Sensitivity Study on the Influence of Cloud Microphysical Parameters on Mixed-Phase Cloud Thermodynamic Phase Partitioning in CAM5

Ivy Tan Yale University, New Haven, Connecticut

Search for other papers by Ivy Tan in
Current site
Google Scholar
PubMed
Close
and
Trude Storelvmo Yale University, New Haven, Connecticut

Search for other papers by Trude Storelvmo in
Current site
Google Scholar
PubMed
Close
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

The influence of six CAM5.1 cloud microphysical parameters on the variance of phase partitioning in mixed-phase clouds is determined by application of a variance-based sensitivity analysis. The sensitivity analysis is based on a generalized linear model that assumes a polynomial relationship between the six parameters and the two-way interactions between them. The parameters, bounded such that they yield realistic cloud phase values, were selected by adopting a quasi–Monte Carlo sampling approach. The sensitivity analysis is applied globally, and to 20°-latitude-wide bands, and over the Southern Ocean at various mixed-phase cloud isotherms and reveals that the Wegener–Bergeron–Findeisen (WBF) time scale for the growth of ice crystals single-handedly accounts for the vast majority of the variance in cloud phase partitioning in mixed-phase clouds, while its interaction with the WBF time scale for the growth of snowflakes plays a secondary role. The fraction of dust aerosols active as ice nuclei in latitude bands, and the parameter related to the ice crystal fall speed and their interactions with the WBF time scale for ice are also significant. All other investigated parameters and their interactions with each other are negligible (<3%). Further analysis comparing three of the quasi–Monte Carlo–sampled simulations with spaceborne lidar observations by CALIOP suggests that the WBF process in CAM5.1 is currently parameterized such that it occurs too rapidly due to failure to account for subgrid-scale variability of liquid and ice partitioning in mixed-phase clouds.

Corresponding author address: Ivy Tan, Geology and Geophysics, Yale University, 210 Whitney Ave., New Haven, CT 06511. E-mail: ivy.tan@yale.edu

Abstract

The influence of six CAM5.1 cloud microphysical parameters on the variance of phase partitioning in mixed-phase clouds is determined by application of a variance-based sensitivity analysis. The sensitivity analysis is based on a generalized linear model that assumes a polynomial relationship between the six parameters and the two-way interactions between them. The parameters, bounded such that they yield realistic cloud phase values, were selected by adopting a quasi–Monte Carlo sampling approach. The sensitivity analysis is applied globally, and to 20°-latitude-wide bands, and over the Southern Ocean at various mixed-phase cloud isotherms and reveals that the Wegener–Bergeron–Findeisen (WBF) time scale for the growth of ice crystals single-handedly accounts for the vast majority of the variance in cloud phase partitioning in mixed-phase clouds, while its interaction with the WBF time scale for the growth of snowflakes plays a secondary role. The fraction of dust aerosols active as ice nuclei in latitude bands, and the parameter related to the ice crystal fall speed and their interactions with the WBF time scale for ice are also significant. All other investigated parameters and their interactions with each other are negligible (<3%). Further analysis comparing three of the quasi–Monte Carlo–sampled simulations with spaceborne lidar observations by CALIOP suggests that the WBF process in CAM5.1 is currently parameterized such that it occurs too rapidly due to failure to account for subgrid-scale variability of liquid and ice partitioning in mixed-phase clouds.

Corresponding author address: Ivy Tan, Geology and Geophysics, Yale University, 210 Whitney Ave., New Haven, CT 06511. E-mail: ivy.tan@yale.edu
Save
  • Atkinson, J. D., and Coauthors, 2013: The importance of feldspar for ice nucleation by mineral dust in mixed-phase clouds. Nature, 498, 355358, doi:10.1038/nature12278.

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

  • Boucher, O., and Coauthors, 2013: Clouds and aerosols. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 571–658. [Available online at http://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_Chapter07_FINAL.pdf.]

    • Search Google Scholar
    • Export Citation
  • Caflisch, R. E., 1998: Monte Carlo and quasi–Monte Carlo methods. Acta Numer., 7, 149, doi:10.1017/S0962492900002804.

  • Cesana, G., and H. Chepfer, 2013: Evaluation of the cloud thermodynamic phase in a climate model using CALIPSO-GOCCP. J. Geophys. Res. Atmos., 118, 79227937, doi:10.1002/jgrd.50376.

    • 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, doi:10.1002/2014JD022932.

    • Search Google Scholar
    • Export Citation
  • Chepfer, H., G. Broginez, P. Goloub, F. M. Bréon, and P. H. Flamant, 1999: Observations of horizontally oriented ice crystals in cirrus clouds with POLDER-1/ADEOS-1. J. Quant. Spectrosc. Radiat. Transfer, 63, 521543, doi:10.1016/S0022-4073(99)00036-9.

    • Search Google Scholar
    • Export Citation
  • Choi, Y.-S., R. S. Lindzen, C.-H. Ho, and J. Kim, 2010: Space observations of cold-cloud phase change. Proc. Natl. Acad. Sci. USA, 107, 11 21111 216, doi:10.1073/pnas.1006241107.

    • Search Google Scholar
    • Export Citation
  • Choi, Y.-S., C.-H. Ho, C.-E. Park, T. Storelvmo, and I. Tan, 2014: Influence of cloud phase composition on climate feedbacks. J. Geophys. Res. Atmos., 119, 36873700, doi:10.1002/2013JD020582.

    • 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, doi:10.1029/2004GL020428.

    • Search Google Scholar
    • Export Citation
  • Conen, F., S. Rodríguez, C. Hüglin, S. Henne, E. Herrmann, N. Bukowiecki, and C. Alewell, 2015: Atmospheric ice nuclei at the high-altitude observatory, Jungfraujoch, Switzerland. Tellus, 67B, 25014, doi:10.3402/tellusb.v67.25014.

    • Search Google Scholar
    • Export Citation
  • Cotton, W. R., G. J. Tripoli, R. M. Rauber, and E. A. 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, doi:10.1175/1520-0450(1986)025<1658:NSOTEO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • DeMott, P. J., and Coauthors, 2015: Integrating laboratory and field data to quantify the immersion freezing ice nucleation activity of mineral dust particles. Atmos. Chem. Phys., 15, 393409, doi:10.5194/acp-15-393-2015.

    • Search Google Scholar
    • Export Citation
  • Eidhammer, T., H. Morrison, A. Bansemer, A. Gettelman, and A. J. Heymsfield, 2014: Comparison of ice cloud properties simulated by the Community Atmosphere Model (CAM5) with in-situ observations. Atmos. Chem. Phys., 14, 10 10310 118, doi:10.5194/acp-14-10103-2014.

    • Search Google Scholar
    • Export Citation
  • Fan, J., S. Ghan, M. Ovchinnikov, X. Liu, P. J. Rasch, and A. Korolev, 2011: Representation of Arctic mixed-phase clouds and the Wegener–Bergeron–Findeisen process in climate models: Perspectives from a cloud-resolving study. J. Geophys. Res., 116, D00T07, doi:10.1029/2010JD015375.

    • 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, doi:10.1256/qj.03.102.

    • Search Google Scholar
    • Export Citation
  • Ghan, S. J., L. R. Leung, and R. C. Easter, 1997: Prediction of cloud droplet number in a general circulation model. J. Geophys. Res., 102, 21 77721 794, doi:10.1029/97JD01810.

    • Search Google Scholar
    • Export Citation
  • Hobbs, P. V., and A. L. Rangno, 1998: Microstructures of low and middle-level clouds over the Beaufort Sea. Quart. J. Roy. Meteor. Soc., 124, 20352071, doi:10.1002/qj.49712455012.

    • Search Google Scholar
    • Export Citation
  • Hobbs, P. V., A. L. Rangno, L. Arthur, M. Shupe, and T. Uttal, 2001: Airborne studies of cloud structures over the Arctic Ocean and comparisons with retrievals from ship-based remote sensing measurements. J. Geophys. Res., 106, 15 02915 044, doi:10.1029/2000JD900323.

    • Search Google Scholar
    • Export Citation
  • Hu, Y., and Coauthors, 2009: CALIPSO/CALIOP cloud phase discrimination algorithm. J. Atmos. Oceanic Technol., 26, 22932309, doi:10.1175/2009JTECHA1280.1.

    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., W. Ebisuzaki, W. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311643, doi:10.1175/BAMS-83-11-1631.

    • Search Google Scholar
    • Export Citation
  • Koffi, B., and Coauthors, 2012: Application of the CALIOP layer product to evaluate the vertical distribution of aerosols estimated by global models: AeroCom phase I results. J. Geophys. Res., 117, D10201, doi:10.1029/2011JD016858.

    • 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, doi:10.1002/2013JD021119.

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

    • Search Google Scholar
    • Export Citation
  • Li, Z.-X., and H. LeTreut, 1992: Cloud-radiation feedbacks in a general circulation model and their dependence on cloud modelling assumptions. Climate Dyn., 7, 133139, doi:10.1007/BF00211155.

    • Search Google Scholar
    • Export Citation
  • Liu, X., and Coauthors, 2012: Toward a minimal representation of aersols in climate models: Description and evaluation in the Community Atmosphere Model CAM5. Geosci. Model Dev., 5, 709739, doi:10.5194/gmd-5-709-2012.

    • 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, doi:10.1175/2009JTECHA1229.1.

    • 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 I: Calculation of SW flux from observed cloud properties. J. Climate, 27, 88368857, doi:10.1175/JCLI-D-14-00287.1.

    • Search Google Scholar
    • Export Citation
  • McFarlane, N., 2011: Parameterizations: Representing key processes in climate models without resolving them. Wiley Interdiscip. Rev.: Climate Change, 2, 482497, doi:10.1002/wcc.122.

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

    • 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, doi:10.5194/acp-15-2445-2015.

    • Search Google Scholar
    • Export Citation
  • Mitchell, J. F. B., C. A. Senior, and W. J. Ingram, 1989: CO2 and climate: A missing feedback? Nature, 341, 132134, doi:10.1038/341132a0.

    • 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, doi:10.1175/2008JCLI2105.1.

    • Search Google Scholar
    • Export Citation
  • Naumann, A. K., A. Seifert, and J.-P. Mellado, 2013: A refined statistical cloud closure using double-Gaussian probability density functions. Geosci. Model Dev., 6, 16411657, doi:10.5194/gmd-6-1641-2013.

    • Search Google Scholar
    • Export Citation
  • Neale, R. B., and Coauthors, 2010: Description of the NCAR Community Atmosphere Model (CAM5.0). NCAR Tech. Note NCAR/TN-486+STR, 274 pp. [Available online at http://www.cesm.ucar.edu/models/cesm1.0/cam/docs/description/cam5_desc.pdf.]

  • Noel, V., and H. Chepfer, 2010: A global view of horizontally oriented crystals in ice clouds from Cloud–Aerosol Lidar And Infrared Pathfinder Satellite Observation (CALIPSO). J. Geophys. Res., 115, D00H23, doi:10.1029/2009JD012365.

    • Search Google Scholar
    • Export Citation
  • Omar, A. H., and Coauthors, 2009: The CALIPSO automated aerosol classification and lidar ratio selection algorithm. J. Atmos. Oceanic Technol., 26, 19942014, doi:10.1175/2009JTECHA1231.1.

    • Search Google Scholar
    • Export Citation
  • Pincus, R., and S. A. Klein, 2000: Unresolved spatial variability and microphysical process rates in large-scale models. J. Geophys. Res., 105, 27 05927 065, doi:10.1029/2000JD900504.

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

    • Search Google Scholar
    • Export Citation
  • Rotstayn, L. D., 2000: On the “tuning” of autoconversion parameterizations in climate models. J. Geophys. Res., 105, 15 49515 507, doi:10.1029/2000JD900129.

    • Search Google Scholar
    • Export Citation
  • Salzmann, M., Y. Ming, J.-C. Golaz, P. A. Ginoux, H. Morrison, A. Gettelman, M. Krämer, and L. J. Donner, 2010: Two-moment bulk stratiform cloud microphysics in the GFDL AM3 GCM: Description, evaluation, and sensitivity tests. Atmos. Chem. Phys., 10, 80378064, doi:10.5194/acp-10-8037-2010.

    • Search Google Scholar
    • Export Citation
  • Shupe, M. D., 2011: Clouds at Arctic atmospheric observatories. Part II: Thermodynamic phase characteristics. J. Appl. Meteor. Climatol., 50, 645661, doi:10.1175/2010JAMC2468.1.

    • Search Google Scholar
    • Export Citation
  • Song, X., and G. J. Zhang, 2011: Microphysics parameterization for convective clouds in a global climate model: Description and single-column model tests. J. Geophys. Res., 116, D02201, doi:10.1029/2010JD014833.

    • Search Google Scholar
    • Export Citation
  • Storelvmo, T., J. E. Kristjánsson, U. Lohmann, T. Iversen, A. Kirkevåg, and Ø. Seland, 2008: Modeling of the Wegener–Bergeron–Findeisen process—Implications for aerosol indirect effects. Environ. Res. Lett., 3, 045001, doi:10.1088/1748-9326/3/4/045001.

    • Search Google Scholar
    • Export Citation
  • Tan, I., T. Storelvmo, and Y.-S. Choi, 2014: Spaceborne lidar observations of the ice-nucleating potential of dust, polluted dust, and smoke aerosols in mixed-phase clouds. J. Geophys. Res. Atmos., 119, 66536665, doi:10.1002/2013JD021333.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 71837192, doi:10.1029/2000JD900719.

    • Search Google Scholar
    • Export Citation
  • Tompkins, A. M., 2002: A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. J. Atmos. Sci., 59, 19171942, doi:10.1175/1520-0469(2002)059<1917:APPFTS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tsushima, Y., and Coauthors, 2006: Importance of the mixed-phase cloud distribution in the control climate for assessing the response of clouds to carbon dioxide increase: A multi-model study. Climate Dyn., 27, 113126, doi:10.1007/s00382-006-0127-7.

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

    • Search Google Scholar
    • Export Citation
  • Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012: Computing and partitioning cloud feedbacks using cloud property histograms. Part II: Attribution to changes in cloud amount, altitude, and optical depth. J. Climate, 25, 37363754, doi:10.1175/JCLI-D-11-00249.1.

    • Search Google Scholar
    • Export Citation
  • Zhao, C., and Coauthors, 2013: A sensitivity study of radiative fluxes at the top of atmosphere to cloud-microphysics and aerosol parameters in the community atmosphere model CAM5. Atmos. Chem. Phys., 13, 10 96910 987, doi:10.5194/acp-13-10969-2013.

    • Search Google Scholar
    • Export Citation
  • Zhou, C., P. Yang, A. E. Dessler, and F. Liang, 2013: Statistical properties of horizontally oriented plates in optically thick clouds from satellite observations. Geosci. Remote Sens. Lett., 10, 986990, doi:10.1109/LGRS.2012.2227451.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2242 600 96
PDF Downloads 3988 262 27