Editorial Type:
Article
Article Type:
Research Article

Indications of a Decrease in the Depth of Deep Convective Cores with Increasing Aerosol Concentration during the CACTI Campaign

Peter G. Veals aDepartment of Atmospheric Sciences, University of Utah, Salt Lake City, Utah

Search for other papers by Peter G. Veals in
Current site
Google Scholar
PubMed Close
,
Adam C. Varble bPacific Northwest National Laboratory, Richland, Washington

Search for other papers by Adam C. Varble in
Current site
Google Scholar
PubMed Close
,
James O. H. Russell aDepartment of Atmospheric Sciences, University of Utah, Salt Lake City, Utah

Search for other papers by James O. H. Russell in
Current site
Google Scholar
PubMed Close
,
Joseph C. Hardin bPacific Northwest National Laboratory, Richland, Washington

Search for other papers by Joseph C. Hardin in
Current site
Google Scholar
PubMed Close
, and
Edward J. Zipser aDepartment of Atmospheric Sciences, University of Utah, Salt Lake City, Utah

Search for other papers by Edward J. Zipser in
Current site
Google Scholar
PubMed Close
Online Publication:
28 Feb 2022
Print Publication:
01 Mar 2022
Collections:
Air–Sea Interactions from the Diurnal to the Intraseasonal during the PISTON, MISOBOB, and CAMP2Ex Observational Campaigns in the Tropics, RELAMPAGO-CACTI: High Impact Weather in Subtropical South America
DOI:
https://doi.org/10.1175/JAS-D-21-0119.1
Page(s):
705–722
Restricted access

Abstract

An aerosol indirect effect on deep convective cores (DCCs), by which increasing aerosol concentration increases cloud-top height via enhanced latent heating and updraft velocity, has been proposed in many studies. However, the magnitude of this effect remains uncertain due to aerosol measurement limitations, modulation of the effect by meteorological conditions, and difficulties untangling meteorological and aerosol effects on DCCs. The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in 2018–19 produced concentrated aerosol and cloud observations in a location with frequent DCCs, providing an opportunity to examine the proposed aerosol indirect effect on DCC depth in a rigorous and robust manner. For periods throughout the campaign with well-mixed boundary layers, we analyze relationships that exist between aerosol variables (condensation nuclei concentration > 10 nm, 0.4% cloud condensation nuclei concentration, 55–1000-nm aerosol concentration, and aerosol optical depth) and meteorological variables [level of neutral buoyancy (LNB), convective available potential energy, midlevel relative humidity, and deep-layer vertical wind shear] with the maximum radar-echo-top height and cloud-top temperature (CTT) of DCCs. Meteorological variables such as LNB and deep-layer shear are strongly correlated with DCC depth. LNB is also highly correlated with three of the aerosol variables. After accounting for meteorological correlations, increasing values of the aerosol variables [with the exception of one formulation of aerosol optical depth (AOD)] are generally correlated at a statistically significant level with a warmer CTT of DCCs. Therefore, for the study region and period considered, increasing aerosol concentration is mostly associated with a decrease in DCC depth.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the RELAMPAGO-CACTI Special Collection.

Corresponding author: Peter G. Veals, peter.veals@utah.edu

Abstract

An aerosol indirect effect on deep convective cores (DCCs), by which increasing aerosol concentration increases cloud-top height via enhanced latent heating and updraft velocity, has been proposed in many studies. However, the magnitude of this effect remains uncertain due to aerosol measurement limitations, modulation of the effect by meteorological conditions, and difficulties untangling meteorological and aerosol effects on DCCs. The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in 2018–19 produced concentrated aerosol and cloud observations in a location with frequent DCCs, providing an opportunity to examine the proposed aerosol indirect effect on DCC depth in a rigorous and robust manner. For periods throughout the campaign with well-mixed boundary layers, we analyze relationships that exist between aerosol variables (condensation nuclei concentration > 10 nm, 0.4% cloud condensation nuclei concentration, 55–1000-nm aerosol concentration, and aerosol optical depth) and meteorological variables [level of neutral buoyancy (LNB), convective available potential energy, midlevel relative humidity, and deep-layer vertical wind shear] with the maximum radar-echo-top height and cloud-top temperature (CTT) of DCCs. Meteorological variables such as LNB and deep-layer shear are strongly correlated with DCC depth. LNB is also highly correlated with three of the aerosol variables. After accounting for meteorological correlations, increasing values of the aerosol variables [with the exception of one formulation of aerosol optical depth (AOD)] are generally correlated at a statistically significant level with a warmer CTT of DCCs. Therefore, for the study region and period considered, increasing aerosol concentration is mostly associated with a decrease in DCC depth.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the RELAMPAGO-CACTI Special Collection.

Corresponding author: Peter G. Veals, peter.veals@utah.edu
Save
Cover Journal of the Atmospheric Sciences
Journal of the Atmospheric Sciences

  • Ackermann, T. P., K. Liou, F. P. Valero, and L. Pfister, 1988: Heating rates in tropical anvils. J. Atmos. Sci., 45, 1606–1623, https://doi.org/10.1175/1520-0469(1988)045<1606:HRITA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Altaraz, O., R. Z. Bar-Or, U. Wollner, and I. Koren, 2013: Relative humidity and its effect on aerosol optical depth in the vicinity of convective clouds. Environ. Res. Lett., 8, 034025, https://doi.org/10.1088/1748-9326/8/3/034025.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Altaraz, O., I. Koren, L. A. Remer, and E. Hirsch, 2014: Cloud invigoration by aerosols—Coupling between microphysics and dynamics. Atmos. Res., 140–141, 38–60, https://doi.org/10.1016/j.atmosres.2014.01.009.

    • Crossref
    • 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, 1337–1342, https://doi.org/10.1126/science.1092779.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • ARM, 2018: Minnis cloud products using VISST algorithm. ARM user facility, accessed 27 July 2021, https://www.arm.gov/capabilities/vaps/visst.

  • Borys, R. D., D. H. Lowenthal, M. A. Wetzel, F. Herrera, A. Gonzalez, and J. Harris, 1998: Chemical and microphysical properties of marine stratiform cloud in the North Atlantic. J. Geophys. Res., 103, 22 073–22 085, https://doi.org/10.1029/98JD02087.

    • Search Google Scholar
    • Export Citation

  • Boucher, O., and J. Quaas, 2013: Water vapour affects both rain and aerosol optical depth. Nat. Geosci., 6, 4–5, https://doi.org/10.1038/ngeo1692.

  • Braga, R. C., and Coauthors, 2017: Further evidence for CCN aerosol concentrations determining the height of warm rain and ice initiation in convective clouds over the Amazon basin. Atmos. Chem. Phys., 17, 14 433–14 456, https://doi.org/10.5194/acp-17-14433-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Breiman, L., 2001: Random forests. Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324.

  • Bryan, G. H., and H. Morrison, 2003: Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics. Mon. Wea. Rev., 140, 202–225, https://doi.org/10.1175/MWR-D-11-00046.1.

    • Crossref
    • 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, 202–225, https://doi.org/10.1175/MWR-D-11-00046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cai, Y., D. C. Montague, W. Mooiweer-Bryan, and T. Deshler, 2008: Performance characteristics of the Ultra High Sensitivity Aerosol Spectrometer for particles between 55 and 800 nm: Laboratory and field studies. J. Aerosol Sci., 39, 759–769, https://doi.org/10.1016/j.jaerosci.2008.04.007.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cecchini, M. A., and Coauthors, 2017: Sensitivities of Amazonian clouds to aerosols and updraft speed. Atmos. Chem. Phys., 17, 10 037–10 050, https://doi.org/10.5194/acp-17-10037-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chand, D., and Coauthors, 2012: Aerosol optical depth increase in partly cloudy conditions. J. Geophys. Res., 117, D17207, https://doi.org/10.1029/2012JD017894.

    • Search Google Scholar
    • Export Citation

  • Chen, Q., I. Koren, O. Altaratz, R. H. Heiblum, G. Dagan, and L. Pinto, 2017: How do changes in warm-phase microphysics affect deep convective clouds? Atmos. Chem. Phys., 17, 9585–9598, https://doi.org/10.5194/acp-17-9585-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chen, T., J. Guo, Z. Li, C. Zhao, H. Liu, M. Cribb, F. Wang, and J. He, 2016: A CloudSat perspective on the cloud climatology and its association with aerosol perturbations in the vertical over eastern China. J. Atmos. Sci., 73, 3599–3616, https://doi.org/10.1175/JAS-D-15-0309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chew, B. N., J. R. Campbell, J. S. Reid, D. M. Giles, E. J. Welton, S. V. Salinas, and S. C. Liew, 2011: Tropical cirrus cloud contamination in sun photometer data. Atmos. Environ., 45, 6724–6731, https://doi.org/10.1016/j.atmosenv.2011.08.017.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dixon, M., 2010: NCAR/lrose-core code. GitHub, accessed 8 January 2015, https://github.com/NCAR/lrose-core.

  • Fan, J., R. Zhang, G. Li, and W.-K. Tao, 2007: Effects of aerosols and relative humidity on cumulus clouds. J. Geophys. Res., 112, D14204, https://doi.org/10.1029/2006JD008136.

    • 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, https://doi.org/10.1029/2009JD012352.

    • Search Google Scholar
    • Export Citation

  • Fan, J., Y. Wang, D. Rosenfeld, and X. Liu, 2016: Review of aerosol–cloud interactions: Mechanisms, significance, and challenges. J. Atmos. Sci., 73, 4221–4252, https://doi.org/10.1175/JAS-D-16-0037.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Fan, J., and Coauthors, 2017: Cloud-resolving intercomparison of an MC3E squall line case: Part I—Convective updrafts. J. Geophys. Res. Atmos., 122, 9351–9378, https://doi.org/10.1002/2017JD026622.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Fan, J., and Coauthors, 2018: Substantial convection and precipitation enhancements by ultrafine aerosol particles. Science, 359, 411–418, https://doi.org/10.1126/science.aan8461.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., 2015: Untangling microphysical impacts on deep convection applying a novel modeling methodology. J. Atmos. Sci., 72, 2446–2464, https://doi.org/10.1175/JAS-D-14-0307.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., 2018: Can the impact of aerosols on deep convection be isolated from meteorological effects in atmospheric observations. J. Atmos. Sci., 75, 3347–3363, https://doi.org/10.1175/JAS-D-18-0105.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., 2019: Separating physical impacts from natural variability using piggybacking technique. Adv. Geosci., 49, 105–111, https://doi.org/10.5194/adgeo-49-105-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., and H. Morrison, 2016: Untangling microphysical impacts on deep convection applying a novel modeling methodology. Part II: Double-moment microphysics. J. Atmos. Sci., 73, 3749–3770, https://doi.org/10.1175/JAS-D-15-0367.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., and H. Morrison, 2017: Modeling condensation in deep convection. J. Atmos. Sci., 74, 2247–2267, https://doi.org/10.1175/JAS-D-16-0255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., and H. Morrison, 2020: Do ultrafine cloud condensation nuclei invigorate deep convection? J. Atmos. Sci., 77, 2567–2583, https://doi.org/10.1175/JAS-D-20-0012.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gregory, L., and Sivaraman, C., 2018: Cimel sunphotometer (CSPHOTAODFILTQAV3). ARM user facility, accessed 22 July 2021, https://doi.org/10.5439/1461660.

    • Crossref
    • Export Citation

  • Gryspeerdt, E., P. Stier, and B. S. Grandey, 2014a: Cloud fraction mediates the aerosol optical depth-cloud top height relationship. Geophys. Res. Lett., 41, 3622–3627, https://doi.org/10.1002/2014GL059524.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gryspeerdt, E., P. Stier, and D. G. Partridge, 2014b: Links between satellite-retrieved aerosol and precipitation. Atmos. Chem. Phys., 14, 9677–9694, https://doi.org/10.5194/acp-14-9677-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gunn, R., and B. B. Phillips, 1957: An experimental investigation of the effect of air pollution on the initiation of rain. J. Meteor., 14, 272–280, https://doi.org/10.1175/1520-0469(1957)014<0272:AEIOTE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Guo, J., and Coauthors, 2016: Delaying precipitation and lightning by air pollution over the Pearl River delta. Part I: Observational analyses. J. Geophys. Res. Atmos., 121, 6472–6488, https://doi.org/10.1002/2015JD023257.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hardin, J., A. Hunzinger, E. Schuman, A. Matthews, N. Bharadwaj, A. Varble, K. Johnson, and S. Giangrande, 2020: CACTI radar b1 processing: Corrections, calibrations, and processing report. ARM Tech. Rep. DOE/SC-ARM-TR-244, 55 pp.

    • Search Google Scholar
    • Export Citation

  • Heikenfeld, M., B. White, L. Labbouz, and P. Stier, 2019: Aerosol effects on deep convection: The propagation of aerosol perturbations through convective cloud microphysics. Atmos. Chem. Phys., 10, 8855–8872, https://doi.org/10.5194/acp-19-2601-2019.

    • Search Google Scholar
    • Export Citation

  • Houze, R. A., Jr., 1982: Cloud clusters and large-scale vertical motions in the tropics. J. Meteor. Soc. Japan, 60, 396–410, https://doi.org/10.2151/jmsj1965.60.1_396.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Igel, M. R., and S. C. van den Heever, 2015: The relative influence of environmental characteristics on tropical deep convective morphology as observed by CloudSat. J. Geophys. Res. Atmos., 120, 4304–4322, https://doi.org/10.1002/2014JD022690.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. T. F. Stocker, Eds., Cambridge University Press, 1535 pp.

  • Jiang, J. H., H. Su, L. Huang, Y. Wang, S. Massie, B. Zhao, A. Omar, and Z. Wang, 2018: Contrasting effects on deep convective clouds by different types of aerosols. Nat. Commun., 9, 3874, https://doi.org/10.1038/s41467-018-06280-4.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Khain, A. P., 2009: Notes on state-of-art investigations of aerosol effects on precipitation: A critical review. Environ. Res. Lett., 4, 015004, https://doi.org/10.1088/1748-9326/4/1/015004.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Khain, A. P., D. Rosenfeld, and A. Pokrovsky, 2005: Aerosol impact on the dynamics and microphysics of deep convective clouds. Quart. J. Roy. Meteor. Soc., 131, 2639–2663, https://doi.org/10.1256/qj.04.62.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Khain, A. P., 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, 1721–1748, https://doi.org/10.1175/2007JAS2515.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Koontz, A., and C. Flynn, 2018: Ultra-High Sensitivity Aerosol Spectrometer (AOSUHSAS). ARM user facility, accessed 22 July 2021, https://doi.org/10.5439/1409033.

    • Crossref
    • Export Citation

  • Koren, I., Y. J. Kaufman, D. Rosenfeld, L. A. Remer, and Y. Rudich, 2005: Aerosol invigoration and restructuring of Atlantic convective clouds. Geophys. Res. Lett., 32, L14828, https://doi.org/10.1029/2005GL023187.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Koren, I., G. Feingold, and L. A. Remer, 2010: The invigoration of deep convective clouds over the Atlantic: Aerosol effect, meteorology or retrieval artifact? Atmos. Chem. Phys., 10, 8855–8872, https://doi.org/10.5194/acp-10-8855-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Koren, I., O. Altaratz, L. A. Remer, G. Feingold, J. V. Martins, and R. H. Heiblum, 2012: Aerosol-induced intensification of rain from the tropics to the mid-latitudes. Nat. Geosci., 5, 118–122, https://doi.org/10.1038/ngeo1364.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kuang, C. J. Wang, C. Salwen, M. Boyer, and A. Singh, 2018: Condensation Particle Counter (AOSCPCF). ARM user facility, accessed 22 July 2021, https://doi.org/10.5439/1046184.

    • Crossref
    • Export Citation

  • Lebo, Z. J., 2014: The sensitivity of a numerically simulated idealized squall line to the vertical distribution of aerosols. J. Atmos. Sci., 71, 4581–4596, https://doi.org/10.1175/JAS-D-14-0068.1.

    • Crossref
    • 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, 5407–5429, https://doi.org/10.5194/acp-11-5407-2011.

    • Crossref
    • 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, 991–1009, https://doi.org/10.1175/MWR-D-13-00156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lebo, Z. J., and H. Morrison, 2015: Effects of horizontal and vertical grid spacing on mixing in simulated squall lines and implications for convective strength and structure. Mon. Wea. Rev., 143, 4355–4375, https://doi.org/10.1175/MWR-D-15-0154.1.

    • Crossref
    • 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, 9941–9964, https://doi.org/10.5194/acp-12-9941-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Li, Z., F. Niu, J. Fan, Y. Liu, D. Rosenfeld, and Y. Ding, 2011: Long-term impacts of aerosols on the vertical development of clouds and precipitation. Nat. Geosci., 4, 888–894, https://doi.org/10.1038/ngeo1313.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lin, J. C., T. Matsui, R. A. Pielke Sr., and C. Kummerow, 2006: Effects of biomass-burning-derived aerosols on precipitation and clouds in the Amazon basin: A satellite-based empirical study. J. Geophys. Res., 111, D19204, https://doi.org/10.1029/2005JD006884.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ma, P., P. J. Rasch, H. Chepfer, D. M. Winker, and S. J. Ghan, 2018: Observational constraint on cloud susceptibility weakened by aerosol retrieval limitations. Nat. Commun., 9, 2640, https://doi.org/10.1038/s41467-018-05028-4.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Marinescu, P. J., and Coauthors, 2021: Impacts of varying concentrations of cloud condensation nuclei on deep convective cloud updrafts—A multimodel assessment. J. Atmos. Sci., 78, 1147–1172, https://doi.org/10.1175/JAS-D-20-0200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mauger, G. S., and J. R. Norris, 2007: Meteorological bias in satellite estimates of aerosol-cloud relationships. Geophys. Res. Lett., 34, L16824, https://doi.org/10.1029/2007GL029952.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • May, R. M., S. C. Arms, P. Marsh, E. Bruning, J. R. Leeman, K. Goebbert, J. E. Thielen, and Z. Bruick, 2020: MetPy: A Python package for meteorological data. GitHub, https://github.com/Unidata/MetPy.

    • Search Google Scholar
    • Export Citation

  • McGill, R., J. W. Tukey, and W. A. Larsen, 1978: Variations of box plots. Amer. Stat., 32, 12–16, https://doi.org/10.1080/00031305.1978.10479236.

    • Search Google Scholar
    • Export Citation

  • Miltenberger, A. K., and Coauthors, 2018: Aerosol-cloud interactions in mixed-phase convective clouds—Part 1: Aerosol perturbations. Atmos. Chem. Phys., 18, 3119–3145, https://doi.org/10.5194/acp-18-3119-2018.

    • Crossref
    • 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., 12, 7689–7705, https://doi.org/10.5194/acp-12-7689-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Morrison, H., and W. W. Grabowski, 2011: Cloud-system resolving model simulations of aerosol indirect effects on tropical deep convection and its thermodynamic environment. Atmos. Chem. Phys., 11, 10 503–10 523, https://doi.org/10.5194/acp-11-10503-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Omar, A. H., and Coauthors, 2013: CALIOP and AERONET aerosol optical depth comparisons: One size fits none. J. Geophys. Res. Atmos., 118, 4748–4766, https://doi.org/10.1002/jgrd.50330.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Pedregosa, F. , and Coauthors, 2011: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res., 12, 2825–2830, https://jmlr.org/papers/v12/pedregosa11a.html.

    • Search Google Scholar
    • Export Citation

  • Peng, J., Z. Li, H. Zhang, J. Liu, and M. Cribb, 2016: Systematic changes in cloud radiative forcing with aerosol loading for deep clouds in the tropics. J. Atmos. Sci., 73, 231–249, https://doi.org/10.1175/JAS-D-15-0080.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Randall, D. A., Harshvardhan, D. A. Dazlich, and T. G. Corsetti, 1989: Interactions among radiation, convection, and large-scale dynamics in a general circulation model. J. Atmos. Sci., 46, 1943–1970, https://doi.org/10.1175/1520-0469(1989)046<1943:IARCAL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rosenfeld, D., 1999: TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall. Geophys. Res. Lett., 26, 3105–3108, https://doi.org/10.1029/1999GL006066.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rosenfeld, D., U. Lohmann, G. B. Raga, C. D. O’Dowd, M. Kulmala, S. Fuzzi, A. Reissell, and M. Andreae, 2008: Flood or drought: How do aerosols affect precipitation? Science, 321, 1309–1313, https://doi.org/10.1126/science.1160606.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stanford, M. W., H. Morrison, A. Varble, J. Berner, W. Wu, G. McFarquhar, and J. Milbrandt, 2019: Sensitivity of simulated deep convection to a stochastic ice microphysics framework. J. Adv. Model. Earth Syst., 11, 3362–3389, https://doi.org/10.1029/2019MS001730.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stolz, D. C., S. A. Rutledge, and J. R. Pierce, 2015: Simultaneous influences of thermodynamics and aerosols on deep convection and lightning in the tropics. J. Geophys. Res. Atmos., 120, 6207–6231, https://doi.org/10.1002/2014JD023033.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Storer, R. L., and S. C. van den Heever, 2013: Microphysical processes evident in aerosol forcing of tropical deep convective clouds. J. Atmos. Sci., 70, 430–446, https://doi.org/10.1175/JAS-D-12-076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Storer, R. L., S. C. van den Heever, and T. S. L’Ecuyer, 2014: Observations of aerosol-induced convective invigoration in the tropical east Atlantic. J. Geophys. Res. Atmos., 119, 3963–3975, https://doi.org/10.1002/2013JD020272.

    • Crossref
    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Toll, V., M. Christensen, J. Quaas, and N. Bellouin, 2019: Weak average liquid-cloud-water response to anthropogenic aerosols. Nature, 572, 51–58, https://doi.org/10.1038/s41586-019-1423-9.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Uin, J., A. Laskin, C. Salwen, and G. Senum, 2018: Cloud Condensation Nuclei Particle Counter (AOSCCN2COLB). ARM user facility, accessed 20 July 2021, https://www.arm.gov/capabilities/instruments/ccn.

  • van den Heever, S. C., and W. R. Cotton, 2007: Urban aerosol impacts on downwind convective storms. J. Appl. Meteor. Climatol., 46, 828–850, https://doi.org/10.1175/JAM2492.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • van den Heever, S. C., G. G. Carrió, 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, 1752–1775, https://doi.org/10.1175/JAS3713.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • van den Heever, S. C., G. L. Stephens, and N. B. Wood, 2011: Aerosol indirect effects on tropical convection characteristics under conditions of radiative–convective equilibrium. J. Atmos. Sci., 68, 699–718, https://doi.org/10.1175/2010JAS3603.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • van der Walt, S., J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, 2014: Scikit-image: Image processing in Python. PeerJ, 2, e453, https://doi.org/10.7717/peerj.453.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Varble, A., 2018: Erroneous attribution of deep convective invigoration to aerosol concentration. J. Atmos. Sci., 75, 1351–1368, https://doi.org/10.1175/JAS-D-17-0217.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Varble, A., and Coauthors, 2014: Evaluation of cloud-resolving and limited area model intercomparison simulations using TWP-ICE observations: 1. Deep convective updraft properties. J. Geophys. Res. Atmos., 119, 13 891–13 918, https://doi.org/10.1002/2013JD021371.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Varble, A., H. Morrison, and E. J. Zipser, 2020: Effects of under-resolved convective dynamics on the evolution of a squall line. Mon. Wea. Rev., 148, 289–311, https://doi.org/10.1175/MWR-D-19-0187.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Varble, A., and Coauthors, 2021: Utilizing a storm-generating hotspot to study convective cloud transitions: The CACTI experiment. Bull. Amer. Meteor. Soc., 102, E1597–E1620, https://doi.org/10.1175/BAMS-D-20-0030.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wall, C., E. Zipser, and C. Liu, 2014: An investigation of the aerosol indirect effect on convective intensity using satellite observations. J. Atmos. Sci., 71, 430–447, https://doi.org/10.1175/JAS-D-13-0158.1.

    • Crossref
    • 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, https://doi.org/10.1029/2004JD005720.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, D., S. Giangrande, Z. Feng, J. C. Hardin, and A. F. Prein, 2020: Updraft and downdraft core size and intensity as revealed by radar wind profilers: MCS observations and idealized model comparisons. J. Geophys. Res. Atmos., 125, e2019JD031774, https://doi.org/10.1029/2019JD031774.

    • Search Google Scholar
    • Export Citation

  • White, B., E. Gryspeerdt, P. Stier, H. Morrison, and G. Thompson, 2017: Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects. Atmos. Chem. Phys., 17, 12 145–12 175, https://doi.org/10.5194/acp-17-12145-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Yan, H., Z. Li, J. Huang, M. Cribb, and J. Liu, 2014: Long-term aerosol-mediated changes in cloud radiative forcing of deep clouds at the top and bottom of the atmosphere over the Southern Great Plains. Atmos. Chem. Phys., 14, 7113–7124, https://doi.org/10.5194/acp-14-7113-2014.

    • Crossref
    • 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, https://doi.org/10.1029/2010GL046052.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, J., J. S. Reid, and B. N. Holben, 2005: An analysis of potential cloud artifacts in MODIS over ocean aerosol optical thickness products. Geophys. Res. Lett., 32, L15803, https://doi.org/10.1029/2005GL023254.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 709 0 0
Full Text Views 2236 681 40
PDF Downloads 666 212 15