Cirrus Cloud Properties as Seen by the CALIPSO Satellite and ECHAM-HAM Global Climate Model

B. Gasparini ETH Zürich, Zürich, Switzerland

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A. Meyer Atmospheric Data Division, Federal Office of Meteorology and Climatology, MeteoSwiss, Payerne, Switzerland

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D. Neubauer ETH Zürich, Zürich, Switzerland

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S. Münch ETH Zürich, Zürich, Switzerland

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U. Lohmann ETH Zürich, Zürich, Switzerland

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Abstract

Cirrus clouds impact the planetary energy balance and upper-tropospheric water vapor transport and are therefore relevant for climate. In this study cirrus clouds at temperatures colder than −40°C simulated by the ECHAM–Hamburg Aerosol Module (ECHAM-HAM) general circulation model are compared to Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite data. The model captures the general cloud cover pattern and reproduces the observed median ice water content within a factor of 2, while extinction is overestimated by about a factor of 3 as revealed by temperature-dependent frequency histograms. Two distinct types of cirrus clouds are found: in situ–formed cirrus dominating at temperatures colder than −55°C and liquid-origin cirrus dominating at temperatures warmer than −55°C. The latter cirrus form in anvils of deep convective clouds or by glaciation of mixed-phase clouds, leading to high ice crystal number concentrations. They are associated with extinction coefficients and ice water content of up to 1 km−1 and 0.1 g m−3, respectively, while the in situ–formed cirrus are associated with smaller extinction coefficients and ice water content. In situ–formed cirrus are nucleated either heterogeneously or homogeneously. The simulated homogeneous ice crystals are similar to liquid-origin cirrus, which are associated with high ice crystal number concentrations. On the contrary, heterogeneously nucleated ice crystals appear in smaller number concentrations. However, ice crystal aggregation and depositional growth smooth the differences between several formation mechanisms, making the attribution to a specific ice nucleation mechanism challenging.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-16-0608.s1.

© 2018 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: Blaž Gasparini, blaz.gasparini@env.ethz.ch

Abstract

Cirrus clouds impact the planetary energy balance and upper-tropospheric water vapor transport and are therefore relevant for climate. In this study cirrus clouds at temperatures colder than −40°C simulated by the ECHAM–Hamburg Aerosol Module (ECHAM-HAM) general circulation model are compared to Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite data. The model captures the general cloud cover pattern and reproduces the observed median ice water content within a factor of 2, while extinction is overestimated by about a factor of 3 as revealed by temperature-dependent frequency histograms. Two distinct types of cirrus clouds are found: in situ–formed cirrus dominating at temperatures colder than −55°C and liquid-origin cirrus dominating at temperatures warmer than −55°C. The latter cirrus form in anvils of deep convective clouds or by glaciation of mixed-phase clouds, leading to high ice crystal number concentrations. They are associated with extinction coefficients and ice water content of up to 1 km−1 and 0.1 g m−3, respectively, while the in situ–formed cirrus are associated with smaller extinction coefficients and ice water content. In situ–formed cirrus are nucleated either heterogeneously or homogeneously. The simulated homogeneous ice crystals are similar to liquid-origin cirrus, which are associated with high ice crystal number concentrations. On the contrary, heterogeneously nucleated ice crystals appear in smaller number concentrations. However, ice crystal aggregation and depositional growth smooth the differences between several formation mechanisms, making the attribution to a specific ice nucleation mechanism challenging.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-16-0608.s1.

© 2018 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: Blaž Gasparini, blaz.gasparini@env.ethz.ch

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  • Avery, M., D. Winker, A. Heymsfield, M. Vaughan, S. Young, Y. Hu, and C. Trepte, 2012: Cloud ice water content retrieved from the CALIOP space-based lidar. Geophys. Res. Lett., 39, L05808, https://doi.org/10.1029/2011GL050545.

    • 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
  • Berry, E., and G. G. Mace, 2014: Cloud properties and radiative effects of the Asian summer monsoon derived from A-Train data. J. Geophys. Res. Atmos., 119, 94929508, https://doi.org/10.1002/2014JD021458.

    • 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
  • Boudala, F. S., G. A. Isaac, Q. Fu, and S. G. Cober, 2002: Parameterization of effective ice particle size for high-latitude clouds. Int. J. Climatol., 22, 12671284, https://doi.org/10.1002/joc.774.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cesana, G., and H. Chepfer, 2012: How well do climate models simulate cloud vertical structure? A comparison between CALIPSO-GOCCP satellite observations and CMIP5 models. Geophys. Res. Lett., 39, 20803, https://doi.org/10.1029/2012GL053153.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, T., W. B. Rossow, and Y. Zhang, 2000: Radiative effects of cloud-type variations. J. Climate, 13, 264286, https://doi.org/10.1175/1520-0442(2000)013<0264:REOCTV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chepfer, H., S. Bony, D. Winker, G. Cesana, J. L. Dufresne, P. Minnis, C. J. Stubenrauch, and S. Zeng, 2010: The GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP). J. Geophys. Res., 115, D00H16, https://doi.org/10.1029/2009JD012251.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chepfer, H., D. Cesana, D. Winker, B. Getzewich, M. Vaughan, and Z. Liu, 2013: Comparison of two different cloud climatologies derived from CALIOP-attenuated backscattered measurements (level 1): The CALIPSO-ST and the CALIPSO-GOCCP. J. Atmos. Oceanic Technol., 30, 725744, https://doi.org/10.1175/JTECH-D-12-00057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Corti, T., and T. Peter, 2009: A simple model for cloud radiative forcing. Atmos. Chem. Phys., 9, 57515758, https://doi.org/10.5194/acp-9-5751-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cziczo, D. J., and Coauthors, 2013: Clarifying the dominant sources and mechanisms of cirrus cloud formation. Science, 340, 13201324, https://doi.org/10.1126/science.1234145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, S., and Coauthors, 2010: In situ and lidar observations of tropopause subvisible cirrus clouds during TC4. J. Geophys. Res., 115, D00J17, https://doi.org/10.1029/2009JD013093.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delanoe, J., and R. J. Hogan, 2010: Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds. J. Geophys. Res., 115, D00H29, https://doi.org/10.1029/2009JD012346.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dinh, T., A. Podglajen, A. Hertzog, B. Legras, and R. Plougonven, 2016: Effect of gravity wave temperature fluctuations on homogeneous ice nucleation in the tropical tropopause layer. Atmos. Chem. Phys., 16, 3546, https://doi.org/10.5194/acp-16-35-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Field, P. R., and R. Wood, 2007: Precipitation and cloud structure in midlatitude cyclones. J. Climate, 20, 233254, https://doi.org/10.1175/JCLI3998.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fusina, F., P. Spichtinger, and U. Lohmann, 2007: Impact of ice supersaturated regions and thin cirrus on radiation in the midlatitudes. J. Geophys. Res., 112, D24S14, https://doi.org/10.1029/2007JD008449.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garnier, A., J. Pelon, M. A. Vaughan, D. M. Winker, C. R. Trepte, and P. Dubuisson, 2015: Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans. Atmos. Meas. Tech., 8, 27592774, https://doi.org/10.5194/amt-8-2759-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gasparini, B., 2016: Cirrus clouds and their geoengineering potential. Ph.D. thesis, ETH Zürich, 168 pp., https://doi.org/10.3929/ethz-b-000000052.

    • Crossref
    • Export Citation
  • Gasparini, B., and U. Lohmann, 2016: Why cirrus cloud seeding cannot substantially cool the planet. J. Geophys. Res. Atmos., 121, 48774893, https://doi.org/10.1002/2015JD024666.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gasparini, B., S. Münch, L. Poncet, M. Feldmann, and U. Lohmann, 2017: Is increasing ice crystal sedimentation velocity in geoengineering simulations a good proxy for cirrus cloud seeding? Atmos. Chem. Phys., 17, 48714885, https://doi.org/10.5194/acp-17-4871-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gates, W. L., 1992: AMIP: The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc., 73, 19621970, https://doi.org/10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gryspeerdt, E., J. Quaas, T. Goren, D. Klocke, and M. Brueck, 2017: Technical note: An automated cirrus classification. Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-723.

    • Search Google Scholar
    • Export Citation
  • Haladay, T., and G. Stephens, 2009: Characteristics of tropical thin cirrus clouds deduced from joint CloudSat and CALIPSO observations. J. Geophys. Res., 114, D00A25, https://doi.org/10.1029/2008JD010675.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., J. R. Holton, and Q. Fu, 2001: The heat balance of the tropical tropopause, cirrus, and stratospheric dehydration. Geophys. Res. Lett., 28, 19691972, https://doi.org/10.1029/2000GL012833.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herzegh, P. H., and P. Hobbs, 1980: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. II: Warm-frontal clouds. J. Atmos. Sci., 37, 597611, https://doi.org/10.1175/1520-0469(1980)037<0597:TMAMSA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., D. Winker, M. Avery, M. Vaughan, G. Diskin, M. Deng, V. Mitev, and R. Matthey, 2014: Relationships between ice water content and volume extinction coefficient from in situ observations for temperatures from 0° to −86°C: Implications for spaceborne lidar retrievals. J. Appl. Meteor. Climatol., 53, 479505, https://doi.org/10.1175/JAMC-D-13-087.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., M. Krämer, N. B. Wood, A. Gettelman, P. R. Field, and G. Liu, 2017a: Dependence of the ice water content and snowfall rate on temperature, globally: Comparison of in situ observations, satellite active remote sensing retrievals, and global climate model simulations. J. Appl. Meteor. Climatol., 56, 189215, https://doi.org/10.1175/JAMC-D-16-0230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., and Coauthors, 2017b: Cirrus clouds. Ice Formation and Evolution in Clouds and Precipitation: Measurement and Modeling Challenges, Meteor. Monogr., No. 58, Amer. Meteor. Soc., 2.1–2.26, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0010.1.

    • Crossref
    • 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
  • Ickes, L., A. Welti, C. Hoose, and U. Lohmann, 2015: Classical nucleation theory of homogeneous freezing of water: Thermodynamic and kinetic parameters. Phys. Chem. Chem. Phys., 17, 55145537, https://doi.org/10.1039/C4CP04184D.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Isotta, F. A., P. Spichtinger, U. Lohmann, and K. von Salzen, 2011: Improvement and implementation of a parameterization for shallow cumulus in the global climate model ECHAM5-HAM. J. Atmos. Sci., 68, 515532, https://doi.org/10.1175/2010JAS3447.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, E. J., and Coauthors, 2016: On the susceptibility of cold tropical cirrus to ice nuclei abundance. J. Atmos. Sci., 73, 24452464, https://doi.org/10.1175/JAS-D-15-0274.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, E. J., and Coauthors, 2017: The NASA Airborne Tropical Tropopause Experiment: High-altitude aircraft measurements in the tropical western Pacific. Bull. Amer. Meteor. Soc., 98, 129143, https://doi.org/10.1175/BAMS-D-14-00263.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joos, H., P. Spichtinger, U. Lohmann, J. F. Gayet, and A. Minikin, 2008: Orographic cirrus in the global climate model ECHAM5. J. Geophys. Res., 113, D18205, https://doi.org/10.1029/2007JD009605.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joos, H., P. Spichtinger, P. Reutter, and F. Fusina, 2014: Influence of heterogeneous freezing on the microphysical and radiative properties of orographic cirrus clouds. Atmos. Chem. Phys., 14, 68356852, https://doi.org/10.5194/acp-14-6835-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanji, Z. A., L. A. Ladino, H. Wex, Y. Boose, M. Burkert-Kohn, D. J. Cziczo, and M. Krämer, 2017: Overview of ice nucleating particles. Ice Formation and Evolution in Clouds and Precipitation: Measurement and Modeling Challenges, Meteor. Monogr., No. 58, Amer. Meteor. Soc., 1.1–1.33, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0006.1.

    • Crossref
    • Export Citation
  • Kärcher, B., 2017: Cirrus clouds and their response to anthropogenic activities. Curr. Climate Change Rep., 3, 4557, https://doi.org/10.1007/s40641-017-0060-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kärcher, B., J. Hendricks, and U. Lohmann, 2006: Physically based parameterization of cirrus cloud formation for use in global atmospheric models. J. Geophys. Res., 111, D01205, https://doi.org/10.1029/2005JD006219.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koffi, B., and Coauthors, 2016: Evaluation of the aerosol vertical distribution in global aerosol models through comparison against CALIOP measurements: AeroCom phase II results. J. Geophys. Res. Atmos., 121, 72547283, https://doi.org/10.1002/2015JD024639.

    • 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
  • Krämer, M., and Coauthors, 2016: A microphysics guide to cirrus clouds—Part 1: Cirrus types. Atmos. Chem. Phys., 16, 34633483, https://doi.org/10.5194/acp-16-3463-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuebbeler, M., U. Lohmann, J. Hendricks, and B. Kärcher, 2014: Dust ice nuclei effects on cirrus clouds. Atmos. Chem. Phys., 14, 30273046, https://doi.org/10.5194/acp-14-3027-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawson, R. P., B. Pilson, B. Baker, Q. Mo, E. Jensen, L. Pfister, and P. Bui, 2008: Aircraft measurements of microphysical properties of subvisible cirrus in the tropical tropopause layer. Atmos. Chem. Phys., 8, 16091620, https://doi.org/10.5194/acp-8-1609-2008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, J., P. Yang, A. E. Dessler, B.-C. Gao, and S. Platnick, 2009: Distribution and radiative forcing of tropical thin cirrus clouds. J. Atmos. Sci., 66, 37213731, https://doi.org/10.1175/2009JAS3183.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J.-L. F., and Coauthors, 2012: An observationally based evaluation of cloud ice water in CMIP3 and CMIP5 GCMs and contemporary reanalyses using contemporary satellite data. J. Geophys. Res., 117, D16105, https://doi.org/10.1029/2012JD017640.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Z., and Coauthors, 2009: The CALIPSO lidar cloud and aerosol discrimination: Version 2 algorithm and initial assessment of performance. J. Atmos. Oceanic Technol., 26, 11981213, https://doi.org/10.1175/2009JTECHA1229.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lohmann, U., and E. Roeckner, 1995: Influence of cirrus cloud radiative forcing on climate and climate sensitivity in a general circulation model. J. Geophys. Res., 100, 16 30516 323, https://doi.org/10.1029/95JD01383.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lohmann, U., and B. Kärcher, 2002: First interactive simulations of cirrus clouds formed by homogeneous freezing in the ECHAM general circulation model. J. Geophys. Res., 107, 4105, https://doi.org/10.1029/2001JD000767.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lohmann, U., and C. Hoose, 2009: Sensitivity studies of different aerosol indirect effects in mixed-phase clouds. Atmos. Chem. Phys., 9, 89178934, https://doi.org/10.5194/acp-9-8917-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lohmann, U., and B. Gasparini, 2017: A cirrus cloud climate dial? Science, 357, 248249, https://doi.org/10.1126/science.aan3325.

  • Lohmann, U., P. Stier, C. Hoose, S. Ferrachat, S. Kloster, E. Roeckner, and J. Zhang, 2007: Cloud microphysics and aerosol indirect effects in the global climate model ECHAM5-HAM. Atmos. Chem. Phys., 7, 34253446, https://doi.org/10.5194/acp-7-3425-2007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luebke, A. E., L. M. Avallone, J. Meyer, C. Rolf, and M. Krämer, 2013: Ice water content of Arctic, midlatitude, and tropical cirrus—Part 2: Extension of the database and new statistical analysis. Atmos. Chem. Phys., 13, 64476459, https://doi.org/10.5194/acp-13-6447-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luebke, A. E., A. Afchine, A. Costa, J. Meyer, C. Rolf, and N. Spelten, 2016: The origin of midlatitude ice clouds and the resulting influence on their microphysical properties. Atmos. Chem. Phys., 16, 57935809, https://doi.org/10.5194/acp-16-5793-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, Z., and W. B. Rossow, 2004: Characterizing tropical cirrus life cycle, evolution, and interaction with upper-tropospheric water vapor using Lagrangian trajectory analysis of satellite observations. J. Climate, 17, 45414563, https://doi.org/10.1175/3222.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madonna, E., 2014: Warm conveyor belts in the ERA-Interim dataset (1979 2010). Part I: Climatology and potential vorticity evolution. J. Climate, 27, 326, https://doi.org/10.1175/JCLI-D-12-00720.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mauritsen, T., and Coauthors, 2012: Tuning the climate of a global model. J. Adv. Model. Earth Syst., 4, M00A01, https://doi.org/10.1029/2012MS000154.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitchell, D. L., A. Garnier, M. Avery, and E. Erfani, 2016: CALIPSO observations of the dependence of homo- and heterogeneous ice nucleation in cirrus clouds on latitude, season and surface condition. Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-1062.

    • Search Google Scholar
    • Export Citation
  • Möhler, O., and Coauthors, 2006: Efficiency of the deposition mode ice nucleation on mineral dust particles. Atmos. Chem. Phys., 6, 30073021, https://doi.org/10.5194/acp-6-3007-2006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Möhler, O., and Coauthors, 2008: The effect of organic coating on the heterogeneous ice nucleation efficiency of mineral dust aerosols. Environ. Res. Lett., 3, 025007, https://doi.org/10.1088/1748-9326/3/2/025007.

    • 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, 2014a: 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
  • Muhlbauer, A., E. Berry, J. M. Comstock, and G. G. Mace, 2014b: Perturbed physics ensemble simulations of cirrus on the cloud system-resolving scale. J. Geophys. Res. Atmos., 119, 47094735, https://doi.org/10.1002/2013JD020709.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nam, C. C. W., and J. Quaas, 2012: Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and Cloudsat satellite data. J. Climate, 25, 49754992, https://doi.org/10.1175/JCLI-D-11-00347.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neubauer, D., U. Lohmann, C. Hoose, and M. G. Frontoso, 2014: Impact of the representation of marine stratocumulus clouds on the anthropogenic aerosol effect. Atmos. Chem. Phys., 14, 11 99712 022, https://doi.org/10.5194/acp-14-11997-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nordeng, T. E., 1994: Extended versions of the convective parametrization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics. ECMWF Tech. Memo. 206, 41 pp.

  • Protopapadaki, S. E., C. J. Stubenrauch, and A. G. Feofilov, 2017: Upper tropospheric cloud systems derived from IR sounders: Properties of cirrus anvils in the tropics. Atmos. Chem. Phys., 17, 38453859, https://doi.org/10.5194/acp-17-3845-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randel, W. J., and E. J. Jensen, 2013: Physical processes in the tropical tropopause layer and their roles in a changing climate. Nat. Geosci., 6, 169176, https://doi.org/10.1038/ngeo1733.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riihimaki, L. D., S. A. McFarlane, C. Liang, S. T. Massie, N. Beagley, and T. D. Toth, 2012: Comparison of methods to determine Tropical Tropopause Layer cirrus formation mechanisms. J. Geophys. Res., 117, D06218, https://doi.org/10.1029/2011JD016832.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 22612287, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schiller, C., M. Krämer, A. Afchine, N. Spelten, and N. Sitnikov, 2008: Ice water content of Arctic, midlatitude, and tropical cirrus. J. Geophys. Res., 113, D24208, https://doi.org/10.1029/2008JD010342.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Segal-Rosenheimer, M., P. B. Russell, J. M. Livingston, S. Ramachandran, J. Redemann, and B. A. Baum, 2013: Retrieval of cirrus properties by Sun photometry: A new perspective on an old issue. J. Geophys. Res. Atmos., 118, 45034520, https://doi.org/10.1002/jgrd.50185.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shi, X., and X. Liu, 2016: Effect of cloud-scale vertical velocity on the contribution of homogeneous nucleation to cirrus formation and radiative forcing. Geophys. Res. Lett., 43, 65886595, https://doi.org/10.1002/2016GL069531.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shi, X., X. Liu, and K. Zhang, 2015: Effects of preexisting ice crystals on cirrus clouds and comparison between different ice nucleation parameterizations with the Community Atmosphere Model (CAM5). Atmos. Chem. Phys., 15, 15031520, https://doi.org/10.5194/acp-15-1503-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spichtinger, P., and M. Krämer, 2013: Tropical tropopause ice clouds: A dynamic approach to the mystery of low crystal numbers. Atmos. Chem. Phys., 13, 98019818, https://doi.org/10.5194/acp-13-9801-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spichtinger, P., K. Gierens, and H. Wernli, 2005: A case study on the formation and evolution of ice supersaturation in the vicinity of a warm conveyor belt’s outflow region. Atmos. Chem. Phys., 5, 973987, https://doi.org/10.5194/acp-5-973-2005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stenke, A., V. Grewe, and M. Ponater, 2008: Lagrangian transport of water vapor and cloud water in the ECHAM4 GCM and its impact on the cold bias. Climate Dyn., 31, 491506, https://doi.org/10.1007/s00382-007-0347-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., 2005: Cloud feedback in the climate system: A critical review. J. Climate, 18, 237273, https://doi.org/10.1175/JCLI-3243.1.

  • Stevens, B., and Coauthors, 2013: Atmospheric component of the MPI-M Earth System Model: ECHAM6. J. Adv. Model. Earth Syst., 5, 146172, https://doi.org/10.1002/jame.20015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stier, P., and Coauthors, 2005: The aerosol-climate model ECHAM5-HAM. Atmos. Chem. Phys., 5, 11251156, https://doi.org/10.5194/acp-5-1125-2005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Storelvmo, T., and N. Herger, 2014: Cirrus cloud susceptibility to the injection of ice nuclei in the upper troposphere. J. Geophys. Res. Atmos., 119, 23752389, https://doi.org/10.1002/2013JD020816.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 17791800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vaughan, M. A., and Coauthors, 2009: Fully automated detection of cloud and aerosol layers in the CALIPSO lidar measurements. J. Atmos. Oceanic Technol., 26, 20342050, https://doi.org/10.1175/2009JTECHA1228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vaughan, M. A., and Coauthors, 2016: Data management system data products catalog. NASA Tech.Rep. PC-SCI-503, 154 pp., https://www-calipso.larc.nasa.gov/products/CALIPSO_DPC_Rev4x10.pdf.

  • Vignati, E., J. Wilson, and P. Stier, 2004: M7: An efficient size-resolved aerosol microphysics module for large-scale aerosol transport models. J. Geophys. Res., 109, D22202, https://doi.org/10.1029/2003JD004485.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Voigt, C., and Coauthors, 2017: ML-CIRRUS: The airborne experiment on natural cirrus and contrail cirrus with the high-altitude long-range research aircraft HALO. Bull. Amer. Meteor. Soc., 98, 271288, https://doi.org/10.1175/BAMS-D-15-00213.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wernli, H., M. Boettcher, H. Joos, A. K. Miltenberger, and P. Spichtinger, 2016: A trajectory-based classification of ERA-Interim ice clouds in the region of the North Atlantic storm track. Geophys. Res. Lett., 43, 66576664, https://doi.org/10.1002/2016GL068922.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winker, D. M., W. H. Hunt, and M. J. McGill, 2007: Initial performance assessment of CALIOP. Geophys. Res. Lett., 34, L19803, https://doi.org/10.1029/2007GL030135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winker, D. M., M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, 2009: Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Oceanic Technol., 26, 23102323, https://doi.org/10.1175/2009JTECHA1281.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winker, D. M., and Coauthors, 2010: The CALIPSO mission: A global 3D view of aerosols and clouds. Bull. Amer. Meteor. Soc., 91, 12111229, https://doi.org/10.1175/2010BAMS3009.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, S. A., and M. A. Vaughan, 2009: The retrieval of profiles of particulate extinction from Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) data: Algorithm description. J. Atmos. Oceanic Technol., 26, 11051119, https://doi.org/10.1175/2008JTECHA1221.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, K., and Coauthors, 2012: The global aerosol-climate model ECHAM-HAM, version 2: Sensitivity to improvements in process representations. Atmos. Chem. Phys., 12, 89118949, https://doi.org/10.5194/acp-12-8911-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, C., and J. E. Penner, 2014: Aircraft soot indirect effect on large-scale cirrus clouds: Is the indirect forcing by aircraft soot positive or negative? J. Geophys. Res. Atmos., 119, 11 30311 320, https://doi.org/10.1002/2014JD021914.

    • Crossref
    • Search Google Scholar
    • Export Citation
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