Abstract

Idealized supercell thunderstorms are simulated with the Weather Research and Forecasting (WRF) Model at 15 cloud condensation nuclei (CCN) concentrations (100–10 000 cm−3) using four environmental soundings with different low-level relative humidity (RH) and vertical wind shear values. The Morrison microphysics scheme is used with explicit prediction of cloud droplet number concentration and a variable shape parameter for the raindrop size distribution (results from simulations with a fixed shape parameter are also presented). Changes in the microphysical process rates with CCN concentration are negligible beyond CCN ≈ 3000 cm−3. Changes in cold pool characteristics with CCN concentration are nonmonotonic and highly dependent on the environmental conditions. In moist conditions with moderate vertical wind shear, the cold pool area is nearly constant with respect to CCN concentration, while the area is reduced by 84% and 22% in the soundings with dry RH and large vertical wind shear, respectively. With the exception of the dry RH sounding, domain-averaged precipitation peaks between 500 and 5000 cm−3, after which it remains constant or slowly decreases. For the dry RH sounding, the domain-averaged precipitation monotonically decreases with CCN concentration. Accumulated precipitation is enhanced (by up to 25 mm) in the most polluted cases near the updrafts, except for the dry RH sounding. The different responses for moist and dry soundings are mostly due to increased (decreased) low-level latent cooling from melting hail (evaporating rain) with increasing CCN concentration in the moist soundings. This compensating effect does not exist when the low-level RH is dry.

1. Introduction

A variety of convective modes and processes have been shown to exhibit sensitivity to aerosol concentration (e.g., Khain et al. 2005; van den Heever and Cotton 2007; Khain and Lynn 2009; Mansell and Ziegler 2013). However, there is a lack of research that examines how the microphysical processes and resulting thermodynamic structure of thunderstorms vary across the wide range of aerosol concentrations that are possible within the atmosphere. Specifically, it is unclear at what aerosol concentration perturbed microphysical processes become evident, and whether these perturbations continue to grow as aerosol concentration increases or if additional increases have negligible influence above a certain threshold. To understand the response of thunderstorms to a variety of aerosol concentrations, the Weather Research and Forecasting (WRF) Model is run at a cloud-resolving horizontal grid spacing of 1 km with the Morrison microphysics scheme, in which the concentration of cloud condensation nuclei (CCN) at 1% supersaturation is varied from 100 to 10 000 cm−3. Four different environmental soundings are tested, all of which are supportive of supercell thunderstorms (i.e., thunderstorms with rotating updrafts), which may produce large hail, damaging straight-line winds, flooding, and tornadoes. Mass budgets are analyzed to quantify the effect of increasing CCN concentration on rates of riming, melting, droplet collection, and evaporation. In addition, these perturbed microphysical processes are linked to changes in the low-level cold pool and to differences in accumulated surface precipitation to understand how supercell thunderstorms respond across the wide spectrum of plausible atmospheric aerosol concentrations.

In the atmosphere, aerosol sources include those that are both anthropogenic (e.g., biomass burning, sulfate emissions, and motor vehicle exhaust) and natural (e.g., evaporation of sea spray, dust lofting, and wildfires) (Penner et al. 1994; Levin and Cotton 2009; Tao et al. 2012). Size radii range from 0.1 μm for Aitken particles to 100 μm for giant aerosols composed of sea salt (Rogers and Yau 1989; Levin and Cotton 2009). In unpolluted conditions over the open ocean, aerosol number concentrations may be as small as 100 cm−3, while environments contaminated by smoke from forest fires may feature concentrations in excess of 10 000 cm−3 (Andreae et al. 2004). Observations from the Southern Great Plains (SGP) site of the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Lamont, Oklahoma, demonstrate that near-surface CCN concentrations on the U.S. Great Plains frequently varied between 1000 and 5000 cm−3 on supercell thunderstorm days and approached 10 000 cm−3 on other days with ordinary convection (Fig. 1). Such widely varying CCN number concentration may play an important role in modifying precipitation development in the supercell thunderstorms that commonly affect the Great Plains, since the growth of precipitating liquid hydrometeors starts when water vapor condenses onto CCN to form cloud droplets. The cloud droplet activation rate is affected by the type and size of the aerosols that serve as CCN, and competition for a limited amount of water makes the aerosol concentration critical in determining how large cloud droplets can grow and, therefore, how efficient autoconversion into raindrops will be. Because aerosols can change the rates of cloud microphysical processes, they can also alter the local temperature and moisture profiles by modifying the latent cooling/heating that results from phase changes of water. In this manner, the buoyancy, precipitation efficiency, and the lifetime of the cloud can all be affected by changes in the aerosol properties. For more information about cloud–aerosol interactions, the reader is directed to Levin and Cotton (2009) and Tao et al. (2012).

Fig. 1.

Maximum daily surface CCN (at supersaturation between 0.9% and 1.1%) and condensation nuclei (CN) number concentrations at the DOE ARM SGP site from 20 Apr to 10 Jun 2011. Days with convective activity (i.e., showers and/or thunderstorms) near the SGP site are indicated in red, and days with supercell thunderstorms are shown in purple. Data were obtained from the DOE ARM online archive (http://www.arm.gov).

Fig. 1.

Maximum daily surface CCN (at supersaturation between 0.9% and 1.1%) and condensation nuclei (CN) number concentrations at the DOE ARM SGP site from 20 Apr to 10 Jun 2011. Days with convective activity (i.e., showers and/or thunderstorms) near the SGP site are indicated in red, and days with supercell thunderstorms are shown in purple. Data were obtained from the DOE ARM online archive (http://www.arm.gov).

With ongoing pollution emissions from urban areas and a recent increase in the number of large wildfires in the western United States and elsewhere (Diaz and Swetnam 2013; Luo et al. 2013; Lang et al. 2014), the need to understand how enhanced aerosol concentrations affect a broad range of convective modes has increased. In polluted environments, previous modeling studies that examined the effects of CCN concentration on thunderstorm characteristics found evidence for delayed onset of precipitation (Tao et al. 2007; van den Heever and Cotton 2007; Storer et al. 2010; Mansell and Ziegler 2013), suppressed raindrop collision and coalescence (van den Heever and Cotton 2007; Fan et al. 2007; Lerach et al. 2008; Storer et al. 2010), decreased cold pool size (Lerach et al. 2008; Storer et al. 2010; Lerach and Cotton 2012), and faster updraft speeds due to enhanced latent heat release (Seifert and Beheng 2006; van den Heever et al. 2006; Fan et al. 2007; Ntelekos et al. 2009; Mansell and Ziegler 2013). However, there is disagreement on whether surface precipitation is enhanced or decreased in polluted environments, with sensitivity to the low-level (0–3 km) relative humidity evident (Fan et al. 2007; Khain et al. 2008; Khain and Lynn 2009; Fan et al. 2009). Many recent modeling studies present results from only two or three different CCN concentrations (e.g., Lerach et al. 2008; Khain and Lynn 2009; Lerach and Cotton 2012), leading to uncertainty in how to apply these results to the broad range of observed CCN concentrations. In addition, while some studies have investigated aerosol effects on supercell thunderstorms (e.g., Seifert and Beheng 2006; Lerach et al. 2008; Khain and Lynn 2009; Storer et al. 2010; Lebo and Seinfeld 2011; Morrison 2012; Lerach and Cotton 2012; Lebo et al. 2012), many studies have focused on nonsupercellular storms. Results from studies of aerosol impacts on other convective modes may not be applicable to supercell thunderstorms owing to the strong vertical wind shear of the environment, which results in updrafts that are strongly influenced by dynamic perturbation pressure as well as buoyancy (Rotunno and Klemp 1985). This study is unique because it examines the influence of aerosol concentration on supercell thunderstorms for 15 CCN concentrations that vary from 100 to 10 000 cm−3 across different low-level relative humidity and vertical wind shear environments. Thus, we will determine whether aerosol-induced perturbations in the microphysics and thermodynamics increase monotonically or nonmonotonically with CCN concentration, whether these perturbations cease to increase beyond a certain limit, and how these changes vary across different environments. While a few other studies (Seifert and Beheng 2006; Fan et al. 2009; Storer et al. 2010; Lebo and Seinfeld 2011; Lerach and Cotton 2012) have tested the sensitivity of thunderstorms in sheared environments to CCN and relative humidity effects, the objective of this research is to investigate how individual microphysical process rates vary across a spectrum of CCN concentrations, up to a larger value (CCN = 10 000 cm−3) than these previous studies.

In section 2, we discuss the WRF Model configuration, the four different soundings used to initialize the model, and the Morrison microphysics scheme as it pertains to this study. Section 3 presents and compares results for each initial sounding. The research is summarized and concluding remarks are provided in section 4.

2. Methods

a. Model configuration

This study uses version 3.3 of the three-dimensional, nonhydrostatic WRF Model (Skamarock et al. 2008) with the Advanced Research WRF (ARW) core. ARW evaluates the equations of motion with a third-order Runge–Kutta integration scheme. An Arakawa C grid is used to stagger the components of the three-dimensional wind one-half grid length away from the thermodynamic variables (e.g., potential temperature), which are evaluated at the center of each grid cell. The model utilizes terrain-following (eta) coordinates.

The chosen model configuration is similar to the idealized supercell thunderstorm test case that is provided with WRF and to those of Morrison and Milbrandt (2011), Lebo et al. (2012), and Morrison (2012). Idealized simulations are chosen so that the effect of CCN concentration on the microphysics and thermodynamics of the supercell thunderstorm can be quantified in the absence of secondary feedbacks from radiative, boundary layer, and surface-layer processes. The domain has a horizontal grid spacing of 1 km and spans 200 km in both the zonal and meridional directions. The horizontal boundaries are periodic to ensure conservation of total mass within the domain. A time step of 2 s is used, except for the acoustic modes, for which a 0.33-s time step is used. In the vertical, an exponentially stretched grid with 70 levels and a nearly constant spacing of approximately 300 m is selected. The model top is at height z = 24 km, and a Rayleigh damper with a damping coefficient of 0.003 s−1 is used within the upper 5 km to eliminate gravity waves that reflect off the upper boundary.

Horizontal and vertical advections are calculated using monotonic fifth- and third-order schemes, respectively. Turbulent diffusion is computed with a 1.5-order turbulent kinetic energy scheme (Skamarock et al. 2008). The Morrison scheme, discussed in more detail below, is used to represent microphysical processes. Radiative transfer, surface fluxes, and Coriolis force are neglected for simplicity. Convection is initiated with a warm perturbation in the potential temperature field. The maximum amplitude of the perturbation is 3 K, it is centered at z = 1.5 km, and it is 20 km wide in the horizontal and 3 km in height. The model equations are integrated for 2 h, with output written every 10 min.

The default sounding (referred to herein as def) used to initialize the model (Fig. 2) is based on Weisman and Klemp (1982), with the environmental wind making a quarter-circle when plotted on a hodograph (Fig. 3). To study the sensitivity of the results to environmental conditions, simulations are also conducted with three additional soundings (Figs. 2 and 3; Table 1), in which the low-level relative humidity and vertical wind shear of the def sounding are modified. These soundings are called low relative humidity (loRH), high relative humidity (hiRH), and high vertical wind shear (hiWS). In the loRH case, the mean surface-to-800-hPa relative humidity from the default input sounding is reduced from approximately 80% to 61% (Fig. 2 and Table 1). The result is an “inverted V” sounding (Brady and Szoke 1989; Johns and Doswell 1992) that is typical of the western U.S. Great Plains in the summer months and that is conducive to high-based, low-precipitation supercell thunderstorms (e.g., Bluestein and Parks 1983; Grant and van den Heever 2014). In the hiRH case, the mean surface-to-800-hPa relative humidity is increased to approximately 91%, yielding a surface-based convective available potential energy (CAPE) of 5138 J kg−1 (compared to 2745 J kg−1 in the def sounding; Table 1). Finally, in the hiWS case, the wind speed is increased by about 5 m s−1 km−1 until it reaches a maximum value of about 25 m s−1 at z = 4.25 km, above which the wind speed is constant. As a result, the hiWS sounding has a 0–3-km bulk shear of about 16 m s−1 compared to about 8 m s−1 in the def sounding (Fig. 3).

Fig. 2.

Skew-T–logp diagram with the soundings used to initialize the WRF Model, including the default (def) sounding and the soundings used for the sensitivity tests: low relative humidity (loRH; dashed line), high relative humidity (hiRH; dotted line), and high vertical wind shear (hiWS). The solid red line is the temperature profile, while the dewpoint temperature profiles are shown in blue. The wind speed and direction are represented by two sets of wind barbs on the right side of the diagram: one set for the hiWS sensitivity test and one set for all other simulations (def).

Fig. 2.

Skew-T–logp diagram with the soundings used to initialize the WRF Model, including the default (def) sounding and the soundings used for the sensitivity tests: low relative humidity (loRH; dashed line), high relative humidity (hiRH; dotted line), and high vertical wind shear (hiWS). The solid red line is the temperature profile, while the dewpoint temperature profiles are shown in blue. The wind speed and direction are represented by two sets of wind barbs on the right side of the diagram: one set for the hiWS sensitivity test and one set for all other simulations (def).

Fig. 3.

Hodograph of the wind profile used to initialize the WRF Model in the high–wind shear case (red line; hiWS) and in all other cases (blue line; def). Each filled circle represents an individual wind vector from the skew T–logp diagram in Fig. 2. The numbers and tick marks along the red and blue lines indicate the height above the surface (km), and the numbers along the concentric circles indicate the wind speed (m s−1).

Fig. 3.

Hodograph of the wind profile used to initialize the WRF Model in the high–wind shear case (red line; hiWS) and in all other cases (blue line; def). Each filled circle represents an individual wind vector from the skew T–logp diagram in Fig. 2. The numbers and tick marks along the red and blue lines indicate the height above the surface (km), and the numbers along the concentric circles indicate the wind speed (m s−1).

Table 1.

Relative humidity, CAPE, and Richardson number for the soundings used to initialize the WRF Model in the default, low relative humidity, high relative humidity, and high vertical wind shear cases.

Relative humidity, CAPE, and Richardson number for the soundings used to initialize the WRF Model in the default, low relative humidity, high relative humidity, and high vertical wind shear cases.
Relative humidity, CAPE, and Richardson number for the soundings used to initialize the WRF Model in the default, low relative humidity, high relative humidity, and high vertical wind shear cases.

Previous modeling studies (e.g., Weisman and Klemp 1982, 1984) have suggested that the convective mode depends on the convective bulk Richardson number R, given by

 
formula

where and are the difference between the density-weighted mean wind calculated over the 0–6-km layer and a 500-m-deep surface layer. Weisman and Klemp (1984) found that 15 < R < 45 strongly favors supercell thunderstorms, with R = 18 producing the fastest updrafts relative to the CAPE. Table 1 shows that R ranges from 12.4 (loRH) to 65.8 (hiRH) for the soundings here. While these values lay outside of the range that Weisman and Klemp (1984) suggested most favors supercell thunderstorms, we nevertheless observe discrete cell development, storm splitting, and substantial updraft rotation (see section 3a) in all of the simulations.

b. Microphysics scheme

The Morrison double-moment microphysics scheme (Morrison and Pinto 2005; Morrison et al. 2005, 2009) is selected to model microphysical processes. The scheme considers five hydrometeor species, all of which are assumed to consist of spherical particles: cloud droplets, cloud ice, rain, snow, and a rimed-ice category. The latter uses bulk density and fall speed characteristics that are typical of either graupel or hail (user selectable). In this research, hail is chosen as the rimed-ice category, which may be more suitable than graupel for studies of continental deep convection (McCumber et al. 1991; Bryan and Morrison 2012). The hydrometeor size distributions N(D) are represented by gamma functions of the form

 
formula

where D is the particle diameter, N0 is the intercept parameter, μ is the shape parameter, and λ is the slope parameter. We predict N0 and λ for each hydrometeor species via Eqs. (3) and (4):

 
formula
 
formula

where q is the hydrometeor mass mixing ratio, Γ is the Euler gamma function, and c is a parameter in the power law that relates the diameter and mass m of the hydrometeors:

 
formula

Since all particles are assumed to be spherical, c = π/6 × ρ, where ρ is the bulk density of the hydrometeor class, given by Table 4 in Morrison and Milbrandt (2011).

For cloud ice, snow, and hail, μ is set to zero. For cloud droplets, the value of μ is a function of the predicted cloud droplet number concentration according to Martin et al. (1994) and varies from 2 to 10. For the simulations presented in sections 3a3c, a variable shape parameter for the raindrop size distribution is used. Specifically, μ is diagnosed with the shape–slope relation from Cao et al. (2008), allowing μ to vary with λ:

 
formula

This relationship was derived from data collected in central Oklahoma by three two-dimensional video disdrometers from May 2005 to May 2007. In total, 14 200 one-minute drop spectra were collected in all seasons in both stratiform and convective rain events that varied in rainfall rate from 0.1 to 100 mm h−1 (Cao et al. 2008). While a few other shape–slope relations exist in the literature for more tropical climates [e.g., Florida, Zhang et al. (2001); India, Narayana Rao et al. (2006)], they are not considered here because shape–slope relations exhibit regional dependence (Cao et al. 2008) and supercell thunderstorms are primarily midlatitude phenomena.

The use of a variable-μ scheme for rain helps address the problem of excessive size sorting that has been noted in double-moment bulk microphysics schemes with fixed μ (Wacker and Seifert 2001; Milbrandt and Yau 2005; Milbrandt and McTaggart-Cowan 2010; Kumjian and Ryzhkov 2012). To determine μ within the microphysics scheme, an initial guess of μ = 0 is made and Eqs. (4) and (6) are iterated until λ converges to within 0.1%. This formula is not extrapolated to values of λ larger than the Cao et al. (2008) data range (20 mm−1), giving a maximum μ of approximately 8.28. The minimum allowable μ for rain is 0. In section 3d, results from the variable-μ scheme are compared to results from a set of μ = 0 simulations with the default sounding. We note that comparing simulations in which the hail shape parameter is fixed versus allowed to vary might also be interesting, although this topic is left unexplored here owing to limited observations of the shape–slope relation for hail.

In this research, the CCN spectrum is represented by a power-law relationship (Pruppacher and Klett 1997):

 
formula

where NCCN is the number concentration of activated cloud condensation nuclei (cm−3), S is the supersaturation ratio (%), C is the CCN concentration (cm−3) at S = 1%, and k is a unitless constant. As S increases, more CCN activate. Tables 9.1 and 9.2 in Pruppacher and Klett (1997) demonstrate that while C is observed to be larger in continental versus maritime air masses, there is no clear dependence of k on the type of air mass or on the value of C. Therefore, in this study, we assume that k is equal to 0.7, which is the average of the values in Pruppacher and Klett (1997). For simplicity, the initial vertical distribution of CCN is assumed to be constant with height and CCN transport and sources and sinks (other than scavenging from cloud droplet nucleation) are not considered. The total number concentration of CCN plus cloud droplets within a grid cell remains constant throughout the simulation so that the CCN concentration reverts to the background value upon cloud evaporation.

Throughout the activation process, mixing is treated as homogeneous; that is, entrainment of subsaturated air causes the mean cloud droplet radius to decrease while the droplet concentration remains constant. At cloud base, droplet activation is performed using the approach from Rogers and Yau (1989), which depends only on updraft speed and assumes that no initial cloud water exists:

 
formula

where wef is the sum of the resolved and subgrid vertical velocity. At cloud base, this relation was shown by Dearden (2009) to produce cloud droplet concentrations to within 10%–20% of those obtained when supersaturation is resolved explicitly (requiring a time step of 0.1 s or shorter). However, within the cloud, Eq. (8) overestimates the number of activated cloud droplets because it fails to account for the reduction in supersaturation due to the condensational growth of existing cloud droplets (Dearden 2009). A different approach is therefore used in the cloud interior, in which the supersaturation is assumed to be in quasi equilibrium; that is, the production of supersaturation from upward motion is assumed to be balanced by the loss from depositional growth of existing hydrometeors [Eq. (9); adapted from Morrison et al. (2005)]:

 
formula

In the above equation, qυ is the water vapor mixing ratio; qsw is the liquid water saturation mixing ratio; qsi is the ice saturation mixing ratio; T is the temperature; g is the gravitational acceleration; cp is the specific heat of air at constant pressure; Q1 = 1 + (dqsw/dT)(Ls/cp); Q2 = 1 + (dqsi/dT)(Ls/cp); Ls is the latent heat of sublimation; and τc, τr, τi, τs, and τh are the saturation relaxation time scales of cloud droplets, rain, ice crystals, snow, and hail, respectively. The supersaturation obtained from Eq. (9) is then substituted into Eq. (7) to obtain the predicted number of activated cloud droplets in the cloud interior. However, if the predicted number of activated cloud droplets is less than the number that already exists, no new CCN are activated. In addition, the number of activated cloud droplets is never allowed to exceed that predicted by Eq. (8).

Ice crystals are produced through primary nucleation, through the Hallett–Mossop process (i.e., rime splintering), and through freezing of cloud droplets (Morrison and Milbrandt 2011). Autoconversion of ice crystals to snow is based on the approach of Harrington et al. (1995) assuming a threshold size of 125 μm. Snow can then be rimed to form hail particles. During melting, snow and hail particles are assumed to have constant mean mass diameter, and the total number concentration of rain, snow, and hail particles is conserved. All ice crystals are melted into rain when the ambient air temperature exceeds 0°C. Liquid water particles that are shed from hailstones are assumed to have a mean mass diameter of 1 mm based on laboratory (Carras and Macklin 1973; Lesins et al. 1980; Rasmussen et al. 1984) and field (Rasmussen and Heymsfield 1987) experiments. These studies consistently showed that water drops shed from hailstones undergoing wet growth have diameters of 0.5–2 mm, with a mode of 1 mm.

Raindrop breakup is treated implicitly by limiting the bulk collection efficiency of raindrops Ec at mean mass diameters Dmr that exceed a specified threshold Dth (Verlinde and Cotton 1993; Morrison et al. 2012). When Dmr is less than Dth, the collection efficiency is assumed to be one. Once Dmr exceeds Dth, the collection efficiency is reduced according to

 
formula

Here, Dth is set to a constant value of 300 μm, which is the default value in the public release of the Morrison scheme in WRF, version 3.3. The size and strength of the low-level cold pool are sensitive to the choice of Dth, since larger values of Dth will increase Dmr and thereby reduce the evaporation rate (Morrison and Milbrandt 2011; Morrison et al. 2012). Further discussion of how the choice of Dth influences the evaporation rate and therefore the cold pool, however, is beyond the scope of this paper.

Simulations are conducted for 15 different values of C that range between 100 and 10 000 cm−3: 100, 250, 500, 750, 1000, 1500, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, and 10 000 cm−3. Note that 10 000 cm−3 is a larger CCN concentration than has been used in previous numerical studies of CCN concentration and supercell thunderstorms [e.g., 1500 cm−3 in Khain and Lynn (2009) and 2000 cm−3 in Lerach and Cotton (2012)]. However, observations from the Department of Energy (DOE)’s ARM site in the SGP (Fig. 1) show that CCN concentrations can reach at least 6000 cm−3 on supercell thunderstorm days (12 May) and can approach 10 000 cm−3 on days with ordinary convection (18 May).

3. Results

a. CCN effects on hydrometeor characteristics and microphysical processes

Reflectivity that exceeds 0 dBZ first appears at time t ≈ 20 min in each of the simulations. A hook echo becomes evident in radar reflectivity at t ≈ 30 min (Fig. 4a). By t ≈ 60 min, the supercell thunderstorm has clearly split into left- and right-moving updrafts, and the hook echo associated with the right mover has become more distinct (Fig. 4b). The two cells move at approximately 90° from each other (Figs. 4c,d), and by t = 120 min, the area with reflectivity greater than 0 dBZ is about 8200 km2 (21% of the domain). The above results are similar for both the CCN = 100-cm−3 and the CCN = 10 000-cm−3 runs. Note that 2–5-km updraft helicity values (Kain et al. 2008) of 1000–2700 m−2 s−2 are present throughout the simulations of def, hiRH, and hiWS, while values in excess of 300 m−2 s−2 are simulated for the loRH case (Fig. 5), justifying our classification of these thunderstorms as supercells.

Fig. 4.

Horizontal cross sections of simulated radar reflectivity (assuming a 10-cm wavelength) at z = 1 km AGL at (a) t = 30, (b) t = 60, (c) t = 90, and (d) t = 120 min using the default sounding and a CCN concentration of 10 000 cm−3.

Fig. 4.

Horizontal cross sections of simulated radar reflectivity (assuming a 10-cm wavelength) at z = 1 km AGL at (a) t = 30, (b) t = 60, (c) t = 90, and (d) t = 120 min using the default sounding and a CCN concentration of 10 000 cm−3.

Fig. 5.

Updraft helicity (integrated over 2–5 km AGL) from t = 10 to 120 min at an interval of 10 min for the CCN = 10 000 cm−3 runs of (a) def, (b) hiRH, (c) loRH, and (d) hiWS.

Fig. 5.

Updraft helicity (integrated over 2–5 km AGL) from t = 10 to 120 min at an interval of 10 min for the CCN = 10 000 cm−3 runs of (a) def, (b) hiRH, (c) loRH, and (d) hiWS.

The next several analyses compare only the cleanest (CCN = 100 cm−3) and dirtiest (CCN = 10 000 cm−3) simulations for each of the four soundings, considering differences in the mean diameters (Fig. 6), number concentrations (Fig. 7), and mass mixing ratios (Fig. 8) of cloud droplets, rain, and hail. Each of these figures consists of conditional (i.e., only nonzero values), domain-averaged vertical profiles at t = 120 min. Because of more CCN, the dirtiest simulation (independent of the initial sounding) has smaller (5 versus 17 μm; Fig. 6), more numerous (~4000 versus ~100 cm−3; Fig. 7) cloud droplets than the cleanest simulation. This result is known as the aerosol indirect effect, first noted by Twomey (1974) and later simulated by Khain et al. (2005), Fan et al. (2007), Tao et al. (2007), Storer et al. (2010), and many other recent studies across a wide variety of convective modes. Because the mean cloud droplet size is smaller in the dirtiest run, fewer cloud droplets make the transition into rain and hail particles, with a 97% (85%)-smaller number concentration of rain (hail) at 5 (10) km compared to the cleanest simulation of the def sounding (Fig. 7a; note that a logarithmic scale is used). Similar trends are seen in the number concentration profiles that result from the other soundings (Figs. 7b–d). Although there are fewer raindrops and hailstones in the dirtiest simulation, the rain and hail particles that are present have 3 times the cloud water mass available for collection (0.9 versus 0.3 g kg−1 at 9 km; Fig. 8a), which leads to mean mass diameters that are 30% larger for rain and 3% larger for hail near the surface in the dirtiest run with the def sounding (Fig. 6a; Table 2). Similar results (fewer, but larger rain and hail particles in polluted conditions) were also simulated by Storer et al. (2010), Lerach and Cotton (2012), and others. For the other soundings, the only notable departure from this pattern is with the loRH sounding, in which the mean mass diameter of rain is about equal in the cleanest and dirtiest runs near the surface (Fig. 6c; Table 2), despite being larger in the dirtiest run from 2 to 8 km (as in the other soundings).

Fig. 6.

Conditional, domain-averaged vertical profiles of hydrometeor mean mass diameter at t = 120 min for cloud droplets (green lines), rain (blue lines), and hail (purple lines) for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings. Results from the cleanest (CCN = 100 cm−3; solid lines) and dirtiest (CCN = 10 000 cm−3; dashed lines) simulations are shown.

Fig. 6.

Conditional, domain-averaged vertical profiles of hydrometeor mean mass diameter at t = 120 min for cloud droplets (green lines), rain (blue lines), and hail (purple lines) for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings. Results from the cleanest (CCN = 100 cm−3; solid lines) and dirtiest (CCN = 10 000 cm−3; dashed lines) simulations are shown.

Fig. 7.

As in Fig. 6, but for hydrometeor number concentration.

Fig. 7.

As in Fig. 6, but for hydrometeor number concentration.

Fig. 8.

As in Fig. 6, but for hydrometeor mass mixing ratio.

Fig. 8.

As in Fig. 6, but for hydrometeor mass mixing ratio.

Table 2.

The percentage change in microphysical and thermodynamic quantities between the cleanest (CCN = 100 cm−3) and dirtiest (CCN = 10 000 cm−3) simulations (DIRTIEST − CLEANEST) at t = 120 min for all cases: default, high relative humidity, low relative humidity, high vertical wind shear, and the default sounding with μ for rain set to zero (zero μ or ZMU). Cold pool characteristics, precipitation, and hydrometeor diameters are calculated at the lowest model level (z = 170 m).

The percentage change in microphysical and thermodynamic quantities between the cleanest (CCN = 100 cm−3) and dirtiest (CCN = 10 000 cm−3) simulations (DIRTIEST − CLEANEST) at t = 120 min for all cases: default, high relative humidity, low relative humidity, high vertical wind shear, and the default sounding with μ for rain set to zero (zero μ or ZMU). Cold pool characteristics, precipitation, and hydrometeor diameters are calculated at the lowest model level (z = 170 m).
The percentage change in microphysical and thermodynamic quantities between the cleanest (CCN = 100 cm−3) and dirtiest (CCN = 10 000 cm−3) simulations (DIRTIEST − CLEANEST) at t = 120 min for all cases: default, high relative humidity, low relative humidity, high vertical wind shear, and the default sounding with μ for rain set to zero (zero μ or ZMU). Cold pool characteristics, precipitation, and hydrometeor diameters are calculated at the lowest model level (z = 170 m).

The differences in the number concentration and mean diameter of the hydrometeors in the cleanest and dirtiest runs lead to important changes in the microphysical process rates. These changes can be analyzed by calculating the liquid (Fig. 9) and ice (Fig. 10) mass budgets, similar to the approach used by Khain et al. (2011) and others. Table 2 summarizes the differences in the vertically integrated microphysical process rates between the cleanest and dirtiest runs for each of the four soundings. Collection of cloud droplets by rain generally proceeds at a slower rate between 2 and 9 km in the dirtiest runs (Fig. 9) at both t = 80 (thin lines) and 120 min (thick lines) owing to the smaller rain mass mixing ratio (Fig. 8). However, because there are more cloud droplets to collect per raindrop, the resulting raindrops have a mean mass diameter that is up to 0.4 mm larger compared to the cleanest runs from 0 to 4 km (Fig. 6). Because of the larger raindrop size in conjunction with the reduced rain number concentration, rain evaporation at these heights is reduced in the dirtiest runs. An exception is the loRH case (Fig. 9c), in which the drier boundary layer is associated with a much higher cloud base (~1.8 versus ~0.5 km in the other soundings) in both the cleanest and dirtiest runs (Fig. 7c), thereby preventing cloud droplet collection by rain below z = 2 km and leading to rain mean mass diameters that are about the same near the surface in both the cleanest and dirtiest runs (Fig. 6c; Table 2). With regard to ice processes, riming of hailstones with cloud droplets (d < 0.1 mm) is increased between 6 and 11 km with more CCN (Fig. 10) in def and hiRH. This trend is not as apparent in hiWS and is reversed in loRH, likely because both hiWS and loRH feature reduced differences in cloud droplet mass between the cleanest and dirtiest runs near 5 km (Figs. 8c,d). On the other hand, riming of hail with raindrops is decreased by up to 2 × 10−4 g kg−1 s−1 in the dirtiest simulation of all of the soundings owing to the reduced rain number concentration. Considering the melting rate, the updraft-influenced (w > 10 m s−1) freezing level is located between 4.0 and 4.4 km in all of the soundings (not shown). The peak melting rate is located at 2.75–3.25 km, although the peak rate is up to 500 m closer to the surface in the dirtiest runs, likely because the larger-diameter hailstones (Fig. 6) have faster fall speeds and, therefore, get closer to the surface before melting substantially. We also note an important difference in the def and hiRH cases compared with the loRH and hiWS cases in Fig. 10: namely, more melting occurs in the lowest 2 km in the dirtiest (relative to the cleanest) runs of def and hiRH at both t = 80 and 120 min, while the opposite trend is apparent in loRH and hiWS. The increased melting at larger CCN concentrations in def and hiRH may be due to the dirtiest run having larger hail number concentrations than the cleanest run from 0 to 2 km (Figs. 7a,b)—a trend that does not occur in loRH (Fig. 7c) or hiWS (Fig. 7d). The additional latent cooling from melting hail in the dirtiest runs of def and hiRH compensates for the decreased evaporative cooling in these runs, thereby impacting the cold pool characteristics in def and hiRH, which will be discussed later.

Fig. 9.

Domain-averaged vertical profiles of the rate of cloud droplet collection by rain (green lines) and rain evaporation rate (blue lines) at t = 80 (thin lines) and 120 min (thick lines) for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings. Solid (dashed) lines represent profiles from the cleanest (dirtiest) simulation.

Fig. 9.

Domain-averaged vertical profiles of the rate of cloud droplet collection by rain (green lines) and rain evaporation rate (blue lines) at t = 80 (thin lines) and 120 min (thick lines) for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings. Solid (dashed) lines represent profiles from the cleanest (dirtiest) simulation.

Fig. 10.

As in Fig. 9, but for the rate of riming hailstones with cloud droplets (green lines), the rate of riming hailstones with rain (blue lines), and the melting rate of hail (purple lines).

Fig. 10.

As in Fig. 9, but for the rate of riming hailstones with cloud droplets (green lines), the rate of riming hailstones with rain (blue lines), and the melting rate of hail (purple lines).

Now considering all of the CCN values (Fig. 11), it is apparent that it is not necessary for the CCN concentration to be increased to 10 000 cm−3 for the microphysical processes to be substantially perturbed. In fact, for each of the soundings, perturbations in the rates of vertically integrated melting, evaporation, and riming of hailstones generally saturate by CCN = 3000 cm−3, after which these rates exhibit little change. The microphysical processes that directly involve cloud droplets (i.e., collection of cloud droplets by rain and riming of hailstones by cloud droplets) are most sensitive to further increases in CCN concentration above 2000–3000 cm−3, although the rate of change in these processes is generally smaller above CCN = 3000 cm−3 in each of the soundings. While Fig. 11 shows results for t = 120 min, similar results are obtained for each of the model outputs from t = 60 to 120 min. These results are also similar to Khain et al. (2011), who used a two-dimensional spectral bin model to conclude that the microphysical processes in a multicell thunderstorm were less sensitive to further increases in CCN concentration above 3000 cm−3. Together, these results suggest that extreme concentrations of CCN, such as those observed downwind of forest fires or highly polluted urban areas, may not be necessary to perturb the microphysical processes substantially. Further, Fig. 11 demonstrates that, after the CCN concentration exceeds 3000 cm−3, additional increases in CCN concentration have progressively less impact on the microphysical processes. An important caveat to this result, however, is that the Morrison microphysics scheme does not include the effects of wet scavenging on the CCN concentration. If the number of CCN available to perturb the microphysical processes is limited by wet scavenging, then larger CCN concentrations might be needed to achieve the maximum perturbation in those processes.

Fig. 11.

Vertically integrated, horizontally averaged microphysical process rates vs CCN concentration at t = 120 min for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings.

Fig. 11.

Vertically integrated, horizontally averaged microphysical process rates vs CCN concentration at t = 120 min for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings.

b. CCN effects on cold pool size and strength

Since the size and strength of the low-level cold pool are determined by the amount of latent cooling caused by evaporating rain and melting hail (e.g., Gilmore and Wicker 1998; Tao et al. 2007; James and Markowski 2010), it is reasonable to expect that changes in the rates of these processes will influence the cold pool. Here, we define the cold pool to be the area at the lowest model level (z = 170 m) that has a perturbation potential temperature colder than −2 K, which is consistent with recent studies by Morrison and Milbrandt (2011) and Morrison (2012). Figure 12 depicts the variation in the size and the mean perturbation potential temperature θ′ of the cold pool with CCN concentration. It is evident from this figure that the relationship between the cold pool characteristics and the CCN concentration is not monotonic—in contrast to what has been reported or suggested in several other studies of supercell thunderstorms (e.g., Lerach et al. 2008; Storer et al. 2010; Lerach and Cotton 2012). In addition, the response of the cold pool to CCN concentration is highly dependent on the initial sounding used: in def (Fig. 12a) and hiRH (Fig. 12b), the cold pool size at t = 120 min remains nearly constant at 6400 km2 regardless of the CCN concentration, while the mean cold pool temperature increases by about 0.4 K from CCN = 100 to 4000 cm−3 before remaining nearly constant at larger CCN concentrations. In contrast, loRH (Fig. 12c) exhibits a rapid decrease in cold pool size at t = 120 min, from 1200 (CCN = 100 cm−3) to 400 km2 (CCN = 3000 cm−3), followed by a much more gradual decrease from 400 to 250 km2 between CCN = 3000 and 10 000 cm−3. The response of the cold pool in hiWS (Fig. 12d) is similar to that of loRH, with a rapid decrease in cold pool size from 7700 (CCN = 100 cm−3) to 6000 km2 (CCN = 3000 cm−3), followed by nearly constant size between CCN = 3000 and 10 000 cm−3. These trends in the cold pool response to CCN concentration are summarized in Table 2.

Fig. 12.

Total area (solid lines) and mean perturbation potential temperature (dashed lines) of the cold pool at the lowest model level (z = 170 m) at t = 100 (blue lines) and 120 min (red lines) vs CCN concentration for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings.

Fig. 12.

Total area (solid lines) and mean perturbation potential temperature (dashed lines) of the cold pool at the lowest model level (z = 170 m) at t = 100 (blue lines) and 120 min (red lines) vs CCN concentration for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings.

To determine why the cold pool response to increasing CCN concentration differs so greatly in the def and hiRH cases (little change in size) compared with the loRH and hiWS cases (rapid decrease in cold pool size between CCN = 100 and 3000 cm−3), we examine the ice mass budget (Fig. 10). In def and hiRH, the rate of melting hail from 0 to 2 km in the dirtiest run actually exceeds that of the cleanest run, which is consistent with larger hail number concentrations (Figs. 7a,b) and more hail mass (Figs. 8a,b) in this layer in the dirtiest run. The increased latent cooling in def and hiRH from melting hail offsets the reduced latent cooling due to less rain evaporation (Figs. 9a,b), resulting in little change in the size of the cold pool as CCN concentration increases (Figs. 12a,b). These trends are not evident in loRH and hiWS, which have much smaller melting rates from 0 to 2 km in the dirtiest run, approximately equal hail number concentration and mass, and cold pools that shrink in size between CCN = 100 and 3000 cm−3.

c. CCN effects on surface precipitation

Similar to that of the cold pool characteristics, the response of the domain-averaged precipitation to increases in CCN concentration (Fig. 13) is dependent on environmental conditions. For def (Fig. 13a), the domain-averaged precipitation increases by about 0.1 mm as the CCN concentration is increased from 100 to 1000 cm−3, rises much more slowly from CCN = 1000 cm−3 to its peak at 5000 cm−3, and then slowly declines by about 0.03 mm between CCN = 5000 and 10 000 cm−3. The pattern for hiRH (Fig. 13b) is similar, but the initial increase in precipitation between CCN = 100 and 1000 cm−3 is larger than in def (0.2 versus 0.1 mm), and the precipitation reaches a peak value between CCN = 2000 and 4000 cm−3 and then remains roughly constant through CCN = 10 000 cm−3, rather than slowly decreasing as in def. In contrast, increasing CCN concentration causes domain-averaged precipitation to decline nearly monotonically in loRH (Fig. 13c), with the most rapid decrease in precipitation from CCN = 100 to 3000 cm−3. In addition, while the absolute change in domain-averaged precipitation in loRH is similar to that of the other soundings (~0.1 mm at t = 120 min), the relative change is much larger in loRH (50% versus ~10%; Table 2) owing to the small total precipitation (~0.16 versus 1.3–2.0 mm). Our finding that supercell thunderstorm precipitation increases (in a domain-averaged sense) for relatively humid low-level conditions (def and hiRH) but decreases for relatively dry low-level conditions (loRH) supports the results of Khain et al. (2008), who found that high (low) relative humidity causes net total condensate gain (loss) owing to enhanced CCN concentrations across a variety of convective modes. Nevertheless, our results show that the largest changes in precipitation for each of the four soundings are achieved between CCN = 100 and 3000 cm−3 (Fig. 13), with much smaller changes at CCN concentrations larger than 3000 cm−3. This pattern mirrors that of the vertically integrated microphysical process rates shown in Fig. 11, which also change much more gradually once the CCN concentration exceeds 3000 cm−3. Similar trends were also found in Khain et al. (2011), in which precipitation in a multicell storm exhibited little sensitivity to additional increases in CCN concentration above 3000 cm−3.

Fig. 13.

Domain-averaged, accumulated surface precipitation at t = 90 (blue line), 100 (green line), 110 (yellow line), and 120 min (red line) vs CCN concentration for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings.

Fig. 13.

Domain-averaged, accumulated surface precipitation at t = 90 (blue line), 100 (green line), 110 (yellow line), and 120 min (red line) vs CCN concentration for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings.

The spatial distribution of the accumulated precipitation (Fig. 14) reveals a few important differences between the cleanest and dirtiest runs for each sounding. First, the most polluted runs of def (Fig. 14a) and hiRH (Fig. 14b) have up to 25 mm more precipitation along (and to the immediate left) of the tracks of both the left- and right-moving updrafts. This trend is also partly reflected in hiWS, although the enhancement in precipitation along the updraft tracks in the most polluted run is up to 18 mm and is less spatially uniform than in def and hiRH. Note that the precipitation reduction along the track of the right-moving hiRH storm from t = 40 to 60 min is due to secondary convective development that follows the main storm. The pattern is completely different in loRH, however, with decreases in accumulated precipitation of up to 18 mm along and to the right of the updraft tracks in the most polluted case. To evaluate whether these changes in precipitation are merely due to shifts in the updraft tracks, purple (black) contours of updraft velocity in the cleanest (dirtiest) simulations are included in Fig. 14 to indicate the approximate updraft tracks. In def, hiRH, and hiWS, the left-moving updraft moves farther to the left in the most polluted run, especially after t = 60 min in hiRH and t = 80 min in def and hiWS. This shift in the track of the left-moving updraft is therefore at least partly responsible for the precipitation enhancement along and to the left of the left-moving updraft in the dirtiest runs of def, hiRH, and hiWS and demonstrates that the CCN concentration can indirectly influence the path of the supercell thunderstorm, likely by changing the characteristics of the low-level cold pool. However, the enhanced precipitation along and to the left of the right-moving updraft cannot be attributed to a track shift, as the path of the right-moving updraft either remains the same (hiWS) or shifts slightly to the right (def and hiRH) in the most polluted runs. It is possible that the 30% (rain) and 3% (hail) increases in the mass mean diameter between the cleanest and dirtiest runs explain this shift in precipitation associated with the right-moving updraft. The larger rain and hail particles in the dirtiest run are not advected as far from the updraft because these heavier particles have faster fall speeds and are not carried aloft where strong horizontal winds are present, according to size sorting theory (Browning and Donaldson 1963; Browning 1964; Hall et al. 1984; Ryzhkov et al. 2005; Tessendorf et al. 2005; Kumjian and Ryzhkov 2012).

Fig. 14.

Difference in accumulated surface precipitation between the dirtiest (CCN = 10 000 cm−3) and cleanest (CCN = 100 cm−3) simulations at t = 120 min (color fill) for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings. The purple and black contours indicate the maximum updraft speeds that were simulated at z = 5 km for the duration of the cleanest and dirtiest simulations, respectively. These contours range from 10 to 30 m s−1 at an interval of 10 m s−1. The approximate locations of the main left- and right-moving updrafts at several times during the simulations are also indicated.

Fig. 14.

Difference in accumulated surface precipitation between the dirtiest (CCN = 10 000 cm−3) and cleanest (CCN = 100 cm−3) simulations at t = 120 min (color fill) for (a) def, (b) hiRH, (c) loRH, and (d) hiWS soundings. The purple and black contours indicate the maximum updraft speeds that were simulated at z = 5 km for the duration of the cleanest and dirtiest simulations, respectively. These contours range from 10 to 30 m s−1 at an interval of 10 m s−1. The approximate locations of the main left- and right-moving updrafts at several times during the simulations are also indicated.

d. Comparison to simulations with rain μ set to zero

The results presented above are from simulations in which μ of the raindrop size distribution [see Eq. (2)] is allowed to vary (we refer to these simulations as varying μ or VMU). However, to our knowledge, the sensitivity of CCN effects to the shape parameter for rain has not yet been explored in the literature. To determine how the choice of the shape parameter affects the results, we now compare results from VMU to simulations in which μ for rain is set to zero (zero μ or ZMU) and the default sounding (Fig. 2) is used.

Figure 15 shows the relationship between CCN concentration and the vertically integrated microphysical process rates (Fig. 15a), cold pool characteristics (Fig. 15b), domain-averaged surface precipitation (Fig. 15c), and the spatial distribution of precipitation (Fig. 15d). Table 2 summarizes the changes in these quantities for ZMU between the cleanest and dirtiest runs. The vertically integrated changes in the microphysical process rates in VMU (Fig. 11a) and ZMU (Fig. 15a) are similar (Table 2). However, while the cold pool area changes little with CCN concentration in VMU (Fig. 12a), the area decreases by 13% from the cleanest to the dirtiest run in ZMU (Fig. 15b; Table 2). There are also differences in the precipitation response to the CCN concentration. Domain-averaged precipitation does not peak until CCN = 5000 cm−3 in VMU (Fig. 13a) but peaks at CCN = 250 cm−3 and declines slowly thereafter in ZMU (Fig. 15c). The precipitation enhancement near and to the left of the updraft tracks in ZMU is also less apparent (Fig. 15d versus Fig. 14a).

Fig. 15.

As in (a) Fig. 11, (b) Fig. 12, (c) Fig. 13, and (d) Fig. 14, but for simulations with the default sounding and the shape parameter in the raindrop size distribution set to zero.

Fig. 15.

As in (a) Fig. 11, (b) Fig. 12, (c) Fig. 13, and (d) Fig. 14, but for simulations with the default sounding and the shape parameter in the raindrop size distribution set to zero.

When the liquid and ice mass budgets for VMU (Figs. 9a, 10a) and ZMU (Figs. 16a,b) are compared, it is clear that the cold pool is more sensitive to CCN concentration in ZMU because a larger reduction in the rain evaporation rate occurs in ZMU below 1 km as the CCN concentration increases (Fig. 16a). In addition, the larger hail melting rates in the z = 0–2-km layer in the dirtiest run of VMU (Fig. 10a) are not present in ZMU (Fig. 16b). Taken together, these two changes indicate that low-level latent cooling decreases more with CCN concentration in ZMU, which explains the greater reduction in cold pool size in those runs. It is likely that the differences in the evaporation and melting rates in VMU and ZMU are related to differences in the amount of raindrop size sorting in the two sets of simulations. Microphysics schemes that assume a value of μ = 0 for rain are known to produce more aggressive raindrop size sorting, with larger (smaller) values of raindrop mean mass diameter near the surface (aloft), than schemes that have μ > 0 (Milbrandt and Yau 2005). Our results confirm this trend. At t = 120 min in the CCN = 100-cm−3 run of ZMU, the raindrop mean mass diameter (not shown) is 14% larger near the surface and 48% smaller at z = 6 km than in VMU.

Fig. 16.

As in (a) Fig. 9 and (b) Fig. 10 at t = 120 min, but for simulations with the default sounding and the shape parameter in the raindrop size distribution set to zero.

Fig. 16.

As in (a) Fig. 9 and (b) Fig. 10 at t = 120 min, but for simulations with the default sounding and the shape parameter in the raindrop size distribution set to zero.

4. Summary and conclusions

In this study, the Weather Research and Forecasting (WRF) Model was run at a horizontal grid spacing of 1 km to investigate how cloud condensation nuclei (CCN) concentration affects the microphysical process rates, particle diameters, cold pool size and strength, and accumulated precipitation in idealized supercell thunderstorms. The Morrison microphysics scheme was modified to include explicit prediction of cloud droplet concentration and a variable shape parameter for the raindrop size distribution. The results were examined across 15 different CCN concentrations between 100 and 10 000 cm−3 and for four environmental soundings with different values of low-level relative humidity and vertical wind shear. While a few other studies (Seifert and Beheng 2006; Fan et al. 2009; Storer et al. 2010; Lebo and Seinfeld 2011; Lerach and Cotton 2012) have tested the sensitivity of thunderstorms in sheared environments to CCN and relative humidity effects, our study has shown how individual microphysical process rates vary across a spectrum of CCN concentrations, up to a larger value (CCN = 10 000 cm−3) than these previous studies. In addition, we have compared results from simulations with a fixed versus a variable shape parameter for the raindrop size distribution, which can qualitatively change some results.

Relative to the cleanest simulation (CCN = 100 cm−3), the dirtiest simulation (CCN = 10 000 cm−3) of each sounding features larger cloud droplet number concentrations (~4000 versus ~100 cm−3; Fig. 7) and smaller concentrations of rain (e.g., 10−4 versus 10−2 cm−3 at z = 5 km) and hail (e.g., 10−3 versus 10−2 cm−3 at z = 10 km) from 2 to 10 km. Fewer cloud droplets undergo autoconversion in the dirtiest simulation because the cloud droplets are smaller (5 versus 17 μm; Fig. 6) owing to increased competition for liquid water. However, the rain and hail particles in the dirtiest runs are up to 30% and 3% larger near the surface (Fig. 6; Table 2), respectively, than in the cleanest runs as they collect the enhanced bulk cloud droplet mass. Because of the reduced concentration and larger mean size of rain and hail particles in the most polluted runs, the mass budget analyses (Figs. 9 and 10) and Table 2 reveal reduced rates of evaporation for all soundings, reduced rates of melting at all heights in loRH and hiWS and reduced rates of melting between 2.25 and 4.25 km in def and hiRH with increased melting below 2.25 km due to larger hail number concentrations in that layer (Figs. 7a,b). Collection of cloud droplets by rain and riming of hail with rain were reduced in the dirtiest run of each sounding (Table 2) owing to smaller rain and hail number concentrations. When all of the different CCN concentrations were examined (100–10 000 cm−3), it was found that the perturbation in these microphysical-process rates, particularly with respect to evaporation, melting, and riming of hail with rain, saturates by CCN ≈ 3000 cm−3 (Fig. 11), independent of the initial sounding. This result indicates that extreme CCN concentrations of 5000–10 000 cm−3 are not necessary to alter substantially the microphysical processes of supercell thunderstorms.

The response of the area and temperature of the low-level cold pool to CCN concentration is found to be nonmonotonic and greatly dependent on the environmental conditions (Fig. 12). For both def and hiRH, the cold pool size shows almost no dependence on CCN concentration due to compensating changes in latent cooling from melting hail versus evaporating rain. Changes in the mean cold pool temperature are also small, with the mean perturbation potential temperature initially increasing by about 0.5 K, reaching a peak at CCN ≈ 3000 cm−3, and then decreasing by about 0.15 K at larger CCN concentrations. The cold pool response to CCN concentration is limited in def and hiRH because, while low-level evaporative cooling decreases with CCN concentration in these simulations, low-level latent cooling from melting hail actually increases by up to almost 50% (Fig. 10) because of larger near-surface hail mixing ratios (Fig. 8) in the dirtiest runs. In contrast, cold pool size decreases dramatically in loRH from 1200 (for CCN = 100 cm−3) to 200 km2 (for CCN = 10 000 cm−3), while the mean temperature increases by 0.45 K. The trends in the cold pool characteristics of hiWS resemble those of loRH. However, the cold pool size (temperature) does not decrease (increase) after CCN = 3000 cm−3 in hiWS, whereas the rates of areal decrease and warming merely slow in loRH after CCN = 3000 cm−3.

When the relationship between domain-averaged precipitation and CCN concentration was examined (Fig. 13; Table 2), it was found that larger CCN concentrations produced more precipitation in def, hiRH, and hiWS, up to a peak accumulation at CCN = 5000 cm−3 in def, CCN = 4000 cm−3 in hiRH, and CCN = 500 cm−3 in hiWS. At larger CCN concentrations, accumulations either remained the same (hiRH) or decreased slightly (def and hiWS). In contrast, domain-averaged precipitation in loRH decreased almost monotonically from 0.16 (CCN = 100 cm−3) to 0.08 mm (CCN = 10 000 cm−3). Maps of the precipitation difference between the cleanest and dirtiest simulations for each sounding (Fig. 14) demonstrate that much of the precipitation enhancement at large CCN concentrations in def, hiRH, and hiWS occurs near and to the left of the tracks of the left- and right-moving updrafts.

Finally, results from simulations with the default sounding and a variable shape parameter μ of the raindrop size distribution were compared to simulations in which μ was set to zero. The size of the cold pool decreased by about 13% as the CCN concentration increased in the μ = 0 runs (Fig. 15b; Table 2), while little change occurred when μ was allowed to vary (Fig. 12a). Domain-averaged precipitation also slowly decreased with CCN concentration when μ = 0 (Fig. 15c), despite having increased between CCN = 100 and 5000 cm−3 in the variable-μ runs (Fig. 13a). The differences between the two sets of simulations were caused by a larger reduction in low-level latent cooling as CCN concentration increased in the μ = 0 simulations (Fig. 16).

The results herein highlight the complex interactions between microphysical processes, precipitation, and thermodynamics in supercell thunderstorms and the sensitivity that these processes display toward pollutant concentration. While changes in the individual microphysical process rates may be fairly large and monotonic, the impacts on the cold pool characteristics and the accumulated precipitation are generally smaller (in a relative sense) and nonmonotonic owing to compensating changes in the microphysical processes. Low-precipitation supercell thunderstorms, however, may be an exception to this statement. Here, an 84% reduction in the cold pool area and a 50% decrease in the domain-averaged precipitation occurred in polluted conditions with dry low-level relative humidity (Table 2). This result indicates that the response of supercell thunderstorms to CCN concentration is highly dependent on the environmental conditions, even in an idealized modeling framework in which the secondary feedback between the initial conditions and physical processes such as radiative transfer and surface fluxes are neglected. Since differences in the relative humidity and vertical wind shear can change the cold pool and precipitation responses to CCN concentration, future studies that examine observational evidence to validate the trends seen in numerical models will likely need to stratify results by environmental conditions. Although unexplored in this paper, it also should be noted that aerosols might further perturb convective processes in their role as ice nuclei—a topic that requires additional research.

Acknowledgments

We thank Dr. David Dowell (NOAA) for his helpful feedback. We also thank Dr. Amy Solomon (NOAA) for modifying the public release of the Morrison microphysics scheme in version 3.1 of the WRF Model to include explicit prediction of cloud droplet concentration. Feedback from three anonymous reviewers substantially improved this manuscript. This material is based upon work supported by the National Science Foundation, in part by a Graduate Research Fellowship (DGE-1144083) and in part by NSF ATM 0910424. Hugh Morrison was partially supported by U.S. DOE ASR DE-SC0008648. We also acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation. Figures were made with NCAR Command Language (NCL), version 6.0.0 (Brown et al. 2012). Any opinions, findings, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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