Implementation and Evaluation of a Double-Plume Convective Parameterization in NCAR CAM5

Wenchao Chu aDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

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Yanluan Lin aDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

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Ming Zhao bNOAA/GFDL, Princeton, New Jersey

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Abstract

Performance of global climate models (GCMs) is strongly affected by the cumulus parameterization (CP) used. Similar to the approach in GFDL AM4, a double-plume CP, which unifies the deep and shallow convection in one framework, is implemented and tested in the NCAR Community Atmospheric Model version 5 (CAM5). Based on the University of Washington (UW) shallow convection scheme, an additional plume was added to represent the deep convection. The shallow and deep plumes share the same cloud model, but use different triggers, fractional mixing rates, and closures. The scheme was tested in single-column, short-term hindcast, and AMIP simulations. Compared with the default combination of the Zhang–McFarlane scheme and UW scheme in CAM5, the new scheme tends to produce a top-heavy mass flux profile during the active monsoon period in the single-column simulations. The scheme increases the intensity of tropical precipitation, closer to TRMM observations. The new scheme increased subtropical marine boundary layer clouds and high clouds over the deep tropics, both in better agreement with observations. Sensitivity tests indicate that regime-dependent fractional entrainment rates of the deep plume are desired to improve tropical precipitation distribution and upper troposphere temperature. This study suggests that a double-plume approach is a promising way to combine shallow and deep convections in a unified framework.

© 2021 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: Yanluan Lin, yanluan@tsinghua.edu.cn

Abstract

Performance of global climate models (GCMs) is strongly affected by the cumulus parameterization (CP) used. Similar to the approach in GFDL AM4, a double-plume CP, which unifies the deep and shallow convection in one framework, is implemented and tested in the NCAR Community Atmospheric Model version 5 (CAM5). Based on the University of Washington (UW) shallow convection scheme, an additional plume was added to represent the deep convection. The shallow and deep plumes share the same cloud model, but use different triggers, fractional mixing rates, and closures. The scheme was tested in single-column, short-term hindcast, and AMIP simulations. Compared with the default combination of the Zhang–McFarlane scheme and UW scheme in CAM5, the new scheme tends to produce a top-heavy mass flux profile during the active monsoon period in the single-column simulations. The scheme increases the intensity of tropical precipitation, closer to TRMM observations. The new scheme increased subtropical marine boundary layer clouds and high clouds over the deep tropics, both in better agreement with observations. Sensitivity tests indicate that regime-dependent fractional entrainment rates of the deep plume are desired to improve tropical precipitation distribution and upper troposphere temperature. This study suggests that a double-plume approach is a promising way to combine shallow and deep convections in a unified framework.

© 2021 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: Yanluan Lin, yanluan@tsinghua.edu.cn
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  • Acker, J. G., and G. Leptoukh, 2007: Online analysis enhances use of NASA Earth science data. Eos, Trans. Amer. Geophys. Union, 88, 1417, https://doi.org/10.1029/2007EO020003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future. J. Climate, 17, 24932525, https://doi.org/10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arakawa, A., and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment, Part I. J. Atmos. Sci., 31, 674701, https://doi.org/10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baba, Y., 2019: Spectral cumulus parameterization based on cloud-resolving model. Climate Dyn., 52, 309334, https://doi.org/10.1007/s00382-018-4137-z.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bechtold, P., J. P. Chaboureau, A. Beljaars, A. K. Betts, M. Köhler, M. Miller, and J. L. Redelsperger, 2004: The simulation of the diurnal cycle of convective precipitation over land in a global model. Quart. J. Roy. Meteor. Soc., 130, 31193137, https://doi.org/10.1256/qj.03.103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bechtold, P., M. Köhler, T. Jung, F. Doblas-Reyes, M. Leutbecher, and Coauthors, 2008: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales. Quart. J. Roy. Meteor. Soc., 134, 13371351, https://doi.org/10.1002/qj.289.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Betts, A. K., and M. J. Miller, 1986: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Quart. J. Roy. Meteor. Soc., 112, 693709, https://doi.org/10.1002/qj.49711247308.

    • 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
  • Bretherton, C. S., and S. Park, 2008: A new bulk shallow-cumulus model and implications for penetrative entrainment feedback on updraft buoyancy. J. Atmos. Sci., 65, 21742193, https://doi.org/10.1175/2007JAS2242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., and S. Park, 2009: A new moist turbulence parameterization in the Community Atmosphere Model. J. Climate, 22, 34223448, https://doi.org/10.1175/2008JCLI2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., J. R. McCaa, and H. Grenier, 2004: A new parameterization for shallow cumulus convection and its application to marine subtropical cloud-topped boundary layers. Part I: Description and 1D results. Mon. Wea. Rev., 132, 864882, https://doi.org/10.1175/1520-0493(2004)132<0864:ANPFSC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and J. M. Fritsch, 2004: A reevaluation of ice–liquid water potential temperature. Mon. Wea. Rev., 132, 24212431, https://doi.org/10.1175/1520-0493(2004)132<2421:AROIWP>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
  • Cui, Z., G. J. Zhang, Y. Wang, and S. Xie, 2021: Understanding the roles of convective trigger functions in the diurnal cycle of precipitation in the NCAR CAM5. J. Climate, 34, 64736489, https://doi.org/10.1175/JCLI-D-20-0699.1.

    • Search Google Scholar
    • Export Citation
  • Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 46054630, https://doi.org/10.1175/JCLI3884.1.

  • Derbyshire, S. H., I. Beau, P. Bechtold, J. Y. Grandpeix, J. M. Piriou, J. L. Redelsperger, and P. M. M. Soares, 2004: Sensitivity of moist convection to environmental humidity. Quart. J. Roy. Meteor. Soc., 130, 30553079, https://doi.org/10.1256/qj.03.130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derbyshire, S. H., A. V. Maidens, S. F. Milton, R. A. Stratton, and M. R. Willett, 2011: Adaptive detrainment in a convective parameterization. Quart. J. Roy. Meteor. Soc., 137, 18561871, https://doi.org/10.1002/qj.875.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donner, L. J., 1986: Sensitivity of the thermal balance in a general circulation model to a parameterization for cumulus convection with radiatively interactive clouds. J. Atmos. Sci., 43, 22772288, https://doi.org/10.1175/1520-0469(1986)043<2277:SOTTBI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gehne, M., T. M. Hamill, G. N. Kiladis, and K. E. Trenberth, 2016: Comparison of global precipitation estimates across a range of temporal and spatial scales. J. Climate, 29, 77737795, https://doi.org/10.1175/JCLI-D-15-0618.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Golaz, J., and Coauthors, 2019: The DOE E3SM Coupled Model version 1: Overview and evaluation at standard resolution. J. Adv. Model. Earth Syst., 11, 20892129, https://doi.org/10.1029/2018MS001603.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gregory, D., 2001: Estimation of entrainment rate in simple models of convective clouds. Quart. J. Roy. Meteor. Soc., 127, 5372, https://doi.org/10.1002/qj.49712757104.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grenier, H., and C. S. Bretherton, 2001: A moist PBL parameterization for large-scale models and its application to subtropical cloud-topped marine boundary layers. Mon. Wea. Rev., 129, 357377, https://doi.org/10.1175/1520-0493(2001)129<0357:AMPPFL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hannay, C., D. L. Williamson, J. J. Hack, J. T. Kiehl, J. G. Olson, S. A. Klein, C. S. Bretherton, and M. Köhler, 2009: Evaluation of forecasted southeast Pacific stratocumulus in the NCAR, GFDL, and ECMWF models. J. Climate, 22, 28712889, https://doi.org/10.1175/2008JCLI2479.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., and C. S. Bretherton, 2011: Simulating deep convection with a shallow convection scheme. Atmos. Chem. Phys., 11, 10 38910 406, https://doi.org/10.5194/acp-11-10389-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hourdin, F., and Coauthors, 2006: The LMDZ4 general circulation model: Climate performance and sensitivity to parameterized physics with emphasis on tropical convection. Climate Dyn., 27, 787813, https://doi.org/10.1007/s00382-006-0158-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hourdin, F., and Coauthors, 2017: The art and science of climate model tuning. Bull. Amer. Meteor. Soc., 98, 589602, https://doi.org/10.1175/BAMS-D-15-00135.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the global precipitation record: GPCP version 2.1. Geophys. Res. Lett., 36, L17808, https://doi.org/10.1029/2009GL040000.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jeevanjee, N., 2017: Vertical velocity in the gray zone. J. Adv. Model. Earth Syst., 9, 23042316, https://doi.org/10.1002/2017MS001059.

  • Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 27842802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and Coauthors, 2012: Exposing global cloud biases in the Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators. J. Climate, 25, 51905207, https://doi.org/10.1175/JCLI-D-11-00469.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, E., 1969: On the distribution and continuity of water substance in atmospheric circulations. On the Distribution and Continuity of Water Substance in Atmospheric Circulations, Meteor. Monogr., No. 10, Amer. Meteor. Soc., 184, https://doi.org/10.1007/978-1-935704-36-2_1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M. F., and D. A. Randall, 2002: Similarity of deep continental cumulus convection as revealed by a three-dimensional cloud-resolving model. J. Atmos. Sci., 59, 25502566, https://doi.org/10.1175/1520-0469(2002)059<2550:SODCCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and R. E. Tuleya, 2004: Impact of CO2-induced warming on simulated hurricane intensity and precipitation: Sensitivity to the choice of climate model and convective parameterization. J. Climate, 17, 34773495, https://doi.org/10.1175/1520-0442(2004)017<3477:IOCWOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuo, H. L., 1965: On formation and intensification of tropical cyclones through latent heat release by cumulus convection. J. Atmos. Sci., 22, 4063, https://doi.org/10.1175/1520-0469(1965)022<0040:OFAIOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuo, H. L., 1974: Further studies of the parameterization of the influence of cumulus convection on large-scale flow. J. Atmos. Sci., 31, 12321240, https://doi.org/10.1175/1520-0469(1974)031<1232:FSOTPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, J. L., 2007: The double-ITCZ problem in IPCC AR4 coupled GCMs: Ocean–atmosphere feedback analysis. J. Climate, 20, 44974525, https://doi.org/10.1175/JCLI4272.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, J. L., and Coauthors, 2006: Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: Convective signals. J. Climate, 19, 26652690, https://doi.org/10.1175/JCLI3735.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., 2019: Impact of cumulus microphysics and entrainment specification on tropical cloud and radiation in GFDL AM2. Earth Syst. Environ., 3, 255266, https://doi.org/10.1007/s41748-019-00099-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., and Coauthors, 2012: TWP-ICE global atmospheric model intercomparison: Convection responsiveness and resolution impact. J. Geophys. Res., 117, D09111, https://doi.org/10.1029/2011JD017018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., M. Zhao, Y. Ming, J.-C. Golaz, L. J. Donner, S. A. Klein, V. Ramaswamy, and S. Xie, 2013: Precipitation partitioning, tropical clouds, and intraseasonal variability in GFDL AM2. J. Climate, 26, 54535466, https://doi.org/10.1175/JCLI-D-12-00442.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., and Coauthors, 2020: Community Integrated Earth System Model (CIESM): Description and evaluation. J. Adv. Model. Earth Syst., 12, e2019MS002036, https://doi.org/10.1029/2019MS002036.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong, 2009: Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748766, https://doi.org/10.1175/2008JCLI2637.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, C., Y. Liu, S. Niu, and A. M. Vogelmann, 2012: Lateral entrainment rate in shallow cumuli: Dependence on dry air sources and probability density functions. Geophys. Res. Lett., 39, L20812, https://doi.org/10.1029/2012GL053646.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, H. Y., and Coauthors, 2014: On the correspondence between mean forecast errors and climate errors in CMIP5 models. J. Climate, 27, 17811798, https://doi.org/10.1175/JCLI-D-13-00474.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, H. Y., and Coauthors, 2015: An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models. J. Adv. Model. Earth Syst., 7, 18101827, https://doi.org/10.1002/2015MS000490.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maher, P., G. K. Vallis, S. C. Sherwood, M. J. Webb, and P. G. Sansom, 2018: The impact of parameterized convection on climatological precipitation in atmospheric global climate models. Geophys. Res. Lett., 45, 37283736, https://doi.org/10.1002/2017GL076826.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manabe, S., J. Smagorinsky, and R. F. Strickler, 1965: Simulated climatology of a general circulation model with a hydrologic cycle. Mon. Wea. Rev., 93, 769798, https://doi.org/10.1175/1520-0493(1965)093<0769:SCOAGC>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., 2000: Convective inhibition, subgrid-scale triggering energy, and stratiform instability in a toy tropical wave model. J. Adv. Model. Earth Syst., 57, 15151535, https://doi.org/10.1175/1520-0469(2000)057<1515:CISSTE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., and R. Neale, 2011: Parameterizing convective organization to escape the entrainment dilemma. J. Adv. Model. Earth Syst., 3, M06004, https://doi.org/10.1029/2011MS000042.

    • 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
  • May, P. T., J. H. Mather, G. Vaughan, C. Jakob, G. M. McFarquhar, K. N. Bower, and G. G. Mace, 2008: The Tropical Warm Pool International Cloud Experiment. Bull. Amer. Meteor. Soc., 89, 629646, https://doi.org/10.1175/BAMS-89-5-629.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moorthi, S., and M. Suarez, 1999: Documentation of version 2 of Relaxed Arakawa-Schubert cumulus parameterization with convective downdrafts. NOAA Tech. Rep. NWS/NCEP 99-01, 44 pp.

    • 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
  • Neale, R. B., and Coauthors, 2012: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCAR/TN-486+STR, 289 pp.

    • Search Google Scholar
    • Export Citation
  • Park, S., 2014: A unified convection scheme (UNICON). Part I: Formulation. J. Atmos. Sci., 71, 39023930, https://doi.org/10.1175/JAS-D-13-0233.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, S., C. S. Bretherton, and P. J. Rasch, 2014: Integrating cloud processes in the Community Atmosphere Model, version 5. J. Climate, 27, 68216856, https://doi.org/10.1175/JCLI-D-14-00087.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., and D. L. Hartmann, 2014: Changes in the distribution of rain frequency and intensity in response to global warming. J. Climate, 27, 83728383, https://doi.org/10.1175/JCLI-D-14-00183.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Phillips, T. J., and Coauthors, 2004: Evaluating parameterizations in general circulation models: Climate simulation meets weather prediction. Bull. Amer. Meteor. Soc., 85, 19031916, https://doi.org/10.1175/BAMS-85-12-1903.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qin, Y., Y. Lin, S. Xu, H. Y. Ma, and S. Xie, 2018: A diagnostic PDF cloud scheme to improve subtropical low clouds in NCAR Community Atmosphere Model (CAM5). J. Adv. Model. Earth Syst., 10, 320341, https://doi.org/10.1002/2017MS001095.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randall, D. A., K. M. Xu, R. J. Somerville, and S. Iacobellis, 1996: Single-column models and cloud ensemble models as links between observations and climate models. J. Climate, 9, 16831697, https://doi.org/10.1175/1520-0442(1996)009<1683:SCMACE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randall, D. A., M. Khairoutdinov, A. Arakawa, and W. Grabowski, 2003: Breaking the cloud. Parameterization deadlock. Bull. Amer. Meteor. Soc., 84, 15471564, https://doi.org/10.1175/BAMS-84-11-1547.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riemann-Campe, K., K. Fraedrich, and F. Lunkeit, 2009: Global climatology of convective available potential energy (CAPE) and convective inhibition (CIN) in ERA-40 reanalysis. Atmos. Res., 93, 534545, https://doi.org/10.1016/j.atmosres.2008.09.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romps, D. M., 2010: A direct measure of entrainment. J. Atmos. Sci., 67, 19081927, https://doi.org/10.1175/2010JAS3371.1.

  • Romps, D. M., 2015: MSE minus CAPE is the true conserved variable for an adiabatically lifted parcel. J. Atmos. Sci., 72, 36393646, https://doi.org/10.1175/JAS-D-15-0054.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmidt, G. A., and Coauthors, 2014: Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J. Adv. Model. Earth Syst., 6, 141184, https://doi.org/10.1002/2013MS000265.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siebesma, A. P., P. 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
  • Slingo, J., and Coauthors, 1994: Mean climate and transience in the tropics of the UGAMP GCM: Sensitivity to convective parameterization. Quart. J. Roy. Meteor. Soc., 120, 881922, https://doi.org/10.1002/qj.49712051807.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2010: Dreary state of precipitation in global models. J. Geophys. Res., 115, D24211, https://doi.org/10.1029/2010JD014532.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stirling, A. J., and R. A. Stratton, 2012: Entrainment processes in the diurnal cycle of deep convection over land. Quart. J. Roy. Meteor. Soc., 138, 11351149, https://doi.org/10.1002/qj.1868.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sui, C. H., M. Satoh, and K. Suzuki, 2020: Precipitation efficiency and its role in cloud-radiative feedbacks to climate variability. J. Meteor. Soc. Japan, 98, 261282, https://doi.org/10.2151/jmsj.2020-024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sundqvist, H., 1988: Parameterization of condensation and associated clouds in models for weather prediction and general circulation simulation. Physically-Based Modelling and Simulation of Climate and Climatic Change, Springer, 433461.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokioka, T., K. Yamazaki, A. Kitoh, and T. Ose, 1988: The equatorial 30-60 day oscillation and the Arakawa-Schubert penetrative cumulus parameterization. J. Meteor. Soc. Japan, 66, 883901, https://doi.org/10.2151/JMSJ1965.66.6_883.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • von Salzen, K., and N. A. McFarlane, 2002: Parameterization of the bulk effects of lateral and cloud-top entrainment in transient shallow cumulus clouds. J. Atmos. Sci., 59, 14051430, https://doi.org/10.1175/1520-0469(2002)059<1405:POTBEO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wagner, T. J., D. D. Turner, L. K. Berg, and S. K. Krueger, 2013: Ground-based remote retrievals of cumulus entrainment rates. J. Atmos. Oceanic Technol., 30, 14601471, https://doi.org/10.1175/JTECH-D-12-00187.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walters, D., and Coauthors, 2019: The Met Office Unified Model global atmosphere 7.0/7.1 and JULES global land 7.0 configurations. Geosci. Model Dev., 12, 19091963, https://doi.org/10.5194/gmd-12-1909-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., and M. Zhang, 2013: An analysis of parameterization interactions and sensitivity of single-column model simulations to convection schemes in CAM4 and CAM5. J. Geophys. Res. Atmos., 118, 88698880, https://doi.org/10.1002/jgrd.50690.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., and G. J. Zhang, 2016: Global climate impacts of stochastic deep convection parameterization in the NCAR CAM 5. J. Adv. Model. Earth Syst., 8, 16411656, https://doi.org/10.1002/2016MS000756.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y.-C., S. Xie, S. Tang, and W. Lin, 2020: Evaluation of an improved convective triggering function: Observational evidence and SCM tests. J. Geophys. Res. Atmos., 125, e2019JD031651, https://doi.org/10.1029/2019JD031651.

    • Search Google Scholar
    • Export Citation
  • Xie, S., and M. Zhang, 2000: Impact of the convection triggering function on single-column model simulations. J. Geophys. Res., 105, 14 98314 996, https://doi.org/10.1029/2000JD900170.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S., T. Hume, C. Jakob, S. A. Klein, R. B. McCoy, and M. Zhang, 2010: Observed large-scale structures and diabatic heating and drying profiles during TWP-ICE. J. Climate, 23, 5779, https://doi.org/10.1175/2009JCLI3071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S., H. Y. Ma, J. S. Boyle, S. A. Klein, and Y. Zhang, 2012: On the correspondence between short-and long-time-scale systematic errors in CAM4/CAM5 for the year of tropical convection. J. Climate, 25, 79377955, https://doi.org/10.1175/JCLI-D-12-00134.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S., and Coauthors, 2019: Improved diurnal cycle of precipitation in E3SM with a revised convective triggering function. J. Adv. Model. Earth Syst., 11, 22902310, https://doi.org/10.1029/2019MS001702.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, B., and Coauthors, 2013: Uncertainty quantification and parameter tuning in the CAM5 Zhang-McFarlane convection scheme and impact of improved convection on the global circulation and climate. J. Geophys. Res. Atmos., 118, 395415, https://doi.org/10.1029/2012JD018213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoshimura, H., R. Mizuta, and H. Murakami, 2015: A spectral cumulus parameterization scheme interpolating between two convective updrafts with semi-Lagrangian calculation of transport by compensatory subsidence. Mon. Wea. Rev., 143, 597621, https://doi.org/10.1175/MWR-D-14-00068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, H., M. Zhang, W. Lin, and X. Zhang, 2017: Cloud transitions: Comparison of temporal variation in the southeastern Pacific with the spatial variation in the northeastern Pacific at low latitudes. Int. J. Climatol., 37, 29232933, https://doi.org/10.1002/joc.4889.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.–Ocean, 33, 407446, https://doi.org/10.1080/07055900.1995.9649539.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., and M. Mu, 2005: Effects of modifications to the Zhang-McFarlane convection parameterization on the simulation of the tropical precipitation in the National Center for Atmospheric Research Community Climate Model, version 3. J. Geophys. Res., 110, D09109, https://doi.org/10.1029/2004JD005617.

    • 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 Space Sci., 7, e2020EA001125, https://doi.org/10.1029/2020EA001125.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, C., and Coauthors, 2012: Toward understanding of differences in current cloud retrievals of ARM ground-based measurements. J. Geophys. Res., 117, D10206, https://doi.org/10.1029/2011JD016792.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., and P. H. Austin, 2005a: Life cycle of numerically simulated shallow cumulus clouds. Part I: Transport. J. Atmos. Sci., 62, 12691290, https://doi.org/10.1175/JAS3414.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., and P. H. Austin, 2005b: Life cycle of numerically simulated shallow cumulus clouds. Part II: Mixing dynamics. J. Atmos. Sci., 62, 12911310, https://doi.org/10.1175/JAS3415.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., and Coauthors, 2018a: The GFDL global atmosphere and land model AM4.0/LM4.0: 1. Simulation characteristics with prescribed SSTs. J. Adv. Model. Earth Syst., 10, 691734, https://doi.org/10.1002/2017MS001208.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., and Coauthors, 2018b: The GFDL global atmosphere and land model AM4.0/LM4.0: 2. Model description, sensitivity studies, and tuning strategies. J. Adv. Model. Earth Syst., 10, 735769, https://doi.org/10.1002/2017MS001209.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, X., Y. Lin, Y. Peng, B. Wang, H. Morrison, and A. Gettelman, 2017: A single ice approach using varying ice particle properties in global climate model microphysics. J. Adv. Model. Earth Syst., 9, 21382157, https://doi.org/10.1002/2017MS000952.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, X., S. A. Klein, H. Y. Ma, P. Bogenschutz, A. Gettelman, and V. E. Larson, 2016: Assessment of marine boundary layer cloud simulations in the CAM with CLUBB and updated microphysics scheme based on ARM observations from the Azores. J. Geophys. Res. Atmos., 121, 84728492, https://doi.org/10.1002/2016JD025274.

    • Crossref
    • Search Google Scholar
    • Export Citation
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