particles, such as frozen dropsŽ y3 .with the density of 0.9 g m are located in the area of comparably strong updraftsand start precipitating over the land as soon as vertical updrafts decrease. Then, graupelparticles begin to contribute to precipitation. Ice crystals and snowflakes are able toenter the land by several tens and even hundreds of kilometers. Therefore, it is mainlymelted drops that cause precipitation over the land.

In Fig. 4, the field of APs concentration at ts2 h is presented. One can see twoŽ .main effects of cloud influence on the APs’ concentration: 1 transport of APs by

Ž .convective motions and 2 decrease of APs concentration due to the activation of CCNand formation of new droplets.

ŽConvective motions transport APs from the boundary layer where 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.

Ž .Fig. 3. Typical drop size spectra in the area of the maximum convective updraft at 1 km xs73.5 km and 1.5Ž . Ž .km xs75 km downwind of maximum low level updraft. As can be seen from a , process of collisions

begins below zs800 m, so that at zs800 m the largest drops reach the 100 mm radius. Symbol lnR denotesŽ .the logarithm of drop radius with the base es2.7 according to Berry and Reinhardt, 1974 method. Symbol

Ž .Log R denotes a logarithm with the base of 10, suitable for plotting the drop radii values.


Fig. 4. Fields of APs’ concentration at ts2 h in E500.

Comparison of APs’ concentration within a cloud with that at the cloud base showsthat 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.

Ž .Fig. 5a–c shows size distributions of APs at ts230 min at different heights a in theŽ .undisturbed area at the upwind side of the convective activity zone xs69 km, b in the

Ž .area of maximum convective updraft, xs73.5 km, and c about 60 km inland from theŽ . Ž .zone of coastal convection. Comparison of figures a and b indicates a sharp size

spectrum cut off at 0.4 mm in the area of cloud activity. APs transported by clouds intoŽ . Žthe middle and upper troposphere have small sizes. Ten kilometers downwind not

.shown the size spectrum of APs at zs3.6 km indicates the concentration of small APsŽwith the radii below 0.4 mm three times as high as compared to the concentration in the

.undisturbed area and the decrease of the concentration of APs’ with the radii greaterthan 0.4 mm by a factor of two. A significant influence of convection on the APs’ size

Ž .spectrum is pronounced at several tens of kilometers figure c and even 100 kmŽ .downwind of convective zone not shown .

4.2. Experiment E500C

In this experiment, initial APs’ concentration was set 500 cmy3 over the wholecomputational region. The main question we are addressing in the experiment is theinfluence 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 thecause of the difference, we compared the maximums of drop concentration during clouddevelopment at 20, 30, 40, and 50 min, when cloud tops were 2, 4, 6, and 6.4 km,


Ž .Fig. 5. Size distributions of APs at ts200 min at different heights a in the unperturbed area at the upwindŽ .side of the convective activity zone, xs69 km, b in the area of the maximum convective updraft, xs73.5

Ž .km, and c 60 km inland from the zone of coastal convection.

respectively. The difference in drop concentration maximums at these time instanceswas 11, 25, 26, and 75 cmy3. In 50 min, the difference in drop concentration maximumsslightly oscillated around 70 cmy3. We interpret the results as follows. The difference indrop concentration at an early stage is caused by the difference of APs’ concentrations atthe 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 ofthe APs are activated. Because in E500C APs’ concentration above the cloud base ishigher, the entrainment increases the drop concentration difference between E500C andE500. Thus, a significant fraction of the drop concentration difference can be attributed


to fresh drop nucleation above the cloud base due to the entrainment of APs throughcloud boundaries. The analysis of the drop size spectra evolution in E500 and E500Cseems to support the conclusion. There is actually no difference in the droplet spectra at

Ž .zs0.4 and 0.8 km not shown . At zs2 km, the difference in the size spectra becomesnoticeable: in E500C, the cloud spectrum contains drops about 1 mm smaller than inE500. The difference in the spectra increases with height, so that at 3.6 km, the dropspectrum 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 thedroplet size led to some delay in rain formation and to a corresponding shift of the rainmaximum 6 to 10 km downwind. Accumulated rain decreased by about 15%. Thus, theeffects of APs located above the cloud base cannot be neglected.

Fig. 6 presents the APs’ concentration field in E500C at 160 min. The minimumvalue of APs’ concentration was 227 cmy3 as compared to the maximum value of 500cmy3. This again shows that in the zone of active convection nucleation scavenging can

Ž .eliminate from the atmosphere up to 40% of APs in case of typical size distribution .

4.3. Experiments E100 and E1000

These experiments were similar to the control experiment E500, except for they3 Ž .maximum concentrations of APs at the surface, which were 100 cm exp. E100 and

y3 Ž .1000 cm exp. E1000 . Fig. 1 shows CWCs in these two experiments, as well as inthe control experiment E500 at ts3000 s. The maximums of CWC in E100, E500 and

Fig. 6. Concentration of APs in E500C at 160 min.


E1000 are 1.1 g my3, 1.65 g my3 and 2.0 g my3, respectively. The difference of theCWC fields in exps. E100 and E1000 is shown in Fig. 7a. From Figs. 2 and 7a one cansee that the CWC in E100 is significantly lower than in E1000. According to the Kessler

Ž .formula 1 , the rate of rainwater production, and, consequently, RWC must also belower than in E500 and E1000. However, this is not the case in the spectral micro-

Ž . Ž .Fig. 7. The fields of a the CWC difference and b RWC difference between experiments E100 and E1000.


Žphysics model, as shown in Fig. 2a,c, Fig. 7b. The maximums of RWC in E100 clear. Ž . y3 y3maritime air , E500 and E1000 relatively dusty air were 1.3 g m , 1.1 g m and

0.85 g my3, respectively. Thus, as we can see in Fig. 7b, the RWC in E100 is nearlytwice as large as in E1000. This difference remains in the convective region all the timeof model integration. We attribute the difference in the RWCs to the fact that drop

Ž y3 .concentration in E1000 with maximum of 260 cm is several times higher than inŽ y3 .Ž .E100 with the maximum of 58 cm Fig. 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,

Ž .while according to the Kessler formula 1 it should be half as low. It means thatŽ .neglecting the effect of drop size in 1 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 dropconcentrations can exceed 1000 cmy3. In case cloud development is simulated in a

Ž .highly dusty air, formula 1 can easily overestimate the rate of rain drop formation bythe factor of 10.

Differences in the CWC and RWC between E100 and E1000 lead to differences inŽ .cloud ice structure. During the period of active ice formation ts3000 s graupel forms

Ž .at the levels 3 to 6 km Fig. 9a,b, Fig. 10a,b by collisions of ice crystals with dropletsŽof greater mass. Because in E1000 the mean drop size is smaller droplets have a lower

.terminal fall velocity than in E100, droplets ascend to higher levels before formingŽgraupel. Thus, graupel particles in E1000 are located at higher levels than in E100 see

.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, aretransported inland faster. Over the land, the largest of them descend because of a fastdecrease of the air vertical velocity inland. The maximum of graupel mass content

Ž .descends from 4 km at 3000 s to 2.5 km at ts4200 s. Fig. 11a–c, Fig. 12a–c . At thesame time, graupel particles in E100 are larger, they grow owing to intensive accretionof small droplets and the largest of them precipitate within 10 km downwind of themaximum convective updraft. As a result, at ts4200 s the graupel mass content inE1000 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 experimentsŽ y1 . Ž .10–15 l . Plate-like crystals form within the layer of 6 to 7 km not shown .

Ž y1 .Concentration of plate-like ice crystals in E1000 up to 500 l is much higher thanŽ y1 .that in E100 up to 100 l . The majority of plate-like crystals forms in the model by

freezing of small droplets at low temperatures. As it was discussed above, the concentra-tion of small cloud droplets in E1000 is much higher than that in E100. It explains thedifference in plate crystal concentration. At the same time, the crystals in E1000 aresmaller than in E100. Their size is usually less than 15 mm. Most particles cannot bedetected by the equipment available. At the same time, small ice crystals influencesignificantly the radiation properties of clouds. This indicates the importance of adequate

Ž . Ž . Ž .Fig. 8. Cloud drop concentrations in a E100 and b E1000 and c the difference between these fields. TheŽ y3 . Ždrop concentration in E1000 with the maximum of 260 cm is several times higher than in E100 with the

y3 .maximum of 58 cm .


description of cloud–aerosol interaction to provide the correct determination of cloudradiation properties.

Ž .Snowflakes form from the very beginning at the heights of 5 to 6 km not shown .The mass of snowflakes is smaller than that of graupel by a factor of 1.5 to 2. The massof 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

Ž .E1000, many crystals mainly 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

ts3000 s to 3–4 km at ts6000 s. We attribute this effect, first of all, to the descent ofice particles within downdrafts on the downwind side of the convective zone and

Ž .sequential entrainment into the areas of suppercooled rain. Ovtchinnikov 1998 dis-cussed the important role of ice recirculation for graupel formation.

Ž .As it was noted above, precipitation over the land x)90 km is related to theŽ .melting of ice particles mainly graupel in the present experiments . Graupel mass

content over the land is greater in E1000 than in E100. Thus, over the land melted rainin 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 ts5400 s are presented, respectively.Radar reflectivity in the spectral HUCM is calculated directly using the size spectra of

Ž .cloud particles according to its definition see Khain and Sednev, 1996 for more detail .The radar reflectivity of supercooled water in E100 is concentrated in the zone of highupdraft velocity, from 75 to 84 km. In E1000, the zone of supercooled water reflectivityextends further inland to 87 km. It is related to a smaller mass of raindrops in E1000, asit was shown above. The radar reflectivity of liquid water in E100 exceeds 40 DBz atxs84 to 89 km. The zone of the maximum radar reflectivity begins from the meltinglevel, which indicates a dominating contribution of large rain droplets formed as a result

Ž .of graupel and frozen drops melting. In E1000 figure b , the zone with radar reflectivityexceeding 40 DBz is located further inland: from 86 to 93 km. The maximum is reachedbelow the 1-km level. This indicates a significant contribution of warm rain at seashorein this case.

In the convective zone below 2.5 km the radar reflectivity of liquid water is greater inŽ .exp. E100 location 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 turnŽ .into graupel or 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 atx)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 inE100, which shows clearly the dominating contribution of melted raindrops.

Thus, we see that difference in AP concentration leads to redistribution of precipita-tion in time and space.

Ž . Ž . Ž .Fig. 9. Graupel mass content in experiments a E100 and b E1000 and c the difference between the massŽ .contents during the period of active ice formation ts3000 s .

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Analysis of vertical velocities in the convective zone shows that in E1000 verticalŽupdraft velocities as well as compensation downdrafts are higher by about 1 mrs or by

. Ž .12% than those in E100 not shown . This increase in updraft is reached in spite oflarger loading in exp. E1000. We attribute this effect to a greater contribution of latentheat of fusion in exp. E1000, because in this experiment a larger fraction of dropsascends above 2-km level and experiences freezing.

[( ) ]4.4. Experiment E500 NH SO4 2 4

This experiment was similar to the control experiment E500, with only one excep-Ž .tion: APs were assumed to consist of NH SO . Size distributions of APs were4 2 4

assumed to be the same as in the control experiment. Because the critical size of APsŽ .and corresponding concentration of nucleated drops depends on their chemical compo-sition, precipitation also depends on the chemical composition of APs. The purpose ofthe experiment is to evaluate the sensitivity of precipitation to the variation of thechemical composition of APs.

wŽ . xIn Fig. 15, the fields of APs’ concentration in experiments E500 and NH SO4 2 4

are shown at 180 min. The concentration of NaCl APs is smaller than that ofŽ .NH SO particles, indicating more effective nucleation and higher concentration in4 2 4

wŽ . xFig. 15. APs’ concentration in experiments E500 and E500 NH SO at 180 min.4 2 4


the first case. As a result, the accumulated rain and precipitation rate in the case ofŽ .NH SO APs turned out to be 20 to 30% higher than in the case of NaCl APs.4 2 4

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 ofcloud–aerosol interaction on the microphysics of mixed-phase clouds and on the APssize distribution in the region of coastal circulation. A situation typical of the EasternMediterranean is simulated. The sea surface is warmer than the land surface. Thebackground wind is directed inland. As a result, the coastal circulation at low levelsturned out to be opposite to the direction of the background flow and a zone of airconvergence arises over the sea at about 20 km offshore.

It was shown that concentration, size distribution, spatial distribution and chemicalcomposition of atmospheric APs crucially influence cloud microphysics, processes ofrain and ice formation. Natural or human-induced increase of atmospheric AP concentra-

Ž .tion can lead to a change of the cloud type from, say, maritime to continental type withŽ .corresponding decrease by 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 asignificant spatial redistribution of precipitation. In case of a low APs’ concentrationwarm rain prevails in the vicinity of the region of cloud formation. Graupel and frozendrops 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 severaltimes lower, a significant fraction of droplets ascends above the freezing level. Thesedrops 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 dropconcentration clouds. Melted rain can take place downwind at a distance of several tensof kilometers from the convective zone. Because of very different ice particle concentra-tions, radiation properties of clouds must be also different. This problem needs a specialconsideration.

It is shown that APs entering clouds above the cloud base influence the evolution ofdrop size spectrum and the rate of rain formation.

The chemical composition of APs influences the concentration of nucleated dropletsŽ .and, therefore, changes accumulated rain significantly in our experiments by 25–30% .

Clouds in a coastal circulation influence significantly the concentration and sizedistribution of APs. First, they decrease of the concentration of largest APs bynucleation scavenging. In our experiments, about 40% of APs were nucleated withinclouds. The remaining APs are transported to middle levels by cloud updrafts and thenenter the land at the levels of 3 to 6 km. In our experiments, the concentration of smallAPs increased several times at these levels. The cut off APs spectrum with a greaterconcentration of small APs remains downwind of the convective zone for several tensand even hundreds of kilometers.

ŽThe schemes of drop nucleation based on the dependence of nucleated drop. Žconcentration on supersaturation at a certain power and autoconversion based on the


.Kessler formula are unsuitable for an adequate description of cloud–aerosol interaction.The Kessler formula predicts incorrect tendency as to the rate of raindrop formationwhile increasing APs’ concentration. Prediction errors of the rate of raindrop formation

Ž .can easily give a 10-fold increase. This indicates that the spectral bin microphysicsŽ .schemes or 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

Ž . ŽReinhardt 1974 used in this study has no numerical spreading in 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 con-ducted a supplemental experiment similar to E500, except with a mass grid containing

Ž43 mass bins. The results showed an increase of the precipitation rate formed 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 gridpoints in the mass mesh had been increased.

Activities connected with updating the model by introducing the new collisionŽ .scheme developed by Bott 1998 is under way. This scheme has no numerical

spreading, conserves the mass and is effective from the computational point of view. Weare going to present the results of the utilization of the scheme in future publications.

Acknowledgements

ŽThe authors are grateful to Christiane Textor the Max-Plank Institute for Meteorol-.ogy, Hamburg for her assistance in calculations and useful discussions, as well as to Dr.

Ž .Danny Rosenfeld The Hebrew University of Jerusalem for useful discussions and forŽ .providing us with observational data. The Israel Ministry of Science grant 6767-95 and

Ž .the Israel Academy of Science grant 572r97 supported the study.

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