• Abdul-Razzak, H., and S. Ghan, 2000: A parameterization of aerosol activation, 2. Multiple aerosol types. J. Geophys. Res., 105, 68376844, https://doi.org/10.1029/1999JD901161.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bacmesiter, J. T., M. J. Suarez, and F. R. Robertson, 2006: Rain reevaporation, boundary layer–convection interactions, and Pacific rainfall patterns in an AGCM. J. Amer. Sci., 63, 33833403, https://doi.org/10.1175/JAS3791.1.

    • Search Google Scholar
    • Export Citation
  • Barahona, D., 2018: On the thermodynamic and kinetic aspects of immersion ice nucleation. Atmos. Chem. Phys., 18, 17 11917 141, https://doi.org/10.5194/acp-18-17119-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barahona, D., and A. Nenes, 2008: Parameterization of cirrus formation in large scale models: Homogeneous nucleation. J. Geophys. Res., 113, D11211, https://doi.org/10.1029/2007JD009355.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barahona, D., and A. Nenes, 2009: Parameterizing the competition between homogeneous and heterogeneous freezing in cirrus cloud formation–monodisperse ice nuclei. Atmos. Chem. Phys., 9, 369381, https://doi.org/10.5194/acp-9-369-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barahona, D., A. Molod, J. Bacmeister, A. Nenes, A. Gettelman, H. Morrison, V. Phillips, and A. Eichmann, 2014: Development of two-moment cloud microphysics for liquid and ice within the Nasa Goddard Earth Observing System Model (GEOS-5). Geosci. Model Dev., 7, 17331766, https://doi.org/10.5194/gmd-7-1733-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barahona, D., A. Molod, and H. Kalesse, 2017: Direct estimation of the global distribution of vertical velocity within cirrus clouds. Sci. Rep., 7, 6840, https://doi.org/10.1038/s41598-017-07038-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, E. A., and L. M. Polvani, 2015: CMIP5 projections of Arctic amplification, of the North American/North Atlantic circulation, and of their relationship. J. Climate, 28, 52545271, https://doi.org/10.1175/JCLI-D-14-00589.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barrett, I., R. J. Hogan, and R. M. Forbes, 2017: Why are mixed-phase altocumulus clouds poorly predicted by large-scale models? Part 2. J. Geophys. Res. Atmos., 122, 99279944, https://doi.org/10.1002/2016JD026322.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barton, N. P., S. A. Klein, and J. S. Boyle, 2014: On the contribution of longwave radiation to global climate model biases in Arctic lower tropospheric stability. J. Climate, 27, 72507269, https://doi.org/10.1175/JCLI-D-14-00126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beesley, J. A., 2000: Estimating the effect of clouds on the Arctic surface energy budget. J. Geophys. Res., 105, 10 10310 117, https://doi.org/10.1029/2000JD900043.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bennartz, R., and Coauthors, 2013: Greenland melt extent enhanced by low-level liquid clouds. Nature, 496, 8386, https://doi.org/10.1038/nature12002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bergeron, T., 1935: On the physics of clouds and precipitation. Proces Verbaux de l’Association de Meteorologie. International Union of Geodesy and Geophysics, 156178.

    • Search Google Scholar
    • Export Citation
  • Bian, H., and Coauthors, 2013: Source attributions of pollution to the western Arctic during the NASA ARCTAS field campaign. Atmos. Chem. Phys., 13, 47074721, https://doi.org/10.5194/acp-13-4707-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bigg, E. K., 1953: The formation of atmospheric ice crystals by the freezing of droplets. Quart. J. Roy. Meteor. Soc., 79, 510519, https://doi.org/10.1002/qj.49707934207.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bodas-Salcedo, A., and Coauthors, 2011: COSP: Satellite simulation software for model assessment. Bull. Amer. Meteor. Soc., 92, 10231043, https://doi.org/10.1175/2011BAMS2856.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boeke, R. C., and P. C. Taylor, 2016: Evaluation of the Arctic surface radiation budget in CMIP5 models. J. Geophys. Res. Atmos., 121, 85258548, https://doi.org/10.1002/2016JD025099.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boeke, R. C., and P. C. Taylor, 2018: Seasonal energy exchange in sea ice retreat regions contributes to differences in projected Arctic warming. Nat. Commun., 9, 5017, https://doi.org/10.1038/s41467-018-07061-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Breen, K. H., D. Barahona, T. Yuan, H. Bian, and S. C. James, 2021: Effect of volcanic emissions on clouds during the 2008 and 2018 Kilauea degassing events. Atmos. Chem. Phys., 21, 77497771, https://doi.org/10.5194/acp-21-7749-2021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carn, S., L. Clarisse, and A. J. Prata, 2016: Multi-decadal satellite measurements of global volcanic degassing. J. Volcanol. Geotherm. Res., 311, 99134, https://doi.org/10.1016/j.jvolgeores.2016.01.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cesana, G., J. E. Kay, H. Chepfer, J. M. English, and G. de Boer, 2012: Ubiquitous low-level liquid-containing Arctic clouds: New observations and climate model constraints from CALIPSO-GOCCP. Geophys. Res. Lett., 39, 2012GL053385, https://doi.org/10.1029/2012GL053385.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cesana, G., D. E. Waliser, X. Jiang, and J.-L.-F. Li, 2015: Multimodel evaluation of cloud phase transition using satellite and reanalysis data. J. Geophys. Res. Atmos., 120, 78717892, https://doi.org/10.1002/2014JD022932.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chin, M., T. Diehl, O. Dubovik, T. F. Eck, B. N. Holben, A. Sinyuk, and D. G. Streets, 2009: Light absorption by pollution, dust, and biomass burning aerosols: A global model study and evaluation with AERONET measurements. Ann. Geophys., 27, 34393464 https://doi.org/10.5194/angeo-27-3439-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chylek, P., and C. Borel, 2004: Mixed phase cloud water/ice structure from high spatial resolution satellite data. Geophys. Res. Lett., 31, L14104, https://doi.org/10.1029/2004GL020428.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Colarco, P., A. da Silva, M. Chin, and T. Diehl, 2010: Online simulations of global aerosol distributions in the NASA GEOS-4 model and comparisons to satellite and ground-based aerosol optical depth. J. Geophys. Res., 115, D14207, https://doi.org/10.1029/2009JD012820.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Connolly, P. J., O. Möhler, P. R. Field, H. Saathoff, R. Burgess, T. Choularton, and M. Gallagher, 2009: Studies of heterogeneous freezing by three different desert dust samples. Atmos. Chem. Phys., 9, 28052824, https://doi.org/10.5194/acp-9-2805-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coopman, Q., J. Riedi, S. Zeng, and T. J. Garrett, 2020: Space-based analysis of the cloud thermodynamic phase transition for varying microphysical and meteorological regimes. Geophys. Res. Lett., 47, e2020GL087122, https://doi.org/10.1029/2020GL087122.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cullather, R. I., Y. K. Lim, L. N. Boisvert, L. Brucker, J. N. Lee, and S. M. Nowicki, 2016: Analysis of the warmest Arctic winter, 2015–2016. Geophys. Res. Lett., 43, 10 80810 816, https://doi.org/10.1002/2016GL071228.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Curry, J. A., and E. E. Ebert, 1992: Annual cycle of radiation fluxes over the Arctic Ocean: Sensitivity to cloud optical properties. J. Climate, 5, 12671280, https://doi.org/10.1175/1520-0442(1992)005<1267:ACORFO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Curry, J. A., W. B. Rossow, D. Randall, and J. L. Schramm, 1996: Overview of Arctic cloud and radiation characteristics. J. Climate, 9, 17311764, https://doi.org/10.1175/1520-0442(1996)009<1731:OOACAR>2.0.CO;2.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Boer, G., E. W. Eloranta, and M. D. Shupe, 2009: Arctic mixed-phase stratiform cloud properties from multiple years of surface-based measurements at two high-latitude locations. J. Amer. Sci., 66, 28742887, https://doi.org/10.1175/2009JAS3029.1.

    • Search Google Scholar
    • Export Citation
  • de Boer, G., T. Hashino, and G. J. Tripoli, 2010: Ice nucleation through immersion freezing in mixed-phase stratiform clouds: Theory and numerical simulations. Atmos. Res., 96, 315324, https://doi.org/10.1016/j.atmosres.2009.09.012.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ehrlich, A., and Coauthors, 2019: A comprehensive in situ and remote sensing data set from the Arctic cloud observations using airborne measurements during polar day (ACLOUD) campaign. Earth Syst. Sci. Data, 11, 18531881, https://doi.org/10.5194/essd-11-1853-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elsaesser, G. S., C. W. O’Dell, M. D. Lebsock, R. Bennartz, T. J. Greenwald, and F. J. Wentz, 2017: The multisensor advanced climatology of liquid water path (MAC-LWP). J. Climate, 30, 10 19310 210, https://doi.org/10.1175/JCLI-D-16-0902.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • English, J., J. E. Kay, A. Gettelman, X. Liu, Y. Wang, Y. Zhang, and H. Chepfer, 2014: Contributions of clouds, surface albedos, and mixed-phase ice nucleation schemes to Arctic radiation biases in CAM5. J. Climate, 27, 51745197, https://doi.org/10.1175/JCLI-D-13-00608.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, S.-M., 2013: Modeling of observed mineral dust aerosols in the Arctic and the impact on winter season low-level clouds. J. Geophys. Res. Atmos., 118, 11 16111 174, https://doi.org/10.1002/jgrd.50842.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, S.-M., and Coauthors, 2012: Inferring ice formation processes from global-scale black carbon profiles observed in the remote atmosphere and model simulations. J. Geophys. Res., 117, D23205, https:/doi.org/10.1029/2012JD018126.

    • Search Google Scholar
    • Export Citation
  • Field, P. R., R. J. Hogan, P. R. A. Brown, A. J. Illingworth, T. W. Choularton, P. H. Kaye, E. Hirst, and R. Greenaway, 2004: Simultaneous radar and aircraft observations of mixed-phase cloud at the 100 m scale. Quart. J. Roy. Meteor. Soc., 130, 18771904, https://doi.org/10.1256/qj.03.102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Findeisen, W., 1938: Kolloid-Meteorologische Vorgänge bei Neiderschlagsbildung. Meteor. Z., 55, 121133.

  • Frey, W. R., and J. E. Kay, 2018: The influence of extratropical cloud phase and amount feedbacks on climate sensitivity. Climate Dyn., 50, 30973116, https://doi.org/10.1007/s00382-017-3796-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fridlind, A. M., A. S. Ackerman, G. McFarquhar, G. Zhang, M. R. Poellot, P. J. DeMott, A. J. Prenni, and A. J. Heymsfield, 2007: Ice properties of single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 2. Model results. J. Geophys. Res., 112, D24202, https://doi.org/10.1029/2007JD008646.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fridlind, A. M., B. V. Diedenhoven, A. S. Ackerman, A. Avramov, A. Mrowiec, H. Morrison, P. Zuidema, and M. D. Shupe, 2012: A FIRE-ACE/SHEBA case study of mixed-phase Arctic boundary layer clouds: Entrainment rate limitations on rapid primary ice nucleation processes. J. Amer. Sci., 69, 365389, https://doi.org/10.1175/JAS-D-11-052.1.

    • Search Google Scholar
    • Export Citation
  • Fu, Q., and S. Hollars, 2004: Testing mixed-phase cloud water vapor parameterizations with SHEBA/FIRE–ACE observations. J. Amer. Sci., 61, 20832091, https://doi.org/10.1175/1520-0469(2004)061<2083:TMCWVP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gaustad, K. L., and D. D. Turner, 2007: MWRRET value-added product: The retrieval of liquid water path and precipitable water vapor from microwave radiometer (MWR) datasets. Tech. Rep. DOE/SC-ARM/TR-081, 19 pp., https://doi.org/10.2172/948370.

    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gettelman, A., and Coauthors, 2010: Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the Community Atmosphere Model. J. Geophys. Res., 115, D18216, https://doi.org/10.1029/2009JD013797.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gettelman, A., H. Morrison, S. Santos, P. Bogenschutz, and P. M. Caldwell, 2015: Advanced two-moment bulk microphysics for global models. Part II: Global model solutions and aerosol–cloud interactions. J. Climate, 28, 12881307, https://doi.org/10.1175/JCLI-D-14-00103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grosvenor, D. P., and R. Wood, 2014: The effect of solar zenith angle on MODIS cloud optical and microphysical retrievals within marine liquid water clouds. Atmos. Chem. Phys., 14, 72917321, https://doi.org/10.5194/acp-14-7291-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoose, C., and O. Möhler, 2012: Heterogeneous ice nucleation on atmospheric aerosols: A review of results from laboratory experiments. Atmos. Chem. Phys., 12, 98179854, https://doi.org/10.5194/acp-12-9817-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoose, C., J. Kristjansson, J.-C. 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ickes, L., A. Welti, and U. Lohmann, 2017: Classical nucleation theory of immersion freezing: Sensitivity of contact angle schemes to thermodynamic and kinetic parameters. Atmos. Chem. Phys., 17, 17131739, https://doi.org/10.5194/acp-17-1713-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Intrieri, J. M., M. D. Shupe, T. Uttal, and B. J. McCarty, 2002: An annual cycle of Arctic cloud characteristics observed by radar and lidar at SHEBA. J. Geophys. Res., 107, 8030, https://doi.org/10.1029/2000JC000423.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H., W. R. Cotton, J. O. Pinto, J. A. Curry, and M. J. Weissbluth, 2000: Cloud resolving simulations of mixed-phase Arctic stratus observed during BASE: Sensitivity to concentration of ice crystals and large-scale heat and moisture advection. J. Amer. Sci., 57, 21052117, https://doi.org/10.1175/1520-0469(2000)057<2105:CRSOMP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kärcher, B., 2003: Simulating gas–aerosol–cirrus interactions: Process-oriented microphysical model and applications. Atmos. Chem. Phys., 3, 16451664, https://doi.org/10.5194/acp-3-1645-2003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karlsson, J., and G. Svensson, 2011: The simulation of Arctic clouds and their influence on the winter surface temperature in present-day climate in the CMIP3 multi-model dataset. Climate Dyn., 36, 623635, https://doi.org/10.1007/s00382-010-0758-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, S., and Coauthors, 2018: Surface irradiances of edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data product. J. Climate, 31, 45014527, https://doi.org/10.1175/JCLI-D-17-0523.1.

    • Search Google Scholar
    • Export Citation
  • Kawai, H., S. Yukimoto, T. Koshiro, N. Oshima, T. Tanaka, H. Yoshimura, and R. Nagasawa, 2019: Significant improvement of cloud representation in the global climate model MRI-ESM2. Geosci. Model Dev., 12, 28752897, https://doi.org/10.5194/gmd-12-2875-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and A. Gettelman, 2009: Cloud influence on and response to seasonal Arctic sea ice loss. J. Geophys. Res., 114, D18204, https://doi.org/10.1029/2009JD011773.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and T. L’Ecuyer, 2013: Observational constraints on Arctic Ocean clouds and radiative fluxes during the early 21st century. J. Geophys. Res. Atmos., 118, 72197236, https://doi.org/10.1002/jgrd.50489.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M., and Y. Kogan, 2000: A new cloud physics parameterization in a large-eddy simulation model of marine stratocumulus. Mon. Wea. Rev., 128, 229243, https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, M. D., S. Platnick, P. Yang, G. T. Arnold, M. A. Gray, J. C. Riedi, S. A. Ackerman, and K.-N. Liou, 2004: Remote sensing of liquid water and ice cloud optical thickness and effective radius in the Arctic: Application of airborne multispectral MAS data. J. Atmos. Oceanic Technol., 21, 857875, https://doi.org/10.1175/1520-0426(2004)021<0857:RSOLWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6, 15871606, https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, S. A., and Coauthors, 2009: Intercomparison of model simulations of mixed-phase clouds observed during the ARM mixed-phase Arctic cloud experiment. I: Single-layer cloud. Quart. J. Roy. Meteor. Soc., 135, 9791002, https://doi.org/10.1002/qj.416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knopf, D. A., and P. A. Alpert, 2013: A water activity based model of heterogeneous ice nucleation kinetics for freezing of water and aqueous solution droplets. Faraday Discuss., 165, 513534, https://doi.org/10.1039/c3fd00035d.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Komurcu, M., and Coauthors, 2014: Intercomparison of the cloud water phase among global climate models. J. Geophys. Res. Atmos., 119, 33723400, https://doi.org/10.1002/2013JD021119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koop, T., B. Luo, A. Tsias, and T. Peter, 2000: Water activity as the determinant for homogeneous ice nucleation in aqueous solutions. Nature, 406, 611614, https://doi.org/10.1038/35020537.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Korolev, A. V., G. A. Isaac, S. G. Cober, J. W. Strapp, and J. Hallett, 2003: Microphysical characterization of mixed-phase clouds. Quart. J. Roy. Meteor. Soc., 129, 3965, https://doi.org/10.1256/qj.01.204.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laaksonen, A., J. Malila, and A. Nenes, 2020: Heterogeneous nucleation of water vapor on different types of black carbon particles. Atmos. Chem. Phys., 20, 13 57913 589, https://doi.org/10.5194/acp-20-13579-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ladino Moreno, L. A., O. Stetzer, and U. Lohmann, 2013: Contact freezing: A review of experimental studies. Atmos. Chem. Phys., 13, 97459769, https://doi.org/10.5194/acp-13-9745-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lance, S., and Coauthors, 2011: Cloud condensation nuclei as a modulator of ice processes in Arctic mixed-phase clouds. Atmos. Chem. Phys., 11, 80038015, https://doi.org/10.5194/acp-11-8003-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Appl. Meteor. Climatol., 22, 10651092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., S. Xie, and S. J. Ghan, 2007: Evaluation of a new mixed-phase cloud microphysics parameterization with CAM3 single-column model and M-PACE observations. Geophys. Res. Lett., 34, L23712, https://doi.org/10.1029/2007GL031446.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., and Coauthors, 2011: Testing cloud microphysics parameterizations in NCAR CAM5 with ISDAC and M-PACE observations. J. Geophys. Res., 116, D00T11, https://doi.org/10.1029/2011JD015889.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., D. Daum, and S. Yum, 2006: Analytical expression for the relative dispersion of the cloud droplet size distribution. Geophys. Res. Lett., 33, L02810, https://doi.org/10.1029/2005GL024052.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., P. Daum, H. Guo, and Y. Peng, 2008: Dispersion bias, dispersion effect, and the aerosol–cloud conundrum. Environ. Res. Lett., 3, 045021, https://doi.org/10.1088/1748-9326/3/4/045021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., and Coauthors, 2018: Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product. J. Climate, 31, 895918, https://doi.org/10.1175/JCLI-D-17-0208.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCoy, D. T., I. Tan, D. Hartmann, M. D. 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.

    • Crossref
    • 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. Climatol., 31, 708721, https://doi.org/10.1175/1520-0450(1992)031<0708:NPINPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mioche, G., and Coauthors, 2017: Vertical distribution of microphysical properties of Arctic springtime low-level mixed-phase clouds over the Greenland and Norwegian seas. Atmos. Chem. Phys., 17, 12 84512 869, https://doi.org/10.5194/acp-17-12845-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mioche, G., O. Jourdan, M. Ceccaldi, and J. Delanoë, 2015: Variability of mixed-phase clouds in the Arctic with a focus on the Svalbard region: A study based on spaceborne active remote sensing. Atmos. Chem. Phys., 15, 24452461, https://doi.org/10.5194/acp-15-2445-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molod, A., L. Takacs, M. Suarez, J. Bacmeister, I. Song, and A. Eichmann, 2012: The GEOS-5 atmospheric general circulation model: Mean climate and development from MERRA to Fortuna. Tech. Rep. Series on Global Modeling and Data Assimilation, Vol. 28, NASA Goddard Space Flight Center, 115 pp.

    • Search Google Scholar
    • Export Citation
  • Molod, A., and Coauthors, 2020: GEOS-S2S version 2: The GMAO high-resolution coupled model and assimilation system for seasonal prediction. J. Geophys. Res. Atmos., 125, https://doi.org/10.1029/2019JD031767.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., and A. Gettelman, 2008: A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: Description and numerical tests. J. Climate, 21, 36423659, https://doi.org/10.1175/2008JCLI2105.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. D. Boer, G. Feingold, J. Harrington, M. D. Shupe, and K. Sulia, 2012: Resilience of persistent Arctic mixed-phase clouds. Nat. Geosci., 5, 1117, https://doi.org/10.1038/ngeo1332.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muhlbauer, A., T. P. Ackerman, J. M. Comstock, G. S. Diskin, S. M. Evans, R. P. Lawson, and R. T. Marchand, 2014: Impact of large-scale dynamics on the microphysical properties of midlatitude cirrus. J. Geophys. Res. Atmos., 119, 39763996, https://doi.org/10.1002/2013JD020035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, D., and T. Koop, 2005: Review of the vapour pressures of ice and supercooled water for atmospheric applications. Quart. J. Roy. Meteor. Soc., 131, 15391565, https://doi.org/10.1256/qj.04.94.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murray, B. J., S. L. Broadley, T. W. Wilson, J. D. Atkinson, and R. H. Wills, 2011: Heterogeneous freezing of water droplets containing kaolinite particles. Atmos. Chem. Phys., 11, 41914207, https://doi.org/10.5194/acp-11-4191-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murray, B. J., D. O’Sullivan, J. D. Atkinson, and M. E. Webb, 2012: Ice nucleation by particles immersed in supercooled cloud droplets. Chem. Soc. Rev., 41, 65196554, https://doi.org/10.1039/c2cs35200a.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naud, C., and A. D. D. Genio, 2006: Observational constraints on the cloud thermodynamic phase in midlatitude storms. J. Climate, 19, 52735288, https://doi.org/10.1175/JCLI3919.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Dell, C. W., F. J. Wentz, and R. Bennartz, 2008: Cloud liquid water path from satellite-based passive microwave observations: A new climatology over the global oceans. J. Climate, 21, 17211739, https://doi.org/10.1175/2007JCLI1958.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ovchinnikov, M., and Coauthors, 2014: Intercomparison of large-eddy simulations of Arctic mixed-phase clouds: Importance of ice size distribution assumptions. J. Adv. Model. Earth Syst., 6, 223248, https://doi.org/10.1002/2013MS000282.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pincus, R., S. Platnick, S. A. Ackerman, R. S. Hemler, and R. J. P. Hofmann, 2012: Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators. J. Climate, 25, 46994720, https://doi.org/10.1175/JCLI-D-11-00267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinto, J. O., 1998: Autumnal mixed-phase cloudy boundary layers in the Arctic. J. Amos. Sci., 55, 20162038, https://doi.org/10.1175/1520-0469(1998)055<2016:AMPCBL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Platnick, S., and Coauthors, 2016: The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua. IEEE Trans. Geosci. Remote Sens., 55, 502525, https://doi.org/10.1109/TGRS.2016.2610522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Popovitcheva, O., E. Kireeva, N. Persiantseva, T. Khokhlova, N. Shonija, V. Tishkova, and B. Demirdjian, 2008: Effect of soot on immersion freezing of water and possible atmospheric implications. Atmos. Res., 90, 326337, https://doi.org/10.1016/j.atmosres.2008.08.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prenni, A. J., and Coauthors, 2007: Can ice-nucleating aerosols affect Arctic seasonal climate? Bull. Amer. Meteor. Soc., 88, 541550, https://doi.org/10.1175/BAMS-88-4-541.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prenni, A. J., P. J. DeMott, D. C. Rogers, S. M. Kreidenweis, G. McFarquhar, G. Zhang, and M. R. Poellot, 2009: Ice nuclei characteristics from M-PACE and their relation to ice formation in clouds. Tellus, 61, 436448, https://doi.org/10.1111/j.1600-0889.2008.00415.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pruppacher, H. R., and J. D. Klett, 2010: Microphysics of Atmospheric Clouds and Precipitation. Springer, 954 pp.

  • Randles, C. A., and Coauthors, 2017: The MERRA-2 aerosol reanalysis, 1980 onward. Part I: System description and data assimilation evaluation. J. Climate, 30, 68236850, https://doi.org/10.1175/JCLI-D-16-0609.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 16091625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M., and Coauthors, 2008: The GEOS-5 Data Assimilation System—Documentation of versions 5.0.1, 5.1.0, and 5.2.0. Technical Report Series on Global Modeling and Data Assimilation, Vol. 27, 97 pp.

    • Search Google Scholar
    • Export Citation
  • Rogers, R. R., and M. K. Yau, 1989: A Short Course in Cloud Physics. Elsevier, 308 pp.

  • Schill, G. P., and Coauthors, 2020: The contribution of black carbon to global ice nucleating particle concentrations relevant to mixed-phase clouds. Proc. Natl. Acad. Sci. USA, 117, 22 70522 711, https://doi.org/10.1073/pnas.2001674117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seethala, C., and A. Horvath, 2010: Global assessment of AMSR-E and MODIS cloud liquid water path retrievals in warm oceanic clouds. J. Geophys. Res., 115, D13202, https://doi.org/10.1029/2009JD012662.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seinfeld, J. H., and S. N. Pandis, 1998: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. John Wiley and Sons, 1326 pp.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and R. G. Barry, 2011: Processes and impacts of Arctic amplification: A research synthesis. Global Planet. Change, 77, 8596, https://doi.org/10.1016/j.gloplacha.2011.03.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., A. P. Barrett, J. C. Stroeve, D. N. Kindig, and M. M. Holland, 2009: The emergence of surface-based Arctic amplification. Cryosphere, 3, 1119, https://doi.org/10.5194/tc-3-11-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shi, Y., and X. Liu, 2019: Dust radiative effects on climate by glaciating mixed-phase clouds. Geophys. Res. Lett., 46, 61286137, https://doi.org/10.1029/2019GL082504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shupe, M. D., V. P. Walden, E. Eloranta, T. Uttal, J. R. Campbell, S. M. Starkweather, and M. Shiobara, 2011: Clouds at Arctic atmospheric observatories. Part I: Occurrence and macrophysical properties. J. Appl. Meteor. Climatol., 50, 626644, https://doi.org/10.1175/2010JAMC2467.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Si, M., E. Evoy, J. Yun, and Y. Xi, 2019: Concentrations, composition, and sources of ice-nucleating particles in the Canadian high Arctic during spring 2016. Atmos. Chem. Phys., 19, 30073024, https://doi.org/10.5194/acp-19-3007-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Silber, I., A. M. Fridlind, A. S. Ackerman, G. V. Cesana, and D. A. Knopf, 2021: The prevalence of precipitation from polar supercooled clouds. Atmos. Chem. Phys., 21, 39493971, https://doi.org/10.5194/acp-21-3949-2021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Solomon, A., M. D. Shupe, P. O. G. Persson, and H. Morrison, 2011: Moisture and dynamical interactions maintaining decoupled Arctic mixed-phase stratocumulus in the presence of a humidity inversion. Atmos. Chem. Phys., 11, 10 12710 148, https://doi.org/10.5194/acp-11-10127-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Solomon, A., G. Feingold, and M. D. Shupe, 2015: The role of ice nuclei recycling in the maintenance of cloud ice in Arctic mixed-phase stratocumulus. Atmos. Chem. Phys., 15, 10 63110 643, https://doi.org/10.5194/acp-15-10631-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sullivan, S. C., D. Lee, L. Oreopoulos, and A. Nenes, 2016: Role of updraft velocity in temporal variability of global cloud hydrometeor number. Proc. Natl. Acad. Sci. USA, 113, 57915796, https://doi.org/10.1073/pnas.1514039113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Szakáll, M., and Coauthors, 2021: Comparative study on immersion freezing utilizing single-droplet levitation methods. Atmos. Chem. Phys., 21, 32893316, https://doi.org/10.5194/acp-21-3289-2021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, I., and T. Storelvmo, 2016: Sensitivity study on the influence of cloud microphysical parameters on mixed-phase cloud thermodynamic phase partitioning in CAM5. J. Amer. Sci., 73, 709728, https://doi.org/10.1175/JAS-D-15-0152.1.

    • Search Google Scholar
    • Export Citation
  • Tan, I., and T. Storelvmo, 2019: Evidence of strong contributions from mixed-phase clouds to Arctic climate change. Geophys. Res. Lett., 46, 28942902, https://doi.org/10.1029/2018GL081871.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, I., L. Oreopoulos, and N. Cho, 2019: The role of thermodynamic phase shifts in cloud optical depth variations with temperature. Geophys. Res. Lett., 46, 45024511, https://doi.org/10.1029/2018GL081590.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taszarek, M., N. Pilguj, J. T. Allen, V. Gensini, H. E. Brooks, and P. Szuster, 2021: Comparison of convective parameters derived from ERA5 and MERRA-2 with rawinsonde data over Europe and North America. J. Climate, 34, 32113237, https://doi.org/10.1175/JCLI-D-20-0484.1.

    • Search Google Scholar
    • Export Citation
  • Taylor, P. C., S. Kato, K.-M. Xu, and M. Cai, 2015: Covariance between Arctic sea ice and clouds within atmospheric state regimes at the satellite footprint level. J. Geophys. Res. Atmos., 120, 12 65612 678, https://doi.org/10.1002/2015JD023520.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, P. C., R. C. Boeke, Y. Li, and D. W. J. Thompson, 2019: Arctic cloud annual cycle biases in climate models. Atmos. Chem. Phys., 19, 87598782, https://doi.org/10.5194/acp-19-8759-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. R., B. H. Kahn, R. O. Green, S. A. Chien, E. M. Middleton, and D. Q. Tran, 2018: Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005–2015. Atmos. Meas. Tech., 11, 10191030, https://doi.org/10.5194/amt-11-1019-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1993: Representation of clouds in large-scale models. Mon. Wea. Rev., 121, 3040, https://doi.org/10.1175/1520-0493(1993)121<3040:ROCILS> 2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ullrich, R., and Coauthors, 2017: A new ice nucleation active site parameterization for desert dust and soot. J. Amer. Sci., 74, 699717, https://doi.org/10.1175/JAS-D-16-0074.1.

    • Search Google Scholar
    • Export Citation
  • Vali, G., P. J. DeMott, O. Möhler, and T. F. Whale, 2015: A proposal for ice nucleation terminology. Atmos. Chem. Phys., 15, 102263102270, https://doi.org/10.5194/acp-15-10263-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vergara-Temprado, J., and Coauthors, 2018: Is black carbon an unimportant ice-nucleating particle in mixed-phase clouds. J. Geophys. Res. Atmos., 123, 42734283, https://doi.org/10.1002/2017JD027831.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verlinde, J., and Coauthors, 2007: The Mixed-Phase Arctic Cloud Experiment. Bull. Amer. Meteor. Soc., 88, 205222, https://doi.org/10.1175/BAMS-88-2-205.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wegener, A., 1911: Thermodynamik der Atmosphäre. J. A. Barth, Leipzig, 331 pp.

  • Welti, A., Z. A. Kanji, F. Lüönd, O. Stetzer, and U. Lohmann, 2014: Exploring the mechanisms of ice nucleation on kaolinite: From deposition nucleation to condensation freezing. J. Amer. Sci., 71, 1636, https://doi.org/10.1175/JAS-D-12-0252.1.

    • Search Google Scholar
    • Export Citation
  • Wiacek, A., T. Peter, and U. Lohmann, 2010: The potential influence of Asian and African mineral dust on ice, mixed-phase and liquid water clouds. Atmos. Chem. Phys., 10, 86498667, https://doi.org/10.5194/acp-10-8649-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S., and Coauthors, 2010: Clouds and more: ARM climate modeling best estimate data: A new data product for climate studies. Bull. Amer. Meteor. Soc., 91, 1320, https://doi.org/10.1175/2009BAMS2891.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S., X. Liu, C. Zhao, and Y. Zhang, 2013: Sensitivity of CAM5-simulated arctic clouds and radiation to ice nucleation parameterization. J. Climate, 26, 59815999, https://doi.org/10.1175/JCLI-D-12-00517.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, C. L., N. Sokolik, and J. Dufek, 2012: Regional radiative impact of volcanic aerosol from the 2009 eruption of Mt. Redoubt. Atmos. Chem. Phys., 12, 36993715, https://doi.org/10.5194/acp-12-3699-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, M., S. Xie, X. Liu, W. Lin, K. Zhang, H.-Y. Ma, X. Zheng, and Y. Zhang, 2020: Toward understanding the simulated phase partitioning of Arctic single-layer mixed-phase clouds in E3SM. Earth Syst. Sci., 7, e2020EA001125, https://doi.org/10.1029/2020EA001125.

    • Search Google Scholar
    • Export Citation
  • Zhao, C., 2011: ARM cloud retrieval ensemble data set (ACRED). DOE/SC-ARM-TR-99, 35 pp.

  • Zobrist, B., T. Koop, B. Luo, C. Marcolli, and T. Peter, 2007: Heterogeneous ice nucleation rate coefficient of water droplets coated by a nonadecanol monolayer. J. Phys. Chem. C, 111, 21492155, https://doi.org/10.1021/jp066080w.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zuidema, P., and Coauthors, 2005: An Arctic springtime mixed-phase cloudy boundary layer observed during SHEBA. J. Amer. Sci., 62, 160176, https://doi.org/10.1175/JAS-3368.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 236 231 16
Full Text Views 174 166 12
PDF Downloads 209 201 14

The Impacts of Immersion Ice Nucleation Parameterizations on Arctic Mixed-Phase Stratiform Cloud Properties and the Arctic Radiation Budget in GEOS-5

Ivy TanaJoint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland
bNASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Ivy Tan in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-4203-4770
and
Donifan BarahonacGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Donifan Barahona in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The influence of four different immersion freezing parameterizations on Arctic clouds and the top-of-the atmosphere (TOA) and surface radiation fluxes is investigated in the fifth version of the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System (GEOS-5) with sea surface temperature, sea ice fraction, and aerosol emissions held fixed. The different parameterizations were derived from a variety of sources, including classical nucleation theory and field and laboratory measurements. Despite the large spread in the ice-nucleating particle (INP) concentrations in the parameterizations, the cloud properties and radiative fluxes had a tendency to form two groups, with the lower INP concentration category producing larger water path and low-level cloud fraction during winter and early spring, whereas the opposite occurred during the summer season. The stability of the lower troposphere was found to strongly correlate with low-cloud fraction and, along with the effect of ice nucleation, ice sedimentation, and melting rates, appears to explain the spring-to-summer reversal pattern in the relative magnitude of the cloud properties between the two categories of simulations. The strong modulation effect of the liquid phase on immersion freezing led to the successful simulation of the characteristic Arctic cloud structure, with a layer rich in supercooled water near cloud top and ice and snow at lower levels. Comparison with satellite retrievals and in situ data suggest that simulations with low INP concentrations more realistically represent Arctic clouds and radiation.

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

Tan’s current affiliation: McGill University, Montreal, Canada

Corresponding author: Ivy Tan, ivy.tan@mcgill.ca

Abstract

The influence of four different immersion freezing parameterizations on Arctic clouds and the top-of-the atmosphere (TOA) and surface radiation fluxes is investigated in the fifth version of the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System (GEOS-5) with sea surface temperature, sea ice fraction, and aerosol emissions held fixed. The different parameterizations were derived from a variety of sources, including classical nucleation theory and field and laboratory measurements. Despite the large spread in the ice-nucleating particle (INP) concentrations in the parameterizations, the cloud properties and radiative fluxes had a tendency to form two groups, with the lower INP concentration category producing larger water path and low-level cloud fraction during winter and early spring, whereas the opposite occurred during the summer season. The stability of the lower troposphere was found to strongly correlate with low-cloud fraction and, along with the effect of ice nucleation, ice sedimentation, and melting rates, appears to explain the spring-to-summer reversal pattern in the relative magnitude of the cloud properties between the two categories of simulations. The strong modulation effect of the liquid phase on immersion freezing led to the successful simulation of the characteristic Arctic cloud structure, with a layer rich in supercooled water near cloud top and ice and snow at lower levels. Comparison with satellite retrievals and in situ data suggest that simulations with low INP concentrations more realistically represent Arctic clouds and radiation.

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

Tan’s current affiliation: McGill University, Montreal, Canada

Corresponding author: Ivy Tan, ivy.tan@mcgill.ca
Save