Aerosol effects on the development of a supercell stormin a double‐moment bulk‐cloud microphysics scheme

Kyo‐Sun Sunny Lim,1 Song‐You Hong,1 Seong Soo Yum,1 Jimy Dudhia,2

and Joseph B. Klemp2

Received 1 March 2010; revised 14 September 2010; accepted 14 October 2010; published 21 January 2011.

[1] This study investigates the aerosol effects on the development of an idealizedthree‐dimensional supercell storm, focusing on storm morphology and precipitation duringa quasi steady state of a storm. The impact of the aerosol concentration on the simulatedstorm is evaluated by varying the initial cloud condensation nuclei (CCN) numberconcentration in the Weather Research and Forecasting Double‐Moment Six‐Classmicrophysics scheme. A right‐moving, quasi‐steady supercell with two diverging echomasses was reproduced, compared with the previous modeling study. In the experimentwith a high CCN number concentration, storm intensity was weakened, and surfaceprecipitation was reduced. On the other hand, the simulation that excluded the graupelsubstance produced a weaker low‐level downdraft, thus less near‐surface vorticity,compared with the simulation that included graupel. The CCN number concentrationsdid not affect the storm structures in the absence of graupel. In addition, the aerosol effectson the surface precipitation with respect to the initial CCN value were diametricallyopposed. The major reason for the different responses to aerosol can be attributed to theexaggerated snow mass loading across the convective core when the graupel species isexcluded. The results indicate that graupel species and related microphysics are crucial to therealistic representation of the aerosol‐precipitation interactions within a supercell storm.

Citation: Lim, K.‐S. S., S.‐Y. Hong, S. S. Yum, J. Dudhia, and J. B. Klemp (2011), Aerosol effects on the development of asupercell storm in a double‐moment bulk‐cloud microphysics scheme, J. Geophys. Res., 116, D02204,doi:10.1029/2010JD014128.

1. Introduction

[2] The increase in aerosol particles in the atmospherecaused by industrialization is known to change cloudmicrophysics and precipitation processes. Increased aerosolconcentrations raise the number concentrations of cloudcondensation nuclei (CCN) and cloud droplets, thus reduc-ing droplet size and increasing cloud reflectance [Twomey,1974, 1977, 1991]. This is generally considered the firstindirect effect of atmospheric aerosol. Previous studiespointed out that a decrease in droplet size is also likely toimpact precipitation [Gunn and Phillips, 1957; Warner,1968; Albrecht, 1989]. The decrease in droplet size cansuppress the conversion of the droplet to drizzle or rain,thereby inhibiting rainfall and prolonging cloud lifetime.This precipitation suppression is commonly considered to bethe second indirect effect of atmospheric aerosol and maysubstantially alter the water mass budget of clouds, alteringtheir persistence and albedo, and, possibly, the climate.

[3] Studies of aerosol effects on large‐scale stratiformclouds via several general circulation models [Lohmannet al., 1999; Ghan et al., 2001; Rotstayn, 1999, 2000;Menon et al., 2002; Rotstayn and Lohmann, 2002; Lohmannand Feichter, 2005; Takemura et al., 2005] suggest that thesuppression of precipitation with increased aerosols, con-sistent with the second indirect effect of aerosols, can alterthe radiative fluxes due to changes in cloud lifetime or liquidwater path. Meanwhile, it is not certain whether the secondindirect effect of aerosol also applies to deep convectiveclouds. Recently, efforts to investigate the effects of aerosolon deep convective clouds have been carried out throughseveral observational and modeling studies [Rosenfeld, 1999,2000;Wang, 2005; Tao et al., 2007; Fan et al. 2007; Lerachet al. 2008].[4] Lerach et al. [2008] simulated a supercell storm using

a bin‐emulating, double‐moment bulk microphysics schemeincluding hail under an environment with a convectiveavailable potential energy (CAPE) of 3130 J kg−1 andveering winds from the surface to 2 km above ground leveland showed a decrease in precipitation with increasingaerosols. Wang [2005] investigated the response of tropicaldeep convection to the increase of CCN concentration with aset of 30 initial CCN profiles ranging from 50 to 6000 cm−3

and found that increasing CCN concentration causes astronger convection, leading to the increase in precipitation

1Department of Atmospheric Sciences and Global EnvironmentLaboratory, Yonsei University, Seoul, South Korea.

2Mesoscale and Microscale Meteorology Division, National Center forAtmospheric Research, Boulder, Colorado, USA.

Copyright 2011 by the American Geophysical Union.0148‐0227/11/2010JD014128

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D02204, doi:10.1029/2010JD014128, 2011

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as well as the expansion of the cloud coverage. However,when the initial CCN concentration exceeds a certain level,many of the above effects become insignificant, implyingthat a more substantial aerosol effect on deep convectivecloud be seen over a clean rather than polluted regions. Witha weak wind shear and a lower CAPE ( = 960 J kg−1), Fanet al. [2007] showed a nonmonotonic precipitation responseto increasing aerosols. The precipitation increased withincreasing aerosols, and then decreased in the extremelyhigh aerosol cases (over 5000 cm−3) owing to suppression ofconvection from depleted water vapor and inefficient coa-lescence. Tao et al. [2007] showed that three different deepconvective cloud systems result in differing responses ofsurface precipitation with increasing aerosol concentrationsduring the mature stage of the simulations. They suggestedthat evaporative cooling in the lower troposphere is a keyprocess in determining whether high CCN reduces orenhances precipitation. Lee et al. [2008] also investigatedaerosol effects on precipitation with five different sets ofinitial soundings and concluded that increasing aerosol caneither decrease or increase precipitation for an imposedlarge‐scale environment supporting cloud development.Aerosol effects on cloud microphysics and precipitationcould be nonmonotonic under the different meteorologicaland aerosol conditions because of the complicated couplingbetween cloud microphysics and storm dynamics [Seifertand Beheng, 2006; Fan et al., 2007; Tao et al., 2007;Yang and Yum, 2007; van den Heever and Cotton, 2007;Lee et al., 2008; G. Li et al., 2008]. Khain [2009] and Khainet al. [2008] analyzed aerosol effects on precipitation usingthe mass and heat budgets and concluded that in the casewhen the condensation loss increases more than the con-densation generation, a decrease in precipitation takes place.They also concluded that many discrepancies between theresults reported in different observational and numericalstudies for aerosol effects on surface precipitation can beattributed to the different atmospheric conditions and cloudtypes analyzed.[5] The purpose of this study is to investigate aerosol

effects on the development of a supercell storm, focusing onstorm morphology and precipitation during a quasi steadystate of a storm. A supercell thunderstorm, characterized bya deep, persistent, rotating updraft [Doswell and Burgess,1993], is the most prolific producer of dense ice particles,such as graupel and hail [Bluestein, 1992]. Thus, the WeatherResearch and Forecasting (WRF) Double‐Moment Six‐Class (WDM6) microphysics scheme [Lim and Hong, 2010]is used, which makes it possible to investigate the aerosoleffects on cloud properties and precipitation processes, incontrast to the WRF Single‐Moment Six‐Class (WSM6)microphysics scheme [Hong and Lim, 2006]. A preliminaryevaluation of the WDM6 microphysics scheme has con-firmed that the skill statistics of precipitation forecasts overKorea from June to August 2008 are generally improved[Hong et al., 2010]. Even though several previous studiesstressed the importance of the microphysical drop size dis-tribution (or size itself) and evaporative cooling rates ontornadogenesis [van den Heever and Cotton, 2004; Snookand Xue, 2008], few studies concerning aerosol‐relatedmicrophysical effects on the evolution of supercell stormshave been reported [Lerach et al., 2008; Khain and Lynn,2009]. Additionally, the graupel effects on the develop-

ment of the supercell storm will be investigated, recognizingthe importance of the dense ice particles in simulating thesupercell storm [Gilmore et al., 2004].[6] Section 2 outlines the numerical experiments conducted

in this study, and their results are discussed in section 3.Concluding remarks appear in section 4.

2. Numerical Experimental Setup

[7] The model used in this study is the AdvancedResearch WRF version 3.1 [Skamarock et al., 2008], whichwas released in April 2009. The WRF model is a state‐of‐the‐art mesoscale numerical weather prediction systemserving both operational forecasting and atmosphericresearch needs. The WDM6 scheme [Lim and Hong, 2010]is a double‐moment bulk‐cloud microphysics scheme basedon the WSM6 microphysics scheme. In addition to theprediction for the mixing ratios of six water species (watervapor, cloud droplets, cloud ice, snow, rain, and graupel),the number concentrations of cloud droplets and raindropsare also predicted in the WDM6 scheme, together with theprognostic variable of cloud condensation nuclei (CCN)number concentration. The ice‐phase microphysics [Honget al., 2004] is identical for both the WDM6 and WSM6schemes. The WRF Double‐Moment Five‐Class (WDM5)scheme, which excludes the graupel substance, has also beendeveloped [Lim and Hong, 2010]. Both the WDM6 andWDM5 schemes were implemented in WRF version 3.1.[8] The formulation of warm‐rain processes such as auto-

conversion and accretion in the WDM6 scheme is based onthe studies of Cohard and Pinty [2000]. Autoconversionparameterization is based on the numerical simulation ofthe stochastic collection equation suggested by Berry andReinhardt [1974], which is built on the observation. Inaddition, the accretion process is obtained by an analyticalintegration of stochastic collection equation. The mass‐weighted mean terminal velocity, which is responsible for thesedimentation of each hydrometeor mass, can be obtained byintegrating the terminal velocity of hydrometeor. Sedimen-tation fluxes for both the number concentration and mixingratio of rain are computed in the WDM6 scheme. Thus, dif-ferential settling between drops can be simulated. The number‐weighted mean terminal velocity, which is responsible for thesedimentation of rain number concentration, is also obtainedby integrating the terminal velocity of rainwater. Meanwhile,the sedimentation fluxes of falling cloud number concentra-tion are neglected, as in cloud mixing ratio.[9] The activated CCN number concentration is predicted

and formulated using a drop activation process based onTwomey’s relationship between the number of activatedCCN and supersaturation [Twomey, 1959; Khairoutdinov andKogan, 2000]. The complete evaporation of cloud drops isassumed to return corresponding CCN particles to the totalCCN count. Any other CCN sink/source terms, except forCCN activation and droplet evaporation, are ignored in theWDM6 scheme. Further details on the CCN activation processare described in Appendix A of Lim and Hong [2010].Obtaining CCN information in both the horizontal and verticaldirections in real time is difficult. In the WDM6 scheme, anidealized CCN spectrum is used based on the Twomey‐typeCCN size distribution, not the detailed trimodal distributionand a typical value of marine type CCN (100 cm−3) is chosen

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in both horizontal and vertical directions, which is consideredto be the initial value of CCN number concentration. Thesaturation mixing ratios over water and ice are calculateddirectly and CCN activation and condensation occur undersupersaturated conditions. The CCN activation is computedjust before the condensation/evaporation process of cloudwater, to ensure that any supersaturated water vapor does not

pass to the dynamical process. Further details on the micro-physical processes of theWDM6 scheme are described by Limand Hong [2010].[10] The 3‐D idealized supercell thunderstorm, which is a

preset option for the WRF model, is used to investigateaerosol effects by changing the initial value of the CCNnumber concentration. For the vertical profiles of potentialtemperature and water vapor mixing ratio, the study adoptsthe soundings used by Weisman and Klemp [1986] andWeisman and Rotunno [2000]. A skew‐T log‐p diagram oftemperature and dew point profiles is shown in Figure 1a. Asymmetric temperature perturbation of 2 K and a 10 kmradius triggers deep convection in a potentially unstableenvironment, and the water vapor mixing ratio is limited to amaximum value of 14 g kg−1 to describe the well‐mixedboundary layer. The environmental wind is similar to the right‐moving supercell case described by Weisman and Klemp[1986]. The low‐level shear turns through a quarter circlewhen plotted on a hodograph and is commonly referred to as“quarter circle shear.” The wind shear between 2.5 km and7.5 km is constant and unidirectional (easterly), with a con-stant wind (no shear) further aloft, as shown in Figure 1b.Figure 2 shows the simulated storm structure using the Klempand Wilhelmson [1978] numerical cloud model in the previ-ous study [Weisman and Klemp, 1986]. The initial stormevolves into a quasi‐steady, right‐moving supercell and therain is now being drawn significantly downshear by thestrong upper level flow with a similar hodograph to that inthis study (compare Figures 1b and 2). The visual structureunder the absence of the ice‐phase microphysical process issimilar to the observational models presented by Lemon andDoswell [1979].[11] The x and y directions of the 40 vertical layer grid are

composed of 321 points with a 500 m grid spacing. The topheight of the model computational area is 20 km. Theseresolutions are sufficient to resolve storm‐scale features,such as the midlevel updraft structure and low‐level meso-cyclogenesis. The model is integrated for 2 h with a timestep of four seconds. The only physical parameterizations inthe model are the microphysics scheme and the 3‐D TKEsubgrid turbulence scheme, while other physical processes,including radiation, vertical diffusion including planetaryboundary layer physics, land surface, and deep convectiondue to the cumulus parameterization scheme, are turned off.[12] Simulations with both the WDM6 and WDM5 micro-

physics schemes are initiated with the nine different initialCCN number concentrations from 100 cm−3 to 8,000 cm−3,which are based on the observed values on the west coast ofthe Korean peninsula [Yum et al., 2005], in order to investi-gate the impact of initial aerosol concentration on the devel-opment of a supercell storm. Three experiments, WDM6_M(maritime type of the CCN number concentration), WDM6_C(continental type of the CCN number concentration), andWDM6_EC (extreme continental type of the CCN numberconcentration), will be primarily discussed. The WDM6_Mrun employs the initial number concentration of CCN of100 cm−3, the WDM6_C starts at 1,000 cm−3, and theWDM6_EC uses 8,000 cm−3. Here, we consider theWDM6_M run as a reference simulation. The CCN experi-ments using the WDM5 scheme are named in the samemanner. All experiments are summarized in Table 1.Gilmoreet al. [2004] found that the amount of accumulated precipi-

Figure 1. (a) Standard skew‐T log‐p plot of initial temper-ature and dew point profiles and (b) hodograph used in thisstudy. Heights are labeled in kilometers above ground level(km agl) on a hodograph.

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tation at the ground is very sensitive to the way the hail/graupel category is parameterized in the simulation of thesupercell storm. Here, the effects of graupel category andgraupel‐related source/sink processes on the development ofa supercell storm are assessed through comparison of theWDM5 and WDM6 runs.[13] Meanwhile, Lim and Hong [2010] employed an initial

CCN number concentration of 100 cm−3 as a default value tosimulate an idealized 2‐D thunderstorm. They showedgreater differences in the droplet concentration between theconvective core and stratiform region in the WDM6 runcompared to the WSM6 run. The reduction of light precip-itation and increase of moderate precipitation accompanyinga marked radar bright band near the freezing level from theWDM6 simulation tend to alleviate existing systematic bia-ses in the case of theWSM6 scheme. They also noted that theWDM6 scheme with an initial CCN number concentration of100 cm−3 produces a comparable amount of cloud water tothe WSM6 scheme.

3. Results

3.1. CCN Effects

[14] The evolution of the storm is shown for each simu-lation by a time series of maximum and minimum verticalvelocities in Figure 3. Updrafts and downdrafts increaseduring the first 60 min of the simulations, before reaching aquasi‐steady phase. During the initial stage of the simula-tion, the warm bubble produces an updraft. Aerosols do nothave a major influence on the maximum vertical velocities,a result similar to that of Tao et al. [2007]. The minimumvertical velocity for the dirty scenario is slightly strongerthan for the clean scenario. However, the difference is notsignificant between the experiments. One may argue that thevelocity up to 60 m s−1 could be too strong. Khain and Lynn[2009] mentioned that the bulk microphysics scheme pro-

duced stronger vertical velocity than the bin microphysicsscheme. However, the magnitude of simulated updraftstrength cannot be validated in an idealized test bed.[15] Figures 4a and 4d show the storm structure defined

with low‐level rain fields, maximum vertical velocities, andstorm‐relative surface wind vectors in the WDM6_M andWDM6_EC runs at 2 h, respectively. The initial stormevolves into a right‐moving, quasi‐steady supercell withtwo diverging echo masses. These characteristics match wellwith the results of Weisman and Klemp [1986] even thoughthey simulated a supercell storm in the absence of ice‐phasemicrophysical processes. However, vertical velocity fromthis study is higher than that from Weisman and Klemp[1986]. Johnson et al. [1993] mentioned that the supercellfeatures are generally less pronounced and simulatedsupercells have a shorter lifetime without ice microphysicalprocesses. The crosses in Figures 4a and 4d indicate theinitial location of the storm. The left storm, located at thenorthern side of line AB, moves in a northeasterly direc-tion, while the storm on the right moves in an easterlydirection relative to the initial location of the storm. Anupdraft develops along the right (left) storm’s right (left)flank in response to the gust front convergence.[16] Previous studies have stressed that wind shear has an

impact on the formation and evolution of a supercell storm

Figure 2. Hodograph and simulated storm structure at 40, 80, and 120 min for the right‐flank supercellusing the numerical cloud model, which is adopted from Weisman and Klemp [1986]. Storm positions arerelative to the ground. Dashed lines represent updraft cell path. Low‐level (1.8 km) rainwater fields(similar to radar reflectivity) are contoured at 2 g kg−1 interval. Regions in which the middle‐level updraft(4.6 km) exceeds 5 m s−1 are shaded. Surface gust fronts are defined by the −1°C perturbation surfaceisotherm. Numbers at the updraft centers represent maximum vertical velocity (m s−1) at the time. Onhodographs, heights are labeled in km agl, and arrows indicate the mean storm motion between 80 and120 min.

Table 1. Summary of the Experiments

CodeMicrophysics

OptionInitial Concentration

of CCN (cm−3)

WDM6_M WDM6 100WDM6_C WDM6 1000WDM6_EC WDM6 8000WDM5_M WDM5 100WDM5_C WDM5 1000WDM5_EC WDM5 8000

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[Weisman and Klemp, 1982, 1986]. Two physical mechan-isms are related to the organizational capacity of verticalwind shear. The first one is the ability of a gust front totrigger new convective cells, and the second is the ability ofan updraft to interact with the vertical wind shear to producean enhanced, quasi‐steady storm structure. Here, the inten-sity of the left storm is stronger than the simulation pre-sented by Weisman and Klemp [1986] in their case D. Thequarter‐circle hodograph used in this study has only abouttwo thirds of the magnitude of shear as in the previoussimulation (compare Figures 1b and 2). This is probablywhy the left‐moving storm in the current simulations isstronger than in the previous simulation. Aside from thedifferences in the hodographs, we would also expectnoticeably stronger storms with a 500 m horizontal grid thanwith the 2 km one used by the previous study.[17] The WDM6_EC run, which is initiated with a CCN

number 80 times higher in concentration than that in theWDM6_M run, shows different distributions of low‐levelrain fields, with dispersed rain amounts shown in the leftstorm (Figure 4d). The low‐level mesocyclone and near‐surface vertical component of the relative vorticity remainedvertically aligned, presenting a pair of vortices in theWDM6_M run (Figures 4b and 4c). However, this pair isweakened in the WDM6_EC run (see Figure 4e). Stirlingand Wakimoto [1989] found through observational analy-sis that a pair of mesoscale vortices is one of the char-acteristics of deep convection. For both the WDM6_M andWDM6_EC runs, the existence of downdrafts originating atthe 6–10 km level on the relative upwind side of the updraft(Figures 4c and 4f) is in good agreement with observation[Lemon and Doswell, 1979]. Air decelerates at the upwindstagnation point, is forced downward and mixes with air

below which then reaches the surface through evaporativecooling and precipitation drag. The WDM6_EC run showsconvergence and a strong updraft over the storm on theright, as in the WDM6_M run. Meanwhile, the strength ofthe left cell and the magnitude of wind near the surface inthe WDM6_EC run are weakened compared with that in theWDM6_M run.[18] Graupel, which has a faster sedimentation velocity

than that of low‐density particles, is mainly located overstrong updraft regions. Large amounts of graupel areresponsible for localized rain over the corresponding regionthrough accretion with cloud droplets. More snow is pro-duced in the WDM6_EC run, especially over the convectivecore (compare Figures 4c and 4f), which induces theenhanced horizontal advection of snow with a slower sedi-mentation velocity relative to that of graupel. More con-version from rain/ice to snow rather than to graupel may bea possible reason for the presence of more snow in theWDM6_EC case. Reduced rain amounts enhance the pro-duction of snow rather than that of graupel through theaccretion process between cloud ice and rain. Exaggeratedsnow mass loading is responsible for the reduced (increased)rain over the strong (weak) echo region in the WDM6_ECrun and the weakened storm intensity. Meanwhile, Wienset al. [2005] reported that the proportion of graupel islarger than that of hail after analyzing observed character-istics of a supercell storm that occurred on 29 June 2000 innorthwestern Kansas. Tessendorf et al. [2005] also notedthat if certain kinematics features are not present, a stormcan only produce graupel particles with little or no hail.Variation of radar reflectivity patterns with height fromobserved and simulated supercell storms is shown inFigure 5. The notch in the low‐level horizontal echo pattern

Figure 3. Time series for the WDM6_M (solid line), WDM6_C (dotted line), and WDM6_EC (dashedline) experiments of (a) maximum vertical velocity and (b) minimum vertical velocity.

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Figure 4. Model simulations at 2 h for (a–c) the WDM6_M run and (d–f) the WDM6_EC run. The low‐level rain field (shaded) at 2 km altitude, maximum vertical velocity (contour), and storm‐relative surfacewind vectors, which are subtracted out in the initial wind field, are shown in Figures 4a and 4d. The crossrepresents the initial location of the simulated storm. Maximum vertical velocities are contoured at 10 m s−1

intervals. Vertical cross sections of the total mixing ratio (shaded) and vertical component of the relativevorticity (contour) along the y direction are shown in Figures 4b and 4e. Contour lines for the vorticity areat ±4, 8, 16, and 32 × 10−2 s−1, and solid (dotted) lines indicate the positive (negative) values. The crosssection for the cold pool is defined by the −0.5°C isotherm of potential temperature perturbation (thickblack line); mixing ratios of graupel (shaded) and snow (thick gray line) and wind vectors are shown inFigures 4c and 4f. Contour lines for the snow are at 0.2, 0.4, 0.8, 1.6, 3.2, and 6.4 g kg−1. Cross‐sectionalregions are denoted in Figures 4a and 4d as lines AB. The unit for the mixing ratio is g kg−1.

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in Figure 5a is associated with a bounded weak‐echo regioncharacterized by steep gradients of rain and denser ice par-ticles or echo‐free vault that extends upward toward theovershooting top of the storm. When we compare the sim-ulated storm on the right (Figure 5b), located at the southernside of line AB in Figure 4a, with the schematic basedon observations of supercell thunderstorms in Alberta(Figure 5a), we can confirm that the simulated radarreflectivity patterns match well with the observation show-ing significant variation with height in the storm and abounded weak‐echo region, which can be seen more clearly

at the 7 km two lines’ point of intersection. The six‐categoryWDM6 microphysics scheme without hail can simulatewell‐known supercell storm features such as an intenseupdraft, large overshooting top, a bounded weak‐echoregion at approximately 60 km along the y direction in theWDM6_M run, and strong midlevel cyclonic vorticity.[19] Figure 6 shows the simulated cloud properties under

the varying CCN number concentrations. Cloud dropletnumber concentration (NC) increases with an increasingCCN number concentration. For the CCN number con-centrations from 100 cm−3 to 8000 cm−3, the NC varies from

Figure 5. Horizontal sections of reflectivity (dBZ) at various altitudes. (a) Schematic based on observa-tions of supercell thunderstorms in Alberta, Canada. (b) Simulated results from the WDM6_M run. Theschematic is from Figure 8.11 of Houze [1993].

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105 cm−3 to 9162 cm−3. The increasing NC is consistentwith the increased activation of aerosols to form clouddroplets at increasing CCN number concentrations, as hasbeen found in previous observational studies [Kaufman andNakajima, 1993; Heymsfield and McFarquhar, 2001; Hudsonand Yum, 2001] and modeling studies [Fan et al., 2007;G. Li et al., 2008; Lim and Hong, 2010]. In contrast, theeffective mean volume radius of cloud droplets decreaseswith an increasing CCN number concentration, reflectinga reduced supersaturation when a large number of clouddroplets are competing for a fixed amount of available watervapor (Figure 6b).[20] Compared with cloud droplets, raindrop properties

show an opposite behavior with respect to the varying CCNnumber concentration (Figures 6c and 6d). The simulatednumber concentration of raindrops (NR) decreases rapidlywith an increasing CCN number concentration because alarge number of small cloud droplets hinders the effectiveautoconversion process from cloud water to rain. Corre-spondingly, the mean volume radius of the raindrops getslarger with increasing CCN number concentrations possiblyowing to more efficient accretion growth with higher cloudcontents. It was reported that the typical volume radius ofthe raindrops is about 1 mm [Rogers and Yau, 1988].

Meanwhile, Zawadzki and De Agostinho Antonio [1988]noted that in cases where there is an indication thatupdraft was present, the drop size distributions are markedlydifferent. The simulated mean volume radius of the rain-drops in Figure 6d is smaller than typical. A reason isattributed to an averaged value for all raindrops. The radiuswas greater than 0.5 mm near the surface, whereas it wasless than 0.2 mm aloft (not shown). A close inspectionreveals the melting processes of snow and graupel are themain source terms for the rain number concentration at themelting level, with significantly higher rain number con-centration with the WDM6 scheme, which, in turn, producessmall size of raindrops aloft. Diagnosed number con-centrations of snow and graupel can cause the deficiency inmelting processes. Further improvement of the WDM6scheme with the prognostic number concentrations of icespecies can provide more realistic microphysical cloudstructures of a supercell. Meanwhile, NR in the WDM6_ECrun is approximately four times smaller than NR in theWDM6_M in this study (Figure 6c).[21] Figure 7 shows the vertical profiles of the whole

domain‐time‐averaged mixing ratios of hydrometeors underthree different CCN concentrations. As discussed earlier,suppressed conversion from cloud water to rain with an

Figure 6. (a, b) Modeled number concentrations and effective mean volume radii of cloud dropletsobtained from time and cloudy area‐averaged values during a 2 h integration time period with the WDM6microphysics scheme, respectively, under varying initial CCN number concentrations. (c, d) Correspondingresults for the raindrops. Cloudy area is defined as either qC > 1.0 × 10−4 g kg−1 or qR > 1.0 × 10−3 g kg−1.

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increasing CCN number concentration results in more cloudwater and hence less rain contents. The highest cloud watermixing ratio appears in the WDM6_EC run with maximumvalues below the freezing level. In contrast, the highestmixing ratio of rain is found in the WDM6_M run. Thesimulated rain mixing ratios are not significantly differentbetween the WDM6_M and WDM6_EC runs. All experi-ments show rapid decreases of the rain amount toward thesurface. One of the main characteristics of the WDM6microphysics scheme is the abundant rainwater mixing ratiobelow/around the melting layer with its rapid reductiontoward the surface [Lim and Hong, 2010]. In the high CCNnumber concentration case, the homogeneous freezing andsubsequent anvil cloud formation may be hindered becauseof the large mass loading of small droplets that decreasesbuoyancy owing to decreased latent heat from dropletfreezing [G. Li et al., 2008]. Thus, the average amount of icedecreases as the CCN number concentration increases(Figure 7c). Fewer graupel particles are produced in the highCCN number concentration case because of the decreasedfreezing process of less rain with an inhibited homogeneousnucleation process of ice. Graupel content is represented inunit of g kg−1 of dry air. Since density of air decreases withheight, the mixing ratio of graupel can be shifted to theupper levels as compared to that of observed graupel content

in unit of kg m−3. Interestingly, the rain and graupel pro-files are quite similar with the WDM6 scheme (Figures 7band 7e). Tessendorf et al. [2005] observed that more andmore of the graupel and small hail categories convert intothe rain category as precipitation descends in a supercellstorm using polarimetric and Doppler radar data along witha simple particle growth model.[22] Meanwhile, the graupel mean volume radius is not

significantly varied and increases slightly from theWDM6_M run to the WDM6_C run (Table 2). Van denHeever and Cotton [2004] showed that the low‐leveldowndrafts are stronger, the cold pools are deeper and moreintense, and the low‐level vertical vorticity is greater in thecases with smaller hailstones. They also concluded that thedifferences in storm dynamics and behavior that occur as aresult of variations in the mean hail diameter are equal inmagnitude to those that occur as a result of excluding hailcompletely. The WDM6_M run, which produces the largestamount of small graupel, confirms the aforementionedcoupling between the cloud microphysics and the stormdynamics (compare Figure 4). Snow and total hydrometeoramounts increase in the WDM6_EC run (Figure 7d). Moreconversion from liquid phases to snow occurs rather than tograupel in the WDM6_EC run. The larger amount of cloud

Figure 7. Vertical profiles of time domain‐averaged mixing ratios of hydrometeors under three differentCCN number concentrations with the WDM6 scheme for (a) cloud water, (b) rain, (c) ice, (d) snow,(e) graupel, and (f) total mixing ratio. All outputs were extracted at 5 min intervals.

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water above the freezing level may contribute to the largeramount of snow though accretion processes.[23] Domain‐averaged total surface precipitation with

respect to the initial CCN number concentration is shown inFigure 8. The total precipitation decreases sharply as theCCN number concentration increases. Tao et al. [2007]showed similar results for a case of a midlatitude conti-nental squall system, which developed in a fairly unstableand relatively dry environment. They showed that evapo-rative cooling was stronger at lower levels in the cleanscenario for a midlatitude continental squall case. Strongerevaporative cooling could enhance the near‐surface coldpool strength, which makes the convergence stronger andresults in enhanced precipitation. When the CCN numberconcentration is relatively large, the total precipitation is notvery sensitive to the CCN number concentration. A possibleexplanation for the reduced precipitation with an increasingaerosol concentration is the suppressed conversion of clouddroplets to raindrops in the early developing stage of a cloudsystem and the reduced convective strength over the strongconvective core region with greater snow mass loading.Figure 9 shows the accumulated surface precipitation during2 h of integration time for the WDM6_M and WDM6_ECruns. The precipitation is mainly reduced over the heavyprecipitation region near the strong updraft core in theWDM6_EC run. These characteristics can also be confirmedin the storm structure with the WDM6_M and WDM6_ECruns (Figure 4).[24] Khain and Lynn [2009] simulated supercell storms

using a 2 km resolution WRF model with a spectral (bin)microphysics scheme and a recent version of the Thompsonbulk‐parameterization scheme. They showed a decrease inprecipitation for high aerosol concentration under lowhumidity. However, the decrease was found to be dependenton environment conditions (air humidity). They mentionedthat the effect of the air humidity on the magnitude and evensign of the precipitation response to aerosols, which wasdiscussed earlier for deep convective clouds and squall lines,can also be extended to supercell storms. Lee et al. [2008]showed that the precipitation increased with increasingaerosols under a high CAPE of around 3,000 J kg−1 andstrong wind shear in a two‐dimensional framework usingthe double‐moment bulk microphysics scheme. Sharpreductions in autoconversion at high aerosol concentrationwere compensated for by increases in the condensation ofcloud liquid and its collection by snow, graupel, and rain.However, under moderate and low CAPEs and a weak windshear, the precipitation decreased with increasing aerosols, aresult that corresponds with this study. (Note that this studyemploys smaller wind shear and a CAPE around 2,200 J kg−1

compared with those of Lee et al. [2008].) Their resultsdemonstrated that increasing aerosols can either decrease orincrease precipitation for an imposed large‐scale environ-ment supporting cloud development. Several modeling studies

showed similar results using a 2‐Dcloud‐resolvingmodel witha bin microphysics scheme [Khain and Pokrovsky, 2004;Khain et al., 2005; Tao et al., 2007; Yang and Yum, 2007].

3.2. Graupel Effects

[25] The importance of graupel substance in simulatingconvective clouds was stressed in numerical modelingstudies [McCumber et al., 1991; Johnson et al., 1993;Gilmore et al., 2004; Y. Li et al., 2008] and in an obser-vational study [Lemon and Doswell, 1979; Tessendorf et al.,2005]. McCumber et al. [1991] evaluated the performanceof several ice parameterizations. They showed that in thecontext of their experimental design, the absence of graupeldiminished the intensity of the radar bright band near themelting level and the bright band was less horizontallyhomogeneous. They also concluded that for tropical cumuli,the optimal mix of bulk ice hydrometeors is cloud ice‐snow‐graupel. Johnson et al. [1993] simulated the 2 August1981 supercell that passed over the southeastern part ofMontana and showed that supercell features such as a rotat-ing intense updraft, bounded weak‐echo region, and largeforward overhanging anvil were generally more pronouncedand have longer lifetime with a hail‐included microphysicsscheme. Y. Li et al. [2008] stressed that graupel played animportant role in the Mesoscale Convective System (MCS)of 11–12 August 1999 during the Kwajalein Experiment(KWAJEX) simulation because the conversion processes ofgraupel are a significant component of latent heat release,which in turn had a significant impact on the dynamic, ther-modynamic, and precipitation characteristics of the evolvingMCS. They also found that deficiencies in the microphysicsparameterization are more likely to be the source of theoverprediction of graupel and radar reflectivity.

Table 2. Cloud Properties Under Three Different Aerosol Conditions Using the WDM6 Microphysics Schemea

WDM6 (5)_M WDM6 (5)_C WDM6 (5)_EC

Snow mean volume radius (mm) 184.6 (382.9) 227.6 (384.2) 253.2 (382.1)Graupel mean volume radius (mm) 371.6 375.1 375.0

aThe values in the parentheses are obtained from the WDM5 microphysics scheme. The threshold of measurable precipitation is 0.002 mm.

Figure 8. Total precipitation with respect to the initialCCN number concentration with the WDM6 microphysicsscheme.

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[26] Lemon and Doswell [1979] obtained a consistentpattern of tornadogenesis after reviewing many supercellstorms observed primarily over the Great Plains of theUnited States. They mentioned that the largest surface hailfall could occur and funnel clouds were often observedduring the second stage of the supercell life cycle, which isclearly defined when a bounded weak echo region isdetected. Tessendorf et al. [2005] addressed the kinematic,microphysical, and electrical aspects of a severe storm thatoccurred in western Kansas on 29 June 2000 observedduring the Severe Thunderstorm Electrification and Precip-itation Study (STEPS) field campaign. They found thatgraupel and small hail could be grown from scratch fromcloud droplet nucleation at cloud base, followed by freezingand continued growth aloft.[27] The WDM5 microphysics scheme also simulates a

right‐moving, quasi‐steady supercell with two divergingecho masses. However, the storms show a hook‐typestructure and move more slowly in the WDM5_M andWDM5_EC runs (Figures 10a and 10d), when compared withthe WDM6_M and WDM6_EC runs (Figures 4a and 4d). Inaddition, the intensity of surface wind and vertical compo-nent of the relative vorticity is weakened in the WDM5 runs.Figures 10c and 10f show the vertical cross sections of snowmass, wind fields, and a cold pool along the strong con-vective cores. A major difference between the WDM5 andWDM6 runs is the weakened cold pool in the WDM5 runs.Presumably this is caused by less liquid‐water loading andweaker low‐level downdrafts, which also results in lessnear‐surface vorticity (see Figures 10b and 10c). The CCNnumber concentrations do not affect the storm structureswith the WDM5 microphysics scheme. In both WDM5 runs,a lot of snow is produced that has small fall speed and getscarried away from the storm at higher levels. Meanwhile, thesnow mean volume radius does not vary much with respectto the initial CCN number concentration in the WDM5 runs(Table 2).[28] Vertical profiles of the time domain‐averaged

hydrometeor mixing ratios are plotted in Figure 11. Cloudwater and rain mixing ratios show similar responses underthe varying CCN number concentration between the WDM5

and WDM6 microphysics schemes. However, the cloudwater mixing ratio in the WDM5_C experiment showsslightly larger values than that in the WDM5_EC experi-ment, and the amount of the rain mixing ratio in theWDM5_EC experiment is the largest below a level of2.5 km. The vertical profile for rain shows two peaks around5.5 km and 3.5 km levels with the WDM5 microphysicsscheme. The peak around 5.5 km level is not shown withthe WDM6 microphysics scheme (compare Figures 7b and11b). The lower peak in the WDM5 rain profile could bedue to melting of snow, while the upper peak is just that rainreplaces graupel owing to the omitted graupel‐producingmicrophysics processes. Without graupel, surplus watervapor would convert into another species such as rain andsnow or ice. The reason for abundant rain in the low CCNcase can be deduced from an enhanced conversion processfrom water vapor to rain with surplus water vapor and aninefficient CCN activation process, in conjunction with theabsence of graupel. Meanwhile, sensitivities in the cloudwater and rain substances between the WDM5_C and theWDM5_EC runs are revealed to be small. Without graupel,the proportion of snow is much greater in the WDM5_M runthan in the WDM6_M run. The largest mixing ratio of snowis revealed with the lowest CCN number concentration. Aninefficient sublimation process from snow to water vaporcould be a reason for more production of snow in theWDM5_M run. Meanwhile, simulated ice properties are notsensitive to the CCN number concentration. The lowest totalmixing ratio occurs in the largest CCN number concentra-tion case (Figure 11e).[29] In terms of the accumulated total precipitation, the

experiments with the WDM5 microphysics scheme showopposite responses from the experiments with the WDM6microphysics scheme under the varying CCN number con-centration (Figure 12a). Increasing rainfall amount with anincreasing CCN number concentration is unique to theWDM5 microphysics scheme. Even though the WDM5 runsshow similar responses to the CCN number concentration interms of the modeled number concentrations of cloud dro-plets and raindrops, as well as the cloud droplet radius, thedifference in the raindrop radius between the WDM5_M

Figure 9. Accumulated surface rainfall (mm) during 2 h of integration time from the (a) WDM6_M runand (b) WDM6_EC run.

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and WDM5_EC runs is much larger than that betweenthe WDM6_M and WDM6_EC runs (Figure 12b). Thespatial distribution of accumulated precipitation is shown inFigure 13, which demonstrates that the surface precipitationis mainly increased in the WDM5_EC run along the rearside of the strong convective region in a view of stormpropagation. The possible reason will be presented in thenext paragraph. In addition, the simulated amount of pre-cipitation is smaller in the WDM5 runs, compared with theWDM6 runs. Reduced precipitation in the WDM5 runs iscaused by significant amounts of snow, which are carriedaway from the cell at upper levels (see Figures 10c and 10f).Khain and Lynn [2009] also showed that the response ofsurface precipitation with respect to variations of the aerosolconcentrations can vary with the method of modeling cloud

microphysics: the explicit bin‐resolving method and thebulk method.[30] Figure 14 shows the differences in the time and

domain‐averaged mixing ratios of vapor and temperaturebetween the WDM5 (6)_M and WDM5 (6)_EC runs. Amore highly saturated environment under the high CCNnumber concentration with the WDM5 microphysicsscheme is responsible for more surface precipitation withoutfurther raindrop evaporation. The mixing ratio and temper-ature profiles with the WDM6 microphysics scheme showdifferent features below the melting level from those of theWDM5 microphysics. A less saturated environment in theWDM6_EC run causes raindrops to evaporate more effec-tively compared with that of the WDM5_EC run. Warmingin the upper troposphere in the WDM6_EC run indicateslatent heat release with a large mass of ice (Figure 11). The

Figure 10. Same as Figure 2 but for the (a–c) WDM5_M run and (d–f) WDM5_EC run. Contour linesfor the snow are at 1, 2, 4, 8, and 16 g kg−1. The cold pool defined by the −0.5°C isotherm of potentialtemperature perturbation (thick black line) is not distinct using the WDM5 microphysics scheme.

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Figure 11. Vertical profiles of the time domain‐averaged mixing ratios of hydrometeors under three dif-ferent CCN number concentrations with the WDM5 scheme for (a) cloud water, (b) rain, (c) ice, (d) snow,and (e) total mixing ratios. All outputs were extracted at 5 min intervals.

Figure 12. (a) Total surface precipitation and (b) domain‐averaged effective mean volume radius ofraindrops, obtained from time and cloudy area‐averaged values during a 2 h integration time period withrespect to the initial CCN number concentration. The solid line with solid circles indicates the resultsobtained from the WDM6 microphysics scheme, and the dotted line with open circles shows that fromthe WDM5 microphysics scheme.

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results for the WDM5 and WDM6 microphysics schemesshow that the presence of graupel can alter the storm mor-phology and the related dynamics and microphysics, andemphasize the effects of aerosols on precipitation. A detailedanalysis of the mechanisms leading to different distributionsof precipitation between the WDM5 and WDM6 micro-physics schemes is beyond the scopes of the work.

4. Concluding Remarks

[31] Aerosol effects on the development of a supercellstorm were investigated, focusing on storm morphology andprecipitation. The impact of the initial aerosol concentrationon the development of a supercell storm was evaluated byvarying the number and mass of aerosols as reflected in the

changes of the CCN number concentration with the double‐moment microphysics schemes. The idealized supercellstorm structure, documented in previous modeling studiesand observations, was well captured with the WDM6microphysics scheme. A few studies concerning aerosol‐related microphysical effects on the evolution of supercellstorms have been reported [Lerach et al., 2008; Khain andLynn, 2009], and theses studies were conducted with binor bin‐emulating bulk microphysics scheme. (We want tonote that Khain and Lynn [2009] simulated a supercell stormnot only with the bin microphysics scheme but also with thebulk microphysics scheme. However, they just investigatedthe aerosol effect by changing the value of the gamma shapeparameter of size distribution for cloud water without a linkbetween cloud condensation nuclei and cloud droplet

Figure 13. Accumulated surface rainfall (mm) during 2 h of integration time from the (a) WDM5_M runand (b) WDM5_EC run.

Figure 14. Vertical profiles of the differences in the time domain‐averaged mixing ratios of vapor (solidlines) and temperature (dotted lines) for the (a) WDM5_EC minus WDM5_M run and (b) WDM6_ECminus WDM6_M run. Units for the mixing ratio and temperature are 10 g kg−1 and degrees Celsius,respectively.

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number concentrations.) Thus, our study can provide areference for aerosol effects on the development of asupercell storm with a double‐moment bulk microphysicsscheme.[32] Hail and graupel are the dominant precipitating ice

particles for a supercell thunderstorm as was noted in pre-vious observation and modeling studies. Thus, it is quitecertain that a more elaborate microphysics scheme, whichhas more ice categories containing graupel and hail such asthat of Ferrier [1994], Meyers et al. [1997], and Noppelet al. [2010], is essential for a realistic simulation of asupercell storm. Ultimately, further improvements of theWDM6 scheme are necessary to include a hail category,which enables us to avoid risks of rendering the ice param-eterization to be case specific. The terminal velocity for adenser ice particle such as hail is faster than for other icespecies. The rate of cooling, which is caused by melting ofice particles and the subsequent evaporation of water off theice‐particle surface, could be reduced and surface precipi-tation could increase with the inclusion of hail. Thus, thelow‐level downdrafts could be weakened, the cold poolscould be shallower and less intense, and storms could moveslowly [van den Heever and Cotton, 2004].[33] The strength of the left cell and the magnitude of

wind near the surface are weakened as the CCN numberconcentration increases with the WDM6 microphysicsscheme. In addition, the total precipitation decreases sharplyas the CCN number concentration increases. When the CCNnumber concentration is relatively large, the total precipi-tation is not very sensitive to the CCN number concentra-tion. Enhancement of the horizontal advection of snow aloftin the high CCN number concentration case leads todecreased (increased) surface rainfall over the strong (weak)echo region. However, without the graupel substance, low‐level downdrafts are weakened and storm moves moreslowly. The CCN number concentrations do not affect thestorm structures with the WDM5 microphysics scheme. Inaddition, snow mass loading is exaggerated across theconvective core, resulting in less liquid‐water loading andsurface precipitation. For the aerosol effects on surfaceprecipitation, varying the CCN number concentrationwithout the graupel substance has opposite impacts onrainfall amounts.[34] The study indicates that the existence of graupel in

the bulk microphysics representation affects not only thestorm structure and dynamics, but also aerosol‐cloud inter-action. Graupel, which has faster sedimentation velocitythan that of low‐density particles, is located over strongupdraft regions and leads to a strong low‐level downdraftand localized precipitation within the well‐organized con-vective cell. Without graupel, CCN effects on storm mor-phology and surface precipitation can differ. We should notethat the effect of aerosols on deep convection is weaker thanthat of ice processes (see Figure 12a). To properly under-stand the effects of aerosols within deep convective simu-lations, it is advisable to represent all the source/sinkprocesses related to the graupel substance, including thesedimentation process, as realistically as possible.

[35] Acknowledgments. This research was supported by the BasicScience Research Program through the National Research Foundation of

Korea (NRF) funded by the Ministry of Education, Science and Technology(MEST) (2010–0000840), and by the Leading Foreign Research InstituteRecruitment Program through NRF funded by MEST (2010‐00715).

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J. Dudhia and J. B. Klemp, Mesoscale and Microscale MeteorologyDivision, National Center for Atmospheric Research, Boulder, CO80307‐3000, USA.S.‐Y. Hong, K.‐S. S. Lim, and S. S. Yum, Department of Atmospheric

Sciences, Yonsei University, Seoul 120‐749, South Korea. (shong@yonsei.ac.kr)

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