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Ongoing Breakthroughs in Convective Parameterization

  • Convection and Climate (C Muller, Section Editor)
  • Published:
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Abstract

Purpose of Review

While the increase of computer power mobilizes a part of the atmospheric modeling community toward models with explicit convection or based on machine learning, we review the part of the literature dedicated to convective parameterization development for large-scale forecast and climate models.

Recent Findings

Many developments are underway to overcome endemic limitations of traditional convective parameterizations, either in unified or multiobject frameworks: scale-aware and stochastic approaches, new prognostic equations or representations of new components such as cold pools. Understanding their impact on the emergent properties of a model remains challenging, due to subsequent tuning of parameters and the limited understanding given by traditional metrics.

Summary

Further effort still needs to be dedicated to the representation of the life cycle of convective systems, in particular their mesoscale organization and associated cloud cover. The development of more process-oriented metrics based on new observations is also needed to help quantify model improvement and better understand the mechanisms of climate change.

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References

  1. Alexander GD, Cotton WR. The use of cloud-resolving simulations of mesoscale convective systems to build a mesoscale parameterization scheme. J Atmos Sci 1998;55(12):2137–61.

    Article  Google Scholar 

  2. Andersen JA, Kuang Z. Moist static energy budget of mjo-like disturbances in the atmosphere of a zonally symmetric aquaplanet. J Climate 2012;25(8):2782–804.

    Article  Google Scholar 

  3. Arakawa A. The cumulus parameterization problem: past, present, and future. J Clim 2004;17(13):2493–525. https://doi.org/10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2.

    Article  Google Scholar 

  4. Arakawa A, Wu CM. A unified representation of deep moist convection in numerical modeling of the atmosphere. Part i. J Atmos Sci 2013;70(7):1977–92. https://doi.org/10.1175/JAS-D-12-0330.1.

    Article  Google Scholar 

  5. Arakawa RA, Schubert WH. Interaction of a cumulus cloud ensemble with the large scale environment. Part I. J Atmos Sci 1974;31:674–701.

    Article  Google Scholar 

  6. Bechtold P, Semane N, Lopez P, Chaboureau JP, Beljaars A, Bormann N. Representing equilibrium and nonequilibrium convection in large-scale models. J Atmos Sci 2014;71(2):734–53.

    Article  Google Scholar 

  7. Becker T, Bretherton CS, Hohenegger C, Stevens B. Estimating bulk entrainment with unaggregated and aggregated convection. Geophys Res Lett 2018;45(1):455–62.

    Article  Google Scholar 

  8. Benedict JJ, Randall DA. Observed characteristics of the mjo relative to maximum rainfall. J Atmos Sci 2007;64(7):2332– 54.

    Article  Google Scholar 

  9. Bengtsson L, Steinheimer M, Bechtold P, Geleyn JF. A stochastic parametrization for deep convection using cellular automata. Q J Roy Meteorol Soc 2013;139(675):1533–43.

    Article  Google Scholar 

  10. Bernstein DN, Neelin JD. Identifying sensitive ranges in global warming precipitation change dependence on convective parameters. Geophys Res Lett 2016;43:5841–50. https://doi.org/10.1002/2016GL069022.

    Article  Google Scholar 

  11. Bhattacharya R, Bordoni S, Suselj K, Teixeira J. Parameterization interactions in global aquaplanet simulations. J Adv Modeling Earth Syst 2018;10:403–20. https://doi.org/10.1002/2017MS000991.

    Article  Google Scholar 

  12. Birch CE, Parker DJ, Marsham JH, Copsey D, Garcia-Carreras L. A seamless assessment of the role of convection in the water cycle of the west african monsoon. J Geophys Res: Atmos 2014;119(6):2890–912. https://doi.org/10.1002/2013JD020887.

    Article  Google Scholar 

  13. Birch CE, Roberts MJ, Garcia-Carreras L, Ackerley D, Reeder MJ, Lock AP, Schiemann R. Sea-breeze dynamics and convection initiation: the influence of convective parameterization in weather and climate model biases. J Climate 2015;28(20):8093–108.

    Article  Google Scholar 

  14. Bogenschutz PA, Krueger SK, Khairoutdinov M. Assumed probability density functions for shallow and deep convection. J Adv Model Earth Syst 2010;2:4.

    Article  Google Scholar 

  15. Böing SJ, Jonker HJ, Siebesma AP, Grabowski WW. Influence of the subcloud layer on the development of a deep convective ensemble. J Atmos Sci 2012;69(9):2682–98.

    Article  Google Scholar 

  16. Böing SJ, Jonker HJ, Nawara WA, Siebesma AP. On the deceiving aspects of mixing diagrams of deep cumulus convection. J Atmos Sci 2014;71(1):56–68.

    Article  Google Scholar 

  17. Bony S, Dufresne JL. Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys Res Lett 2005;32:20.

    Article  Google Scholar 

  18. Bouniol D, Roca R, Fiolleau T, Poan DE. Macrophysical, microphysical, and radiative properties of tropical mesoscale convective systems over their life cycle. J Climate 2016;29(9):3353–71.

    Article  Google Scholar 

  19. Caldwell PM, Zelinka MD, Klein SA. Evaluating emergent constraints on equilibrium climate sensitivity. J Climate 2018;31 (10):3921–42.

    Article  Google Scholar 

  20. Cesana G, Del Genio AD, Ackerman AS, Kelley M, Elsaesser G, Fridlind AM, Cheng Y, Yao MS. 2018. Evaluating models’ response of tropical low clouds to sst forcings using calipso observations. Atmos Chem Phys, submitted.

  21. Chen B, Mapes BE. Effects of a simple convective organization scheme in a two-plume gcm. J Adv Model Earth Syst 2018;10(3):867–80.

    Article  Google Scholar 

  22. Cheruy F, Campoy A, Dupont JC, Ducharne A, Hourdin F, Haeffelin M, Chiriaco M, Idelkadi A. Combined influence of atmospheric physics and soil hydrology on the simulated meteorology at the SIRTA atmospheric observatory. Clim Dyn 2013;40:2251–69. https://doi.org/10.1007/s00382-012-1469-y.

    Article  Google Scholar 

  23. Colin M, Sherwood S, Geoffroy O, Bony S, Fuchs D. Identifying the sources of convective memory in cloud-resolving simulations. J Atmos Sci 2018;0(0):null. https://doi.org/10.1175/JAS-D-18-0036.1.

    Article  Google Scholar 

  24. Collis S, Protat A, May PT, Williams C. Statistics of storm updraft velocities from twp-ice including verification with profiling measurements. J Appl Meteorol Climatol 2013;52(8):1909–22.

    Article  Google Scholar 

  25. Couvreux F, Roehrig R, Rio C, Lefebvre MP, Caian M, Komori T, Derbyshire S, Guichard F, Favot F, D’Andrea F, et al. Representation of daytime moist convection over the semi-arid tropics by parametrizations used in climate and meteorological models. Q J Roy Meteorol Soc 2015;141(691): 2220–36.

    Article  Google Scholar 

  26. Dai A. Precipitation characteristics in eighteen coupled climate models. J Climate 2006;19(18):4605–30.

    Article  Google Scholar 

  27. D’Andrea F, Gentine P, Betts AK, Lintner BR. Triggering deep convection with a probabilistic plume model. J Atmos Sci 2014;71:3881–901. https://doi.org/10.1175/JAS-D-13-0340.1.

    Article  Google Scholar 

  28. Davies L, Plant RS, Derbyshire SH. A simple model of convection with memory. J Geophys Res: Atmos 2009;114:D17.

    Article  Google Scholar 

  29. Davies L, Plant R, Derbyshire S. Departures from convective equilibrium with a rapidly varying surface forcing. Q J Roy Meteorol Soc 2013;139(676):1731–46.

    Article  Google Scholar 

  30. Del Genio A, Yao M. 1993. Efficient cumulus parameterization for long-term climate studies: the GISS scheme, pp 181–184.

  31. Del Genio AD, Wu J. The role of entrainment in the diurnal cycle of continental convection. J Climate 2010; 23(10):2722–38.

    Article  Google Scholar 

  32. Del Genio AD, Yao MS, Kovari W, Lo KK. A prognostic cloud water parameterization for global climate models. J Climate 1996;9(2):270–304.

    Article  Google Scholar 

  33. Del Genio AD, Kovari W, Yao MS, Jonas J. Cumulus microphysics and climate sensitivity. J Climate 2005;18(13):2376– 87.

    Article  Google Scholar 

  34. Del Genio AD, Chen Y, Kim D, Yao MS. The mjo transition from shallow to deep convection in cloudsat/calipso data and giss gcm simulations. J Climate 2012;25(11):3755–70.

    Article  Google Scholar 

  35. Del Genio AD, Wu J, Wolf AB, Chen Y, Yao MS, Kim D. Constraints on cumulus parameterization from simulations of observed mjo events. J Climate 2015;28(16):6419–42.

    Article  Google Scholar 

  36. Deng Q, Khouider B, Majda AJ. The mjo in a coarse-resolution gcm with a stochastic multicloud parameterization. J Atmos Sci 2015;72(1):55–74.

    Article  Google Scholar 

  37. Diallo FB, Hourdin F, Rio C, Traore AK, Mellul L, Guichard F, Kergoat L. The surface energy budget computed at the grid-scale of a climate model challenged by station data in West Africa. J Adv Modeling Earth Syst 2017;9(7):2710–38. https://doi.org/10.1002/2017MS001081. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017MS001081.

    Article  Google Scholar 

  38. Dixit V, Geoffroy O, Sherwood SC. Control of ITCZ width by low-level radiative heating from upper-level clouds in aquaplanet simulations. Geophys Res Lett 2018;45:5788–97. https://doi.org/10.1029/2018GL078292.

    Article  Google Scholar 

  39. Donner LJ. A cumulus parameterization including mass fluxes, vertical momentum dynamics, and mesoscale effects. J Atmos Sci 1993;50(6):889–906. https://doi.org/10.1175/1520-0469(1993)050<0889:ACPIMF>2.0.CO;2.

    Article  Google Scholar 

  40. Donner LJ, Phillips VT. Boundary layer control on convective available potential energy: implications for cumulus parameterization. J Geophys Res: Atmos 2003;108:D22. https://doi.org/10.1029/2003JD003773.

    Article  Google Scholar 

  41. Donner LJ, Horowitz LW, Fiore AM, Seman CJ, Blake DR, Blake NJ. Transport of radon-222 and methyl iodide by deep convection in the gfdl global atmospheric model am2. J Geophys Res: Atmos 2007;112: D17. https://doi.org/10.1029/2006JD007548.

    Article  CAS  Google Scholar 

  42. Donner LJ, Wyman BL, Hemler RS, Horowitz LW, Ming Y, Zhao M, Golaz JC, Ginoux P, Lin SJ, Schwarzkopf MD, et al. The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component am3 of the gfdl global coupled model cm3. J Climate 2011; 24(13):3484–519.

    Article  Google Scholar 

  43. Donner LJ, O’Brien TA, Rieger D, Vogel B, Cooke WF. Are atmospheric updrafts a key to unlocking climate forcing and sensitivity? Atmos Chem Phys 2016;16(20):12,983–92.

    Article  CAS  Google Scholar 

  44. Dorrestijn J, Crommelin DT, Siebesma AP, Jonker HJ. Stochastic parameterization of shallow cumulus convection estimated from high-resolution model data. Theor Comput Fluid Dyn 2013;27(1–2):133–48.

    Article  Google Scholar 

  45. Dorrestijn J, Crommelin DT, Siebesma AP, Jonker HJ, Selten F. Stochastic convection parameterization with markov chains in an intermediate-complexity gcm. J Atmos Sci 2016;73(3):1367–82.

    Article  Google Scholar 

  46. Doswell I, Charles A, Brooks HE, Maddox RA. Flash flood forecasting: an ingredients-based methodology. Weather Forecast 1996;11(4):560–81.

    Article  Google Scholar 

  47. Elsaesser GS, Del Genio AD, Jiang JH, van Lier-Walqui M. An improved convective ice parameterization for the nasa giss global climate model and impacts on cloud ice simulation. J Climate 2017;30(1): 317–36.

    Article  Google Scholar 

  48. Emanuel KA. A scheme for representing cumulus convection in large-scale models. J Atmos Sci 1991;48: 2313–35.

    Article  Google Scholar 

  49. Emanuel KA, Zivkovic-Rothman M. Development and evaluation of a convection scheme for use in climate models. J Atmos Sci 1999;56:1766–82. https://doi.org/10.1175/1520-0469(1999)056<1766:DAEOAC>2.0.CO;2.

    Article  Google Scholar 

  50. Fan J, Rosenfeld D, Zhang Y, Giangrande SE, Li Z, Machado LA, Martin ST, Yang Y, Wang J, Artaxo P, et al. Substantial convection and precipitation enhancements by ultrafine aerosol particles. Science 2018;359(6374):411–8.

    Article  CAS  Google Scholar 

  51. Feingold G. Modeling of the first indirect effect: analysis of measurement requirements. Geophys Res Lett 2003; 30:19. https://doi.org/10.1029/2003GL017967.

    Article  CAS  Google Scholar 

  52. Feng Z, Hagos S, Rowe AK, Burleyson CD, Martini MN, de Szoeke SP. Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the amie/dynamo field campaign. J Adv Model Earth Syst 2015;7(2):357–81.

    Article  Google Scholar 

  53. Fiolleau T, Roca R. An algorithm for the detection and tracking of tropical mesoscale convective systems using infrared images from geostationary satellite. IEEE Trans Geosci Remote Sens 2013;51(7):4302–15.

    Article  Google Scholar 

  54. Folkins I, Bernath P, Boone C, Donner LJ, Eldering A, Lesins G, Martin RV, Sinnhuber BM, Walker K. Testing convective parameterizations with tropical measurements of hno3, co, h2o, and o3: implications for the water vapor budget. J Geophys Res: Atmos 2006;111:D23. https://doi.org/10.1029/2006JD007325.

    Article  CAS  Google Scholar 

  55. Fu R, Del Genio AD, Rossow WB. Behavior of deep convective clouds in the tropical pacific deduced from isccp radiances. J Clim 1990;3(10):1129–52.

    Article  Google Scholar 

  56. Gentine P, Pritchard M, Rasp S, Reinaudi G, Yacalis G. Could machine learning break the convection parameterization deadlock? Geophys Res Lett 2018;45(11):5742–51. https://doi.org/10.1029/2018GL078202.

    Article  Google Scholar 

  57. Geoffroy O, Sherwood SC, Fuchs D. On the role of the stratiform cloud scheme in the inter-model spread of cloud feedback. J Adv Model Earth Syst 2017;9:423–37. https://doi.org/10.1002/2016MS000846.

    Article  Google Scholar 

  58. Giangrande SE, Collis S, Straka J, Protat A, Williams C, Krueger S. A summary of convective-core vertical velocity properties using arm uhf wind profilers in oklahoma. J Appl Meteorol Climatol 2013; 52(10):2278–95.

    Article  Google Scholar 

  59. Giangrande SE, Toto T, Jensen MP, Bartholomew MJ, Feng Z, Protat A, Williams CR, Schumacher C, Machado L. Convective cloud vertical velocity and mass-flux characteristics from radar wind profiler observations during goamazon2014/5. J Geophys Res: Atmos 2016;121:21.

    Article  CAS  Google Scholar 

  60. Glenn IB, Krueger SK. Downdrafts in the near cloud environment of deep convective updrafts. J Adv Model Earth Syst 2014;6(1):1–8.

    Article  Google Scholar 

  61. Goswami B, Khouider B, Phani R, Mukhopadhyay P, Majda A. Implementation and calibration of a stochastic multicloud convective parameterization in the ncep c limate f orecast s ystem (cfsv2). J Adv Model Earth Syst 2017;9(3):1721–39.

    Article  Google Scholar 

  62. Goswami B, Khouider B, Phani R, Mukhopadhyay P, Majda A. Improving synoptic and intraseasonal variability in cfsv2 via stochastic representation of organized convection. Geophys Res Lett 2017;44(2): 1104–13.

    Article  Google Scholar 

  63. Grabowski WW. Towards global large eddy simulation: super-parameterization revisited. J Met Soc Japan 2016; 94(4):327–44. https://doi.org/10.2151/jmsj.2016-017.

    Article  Google Scholar 

  64. Grandpeix J, Lafore J. A density current parameterization coupled with Emanuel’s convection scheme. Part I: the models. J Atmos Sci 2010;67:881–97. https://doi.org/10.1175/2009JAS3044.1.

    Article  Google Scholar 

  65. Grandpeix J, Lafore J, Cheruy F. A density current parameterization coupled with Emanuel’s convection scheme. Part II: 1D simulations. J Atmos Sci 2010;67:898–922. https://doi.org/10.1175/2009JAS3045.1.

    Article  Google Scholar 

  66. Grandpeix JY, Phillips V, Tailleux R. Improved mixing representation in Emanuel’s convection scheme. Q J R Meteorol Soc 2004;130:3207–22.

    Article  Google Scholar 

  67. Grant LD, Lane TP, van den Heever SC. 2018. The role of cold pools in tropical oceanic convective systems. Journal of the Atmospheric Sciences.

  68. Gregory D, Rowntree PR. A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon Weather Rev 1990;118(7):1483–506. https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2.

    Article  Google Scholar 

  69. Grell GA. Prognostic evaluation of assumptions used by cumulus parameterizations. Mon Weather Rev 1993;121 (3):764–87. https://doi.org/10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2.

    Article  Google Scholar 

  70. Guo H, Golaz JC, Donner LJ, Ginoux P, Hemler RS. Multivariate probability density functions with dynamics in the GFDL atmospheric general circulation model: global tests. J Climate 2014;27:2087–108. https://doi.org/10.1175/JCLI-D-13-00347.1.

    Article  Google Scholar 

  71. Guo H, Golaz JC, Donner LJ, Wyman B, Zhao M, Ginoux P. CLUBB as a unified cloud parameterization: opportunities and challenges. Geophys Res Lett 2015;42:4540–47 . https://doi.org/10.1002/2015GL063672.

    Article  Google Scholar 

  72. Guérémy JF. A continuous buoyancy based convection scheme: one- and three-dimensional validation. Tellus A 2011;63(4):687–706. https://doi.org/10.1111/j.1600-0870.2011.00521.x.

    Article  Google Scholar 

  73. Hagos S, Feng Z, Plant RS, Houze JRA, Xiao H. A stochastic framework for modeling the population dynamics of convective clouds. J Adv Model Earth Syst 2018;10(2):448–65.

    Article  Google Scholar 

  74. Hannah WM. Entrainment versus dilution in tropical deep convection. J Atmos Sci 2017;74(11):3725–47.

    Article  Google Scholar 

  75. Harrop BE, Ma PL, Rasch PJ, Neale RB, Hannay C. The role of convective gustiness in reducing seasonal precipitation biases in the tropical west pacific. J Adv Modeling Earth Syst 2018;10:961–70. https://doi.org/10.1002/2017MS001157.

    Article  Google Scholar 

  76. Hartmann DL, Larson K. An important constraint on tropical cloud-climate feedback. Geophys Res Lett 2002;29(20):12–1.

    Article  Google Scholar 

  77. Hartmann DL, Gasparini B, Berry SE, Blossey PN. The life cycle and net radiative effect of tropical anvil clouds. J Adv Model Earth Syst 2018;10(12):3012–29.

    Article  Google Scholar 

  78. Heus T, Jonker HJ. Subsiding shells around shallow cumulus clouds. J Atmos Sci 2008;65(3):1003–18.

    Article  Google Scholar 

  79. Heymsfield AJ, Schmitt C, Bansemer A. Ice cloud particle size distributions and pressure-dependent terminal velocities from in situ observations at temperatures from 0 to- 86 c. J Atmos Sci 2013;70(12):4123–54.

    Article  Google Scholar 

  80. Hirota N, Takayabu YN, Watanabe M, Kimoto M, Chikira M. Role of convective entrainment in spatial distributions of and temporal variations in precipitation over tropical oceans. J Climate 2014;27(23):8707–23.

    Article  Google Scholar 

  81. Hohenegger C, Bretherton CS. Simulating deep convection with a shallow convection scheme. Atmos Chem Phys 2011;11(20):10,389–406.

    Article  CAS  Google Scholar 

  82. Holloway C, Woolnough S, Lister G. Precipitation distributions for explicit versus parametrized convection in a large-domain high-resolution tropical case study. Q J Roy Meteorol Soc 2012;138(668):1692–708.

    Article  Google Scholar 

  83. Holloway CE, Neelin JD. Moisture vertical structure, column water vapor, and tropical deep convection. J Atmos Sci 2009;66(6):1665–83.

    Article  Google Scholar 

  84. Holloway CE, Woolnough SJ, Lister GM. The effects of explicit versus parameterized convection on the mjo in a large-domain high-resolution tropical case study. Part i: Characterization of large-scale organization and propagation. J Atmos Sci 2013;70(5):1342–69.

    Article  Google Scholar 

  85. Holloway CE, Woolnough SJ, Lister GM. The effects of explicit versus parameterized convection on the mjo in a large-domain high-resolution tropical case study. Part ii: processes leading to differences in mjo development. J Atmos Sci 2015;72(7):2719–43.

    Article  Google Scholar 

  86. Hourdin F, Couvreux F, Menut L. Parameterisation of the dry convective boundary layer based on a mass flux representation of thermals. J Atmos Sci 2002;59:1105–23.

    Article  Google Scholar 

  87. Hourdin F, Grandpeix JY, Rio C, Bony S, Jam A, Cheruy F, Rochetin N, Fairhead L, Idelkadi A, Musat I, Dufresne JL, Lahellec A, Lefebvre MP, Roehrig R. LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection. Clim Dyn 2013;40:2193–222. https://doi.org/10.1007/s00382-012-1343-y.

    Article  Google Scholar 

  88. Hourdin F, Gueye M, Diallo B, Dufresne JL, Escribano J, Menut L, Marticoréna B, Siour G, Guichard F. Parameterization of convective transport in the boundary layer and its impact on the representation of the diurnal cycle of wind and dust emissions. Atmos Chem Phys 2015;15:6775–88. https://doi.org/10.5194/acp-15-6775-2015.

    Article  CAS  Google Scholar 

  89. Hourdin F, Gǎinusa-Bogdan A, Braconnot P, Dufresne JL, Traore AK, Rio C. Air moisture control on ocean surface temperature, hidden key to the warm bias enigma. Geophys Res Lett 2015;42:10. https://doi.org/10.1002/2015GL066764.

    Article  Google Scholar 

  90. Hourdin F, Mauritsen T, Gettelman A, Golaz JC, Balaji V, Duan Q, Folini D, Ji D, Klocke D, Qian Y, Rauser F, Rio C, Tomassini L, Watanabe M, Williamson D. The art and science of climate model tuning. Bull Am Meteorol Soc 2017;98:589–602. https://doi.org/10.1175/BAMS-D-15-00135.1.

    Article  Google Scholar 

  91. Houston AL, Wilhelmson RB. The dependence of storm longevity on the pattern of deep convection initiation in a low-shear environment. Mon Weather Rev 2011;139(10):3125–38.

    Article  Google Scholar 

  92. Houze JRA. Mesoscale convective systems. Rev Geophys 2004;42:4.

    Article  Google Scholar 

  93. Houze RA, Churchill DD. Mesoscale organization and cloud microphysics in a bay of bengal depression. J Atmos Sci 1987;44(14):1845–68.

    Article  Google Scholar 

  94. Huang XY. The organization of moist convection by internal gravity waves. Tellus A: Dyn Meteorol Oceanogr 1990;42(2):270–85.

    Article  Google Scholar 

  95. Jakob C. Accelerating progress in global atmospheric model development through improved parameterizations. Bull Am Meteorol Soc 2010;91(7):869–76. https://doi.org/10.1175/2009BAMS2898.1.

    Article  Google Scholar 

  96. Jam A, Hourdin F, Rio C, Couvreux F. Resolved versus parametrized boundary-layer plumes. Part iii: derivation of a statistical scheme for cumulus clouds. Boundary-layer Meteorol 2013;147(3):421–41.

    Article  Google Scholar 

  97. Jeevanjee N, Romps DM. Effective buoyancy, inertial pressure, and the mechanical generation of boundary layer mass flux by cold pools. J Atmos Sci 2015;72(8):3199–13. https://doi.org/10.1175/JAS-D-14-0349.1.

    Article  Google Scholar 

  98. Johnson RH, Rickenbach TM, Rutledge SA, Ciesielski PE, Schubert WH. Trimodal characteristics of tropical convection. J Clim 1999; 12 (8): 2397–418. https://doi.org/10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.

    Article  Google Scholar 

  99. Jones TR, Randall DA. Quantifying the limits of convective parameterizations. J Geophys Res: Atmos 2011; 116:D8.

    Google Scholar 

  100. Kain JS, Fritsch JM. A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 1990;47(23):2784–802. https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2.

    Article  Google Scholar 

  101. Kay J, Wood R. 2008. Timescale analysis of aerosol sensitivity during homogeneous freezing and implications for upper tropospheric water vapor budgets. Geophys Res Lett, 35. https://doi.org/10.1029/2007GL032628.

  102. Kay JE, Wall C, Yettella V, Medeiros B, Hannay C, Caldwell P, Bitz C. Global climate impacts of fixing the southern ocean shortwave radiation bias in the community earth system model (cesm). J Climate 2016;29(12):4617–36.

    Article  Google Scholar 

  103. Keane R, Plant R. Large-scale length and time-scales for use with stochastic convective parametrization. Q J Roy Meteorol Soc 2012;138(666):1150–64.

    Article  Google Scholar 

  104. Keane RJ, Craig GC, Keil C, Zängl G. The plant–craig stochastic convection scheme in icon and its scale adaptivity. J Atmos Sci 2014;71(9):3404–15.

    Article  Google Scholar 

  105. Keane RJ, Plant RS, Tennant WJ. Evaluation of the plant–craig stochastic convection scheme (v2 0) in the ensemble forecasting system mogreps-r (24 km) based on the unified model (v7 3). Geoscie Model Develop 2016;9(5):1921–35.

    Article  Google Scholar 

  106. Khain A. Notes on state-of-the-art investigations of aerosol effects on precipitation: a critical review. Environ Res Lett 2009;4(1):015,004.

    Article  CAS  Google Scholar 

  107. Khairoutdinov M, Randall D. High-resolution simulation of shallow-to-deep convection transition over land. J Atmos Sci 2006;63(12):3421–36.

    Article  Google Scholar 

  108. Khouider B, Moncrieff MW. Organized convection parameterization for the itcz. J Atmos Sci 2015;72(8): 3073–96.

    Article  Google Scholar 

  109. Khouider B, Biello J, Majda AJ, et al. A stochastic multicloud model for tropical convection. Commun Math Sci 2010;8(1):187–216.

    Article  Google Scholar 

  110. Kiehl J. On the observed near cancellation between longwave and shortwave cloud forcing in tropical regions. J Climate 1994;7(4):559–65.

    Article  Google Scholar 

  111. Kim D, Sobel AH, Maloney ED, Frierson DM, Kang IS. A systematic relationship between intraseasonal variability and mean state bias in agcm simulations. J Climate 2011;24(21):5506–20.

    Article  Google Scholar 

  112. Kim D, Ahn MS, Kang IS, Del Genio AD. Role of longwave cloud–radiation feedback in the simulation of the Madden–Julian oscillation. J Climate 2015;28(17):6979–94.

    Article  Google Scholar 

  113. Klein S, Hall A, Norris J, Pincus R. 2017. Low-cloud feedbacks from cloud-controlling factors: a review, vol 65.

  114. Klingaman NP, Jiang X, Xavier PK, Petch J, Waliser D, Woolnough SJ. Vertical structure and physical processes of the Madden-Julian oscillation: synthesis and summary. J Geophys Res: Atmos 2015;120(10): 4671–4689.

    Google Scholar 

  115. Köhler M, Ahlgrimm M, Beljaars A. Unified treatment of dry convective and stratocumulus-topped boundary layers in the ECMWF model. Q J R Meteorol Soc 2011;137:43–57. https://doi.org/10.1002/qj.713.

    Article  Google Scholar 

  116. Kumar VV, Jakob C, Protat A, Williams CR, May PT. Mass-flux characteristics of tropical cumulus clouds from wind profiler observations at Darwin, Australia. J Atmos Sci 2015;72(5):1837–55.

    Article  Google Scholar 

  117. Kuo YH, Schiro KA, Neelin JD. Convective transition statistics over tropical oceans for climate model diagnostics: Observational baseline. J Atmos Sci 2018;75(5):1553–70.

    Article  Google Scholar 

  118. Kwon YC, Hong SY. A mass-flux cumulus parameterization scheme across gray-zone resolutions. Mon Weather Rev 2017; 145 (2): 583–98. https://doi.org/10.1175/MWR-D-16-0034.1.

    Article  Google Scholar 

  119. Labbouz L, Kipling Z, Stier P, Protat A. How well can we represent the spectrum of convective clouds in a climate model? Comparisons between internal parameterization variables and radar observations. J Atmos Sci 2018;75(5):1509–24.

    Article  Google Scholar 

  120. Lane TP, Moncrieff MW. Characterization of momentum transport associated with organized moist convection and gravity waves. J Atmos Sci 2010;67(10):3208–25.

    Article  Google Scholar 

  121. Lane TP, Moncrieff MW. Long-lived mesoscale systems in a low–convective inhibition environment. Part i: upshear propagation. J Atmos Sci 2015;72(11):4297–318.

    Article  Google Scholar 

  122. Lang S, Tao WK. The next-generation goddard convective-stratiform heating algorithm: new tropical and warm season retrievals for gpm. J Clim 2018;31:5997–6026.

    Article  Google Scholar 

  123. Lee SS, Donner LJ, Phillips VT. Impacts of aerosol chemical composition on microphysics and precipitation in deep convection. Atmos Res 2009;94(2):220–37. https://doi.org/10.1016/j.atmosres.2009.05.015, http://www.sciencedirect.com/science/article/pii/S016980950900163X.

    Article  CAS  Google Scholar 

  124. LeMone MA, Pennell WT. The relationship of trade wind cumulus distribution to subcloud layer fluxes and structure. Mon Weather Rev 1976;104(5):524–39. https://doi.org/10.1175/1520-0493(1976)104<0524:TROTWC>2.0.CO;2.

    Article  Google Scholar 

  125. Li JL, Waliser D, Stephens G, Lee S. Characterizing and understanding cloud ice and radiation budget biases in global climate models and reanalysis. Meteorol Monogr 2016;56:13–1.

    Article  CAS  Google Scholar 

  126. Locatelli R, Bousquet P, Hourdin F, Saunois M, Cozic A, Couvreux F, Grandpeix JY, Lefebvre MP, Rio C, Bergamaschi P, Chambers SD, Karstens U, Kazan V, van der Laan S, Meijer HAJ, Moncrieff J, Ramonet M, Scheeren HA, Schlosser C, Schmidt M, Vermeulen A, Williams AG. Atmospheric transport and chemistry of trace gases in LMDz5B: evaluation and implications for inverse modelling. Geosc Model Dev 2015;8:129–50. https://doi.org/10.5194/gmd-8-129-2015.

    Article  Google Scholar 

  127. Mapes B, Neale R. Parameterizing convective organization to escape the entrainment dilemma. J Adv Model Earth Syst 2011;3:2.

    Article  Google Scholar 

  128. Mapes B, Tulich S, Lin J, Zuidema P. The mesoscale convection life cycle: building block or prototype for large-scale tropical waves? Dyn Atmos Oceans 2006;42(1–4):3–29.

    Article  Google Scholar 

  129. Masunaga H, Luo ZJ. Convective and large-scale mass flux profiles over tropical oceans determined from synergistic analysis of a suite of satellite observations. J Geophys Res: Atmos 2016;121(13):7958–74.

    Google Scholar 

  130. Mathon V, Laurent H, Lebel T. Mesoscale convective system rainfall in the Sahel. J Appl Meteorol 2002; 41(11):1081–92.

    Article  Google Scholar 

  131. Mauritsen T, Stevens B. Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nat Geosci 2015;8(5):346.

    Article  CAS  Google Scholar 

  132. McFiggans G, Artaxo P, Baltensperger U, Coe H, Facchini MC, Feingold G, Fuzzi S, Gysel M, Laaksonen A, Lohmann U, Mentel TF, Murphy DM, O’Dowd CD, Snider JR, Weingartner E. The effect of physical and chemical aerosol properties on warm cloud droplet activation. Atmos Chem Phys 2006;6(9):2593–649. https://doi.org/10.5194/acp-6-2593-2006. https://www.atmos-chem-phys.net/6/2593/2006/.

    Article  CAS  Google Scholar 

  133. Mitchell D, Mishra S, Lawson R. Representing the ice fall speed in climate models: results from tropical composition, cloud and climate coupling (tc4) and the indirect and semi-direct aerosol campaign (isdac). J Geophys Res: Atmos 2011;116:D1.

    Article  Google Scholar 

  134. Miyamoto Y, Kajikawa Y, Yoshida R, Yamaura T, Yashiro H, Tomita H. Deep moist atmospheric convection in a subkilometer global simulation. Geophys Res Lett 2013;40(18):4922–26. https://doi.org/10.1002/grl.50944.

    Article  Google Scholar 

  135. Moncrieff MW, Liu C. Convection initiation by density currents: role of convergence, shear, and dynamical organization. Mon Weather Rev 1999;127(10):2455–64.

    Article  Google Scholar 

  136. Moncrieff MW, Liu C, Bogenschutz P. Simulation, modeling, and dynamically based parameterization of organized tropical convection for global climate models. J Atmos Sci 2017;74(5):1363–80.

    Article  Google Scholar 

  137. Morita J, Takayabu YN, Shige S, Kodama Y. Analysis of rainfall characteristics of the Madden–Julian oscillation using trmm satellite data. Dyn Atmos Oceans 2006;42(1–4):107–26.

    Article  Google Scholar 

  138. Morrison H. Impacts of updraft size and dimensionality on the perturbation pressure and vertical velocity in cumulus convection. Part i: simple, generalized analytic solutions. J Atmos Sci 2016;73(4):1441–54.

    Article  Google Scholar 

  139. Morrison H. Impacts of updraft size and dimensionality on the perturbation pressure and vertical velocity in cumulus convection. Part ii: comparison of theoretical and numerical solutions and fully dynamical simulations. J Atmos Sci 2016;73(4):1455–80.

    Article  Google Scholar 

  140. Morrison H, Gettelman A. 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 2008;21(15):3642–59.

    Article  Google Scholar 

  141. Moseley C, Hohenegger C, Berg P, Haerter JO. Intensification of convective extremes driven by cloud–cloud interaction. Nat Geosci 2016;9(10):748.

    Article  CAS  Google Scholar 

  142. Nesbitt SW, Zipser EJ. The diurnal cycle of rainfall and convective intensity according to three years of trmm measurements. J Climate 2003;16(10):1456–75.

    Article  Google Scholar 

  143. Nie J, Kuang Z, Jacob DJ, Guo J. Representing effects of aqueous phase reactions in shallow cumuli in global models. J Geophys Res 2016;121:5769–87. https://doi.org/10.1002/2015JD024208.

    Article  Google Scholar 

  144. Nitta T, Esbensen S. Heat and moisture budget analyses using bomex data. Mon Weather Rev 1974; 102(1):17–28. https://doi.org/10.1175/1520-0493(1974)102<0017:HAMBAU>2.0.CO;2.

    Article  Google Scholar 

  145. O’Gorman PA, Dwyer JG. Using machine learning to parameterize moist convection: potential for modeling of climate, climate change, and extreme events. J Adv Model Earth Sys 2018;0:0. https://doi.org/10.1029/2018MS001351.

    Article  Google Scholar 

  146. Ong H, Wu CM, Kuo HC. Effects of artificial local compensation of convective mass flux in the cumulus parameterization. J Adv Model Earth Syst 2017;9(4):1811–27. https://doi.org/10.1002/2017MS000926.

    Article  Google Scholar 

  147. Orr A, Bechtold P, Scinocca J, Ern M, Janiskova M. Improved middle atmosphere climate and forecasts in the ecmwf model through a nonorographic gravity wave drag parameterization. J Climate 2010;23 (22):5905–26.

    Article  Google Scholar 

  148. Oueslati B, Bellon G. The double itcz bias in cmip5 models: interaction between sst, large-scale circulation and precipitation. Climate Dynam 2015; 44: 585–607. https://doi.org/10.1007/s00382-015-2468-6.

    Article  Google Scholar 

  149. Pan DM, Randall DD. A cumulus parameterization with a prognostic closure. Q J Roy Meteorol Soc 1998; 124(547):949–81.

    Google Scholar 

  150. Park S. A unified convection scheme (UNICON). Part I: formulation. J Atmos Sci 2014;71:3902–30. https://doi.org/10.1175/JAS-D-13-0233.1.

    Article  Google Scholar 

  151. Park S. A unified convection scheme (UNICON). Part II: simulation. J Atmos Sci 2014;71:3931–73. https://doi.org/10.1175/JAS-D-13-0234.1.

    Article  Google Scholar 

  152. Perraud E, Couvreux F, Malardel S, Lac C, Masson V, Thouron O. Evaluation of statistical distributions for the parametrization of subgrid boundary-layer clouds. Boundary-layer Meteorol 2011;140(2):263–94.

    Article  Google Scholar 

  153. Peters JM. The impact of effective buoyancy and dynamic pressure forcing on vertical velocities within two-dimensional updrafts. J Atmos Sci 2016;73(11):4531–51. https://doi.org/10.1175/JAS-D-16-0016.1.

    Article  Google Scholar 

  154. Peters K, Jakob C, Davies L, Khouider B, Majda AJ. Stochastic behavior of tropical convection in observations and a multicloud model. J Atmos Sci 2013;70(11):3556–75.

    Article  Google Scholar 

  155. Peters K, Crueger T, Jakob C, Möbis B. Improved mjo-simulation in echam 6.3 by coupling a stochastic multicloud model to the convection scheme. J Adv Modeling Earth Syst 2017;9(1):193–219.

    Article  Google Scholar 

  156. Piriou JM, Redelsperger JL, Geleyn JF, Lafore JP, Guichard F. An approach for convective parameterization with memory: separating microphysics and transport in grid-scale equations. J Atmos Sci 2007;64 (11):4127–39.

    Article  Google Scholar 

  157. Plant R, Craig GC. A stochastic parameterization for deep convection based on equilibrium statistics. J Atmos Sci 2008;65(1):87–105.

    Article  Google Scholar 

  158. Qin Y, Lin Y. Alleviated double itcz problem in the ncar cesm1: a new cloud scheme and the working mechanisms. J Adv Model Earth Syst 2018;10(9):2318–32.

    Article  Google Scholar 

  159. Qin Y, Lin Y, Xu S, Ma HY, Xie S. A diagnostic pdf cloud scheme to improve subtropical low clouds in ncar community atmosphere model (cam 5). J Adv Model Earth Syst 2018;10(2):320–41.

    Article  Google Scholar 

  160. Randall D, Khairoutdinov M, Arakawa A, Grabowski W. Breaking the cloud parameterization deadlock. Bull Am Meteorol Soc 2003;84(11):1547–64. https://doi.org/10.1175/BAMS-84-11-1547.

    Article  Google Scholar 

  161. Raymond DJ. Convective processes and tropical atmospheric circulations. Q J Roy Meteorol Soc 1994;120 (520):1431–55.

    Article  Google Scholar 

  162. Raymond DJ, Blyth AM. A stochastic model for non precipitating cumulus clouds. J Atmos Sci 1986;43: 2708–18.

    Article  Google Scholar 

  163. Riette S, Lac C. A new framework to compare mass-flux schemes within the AROME numerical weather prediction model. Boundary-layer Meteorol 2016;160:269–7. https://doi.org/10.1007/s10546-016-0146-9.

    Article  Google Scholar 

  164. Riley EM, Mapes BE, Tulich SN. Clouds associated with the Madden–Julian oscillation: a new perspective from cloudsat. J Atmos Sci 2011;68(12):3032–51.

    Article  Google Scholar 

  165. Rio C, Hourdin F, Grandpeix JY, Lafore JP. Shifting the diurnal cycle of parameterized deep convection over land. Geophys Res Lett 2009;36:7.

    Article  Google Scholar 

  166. Rio C, Grandpeix JY, Hourdin F, Guichard F, Couvreux F, Lafore JP, Fridlind A, Mrowiec A, Roehrig R, Rochetin N, Lefebvre MP, Idelkadi A. Control of deep convection by sub-cloud lifting processes: the ALP closure in the LMDZ5B general circulation model. Clim Dyn 2013;40:2271–92 . https://doi.org/10.1007/s00382-012-1506-x.

    Article  Google Scholar 

  167. Rochetin N, Couvreux F, Grandpeix JY, Rio C. Deep convection triggering by boundary layer thermals. Part I: LES analysis and stochastic triggering formulation. J Atmos Sci 2014;71:496–514. https://doi.org/10.1175/JAS-D-12-0336.1.

    Article  Google Scholar 

  168. Rochetin N, Grandpeix JY, Rio C, Couvreux F. Deep convection triggering by boundary layer thermals. Part II: stochastic triggering parameterization for the LMDZ GCM. J Atmos Sci 2014;71:515–38. https://doi.org/10.1175/JAS-D-12-0337.1.

    Article  Google Scholar 

  169. Roehrig R, Bouniol D, Guichard F, Hourdin F, Redelsperger JL. The present and future of the west african monsoon: a process-oriented assessment of cmip5 simulations along the amma transect. J Climate 2013; 26(17):6471–505.

    Article  Google Scholar 

  170. Romps DM. A direct measure of entrainment. J Atmos Sci 2010;67(6):1908–27.

    Article  Google Scholar 

  171. Romps DM. The stochastic parcel model: a deterministic parameterization of stochastically entraining convection. J Adv Model Earth Syst 2016;8(1):319–44.

    Article  Google Scholar 

  172. Romps DM, Kuang Z. Nature versus nurture in shallow convection. J Atmos Sci 2010;67(5):1655–66.

    Article  Google Scholar 

  173. Rosenfeld D, Lohmann U, Raga GB, O’dowd CD, Kulmala M, Fuzzi S, Reissell A, Andreae MO. Flood or drought: how do aerosols affect precipitation? Science 2008;321(5894):1309–13.

    Article  CAS  Google Scholar 

  174. Rossow WB, Tselioudis G, Polak A, Jakob C. Tropical climate described as a distribution of weather states indicated by distinct mesoscale cloud property mixtures. Geophys Res Lett 2005;32:21.

    Article  Google Scholar 

  175. Rotunno R, Klemp JB, Weisman ML. A theory for strong, long-lived squall lines. J Atmos Sci 1988; 45(3):463–85.

    Article  Google Scholar 

  176. Rozbicki JJ, Young GS, Qian L. Test of a convective wake parameterization in the single-column version of ccm3. Mon Weather Rev 1999;127(6):1347–61.

    Article  Google Scholar 

  177. Sacks J, Welch WJ, Mitchell TJ, Wynn HP. Design and analysis of computer experiments. Statist Sci 1989;4(4):409–23. https://doi.org/10.1214/ss/1177012413.

    Article  Google Scholar 

  178. Sakradzija M, Seifert A, Dipankar A. A stochastic scale-aware parameterization of shallow cumulus convection across the convective gray zone. J Adv Model Earth Syst 2016;8(2):786–812.

    Article  Google Scholar 

  179. Sassen K, Wang Z. Classifying clouds around the globe with the cloudsat radar: 1-year of results. Geophys Res Lett 2008;35:4.

    Google Scholar 

  180. Schiro KA, Neelin JD. Tropical continental downdraft characteristics: mesoscale systems versus unorganized convection. Atmos Chem Phys 2018;18:1997–2010.

    Article  CAS  Google Scholar 

  181. Schlemmer L, Hohenegger C. The formation of wider and deeper clouds as a result of cold-pool dynamics. J Atmos Sci 2014;71(8):2842–58.

    Article  Google Scholar 

  182. Schmidt GA, Bader D, Donner LJ, Elsaesser GS, Golaz JC, Hannay C, Molod A, Neale RB, Saha S. Practice and philosophy of climate model tuning across six US modeling centers. Geosci Model Dev 2017;10:3207–23. https://doi.org/10.5194/gmd-10-3207-2017.

    Article  CAS  Google Scholar 

  183. Schneider T, Lan S, Stuart A, Teixeira J. Earth system modeling 2.0: a blueprint for models that learn from observations and targeted high-resolution simulations. Geophys Res Lett 2017;44(24):12,396–417. https://doi.org/10.1002/2017GL076101.

    Article  Google Scholar 

  184. Schumacher C, Houze JRA. Stratiform rain in the tropics as seen by the trmm precipitation radar. J Climate 2003;16(11):1739–56.

    Article  Google Scholar 

  185. Schumacher C, Houze JRA. Stratiform precipitation production over sub-saharan africa and the tropical east atlantic as observed by trmm. Quarterly Journal of the Royal Meteorological Society: A journal of the Atmospheric Sciences, Applied Meteorology and Physical Oceanography 2006;132(620):2235–55.

    Article  Google Scholar 

  186. Schumacher C, Houze JRA, Kraucunas I. The tropical dynamical response to latent heating estimates derived from the trmm precipitation radar. J Atmos Sci 2004;61(12):1341–58.

    Article  Google Scholar 

  187. Sherwood SC, Bony S, Dufresne JL. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 2014;505:37–42. https://doi.org/10.1038/nature12829.

    Article  CAS  Google Scholar 

  188. Shige ea S. Spectral retrieval of latent heating profiles from trmm pr data. Part iv: comparisons of lookup tables from two- and three-dimensional simulations. J Clim 2009;22:5577–94.

    Article  Google Scholar 

  189. Simpson J, Wiggert V. Models of precipitating cumulus towers. Mon Wea Rev 1969;97(7):471–89.

    Article  Google Scholar 

  190. Slingo JM. The development and verification of a cloud prediction scheme for the ecmwf model. Q J Roy Meteorol Soc 1987;113(477):899–927. https://doi.org/10.1002/qj.49711347710.

    Article  Google Scholar 

  191. Song X, Zhang GJ, Li JL. Evaluation of microphysics parameterization for convective clouds in the ncar community atmosphere model cam5. J Climate 2012;25(24):8568–90.

    Article  Google Scholar 

  192. Storer RL, Griffin BM, Höft J, Weber JK, Raut E, Larson VE, Wang M, Rasch PJ. Parameterizing deep convection using the assumed probability density function method. Geosc Model Dev 2015;8: 1–19. https://doi.org/10.5194/gmd-8-1-2015.

    Article  Google Scholar 

  193. Storer RL, Zhang GJ, Song X. Effects of convective microphysics parameterization on large-scale cloud hydrological cycle and radiative budget in tropical and midlatitude convective regions. J Climate 2015;28(23):9277–97.

    Article  Google Scholar 

  194. Stratton R, Stirling A. Improving the diurnal cycle of convection in gcms. Q J Roy Meteorol Soc 2012; 138(666):1121–34.

    Article  Google Scholar 

  195. Sušelj K, Teixeira J, Chung D. A unified model for moist convective boundary layers based on a stochastic eddy-diffusivity/mass-flux parameterization. J Atmos Sci 2013;70(7):1929–53.

    Article  Google Scholar 

  196. Takahashi H, Luo ZJ, Stephens GL. Level of neutral buoyancy, deep convective outflow, and convective core: New perspectives based on 5 years of cloudsat data. J Geophys Res: Atmos 2017;122(5):2958–69.

    Google Scholar 

  197. Talib J, Woolnough SJ, Klingaman NP, Holloway CE. The role of the cloud radiative effect in the sensitivity of the intertropical convergence zone to convective mixing. J Climate 2018;31:6821–38. https://doi.org/10.1175/JCLI-D-17-0794.1.

    Article  Google Scholar 

  198. Tan Z, Kaul CM, Pressel KG, Cohen Y, Schneider T, Teixeira J. An extended eddy-diffusivity mass-flux scheme for unified representation of subgrid-scale turbulence and convection. J Adv Model Earth Syst 2018;10(3):770–800.

    Article  Google Scholar 

  199. Tao WK, Moncrieff MW. Multiscale cloud system modeling. Rev Geophys 2009;47:4. https://doi.org/10.1029/2008RG000276.

    Article  Google Scholar 

  200. Tiedtke M. A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Wea Rev 1989;117:1179–800.

    Article  Google Scholar 

  201. Tiedtke M. Representation of clouds in large-scale models. Mon Weather Rev 1993;121(11):3040–61.

    Article  Google Scholar 

  202. Tompkins AM. 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 2002;59(12):1917–42.

    Article  Google Scholar 

  203. Van Weverberg K, Morcrette CJ, Petch J, Klein SA, Ma HY, Zhang C, Xie S, Tang Q, Gustafson WI, Qian Y, Berg LK, Liu Y, Huang M, Ahlgrimm M, Forbes R, Bazile E, Roehrig R, Cole J, Merryfield W, Lee WS, Cheruy F, Mellul L, Wang YC, Johnson K, Thieman MM. Causes: attribution of surface radiation biases in nwp and climate models near the U.S. Southern great plains. J Geophys Res: Atmos 2018;123(7):3612–44. https://doi.org/10.1002/2017JD027188.

    Article  Google Scholar 

  204. Varble A. Erroneous attribution of deep convective invigoration to aerosol concentration. J Atmos Sci 2018; 75(4):1351–68.

    Article  Google Scholar 

  205. Ventrice MJ, Thorncroft CD. The role of convectively coupled atmospheric kelvin waves on african easterly wave activity. Mon Weather Rev 2013;141(6):1910–24.

    Article  Google Scholar 

  206. Vial J, Bony S, Stevens B, Vogel R. 2018. Mechanisms and model diversity of trade-wind shallow cumulus cloud feedbacks: a review, p 159. https://doi.org/10.1007/978-3-319-77273-8_8.

  207. Vignon E, Hourdin F, Genthon C, Van de Wiel BJH, Gallée H, Madeleine JB, Beaumet J. Modeling the dynamics of the atmospheric boundary layer over the antarctic plateau with a general circulation model. J Adv Model Earth Syst 2018;10:98–125. https://doi.org/10.1002/2017MS001184.

    Article  Google Scholar 

  208. Wang Y, Zhang GJ. Global climate impacts of stochastic deep convection parameterization in the ncar cam5. J Adv Model Earth Syst 2016;8(4):1641–56.

    Article  Google Scholar 

  209. Webb MJ, Lock AP, Bretherton CS, Bony S, Cole JNS, Idelkadi A, Kang SM, Koshiro T, Kawai H, Ogura T, Roehrig R, Shin Y, Mauritsen T, Sherwood SC, Vial J, Watanabe M, Woelfle MD, Zhao M. The impact of parametrized convection on cloud feedback. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2015;373(2054): 20140,414. https://doi.org/10.1098/rsta.2014.0414.

    Article  Google Scholar 

  210. White B, Gryspeerdt EG, Stier P, Morrison H, Thompson G, Kipling Z. Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects. Atmos Chem Phys 2017;17(1):12,145–75.

    Article  CAS  Google Scholar 

  211. Williamson D, Blaker AT, Hampton C, Salter J. Identifying and removing structural biases in climate models with history matching. Clim Dyn 2015;45:1299–324. https://doi.org/10.1007/s00382-014-2378-z.

    Article  Google Scholar 

  212. Woelfle MD, Yu S, Bretherton CS, Pritchard MS. Sensitivity of coupled tropical pacific model biases to convective parameterization in CESM1. J Adv Model Earth Syst 2018;10:126–44. https://doi.org/10.1002/2017MS001176.

    Article  Google Scholar 

  213. Wu CM, Arakawa A. A unified representation of deep moist convection in numerical modeling of the atmosphere. Part ii. J Atmos Sci 2014;71(6):2089–103. https://doi.org/10.1175/JAS-D-13-0382.1.

    Article  Google Scholar 

  214. Wu X, Deng L, Song X, Zhang GJ. Coupling of convective momentum transport with convective heating in global climate simulations. J Atmos Sci 2007;64(4):1334–49. https://doi.org/10.1175/JAS3894.1.

    Article  Google Scholar 

  215. Xiang B, Zhao M, Held IM, Golaz JC. Predicting the severity of spurious “double ITCZ” problem in CMIP5 coupled models from AMIP simulations. Geophys Res Lett 2017;44:1520–27. https://doi.org/10.1002/2016GL071992.

    Article  Google Scholar 

  216. Xu W, Rutledge SA. Morphology, intensity, and rainfall production of mjo convection: Observations from dynamo shipborne radar and trmm. J Atmos Sci 2015;72(2):623–40.

    Article  Google Scholar 

  217. Yanai M, Esbensen S, Chu JH. Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J Atmos Sci 1973;30(4):611–27. https://doi.org/10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2.

    Article  Google Scholar 

  218. Yano JI, Moncrieff MW. Numerical archetypal parameterization for mesoscale convective systems. J Atmos Sci 2016;73(7):2585–602.

    Article  Google Scholar 

  219. Yano JI, Moncrieff MW. Convective organization in evolving large-scale forcing represented by a highly truncated numerical archetype. J Atmos Sci 2018;75(8):2827–47.

    Article  Google Scholar 

  220. Yuter SE, Houze JRA. The natural variability of precipitating clouds over the western pacific warm pool. Q J Roy Meteorol Soc 1998;124(545):53–99.

    Article  Google Scholar 

  221. Zelinka MD, Hartmann DL. The observed sensitivity of high clouds to mean surface temperature anomalies in the tropics. J Geophys Res: Atmos 2011;116:D23. https://doi.org/10.1029/2011JD016459.

    Article  Google Scholar 

  222. Zelinka MD, Zhou C, Klein SA. Insights from a refined decomposition of cloud feedbacks. Geophys Res Lett 2016;43(17):9259–69.

    Article  Google Scholar 

  223. Zhang G, McFarlane NA. Sensitivity of climate simulations to the parameterization of cumulus convection in the canadian climate centre general circulation model. Atmos Ocean 1995;33(3):407–46. https://doi.org/10.1080/07055900.1995.9649539.

    Article  Google Scholar 

  224. Zhang GJ. Convective quasi-equilibrium in midlatitude continental environment and its effect on convective parameterization. J Geophys Res: Atmos 2002;107(D14):ACL 12–1–16. https://doi.org/10.1029/2001JD001005.

    Article  Google Scholar 

  225. Zhang GJ. Convective quasi-equilibrium in the tropical western pacific: comparison with midlatitude continental environment. J Geophys Res: Atmos 2003;108:D19. https://doi.org/10.1029/2003JD003520.

    Article  Google Scholar 

  226. Zhang GJ, Cho HR. Parameterization of the vertical transport of momentum by cumulus clouds. Part ii: application. J Atmos Sci 1991;48(22):2448–2457. https://doi.org/10.1175/1520-0469(1991)048<2448:POTVTO>2.0.CO;2.

    Article  Google Scholar 

  227. Zhang GJ, Wu X, Zeng X, Mitovski T. Estimation of convective entrainment properties from a cloud-resolving model simulation during twp-ice. Clim Dyn 2016;47(7–8):2177–92.

    Article  Google Scholar 

  228. Zhang M, Bretherton CS, Blossey PN, Austin PH, Bacmeister JT, Bony S, Brient F, Cheedela SK, Cheng A, Del Genio AD, et al. Cgils: results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models. J Adv Model Earth Syst 2013;5(4):826–42.

    Article  Google Scholar 

  229. Zhao M, Golaz JC, Held I, Ramaswamy V, Lin SJ, Ming Y, Ginoux P, Wyman B, Donner L, Paynter D, et al. Uncertainty in model climate sensitivity traced to representations of cumulus precipitation microphysics. J Climate 2016;29(2):543–60.

    Article  Google Scholar 

  230. Zhao M, Golaz JC, Held IM, Guo H, Balaji V, Benson R, Chen JH, Chen X, Donner LJ, Dunne JP, Dunne K, Durachta J, Fan SM, Freidenreich SM, Garner ST, Ginoux P, Harris LM, Horowitz LW, Krasting JP, Langenhorst AR, Liang Z, Lin P, Lin SJ, Malyshev SL, Mason E, Milly PCD, Ming Y, Naik V, Paulot F, Paynter D, Phillipps P, Radhakrishnan A, Ramaswamy V, Robinson T, Schwarzkopf D, Seman CJ, Shevliakova E, Shen Z, Shin H, Silvers LG, Wilson JR, Winton M, Wittenberg AT, Wyman B, Xiang B. The gfdl global atmosphere and land model am4.0/lm4.0: 2. model description, sensitivity studies, and tuning strategies. J Adv Model Earth Syst 2018;10(3):735–69. https://doi.org/10.1002/2017MS001209.

    Article  Google Scholar 

  231. Zuidema P, Torri G, Muller C, Chandra A. A survey of precipitation-induced atmospheric cold pools over oceans and their interactions with the larger-scale environment. Surv Geophys 2017;38(6):1283–305.

    Article  Google Scholar 

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Acknowledgements

CR and FH are supported by CNRS and thank the LEFE/INSU french national program DEPHY. AD was supported by the NASA Precipitation Measurement Missions, CloudSat/CALIPSO Mission, and Modeling and Analysis Programs and by the DOE Atmospheric System Research Program. The authors thank Jean-Yves Grandpeix for useful discussions as well as two anonymous reviewers who helped improve the manuscript.

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Authors and Affiliations

  1. Centre National de Recherches Météorologiques, Université de Toulouse, Météo-France, CNRS, Toulouse, France

    Catherine Rio

  2. NASA Goddard Institute for Space Studies, New York, NY, USA

    Anthony D. Del Genio

  3. Laboratoire de Météorologie Dynamique, IPSL, CNRS, Sorbonne Université, Paris, France

    Frédéric Hourdin

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  1. Catherine Rio
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  2. Anthony D. Del Genio
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Correspondence to Catherine Rio.

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Rio, C., Del Genio, A.D. & Hourdin, F. Ongoing Breakthroughs in Convective Parameterization. Curr Clim Change Rep 5, 95–111 (2019). https://doi.org/10.1007/s40641-019-00127-w

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