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

Yishi Hu aUniversity of Wyoming, Laramie, Wyoming

Search for other papers by Yishi Hu in
Current site
Google Scholar
PubMed
Close
,
Zachary J. Lebo aUniversity of Wyoming, Laramie, Wyoming

Search for other papers by Zachary J. Lebo in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-1064-4833
,
Bart Geerts aUniversity of Wyoming, Laramie, Wyoming

Search for other papers by Bart Geerts in
Current site
Google Scholar
PubMed
Close
,
Yonggang Wang bState University of New York at Oswego, Oswego, New York

Search for other papers by Yonggang Wang in
Current site
Google Scholar
PubMed
Close
, and
Yazhe Hu aUniversity of Wyoming, Laramie, Wyoming

Search for other papers by Yazhe Hu in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Yishi Hu and Z. J. Lebo’s current affiliation: School of Meteorology, University of Oklahoma, Norman, Oklahoma

© 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: Zachary J. Lebo, zachary.lebo@ou.edu

Abstract

Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Yishi Hu and Z. J. Lebo’s current affiliation: School of Meteorology, University of Oklahoma, Norman, Oklahoma

© 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: Zachary J. Lebo, zachary.lebo@ou.edu
Save
  • Alexander, S., G. McFarquhar, R. Marchand, A. Protat, E. Vignon, G. Mace, and A. 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
  • Andsager, K., K. Beard, and N. Laird, 1999: Laboratory measurements of axis ratios for large raindrops. J. Atmos. Sci., 56, 26732683, https://doi.org/10.1175/1520-0469(1999)056<2673:LMOARF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Atlas, R., C. Bretherton, P. 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
  • Barklie, R. H. D., and N. R. Gokhale, 1959: The freezing of supercooled water drops. Alberta hail, 1958 and related studies, McGill University Stormy Weather Group Science Rep. MW-30, 43–64.

  • Barrett, P., A. Blyth, P. Brown, and S. Abel, 2020: The structure of turbulence and mixed-phase cloud microphysics in a highly supercooled altocumulus cloud. Atmos. Chem. Phys., 20, 19211939, https://doi.org/10.5194/acp-20-1921-2020.

    • Search Google Scholar
    • Export Citation
  • Bennartz, R., 2007: Global assessment of marine boundary layer cloud droplet number concentration from satellite. J. Geophys. Res., 112, D02201, https://doi.org/10.1029/2006JD007547.

    • Search Google Scholar
    • Export Citation
  • Bigg, E., 1953: The supercooling of water. Proc. Phys. Soc., 66, 688694, https://doi.org/10.1088/0370-1301/66/8/309.

  • 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
  • Bodas-Salcedo, A., T. Andrews, A. V. Karmalkar, and M. A. Ringer, 2016: Cloud liquid water path and radiative feedbacks over the Southern Ocean. Geophys. Res. Lett., 43, 10 93810 946, https://doi.org/10.1002/2016GL070770.

    • Search Google Scholar
    • Export Citation
  • Brandes, E., G. Zhang, and J. Vivekanandan, 2002: Experiments in rainfall estimation with a polarimetric radar in a subtropical environment. J. Appl. Meteor., 41, 674685, https://doi.org/10.1175/1520-0450(2002)041<0674:EIREWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ceppi, P., D. McCoy, and D. Hartmann, 2016: Observational evidence for a negative shortwave cloud feedback in middle to high latitudes. Geophys. Res. Lett., 43, 13311339, https://doi.org/10.1002/2015GL067499.

    • Search Google Scholar
    • Export Citation
  • Cohard, J.-M., and J.-P. Pinty, 2000: A comprehensive two-moment warm microphysical bulk scheme. I: Description and tests. Quart. J. Roy. Meteor. Soc., 126, 18151842, https://doi.org/10.1256/smsqj.56613.

    • Search Google Scholar
    • Export Citation
  • Cooper, W., 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.

  • Cotton, W., G. Tripoli, R. Rauber, and E. Mulvihill, 1986: Numerical simulation of the effects of varying ice crystal nucleation rates and aggregation processes on orographic snowfall. J. Climate Appl. Meteor., 25, 16581680, https://doi.org/10.1175/1520-0450(1986)025<1658:NSOTEO>2.0.CO;2.

    • 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
  • Danabasoglu, G., and Coauthors, 2020: The Community Earth System Model version 2 (CESM2). J. Adv. Model. Earth Syst., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916.

    • Search Google Scholar
    • Export Citation
  • de Boer, G., G. Feingold, J. Harrington, M. Shupe, K. Sulia, and H. Morrison, 2011: Resilience of persistent Arctic mixed-phase clouds. Nat. Geosci., 5, 1117, https://doi.org/10.1038/ngeo1332.

    • Search Google Scholar
    • Export Citation
  • DeMott, P., and Coauthors, 2010: Predicting global atmospheric ice nuclei distributions and their impact on climate. Proc. Natl. Acad. Sci. USA, 107, 11 21711 222, https://doi.org/10.1073/pnas.0910818107.

    • 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 the airborne Wyoming 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
  • Forbes, R., and M. Ahlgrimm, 2014: On the representation of high-latitude boundary layer mixed-phase cloud in the ECMWF global model. Mon. Wea. Rev., 142, 34253445, https://doi.org/10.1175/MWR-D-13-00325.1.

    • Search Google Scholar
    • Export Citation
  • Gettelman, A., and H. Morrison, 2015: Advanced two-moment bulk microphysics for global models. Part I: Off-line tests and comparison with other schemes. J. Climate, 28, 12681287, https://doi.org/10.1175/JCLI-D-14-00102.1.

    • Search Google Scholar
    • Export Citation
  • Hallett, J., and S. 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
  • Hines, K., D. Bromwich, I. Silber, L. Russell, and L. Bai, 2021: Predicting frigid mixed-phase clouds for pristine coastal Antarctica. J. Geophys. Res. Atmos., 126, e2021JD035112, https://doi.org/10.1029/2021JD035112.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, https://doi.org/10.1175/MWR3199.1.

    • Search Google Scholar
    • Export Citation
  • Hoose, C., J. Kristjánsson, J.-P. Chen, and A. Hazra, 2010: A classical-theory-based parameterization of heterogeneous ice nucleation by mineral dust, soot, and biological particles in a global climate model. J. Atmos. Sci., 67, 24832503, https://doi.org/10.1175/2010JAS3425.1.

    • Search Google Scholar
    • Export Citation
  • Hu, Y., B. Geerts, M. Deng, C. D. Grasmick, Y. Wang, C. P. Lackner, Y. Hu, Z. J. Lebo, and D. Zhang, 2023: Vertical structure and ice production processes of shallow convective postfrontal clouds over the Southern Ocean in MARCUS. Part I: Observational study. J. Atmos. Sci., 80, 12851306, https://doi.org/10.1175/JAS-D-21-0243.1.

    • Search Google Scholar
    • Export Citation
  • Huang, Y., S. T. Siems, M. J. Manton, and G. Thompson, 2014: An evaluation of WRF simulations of clouds over the Southern Ocean with A-Train observations. Mon. Wea. Rev., 142, 647667, https://doi.org/10.1175/MWR-D-13-00128.1.

    • Search Google Scholar
    • Export Citation
  • Huang, Y., A. Protat, S. T. Siems, and M. J. Manton, 2015: A-Train observations of maritime midlatitude storm-track cloud systems: Comparing the Southern Ocean against the North Atlantic. J. Climate, 28, 19201939, https://doi.org/10.1175/JCLI-D-14-00169.1.

    • Search Google Scholar
    • Export Citation
  • Hyder, P., and Coauthors, 2018: Critical Southern Ocean climate model biases traced to atmospheric model cloud errors. Nat. Commun., 9, 3625, https://doi.org/10.1038/s41467-018-05634-2.

    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945, https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kahn, B., S. Nasiri, M. Schreier, and B. Baum, 2011: Impacts of subpixel cloud heterogeneity on infrared thermodynamic phase assessment. J. Geophys. Res., 116, D20201, https://doi.org/10.1029/2011JD015774.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kay, J. E., C. Wall, V. Yettella, B. Medeiros, C. Hannay, P. Caldwell, and C. Bitz, 2016: Global climate impacts of fixing the Southern Ocean shortwave radiation bias in the Community Earth System Model (CESM). J. Climate, 29, 46174636, https://doi.org/10.1175/JCLI-D-15-0358.1.

    • Search Google Scholar
    • Export Citation
  • Koontz, A., 2017: Cloud condensation nuclei particle counter (aosccn1colspectra). ARM Data Center, accessed 17 May 2022, https://doi.org/10.5439/1342134.

  • 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.

  • Lebo, Z. J., N. C. Johnson, and J. Y. Harrington, 2008: Radiative influences on ice crystal and droplet growth within mixed-phase stratus clouds. J. Geophys. Res., 113, D09203, https://doi.org/10.1029/2007JD009262.

    • Search Google Scholar
    • Export Citation
  • Mace, G. G., 2010: Cloud properties and radiative forcing over the maritime storm tracks of the Southern Ocean and North Atlantic derived from A-Train. J. Geophys. Res., 115, D10201, https://doi.org/10.1029/2009JD012517.

    • 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, 2021: 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
  • Marchand, R., T. Ackerman, E. Westwater, S. Clough, K. Cady-Pereira, and J. Liljegren, 2003: An assessment of microwave absorption models and retrievals of cloud liquid water using clear-sky data. J. Geophys. Res., 108, 4773, https://doi.org/10.1029/2003JD003843.

    • Search Google Scholar
    • Export Citation
  • McCluskey, C., 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., I. Tan, D. Hartmann, M. Zelinka, and T. Storelvmo, 2016: On the relationships among cloud cover, mixed-phase partitioning, and planetary albedo in GCMs. J. Adv. Model. Earth Syst., 8, 650668, https://doi.org/10.1002/2015MS000589.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., 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. DeMott, and W. 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
  • Milbrandt, J. A., and M. K. Yau, 2005: A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter. J. Atmos. Sci., 62, 30513064, https://doi.org/10.1175/JAS3534.1.

    • Search Google Scholar
    • Export Citation
  • Milbrandt, J. A., and H. Morrison, 2016: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part III: Introduction of multiple free categories. J. Atmos. Sci., 73, 975995, https://doi.org/10.1175/JAS-D-15-0204.1.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., and J. A. Milbrandt, 2015: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. J. Atmos. Sci., 72, 287311, https://doi.org/10.1175/JAS-D-14-0065.1.

    • 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, https://doi.org/10.1175/2008MWR2556.1.

    • Search Google Scholar
    • Export Citation
  • Naud, C., J. Booth, and A. Delgenio, 2014: Evaluation of Era-Interim and MERRA cloudiness in the Southern Ocean. J. Climate, 27, 21092124, https://doi.org/10.1175/JCLI-D-13-00432.1.

    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Search Google Scholar
    • Export Citation
  • Oue, M., A. Tatarevic, P. Kollias, D. Wang, K. Yu, and A. Vogelmann, 2020: The Cloud-resolving model Radar Simulator (CR-SIM) version 3.3: Description and applications of a virtual observatory. Geosci. Model Dev., 13, 19751998, https://doi.org/10.5194/gmd-13-1975-2020.

    • Search Google Scholar
    • Export Citation
  • Papritz, L., S. Pfahl, H. Sodemann, and H. Wernli, 2015: A climatology of cold air outbreaks and their impact on air–sea heat fluxes in the high-latitude South Pacific. J. Climate, 28, 342364, https://doi.org/10.1175/JCLI-D-14-00482.1.

    • Search Google Scholar
    • Export Citation
  • Pruppacher, H., and J. Klett, 1997: Microphysics of Clouds and Precipitation. Atmospheric and Oceanographic Sciences Library, Vol. 18, Kluwer Academic, 433446.

  • Ryzhkov, A., M. Pinsky, A. Pokrovsky, and A. Khain, 2011: Polarimetric radar observation operator for a cloud model with spectral microphysics. J. Appl. Meteor. Climatol., 50, 873894, https://doi.org/10.1175/2010JAMC2363.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2019: A description of the Advanced Research WRF Model version 4. NCAR Tech. Note NCAR/TN-556+STR, 145 pp., https://doi.org/10.5065/1dfh-6p97.

  • Sotiropoulou, G., E. Vignon, G. Young, H. Morrison, S. O’Shea, T. Lachlan-Cope, A. Berne, and A. Nenes, 2021: Secondary ice production in summer clouds over the Antarctic coast: An underappreciated process in atmospheric models. Atmos. Chem. Phys., 21, 755771, https://doi.org/10.5194/acp-21-755-2021.

    • Search Google Scholar
    • Export Citation
  • Tan, I., T. Storelvmo, and M. D. Zelinka, 2016: Observational constraints on mixed-phase clouds imply higher climate sensitivity. Science, 352, 224227, https://doi.org/10.1126/science.aad5300.

    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., J.-P. Chen, Z. Li, C. Wang, and C. Zhang, 2012: Impact of aerosols on convective clouds and precipitation. Rev. Geophys., 50, RG2001, https://doi.org/10.1029/2011RG000369.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, https://doi.org/10.1175/2008MWR2387.1.

    • Search Google Scholar
    • Export Citation
  • Twomey, S., 1974: Pollution and the planetary albedo. Atmos. Environ., 8, 12511256, https://doi.org/10.1016/0004-6981(74)90004-3.

  • Vignon, E., N. Besic, N. Jullien, J. Gehring, and A. Berne, 2019: Microphysics of snowfall over coastal East Antarctica simulated by polar WRF and observed by radar. J. Geophys. Res. Atmos., 124, 11 45211 476, https://doi.org/10.1029/2019JD031028.

    • Search Google Scholar
    • Export Citation
  • Vignon, E., and Coauthors, 2021: Challenging and improving the simulation of mid-level mixed-phase clouds over the high-latitude Southern Ocean. J. Geophys. Res. Atmos., 126, e2020JD033490, https://doi.org/10.1029/2020JD033490.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., D. Zhang, X. Liu, and Z. Wang, 2018: Distinct contributions of ice nucleation, large-scale environment, and shallow cumulus detrainment to cloud phase partitioning with NCAR CAM5. J. Geophys. Res. Atmos., 123, 11321154, https://doi.org/10.1002/2017JD027213.

    • 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
  • Young, G., T. Lachlan-Cope, S. O’Shea, C. Dearden, C. Listowski, K. Bower, T. Choularton, and M. Gallagher, 2019: Radiative effects of secondary ice enhancement in coastal Antarctic clouds. Geophys. Res. Lett., 46, 23122321, https://doi.org/10.1029/2018GL080551.

    • Search Google Scholar
    • Export Citation
  • Young, K., 1974: The role of contact nucleation in ice phase initiation in clouds. J. Atmos. Sci., 31, 768776, https://doi.org/10.1175/1520-0469(1974)031<0768:TROCNI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 803 360 31
Full Text Views 343 233 10
PDF Downloads 408 258 8