A Novel Framework for Evaluating and Improving Parameterized Subtropical Marine Boundary Layer Cloudiness

Mark Smalley Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Kay Sušelj Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Matthew Lebsock Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Joao Teixeira Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Abstract

A single-column model (SCM) is used to simulate a variety of environmental conditions between Los Angeles, California, and Hawaii in order to identify physical elements of parameterizations that are required to reproduce the observed behavior of marine boundary layer (MBL) cloudiness. The SCM is composed of the JPL eddy-diffusivity/mass-flux (EDMF) mixing formulation and the RRTMG radiation model. Model forcings are provided by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2). Simulated low cloud cover (LCC), rain rate, albedo, and liquid water path are compared to collocated pixel-level observations from A-Train satellites. This framework ensures that the JPL EDMF is able to simulate a continuum of real-world conditions. First, the JPL EDMF is shown to reproduce the observed mean LCC as a function of lower-tropospheric stability. Joint probability distributions of lower-tropospheric cloud fraction, height, and lower-tropospheric stability (LTS) show that the JPL EDMF improves upon its MERRA2 input but struggles to match the frequency of observed intermediate-range LCC. We then illustrate the physical roles of plume lateral entrainment and eddy-diffusivity mixing length in producing a realistic behavior of LCC as a function of LTS. In low-LTS conditions, LCC is mostly sensitive to the ability of convection to mix moist air out of the MBL. In high-LTS conditions, LCC is also sensitive to the turbulent mixing of free-tropospheric air into the MBL. In the intermediate LTS regime typical of stratocumulus–cumulus transition there is proportional sensitivity to both mixing mechanisms, emphasizing the utility of a combined eddy-diffusivity/mass-flux approach for representing mixing processes.

© 2019 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: Mark Smalley, mark.a.smalley@jpl.nasa.gov

Abstract

A single-column model (SCM) is used to simulate a variety of environmental conditions between Los Angeles, California, and Hawaii in order to identify physical elements of parameterizations that are required to reproduce the observed behavior of marine boundary layer (MBL) cloudiness. The SCM is composed of the JPL eddy-diffusivity/mass-flux (EDMF) mixing formulation and the RRTMG radiation model. Model forcings are provided by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2). Simulated low cloud cover (LCC), rain rate, albedo, and liquid water path are compared to collocated pixel-level observations from A-Train satellites. This framework ensures that the JPL EDMF is able to simulate a continuum of real-world conditions. First, the JPL EDMF is shown to reproduce the observed mean LCC as a function of lower-tropospheric stability. Joint probability distributions of lower-tropospheric cloud fraction, height, and lower-tropospheric stability (LTS) show that the JPL EDMF improves upon its MERRA2 input but struggles to match the frequency of observed intermediate-range LCC. We then illustrate the physical roles of plume lateral entrainment and eddy-diffusivity mixing length in producing a realistic behavior of LCC as a function of LTS. In low-LTS conditions, LCC is mostly sensitive to the ability of convection to mix moist air out of the MBL. In high-LTS conditions, LCC is also sensitive to the turbulent mixing of free-tropospheric air into the MBL. In the intermediate LTS regime typical of stratocumulus–cumulus transition there is proportional sensitivity to both mixing mechanisms, emphasizing the utility of a combined eddy-diffusivity/mass-flux approach for representing mixing processes.

© 2019 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: Mark Smalley, mark.a.smalley@jpl.nasa.gov
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  • Abdul-Razzak, H., and S. J. 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
  • 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.

  • Cheinet, S., and J. Teixeira, 2003: A simple formulation for the eddy-diffusivity parameterization of cloud-topped boundary layers. Geophys. Res. Lett., 30, 1930, https://doi.org/10.1029/2003GL017377.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Roode, S. R., and Coauthors, 2016: Large-eddy simulations of EUCLIPSE–GASS Lagrangian stratocumulus-to-cumulus transitions: Mean state, turbulence, and decoupling. J. Atmos. Sci., 73, 24852508, https://doi.org/10.1175/JAS-D-15-0215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Efron, B., 1979: Bootstrap methods: Another look at the jackknife. Ann. Stat., 7, 126, https://doi.org/10.1214/aos/1176344552.

  • 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
  • Golaz, J. C., V. E. Larson, and W. R. Cotton, 2002: A PDF-based model for boundary layer clouds. Part I: Method and model description. J. Atmos. Sci., 59, 35403551, https://doi.org/10.1175/1520-0469(2002)059<3540:APBMFB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., 1998: Toward cloud resolving modeling of large-scale tropical circulations: A simple cloud microphysics parameterization. J. Atmos. Sci., 55, 32833298, https://doi.org/10.1175/1520-0469(1998)055<3283:TCRMOL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Greenwald, T. J., R. Bennartz, M. Lebsock, and J. Teixeira, 2018: An uncertainty data set for passive microwave satellite observations of warm cloud liquid water path. J. Geophys. Res., 123, 36683687, https://doi.org/10.1002/2017JD027638.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffin, B. M., and V. E. Larson, 2016: A new subgrid-scale representation of hydrometeor fields using a multivariate PDF. Geosci. Model Dev., 9, 20312053, https://doi.org/10.5194/gmd-9-2031-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gustafson, W. I., A. M. Vogelmann, X. Cheng, S. Endo, B. Krishna, Z. Li, T. Toto, and H. Xiao, 2017: Recommendation for the implementation of the LASSO workflow. DOE Atmospheric radiation Measurement Climate Research Facility. Tech. Doc. DOE/SC-ARM-17-031, https://doi.org/10.2172/1406259.

    • Crossref
    • Export Citation
  • Huang, H. Y., A. Hall, and J. Teixeira, 2013: Evaluation of the WRF PBL parameterizations for marine boundary layer clouds: Cumulus and stratocumulus. Mon. Wea. Rev., 141, 22652271, https://doi.org/10.1175/MWR-D-12-00292.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., E. J. Mlawer, S. A. Clough, and J. J. Morcrette, 2000: Impact of an improved longwave radiation model, RRTM, on the energy budget and thermodynamic properties of the NCAR community climate model, CCM3. Geophys. Res. Lett., 105, 14 87314 890, https://doi.org/10.1029/2000JD900091.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jakob, C., 2003: An Improved strategy for the evaluation of cloud parameterizations in GCMs. Bull. Amer. Meteor. Soc., 84, 13871401, https://doi.org/10.1175/BAMS-84-10-1387.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalmus, P., M. Lebsock, and J. Teixeira, 2014: Observational boundary layer energy and water budgets of the stratocumulus-to-cumulus transition. J. Climate, 27, 91559170, https://doi.org/10.1175/JCLI-D-14-00242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karlsson, J., G. Svensson, and H. Rodhe, 2008: Cloud radiative forcing of subtropical low level clouds in global models. Climate Dyn., 30, 779788, https://doi.org/10.1007/s00382-007-0322-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, S., S. Sun-Mack, W. F. Miller, F.G. Rose, Y. Chen, P. Minnis, and B. A. Wielicki, 2010: Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles. J. Geophys. Res., 115, D00H28, https://doi.org/10.1029/2009JD012277.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, S., and Coauthors, 2011: Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties. J. Geophys. Res., 116, D19209, https://doi.org/10.1029/2011JD016050.

    • 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
  • Klein, S., 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
  • Kubar, T. L., G. L. Stephens, M. Lebsock, V. E. Larson, and P. A. Bogenschutz, 2015: Regional assessments of low clouds against large-scale stability in CAM5 and CAM-CLUBB using MODIS and ERA-Interim reanalysis data. J. Climate, 28, 16851706, https://doi.org/10.1175/JCLI-D-14-00184.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • L’Ecuyer, T., and G. Stephens, 2002: An estimation-based precipitation retrieval algorithm for attenuating radars. J. Appl. Meteor. Climatol., 41, 272285, https://doi.org/10.1175/1520-0450(2002)041<0272:AEBPRA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lebsock, M., and T. S. L’Ecuyer, 2011: The retrieval of warm rain from CloudSat. J. Geophys. Res., 116, D20209, https://doi.org/10.1029/2011JD016076.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lebsock, M., and H. Su, 2014: Application of active spaceborne remote sensing for understanding biases between passive cloud water path retrievals. J. Geophys. Res. Atmos., 119, 89628979, https://doi.org/10.1002/2014JD021568.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lock, A. P., A. R. Brown, M. R. Bush, G. M. Martin, and R. N. B. Smith, 2000: A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests. Mon. Wea. Rev., 128, 31873199, https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mace, G. G., and Q. Zhang, 2014: The CloudSat radar-lidar geometrical profile product (RL-GeoProf): Updates, improvements, and selected results. J. Geophys. Res. Atmos., 119, 94419462, https://doi.org/10.1002/2013JD021374.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marchand, R., G. G. Mace, T. Ackerman, and G. Stephens, 2008: Hydrometeor detection using CloudSat—An Earth-orbiting 94-GHz cloud radar. J. Atmos. Oceanic Technol., 25, 519533, https://doi.org/10.1175/2007JTECHA1006.1.

    • 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
  • Nam, C., S. Bony, J. L. Dufresne, and H. Chepfer, 2012: The too few, too bright tropical low-cloud problem in CMIP5 models. Geophys. Res. Lett., 39, L21801, https://doi.org/10.1029/2012GL053421.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neggers, R. A. J., A. P. Siebesma, and T. Heus, 2012: Continuous single-column model evaluation at a permanent meteorological supersite. Bull. Amer. Meteor. Soc., 93, 13891400, https://doi.org/10.1175/BAMS-D-11-00162.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nuijens, L., and B. Stevens, 2012: The influence of wind speed on shallow marine cumulus convection. J. Atmos. Sci., 69, 168184, https://doi.org/10.1175/JAS-D-11-02.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pincus, R., H. W. Barker, and J.-J. Morcrette, 2003: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. J. Geophys. Res., 108, 4376, https://doi.org/10.1029/2002JD003322.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Platnick, S., and Coauthors, 2017: 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
  • Rapp, A. D., M. Lebsock, and T. S. L’Ecuyer, 2013: Low cloud precipitation climatology in the southeastern Pacific marine stratocumulus region using CloudSat. Environ. Res. Lett., 8, 014027, https://doi.org/10.1088/1748-9326/8/1/014027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romps, D. M., and Z. Kuang, 2010: Nature versus nurture in shallow convection. J. Atmos. Sci., 67, 16551666, https://doi.org/10.1175/2009JAS3307.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sandu, I., and B. Stevens, 2011: On the factors modulating the stratocumulus to cumulus transitions. J. Atmos. Sci., 68, 18651881, https://doi.org/10.1175/2011JAS3614.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sandu, I., B. Stevens, and R. Pincus, 2010: On the transitions in marine boundary layer cloudiness. Atmos. Chem. Phys., 10, 23 58923 622, https://doi.org/10.5194/acp-10-2377-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siebesma, A. P., and J. Teixeira, 2000: An advection-diffusion scheme for the convective boundary layer: Description and 1D results. 14th Symp. on Boundary Layer and Turbulence, Aspen, CO, Amer. Meteor. Soc., 4.16, https://ams.confex.com/ams/AugAspen/techprogram/paper_14840.htm.

  • Siebesma, A. P., P. M. M. Soares, and J. Teixeira, 2007: A combined eddy-diffusivity mass-flux approach for the convective boundary layer. J. Atmos. Sci., 64, 12301248, https://doi.org/10.1175/JAS3888.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G., D. Winker, J. Pelon, C. Trepte, D. Vane, C. Yuhas, T. L’Ecuyer, and M. Lebsock, 2018: CloudSat and CALIPSO within the A-Train: Ten years of actively observing the earth system. Bull. Amer. Meteor. Soc., 99, 569581, https://doi.org/10.1175/BAMS-D-16-0324.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sušelj, K., J. Teixeira, and G. Matheou, 2012: Eddy diffusivity/mass flux and shallow cumulus boundary layer: An updraft PDF multiple mass flux scheme. J. Atmos. Sci., 69, 15131533, https://doi.org/10.1175/JAS-D-11-090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sušelj, K., J. Teixeira, and D. Chung, 2013: A unified model for moist convective boundary layers based on a stochastic eddy-diffusivity/mass-flux parameterization. J. Atmos. Sci., 70, 19291953, https://doi.org/10.1175/JAS-D-12-0106.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sušelj, K., T. F. Hogan, and J. Teixeira, 2014: Implementation of a stochastic eddy-diffusivity/mass-flux parameterization into the Navy Global Environmental Model. Wea. Forecasting, 29, 13741390, https://doi.org/10.1175/WAF-D-14-00043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sušelj, K., M. Kurowski, and J. Teixeira, 2019: On the factors controlling the development of shallow convection in eddy-diffusivity/mass-flux models. J. Atmos. Sci., 76, 433456, https://doi.org/10.1175/JAS-D-18-0121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teixeira, J., and Coauthors, 2011: Tropical and subtropical cloud transitions in weather and climate prediction models: The GCSS/WGNE Pacific Cross-Section Intercomparison (GPCI). J. Climate, 24, 52235256, https://doi.org/10.1175/2011JCLI3672.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teixeira, J., and S. Cheinet, 2004: A simple mixing length formulation for the eddy-diffusivity parameterization of dry convection. Bound.-Layer Meteor., 110, 435453, https://doi.org/10.1023/B:BOUN.0000007230.96303.0d.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., T. Meissner, C. Gentemann, and M. Brewer, 2014: Remote sensing systems Aqua ASMR-E daily environmental suite on 0.25° grid, version 7.0. Remote Sensing Systems, Santa Rosa, CA, accessed 1 August 2019, http://www.remss.com/missions/amsr.

  • Wielicki, B. A., B. R. Barkstrom, E. F. Harrison, R. B. Lee III, G. L. Smith, and J. E. Cooper, 1996: Clouds and the Earth’s Radiant Energy System (CERES): An Earth Observing System Experiment. Bull. Amer. Meteor. Soc., 77, 853868, https://doi.org/10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, R., 2012: Stratocumulus clouds. Mon. Wea. Rev., 140, 23732423, https://doi.org/10.1175/MWR-D-11-00121.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, R., and Coauthors, 2015: Clouds, aerosols, and precipitation in the marine boundary layer: An ARM mobile facility deployment. Bull. Amer. Meteor. Soc., 96, 419440, https://doi.org/10.1175/BAMS-D-13-00180.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamaguchi, T., G. Feingold, and J. Kazil, 2017: Stratocumulus to cumulus transition by drizzle. J. Adv. Model. Earth Syst., 9, 23332349, https://doi.org/10.1002/2017MS001104.

    • Crossref
    • Search Google Scholar
    • Export Citation
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