• Andreae, M. O., 2009: Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions. Atmos. Chem. Phys., 9, 543556, doi:10.5194/acp-9-543-2009.

    • Search Google Scholar
    • Export Citation
  • Andreae, M. O., D. Rosenfeld, P. Artaxo, A. A. Costa, G. P. Frank, K. M. Longo, and M. A. F. Silva-Dias, 2004: Smoking rain clouds over the Amazon. Science, 303, 13371342, doi:10.1126/science.1092779.

    • Search Google Scholar
    • Export Citation
  • Beard, K. V., 1976: Terminal velocity and shape of cloud and precipitation drops aloft. J. Atmos. Sci., 33, 851864, doi:10.1175/1520-0469(1976)033<0851:TVASOC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Berg, W., T. L’Ecuyer, and S. van den Heever, 2008: Evidence for the impact of aerosols on the onset and microphysical properties of rainfall from a combination of satellite observations and cloud-resolving model simulations. J. Geophys. Res., 113, D14S23, doi:10.1029/2007JD009649.

    • Search Google Scholar
    • Export Citation
  • Bluestein, H. B., E. W. McCaul Jr., G. P. Byrd, and G. R. Woodall, 1988: Mobile sounding observations of a tornadic storm near the dry line: The Canadian, Texas, storm of 7 May 1986. Mon. Wea. Rev., 116, 17901804, doi:10.1175/1520-0493(1988)116<1790:MSOOAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and H. Morrison, 2012: Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics. Mon. Wea. Rev., 140, 202225, doi:10.1175/MWR-D-11-00046.1.

    • Search Google Scholar
    • Export Citation
  • Bunkers, M. J., M. R. Hjelmefelt, and P. L. Smith, 2006: An observational examination of long-lived supercells. Part I: Characteristics, evolution, and demise. Wea. Forecasting, 21, 673688, doi:10.1175/WAF949.1.

    • Search Google Scholar
    • Export Citation
  • Ekman, A. M. L., A. Engstrom, and A. Soderberg, 2011: Impact of two-way aerosol–cloud interaction and changes in aerosol size distribution on simulated aerosol-induced deep convective cloud sensitivity. J. Atmos. Sci., 68, 685697, doi:10.1175/2010JAS3651.1.

    • Search Google Scholar
    • Export Citation
  • Fan, J., and Coauthors, 2009: Dominant role by vertical wind shear in regulating aerosol effects on deep convective clouds. J. Geophys. Res., 114, D22206, doi:10.1029/2009JD012352.

    • Search Google Scholar
    • Export Citation
  • Foote, G. B., and P. S. Du Toit, 1969: Terminal velocity of raindrops aloft. J. Appl. Meteor., 8, 249253, doi:10.1175/1520-0450(1969)008<0249:TVORA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fridlind, A. M., and Coauthors, 2004: Evidence for the predominance of mid-tropospheric aerosols as subtropical anvil cloud nuclei. Science, 304, 718722, doi:10.1126/science.1094947.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., 2006: Indirect impact of atmospheric aerosol in idealized simulations of convective–radiative quasi equilibrium. J. Climate, 19, 46644682, doi:10.1175/JCLI3857.1.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., M. Sato, and R. Ruedy, 1997: Radiative forcing and climate response. J. Geophys. Res., 102, 68316864, doi:10.1029/96JD03436.

    • Search Google Scholar
    • Export Citation
  • Heiblum, R. H., I. Koren, and O. Altaratz, 2012: New evidence of cloud invigoration from TRMM measurements of rain center of gravity. Geophys. Res. Lett., 39, L08803, doi:10.1029/2012GL051158.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., M. I. Biggerstaf, S. A. Rutledge, and B. F. Smull, 1989: Interpretation of Doppler weather radar displays of midlatitude mesoscale convective systems. Bull. Amer. Meteor. Soc., 70, 608–619, doi:10.1175/1520-0477(1989)070<0608:IODWRD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jorgensen, D. P., and M. A. LeMone, 1989: Vertical velocity characteristics of oceanic convection. J. Atmos. Sci., 46, 621640, doi:10.1175/1520-0469(1989)046<0621:VVCOOC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Khain, A., and B. Lynn, 2009: Simulation of a supercell storm in clean and dirty atmosphere using weather research and forecasting model with spectral bin microphysics. J. Geophys. Res., 114, D19209, doi:10.1029/2009JD011827.

    • Search Google Scholar
    • Export Citation
  • Khain, A., A. Pokrovsky, M. Pinsky, A. Seifert, and V. Phillips, 2004: Simulation of effects of atmospheric aerosols on deep turbulent convective clouds using a spectral microphysics mixed-phase cumulus cloud model. Part I: Model description and possible applications. J. Atmos. Sci., 61, 29632982, doi:10.1175/JAS-3350.1.

    • Search Google Scholar
    • Export Citation
  • Khain, A., D. Rosenfeld, and A. Pokrovsky, 2005: Aerosol impact on the dynamics and microphysics of deep convective clouds. Quart. J. Roy. Meteor. Soc., 131, 26392663, doi:10.1256/qj.04.62.

    • Search Google Scholar
    • Export Citation
  • Khain, A., N. BenMoshe, and A. Pokrovsky, 2008: Factors determining the impact of aerosols on surface precipitation from clouds: An attempt at classification. J. Atmos. Sci., 65, 17211748, doi:10.1175/2007JAS2515.1.

    • Search Google Scholar
    • Export Citation
  • Koren, I., J. V. Martins, L. A. Remer, and H. Afargan, 2008: Smoke invigoration versus inhibition of clouds over the Amazon. Science, 321, 946949, doi:10.1126/science.1159185.

    • Search Google Scholar
    • Export Citation
  • Koren, I., G. Feingold, and L. A. Remer, 2010a: The invigoration of deep convective clouds of the Atlantic: Aerosol effect, meteorology, or retrieval artifact? Atmos. Chem. Phys., 10, 88558872, doi:10.5194/acp-10-8855-2010.

    • Search Google Scholar
    • Export Citation
  • Koren, I., L. A. Remer, O. Altaratz, J. V. Martins, and A. Davidi, 2010b: Aerosol-induced changes of convective cloud anvils produce strong climate warming. Atmos. Chem. Phys., 10, 50015010, doi:10.5194/acp-10-5001-2010.

    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., and J. H. Seinfeld, 2011: Theoretical basis for convective invigoration due to increased aerosol concentration. Atmos. Chem. Phys., 11, 54075429, doi:10.5194/acp-11-5407-2011.

    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., and H. Morrison, 2014: Dynamical effects of aerosol perturbations on simulated idealized squall lines. Mon. Wea. Rev., 142,9911009, doi:10.1175/MWR-D-13-00156.1.

    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., H. Morrison, and J. H. Seinfeld, 2012: Are simulated aerosol-induced effects on deep convective clouds strongly dependent on saturation adjustment? Atmos. Chem. Phys., 12, 99419964, doi:10.5194/acp-12-9941-2012.

    • Search Google Scholar
    • Export Citation
  • Lee, S. S., 2011: Dependence of aerosol-precipitation interactions on humidity in a multiple-cloud system. Atmos. Chem. Phys., 11, 21792196, doi:10.5194/acp-11-2179-2011.

    • Search Google Scholar
    • Export Citation
  • Lee, S. S., L. J. Donner, V. T. J. Phillips, and Y. Ming, 2008a: The dependence of aerosol effects on clouds and precipitation on cloud-system organization, shear and stability. J. Geophys. Res., 113, D16202, doi:10.1029/2007JD009224.

    • Search Google Scholar
    • Export Citation
  • Lee, S. S., L. J. Donner, V. T. J. Phillips, and Y. Ming, 2008b: Examination of aerosol effects on precipitation in deep convective clouds during the 1997 ARM summer experiment. Quart. J. Roy. Meteor. Soc., 134, 12011220, doi:10.1002/qj.287.

    • Search Google Scholar
    • Export Citation
  • Markowski, P., and Y. Richardson, 2010: Mesoscale Meteorology in Midlatitudes. Wiley-Blackwell, 407 pp.

  • May, P. T., V. N. Bringi, and M. Thurai, 2011: Do we observe aerosol impacts on DSDs in strongly forced tropical thunderstorms? J. Atmos. Sci., 68, 19021910, doi:10.1175/2011JAS3617.1.

    • Search Google Scholar
    • Export Citation
  • Mitra, S. K., J. Brinkmann, and H. T. Pruppacher, 1992: A wind tunnel study on the drop-to-particle conversion. J. Aerosol Sci., 23, 245256, doi:10.1016/0021-8502(92)90326-Q.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., 2012: On the robustness of aerosol effects on an idealized supercell storm simulated with a cloud system-resolving model. Atmos. Chem. Phys. Discuss., 12, 10 493–10 533, doi:10.5194/acpd-12-10493-2012.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 9911007, doi:10.1175/2008MWR2556.1.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., S. A. Tessendorf, J. Ikeda, and G. Thompson, 2012: Sensitivity of a simulated midlatitude squall line to parameterization of raindrop breakup. Mon. Wea. Rev., 140, 24372460, doi:10.1175/MWR-D-11-00283.1.

    • Search Google Scholar
    • Export Citation
  • Muhlbauer, A., and Coauthors, 2013: Reexamination of the state of the art of cloud modeling shows real improvements. Bull. Amer. Meteor. Soc., 94, ES45–ES48, doi:10.1175/BAMS-D-12-00188.1.

    • Search Google Scholar
    • Export Citation
  • Noppel, H., U. Blahak, A. Seifert, and K. D. Beheng, 2010: Simulations of a hailstorm and the impact of CCN using an advanced two-moment cloud microphysics scheme. Atmos. Res., 96, 286301, doi:10.1016/j.atmosres.2009.09.008.

    • Search Google Scholar
    • Export Citation
  • Parker, M. D., and R. H. Johnson, 2000: Organizational modes of midlatitude mesoscale convective systems. Mon. Wea. Rev., 128, 34133436, doi:10.1175/1520-0493(2001)129<3413:OMOMMC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Pruppacher, H. R., and J. D. Klett, 1997: Microphysics of Clouds and Precipitation. Kluwer Academic Publishers, 954 pp.

  • Romps, D. M., and Z. Kuang, 2010: Do undiluted convective plumes exist in the upper tropical troposphere? J. Atmos. Sci., 67, 468484, doi:10.1175/2009JAS3184.1.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., and W. L. Woodley, 2000: Deep convective clouds with sustained supercooled liquid water down to −37.5°C. Nature, 405, 440442, doi:10.1038/35013030.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., U. Lohmann, G. B. Raga, C. D. O’Dowd, M. Kulmala, S. Fuzzi, A. Reissell, and M. O. Andreae, 2008: Flood or drought: How do aerosols affect precipitation? Science, 321, 13091313, doi:10.1126/science.1160606.

    • Search Google Scholar
    • Export Citation
  • Rotunno, R., J. B. Klemp, and M. L. Weisman, 1988: A theory for strong, long-lived squall lines. J. Atmos. Sci., 45, 463485, doi:10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Seifert, A., C. Köhler, and K. D. Beheng, 2012: Aerosol-cloud-precipitation effects over Germany as simulated by a convective-scale numerical weather prediction model. Atmos. Chem. Phys., 12, 709725, doi:10.5194/acp-12-709-2012.

    • Search Google Scholar
    • Export Citation
  • Seigel, R. B., and S. C. van den Heever, 2013: Squall-line intensification via hydrometeor recirculation. J. Atmos. Sci., 70, 20122031, doi:10.1175/JAS-D-12-0266.1.

    • Search Google Scholar
    • Export Citation
  • Seigel, R. B., S. C. van den Heever, and S. M. Saleeby, 2013: Mineral dust indirect effects and cloud radiative feedbacks of a simulated idealized nocturnal squall line. Atmos. Chem. Phys., 13, 44674485, doi:10.5194/acp-13-4467-2013.

    • Search Google Scholar
    • Export Citation
  • Simpson, J., 1971: On cumulus entrainment and one-dimensional models. J. Atmos. Sci., 28, 449455, doi:10.1175/1520-0469(1971)028<0449:OCEAOD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3_bw.pdf.]

  • Storer, R. L., and S. C. van den Heever, 2013: Microphysical processes evident in aerosol forcing of tropical deep convection. J. Atmos. Sci., 70, 430446, doi:10.1175/JAS-D-12-076.1.

    • Search Google Scholar
    • Export Citation
  • Storer, R. L., S. C. van den Heever, and G. L. Stephens, 2010: Modeling aerosol impacts on convective storms in different environments. J. Atmos. Sci., 67, 39043915, doi:10.1175/2010JAS3363.1.

    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., X. Li, A. Khain, T. Matsui, S. Lang, and J. Simpson, 2007: Role of atmospheric aerosol concentration on deep convective precipitation: Cloud-resolving model simulations. J. Geophys. Res.,112, D24S18, doi:10.1029/2007JD008728.

  • Teller, A., and Z. Levin, 2006: The effects of aerosols on precipitation and dimensions of subtropical clouds: A sensitivity study using a numerical cloud model. Atmos. Chem. Phys., 6, 6780, doi:10.5194/acp-6-67-2006.

    • Search Google Scholar
    • Export Citation
  • Tompkins, A. M., 2001: Organization of tropical convection in low vertical wind shears: The role of water vapor. J. Atmos. Sci., 58, 529545, doi:10.1175/1520-0469(2001)058<0529:OOTCIL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., 2013: Mesoscale-Convective Processes in the Atmosphere. Cambridge University Press, 377 pp.

  • Van den Heever, S. C., and W. R. Cotton, 2007: Urban aerosol impacts on downwind convective storms. J. Appl. Meteor. Climatol., 46, 828850, doi:10.1175/JAM2492.1.

    • Search Google Scholar
    • Export Citation
  • Van den Heever, S. C., G. G. Carri, W. R. Cotton, P. J. DeMott, and A. J. Prenni, 2006: Impacts of nucleating aerosol on Florida storms. Part I: Mesoscale simulations. J. Atmos. Sci., 63, 17521775, doi:10.1175/JAS3713.1.

    • Search Google Scholar
    • Export Citation
  • Wang, C., 2005: A modeling study of the response of tropical deep convection to the increase of cloud condensation nuclei concentration: 1. Dynamics and microphysics. J. Geophys. Res., 110, D21211, doi:10.1029/2004JD005720.

    • Search Google Scholar
    • Export Citation
  • Xu, K., and D. A. Randall, 2001: Updraft and downdraft statistics of simulated tropical and midlatitude cumulus convection. J. Atmos. Sci., 58, 16301649, doi:10.1175/1520-0469(2001)058<1630:UADSOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Yuan, T., L. A. Remer, K. E. Pickering, and H. Yu, 2011: Observational evidence of aerosol enhancement of lightning activity and convective invigoration. Geophys. Res. Lett., 38, L04701, doi:10.1029/2010GL046052.

    • Search Google Scholar
    • Export Citation
  • Yuter, S. E., M. A. Miller, M. D. Parker, P. M. Markowski, Y. Richardson, H. Brooks, and J. M. Straka, 2013: Comment on “Why do tornados and hailstorms rest on weekends?” by D. Rosenfeld and T. Bell. J. Geophys. Res. Atmos., 118, 7332–7338, doi:10.1002/jgrd.50526.

    • Search Google Scholar
    • Export Citation
  • Ziegler, C. L., E. R. Mansell, J. M. Straka, D. R. MacGorman, and D. W. Burgess, 2010: The impact of spatial variations of low-level stability on the life cycle of a simulated supercell storm. Mon. Wea. Rev., 138, 17381766, doi:10.1175/2009MWR3010.1.

    • Search Google Scholar
    • Export Citation
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    Skew T–logp diagram of the sounding used to initialize the model. Temperature (black) and dewpoint temperature (blue) are shown. The parcel following a moist adiabat lifted from the lifting condensation level (LCL) is also displayed (red). In this case, Δu = 12 m s−1.

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    The (a) magnitude of cu and (b) change in cu [Δ(cu)] relative to the clean scenario as a function of time. The scenarios depicted include clean (black), polluted low (blue), polluted mid (red), and polluted (green).

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    Vertical profiles of (a) the mean convective mass flux and (b) the relative change in the mean convective mass flux (assuming a threshold vertical velocity of 2 m s−1). The clean (black), polluted low (blue), polluted mid (red), and polluted (green) scenarios are portrayed.

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    Vertical profiles of horizontally averaged domain-wide (a)–(c) hydrometeor mixing ratios qk (where k corresponds to the hydrometeor type—“c” for cloud, “i” for ice, and “g” for graupel) and (d)–(f) relative change in spherical-equivalent mean-volume radius Δrk. Profiles are shown for (a),(d) cloud; (b),(e) cloud ice; and (c),(f) graupel. For qk, profiles for all simulations [i.e., clean (black), polluted low (blue), polluted mid (red), and polluted (green)] are shown. For Δrk, because the relative change is portrayed, only results from the sensitivity simulations are shown. The green and blue lines are dashed only to better depict very small differences that would otherwise be masked by two lines falling directly on top of each other. A threshold of 1 × 10−10 kg kg−1 was used to conditionally average the mass mixing ratios. The results are insensitive to the chosen threshold.

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    Relative changes in the horizontally averaged (a) rainwater mixing ratio, (b) number mixing ratio, and (c) mean drop size behind the leading edge of the cold pool. All changes are shown relative to the clean case. The polluted low (blue), polluted mid (red), and polluted (green) scenarios are shown. The profiles are temporally averaged over the final 4 h of the simulations.

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    Horizontally averaged (a) latent cooling rate, (b) buoyancy, and (c) relative change in buoyancy behind the leading edge of the cold pool. The scenarios depicted include the clean (black), polluted low (blue), polluted mid (red), and polluted (green) cases. The relative changes in (c) are with respect to the clean case. The profiles are temporally averaged over the final 4 h of the simulations.

  • View in gallery

    Vertical profiles of temporally and horizontally averaged latent heating rates. The heating rates are separated into warming (red), cooling (blue), and net (black). The clean scenario is used as the reference state (solid) in all panels. The dashed curves correspond to the (a) polluted low, (b) polluted mid, and (c) polluted aerosol scenarios. The averaging is performed over the last 4 h of the simulations.

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    Domain-averaged (a) cumulative precipitation and (b) change in precipitation relative to the clean (black) scenario for the polluted low (blue), polluted mid (red), and polluted (green) cases.

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    Line-averaged low-level (below approximately 3 km) to midlevel (between approximately 3 and 10 km) tracer fraction (unitless, color contours) as a function of height and line-normal distance at 8 h. Cool (warm) colors represent regions dominated by the midlevel (low level) tracer. White areas correspond to regions in which the concentration of at least one of the tracers is less than 1 × 10−10 kg kg−1. Note that as one of the tracer mixing ratios approaches the chosen threshold, the ratio approaches either zero or infinity; these very small and very large values are due to the absence of one of the tracers at a particular location.

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    Scatterplot of (a) the low-level tracer mixing ratio and (b) the midlevel tracer mixing ratio with respect to w between 4 and 9 km (approximately the extent of the mixed-phase region). Every one hundredth point is displayed for w ≥ 1 m s−1. The largest values tend to occur in the strongest updrafts, while the largest values tend to occur in the weakest updrafts.

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    As in Fig. 9, but for Δx = Δy = 500 m instead of 1 km.

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The Sensitivity of a Numerically Simulated Idealized Squall Line to the Vertical Distribution of Aerosols

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  • 1 Cooperative Institute for Research in Environmental Sciences, University of Colorado, and Chemical Sciences Division, NOAA/Earth System Research Laboratory, Boulder, Colorado
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Abstract

Changes in the aerosol number concentration are reflected by changes in raindrop size and number concentration that ultimately affect the strength of cold pools via evaporation. Therefore, aerosol perturbations can potentially alter the balance between cold pool–induced and low-level wind shear–induced circulations. In the present work, simulations with increased aerosol loadings below approximately 3 km, between approximately 3 and 10 km, and at all vertical levels are performed to specifically address both the overall sensitivity of a squall line to the vertical distribution of aerosols and the extent to which low-level aerosols can affect the convective strength of the system. The results suggest that low-level aerosol perturbations have a negligible effect on the overall storm strength even though they act to enhance low-level latent heating rates. A tracer analysis shows that the low-level aerosols are either predominantly detrained at or below the freezing level or are rapidly lifted to the top of the troposphere or the lower stratosphere within the strongest convective cores. Moreover, it is shown that midlevel aerosol perturbations have nearly the same effect as perturbing the entire domain, increasing the convective updraft mass flux by more than 10%. These changes in strength are driven by a complex chain of events caused by smaller supercooled droplets, larger graupel, and larger raindrops. Combined, these changes tend to reduce the low-level bulk evaporation rate, thus weakening the cold pool and enhancing updraft strength. The results presented herein suggest that midlevel aerosol perturbations may exhibit a much larger effect on squall lines, at least in the context of this idealized framework.

Corresponding author address: Zachary J. Lebo, NOAA/Earth System Research Laboratory, 325 Broadway R/CSD-2, Boulder, CO 80305. E-mail: zach.lebo@noaa.gov

Abstract

Changes in the aerosol number concentration are reflected by changes in raindrop size and number concentration that ultimately affect the strength of cold pools via evaporation. Therefore, aerosol perturbations can potentially alter the balance between cold pool–induced and low-level wind shear–induced circulations. In the present work, simulations with increased aerosol loadings below approximately 3 km, between approximately 3 and 10 km, and at all vertical levels are performed to specifically address both the overall sensitivity of a squall line to the vertical distribution of aerosols and the extent to which low-level aerosols can affect the convective strength of the system. The results suggest that low-level aerosol perturbations have a negligible effect on the overall storm strength even though they act to enhance low-level latent heating rates. A tracer analysis shows that the low-level aerosols are either predominantly detrained at or below the freezing level or are rapidly lifted to the top of the troposphere or the lower stratosphere within the strongest convective cores. Moreover, it is shown that midlevel aerosol perturbations have nearly the same effect as perturbing the entire domain, increasing the convective updraft mass flux by more than 10%. These changes in strength are driven by a complex chain of events caused by smaller supercooled droplets, larger graupel, and larger raindrops. Combined, these changes tend to reduce the low-level bulk evaporation rate, thus weakening the cold pool and enhancing updraft strength. The results presented herein suggest that midlevel aerosol perturbations may exhibit a much larger effect on squall lines, at least in the context of this idealized framework.

Corresponding author address: Zachary J. Lebo, NOAA/Earth System Research Laboratory, 325 Broadway R/CSD-2, Boulder, CO 80305. E-mail: zach.lebo@noaa.gov

1. Introduction

The sensitivity of deep convective clouds to anthropogenic aerosol perturbations has received considerable attention over the last decade (e.g., Khain et al. 2004; Wang 2005; Grabowski 2006; Teller and Levin 2006; Van den Heever et al. 2006; Tao et al. 2007; Lee et al. 2008a; Rosenfeld et al. 2008; Fan et al. 2009; Khain and Lynn 2009; Koren et al. 2010b; Noppel et al. 2010; Ekman et al. 2011; Seifert et al. 2012; Morrison 2012; Storer and van den Heever 2013; Seigel et al. 2013; Lebo and Morrison 2014). The fundamental connection between hygroscopic aerosols and cloud droplets provides the foundation for changes in ambient aerosol number concentrations to alter bulk cloud properties and potentially have substantial effects on cloud dynamics. Owing to the existence of a large mixed-phase region in which various hydrometeor species interact simultaneously, deep convective clouds are inherently complex. Therefore, understanding the potential indirect effects of aerosols on the structure and strength of these systems is challenging. However, recent advances in computational efficiency and numerical algorithms that explicitly treat the aerosol–cloud particle system have produced new insights into potential pathways for an increase in aerosol loading to alter deep convective cloud updraft strength and structure.

Recent investigations into the sensitivity of deep convective clouds to increased aerosol loading have provided mixed results. Several modeling (e.g., Tao et al. 2007; Fan et al. 2009; Lebo and Seinfeld 2011; Lebo et al. 2012) and observational (e.g., Andreae et al. 2004; Koren et al. 2008, 2010a; Yuan et al. 2011; Heiblum et al. 2012) studies have reported that polluted conditions tend to be associated with stronger deep convection and/or increased precipitation, while other modeling (e.g., Khain and Lynn 2009; Morrison 2012; Seigel et al. 2013) and observational (e.g., Rosenfeld and Woodley 2000) studies have found little sensitivity, or even a reduction in convective strength and/or precipitation. However, most previous studies have used a variety of metrics (e.g., mean convective mass flux, domain-averaged cumulative precipitation, and maximum updraft velocity) to determine the sensitivity of deep convective clouds to aerosol perturbations; therefore, it is often challenging to directly compare the findings of these studies. Convective invigoration (or the increase in updraft strength due to an increase in aerosol loading) has often been explained via increased latent heating rates in polluted conditions. Latent heating rates are thought to increase as a result of suppressed collision–coalescence below the freezing level in polluted conditions that leads to additional liquid water being lofted above the freezing level, which enhances freezing and ice microphysical processes (e.g., Rosenfeld et al. 2008). However, more recent work has suggested that the latent heating effects may be minor in comparison to the effects on the cold pool intensity and depth, especially with regard to squall lines (e.g., Khain et al. 2005; Van den Heever and Cotton 2007; Tao et al. 2007; Lee et al. 2008a,b; Storer et al. 2010; Seigel et al. 2013; Lebo and Morrison 2014). Several observational (e.g., Berg et al. 2008; May et al. 2011) and modeling (e.g., Storer et al. 2010; Storer and van den Heever 2013; Lebo and Morrison 2014) studies have demonstrated that increased aerosol loading leads to larger raindrops via an increase in supercooled liquid water and a subsequent increase in riming; these larger rimed particles ultimately melt and produce larger raindrops. The larger drops tend to reduce bulk low-level evaporative cooling, which produces a less negatively buoyant cold pool and ultimately a weaker cold pool. In a weak wind shear environment (where the ratio of the cold pool intensity c to the change in the line-normal wind over the depth of the low-level shear layer Δu is greater than 1), the weakened cold pool allows the system to become more optimal [i.e., more upright with stronger updrafts in the context of Rotunno–Klemp–Weisman (RKW) theory; Rotunno et al. (1988)]. The combined effect of a weakened cold pool and enhanced convective updrafts has also been shown to lead to a substantial increase in precipitation in numerically simulated idealized squall lines (e.g., Lebo and Morrison 2014).

Furthermore, several studies have demonstrated that aerosol-induced effects may be a function of the environmental conditions. For example, both Fan et al. (2009) and Lebo and Morrison (2014) showed that the strength of the low-level environmental shear is crucial in determining the sign and magnitude of aerosol-induced changes in convective updraft strength. Furthermore, Khain et al. (2008) demonstrated that the sign of the precipitation response to an aerosol perturbation is largely dependent on the ambient relative humidity, while Storer et al. (2010) suggested that the aerosol effect may be dependent on the convective available potential energy (CAPE). However, it is important to frame the aerosol-induced responses in the context of changes in the environmental conditions; even small changes in the environment are likely to overwhelm any changes in convection that are attributable to changes in the aerosol number concentration.

An important assumption in many previous studies (e.g., Fan et al. 2009; Khain and Lynn 2009; Lebo et al. 2012) is that the increase in aerosol loading is distributed throughout the model domain (both horizontally and vertically). However, only a few studies have examined the effects of low-level (e.g., Van den Heever et al. 2006; Lee 2011) and mid- to upper-level (e.g., Fridlind et al. 2004) aerosol perturbations on numerically simulated deep convective clouds. The vertical homogeneity of the aerosol perturbation is an important assumption given that the basis of many previous results regarding latent heating effects are founded in the idea that a low-level aerosol source/perturbation is ingested into the cloud at or just above the cloud base, which suppresses collision–coalescence and permits the now smaller droplets to be more readily lofted above the freezing level. One may then presume that perturbing only the aerosol number concentration below the freezing level should have the same effect as perturbing the entire vertical domain (at least qualitatively). A potential shortcoming of this theoretical framework lies in the fact that convective cores, especially within squall lines, can be quite strong with updraft velocities often exceeding 10 m s−1 (e.g., Jorgensen and LeMone 1989; Xu and Randall 2001), which suggests that only the largest raindrops with terminal fall speeds up to 10 m s−1 (e.g., Foote and Du Toit 1969; Beard 1976) are capable of falling relative to the rapidly rising air within the convective cores. Qualitatively, small changes in the raindrop sizes within a core will likely have a small effect on the net fall speed of the particles given the strength of the updraft cores and vertical transport of hydrometeors in the convective cores; therefore, only small effects on the system-wide latent heating rates above the freezing level may be expected because of increased low-level aerosol loadings. This point has also been made by Yuter et al. (2013).

In this study, the sensitivity of aerosol effects on deep convective clouds (in the context of a numerically simulated squall line) to the vertical distribution of aerosols is systematically analyzed. Two important and fundamental questions are addressed in this study. 1) Do low-level aerosol perturbations lead to increased supercooled liquid water aloft, enhanced latent heating, and a subsequent “invigoration” effect within an idealized squall line in a weak shear environment? 2) How sensitive are deep convective clouds to the vertical distribution of aerosols? The results are presented in the context of both enhanced latent heating and RKW theory for an idealized squall line. Before presenting the results, a brief outline of the numerical model is provided in section 2. The results are discussed in section 3, and the primary conclusions of this work and a discussion on the potential implications of this work are described in section 4.

2. Model description and analysis procedure

a. Numerical model

The two-moment bulk microphysics model of Morrison et al. (2009), which is coupled to the Weather Research and Forecasting (WRF) Model, version 3.3.1 (Skamarock et al. 2008), and includes an explicit representation of aerosol effects on clouds following Lebo et al. (2012), is used in this study. The dynamical framework is analogous to that used in Lebo and Morrison (2014). The domain is 124 km × 714 km in the line-parallel and line-normal directions, respectively, with a horizontal grid spacing of 1 km. The vertical dimension comprises 80 levels with a grid spacing of approximately 250 m. A time step of 2.5 s is used, and the simulations are performed for 8 h (the first 4 h correspond to a spinup period, whereas the latter 4 h are analyzed in section 3). The model boundaries are open in the line-normal direction and periodic in the line-parallel direction. Advection of scalars is calculated using fifth- and third-order monotonic advection schemes in the horizontal and vertical, respectively.

Convection is triggered in the model following Ziegler et al. (2010) and Lebo and Morrison (2014). The vertical velocity (w) field is directly forced over the first hour of the simulations. The forcing is applied within a half cylinder with radii of 10 km in the x direction and 2.5 km in the z direction. The maximum acceleration is assumed to be 0.5 m s−2 and is located at the center of the half cylinder. The w forcing is uniform in the line-parallel dimension. Moreover, the w forcing decays radially from the center according to a cosine function of the radius. Random thermal perturbations (amplitude of 0.1 K) are applied to the initial sounding within a region that is 40 km wide in the x direction and centered on the w forcing region; the random perturbation region is 4 km deep.

Because the simulations are specifically designed to be idealized, the effects of radiation, surface fluxes, and Coriolis acceleration are neglected in the present study. Radiation and surface fluxes are important factors that contribute to both the stability of the atmosphere and cold pool recovery. The former is particularly important regarding the vertical positioning of the aerosols in the atmosphere because this will affect the vertical stratification of solar radiation absorption and thus change the prestorm environmental sounding, which is the semidirect effect (Hansen et al. 1997). It is inherently challenging to discern indirect aerosol effects from direct effects and feedbacks on the environmental sounding. Therefore, to limit the degrees of freedom and focus on the indirect aerosol effects, these factors (i.e., the semidirect and direct effects) are neglected in the present study. Determining how the aerosol effects act as a function of solar radiation absorption and surface flux magnitude is beyond the scope of this study.

WRF is initialized with a modified sounding (Fig. 1) from the observationally based squall-line case study of the Eighth International Cloud Modeling Workshop held in July 2012 in Warsaw, Poland (Muhlbauer et al. 2013). The initial sounding comprises the 0000 UTC 20 June 2007 sounding from Norman, Oklahoma (OUN), and is smoothed using a 1.25-km running average. The chosen sounding has a mixed-layer CAPE (MLCAPE) of 2442 J kg−1. The MLCAPE is reported herein because it is more indicative of the sounding’s instability compared with other measures that are typically reported in the literature [i.e., most unstable CAPE (MUCAPE)]. Details of the observed squall line from this case are given by Morrison et al. (2012).

Fig. 1.
Fig. 1.

Skew T–logp diagram of the sounding used to initialize the model. Temperature (black) and dewpoint temperature (blue) are shown. The parcel following a moist adiabat lifted from the lifting condensation level (LCL) is also displayed (red). In this case, Δu = 12 m s−1.

Citation: Journal of the Atmospheric Sciences 71, 12; 10.1175/JAS-D-14-0068.1

To specifically analyze the sensitivity of squall lines to the vertical distribution of aerosols, a set of four simulations is performed. In the first simulation, the aerosol number concentration represents clean background conditions (clean scenario, 100 cm−3). In the next two simulations, aerosols are increased by a factor of 10 (to 1000 cm−3) below approximately 3 km (polluted low scenario) and between approximately 3 and 10 km (polluted mid scenario); the aerosols elsewhere remain at their background (or clean) concentrations. For reference, the freezing level is located at approximately 4.5 km (on average). Furthermore, a simulation, which is performed in a manner analogous to those performed in many previous studies, includes an aerosol perturbation everywhere in the domain (polluted scenario, 1000 cm−3). Lebo and Morrison (2014, their Fig. 12) showed that in relatively weak low-level wind shear environments, the response in both convective updraft strength and cumulative precipitation to an aerosol perturbation was qualitatively the same for an aerosol perturbation from 100 to 200, 500, 1000, or 2000 cm−3 using a similar initial sounding and model configuration (i.e., an increased convective mass flux was found for all aerosol perturbations in a weak-shear environment), thus warranting the choice of a single polluted aerosol number concentration in this study. Moreover, for comparative purposes, the aerosol number concentrations used here roughly correspond to an aerosol optical thickness at 500 nm (AOT500) of 0.051 and 0.225 for 100 and 1000 cm−3, respectively [see Figs. 1 and 2 in Andreae (2009)]. The additional aerosols are evenly distributed according to the distribution parameters (i.e., each bin receives 10 times more particles in the polluted regions of the domain). The aerosols are assumed to be horizontally homogeneous in all scenarios and vertically homogeneous in the clean and polluted scenarios. In the polluted low and polluted mid scenarios, the aerosols are vertically homogeneous within each of the clean and polluted regions of the domain. Moreover, the added aerosols are assumed to act only as cloud condensation nuclei (i.e., the ice nuclei concentration remains constant in all scenarios). For all scenarios, the aerosols are assumed to be ammonium sulfate (for the purpose of including the aerosol density and ionic dissociation) and lognormally distributed with a geometric mean diameter of 0.1 μm and spectral width of 1.8 (Lebo et al. 2012). Aerosol activation is considered following Köhler theory and assuming that the aerosol particles are completely soluble. Moreover, aerosol regeneration is predicted whereby a single aerosol particle is produced following the complete evaporation of one cloud droplet (Mitra et al. 1992).

Furthermore, passive tracers, initialized to 1 kg kg−1, are added after 4 h below approximately 3 km and between approximately 3 and 10 km to explicitly analyze mixing in and around the squall line during the mature phase of the squall lines. The tracers are assumed to be horizontally homogeneous. A detailed discussion and analysis of the tracers is provided in section 3c.

b. RKW theory

Provided that the majority of the results presented below are based on RKW theory (Rotunno et al. 1988), a brief overview of the theory and its potential relationship to aerosol effects is warranted. The physical basis of RKW theory as it applies to squall lines lies in the balance between cyclonic vorticity that is generated by the cold pool and anticyclonic vorticity generated by low-level environmental wind shear. Numerically, this relationship or ratio is defined as cu, where Δu is the change in the line-normal wind between the surface and the top of the shear layer and c can be represented as follows (e.g., Rotunno et al. 1988):
e1
where H is the top of the cold pool and BL corresponds to the buoyancy at some point L behind the leading edge of the cold pool. The leading edge of the cold pool is defined by the location at which the 0.005 m s−2 buoyancy isosurface intersects the surface (Tompkins 2001). Equation (1) is derived for an idealized hydrostatic density current (c is the density current propagation speed) and thus, in more realistic three-dimensional nonhydrostatic simulations, a precise determination of L is rather challenging. Following RKW theory and Markowski and Richardson (2010), L corresponds to the point behind the leading edge of the cold pool at which the zonal flow relative to the leading edge of the cold pool is stationary; the pressure within the cold pool at L should be nearly hydrostatic. For this study, L is chosen to be a fixed point 30 km behind the leading edge of the cold pool. Moreover, a characteristic value of c is determined following Lebo and Morrison (2014) in which c is computed for all horizontal points within 10 km of L in the zonal direction (line normal) and then averaged across this zonal band and in the meridional direction (line parallel). The results are qualitatively insensitive to the chosen L as long as L is not within 10–15 km of the leading edge of the cold pool where large nonhydrostatic effects occur. Moreover, L is a function of the meridional position; that is, L varies in the line-parallel direction according to the precise location of the cold pool leading edge for a given zonal transect. Furthermore, H is determined by the level of neutral buoyancy at L.

Equation (1) provides insight into the connection between aerosol perturbations, cold pool intensity, and ultimately squall-line strength and structure. Increased aerosol loading (which tends to increase the droplet number concentration and potentially alter the number concentrations of other hydrometeors) leads to changes in both hydrometer mass mixing ratios and latent heating rates (discussed in section 3). Changes in the mass and/or number mixing ratios can have a substantial effect on the hydrometeor sizes. Generally, the magnitude of BL decreases with added aerosol; therefore, both c and the ratio cu also decrease [because BL is negative in the cold pool, decreasing the magnitude of BL acts to decrease c; Eq. (1)]. If we assume that cu > 1 for a given low-level environmental shear scenario and clean background aerosol loadings, then cu → 1 as the aerosol loading increases (for a fixed Δu). This would suggest more upright and stronger updrafts. This hypothetical pathway will be discussed in more detail below. For the purpose of this analysis, Δu is set to 12 m s−1 (over the lowest 5 km), representing relatively weak low-level wind shear for the chosen sounding and corresponding to an environmental framework that has been shown to be largely sensitive to aerosol perturbations (e.g., Fan et al. 2009; Lebo and Morrison 2014). This choice of Δu produces a well-defined trailing stratiform (TS) linear mesoscale convective system (shown later). According to Parker and Johnson (2000), this squall-line type is the most common; the idealized line-parallel shear profile used herein correspond qualitatively well to their depiction of an average TS system. Regardless, it is important to keep in mind that changes in Δu are likely to have a larger effect on squall-line structure and strength than changes in aerosol loading. Fan et al. (2009) and Lebo and Morrison (2014) provide good overviews of aerosol effects for various shear environments.

3. Results

a. Convective strength

The sensitivity of the cold pool intensity to elevated aerosol loadings is shown in Fig. 2. As shown in Lebo and Morrison (2014) and corroborated here, we see a clear decrease in cu for an increase in aerosol loading (black vs green curves in Fig. 2). However, the additional simulations with aerosol loading constrained to low (below approximately 3 km) and mid- (between approximately 3 and 10 km) levels provide our first evidence of the important role of low- versus midlevel aerosol pollution in this idealized framework. Figure 2 shows that when the increased aerosol loading is only present at low levels (blue), little to no change in cu is predicted relative to the clean case (with even a slight increase between 4 and 6 h). However, when the increased aerosol loading occurs in the midlevels (red), the change in cu is nearly the same as that for polluting the entire domain (green). As shown in Lebo and Morrison (2014) and according to RKW theory, a reduction in cu should correspond to more upright, stronger updrafts. This finding is shown here in terms of the mean convective mass flux profile (MFz) and the change in MFz relative to the clean scenario (Fig. 3). The mean convective mass flux profiles are computed from the product of w and air density ρ for all locations in which w ≥ 2 m s−1. The product is added across each horizontal cross section and divided by the total number of grid points meeting this criterion. A lower threshold value for w was also analyzed (i.e., 1 m s−1); little to no sensitivity in the quantitative (regarding the relative changes due to the aerosols perturbations) and qualitative results was found. Comparing Figs. 2 and 3, we see a clear correspondence between decreased cu and increased mean convective mass flux for the polluted mid and polluted scenarios in this study. This result suggests that low-level aerosol perturbations may have a very small effect on the cold pool intensity and, consequently, storm strength, especially in the context of the idealized squall line simulated in a relatively high-CAPE environment in this study.

Fig. 2.
Fig. 2.

The (a) magnitude of cu and (b) change in cu [Δ(cu)] relative to the clean scenario as a function of time. The scenarios depicted include clean (black), polluted low (blue), polluted mid (red), and polluted (green).

Citation: Journal of the Atmospheric Sciences 71, 12; 10.1175/JAS-D-14-0068.1

Fig. 3.
Fig. 3.

Vertical profiles of (a) the mean convective mass flux and (b) the relative change in the mean convective mass flux (assuming a threshold vertical velocity of 2 m s−1). The clean (black), polluted low (blue), polluted mid (red), and polluted (green) scenarios are portrayed.

Citation: Journal of the Atmospheric Sciences 71, 12; 10.1175/JAS-D-14-0068.1

The simulations suggest that the reason for the enhanced convection in the polluted mid and polluted scenarios is related to changes in not only cloud water but also the ice and graupel mass mixing ratios and sizes. Figure 4 depicts vertical profiles of the mass mixing ratios qk (where the subscript k represents the hydrometeor type—c for cloud, i for ice, and g for graupel) and the change in the mean radius Δrk for the same hydrometeor classes, assuming that all ice species are treated as spheres in these simulations. The bulk snow characteristics are not shown because the aerosol perturbations have nearly no effect on either the size or number of snow crystals. Moreover, the domain-averaged rainwater characteristics are also not shown here because they largely depend on the analysis region (i.e., averaging over the updrafts, the trailing stratiform region, or the entire domain).

Fig. 4.
Fig. 4.

Vertical profiles of horizontally averaged domain-wide (a)–(c) hydrometeor mixing ratios qk (where k corresponds to the hydrometeor type—“c” for cloud, “i” for ice, and “g” for graupel) and (d)–(f) relative change in spherical-equivalent mean-volume radius Δrk. Profiles are shown for (a),(d) cloud; (b),(e) cloud ice; and (c),(f) graupel. For qk, profiles for all simulations [i.e., clean (black), polluted low (blue), polluted mid (red), and polluted (green)] are shown. For Δrk, because the relative change is portrayed, only results from the sensitivity simulations are shown. The green and blue lines are dashed only to better depict very small differences that would otherwise be masked by two lines falling directly on top of each other. A threshold of 1 × 10−10 kg kg−1 was used to conditionally average the mass mixing ratios. The results are insensitive to the chosen threshold.

Citation: Journal of the Atmospheric Sciences 71, 12; 10.1175/JAS-D-14-0068.1

Figures 4a and 4d show that for an increase in aerosol loading throughout the vertical (green), the cloud water mixing ratio tends to increase while the droplet sizes decrease, respectively. Moreover, Fig. 4a also shows how the vertical position of the aerosol perturbation directly affects the cloud water primarily within the vertical extent of the perturbation; the changes in cloud water (both mass and size) do extend beyond the perturbation region by approximately 1 km in the polluted low and polluted mid scenarios. Moreover, unless the mixed-phase processes are enhanced, the aerosol perturbation appears to affect predominately the cloud water characteristics (i.e., polluted low case). In other words, the effect of a low-level aerosol perturbation results in a nearly localized effect on the cloud water mixing ratio via reduced autoconversion and accretion; small (~5%) changes in qi and ri are also found. However, in the case of midlevel pollution, the effects on the cloud water mixing ratios and droplet sizes largely alter the mixed-phase processes, which extend the effects to other hydrometeor categories in a manner analogous to polluting the entire column (red and green). Specifically, the amount of supercooled liquid increases, which enhances riming and produces larger graupel that ultimately leads to larger and less numerous raindrops (Fig. 5).

Fig. 5.
Fig. 5.

Relative changes in the horizontally averaged (a) rainwater mixing ratio, (b) number mixing ratio, and (c) mean drop size behind the leading edge of the cold pool. All changes are shown relative to the clean case. The polluted low (blue), polluted mid (red), and polluted (green) scenarios are shown. The profiles are temporally averaged over the final 4 h of the simulations.

Citation: Journal of the Atmospheric Sciences 71, 12; 10.1175/JAS-D-14-0068.1

To better illustrate the aerosol-induced effects on the rain in the trailing stratiform region, Fig. 5 shows the relative change in qr, Nr, and rr (the subscript r corresponds to rain) behind the leading edge of the cold pool [for simplicity, this region is defined as a band between 20 and 200 km behind the leading edge of the cold pool and encompasses the region used to calculate c in Eq. (1)]. For the polluted and polluted mid cases, qr increases by up to 15% below 2 km; a somewhat larger increase is predicted in the polluted low case. Moreover, Fig. 5b shows that Nr decreases by 40%–80% in the polluted and polluted mid scenarios, while an increase is actually found for the polluted low scenario. These changes combine to produce an increase in the mean raindrop size by 6%–15% for the polluted mid and polluted cases; a smaller decrease in the mean raindrop size (5%–10%) is found in the presence of a low-level aerosol perturbation. Because diffusional growth of droplets is proportional to the droplet radius (Pruppacher and Klett 1997), these changes (especially those shown in Figs. 5b and 5c) suggest that the bulk evaporation rate should be reduced in the polluted and polluted mid cases. This decrease in the evaporation rate is reflected in the mean latent cooling rate behind the leading edge of the cold pool that is shown in Fig. 6. In fact, Fig. 6a suggests that the latent cooling rate decreases throughout most of the vertical for the polluted and polluted mid cases and that there are small changes to the latent cooling rate when only the low-level aerosol number concentration is perturbed. However, the decreased latent cooling rate (in the polluted mid and polluted cases) is partially offset by the added hydrometeor mass within the cold pool (Fig. 5). The combined effect of changes in latent heating, hydrometeor loading, and the water vapor mixing ratio in the cold pool on the buoyancy is shown in Figs. 6b and 6c; the magnitude of the buoyancy decreases (i.e., the cold pool becomes less negatively buoyant) by 10%–20% below 3 km in the polluted and polluted mid cases. This corresponds well with the reduction in cu shown in Fig. 2. Moreover, for the polluted low case, Figs. 6b and 6c demonstrate that the overall effect of the aerosol-induced changes on the cold pool is to slightly increase the magnitude of the buoyancy, which again is nicely reflected in the changes in cu that are shown in Fig. 2.

Fig. 6.
Fig. 6.

Horizontally averaged (a) latent cooling rate, (b) buoyancy, and (c) relative change in buoyancy behind the leading edge of the cold pool. The scenarios depicted include the clean (black), polluted low (blue), polluted mid (red), and polluted (green) cases. The relative changes in (c) are with respect to the clean case. The profiles are temporally averaged over the final 4 h of the simulations.

Citation: Journal of the Atmospheric Sciences 71, 12; 10.1175/JAS-D-14-0068.1

To further demonstrate that the majority of the enhancement in the convective strength is related to changes in the mixed-phase region that ultimately affect the cold pool (via enhanced graupel formation and subsequently larger and less numerous melted raindrops) and not changes in latent heating rates as would be expected following the traditional convective invigoration arguments (e.g., Rosenfeld et al. 2008; Storer et al. 2010; Lebo et al. 2012), profiles of latent heating rates (averaged horizontally over the entire domain and temporally over the last 4 h) are shown in Fig. 7; each panel compares the respective aerosol perturbation case to the clean scenario. We see that in the polluted and polluted low scenarios (the two simulations in which the aerosols are added to the lower levels), the magnitudes of both the warming (red) and cooling (blue) rates increase below approximately 4 km. However, the relative change in warming largely offsets the change in cooling; hence, little effect on the net heating rate (black) is observed in the presence of a low-level aerosol perturbation. Moreover, the domain-averaged net latent heating rate is positive due to condensation along the leading edge of the cold pool that largely outweighs the cooling due to evaporation within the cold pool except directly above the surface.

Fig. 7.
Fig. 7.

Vertical profiles of temporally and horizontally averaged latent heating rates. The heating rates are separated into warming (red), cooling (blue), and net (black). The clean scenario is used as the reference state (solid) in all panels. The dashed curves correspond to the (a) polluted low, (b) polluted mid, and (c) polluted aerosol scenarios. The averaging is performed over the last 4 h of the simulations.

Citation: Journal of the Atmospheric Sciences 71, 12; 10.1175/JAS-D-14-0068.1

Furthermore, when the aerosols are only added to the midlevels (i.e., polluted mid scenario), the changes in latent heating rates are negligible throughout most of the vertical (Fig. 7b). In fact, this should be expected given that the water vapor mixing decreases very rapidly with height. Therefore, small changes in the vertical velocity that are induced by aerosol perturbations will have only small effects on the ambient supersaturation and thus the vapor deposition and latent heating rates. Moreover, the lack of a response in the latent heating profiles for the polluted mid scenario suggests that the convective enhancement shown in Fig. 3 must arise from a different mechanism (i.e., the feedbacks on the bulk microphysical properties in the cold pool and the cold pool intensity that were shown and discussed earlier in this subsection).

The results presented herein regarding the aerosol-induced effect on the cold pool intensity have also been demonstrated in previous studies (e.g., Khain et al. 2005; Van den Heever and Cotton 2007; Tao et al. 2007; Lee et al. 2008b,a; Storer et al. 2010; Seigel et al. 2013; Lebo and Morrison 2014). However, the current work suggests that these aerosol-induced effects on the cold pool intensity may be due to midlevel aerosol perturbations, at least in the idealized high-CAPE case presented here. Moreover, other studies (e.g., Seigel and van den Heever 2013) have suggested that changes in the bulk microphysical properties of squall lines can have a more pronounced effect on the convective strength via a hydrometeor recirculation mechanism near the melting level. With that said, a direct comparison with these results is particularly challenging because of large differences in the cold pool structure in both Seigel and van den Heever (2013) and the present work. Furthermore, the connection between a weaker cold pool and stronger updrafts opposes arguments made in a few previous studies (e.g., Tao et al. 2007; Lee et al. 2008a). Tao et al. (2007) showed that an aerosol-induced stronger cold pool caused more precipitation, while Lee et al. (2008a) demonstrated that increased aerosol loading increased the cold pool strength and subsequently increased low-level convergence. These differences may be related to the use of two-dimensional (2D) models in the previous studies compared with the three-dimensional (3D) model used in this study.

b. Precipitation

The changes in convective strength and the bulk hydrometeor characteristics that were discussed in section 3a could have an effect on the surface precipitation. It might be expected that the increased latent heating rates in the polluted and polluted low cases would lead to similar changes in the cumulative precipitation. However, Fig. 8 suggests that this is not the case. In fact, the polluted and polluted mid cases exhibit similar trends in the domain-averaged cumulative precipitation. The increased precipitation in these two cases arises from the less-numerous larger raindrops behind the leading edge of the cold pool (Fig. 5) that fall faster and evaporate less readily (in a bulk sense, Fig. 6), which ultimately increases the precipitation rate and the domain-averaged cumulative precipitation (Fig. 8). The smaller increase in the domain-averaged cumulative precipitation for the polluted low scenario arises from a combination of increased rainwater mass and smaller drops (Fig. 5).

Fig. 8.
Fig. 8.

Domain-averaged (a) cumulative precipitation and (b) change in precipitation relative to the clean (black) scenario for the polluted low (blue), polluted mid (red), and polluted (green) cases.

Citation: Journal of the Atmospheric Sciences 71, 12; 10.1175/JAS-D-14-0068.1

c. Tracer analysis

As discussed in section 2, tracers are added to the domain after 4 h to investigate the transport and mixing of air in and around the simulated squall line to help elucidate causes of the findings presented in sections 3a and 3b. The most important tracers for this analysis are the low- and midlevel tracers, which are located below approximately 3 km and between approximately 3 and 10 km, respectively. Figure 9 shows the line-averaged ratio of low- to midlevel tracer mixing ratios (%) for the clean scenario, where warm (cool) colors correspond to regions dominated by the low-level (midlevel) tracer [note that as one tracer’s mixing ratio approaches the predefined threshold (i.e., 1 × 10−1 kg kg−1) the tracer ratio approaches zero or infinity; hence, very large and very small values are attributable to the absence of one of the tracers]. The low-level tracer clearly dominates at low levels and at the top of the anvil, while the midlevel tracer is dominant in the midtroposphere and lower extent of the anvil. This suggests that, on average, the midlevel tracer is not transported far from its original location in the atmosphere when the cloud-free midlevel air interacts with the updrafts and downdrafts in the squall line. However, this finding does not suggest that the midlevel air is not entrained in the convective cores. Instead, Fig. 9 simply suggests that a majority of the air within the cores had to have originated from lower levels.

Fig. 9.
Fig. 9.

Line-averaged low-level (below approximately 3 km) to midlevel (between approximately 3 and 10 km) tracer fraction (unitless, color contours) as a function of height and line-normal distance at 8 h. Cool (warm) colors represent regions dominated by the midlevel (low level) tracer. White areas correspond to regions in which the concentration of at least one of the tracers is less than 1 × 10−10 kg kg−1. Note that as one of the tracer mixing ratios approaches the chosen threshold, the ratio approaches either zero or infinity; these very small and very large values are due to the absence of one of the tracers at a particular location.

Citation: Journal of the Atmospheric Sciences 71, 12; 10.1175/JAS-D-14-0068.1

Furthermore, the low-level tracer is either quickly detrained from the updraft cores (not all convective plumes reach the level of free convection) or is ejected into the upper troposphere and lower stratosphere. Some of the low-level tracer is detrained in the midtroposphere; however, the amount is small in comparison to the ambient midlevel tracer due to the small number of convective cores present in the line at any given time. Figure 10a presents a scatterplot of with respect to w to demonstrate the relationship between the low-level tracer and the convective core strengths. Here, we see that the highest values generally occur in conjunction with high w, confirming that the large low-level tracer mixing ratios tend to occur within the strongest updraft cores that are less readily detrained in the midlevels, and that results in strong vertical transport to the upper troposphere and lower stratosphere. However, there are relatively few points in which w is large (i.e., there are few very strong updraft cores). Moreover, Fig. 10 suggests that for moderate w (i.e., 5–10 m s−1), and are smaller but of equal magnitude, suggesting that the ratio of the tracers is approximately 1 in these areas (i.e., the effects of detrainment/entrainment become more substantial as the strength of the convective core decreases or along the perimeter of the convective core, as expected).

Fig. 10.
Fig. 10.

Scatterplot of (a) the low-level tracer mixing ratio and (b) the midlevel tracer mixing ratio with respect to w between 4 and 9 km (approximately the extent of the mixed-phase region). Every one hundredth point is displayed for w ≥ 1 m s−1. The largest values tend to occur in the strongest updrafts, while the largest values tend to occur in the weakest updrafts.

Citation: Journal of the Atmospheric Sciences 71, 12; 10.1175/JAS-D-14-0068.1

To tie the previous findings back to the aerosol perturbation, the tracer analysis suggests that the low-level aerosols could have little to no effect on the midlevel microphysics, and thus, especially in the case of this idealized squall line, little to no effect on the strength of the entire system because the low-level tracers are most prominent at the top of the anvil and at low levels; only a moderate amount of the low-level tracer is detrained at the midlevels. The midlevel tracer is predominant in the midlevels, especially in regions with weaker updrafts (i.e., <10 m s−1) (Figs. 9 and 10b). These weaker updrafts tend to transport the tracer over much shorter vertical distances than the low-level tracer (i.e., the lower anvil and the eastern and western extents tend to be dominated by the midlevel tracer). Therefore, the midlevel aerosols have a more pronounced effect on the mixed-phase bulk microphysical properties than low-level aerosols. However, a potential caveat to this analysis is that the tracers are massless and hydrometeors are not. Therefore, it should be expected that the hydrometeors will follow different trajectories than the tracers in the presence of a nonzero horizontal wind. However, given the strength of the updrafts compared to the terminal fall speeds of the hydrometeors, the difference is thought to be small within the regions of strongest convection.

d. Potential sensitivity to grid resolution

While the horizontal grid spacing employed in this study is fairly typical for most contemporary mesoscale modeling studies that have investigated aerosol effects, the simulations are far from turbulent-eddy resolving. Reducing the horizontal grid spacing typically acts to reduce the width of the convective cores and increases the convective strength (e.g., Bryan and Morrison 2012). Standard entrainment theory then would suggest that these smaller convective cores should detrain more cloudy air and entrain more environmental air (e.g., Simpson 1971). However, the increased core strength may suppress this effect by decreasing the in-core residence time of parcels that originate at low levels and that are transported upward through the troposphere.

Furthermore, convective updraft cores, at least in supercellular environments, tend to remain undiluted as parcels ascend from the lower to upper troposphere according to observational evidence (e.g., Bluestein et al. 1988; Bunkers et al. 2006; Trapp 2013). On the contrary, Romps and Kuang (2010) suggested that in the framework of tropical convection, convective cores remain undiluted only up to 4–5 km. While the literature provides little evidence regarding the extent to which squall-line updrafts are diluted in the mid- to upper troposphere, Fig. 9 suggests that the convective line contains relatively small amounts of low-level air in the midlevels. There are two potential reasons for this finding: 1) the convective cores remain undiluted throughout most of the troposphere or 2) the convective cores account for only a small fraction of the convective line, therefore limiting the squall line’s ability to detrain low-level air into the midlevels. To further solidify the results presented herein, an additional simulation (for the clean scenario only) is performed with twice the horizontal resolution [i.e., Δx = Δy = 500 m (simulations with even higher resolutions and the explicit binned aerosol treatment are currently prohibited by computing resources)]. All other parameters are identical to the simulations presented previously in this study. Figure 11 shows the same tracer analysis as in Fig. 9, except for the higher-resolution simulation. Overall, Figs. 9 and 11 are qualitatively very similar. The most important similarity in the context of mixing as a function of grid spacing is that the magnitude of the tracer ratio (i.e., ) remains relatively low (i.e., less than 1) in the midlevels; the dominance of the low-level tracer is still confined to the top of the convective cores and the top of the anvil.

Fig. 11.
Fig. 11.

As in Fig. 9, but for Δx = Δy = 500 m instead of 1 km.

Citation: Journal of the Atmospheric Sciences 71, 12; 10.1175/JAS-D-14-0068.1

4. Conclusions and discussion

The sensitivity of a numerically simulated squall line to the vertical distribution of aerosols was analyzed using 3D cloud-resolving model (CRM) simulations. To specifically address this sensitivity, four scenarios were simulated: clean (Na = 100 cm−3 at all vertical levels), polluted low (as in the clean case, but with Na = 1000 cm−3 below approximately 3 km), polluted mid (as in the clean case, but with Na = 1000 cm−3 between approximately 3 and 10 km, and polluted (Na = 1000 cm−3 at all levels). The idealized simulations in this study suggest that low-level aerosol perturbations may have a very small effect on squall-line strength, especially compared with the effects of a midlevel aerosol perturbation. The reason for this finding is that the low-level aerosols activate and are subsequently projected through the convective cores and into the anvil or are quickly detrained; not all convective plumes reach the level of free convection [analogous to Houze et al. (1989, their Fig. 1)]. Moreover, midlevel air (and consequently, midlevel aerosol) remains within the mixed-phase region or is advected into the lower anvil, especially away from the center of the anvil. Therefore, a midlevel aerosol perturbation is more likely to have a substantial effect on the mixed-phase processes and change the squall-line strength (>10% increase in convective updraft mass flux) via enhanced riming, larger melted raindrops, and suppressed evaporative cooling in the cold pool. The suppression in evaporative cooling acts to weaken the cold pool (reduced production of negative buoyancy). Following RKW theory for squall lines in which cu > 1, the weakened cold pool supports more vigorous updrafts as a result of the better balance between cold pool–induced vorticity and low-level environmental wind shear–induced vorticity. Furthermore, the results of this study regarding midlevel aerosols are generally consistent with the modeling results of Fridlind et al. (2004) in which it was shown that midlevel aerosols were necessary to accurately simulate deep convective storms in the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida-Area Cirrus Experiment (CRYSTAL-FACE).

A caveat of this work is that the results are difficult to generalize to all mesoscale convective systems (MCSs). This should come as no surprise given the drastically different responses predicted by CRMs for aerosol perturbations in different cloud regimes and environmental conditions [see Khain et al.’s (2008) Fig. 17 for a general overview of the different responses]. Moreover, the mesoscale dynamical structure of a squall line is not identical to other MCSs (e.g., supercells). Regardless, the aerosol sensitivity was analyzed in the context of a weak low-level wind shear environment. Both Fan et al. (2009) and Lebo and Morrison (2014) provide a comprehensive analysis of aerosol effects on deep convection in different shear environments, suggesting that a larger effect is found in weak-shear environments with relatively high CAPE. Therefore, it is plausible that the results shown here are largely suppressed in stronger low-level shear environments. Furthermore, one must always keep in mind that the responses shown here to the various aerosol perturbations are quite small in comparison to changing the environmental context (e.g., low-level wind shear, moisture profile, temperature profile, upper-level wind shear, CAPE, and rotating shear) of the convective system.

It was shown that polluting the midlevels has nearly the same effect as polluting the entire model domain. This result raises an important question: do the results presented herein suggest that distant pollution sources are more important than local sources with regard to aerosol effects on squall lines? Locally produced aerosols are likely to remain at low levels because the vertical mixing time scale in the absence of a larger-scale driving mechanism (e.g., deep convection) is relatively long. Moreover, given that midlevel winds tend to be quite strong compared to their low-level counterparts, advection plays a larger factor for the ambient midlevel aerosols, suggesting that distant sources are likely to produce the ambient midlevel aerosols. Addressing this question is beyond the scope of the present study; simulations over a large domain and with an explicit representation of both local and distant emissions are necessary to explicitly address this issue in the future.

Acknowledgments

The case presented here stems from a squall-line case study that was part of the Eighth International Cloud Modeling Workshop held in Warsaw, Poland. Funding for this work was provided by the Advanced Study Program at the National Center for Atmospheric Research, which is funded by the National Science Foundation, and NOAA's Climate Goal. A particular thank you goes to Hugh Morrison and Graham Feingold for their assistance with this work. Additionally, I would like to thank the four reviewers for providing useful comments and suggestions that greatly improved this paper.

REFERENCES

  • Andreae, M. O., 2009: Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions. Atmos. Chem. Phys., 9, 543556, doi:10.5194/acp-9-543-2009.

    • Search Google Scholar
    • Export Citation
  • Andreae, M. O., D. Rosenfeld, P. Artaxo, A. A. Costa, G. P. Frank, K. M. Longo, and M. A. F. Silva-Dias, 2004: Smoking rain clouds over the Amazon. Science, 303, 13371342, doi:10.1126/science.1092779.

    • Search Google Scholar
    • Export Citation
  • Beard, K. V., 1976: Terminal velocity and shape of cloud and precipitation drops aloft. J. Atmos. Sci., 33, 851864, doi:10.1175/1520-0469(1976)033<0851:TVASOC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Berg, W., T. L’Ecuyer, and S. van den Heever, 2008: Evidence for the impact of aerosols on the onset and microphysical properties of rainfall from a combination of satellite observations and cloud-resolving model simulations. J. Geophys. Res., 113, D14S23, doi:10.1029/2007JD009649.

    • Search Google Scholar
    • Export Citation
  • Bluestein, H. B., E. W. McCaul Jr., G. P. Byrd, and G. R. Woodall, 1988: Mobile sounding observations of a tornadic storm near the dry line: The Canadian, Texas, storm of 7 May 1986. Mon. Wea. Rev., 116, 17901804, doi:10.1175/1520-0493(1988)116<1790:MSOOAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and H. Morrison, 2012: Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics. Mon. Wea. Rev., 140, 202225, doi:10.1175/MWR-D-11-00046.1.

    • Search Google Scholar
    • Export Citation
  • Bunkers, M. J., M. R. Hjelmefelt, and P. L. Smith, 2006: An observational examination of long-lived supercells. Part I: Characteristics, evolution, and demise. Wea. Forecasting, 21, 673688, doi:10.1175/WAF949.1.

    • Search Google Scholar
    • Export Citation
  • Ekman, A. M. L., A. Engstrom, and A. Soderberg, 2011: Impact of two-way aerosol–cloud interaction and changes in aerosol size distribution on simulated aerosol-induced deep convective cloud sensitivity. J. Atmos. Sci., 68, 685697, doi:10.1175/2010JAS3651.1.

    • Search Google Scholar
    • Export Citation
  • Fan, J., and Coauthors, 2009: Dominant role by vertical wind shear in regulating aerosol effects on deep convective clouds. J. Geophys. Res., 114, D22206, doi:10.1029/2009JD012352.

    • Search Google Scholar
    • Export Citation
  • Foote, G. B., and P. S. Du Toit, 1969: Terminal velocity of raindrops aloft. J. Appl. Meteor., 8, 249253, doi:10.1175/1520-0450(1969)008<0249:TVORA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fridlind, A. M., and Coauthors, 2004: Evidence for the predominance of mid-tropospheric aerosols as subtropical anvil cloud nuclei. Science, 304, 718722, doi:10.1126/science.1094947.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., 2006: Indirect impact of atmospheric aerosol in idealized simulations of convective–radiative quasi equilibrium. J. Climate, 19, 46644682, doi:10.1175/JCLI3857.1.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., M. Sato, and R. Ruedy, 1997: Radiative forcing and climate response. J. Geophys. Res., 102, 68316864, doi:10.1029/96JD03436.

    • Search Google Scholar
    • Export Citation
  • Heiblum, R. H., I. Koren, and O. Altaratz, 2012: New evidence of cloud invigoration from TRMM measurements of rain center of gravity. Geophys. Res. Lett., 39, L08803, doi:10.1029/2012GL051158.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., M. I. Biggerstaf, S. A. Rutledge, and B. F. Smull, 1989: Interpretation of Doppler weather radar displays of midlatitude mesoscale convective systems. Bull. Amer. Meteor. Soc., 70, 608–619, doi:10.1175/1520-0477(1989)070<0608:IODWRD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jorgensen, D. P., and M. A. LeMone, 1989: Vertical velocity characteristics of oceanic convection. J. Atmos. Sci., 46, 621640, doi:10.1175/1520-0469(1989)046<0621:VVCOOC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Khain, A., and B. Lynn, 2009: Simulation of a supercell storm in clean and dirty atmosphere using weather research and forecasting model with spectral bin microphysics. J. Geophys. Res., 114, D19209, doi:10.1029/2009JD011827.

    • Search Google Scholar
    • Export Citation
  • Khain, A., A. Pokrovsky, M. Pinsky, A. Seifert, and V. Phillips, 2004: Simulation of effects of atmospheric aerosols on deep turbulent convective clouds using a spectral microphysics mixed-phase cumulus cloud model. Part I: Model description and possible applications. J. Atmos. Sci., 61, 29632982, doi:10.1175/JAS-3350.1.

    • Search Google Scholar
    • Export Citation
  • Khain, A., D. Rosenfeld, and A. Pokrovsky, 2005: Aerosol impact on the dynamics and microphysics of deep convective clouds. Quart. J. Roy. Meteor. Soc., 131, 26392663, doi:10.1256/qj.04.62.

    • Search Google Scholar
    • Export Citation
  • Khain, A., N. BenMoshe, and A. Pokrovsky, 2008: Factors determining the impact of aerosols on surface precipitation from clouds: An attempt at classification. J. Atmos. Sci., 65, 17211748, doi:10.1175/2007JAS2515.1.

    • Search Google Scholar
    • Export Citation
  • Koren, I., J. V. Martins, L. A. Remer, and H. Afargan, 2008: Smoke invigoration versus inhibition of clouds over the Amazon. Science, 321, 946949, doi:10.1126/science.1159185.

    • Search Google Scholar
    • Export Citation
  • Koren, I., G. Feingold, and L. A. Remer, 2010a: The invigoration of deep convective clouds of the Atlantic: Aerosol effect, meteorology, or retrieval artifact? Atmos. Chem. Phys., 10, 88558872, doi:10.5194/acp-10-8855-2010.

    • Search Google Scholar
    • Export Citation
  • Koren, I., L. A. Remer, O. Altaratz, J. V. Martins, and A. Davidi, 2010b: Aerosol-induced changes of convective cloud anvils produce strong climate warming. Atmos. Chem. Phys., 10, 50015010, doi:10.5194/acp-10-5001-2010.

    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., and J. H. Seinfeld, 2011: Theoretical basis for convective invigoration due to increased aerosol concentration. Atmos. Chem. Phys., 11, 54075429, doi:10.5194/acp-11-5407-2011.

    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., and H. Morrison, 2014: Dynamical effects of aerosol perturbations on simulated idealized squall lines. Mon. Wea. Rev., 142,9911009, doi:10.1175/MWR-D-13-00156.1.

    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., H. Morrison, and J. H. Seinfeld, 2012: Are simulated aerosol-induced effects on deep convective clouds strongly dependent on saturation adjustment? Atmos. Chem. Phys., 12, 99419964, doi:10.5194/acp-12-9941-2012.

    • Search Google Scholar
    • Export Citation
  • Lee, S. S., 2011: Dependence of aerosol-precipitation interactions on humidity in a multiple-cloud system. Atmos. Chem. Phys., 11, 21792196, doi:10.5194/acp-11-2179-2011.

    • Search Google Scholar
    • Export Citation
  • Lee, S. S., L. J. Donner, V. T. J. Phillips, and Y. Ming, 2008a: The dependence of aerosol effects on clouds and precipitation on cloud-system organization, shear and stability. J. Geophys. Res., 113, D16202, doi:10.1029/2007JD009224.

    • Search Google Scholar
    • Export Citation
  • Lee, S. S., L. J. Donner, V. T. J. Phillips, and Y. Ming, 2008b: Examination of aerosol effects on precipitation in deep convective clouds during the 1997 ARM summer experiment. Quart. J. Roy. Meteor. Soc., 134, 12011220, doi:10.1002/qj.287.

    • Search Google Scholar
    • Export Citation
  • Markowski, P., and Y. Richardson, 2010: Mesoscale Meteorology in Midlatitudes. Wiley-Blackwell, 407 pp.

  • May, P. T., V. N. Bringi, and M. Thurai, 2011: Do we observe aerosol impacts on DSDs in strongly forced tropical thunderstorms? J. Atmos. Sci., 68, 19021910, doi:10.1175/2011JAS3617.1.

    • Search Google Scholar
    • Export Citation
  • Mitra, S. K., J. Brinkmann, and H. T. Pruppacher, 1992: A wind tunnel study on the drop-to-particle conversion. J. Aerosol Sci., 23, 245256, doi:10.1016/0021-8502(92)90326-Q.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., 2012: On the robustness of aerosol effects on an idealized supercell storm simulated with a cloud system-resolving model. Atmos. Chem. Phys. Discuss., 12, 10 493–10 533, doi:10.5194/acpd-12-10493-2012.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 9911007, doi:10.1175/2008MWR2556.1.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., S. A. Tessendorf, J. Ikeda, and G. Thompson, 2012: Sensitivity of a simulated midlatitude squall line to parameterization of raindrop breakup. Mon. Wea. Rev., 140, 24372460, doi:10.1175/MWR-D-11-00283.1.

    • Search Google Scholar
    • Export Citation
  • Muhlbauer, A., and Coauthors, 2013: Reexamination of the state of the art of cloud modeling shows real improvements. Bull. Amer. Meteor. Soc., 94, ES45–ES48, doi:10.1175/BAMS-D-12-00188.1.

    • Search Google Scholar
    • Export Citation
  • Noppel, H., U. Blahak, A. Seifert, and K. D. Beheng, 2010: Simulations of a hailstorm and the impact of CCN using an advanced two-moment cloud microphysics scheme. Atmos. Res., 96, 286301, doi:10.1016/j.atmosres.2009.09.008.

    • Search Google Scholar
    • Export Citation
  • Parker, M. D., and R. H. Johnson, 2000: Organizational modes of midlatitude mesoscale convective systems. Mon. Wea. Rev., 128, 34133436, doi:10.1175/1520-0493(2001)129<3413:OMOMMC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Pruppacher, H. R., and J. D. Klett, 1997: Microphysics of Clouds and Precipitation. Kluwer Academic Publishers, 954 pp.

  • Romps, D. M., and Z. Kuang, 2010: Do undiluted convective plumes exist in the upper tropical troposphere? J. Atmos. Sci., 67, 468484, doi:10.1175/2009JAS3184.1.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., and W. L. Woodley, 2000: Deep convective clouds with sustained supercooled liquid water down to −37.5°C. Nature, 405, 440442, doi:10.1038/35013030.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., U. Lohmann, G. B. Raga, C. D. O’Dowd, M. Kulmala, S. Fuzzi, A. Reissell, and M. O. Andreae, 2008: Flood or drought: How do aerosols affect precipitation? Science, 321, 13091313, doi:10.1126/science.1160606.

    • Search Google Scholar
    • Export Citation
  • Rotunno, R., J. B. Klemp, and M. L. Weisman, 1988: A theory for strong, long-lived squall lines. J. Atmos. Sci., 45, 463485, doi:10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Seifert, A., C. Köhler, and K. D. Beheng, 2012: Aerosol-cloud-precipitation effects over Germany as simulated by a convective-scale numerical weather prediction model. Atmos. Chem. Phys., 12, 709725, doi:10.5194/acp-12-709-2012.

    • Search Google Scholar
    • Export Citation
  • Seigel, R. B., and S. C. van den Heever, 2013: Squall-line intensification via hydrometeor recirculation. J. Atmos. Sci., 70, 20122031, doi:10.1175/JAS-D-12-0266.1.

    • Search Google Scholar
    • Export Citation
  • Seigel, R. B., S. C. van den Heever, and S. M. Saleeby, 2013: Mineral dust indirect effects and cloud radiative feedbacks of a simulated idealized nocturnal squall line. Atmos. Chem. Phys., 13, 44674485, doi:10.5194/acp-13-4467-2013.

    • Search Google Scholar
    • Export Citation
  • Simpson, J., 1971: On cumulus entrainment and one-dimensional models. J. Atmos. Sci., 28, 449455, doi:10.1175/1520-0469(1971)028<0449:OCEAOD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3_bw.pdf.]

  • Storer, R. L., and S. C. van den Heever, 2013: Microphysical processes evident in aerosol forcing of tropical deep convection. J. Atmos. Sci., 70, 430446, doi:10.1175/JAS-D-12-076.1.

    • Search Google Scholar
    • Export Citation
  • Storer, R. L., S. C. van den Heever, and G. L. Stephens, 2010: Modeling aerosol impacts on convective storms in different environments. J. Atmos. Sci., 67, 39043915, doi:10.1175/2010JAS3363.1.

    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., X. Li, A. Khain, T. Matsui, S. Lang, and J. Simpson, 2007: Role of atmospheric aerosol concentration on deep convective precipitation: Cloud-resolving model simulations. J. Geophys. Res.,112, D24S18, doi:10.1029/2007JD008728.

  • Teller, A., and Z. Levin, 2006: The effects of aerosols on precipitation and dimensions of subtropical clouds: A sensitivity study using a numerical cloud model. Atmos. Chem. Phys., 6, 6780, doi:10.5194/acp-6-67-2006.

    • Search Google Scholar
    • Export Citation
  • Tompkins, A. M., 2001: Organization of tropical convection in low vertical wind shears: The role of water vapor. J. Atmos. Sci., 58, 529545, doi:10.1175/1520-0469(2001)058<0529:OOTCIL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., 2013: Mesoscale-Convective Processes in the Atmosphere. Cambridge University Press, 377 pp.

  • Van den Heever, S. C., and W. R. Cotton, 2007: Urban aerosol impacts on downwind convective storms. J. Appl. Meteor. Climatol., 46, 828850, doi:10.1175/JAM2492.1.

    • Search Google Scholar
    • Export Citation
  • Van den Heever, S. C., G. G. Carri, W. R. Cotton, P. J. DeMott, and A. J. Prenni, 2006: Impacts of nucleating aerosol on Florida storms. Part I: Mesoscale simulations. J. Atmos. Sci., 63, 17521775, doi:10.1175/JAS3713.1.

    • Search Google Scholar
    • Export Citation
  • Wang, C., 2005: A modeling study of the response of tropical deep convection to the increase of cloud condensation nuclei concentration: 1. Dynamics and microphysics. J. Geophys. Res., 110, D21211, doi:10.1029/2004JD005720.

    • Search Google Scholar
    • Export Citation
  • Xu, K., and D. A. Randall, 2001: Updraft and downdraft statistics of simulated tropical and midlatitude cumulus convection. J. Atmos. Sci., 58, 16301649, doi:10.1175/1520-0469(2001)058<1630:UADSOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Yuan, T., L. A. Remer, K. E. Pickering, and H. Yu, 2011: Observational evidence of aerosol enhancement of lightning activity and convective invigoration. Geophys. Res. Lett., 38, L04701, doi:10.1029/2010GL046052.

    • Search Google Scholar
    • Export Citation
  • Yuter, S. E., M. A. Miller, M. D. Parker, P. M. Markowski, Y. Richardson, H. Brooks, and J. M. Straka, 2013: Comment on “Why do tornados and hailstorms rest on weekends?” by D. Rosenfeld and T. Bell. J. Geophys. Res. Atmos., 118, 7332–7338, doi:10.1002/jgrd.50526.

    • Search Google Scholar
    • Export Citation
  • Ziegler, C. L., E. R. Mansell, J. M. Straka, D. R. MacGorman, and D. W. Burgess, 2010: The impact of spatial variations of low-level stability on the life cycle of a simulated supercell storm. Mon. Wea. Rev., 138, 17381766, doi:10.1175/2009MWR3010.1.

    • Search Google Scholar
    • Export Citation
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