Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part I: Observational Study

Yazhe Hu aDepartment of Atmospheric Sciences, University of Wyoming, Laramie, Wyoming

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Bart Geerts aDepartment of Atmospheric Sciences, University of Wyoming, Laramie, Wyoming

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Min Deng aDepartment of Atmospheric Sciences, University of Wyoming, Laramie, Wyoming

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Coltin Grasmick aDepartment of Atmospheric Sciences, University of Wyoming, Laramie, Wyoming

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Yonggang Wang bDepartment of Atmospheric and Geological Sciences, State University of New York at Oswego, Oswego, New York

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Christian Philipp Lackner aDepartment of Atmospheric Sciences, University of Wyoming, Laramie, Wyoming

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Yishi Hu cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Zachary J. Lebo cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Damao Zhang dPacific Northwest National Laboratory, Richland, Washington

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Abstract

A study of the vertical structure of postfrontal shallow clouds in the marine boundary layer over the Southern Ocean is presented. The central question of this two-part study regards cloud phase (liquid/ice) of precipitation, and the associated growth mechanisms. In this first part, data from the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign are analyzed, starting with a 75-h case with continuous sea surface-based thermal instability, modest surface heat fluxes, an open-cellular mesoscale organization, and very few ice nucleating particles (INPs). The clouds are mostly precipitating and shallow (tops mostly around 2 km above sea level), with weak up- and downdrafts, and with cloud-top temperatures generally around −18° to −10°C. The case study is extended to three other periods of postfrontal shallow clouds in MARCUS. While abundant supercooled liquid water is commonly present, an experimental cloud-phase algorithm classifies nearly two-thirds of clouds in the 0° to −5°C layer as containing ice (cloud ice, snow, or mixed phase), implying that much of the precipitation grows through cold-cloud processes. The best predictors of ice presence are cloud-top temperature, cloud depth, and INP concentration. Measures of convective activity and turbulence are found to be poor indicators of ice presence in the studied environment. The water-phase distribution in this cloud regime is explored through numerical simulations in Part II.

Significance Statement

Climate models generally predict a lower albedo than observed over the Southern Ocean, and this is largely attributed to a lack of cloudiness, especially in the postfrontal cold sector of midlatitude cyclones. This in turn may be due to an excess of ice in these simulated clouds, resulting in rapid precipitation fallout and an overly brief cloud lifespan. The objective of this study is to examine whether shallow postfrontal clouds over the Southern Ocean are dominated by supercooled drops, or by snow and ice, using data collected by a U.S. Department of Energy Atmospheric Radiation Measurement Mobile Facility deployed aboard an Australian Antarctic supply vessel. We find that these clouds contain much supercooled liquid, even though cloud-top temperatures generally are around −18° to −8°C, and that about two-thirds of the clouds just above the freezing level contain ice. Much of the precipitation appears to grow through cold-cloud processes above the freezing level, rather than drizzle/rain. Updrafts and/or turbulence in convection or in cloud-top generating cells do not initiate much ice, compared to observations elsewhere in a similar temperature range. This may be attributable to the extremely low concentration of ice nucleating particles in this environment. Ultimately, the deepest clouds with the coldest cloud tops are most likely to be ice dominated.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bart Geerts, geerts@uwyo.edu

Abstract

A study of the vertical structure of postfrontal shallow clouds in the marine boundary layer over the Southern Ocean is presented. The central question of this two-part study regards cloud phase (liquid/ice) of precipitation, and the associated growth mechanisms. In this first part, data from the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign are analyzed, starting with a 75-h case with continuous sea surface-based thermal instability, modest surface heat fluxes, an open-cellular mesoscale organization, and very few ice nucleating particles (INPs). The clouds are mostly precipitating and shallow (tops mostly around 2 km above sea level), with weak up- and downdrafts, and with cloud-top temperatures generally around −18° to −10°C. The case study is extended to three other periods of postfrontal shallow clouds in MARCUS. While abundant supercooled liquid water is commonly present, an experimental cloud-phase algorithm classifies nearly two-thirds of clouds in the 0° to −5°C layer as containing ice (cloud ice, snow, or mixed phase), implying that much of the precipitation grows through cold-cloud processes. The best predictors of ice presence are cloud-top temperature, cloud depth, and INP concentration. Measures of convective activity and turbulence are found to be poor indicators of ice presence in the studied environment. The water-phase distribution in this cloud regime is explored through numerical simulations in Part II.

Significance Statement

Climate models generally predict a lower albedo than observed over the Southern Ocean, and this is largely attributed to a lack of cloudiness, especially in the postfrontal cold sector of midlatitude cyclones. This in turn may be due to an excess of ice in these simulated clouds, resulting in rapid precipitation fallout and an overly brief cloud lifespan. The objective of this study is to examine whether shallow postfrontal clouds over the Southern Ocean are dominated by supercooled drops, or by snow and ice, using data collected by a U.S. Department of Energy Atmospheric Radiation Measurement Mobile Facility deployed aboard an Australian Antarctic supply vessel. We find that these clouds contain much supercooled liquid, even though cloud-top temperatures generally are around −18° to −8°C, and that about two-thirds of the clouds just above the freezing level contain ice. Much of the precipitation appears to grow through cold-cloud processes above the freezing level, rather than drizzle/rain. Updrafts and/or turbulence in convection or in cloud-top generating cells do not initiate much ice, compared to observations elsewhere in a similar temperature range. This may be attributable to the extremely low concentration of ice nucleating particles in this environment. Ultimately, the deepest clouds with the coldest cloud tops are most likely to be ice dominated.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bart Geerts, geerts@uwyo.edu
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  • Ahn, E., Y. Huang, T. H. Chubb, D. Baumgardner, P. Isaac, M. de Hoog, S. T. Siems, and M. J. Manton, 2017: In situ observations of wintertime low-altitude clouds over the Southern Ocean. Quart. J. Roy. Meteor. Soc., 143, 13811394, https://doi.org/10.1002/qj.3011.

    • Search Google Scholar
    • Export Citation
  • Ahn, E., Y. Huang, S. T. Siems, and M. J. Manton, 2018: A comparison of cloud microphysical properties derived from MODIS and CALIPSO with in situ measurements over the wintertime Southern Ocean. J. Geophys. Res. Atmos., 123, 11 12011 140, https://doi.org/10.1029/2018JD028535.

    • Search Google Scholar
    • Export Citation
  • Alexander, S. P., G. M. McFarquhar, R. Marchand, A. Protat, É. Vignon, G. G. Mace, and A. R. Klekociuk, 2021: Mixed-phase clouds and precipitation in Southern Ocean cyclones and cloud systems observed poleward of 64°S by ship-based cloud radar and lidar. J. Geophys. Res. Atmos., 126, e2020JD033626, https://doi.org/10.1029/2020JD033626.

    • Search Google Scholar
    • Export Citation
  • Atlas, R. L., C. S. Bretherton, P. N. Blossey, A. Gettelman, C. Bardeen, P. Lin, and Y. Ming, 2020: How well do large-eddy simulations and global climate models represent observed boundary layer structures and low clouds over the summertime Southern Ocean? J. Adv. Model. Earth Syst., 12, e2020MS002205, https://doi.org/10.1029/2020MS002205.

    • Search Google Scholar
    • Export Citation
  • Bodas-Salcedo, A., and Coauthors, 2014: Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models. J. Climate, 27, 4156, https://doi.org/10.1175/JCLI-D-13-00169.1.

    • Search Google Scholar
    • Export Citation
  • Choi, Y.-S., C.-H. Ho, S.-W. Kim, and R. S. Lindzen, 2010: Observational diagnosis of cloud phase in the winter Antarctic atmosphere for parameterizations in climate models. Adv. Atmos. Sci., 27, 12331245, https://doi.org/10.1007/s00376-010-9175-3.

    • Search Google Scholar
    • Export Citation
  • Cooper, W. A., 1986: Ice initiation in natural clouds. Precipitation Enhancement: A Scientific Challenge, Meteor. Monogr., No. 43, Amer. Meteor. Soc., 29–32, https://doi.org/10.1175/0065-9401-21.43.29.

  • Crewell, S., and U. Löhnert, 2003: Accuracy of cloud liquid water path from ground-based microwave radiometry 2. Sensor accuracy and synergy. Radio Sci., 38, 8042, https://doi.org/10.1029/2002RS002634.

    • Search Google Scholar
    • Export Citation
  • Crosier, J., and Coauthors, 2011: Observations of ice multiplication in a weakly convective cell embedded in supercooled mid-level stratus. Atmos. Chem. Phys., 11, 257273, https://doi.org/10.5194/acp-11-257-2011.

    • Search Google Scholar
    • Export Citation
  • D’Alessandro, J. J., M. Diao, C. Wu, X. Liu, J. B. Jensen, and B. B. Stephens, 2019: Cloud phase and relative humidity distributions over the Southern Ocean in austral summer based on in situ observations and CAM5 simulations. J. Climate, 32, 27812805, https://doi.org/10.1175/JCLI-D-18-0232.1.

    • Search Google Scholar
    • Export Citation
  • D’Alessandro, J. J., G. M. McFarquhar, W. Wu, J. L. Stith, J. B. Jensen, and R. M. Rauber, 2021: Characterizing the occurrence and spatial heterogeneity of liquid, ice and mixed phase low-level clouds over the Southern Ocean using in situ observations acquired during SOCRATES. J. Geophys. Res. Atmos., 126, e2020JD034482, https://doi.org/10.1029/2020JD034482.

    • Search Google Scholar
    • Export Citation
  • Deng, M., and G. G. Mace, 2006: Cirrus microphysical properties and air motion statistics using cloud radar Doppler moments. Part I: Algorithm description. J. Appl. Meteor. Climatol., 45, 16901709, https://doi.org/10.1175/JAM2433.1.

    • Search Google Scholar
    • Export Citation
  • Deng, M., J. French, B. Geerts, S. Haimov, L. Oolman, D. Plummer, and Z. Wang, 2022: Retrieval and evaluation of ice water content from an airborne cloud radar in orographic wintertime clouds during SNOWIE. J. Atmos. Oceanic Technol., 39, 207221, https://doi.org/10.1175/JTECH-D-21-0085.1.

    • Search Google Scholar
    • Export Citation
  • Eloranta, E. W., 2005: High spectral resolution lidar. Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, C. Weitkamp, Ed., Springer Series in Optical Sciences, Vol. 102, Springer, 143–163.

  • Field, P. R., and Coauthors, 2017: Secondary ice production: Current state of the science and recommendations for the future. Ice Formation and Evolution in Clouds and Precipitation: Measurement and Modeling Challenges, Meteor. Monogr., No. 58, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0014.1.

  • Fletcher, J. K., S. Mason, and C. Jakob, 2016: A climatology of clouds in marine cold air outbreaks in both hemispheres. J. Climate, 29, 66776692, https://doi.org/10.1175/JCLI-D-15-0783.1.

    • Search Google Scholar
    • Export Citation
  • Fletcher, N. H., 1962: Physics of Rain Clouds. Cambridge University Press, 386 pp.

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

    • Search Google Scholar
    • Export Citation
  • Freeman, N. M., and N. S. Lovenduski, 2016: Mapping the Antarctic polar front: Weekly realizations from 2002 to 2014. Earth Syst. Sci. Data, 8, 191198, https://doi.org/10.5194/essd-8-191-2016.

    • Search Google Scholar
    • Export Citation
  • Grasmick, C., B. Geerts, J. R. French, S. Haimov, and R. M. Rauber, 2022: Estimating microphysics properties in ice-dominated clouds from airborne Ka–W-band dual-wavelength ratio reflectivity in close proximity to in situ probes. J. Atmos. Oceanic Technol., 39, 18151833, https://doi.org/10.1175/JTECH-D-21-0147.1.

    • Search Google Scholar
    • Export Citation
  • Hallett, J., and S. C. Mossop, 1974: Production of secondary ice particles during the riming process. Nature, 249, 2628 https://doi.org/10.1038/249026a0.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., and Coauthors, 2008: Testing IWC retrieval methods using radar and ancillary measurements with in situ data. J. Appl. Meteor. Climatol., 47, 135163, https://doi.org/10.1175/2007JAMC1606.1.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., S. Y. Matrosov, and N. B. Wood, 2016: Toward improving ice water content and snow-rate retrievals from radars. Part I: X and W bands, emphasizing CloudSat. J. Appl. Meteor. Climatol., 55, 20632090, https://doi.org/10.1175/JAMC-D-15-0290.1.

    • Search Google Scholar
    • Export Citation
  • Hu, Y., Z. Liu, D. Winker, M. Vaughan, V. Noel, L. Bissonnette, G. Roy, and M. McGill, 2006: Simple relationship between lidar multiple scattering and depolarization for water clouds. Opt. Lett., 31, 18091811, https://doi.org/10.1364/OL.31.001809.

    • Search Google Scholar
    • Export Citation
  • Hu, Y., S. Rodier, K. Xu, W. Sun, J. Huang, B. Lin, P. Zhai, and D. Josset, 2010: Occurrence, liquid water content, and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements. J. Geophys. Res., 115, D00H34, https://doi.org/10.1029/2009JD012384.

    • Search Google Scholar
    • Export Citation
  • Hu, Y., Z. J. Lebo, B. Geerts, Y. Hu, and Y. Wang, 2023: Vertical structure and ice production processes of shallow convective postfrontal clouds over the Southern Ocean in MARCUS. Part II: Modeling study. J. Atmos. Sci., 80, 13071327, https://doi.org/10.1175/JAS-D-21-0272.1.

    • Search Google Scholar
    • Export Citation
  • Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2021: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) version 2.1. J. Climate, 34, 29232939, https://doi.org/10.1175/JCLI-D-20-0166.1.

    • Search Google Scholar
    • Export Citation
  • Huang, Y., 2017: Observations and simulations of cloud thermodynamic phase over the Southern Ocean. Ph.D. thesis, Monash University, 179 pp., https://doi.org/10.4225/03/58b4eb811b885.

  • Huang, Y., T. Chubb, D. Baumgardner, M. deHoog, S. T. Siems, and M. J. Manton, 2017: Evidence for secondary ice production in Southern Ocean open cellular convection. Quart. J. Roy. Meteor. Soc., 143, 16851703, https://doi.org/10.1002/qj.3041.

    • Search Google Scholar
    • Export Citation
  • Jensen, M., S. Giangrande, T. Fairless, and A. Zhou, 2017: Interpolated sonde (INTERPOLATEDSONDE): ARM Mobile Facility (MAR) Hobart, AUS to Antarctic coast—Resupply ship Aurora Australis; AMF2 (M1). Subset used: 31 October 2017–24 March 2018. ARM Data Center, accessed 10 January 2020, https://doi.org/10.5439/1095316.

    • Search Google Scholar
    • Export Citation
  • Keeler, J. M., B. F. Jewett, R. M. Rauber, G. M. McFarquhar, R. M. Rasmussen, L. Xue, C. Liu, and G. Thompson, 2016: Dynamics of cloud-top generating cells in winter cyclones. Part II: Radiative and instability forcing. J. Atmos. Sci., 73, 15291553, https://doi.org/10.1175/JAS-D-15-0127.1.

    • Search Google Scholar
    • Export Citation
  • Kollias, P., B. P. Treserras, and A. Protat, 2019: Calibration of the 2007–2017 record of atmospheric radiation measurements cloud radar observations using CloudSat. Atmos. Meas. Tech., 12, 49494964, https://doi.org/10.5194/amt-12-4949-2019.

    • Search Google Scholar
    • Export Citation
  • Kolstad, E. W., T. J. Bracegirdle, and I. A. Seierstad, 2009: Marine cold-air outbreaks in the North Atlantic: Temporal distribution and associations with large-scale atmospheric circulation. Climate Dyn., 33, 187197, https://doi.org/10.1007/s00382-008-0431-5.

    • Search Google Scholar
    • Export Citation
  • Korolev, A., and Coauthors, 2017: Mixed-phase clouds: Progress and challenges. Ice Formation and Evolution in Clouds and Precipitation: Measurement and Modeling Challenges, Meteor. Monogr., No. 58, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1.

  • Kyrouac, J., and S. Springston, 2017: Meteorological measurements associated with the Aerosol Observing System (AOSMET): ARM Mobile Facility (MAR) Hobart, AUS to Antarctic coast—Resupply ship Aurora Australis; AMF2 (M1). Subset used: 29 October 2017–24 March 2018. ARM Data Center, accessed 10 January 2020, https://doi.org/10.5439/1025153.

  • Lang, F., Y. Huang, A. Protat, S. C. H. Truong, S. T. Siems, and M. J. Manton, 2021: Shallow convection and precipitation over the Southern Ocean: A case study during the CAPRICORN 2016 field campaign. J. Geophys. Res. Atmos., 126, e2020JD034088, https://doi.org/10.1029/2020JD034088.

    • Search Google Scholar
    • Export Citation
  • Lawson, R. P., S. Woods, and H. Morrison, 2015: The microphysics of ice and precipitation development in tropical cumulus clouds. J. Atmos. Sci., 72, 24292445, https://doi.org/10.1175/JAS-D-14-0274.1.

    • Search Google Scholar
    • Export Citation
  • Lenaerts, J. T. M., K. Van Tricht, S. Lhermitte, and T. S. L’Ecuyer, 2017: Polar clouds and radiation in satellite observations, reanalyses, and climate models. Geophys. Res. Lett., 44, 33553364, https://doi.org/10.1002/2016GL072242.

    • Search Google Scholar
    • Export Citation
  • Liao, L., and R. Meneghini, 2022: GPM DPR retrievals: Algorithm, evaluation, and validation. Remote Sens., 14, 843, https://doi.org/10.3390/rs14040843.

    • Search Google Scholar
    • Export Citation
  • Luke, E. P., F. Yang, P. Kollias, A. M. Vogelmann, and M. Maahn, 2021: New insights into ice multiplication using remote-sensing observations of slightly supercooled mixed-phase clouds in the Arctic. Proc. Natl. Acad. Sci. USA, 118, e2021387118, https://doi.org/10.1073/pnas.2021387118.

    • Search Google Scholar
    • Export Citation
  • Mace, G. G., and A. Protat, 2018: Clouds over the Southern Ocean as observed from the R/V Investigator during CAPRICORN. Part I: Cloud occurrence and phase partitioning. J. Appl. Meteor. Climatol., 57, 17831803, https://doi.org/10.1175/JAMC-D-17-0194.1.

    • Search Google Scholar
    • Export Citation
  • Mace, G. G., Q. Zhang, M. Vaughan, R. Marchand, G. Stephens, C. Trepte, and D. Winker, 2009: A description of hydrometeor layer occurrence statistics derived from the first year of merged CloudSat and CALIPSO data. J. Geophys. Res., 114, D00A26, https://doi.org/10.1029/2007JD009755.

    • Search Google Scholar
    • Export Citation
  • Mace, G. G., A. Protat, and S. Benson, 2021a: Mixed-phase clouds over the Southern Ocean as observed from satellite and surface based lidar and radar. J. Geophys. Res. Atmos., 126, e2021JD034569, https://doi.org/10.1029/2021JD034569.

    • Search Google Scholar
    • Export Citation
  • Mace, G. G., and Coauthors, 2021b: Southern Ocean cloud properties derived from CAPRICORN and MARCUS data. J. Geophys. Res. Atmos., 126, e2020JD033368, https://doi.org/10.1029/2020JD033368.

    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., and A. J. Heymsfield, 2008: Estimating ice content and extinction in precipitating cloud systems from CloudSat radar measurements. J. Geophys. Res., 113, D00A05, https://doi.org/10.1029/2007JD009633.

    • Search Google Scholar
    • Export Citation
  • McCluskey, C. S., and Coauthors, 2018: Observations of ice nucleating particles over Southern Ocean waters. Geophys. Res. Lett., 45, 11 98911 997, https://doi.org/10.1029/2018GL079981.

    • Search Google Scholar
    • Export Citation
  • McCoy, D. T., D. L. Hartmann, and D. P. Grosvenor, 2014: Observed Southern Ocean cloud properties and shortwave reflection. Part II: Phase changes and low cloud feedback. J. Climate, 27, 88588868, https://doi.org/10.1175/JCLI-D-14-00288.1.

    • Search Google Scholar
    • Export Citation
  • McCoy, I. L., R. Wood, and J. K. Fletcher, 2017: Identifying meteorological controls on open and closed mesoscale cellular convection associated with marine cold air outbreaks. J. Geophys. Res. Atmos., 122, 11 67811 702, https://doi.org/10.1002/2017JD027031.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G., and Coauthors, 2021: Observations of clouds, aerosols, precipitation, and surface radiation over the Southern Ocean: An overview of CAPRICORN, MARCUS, MICRE, and SOCRATES. Bull. Amer. Meteor. Soc., 102, E894E928, https://doi.org/10.1175/BAMS-D-20-0132.1.

    • Search Google Scholar
    • Export Citation
  • Meyers, M. P., P. J. DeMott, and W. R. Cotton, 1992: New primary ice-nucleation parameterizations in an explicit cloud model. J. Appl. Meteor., 31, 708721, https://doi.org/10.1175/1520-0450(1992)031<0708:NPINPI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Morrison, A. E., S. T. Siems, and M. J. Manton, 2011: A three-year climatology of cloud-top phase over the Southern Ocean and North Pacific. J. Climate, 24, 24052418, https://doi.org/10.1175/2010JCLI3842.1.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., and Coauthors, 2011: Intercomparison of cloud model simulations of Arctic mixed-phase boundary layer clouds observed during SHEBA/FIRE-ACE. J. Adv. Model. Earth Syst., 3, M05001, https://doi.org/10.1029/2011MS000066.

    • Search Google Scholar
    • Export Citation
  • Muhlbauer, A., I. L. McCoy, and R. Wood, 2014: Climatology of stratocumulus cloud morphologies: Microphysical properties and radiative effects. Atmos. Chem. Phys., 14, 66956716, https://doi.org/10.5194/acp-14-6695-2014.

    • Search Google Scholar
    • Export Citation
  • Naud, C. M., J. F. Booth, K. Lamer, R. Marchand, A. Protat, and G. M. McFarquhar, 2020: On the relationship between the marine cold air outbreak M parameter and low-level cloud heights in the midlatitudes. J. Geophys. Res. Atmos., 125, e2020JD032465, https://doi.org/10.1029/2020JD032465.

    • Search Google Scholar
    • Export Citation
  • Plummer, D. M., G. M. McFarquhar, R. M. Rauber, B. F. Jewett, and D. C. Leon, 2015: Microphysical properties of convectively generated fall streaks within the stratiform comma head region of continental winter cyclones. J. Atmos. Sci., 72, 24652483, https://doi.org/10.1175/JAS-D-14-0354.1.

    • Search Google Scholar
    • Export Citation
  • Rangno, A. L., and P. V. Hobbs, 1991: Ice particle concentrations and precipitation development in small polar maritime cumuliform clouds. Quart. J. Roy. Meteor. Soc., 117, 207241, https://doi.org/10.1002/qj.49711749710.

    • Search Google Scholar
    • Export Citation
  • Rauber, R. M., and A. Tokay, 1991: An explanation for the existence of supercooled water at the top of cold clouds. J. Atmos. Sci., 48, 10051023, https://doi.org/10.1175/1520-0469(1991)048<1005:AEFTEO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rauber, R. M., and Coauthors, 2015: The role of cloud-top generating cells and boundary layer circulations in the finescale radar structure of a winter cyclone over the Great Lakes. Mon. Wea. Rev., 143, 22912318, https://doi.org/10.1175/MWR-D-14-00350.1.

    • Search Google Scholar
    • Export Citation
  • Rosenow, A. A., D. M. Plummer, R. M. Rauber, G. M. McFarquhar, B. F. Jewett, and D. Leon, 2014: Vertical velocity and physical structure of generating cells and convection in the comma head region of continental winter cyclones. J. Atmos. Sci., 71, 15381558, https://doi.org/10.1175/JAS-D-13-0249.1.

    • Search Google Scholar
    • Export Citation
  • Sassen, K., 2005: Polarization in lidar. Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, C. Weitkamp, Ed., Springer Series in Optical Sciences, Vol. 102, Springer, 19–42.

  • Shupe, M. D., 2007: A ground-based multisensor cloud phase classifier. Geophys. Res. Lett., 34, L22809, https://doi.org/10.1029/2007GL031008.

    • Search Google Scholar
    • Export Citation
  • Shupe, M. D., P. Kollias, S. Y. Matrosov, and T. L. Schneider, 2004: Deriving mixed-phase cloud properties from Doppler radar spectra. J. Atmos. Oceanic Technol., 21, 660670, https://doi.org/10.1175/1520-0426(2004)021<0660:DMCPFD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Shupe, M. D., S. Y. Matrosov, and T. Uttal, 2006: Arctic mixed-phase cloud properties derived from surface-based sensors at SHEBA. J. Atmos. Sci., 63, 697711, https://doi.org/10.1175/JAS3659.1.

    • Search Google Scholar
    • Export Citation
  • Sivaraman, C., D. Flynn, L. Riihimaki, and J. Comstock, 2020: Cloud mask from micropulse lidar (30SMPLCMASK1ZWANG): ARM Mobile Facility (ANX) Andenes, Norway; AMF1 (main site for COMBLE) (M1). Subset used: 11 February–1 June 2020. ARM Data Center, accessed 1 October 2020, https://doi.org/10.5439/1508389.

    • Search Google Scholar
    • Export Citation
  • Steiner, M., R. A. Houze Jr., and S. E. Yuter, 1995: Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteor., 34, 19782007, https://doi.org/10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and J. T. Fasullo, 2010: Simulation of present-day and twenty-first-century energy budgets of the Southern Oceans. J. Climate, 23, 440454, https://doi.org/10.1175/2009JCLI3152.1.

    • Search Google Scholar
    • Export Citation
  • Wang, M., and K. Johnson, 2017: Marine W-band ARM Cloud Radar, Active Remote Sensing of Clouds (ARSCLWACR1KOLLIASSHP). ARM, https://doi.org/10.5439/1498736.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., and Coauthors, 2020: Microphysical properties of generating cells over the Southern Ocean: Results from SOCRATES. J. Geophys. Res. Atmos., 125, e2019JD032237, https://doi.org/10.1029/2019JD032237.

    • Search Google Scholar
    • Export Citation
  • Westwater, E. R., Y. Han, M. D. Shupe, and S. Y. Matrosov, 2001: Analysis of integrated cloud liquid and precipitable water vapor retrievals from microwave radiometers during the Surface Heat Budget of the Arctic Ocean project. J. Geophys. Res., 106, 32 01932 030, https://doi.org/10.1029/2000JD000055.

    • Search Google Scholar
    • Export Citation
  • Wood, R., and C. S. Bretherton, 2006: On the relationship between stratiform low cloud cover and lower-tropospheric stability. J. Climate, 19, 64256432, https://doi.org/10.1175/JCLI3988.1.

    • Search Google Scholar
    • Export Citation
  • Wood, R., and D. L. Hartmann, 2006: Spatial variability of liquid water path in marine low cloud: The importance of mesoscale cellular convection. J. Climate, 19, 17481764, https://doi.org/10.1175/JCLI3702.1.

    • Search Google Scholar
    • Export Citation
  • Yu, C.-K., P.-R. Hsieh, S. E. Yuter, L.-W. Cheng, C.-L. Tsai, C.-Y. Lin, and Y. Chen, 2016: Measuring droplet fall speed with a high-speed camera: Indoor accuracy and potential outdoor applications. Atmos. Meas. Tech., 9, 17551766, https://doi.org/10.5194/amt-9-1755-2016.

    • Search Google Scholar
    • Export Citation
  • Zaremba, T. J., R. M. Rauber, G. M. McFarquhar, M. Hayman, J. A. Finlon, and D. M. Stechman, 2020: Phase characterization of cold sector Southern Ocean cloud tops: Results from SOCRATES. J. Geophys. Res. Atmos., 125, e2020JD033673, https://doi.org/10.1029/2020JD033673.

    • Search Google Scholar
    • Export Citation
  • Zhang, D., 2017: MWR retrievals (MWRRET1LILJCLOU): ARM Mobile Facility (MAR) Hobart, AUS to Antarctic coast—Resupply ship Aurora Australis; AMF2 (M1). Subset used: 21 October 2017– 23 March 2018. ARM Data Center, accessed 10 January 2020, https://doi.org/10.5439/1027369.

    • Search Google Scholar
    • Export Citation
  • Zhang, D., and M. Levin, 2019: Thermodynamic cloud phase (THERMOCLDPHASE), version 1.4.6. ARM Data Center, accessed 6 December 2022, https://doi.org/10.5439/1871014.

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