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

Aerosol effects on the development of a storm in a double‐moment bulk‐ 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 idealized three‐dimensional supercell storm, focusing on storm morphology and during a quasi steady state of a storm. The impact of the aerosol concentration on the simulated storm is evaluated by varying the initial cloud condensation nuclei (CCN) number concentration in the Weather Research and Forecasting Double‐Moment Six‐Class microphysics scheme. A right‐moving, quasi‐steady supercell with two diverging echo masses was reproduced, compared with the previous modeling study. In the experiment with a high CCN number concentration, storm intensity was weakened, and surface precipitation was reduced. On the other hand, the simulation that excluded the graupel substance produced a weaker low‐level downdraft, thus less near‐surface vorticity, compared with the simulation that included graupel. The CCN number concentrations did not affect the storm structures in the absence of graupel. In addition, the aerosol effects on the surface precipitation with respect to the initial CCN value were diametrically opposed. The major reason for the different responses to aerosol can be attributed to the exaggerated snow mass loading across the convective core when the graupel species is excluded. The results indicate that graupel species and related microphysics are crucial to the realistic 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 a supercell storm in a double‐moment bulk‐cloud microphysics scheme, J. Geophys. Res., 116, D02204, doi:10.1029/2010JD014128.

1. Introduction [3] Studies of aerosol effects on large‐scale stratiform via several general circulation models [Lohmann [2] The increase in aerosol particles in the atmosphere et al., 1999; Ghan et al., 2001; Rotstayn, 1999, 2000; caused by industrialization is known to change cloud Menon et al., 2002; Rotstayn and Lohmann, 2002; Lohmann microphysics and precipitation processes. Increased aerosol and Feichter, 2005; Takemura et al., 2005] suggest that the concentrations raise the number concentrations of cloud suppression of precipitation with increased aerosols, con- condensation nuclei (CCN) and cloud droplets, thus reduc- sistent with the second indirect effect of aerosols, can alter ing droplet size and increasing cloud reflectance [Twomey, the radiative fluxes due to changes in cloud lifetime or liquid 1974, 1977, 1991]. This is generally considered the first water path. Meanwhile, it is not certain whether the second indirect effect of atmospheric aerosol. Previous studies indirect effect of aerosol also applies to deep convective pointed out that a decrease in droplet size is also likely to clouds. Recently, efforts to investigate the effects of aerosol impact precipitation [Gunn and Phillips, 1957; Warner, on deep convective clouds have been carried out through 1968; Albrecht, 1989]. The decrease in droplet size can several observational and modeling studies [Rosenfeld, 1999, suppress the conversion of the droplet to drizzle or , 2000; Wang, 2005; Tao et al., 2007; Fan et al. 2007; Lerach thereby inhibiting rainfall and prolonging cloud lifetime. et al. 2008]. This precipitation suppression is commonly considered to be [4] Lerach et al. [2008] simulated a supercell storm using the second indirect effect of atmospheric aerosol and may a bin‐emulating, double‐moment bulk microphysics scheme substantially alter the water mass budget of clouds, altering including under an environment with a convective their persistence and albedo, and, possibly, the climate. −1 available potential energy (CAPE) of 3130 J kg and veering from the surface to 2 km above ground level

1 and showed a decrease in precipitation with increasing Department of Atmospheric Sciences and Global Environment aerosols. Wang [2005] investigated the response of tropical Laboratory, Yonsei University, Seoul, South Korea. 2Mesoscale and Microscale Meteorology Division, National Center for deep convection to the increase of CCN concentration with a −3 Atmospheric Research, Boulder, Colorado, USA. set of 30 initial CCN profiles ranging from 50 to 6000 cm and found that increasing CCN concentration causes a Copyright 2011 by the American Geophysical Union. stronger convection, leading to the increase in precipitation 0148‐0227/11/2010JD014128

D02204 1of16 D02204 LIM ET AL.: AEROSOL EFFECTS ON A SUPERCELL STORM D02204 as well as the expansion of the cloud coverage. However, ment of the supercell storm will be investigated, recognizing when the initial CCN concentration exceeds a certain level, the importance of the dense ice particles in simulating the many of the above effects become insignificant, implying supercell storm [Gilmore et al., 2004]. that a more substantial aerosol effect on deep convective [6] Section 2 outlines the numerical experiments conducted cloud be seen over a clean rather than polluted regions. With in this study, and their results are discussed in section 3. a weak shear and a lower CAPE ( = 960 J kg−1), Fan Concluding remarks appear in section 4. et al. [2007] showed a nonmonotonic precipitation response to increasing aerosols. The precipitation increased with increasing aerosols, and then decreased in the extremely 2. Numerical Experimental Setup −3 high aerosol cases (over 5000 cm ) owing to suppression of [7] The model used in this study is the Advanced convection from depleted water vapor and inefficient coa- Research WRF version 3.1 [Skamarock et al., 2008], which lescence. Tao et al. [2007] showed that three different deep was released in April 2009. The WRF model is a state‐of‐ convective cloud systems result in differing responses of the‐art mesoscale numerical weather prediction system surface precipitation with increasing aerosol concentrations serving both operational forecasting and atmospheric during the mature stage of the simulations. They suggested research needs. The WDM6 scheme [Lim and Hong, 2010] that evaporative cooling in the lower troposphere is a key is a double‐moment bulk‐cloud microphysics scheme based process in determining whether high CCN reduces or on the WSM6 microphysics scheme. In addition to the enhances precipitation. Lee et al. [2008] also investigated prediction for the mixing ratios of six water species (water aerosol effects on precipitation with five different sets of vapor, cloud droplets, cloud ice, snow, rain, and graupel), initial soundings and concluded that increasing aerosol can the number concentrations of cloud droplets and raindrops either decrease or increase precipitation for an imposed are also predicted in the WDM6 scheme, together with the large‐scale environment supporting cloud development. prognostic variable of cloud condensation nuclei (CCN) Aerosol effects on cloud microphysics and precipitation number concentration. The ice‐phase microphysics [Hong could be nonmonotonic under the different meteorological et al., 2004] is identical for both the WDM6 and WSM6 and aerosol conditions because of the complicated coupling schemes. The WRF Double‐Moment Five‐Class (WDM5) between cloud microphysics and storm dynamics [Seifert scheme, which excludes the graupel substance, has also been and Beheng, 2006; Fan et al., 2007; Tao et al., 2007; developed [Lim and Hong, 2010]. Both the WDM6 and Yang and Yum, 2007; van den Heever and Cotton, 2007; WDM5 schemes were implemented in WRF version 3.1. Lee et al., 2008; G. Li et al., 2008]. Khain [2009] and Khain [8] The formulation of warm‐rain processes such as auto- et al. [2008] analyzed aerosol effects on precipitation using conversion and accretion in the WDM6 scheme is based on the mass and heat budgets and concluded that in the case the studies of Cohard and Pinty [2000]. Autoconversion when the condensation loss increases more than the con- parameterization is based on the numerical simulation of densation generation, a decrease in precipitation takes place. the stochastic collection equation suggested by Berry and They also concluded that many discrepancies between the Reinhardt [1974], which is built on the observation. In results reported in different observational and numerical addition, the accretion process is obtained by an analytical studies for aerosol effects on surface precipitation can be integration of stochastic collection equation. The mass‐ attributed to the different atmospheric conditions and cloud weighted mean terminal velocity, which is responsible for the types analyzed. sedimentation of each hydrometeor mass, can be obtained by [5] The purpose of this study is to investigate aerosol integrating the terminal velocity of hydrometeor. Sedimen- effects on the development of a supercell storm, focusing on tation fluxes for both the number concentration and mixing storm morphology and precipitation during a quasi steady ratio of rain are computed in the WDM6 scheme. Thus, dif- state of a storm. A supercell , characterized by ferential settling between drops can be simulated. The number‐ a deep, persistent, rotating updraft [Doswell and Burgess, weighted mean terminal velocity, which is responsible for the 1993], is the most prolific producer of dense ice particles, sedimentation of rain number concentration, is also obtained such as graupel and hail [Bluestein, 1992]. Thus, the Weather by integrating the terminal velocity of rainwater. Meanwhile, Research and Forecasting (WRF) Double‐Moment Six‐ the sedimentation fluxes of falling cloud number concentra- Class (WDM6) microphysics scheme [Lim and Hong, 2010] tion are neglected, as in cloud mixing ratio. is used, which makes it possible to investigate the aerosol [9] The activated CCN number concentration is predicted effects on cloud properties and precipitation processes, in and formulated using a drop activation process based on contrast to the WRF Single‐Moment Six‐Class (WSM6) Twomey’s relationship between the number of activated microphysics scheme [Hong and Lim, 2006]. A preliminary CCN and supersaturation [Twomey, 1959; Khairoutdinov and evaluation of the WDM6 microphysics scheme has con- Kogan, 2000]. The complete evaporation of cloud drops is firmed that the skill statistics of precipitation forecasts over assumed to return corresponding CCN particles to the total Korea from June to August 2008 are generally improved CCN count. Any other CCN sink/source terms, except for [Hong et al., 2010]. Even though several previous studies CCN activation and droplet evaporation, are ignored in the stressed the importance of the microphysical drop size dis- WDM6 scheme. Further details on the CCN activation process tribution (or size itself) and evaporative cooling rates on are described in Appendix A of Lim and Hong [2010]. tornadogenesis [van den Heever and Cotton, 2004; Snook Obtaining CCN information in both the horizontal and vertical and Xue, 2008], few studies concerning aerosol‐related directions in real time is difficult. In the WDM6 scheme, an microphysical effects on the evolution of supercell storms idealized CCN spectrum is used based on the Twomey‐type have been reported [Lerach et al., 2008; Khain and Lynn, CCN size distribution, not the detailed trimodal distribution 2009]. Additionally, the graupel effects on the develop- and a typical value of marine type CCN (100 cm−3)ischosen

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pass to the dynamical process. Further details on the micro- physical processes of the WDM6 scheme are described by Lim and Hong [2010]. [10] The 3‐D idealized supercell thunderstorm, which is a preset option for the WRF model, is used to investigate aerosol effects by changing the initial value of the CCN number concentration. For the vertical profiles of potential temperature and water vapor mixing ratio, the study adopts the soundings used by Weisman and Klemp [1986] and Weisman and Rotunno [2000]. A skew‐T log‐p diagram of temperature and dew point profiles is shown in Figure 1a. A symmetric temperature perturbation of 2 K and a 10 km radius triggers deep convection in a potentially unstable environment, and the water vapor mixing ratio is limited to a maximum value of 14 g kg−1 to describe the well‐mixed boundary 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 circle when plotted on a hodograph and is commonly referred to as “quarter circle shear.” The wind shear between 2.5 km and 7.5 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 Klemp and Wilhelmson [1978] numerical cloud model in the previ- ous study [Weisman and Klemp, 1986]. The initial storm evolves into a quasi‐steady, right‐moving supercell and the rain is now being drawn significantly downshear by the strong upper level flow with a similar hodograph to that in this study (compare Figures 1b and 2). The visual structure under the absence of the ice‐phase microphysical process is similar to the observational models presented by Lemon and Doswell [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 top height of the model computational area is 20 km. These resolutions 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 time step of four seconds. The only physical parameterizations in the model are the microphysics scheme and the 3‐D TKE subgrid turbulence scheme, while other physical processes, including radiation, vertical diffusion including planetary boundary layer physics, land surface, and deep convection due 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 initial CCN number concentrations from 100 cm−3 to 8,000 cm−3, which are based on the observed values on the west coast of the Korean peninsula [Yum et al., 2005], in order to investi- Figure 1. (a) Standard skew‐T log‐p plot of initial temper- gate the impact of initial aerosol concentration on the devel- ature and dew point profiles and (b) hodograph used in this opment of a supercell storm. Three experiments, WDM6_M study. Heights are labeled in kilometers above ground level (maritime type of the CCN number concentration), WDM6_C (km agl) on a hodograph. (continental type of the CCN number concentration), and WDM6_EC (extreme continental type of the CCN number concentration), will be primarily discussed. The WDM6_M in both horizontal and vertical directions, which is considered run employs the initial number concentration of CCN of to be the initial value of CCN number concentration. The 100 cm−3, the WDM6_C starts at 1,000 cm−3, and the saturation mixing ratios over water and ice are calculated WDM6_EC uses 8,000 cm−3. Here, we consider the directly and CCN activation and condensation occur under WDM6_M run as a reference simulation. The CCN experi- supersaturated conditions. The CCN activation is computed ments using the WDM5 scheme are named in the same just before the condensation/evaporation process of cloud manner. All experiments are summarized in Table 1. Gilmore water, to ensure that any supersaturated water vapor does not et al. [2004] found that the amount of accumulated precipi-

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Figure 2. Hodograph and simulated storm structure at 40, 80, and 120 min for the right‐flank supercell using the numerical cloud model, which is adopted from Weisman and Klemp [1986]. Storm positions are relative 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 surface isotherm. Numbers at the updraft centers represent maximum vertical velocity (m s−1) at the time. On hodographs, heights are labeled in km agl, and arrows indicate the mean storm motion between 80 and 120 min. tation at the ground is very sensitive to the way the hail/ duced stronger vertical velocity than the bin microphysics graupel category is parameterized in the simulation of the scheme. However, the magnitude of simulated updraft supercell storm. Here, the effects of graupel category and strength cannot be validated in an idealized test bed. graupel‐related source/sink processes on the development of [15] Figures 4a and 4d show the storm structure defined a supercell storm are assessed through comparison of the with low‐level rain fields, maximum vertical velocities, and WDM5 and WDM6 runs. storm‐relative surface wind vectors in the WDM6_M and [13] Meanwhile, Lim and Hong [2010] employed an initial WDM6_EC runs at 2 h, respectively. The initial storm CCN number concentration of 100 cm−3 as a default value to evolves into a right‐moving, quasi‐steady supercell with simulate an idealized 2‐D thunderstorm. They showed two diverging echo masses. These characteristics match well greater differences in the droplet concentration between the with the results of Weisman and Klemp [1986] even though convective core and stratiform region in the WDM6 run they simulated a supercell storm in the absence of ice‐phase compared to the WSM6 run. The reduction of light precip- microphysical processes. However, vertical velocity from itation and increase of moderate precipitation accompanying this study is higher than that from Weisman and Klemp a marked radar bright band near the freezing level from the [1986]. Johnson et al. [1993] mentioned that the supercell WDM6 simulation tend to alleviate existing systematic bia- features are generally less pronounced and simulated ses in the case of the WSM6 scheme. They also noted that the have a shorter lifetime without ice microphysical WDM6 scheme with an initial CCN number concentration of processes. The crosses in Figures 4a and 4d indicate the 100 cm−3 produces a comparable amount of cloud water to initial location of the storm. The left storm, located at the the WSM6 scheme. northern side of line AB, moves in a northeasterly direc- tion, while the storm on the right moves in an easterly 3. Results direction relative to the initial location of the storm. An updraft develops along the right (left) storm’s right (left) 3.1. CCN Effects flank in response to the gust front convergence. [14] The evolution of the storm is shown for each simu- [16] Previous studies have stressed that wind shear has an lation by a time series of maximum and minimum vertical impact on the formation and evolution of a supercell storm velocities in Figure 3. Updrafts and downdrafts increase during the first 60 min of the simulations, before reaching a quasi‐steady phase. During the initial stage of the simula- Table 1. Summary of the Experiments tion, the warm bubble produces an updraft. Aerosols do not Microphysics Initial Concentration have a major influence on the maximum vertical velocities, Code Option of CCN (cm−3) a result similar to that of Tao et al. [2007]. The minimum WDM6_M WDM6 100 vertical velocity for the dirty scenario is slightly stronger WDM6_C WDM6 1000 than for the clean scenario. However, the difference is not WDM6_EC WDM6 8000 significant between the experiments. One may argue that the WDM5_M WDM5 100 velocity up to 60 m s−1 could be too strong. Khain and Lynn WDM5_C WDM5 1000 [2009] mentioned that the bulk microphysics scheme pro- WDM5_EC WDM5 8000

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

[Weisman and Klemp, 1982, 1986]. Two physical mechan- below which then reaches the surface through evaporative isms are related to the organizational capacity of vertical cooling and precipitation drag. The WDM6_EC run shows wind shear. The first one is the ability of a gust front to convergence and a strong updraft over the storm on the trigger new convective cells, and the second is the ability of right, as in the WDM6_M run. Meanwhile, the strength of an updraft to interact with the vertical wind shear to produce the left cell and the magnitude of wind near the surface in an enhanced, quasi‐steady storm structure. Here, the inten- the WDM6_EC run are weakened compared with that in the sity of the left storm is stronger than the simulation pre- WDM6_M run. sented by Weisman and Klemp [1986] in their case D. The [18] Graupel, which has a faster sedimentation velocity quarter‐circle hodograph used in this study has only about than that of low‐density particles, is mainly located over two thirds of the magnitude of shear as in the previous strong updraft regions. Large amounts of graupel are simulation (compare Figures 1b and 2). This is probably responsible for localized rain over the corresponding region why the left‐moving storm in the current simulations is through accretion with cloud droplets. More snow is pro- stronger than in the previous simulation. Aside from the duced in the WDM6_EC run, especially over the convective differences in the hodographs, we would also expect core (compare Figures 4c and 4f), which induces the noticeably stronger storms with a 500 m horizontal grid than enhanced horizontal advection of snow with a slower sedi- with the 2 km one used by the previous study. mentation velocity relative to that of graupel. More con- [17] The WDM6_EC run, which is initiated with a CCN version from rain/ice to snow rather than to graupel may be number 80 times higher in concentration than that in the a possible reason for the presence of more snow in the WDM6_M run, shows different distributions of low‐level WDM6_EC case. Reduced rain amounts enhance the pro- rain fields, with dispersed rain amounts shown in the left duction of snow rather than that of graupel through the storm (Figure 4d). The low‐level and near‐ accretion process between cloud ice and rain. Exaggerated surface vertical component of the relative vorticity remained snow mass loading is responsible for the reduced (increased) vertically aligned, presenting a pair of vortices in the rain over the strong (weak) echo region in the WDM6_EC WDM6_M run (Figures 4b and 4c). However, this pair is run and the weakened storm intensity. Meanwhile, Wiens weakened in the WDM6_EC run (see Figure 4e). Stirling et al. [2005] reported that the proportion of graupel is and Wakimoto [1989] found through observational analy- larger than that of hail after analyzing observed character- sis that a pair of mesoscale vortices is one of the char- istics of a supercell storm that occurred on 29 June 2000 in acteristics of deep convection. For both the WDM6_M and northwestern Kansas. Tessendorf et al. [2005] also noted WDM6_EC runs, the existence of downdrafts originating at that if certain kinematics features are not present, a storm the 6–10 km level on the relative upwind side of the updraft can only produce graupel particles with little or no hail. (Figures 4c and 4f) is in good agreement with observation Variation of radar reflectivity patterns with height from [Lemon and Doswell, 1979]. Air decelerates at the upwind observed and simulated supercell storms is shown in stagnation point, is forced downward and mixes with air Figure 5. The notch in the low‐level horizontal echo pattern

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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 surface wind vectors, which are subtracted out in the initial wind field, are shown in Figures 4a and 4d. The cross represents 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 relative vorticity (contour) along the y direction are shown in Figures 4b and 4e. Contour lines for the vorticity are at ±4, 8, 16, and 32 × 10−2 s−1, and solid (dotted) lines indicate the positive (negative) values. The cross section for the cold pool is defined by the −0.5°C isotherm of potential temperature perturbation (thick black line); mixing ratios of graupel (shaded) and snow (thick gray line) and wind vectors are shown in Figures 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‐sectional regions are denoted in Figures 4a and 4d as lines AB. The unit for the mixing ratio is g kg−1.

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Figure 5. Horizontal sections of reflectivity (dBZ) at various altitudes. (a) Schematic based on observa- tions of supercell in Alberta, Canada. (b) Simulated results from the WDM6_M run. The schematic is from Figure 8.11 of Houze [1993]. in Figure 5a is associated with a bounded weak‐echo region at the 7 km two lines’ point of intersection. The six‐category characterized by steep gradients of rain and denser ice par- WDM6 microphysics scheme without hail can simulate ticles or echo‐free vault that extends upward toward the well‐known supercell storm features such as an intense overshooting top of the storm. When we compare the sim- updraft, large overshooting top, a bounded weak‐echo ulated storm on the right (Figure 5b), located at the southern region at approximately 60 km along the y direction in the side of line AB in Figure 4a, with the schematic based WDM6_M run, and strong midlevel cyclonic vorticity. on observations of supercell thunderstorms in Alberta [19] Figure 6 shows the simulated cloud properties under (Figure 5a), we can confirm that the simulated radar the varying CCN number concentrations. Cloud droplet reflectivity patterns match well with the observation show- number concentration (NC) increases with an increasing ing significant variation with height in the storm and a CCN number concentration. For the CCN number con- −3 −3 bounded weak‐echo region, which can be seen more clearly centrations from 100 cm to 8000 cm , the NC varies from

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Figure 6. (a, b) Modeled number concentrations and effective mean volume radii of cloud droplets obtained from time and cloudy area‐averaged values during a 2 h integration time period with the WDM6 microphysics scheme, respectively, under varying initial CCN number concentrations. (c, d) Corresponding −4 −1 −3 −1 results for the raindrops. Cloudy area is defined as either qC >1.0×10 gkg or qR >1.0×10 gkg .

−3 −3 105 cm to 9162 cm . The increasing NC is consistent Meanwhile, Zawadzki and De Agostinho Antonio [1988] with the increased activation of aerosols to form cloud noted that in cases where there is an indication that droplets at increasing CCN number concentrations, as has updraft was present, the drop size distributions are markedly been found in previous observational studies [Kaufman and different. The simulated mean volume radius of the rain- Nakajima, 1993; Heymsfield and McFarquhar,2001;Hudson drops in Figure 6d is smaller than typical. A reason is and Yum, 2001] and modeling studies [Fan et al., 2007; attributed to an averaged value for all raindrops. The radius G. Li et al., 2008; Lim and Hong, 2010]. In contrast, the was greater than 0.5 mm near the surface, whereas it was effective mean volume radius of cloud droplets decreases less than 0.2 mm aloft (not shown). A close inspection with an increasing CCN number concentration, reflecting reveals the melting processes of snow and graupel are the a reduced supersaturation when a large number of cloud main source terms for the rain number concentration at the droplets are competing for a fixed amount of available water melting level, with significantly higher rain number con- vapor (Figure 6b). centration with the WDM6 scheme, which, in turn, produces [20] Compared with cloud droplets, raindrop properties small size of raindrops aloft. Diagnosed number con- show an opposite behavior with respect to the varying CCN centrations of snow and graupel can cause the deficiency in number concentration (Figures 6c and 6d). The simulated melting processes. Further improvement of the WDM6 number concentration of raindrops (NR) decreases rapidly scheme with the prognostic number concentrations of ice with an increasing CCN number concentration because a species can provide more realistic microphysical cloud large number of small cloud droplets hinders the effective structures of a supercell. Meanwhile, NR in the WDM6_EC autoconversion process from cloud water to rain. Corre- run is approximately four times smaller than NR in the spondingly, the mean volume radius of the raindrops gets WDM6_M in this study (Figure 6c). larger with increasing CCN number concentrations possibly [21] Figure 7 shows the vertical profiles of the whole owing to more efficient accretion growth with higher cloud domain‐time‐averaged mixing ratios of hydrometeors under contents. It was reported that the typical volume radius of three different CCN concentrations. As discussed earlier, the raindrops is about 1 mm [Rogers and Yau, 1988]. suppressed conversion from cloud water to rain with an

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Figure 7. Vertical profiles of time domain‐averaged mixing ratios of hydrometeors under three different CCN 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. increasing CCN number concentration results in more cloud in unit of kg m−3. Interestingly, the rain and graupel pro- water and hence less rain contents. The highest cloud water files are quite similar with the WDM6 scheme (Figures 7b mixing ratio appears in the WDM6_EC run with maximum and 7e). Tessendorf et al. [2005] observed that more and values below the freezing level. In contrast, the highest more of the graupel and small hail categories convert into mixing ratio of rain is found in the WDM6_M run. The the rain category as precipitation descends in a supercell simulated rain mixing ratios are not significantly different storm using polarimetric and Doppler radar data along with between the WDM6_M and WDM6_EC runs. All experi- a simple particle growth model. ments show rapid decreases of the rain amount toward the [22] Meanwhile, the graupel mean volume radius is not surface. One of the main characteristics of the WDM6 significantly varied and increases slightly from the microphysics scheme is the abundant rainwater mixing ratio WDM6_M run to the WDM6_C run (Table 2). Van den below/around the melting layer with its rapid reduction Heever and Cotton [2004] showed that the low‐level toward the surface [Lim and Hong, 2010]. In the high CCN downdrafts are stronger, the cold pools are deeper and more number concentration case, the homogeneous freezing and intense, and the low‐level vertical vorticity is greater in the subsequent anvil cloud formation may be hindered because cases with smaller hailstones. They also concluded that the of the large mass loading of small droplets that decreases differences in storm dynamics and behavior that occur as a buoyancy owing to decreased latent heat from droplet result of variations in the mean hail diameter are equal in freezing [G. Li et al., 2008]. Thus, the average amount of ice magnitude to those that occur as a result of excluding hail decreases as the CCN number concentration increases completely. The WDM6_M run, which produces the largest (Figure 7c). Fewer graupel particles are produced in the high amount of small graupel, confirms the aforementioned CCN number concentration case because of the decreased coupling between the cloud microphysics and the storm freezing process of less rain with an inhibited homogeneous dynamics (compare Figure 4). Snow and total hydrometeor nucleation process of ice. Graupel content is represented in amounts increase in the WDM6_EC run (Figure 7d). More unit of g kg−1 of dry air. Since density of air decreases with conversion from liquid phases to snow occurs rather than to height, the mixing ratio of graupel can be shifted to the graupel in the WDM6_EC run. The larger amount of cloud upper levels as compared to that of observed graupel content

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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. water above the freezing level may contribute to the larger showed similar results using a 2‐Dcloud‐resolving model with amount of snow though accretion processes. a bin microphysics scheme [Khain and Pokrovsky, 2004; [23] Domain‐averaged total surface precipitation with Khain et al.,2005;Tao et al.,2007;Yang and Yum,2007]. respect to the initial CCN number concentration is shown in Figure 8. The total precipitation decreases sharply as the 3.2. Graupel Effects CCN number concentration increases. Tao et al. [2007] [25] The importance of graupel substance in simulating showed similar results for a case of a midlatitude conti- convective clouds was stressed in numerical modeling nental squall system, which developed in a fairly unstable studies [McCumber et al., 1991; Johnson et al., 1993; and relatively dry environment. They showed that evapo- Gilmore et al., 2004; Y. Li et al., 2008] and in an obser- rative cooling was stronger at lower levels in the clean vational study [Lemon and Doswell, 1979; Tessendorf et al., scenario for a midlatitude continental squall case. Stronger 2005]. McCumber et al. [1991] evaluated the performance evaporative cooling could enhance the near‐surface cold of several ice parameterizations. They showed that in the pool strength, which makes the convergence stronger and context of their experimental design, the absence of graupel results in enhanced precipitation. When the CCN number diminished the intensity of the radar bright band near the concentration is relatively large, the total precipitation is not melting level and the bright band was less horizontally very sensitive to the CCN number concentration. A possible homogeneous. They also concluded that for tropical cumuli, explanation for the reduced precipitation with an increasing the optimal mix of bulk ice hydrometeors is cloud ice‐ aerosol concentration is the suppressed conversion of cloud snow‐graupel. Johnson et al. [1993] simulated the 2 August droplets to raindrops in the early developing stage of a cloud 1981 supercell that passed over the southeastern part of system and the reduced convective strength over the strong Montana and showed that supercell features such as a rotat- convective core region with greater snow mass loading. ing intense updraft, bounded weak‐echo region, and large Figure 9 shows the accumulated surface precipitation during forward overhanging anvil were generally more pronounced 2 h of integration time for the WDM6_M and WDM6_EC and have longer lifetime with a hail‐included microphysics runs. The precipitation is mainly reduced over the heavy scheme. Y. Li et al. [2008] stressed that graupel played an precipitation region near the strong updraft core in the important role in the Mesoscale Convective System (MCS) WDM6_EC run. These characteristics can also be confirmed of 11–12 August 1999 during the Kwajalein Experiment in the storm structure with the WDM6_M and WDM6_EC (KWAJEX) simulation because the conversion processes of runs (Figure 4). graupel are a significant component of latent heat release, [24] Khain and Lynn [2009] simulated supercell storms which in turn had a significant impact on the dynamic, ther- using a 2 km resolution WRF model with a spectral (bin) modynamic, and precipitation characteristics of the evolving microphysics scheme and a recent version of the Thompson MCS. They also found that deficiencies in the microphysics bulk‐parameterization scheme. They showed a decrease in parameterization are more likely to be the source of the precipitation for high aerosol concentration under low overprediction of graupel and radar reflectivity. humidity. However, the decrease was found to be dependent on environment conditions (air humidity). They mentioned that the effect of the air humidity on the magnitude and even sign of the precipitation response to aerosols, which was discussed 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 increasing aerosols under a high CAPE of around 3,000 J kg−1 and strong wind shear in a two‐dimensional framework using the double‐moment bulk microphysics scheme. Sharp reductions in autoconversion at high aerosol concentration were compensated for by increases in the condensation of cloud liquid and its collection by snow, graupel, and rain. However, under moderate and low CAPEs and a weak wind shear, the precipitation decreased with increasing aerosols, a result that corresponds with this study. (Note that this study employs smaller wind shear and a CAPE around 2,200 J kg−1 compared with those of Lee et al. [2008].) Their results demonstrated that increasing aerosols can either decrease or Figure 8. Total precipitation with respect to the initial increase precipitation for an imposed large‐scale environ- CCN number concentration with the WDM6 microphysics ment supporting cloud development. Several modeling studies scheme.

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

[26] Lemon and Doswell [1979] obtained a consistent and WDM6 microphysics schemes. However, the cloud pattern of tornadogenesis after reviewing many supercell water mixing ratio in the WDM5_C experiment shows storms observed primarily over the Great Plains of the slightly larger values than that in the WDM5_EC experi- United States. They mentioned that the largest surface hail ment, and the amount of the rain mixing ratio in the fall could occur and funnel clouds were often observed WDM5_EC experiment is the largest below a level of during the second stage of the supercell life cycle, which is 2.5 km. The vertical profile for rain shows two peaks around clearly defined when a bounded weak echo region is 5.5 km and 3.5 km levels with the WDM5 microphysics detected. Tessendorf et al. [2005] addressed the kinematic, scheme. The peak around 5.5 km level is not shown with microphysical, and electrical aspects of a severe storm that the WDM6 microphysics scheme (compare Figures 7b and occurred in western Kansas on 29 June 2000 observed 11b). The lower peak in the WDM5 rain profile could be during the Severe Thunderstorm Electrification and Precip- due to melting of snow, while the upper peak is just that rain itation Study (STEPS) field campaign. They found that replaces graupel owing to the omitted graupel‐producing graupel and small hail could be grown from scratch from microphysics processes. Without graupel, surplus water cloud droplet nucleation at cloud base, followed by freezing vapor would convert into another species such as rain and and continued growth aloft. snow or ice. The reason for abundant rain in the low CCN [27] The WDM5 microphysics scheme also simulates a case can be deduced from an enhanced conversion process right‐moving, quasi‐steady supercell with two diverging from water vapor to rain with surplus water vapor and an echo masses. However, the storms show a hook‐type inefficient CCN activation process, in conjunction with the structure and move more slowly in the WDM5_M and absence of graupel. Meanwhile, sensitivities in the cloud WDM5_EC runs (Figures 10a and 10d), when compared with water and rain substances between the WDM5_C and the the WDM6_M and WDM6_EC runs (Figures 4a and 4d). In WDM5_EC runs are revealed to be small. Without graupel, addition, the intensity of surface wind and vertical compo- the proportion of snow is much greater in the WDM5_M run nent of the relative vorticity is weakened in the WDM5 runs. than in the WDM6_M run. The largest mixing ratio of snow Figures 10c and 10f show the vertical cross sections of snow is revealed with the lowest CCN number concentration. An mass, wind fields, and a cold pool along the strong con- inefficient sublimation process from snow to water vapor vective cores. A major difference between the WDM5 and could be a reason for more production of snow in the WDM6 runs is the weakened cold pool in the WDM5 runs. WDM5_M run. Meanwhile, simulated ice properties are not Presumably this is caused by less liquid‐water loading and sensitive to the CCN number concentration. The lowest total weaker low‐level downdrafts, which also results in less mixing ratio occurs in the largest CCN number concentra- near‐surface vorticity (see Figures 10b and 10c). The CCN tion case (Figure 11e). number concentrations do not affect the storm structures [29] In terms of the accumulated total precipitation, the with the WDM5 microphysics scheme. In both WDM5 runs, experiments with the WDM5 microphysics scheme show a lot of snow is produced that has small fall speed and gets opposite responses from the experiments with the WDM6 carried away from the storm at higher levels. Meanwhile, the microphysics scheme under the varying CCN number con- snow mean volume radius does not vary much with respect centration (Figure 12a). Increasing rainfall amount with an to the initial CCN number concentration in the WDM5 runs increasing CCN number concentration is unique to the (Table 2). WDM5 microphysics scheme. Even though the WDM5 runs [28] Vertical profiles of the time domain‐averaged show similar responses to the CCN number concentration in hydrometeor mixing ratios are plotted in Figure 11. Cloud terms of the modeled number concentrations of cloud dro- water and rain mixing ratios show similar responses under plets and raindrops, as well as the cloud droplet radius, the the varying CCN number concentration between the WDM5 difference in the raindrop radius between the WDM5_M

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Figure 10. Same as Figure 2 but for the (a–c) WDM5_M run and (d–f) WDM5_EC run. Contour lines for the snow are at 1, 2, 4, 8, and 16 g kg−1. The cold pool defined by the −0.5°C isotherm of potential temperature perturbation (thick black line) is not distinct using the WDM5 microphysics scheme. and WDM5_EC runs is much larger than that between microphysics: the explicit bin‐resolving method and the the WDM6_M and WDM6_EC runs (Figure 12b). The bulk method. spatial distribution of accumulated precipitation is shown in [30] Figure 14 shows the differences in the time and Figure 13, which demonstrates that the surface precipitation domain‐averaged mixing ratios of vapor and temperature is mainly increased in the WDM5_EC run along the rear between the WDM5 (6)_M and WDM5 (6)_EC runs. A side of the strong convective region in a view of storm more highly saturated environment under the high CCN propagation. The possible reason will be presented in the number concentration with the WDM5 microphysics next paragraph. In addition, the simulated amount of pre- scheme is responsible for more surface precipitation without cipitation is smaller in the WDM5 runs, compared with the further raindrop evaporation. The mixing ratio and temper- WDM6 runs. Reduced precipitation in the WDM5 runs is ature profiles with the WDM6 microphysics scheme show caused by significant amounts of snow, which are carried different features below the melting level from those of the away from the cell at upper levels (see Figures 10c and 10f). WDM5 microphysics. A less saturated environment in the Khain and Lynn [2009] also showed that the response of WDM6_EC run causes raindrops to evaporate more effec- surface precipitation with respect to variations of the aerosol tively compared with that of the WDM5_EC run. Warming concentrations can vary with the method of modeling cloud in the upper troposphere in the WDM6_EC run indicates latent heat release with a large mass of ice (Figure 11). The

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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 of raindrops, obtained from time and cloudy area‐averaged values during a 2 h integration time period with respect to the initial CCN number concentration. The solid line with solid circles indicates the results obtained from the WDM6 microphysics scheme, and the dotted line with open circles shows that from the WDM5 microphysics scheme.

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Figure 13. Accumulated surface rainfall (mm) during 2 h of integration time from the (a) WDM5_M run and (b) WDM5_EC run. results for the WDM5 and WDM6 microphysics schemes changes of the CCN number concentration with the double‐ show that the presence of graupel can alter the storm mor- moment microphysics schemes. The idealized supercell phology and the related dynamics and microphysics, and storm structure, documented in previous modeling studies emphasize the effects of aerosols on precipitation. A detailed and observations, was well captured with the WDM6 analysis of the mechanisms leading to different distributions microphysics scheme. A few studies concerning aerosol‐ of precipitation between the WDM5 and WDM6 micro- related microphysical effects on the evolution of supercell physics schemes is beyond the scopes of the work. storms have been reported [Lerach et al., 2008; Khain and Lynn, 2009], and theses studies were conducted with bin or bin‐emulating bulk microphysics scheme. (We want to 4. Concluding Remarks note that Khain and Lynn [2009] simulated a supercell storm [31] Aerosol effects on the development of a supercell not only with the bin microphysics scheme but also with the storm were investigated, focusing on storm morphology and bulk microphysics scheme. However, they just investigated precipitation. The impact of the initial aerosol concentration the aerosol effect by changing the value of the gamma shape on the development of a supercell storm was evaluated by parameter of size distribution for cloud water without a link varying the number and mass of aerosols as reflected in the between cloud condensation nuclei and cloud droplet

Figure 14. Vertical profiles of the differences in the time domain‐averaged mixing ratios of vapor (solid lines) and temperature (dotted lines) for the (a) WDM5_EC minus WDM5_M run and (b) WDM6_EC minus WDM6_M run. Units for the mixing ratio and temperature are 10 g kg−1 and degrees Celsius, respectively.

14 of 16 D02204 LIM ET AL.: AEROSOL EFFECTS ON A SUPERCELL STORM D02204 number concentrations.) Thus, our study can provide a Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (2010–0000840), and by the Leading Foreign Research Institute reference for aerosol effects on the development of a Recruitment Program through NRF funded by MEST (2010‐00715). supercell storm with a double‐moment bulk microphysics scheme. References [32] Hail and graupel are the dominant precipitating ice Albrecht, B. A. (1989), Aerosols, cloud microphysics, and fractional cloud- particles for a supercell thunderstorm as was noted in pre- iness, Science, 245, 1227–1230, doi:10.1126/science.245.4923.1227. vious observation and modeling studies. Thus, it is quite Berry, E. X., and R. L. Reinhardt (1974), An analysis of cloud drop certain that a more elaborate microphysics scheme, which growth by collection. Part II: Single initial distributions, J. Atmos. 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