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

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

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

    • Search Google Scholar
    • Export Citation
  • Bechtold, P., , N. Semane, , P. Lopez, , J.-P. Chaboureau, , A. Beljaars, , and N. Bormann, 2014: Representing equilibrium and nonequilibrium convection in large-scale models. J. Atmos. Sci., 71, 734753, doi:10.1175/JAS-D-13-0163.1.

    • Search Google Scholar
    • Export Citation
  • Bellucci, A., , S. Gualdi, , and A. Navarra, 2010: The double-ITCZ syndrome in coupled general circulation models: The role of large-scale vertical circulation regimes. J. Climate, 23, 11271145, doi:10.1175/2009JCLI3002.1.

    • Search Google Scholar
    • Export Citation
  • Chikira, M., 2010: A cumulus parameterization with state-dependent entrainment rate. Part II: Impact on climatology in a general circulation model. J. Atmos. Sci., 67, 21942211, doi:10.1175/2010JAS3317.1.

    • Search Google Scholar
    • Export Citation
  • Chikira, M., 2014: Eastward-propagating intraseasonal oscillation represented by Chikira–Sugiyama cumulus parameterization. Part II: Understanding moisture variation under weak temperature gradient balance. J. Atmos. Sci., 71, 615639, doi:10.1175/JAS-D-13-038.1.

    • Search Google Scholar
    • Export Citation
  • Chikira, M., , and M. Sugiyama, 2010: A cumulus parameterization with state-dependent entrainment rate. Part I: Description and sensitivity to temperature and humidity profiles. J. Atmos. Sci., 67, 21712193, doi:10.1175/2010JAS3316.1.

    • Search Google Scholar
    • Export Citation
  • Chikira, M., , and M. Sugiyama, 2013: Eastward-propagating intraseasonal oscillation represented by Chikira–Sugiyama cumulus parameterization. Part I: Comparison with observation and reanalysis. J. Atmos. Sci., 70, 39203939, doi:10.1175/JAS-D-13-034.1.

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

  • Del Genio, A. D., , and J. Wu, 2010: The role of entrainment in the diurnal cycle of continental convection. J. Climate, 23, 27222738, doi:10.1175/2009JCLI3340.1.

    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., , Y. Chen, , D. Kim, , and M.-S. Yao, 2012: The MJO transition from shallow to deep convection in CloudSat/CALIPSO data and GISS GCM simulations. J. Climate, 25, 37553770, doi:10.1175/JCLI-D-11-00384.1.

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

    • Search Google Scholar
    • Export Citation
  • de Rooy, W. C., and et al. , 2013: Entrainment and detrainment in cumulus convection: An overview. Quart. J. Roy. Meteor. Soc., 139, 119, doi:10.1002/qj.1959.

    • Search Google Scholar
    • Export Citation
  • de Szoeke, S. P., , and S.-P. Xie, 2008: The tropical eastern Pacific seasonal cycle: Assessment of errors and mechanisms in IPCC AR4 coupled ocean–atmosphere general circulation models. J. Climate, 21, 25732590, doi:10.1175/2007JCLI1975.1.

    • Search Google Scholar
    • Export Citation
  • Emori, S., , T. Nozawa, , A. Numaguti, , and I. Uno, 2001: Importance of cumulus parameterization for precipitation simulation over East Asia in June. J. Meteor. Soc. Japan, 79, 939947, doi:10.2151/jmsj.79.939.

    • Search Google Scholar
    • Export Citation
  • Esbensen, S., 1978: Bulk thermodynamic effects and properties of small tropical cumuli. J. Atmos. Sci., 35, 826837, doi:10.1175/1520-0469(1978)035<0826:BTEAPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Grant, A., , and A. Brown, 1999: A similarity hypothesis for shallow-cumulus transports. Quart. J. Roy. Meteor. Soc., 125, 19131936, doi:10.1002/qj.49712555802.

    • 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, doi:10.1002/qj.49712757104.

    • Search Google Scholar
    • Export Citation
  • Gregory, D., , and P. R. Rowntree, 1990: A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Wea. Rev., 118, 14831506, doi:10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hirons, L., , P. Inness, , F. Vitart, , and P. Bechtold, 2013: Understanding advances in the simulation of intraseasonal variability in the ECMWF model. Part II: The application of process-based diagnostics. Quart. J. Roy. Meteor. Soc., 139, 14271444, doi:10.1002/qj.2059.

    • Search Google Scholar
    • Export Citation
  • Hirota, N., , and M. Takahashi, 2012: A tripolar pattern as an internal mode of the East Asian summer monsoon. Climate Dyn., 39, 22192238, doi:10.1007/s00382-012-1416-y.

    • Search Google Scholar
    • Export Citation
  • Hirota, N., , and Y. N. Takayabu, 2012: Inter-model differences of future precipitation changes in CMIP3 and MIROC5 climate models. J. Meteor. Soc. Japan, 90A, 307316, doi:10.2151/jmsj.2012-A16.

    • Search Google Scholar
    • Export Citation
  • Hirota, N., , and Y. N. Takayabu, 2013: Reproducibility of precipitation distribution over the tropical oceans in CMIP5 multi-climate models compared to CMIP3. Climate Dyn.,41, 2909–2920, doi:10.1007/s00382-013-1839-0.

  • Hirota, N., , Y. N. Takayabu, , M. Watanabe, , and M. Kimoto, 2011: Precipitation reproducibility over tropical oceans and its relationship to the double ITCZ problem in CMIP3 and MIROC5 climate models. J. Climate, 24, 48594873, doi:10.1175/2011JCLI4156.1.

    • Search Google Scholar
    • Export Citation
  • Holloway, C. E., , and J. D. Neelin, 2009: Moisture vertical structure, column water vapor, and tropical deep convection. J. Atmos. Sci., 66, 16651683, doi:10.1175/2008JAS2806.1.

    • Search Google Scholar
    • Export Citation
  • Jensen, M. P., , and A. D. Del Genio, 2006: Factors limiting convective cloud-top height at the ARM Nauru Island climate research facility. J. Climate, 19, 21052117, doi:10.1175/JCLI3722.1.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., , T. M. Rickenbach, , S. A. Rutledge, , P. E. Ciesielski, , and W. H. Schubert, 1999: Trimodal characteristics of tropical convection. J. Climate, 12, 23972418, doi:10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M., , and D. Randall, 2006: High-resolution simulation of shallow-to-deep convection transition over land. J. Atmos. Sci., 63, 34213436, doi:10.1175/JAS3810.1.

    • Search Google Scholar
    • Export Citation
  • Kim, D., , A. H. Sobel, , E. D. Maloney, , D. M. Frierson, , and I.-S. Kang, 2011: A systematic relationship between intraseasonal variability and mean state bias in AGCM simulations. J. Climate, 24, 55065520, doi:10.1175/2011JCLI4177.1.

    • Search Google Scholar
    • Export Citation
  • Kim, D., , A. H. Sobel, , A. D. Del Genio, , Y. Chen, , S. J. Camargo, , M.-S. Yao, , M. Kelley, , and L. Nazarenko, 2012: The tropical subseasonal variability simulated in the NASA GISS general circulation model. J. Climate, 25, 46414659, doi:10.1175/JCLI-D-11-00447.1.

    • Search Google Scholar
    • Export Citation
  • Kuang, Z., , and C. S. Bretherton, 2006: A mass flux scheme view of a high-resolution simulation of a transition from shallow to deep cumulus convection. J. Atmos. Sci., 63, 18951909, doi:10.1175/JAS3723.1.

    • Search Google Scholar
    • Export Citation
  • Lin, C., 1999: Some bulk properties of cumulus ensembles simulated by a cloud-resolving model. Part II: Entrainment profiles. J. Atmos. Sci., 56, 37363748, doi:10.1175/1520-0469(1999)056<3736:SBPOCE>2.0.CO;2.

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

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

  • Maloney, E. D., , and D. L. Hartmann, 2001: The sensitivity of intraseasonal variability in the NCAR CCM3 to changes in convective parameterization. J. Climate, 14, 20152034, doi:10.1175/1520-0442(2001)014<2015:TSOIVI>2.0.CO;2.

    • 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, doi:10.1175/1520-0493(1965)093<0769:SCOAGC>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mechoso, C. R., and et al. , 1995: The seasonal cycle over the tropical Pacific in coupled ocean–atmosphere general circulation models. Mon. Wea. Rev., 123, 28252838, doi:10.1175/1520-0493(1995)123<2825:TSCOTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., , C. Covey, , B. McAvaney, , M. Latif, , and R. J. Stouffer, 2005: Overview of the Coupled Model Intercomparison Project. Bull. Amer. Meteor. Soc., 86, 8993, doi:10.1175/BAMS-86-1-89.

    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., , and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys., 20, 851875, doi:10.1029/RG020i004p00851.

    • Search Google Scholar
    • Export Citation
  • Miura, H., , T. Maeda, , and M. Kimoto, 2012: A comparison of the Madden–Julian oscillation simulated by different versions of the MIROC climate model. SOLA, 8, 165169, doi:10.2151/sola.2012-040.

    • Search Google Scholar
    • Export Citation
  • Möbis, B., , and B. Stevens, 2012: Factors controlling the position of the intertropical convergence zone on an aquaplanet. J. Adv. Model. Earth Syst.,4, M00A04, doi:10.1029/2012MS000199.

  • Nakanishi, M., , and H. Niino, 2004: An improved Mellor–Yamada level-3 model with condensation physics: Its design and verification. Bound.-Layer Meteor., 112, 131, doi:10.1023/B:BOUN.0000020164.04146.98.

    • Search Google Scholar
    • Export Citation
  • Oueslati, B., , and G. Bellon, 2013: Convective entrainment and large-scale organization of tropical precipitation: Sensitivity of the CNRM-CM5 hierarchy of models. J. Climate, 26, 29312946, doi:10.1175/JCLI-D-12-00314.1.

    • Search Google Scholar
    • Export Citation
  • Pan, D.-M., , and D. D. A. Randall, 1998: A cumulus parameterization with a prognostic closure. Quart. J. Roy. Meteor. Soc., 124, 949981, doi:10.1002/qj.49712454714.

    • Search Google Scholar
    • Export Citation
  • Philander, S. G. H., , D. Gu, , D. Halpern, , G. Lambert, , N.-C. Lau, , T. Li, , and R. C. Pacanowski, 1996: Why the ITCZ is mostly north of the equator. J. Climate, 9, 29582972, doi:10.1175/1520-0442(1996)009<2958:WTIIMN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Redelsperger, J., , D. Parsons, , and F. Guichard, 2002: Recovery processes and factors limiting cloud-top height following the arrival of a dry intrusion observed during TOGA COARE. J. Atmos. Sci., 59, 24382457, doi:10.1175/1520-0469(2002)059<2438:RPAFLC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sekiguchi, M., , and T. Nakajima, 2008: A k-distribution-based radiation code and its computational optimization for an atmospheric general circulation model. J. Quant. Spectrosc. Radiat. Transfer, 109, 27792793, doi:10.1016/j.jqsrt.2008.07.013.

    • Search Google Scholar
    • Export Citation
  • Siebesma, A., , and J. Cuijpers, 1995: Evaluation of parametric assumptions for shallow cumulus convection. J. Atmos. Sci., 52, 650666, doi:10.1175/1520-0469(1995)052<0650:EOPAFS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Simpson, J., , and V. Wiggert, 1969: Models of precipitating cumulus towers. Mon. Wea. Rev., 97, 471489, doi:10.1175/1520-0493(1969)097<0471:MOPCT>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Song, X., , and G. Zhang, 2009: Convection parameterization, tropical pacific double ITCZ, and upper-ocean biases in the NCAR CCSM3. Part I: Climatology and atmospheric feedback. J. Climate, 22, 42994315, doi:10.1175/2009JCLI2642.1.

    • Search Google Scholar
    • Export Citation
  • Takata, K., , S. Emori, , and T. Watanabe, 2003: Development of the minimal advanced treatments of surface interaction and runoff. Global Planet. Change, 38, 209222, doi:10.1016/S0921-8181(03)00030-4.

    • Search Google Scholar
    • Export Citation
  • Takayabu, Y. N., , S. Shige, , W.-K. Tao, , and N. Hirota, 2010: Shallow and deep latent heating modes over tropical oceans observed with TRMM PR spectral latent heating data. J. Climate, 23, 20302046, doi:10.1175/2009JCLI3110.1.

    • Search Google Scholar
    • Export Citation
  • Takemi, T., , O. Hirayama, , and C. Liu, 2004: Factors responsible for the vertical development of tropical oceanic cumulus convection. Geophys. Res. Lett., 31, L11109, doi:10.1029/2004GL020225.

    • Search Google Scholar
    • Export Citation
  • Takemura, T., , T. Nozawa, , S. Emori, , T. Y. Nakajima, , and T. Nakajima, 2005: Simulation of climate response to aerosol direct and indirect effects with aerosol transport-radiation model. J. Geophys. Res., 110, D02202, doi:10.1029/2004JD005029.

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

    • 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.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., , L. Zhou, , and K. Hamilton, 2007: Effect of convective entrainment/detrainment on the simulation of the tropical precipitation diurnal cycle. Mon. Wea. Rev., 135, 567585, doi:10.1175/MWR3308.1.

    • Search Google Scholar
    • Export Citation
  • Watanabe, M., , S. Emori, , M. Satoh, , and H. Miura, 2009: A PDF-based hybrid prognostic cloud scheme for general circulation models. Climate Dyn., 33, 795816, doi:10.1007/s00382-008-0489-0.

    • Search Google Scholar
    • Export Citation
  • Watanabe, M., and et al. , 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 63126335, doi:10.1175/2010JCLI3679.1.

    • Search Google Scholar
    • Export Citation
  • Watanabe, M., , M. Chikira, , Y. Imada, , and M. Kimoto, 2011: Convective control of ENSO simulated in MIROC. J. Climate, 24, 543562, doi:10.1175/2010JCLI3878.1.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M., , and G. N. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber–frequency domain. J. Atmos. Sci., 56, 374399, doi:10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., and et al. , 2007: A regional ocean–atmosphere model for eastern Pacific climate: Towards reducing tropical biases. J. Climate, 20, 15041522, doi:10.1175/JCLI4080.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., , and X. Song, 2010: Convection parameterization, tropical Pacific double ITCZ, and upper-ocean biases in the NCAR CCSM3. Part II: Coupled feedback and the role of ocean heat transport. J. Climate, 23, 800812, doi:10.1175/2009JCLI3109.1.

    • Search Google Scholar
    • Export Citation
  • Zipser, E. J., 2003: Some views on “hot towers” after 50 years of tropical field programs and two years of TRMM data. Cloud Systems, Hurricanes, and the Tropical Rainfall Measuring Mission (TRMM): A Tribute to Dr. Joanne Simpson, Meteor. Monogr., No 29, Amer. Meteor. Soc., 75116, doi:10.1175/0065-9401(2003)029<0049:CSVOHT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    Vertical profiles of fractional entrainment rate (color; km−1) for convection with different cloud-top heights (abscissa; hPa) over the tropical oceans (30°S–30°N) realized in model experiments: (a) Ctl, (b) NoEnt, (c) AS, and (d) ASRH. The right-hand side panels in (a)–(d) show the average profile of all convection.

  • View in gallery

    Climatological annual precipitation (color; mm day−1) and SST (black contours; contour interval : 3°C) from (a) observations (TRMM PR2A25 and Hadley SST) and from model experiments (b) Ctl, (c) NoEnt, (d) AS, and (e) ASRH.

  • View in gallery

    (a),(b) Vertical pressure velocity (hPa day−1); (c),(d) relative humidity (%); (e),(f) diabatic heating (K day−1); (g),(h) radiative heating (K day−1); and (i),(j) detrainment mass flux (kg m−2 s−1) for (left) Ctl and (right) difference between NoEnt and Ctl. Vectors in (a),(b) show zonal wind (m s−1) and vertical pressure velocity (hPa day−1).

  • View in gallery

    Horizontal advection of moist static energy (J kg−1 s−1) at 850 hPa for (a) Ctl and (b) difference between NoEnt and Ctl. Vectors show zonal and meridional winds (m s−1).

  • View in gallery

    Linear responses of vertical pressure velocity (hPa day−1) to (a) total diabatic heating, (b) latent heating, (c) radiative heating, and (d) heating associated with turbulent mixing for NoEnt minus Ctl.

  • View in gallery

    Temporal precipitation standard deviation (interval: 2 mm day−1) for (a) TRMM PR2A25, (b) Ctl, (c) NoEnt, (d) AS, and (e) ASRH. Values larger than 5.5 mm day−1 are shaded.

  • View in gallery

    Power spectra of the precipitation time frequency (mm day−1)2 for (a) TRMM 3B42, (b) Ctl, (c) NoEnt, (d) AS, and (e) ASRH. The abscissa indicates the period (days) in a logarithmic scale. Note that the ordinate scale values in (e) are 4 times larger than those in (a)–(d).

  • View in gallery

    Weighted probability distribution functions for precipitation intensity [abscissa: dBR = 10 log10(precipitation rate)] for TRMM PR2A25, Ctl, NoEnt, AS, and ASRH.

  • View in gallery

    Composites of the precipitation events for (a),(c),(e),(g),(i) Ctl; (b),(d),(f),(h),( j) NoEnt; (k),(m),(o),(q),(s) AS; and (l),(n),(p),(r),(t) ASRH: (a),(b),(k),(l) precipitation (mm day−1); (c),(d),(m),(n) probability density of cloud-top height [contour interval (CI): 7% (100 hPa)−1]; (e),(f),(o),(p) detrainment mass flux (CI: 0.001 kg m−2 s−1); (g),(h),(q),(r) relative humidity (CI: 5%); and (i),(j),(s),(t) temperature anomaly from the climatological average (CI: 0.5K). Values >7 in (c),(d),(m),(n); >0.003 in (e),(f),(o),(p); >70 in (g),(h),(q),(r); and >0 in (i),(j),(s),(t) are shaded. The time evolution of the events is shown in (a),(b),(k),(l) and the left-hand panels in (c)–(j) and (m)–(t). The right-hand panels in (c)–(j) and (m)–(t) show the average from day −20 to day +20.

  • View in gallery

    (a),(b) As in Fig. 9c, but for the cloud top diagnosed using climatological humidity in (a) and temperature in (b). (c) As in Fig. 9g, but for a composite of the climatological humidity.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 92 92 8
PDF Downloads 46 46 2

Role of Convective Entrainment in Spatial Distributions of and Temporal Variations in Precipitation over Tropical Oceans

View More View Less
  • 1 National Institute of Polar Research, and Atmosphere and Ocean Research Institute, University of Tokyo, Tokyo, Japan
  • | 2 Atmosphere and Ocean Research Institute, University of Tokyo, Tokyo, Japan
  • | 3 Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
© Get Permissions
Full access

Abstract

The authors demonstrate that an appropriate treatment of convective entrainment is essential for determining spatial distributions of and temporal variations in precipitation. Four numerical experiments are performed using atmospheric models with different entrainment characteristics: a control experiment (Ctl), a no-entrainment experiment (NoEnt), an original Arakawa–Schubert experiment (AS), and an AS experiment with a simple empirical suppression of convection depending on cloud-layer humidity (ASRH). The fractional entrainment rates of AS and ASRH are constant for each cloud type and are very small in the lower troposphere compared with those in the Ctl, in which half of the buoyancy-generated energy is consumed by entrainment. Spatial and temporal variations in the observed precipitation are satisfactorily reproduced in the Ctl, but their amplitudes are underestimated with a so-called double intertropical convergence zone bias in the NoEnt and AS. The spatial variation is larger in the Ctl because convection is more active over humid ascending regions and more suppressed over dry subsidence regions. Feedback processes involving convection, the large-scale circulation, free tropospheric moistening by congestus, and radiation enhance the variations. The temporal evolution of precipitation events is also more realistic in the Ctl, because congestus moistens the midtroposphere, and large precipitation events occur once sufficient moisture is available. The large entrainment in the lower troposphere, increasing free tropospheric moistening by congestus and enhancing the coupling of convection to free tropospheric humidity, is suggested to be important for the realistic spatial and temporal variations.

Corresponding author address: Nagio Hirota, National Institute of Polar Research, 10-3, Midoricho, Tachikawa, Tokyo 190-8518, Japan. E-mail: nagio@aori.u-tokyo.ac.jp

Abstract

The authors demonstrate that an appropriate treatment of convective entrainment is essential for determining spatial distributions of and temporal variations in precipitation. Four numerical experiments are performed using atmospheric models with different entrainment characteristics: a control experiment (Ctl), a no-entrainment experiment (NoEnt), an original Arakawa–Schubert experiment (AS), and an AS experiment with a simple empirical suppression of convection depending on cloud-layer humidity (ASRH). The fractional entrainment rates of AS and ASRH are constant for each cloud type and are very small in the lower troposphere compared with those in the Ctl, in which half of the buoyancy-generated energy is consumed by entrainment. Spatial and temporal variations in the observed precipitation are satisfactorily reproduced in the Ctl, but their amplitudes are underestimated with a so-called double intertropical convergence zone bias in the NoEnt and AS. The spatial variation is larger in the Ctl because convection is more active over humid ascending regions and more suppressed over dry subsidence regions. Feedback processes involving convection, the large-scale circulation, free tropospheric moistening by congestus, and radiation enhance the variations. The temporal evolution of precipitation events is also more realistic in the Ctl, because congestus moistens the midtroposphere, and large precipitation events occur once sufficient moisture is available. The large entrainment in the lower troposphere, increasing free tropospheric moistening by congestus and enhancing the coupling of convection to free tropospheric humidity, is suggested to be important for the realistic spatial and temporal variations.

Corresponding author address: Nagio Hirota, National Institute of Polar Research, 10-3, Midoricho, Tachikawa, Tokyo 190-8518, Japan. E-mail: nagio@aori.u-tokyo.ac.jp

1. Introduction

Tropical convection plays a crucial role in the climate system and drives atmospheric circulations. It is well known that convection over tropical oceans has a “trimodal structure” consisting of deep convection, congestus, and shallow convection (Johnson et al. 1999). Deep convection, with its cloud top above 300 hPa, is most frequently observed where the sea surface temperature (SST) is very high (>28°C), because a high SST supplies considerable heat and moisture from the sea surface, fueling convective activity. On the other hand, shallow convection and congestus are more dominant over large-scale subsidence regions where SST is relatively lower. Trade cumulus, an example of shallow convection, is confined below temperature inversions at approximately 800 hPa, while congestus has its cloud top at approximately 600 hPa, where the stable melting layer occurs. The latent heating of fusion is considered to play an important role in separating the congestus cloud-top height from the deep convection cloud-top height (Redelsperger et al. 2002; Zipser 2003; Takemi et al. 2004; Jensen and Del Genio 2006). A convective parcel above the freezing level at approximately 600 hPa can gain additional buoyancy from the latent heating of fusion that contributes to the development of deep convection.

Recent studies suggest that free tropospheric humidity is important in the dynamical suppression of deep convection over large-scale subsidence regions (Zipser 2003; Jensen and Del Genio 2006; Wang et al. 2007; Holloway and Neelin 2009; Lin et al. 2012). For example, Takayabu et al. (2010) explained that, because the midtroposphere is very dry over subsidence regions, the entrainment of dry environmental air into a convective parcel effectively decreases the parcel’s buoyancy, thus suppressing deep convection. Jensen and Del Genio (2006) performed sensitivity tests using a simple convective parcel model, and their results support the quantitative importance of buoyancy reduction by entrainment.

In most general circulation models with horizontal resolutions of over 10 km, convective activities are represented by convective parameterizations. Convective parameterization schemes, using either the moist adjustment approach (e.g., Manabe et al. 1965) or the mass flux approach (e.g., Arakawa and Schubert 1974), are designed to remove atmospheric instability produced by large-scale forcing with consideration of convective entrainment (Arakawa 2004). Such entrainment is responsible for interactions between convection and environment.

Entrainment formulations in convective parameterizations have been extensively examined and compared with observations, cloud-resolving models (CRMs), and large-eddy simulations (LESs). Based on studies of convective plumes in water tank experiments conducted by Simpson and Wiggert (1969), the fractional entrainment rate (later simply called the entrainment rate) was set to be inversely proportional to cloud radius in many schemes (e.g., Arakawa and Schubert 1974; Tiedtke 1989; Gregory and Rowntree 1990). However, the entrainment values suggested for use in such methods are an order of magnitude smaller than those inferred from observations, CRMs, and LESs (Esbensen 1978; Siebesma and Cuijpers 1995; Del Genio and Wu 2010; de Rooy et al. 2013). The models using smaller entrainment values showed that the sensitivity of convection depth to environmental humidity is insufficient (Derbyshire et al. 2004).

Weak sensitivity of convection to humidity is suggested to be a probable cause of the so-called double intertropical convergence zone (ITCZ) bias (Song and Zhang 2009; Zhang and Song 2010; Chikira 2010; Bellucci et al. 2010; Hirota et al. 2011), which has long been recognized as one of the major deficiencies in climate models (Meehl et al. 2005). Double ITCZ bias is the overestimation of precipitation over the southeastern Pacific by a model, corresponding to an ITCZ in the Southern Hemisphere alongside the observed ITCZ in the Northern Hemisphere. Hirota et al. (2011) and Hirota and Takayabu (2013) examined output from climate models from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), respectively, and demonstrated that models with a weak convection sensitivity to midtropospheric humidity tend to suffer from the double ITCZ bias. The dynamical suppression of deep convection is insufficient in these models, and convection is too active over the southeastern Pacific despite the dry troposphere.

Furthermore, several feedback processes involving convective activities, the large-scale circulation, surface evaporation, and radiation are suggested to be important for the formation of the double ITCZ bias (Bellucci et al. 2010; Kim et al. 2011; Oueslati and Bellon 2013; Möbis and Stevens 2012). Latent heating of convective activities drives upward motion, and the associated low-level convergence of the large-scale circulation supplies moisture that further enhances convective activities (Oueslati and Bellon 2013). The low-level winds also affect surface evaporation, which connects back to the convective activities (Möbis and Stevens 2012; Kim et al. 2011). Convective clouds cause a significant radiative warming that adds to the latent heating and enhance the dynamical response (Oueslati and Bellon 2013).

Although this study uses an atmospheric model and concentrates on understanding the role of convective entrainment, air–sea interactions are also known to be important to the formation of the double ITCZ bias (Mechoso et al. 1995; Xie et al. 2007; de Szoeke and Xie 2008). For example, Philander et al. (1996) suggested that low-level stratus clouds, by reducing the incoming solar radiation, decrease the SSTs and therefore lead to a suppression of convection over the southeastern Pacific. In fact, the double ITCZ bias is somewhat smaller in atmospheric models, in which the observed SST is prescribed, than in coupled atmosphere–ocean models.

The sensitivity of convection to atmospheric humidity has also been suggested to be important to some other aspects of climate variations. For example, the dominant intraseasonal oscillation (30–90 days) over the tropics known as the Madden–Julian oscillation (MJO) is better represented in models that have a strong link between convection and humidity (Tokioka et al. 1988; Lin et al. 2006; Bechtold et al. 2008; Kim et al. 2011, 2012; Del Genio et al. 2012; Miura et al. 2012; Hirons et al. 2013).

The diurnal cycles over the continental tropics in most global models show peak rainfall near noon, which is 2–3 h earlier than is observed (Dai 2006). This discrepancy is mitigated when the entrainment rates are increased in convective parameterizations (Wang et al. 2007; Del Genio and Wu 2010; Bechtold et al. 2014). Emori et al. (2001) improved baiu front simulations by introducing the empirical suppression of convection for dry environments. Hirota and Takayabu (2012) examined global warming projections and suggested that the coupling strength of convection and tropospheric humidity systematically affects the magnitude of circulation changes in the tropics.

As described above, convective entrainment seems to be a critical process that affects various aspects of climate variations. In the present study, we examine the role of convective entrainment in the spatial distributions and temporal variations of precipitation using numerical experiments in a global atmospheric model.

We use the atmospheric part of the Model for Interdisciplinary Research on Climate, version 5 (MIROC5; Watanabe et al. 2010). This is the current version developed for the CMIP5 at the Atmosphere and Ocean Research Institute (The University of Tokyo), the National Institute for Environmental Studies, and the Japan Agency for Marine-Earth Science and Technology. Previous studies have shown that MIROC5 satisfactorily reproduces many aspects of the tropical climate, including the climatological mean field (Hirota et al. 2011; Hirota and Takayabu 2013), the El Niño–Southern Oscillation (Watanabe et al. 2011), and the MJO (Chikira and Sugiyama 2013; Chikira 2014).

This study is complementary to those published by Hirota et al. (2011) and Hirota and Takayabu (2013), in which output data from the CMIP models were analyzed and a general tendency that models with weak convection sensitivity to midtropospheric humidity suffer from the double ITCZ bias was observed. Here we verify their conclusions more directly using sensitivity experiments in which only convective entrainment is altered. We also improve our understanding of associated processes using more detailed experimental data (only daily data are available for the major variables in the CMIP archive). In particular, we show how entrainment affects the double ITCZ bias through the feedback processes of convection, the large-scale circulation, and radiation. We also examine the time evolution of precipitation events and clarify how convection interacts with the environment. We try to identify vertical profiles of entrainment rates necessary for reproducing realistic spatial and temporal variations of the precipitation in MIROC5. The model and the data used in our analyses are described in section 2, the results are provided in section 3, and a discussion and summary are stated in section 4.

2. Methodology

We use the atmospheric part of MIROC5 with horizontal resolution of T42 spectral truncation (~2.8°) and 40 vertical levels of hybrid sigma–pressure (σp) vertical coordinates. The convective scheme is based on that published by Arakawa and Schubert (1974), and a new entrainment scheme developed by Chikira and Sugiyama (2010) has recently been added. In this scheme, the cloud-base mass flux of a convective parcel is determined using a prognostic convective kinetic energy closure (Pan and Randall 1998). The ascending convective parcel entrains environmental air until it reaches the cloud top, where its vertical velocity becomes negative. Detrainment occurs only at the cloud top. These calculations are performed for an ensemble of cumulus cloud types with different entrainment characteristics. Detrainments and mass flux–induced compensating subsidence influence the large-scale environment, and these constitute the grid-scale convective effects of the cumulus ensemble.

The entrainment rate varies depending on the cloud types involved, but it is constant for each cloud type in the original Arakawa and Schubert (1974) scheme. However, recent studies (including results from observations, LESs, and CRMs) have indicated that entrainment rates vary widely, especially with respect to height (Lin 1999; Siebesma and Cuijpers 1995; Gregory 2001; Del Genio and Wu 2010; de Rooy et al. 2013). Chikira and Sugiyama (2010) have used the entrainment rates proposed by Gregory (2001), formulated as
eq1
where and B are the updraft velocity and the buoyancy of the convective parcel, respectively, and a and Cϵ are dimensionless constants. The parameter a represents a buoyancy force ratio that is used to accelerate the mean updraft velocity, whereas Cϵ represents the fraction by which buoyancy-generated energy is decreased by entrainment. The underlying concept of this formulation is supported for both shallow convection and deep convection by comparisons with LESs (Grant and Brown 1999) and CRMs (Del Genio and Wu 2010).

MIROC5 includes several other schemes, such as a probability distribution function (PDF)-based prognostic cloud scheme (Watanabe et al. 2009), a two-stream k-distribution scheme for radiation with 111 channels (Sekiguchi and Nakajima 2008), level 2.5 of the Mellor–Yamada turbulence scheme (Mellor and Yamada 1982) revised by Nakanishi and Niino (2004), an orographic gravity wave drag scheme, a land surface model (Takata et al. 2003), and a prognostic aerosol scheme with direct and indirect effects (Takemura et al. 2005).

Four experiments with different entrainment characteristics are performed in this study (summarized in Table 1): a control experiment (Ctl), a no-entrainment experiment (NoEnt), an original Arakawa–Shubert experiment (AS), and an AS using an empirical suppression method (ASRH) published by Emori et al. (2001). The entrainment rates of convection over the tropical oceans (30°S–30°N) realized in the experiments are shown in Fig. 1. The Ctl uses the entrainment scheme published by Chikira and Sugiyama (2010) with standard MIROC5 parameters used in CMIP5. The value of Cϵ is 0.52, suggesting that about half of the buoyancy-generated energy is consumed by entrainment. An ensemble of 14 cumulus clouds with different cloud-base updraft velocities (and, therefore, different entrainment characteristics) is assessed, with an updraft velocity range of 0.1–1.4 m s−1. The entrainment rate in the Ctl is largest in the lower troposphere with 1 km−1 because the updraft velocity is small. Entrainment rates in the AS and ASRH are vertically constant for each cloud type (but vary slightly with the cloud-top height depending on environmental conditions), ranging from 0.01 to 2 km−1, whereas the entrainment rates in the NoEnt are zero. The entrainment rate of the Ctl in the lower troposphere is significantly larger than those of the AS and ASRH, especially for convection with middle-to-high cloud-top height, which is consistent with the results from CRMs and LESs. The only difference between the AS and the ASRH is that the ASRH employs the empirical suppression of cumulus convection when the relative humidity averaged over the cloud layer is below 80%, which is intended to enhance the link between convection and tropospheric humidity (Emori et al. 2001). More information on the Ctl, AS, and ASRH has been published by Chikira and Sugiyama (2010). Note that the MIROC was first developed with the convective parameterization of AS, the empirical suppression in the ASRH was introduced to MIROC3.2 in CMIP3, and further improvements in the MIROC5 (Ctl) in CMIP5 were achieved by Chikira and Sugiyama (2010). In this study, we compare an additional NoEnt in an attempt to understand the role of entrainment more clearly.

Table 1.

Summary of entrainment schemes, the double ITCZ indices (DI; mm day−1), precipitation P differences of subsidence and ascending regions (P↑ − P↓; mm day−1), and temporal standard deviations (std dev; mm day−1) for TRMM, Ctl, NoEnt, AS, and ASRH.

Table 1.
Fig. 1.
Fig. 1.

Vertical profiles of fractional entrainment rate (color; km−1) for convection with different cloud-top heights (abscissa; hPa) over the tropical oceans (30°S–30°N) realized in model experiments: (a) Ctl, (b) NoEnt, (c) AS, and (d) ASRH. The right-hand side panels in (a)–(d) show the average profile of all convection.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-13-00701.1

The observational precipitation datasets from the Tropical Rainfall Measuring Mission (TRMM PR2A25 and 3B42 datasets) and SST compiled by the Hadley Centre are utilized in this study. They are linearly interpolated onto the spectral T42 (~2.8°) horizontal grid. Each model experiment is run for six years, and the last five years are analyzed. We have confirmed that extending the experiment periods does not influence our results, the differences between the four experiments being significantly larger than the internal variations in the atmospheric model (not shown). The climatological average SST from observations is prescribed in the experiments. Temporal precipitation variabilities are compared between 6-hourly snapshots obtained from the experiments and orbital snapshots obtained from the TRMM PR2A25, except for the calculation of power spectrum in section 3c, where the TRMM 3B42 is used.

3. Results

a. Spatial variations

The mean annual climatological precipitation observed over tropical oceans using TRMM PR2A25 along with SST contours is shown in Fig. 2a. Heavy precipitation is observed over the warm pool stretching from the Indian Ocean to the western Pacific, and two convergence zones, the ITCZ and the South Pacific convergence zone (SPCZ), extend eastward and southeastward, respectively. The general similarity between the precipitation distribution and the SST distribution suggests that the SST controlled the convection. However, some exceptions are identified over subsidence regions, such as the southeastern Pacific (indicated by a black box in Fig. 2), where moderate precipitation associated with congestus and shallow convection is observed despite relatively high SSTs being encountered.

Fig. 2.
Fig. 2.

Climatological annual precipitation (color; mm day−1) and SST (black contours; contour interval : 3°C) from (a) observations (TRMM PR2A25 and Hadley SST) and from model experiments (b) Ctl, (c) NoEnt, (d) AS, and (e) ASRH.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-13-00701.1

The climatological precipitation and SST distributions from the four experiments are shown in Figs. 2b–e. The Ctl satisfactorily reproduces the precipitation distributions, including the SPCZ extending southeastward from the western Pacific. However, in NoEnt and AS, the SPCZ extends eastward rather than southeastward, showing the double ITCZ structure. The double ITCZ index, defined as regional average precipitation over the southeastern Pacific (150°–100°W, 20°S–0°; Bellucci et al. 2010), is 1.4 mm day−1 in the TRMM data, 2.5 mm day−1 in the Ctl, 3.4 mm day−1 in the NoEnt, and 2.9 mm day−1 in the AS (Table 1), indicating that convection over the southeastern Pacific is more realistically suppressed in the Ctl. Generally, NoEnt and AS overestimate precipitation over subsidence regions in the subtropics and underestimate precipitation in ascending regions, such as the Maritime Continent and ITCZ. The difference between precipitation over subsidence regions (where the vertical velocity at 500 hPa is downward) and ascending regions (where the vertical velocity at 500 hPa is upward) in the tropical oceans (30°S–30°N) is 3.88 mm day−1 in TRMM data, 3.71 mm day−1 in Ctl, 3.10 mm day−1 in NoEnt, and 3.10 mm day−1 in AS (Table 1). These results indicate that the spatial precipitation variations are underestimated in NoEnt and AS and are enhanced by convective entrainment in Ctl. Furthermore, we have performed an additional experiment. The entrainment rate realized in the Ctl is averaged climatologically and is averaged horizontally over the tropics (30°S–30°N), removing its horizontal and temporal variations. We prescribe the averaged entrainment rate and run the model. The experiment shows similar performance with the Ctl, suggesting the importance of the vertical profile of entrainment rate.

The magnitudes of the spatial variations in ASRH are in between those in Ctl and NoEnt/AS; the double ITCZ index is 2.8 mm day−1, and the precipitation difference between subsidence and ascending regions is 3.4 mm day−1. Compared with AS, the empirical suppression of convection in ASRH decreases precipitation over some subsidence regions, including the southeastern Pacific, and increases precipitation over some ascending regions, such as the central Pacific ITCZ and SPCZ. However, the large dry bias over the Maritime Continent remains similar to that observed in NoEnt and AS.

It should be noted that precipitation distribution over the Indian Ocean shows similar biases in all of the experiments, suggesting that they are not considerably affected by convective entrainment. In fact, the reproducibility of the Indian Ocean precipitation is largely improved when the model is coupled with the ocean component (not shown).

The convective activities are interacting with the large-scale circulations, radiative processes, and environmental humidity. Figure 3 shows longitude–height cross sections of pressure vertical velocity, relative humidity, total diabatic heating, radiative heating, and cumulus detrainment mass flux for the Ctl and for differences between the NoEnt and the Ctl. Furthermore, the moist static energy and wind field at 850 hPa from the Ctl and differences with the NoEnt are shown in Fig. 4.

Fig. 3.
Fig. 3.

(a),(b) Vertical pressure velocity (hPa day−1); (c),(d) relative humidity (%); (e),(f) diabatic heating (K day−1); (g),(h) radiative heating (K day−1); and (i),(j) detrainment mass flux (kg m−2 s−1) for (left) Ctl and (right) difference between NoEnt and Ctl. Vectors in (a),(b) show zonal wind (m s−1) and vertical pressure velocity (hPa day−1).

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-13-00701.1

Fig. 4.
Fig. 4.

Horizontal advection of moist static energy (J kg−1 s−1) at 850 hPa for (a) Ctl and (b) difference between NoEnt and Ctl. Vectors show zonal and meridional winds (m s−1).

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-13-00701.1

In the Ctl, the negative and positive vertical pressure velocity over the western and eastern Pacific, respectively, represents the Walker circulation (Fig. 3a), and is consistent with the east–west contrast of diabatic heating (Fig. 3e). Relative humidity in the free troposphere is higher in the western Pacific compared to the eastern Pacific (Fig. 3c). The maxima of relative humidity at 600 hPa over the western Pacific is located where large detrainment of congestus occurs (Fig. 3i).

Feedback processes involving convective activities, the large-scale circulation, and environmental humidity may be at work (Oueslati and Bellon 2013). Convection is more active over the western Pacific than over the eastern Pacific; the associated diabatic heating drives the large-scale circulation with the dry descending branch and the moist ascending branch; thus, the convection is further enhanced over the western Pacific and suppressed over the eastern Pacific. Since the Ctl has large entrainment rates in the lower troposphere (Fig. 1a), convection is more active in humid environment and more suppressed in dry environment. The moistening by congestus in the western Pacific plays a role in creating the east–west contrast of the free tropospheric humidity. The cloud associated with convective activities also affects radiation. The radiative heating is everywhere negative, and its vertical profile with minima around 850 and 600–500 hPa (Fig. 3g) are associated with shallow convection and congestus, respectively.

The difference of the NoEnt minus the Ctl shows that the east–west contrast of the circulation, humidity, and total diabatic heating is weaker in the NoEnt (Figs. 3b,d,f). Meanwhile, the detrainment in the free troposphere and the radiative cooling at 850–500 hPa are smaller in the NoEnt, corresponding to smaller cloud amount in the lower troposphere (Figs. 3h,j). These results suggest that the feedback process described in the previous paragraph is also weaker for two reasons. The first reason is that the convection is not sensitive enough to environmental humidity, so deep convection is not much suppressed over the dry eastern Pacific, and the anomalous subsidence over the western Pacific dries the atmosphere, suppressing deep convection. The other reason is that the moistening by congestus over the western Pacific is insufficient.

The large-scale circulation also affects surface evaporation, which influences the boundary layer moist static energy, which couples back to the convective activities (Möbis and Stevens 2012; Kim et al. 2011). Compared to the Ctl, the NoEnt shows weaker trade easterlies over the tropical Pacific with maxima of the weakening located over the southeastern Pacific (Figs. 4a,b). The anomalous westerlies advect moist static energy from the western Pacific to the southeastern Pacific, favoring the convection.

The anomalies of the NoEnt described above are similar to those of the AS and the ASRH, but those of the ASRH are about 50% smaller in magnitude (not shown). The ASRH produces deep clouds instead of congestus clouds because its midtropospheric entrainment rate is too small (Fig. 1d). This suggests that the coupling of convection and free tropospheric humidity in the ASRH is stronger than that in the NoEnt and AS but not as strong as that in the Ctl.

It is worth discussing which type of heating is responsible for driving the large-scale circulation anomaly shown in Fig. 3b. The total diabatic heating in the free troposphere mainly corresponds to the latent heating and the radiative cooling, whereas the heating associated with turbulent mixing contributes only in the boundary layer. To examine the relative contribution, we calculate linear responses to each heating anomaly using a linearized dry primitive model described in Hirota and Takahashi (2012). The governing equations of the model are the primitive equations linearized about the climatological basic state in σ coordinates (p divided by surface pressure). The horizontal resolution of the model is spectrally truncated at T21 (~5.6°), and the model has 20 levels. Rayleigh friction, Newtonian cooling, and ∇4 horizontal diffusion are included. The e-folding time of friction and cooling is set to 3 days below the σ = 0.85 level and 30 days for the other levels, whereas that of horizontal diffusion is 1 day for the largest wavenumber. The linear model is integrated for 20 days, with prescribed heating imposed at each time step. A quasi-stationary solution is obtained after around 15 days; therefore, results are presented as an average of days 15–20.

The linear responses to each heating anomaly (e.g., Figs. 3f,h) are shown in Fig. 5. The model well captures the weakening of the Walker circulation in response to the total diabatic heating anomaly (Figs. 3b, 5a). This response is mostly explained by the response to the latent heating anomaly (Fig. 5b). The radiative heating also contributes mainly in the lower troposphere over the eastern Pacific (Fig. 5c), whereas the turbulent mixing shows almost no contribution (Fig. 5d).

Fig. 5.
Fig. 5.

Linear responses of vertical pressure velocity (hPa day−1) to (a) total diabatic heating, (b) latent heating, (c) radiative heating, and (d) heating associated with turbulent mixing for NoEnt minus Ctl.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-13-00701.1

b. Temporal variations

Temporal precipitation variations are examined in this subsection. The standard deviations of the 6-hourly precipitation are shown in Fig. 6. The average standard deviations over the tropical oceans (30°S–30°N) are 6.1 mm day−1 in TRMM PR2A25 data, 6.5 mm day−1 in Ctl, 5.0 mm day−1 in NoEnt, 4.7 mm day−1 in AS, and 9.1 mm day−1 in ASRH (Table 1). Similar to the spatial variations discussed in the previous subsection, the temporal variations are significantly underestimated in NoEnt and AS, whereas Ctl shows reasonable agreement with the TRMM data, and ASRH shows excessive temporal variations.

Fig. 6.
Fig. 6.

Temporal precipitation standard deviation (interval: 2 mm day−1) for (a) TRMM PR2A25, (b) Ctl, (c) NoEnt, (d) AS, and (e) ASRH. Values larger than 5.5 mm day−1 are shaded.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-13-00701.1

Next, we examine time scales of the precipitation time series by comparing the power spectra for TRMM 3B42 and for each experiment. The power spectra are calculated for each year and for each grid over the tropical oceans (30°S–30°N), and their averages are shown in Fig. 7. Note that the statistical errors in the averaged spectra are very small because of the large number of samples (5 yr multiplied by the number of ocean grids). All the spectra show large peaks corresponding to the semidiurnal and diurnal variations. In addition, another peak, with a period of approximately 4–10 days is found in the observation. A similar peak is identified around the period of 5–15 days in the Ctl but not in the other experiments. The power of the ASRH is similar to that of the Ctl for periods longer than 7 days, but it is very large for periods of 0.5–7 days, corresponding to the large overestimation of the precipitation standard deviations shown in Fig. 6e. We have also examined the space–time spectrum of the symmetric component, as described by Wheeler and Kiladis (1999) [see Fig. 10 in Chikira and Sugiyama (2010) and Fig. 1 in Chikira and Sugiyama (2013)]. The Ctl reasonably reproduces the spectral maxima of the MJO, the Kelvin waves, and the equatorial Rossby waves, though the maxima of the MJO and the Kelvin waves with the period of 1–4 days are smaller compared to observations. The underestimation of the high-frequency Kelvin waves corresponds to the underestimation of the power around the period of 1–4 days in Fig. 7b.

Fig. 7.
Fig. 7.

Power spectra of the precipitation time frequency (mm day−1)2 for (a) TRMM 3B42, (b) Ctl, (c) NoEnt, (d) AS, and (e) ASRH. The abscissa indicates the period (days) in a logarithmic scale. Note that the ordinate scale values in (e) are 4 times larger than those in (a)–(d).

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-13-00701.1

The differences in the temporal variations are more clearly illustrated using weighted PDFs of precipitation intensity (Fig. 8). PDFs are derived by binning all snapshot grid data (T42 spectral truncation is ~2.8° horizontal resolution) from the tropical oceans (30°S–30°N) according to grid precipitation using a dBR scale, defined as 10 log10(precipitation rate). The bin interval is set to 2 dBR (…, 4, 6, 10, 16, 25, 40, 63, … mm day−1). The PDFs are then weighted by the precipitation amount. The weighted PDFs indicate the contribution of each bin to the total precipitation, and the sum of all bins is unity because of normalization to the total precipitation. The weighted PDF for the TRMM PR2A25 observation has a maximum at 14 dBR (25 mm day−1), and approximately 65% of the total precipitation is contributed by the bins with precipitation intensities of 10–18 dBR (10–63 mm day−1). The weighted PDF for the Ctl is very similar to the weighted PDF for the TRMM data, with a maximum at 14 dBR (25 mm day−1). However, NoEnt and AS underestimate heavy precipitation above 14 dBR (25 mm day−1) and overestimate weaker precipitation at approximately 10 dBR (10 mm day−1), which is consistent with the results shown in Figs. 6 and 7 and those in Fig. 3b of Hirons et al. (2013). Interestingly, the ASRH shows two maxima at 6 dBR (4.0 mm day−1) and 16 dBR (40 mm day−1), reflecting the simple empirical suppression condition. The larger maximum is obtained from the convective scheme with relative humidity above 80%, and the smaller maximum is obtained from a large (grid)-scale condensation scheme with relative humidity below 80% (not shown).

Fig. 8.
Fig. 8.

Weighted probability distribution functions for precipitation intensity [abscissa: dBR = 10 log10(precipitation rate)] for TRMM PR2A25, Ctl, NoEnt, AS, and ASRH.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-13-00701.1

c. Temporal evolution

Finally, we compare the temporal evolution of precipitation events to examine the interactions between convection and free tropospheric humidity associated with convective entrainment. The following analyses are performed for the four experiments. Given the agreements in the spatial and temporal variations between the Ctl and TRMM data (Figs. 2, 68), we expect the temporal evolution to also be more realistic using the Ctl.

The precipitation events discussed here are defined as minima in outgoing longwave radiation (OLR), which is a measure of convective activity, in the 8-day running periods over the 22.5° × 22.5° running domains. For example, a grid point of 11.25°E, 11.25°N on 5 January is considered an event if its OLR is minimum with respect to the domain of 0°–22.5°E, 0°–22.5°N and minimum over the 8 days during 1–8 January. Each grid point over the tropical oceans (30°S–30°N) of the five years is tested. Then the top 90 selected events (about once every 20 days) are averaged, and the lag composites are shown in Fig. 9. Day +0 indicates the events of OLR minima. This definition selects precipitation events associated with large-scale atmospheric phenomena, such as MJO, Kelvin waves, and Rossby waves. We have tested many other definitions with different running periods and domains and confirmed that our results are robust and not sensitive to the details of the definitions (not shown).

Fig. 9.
Fig. 9.

Composites of the precipitation events for (a),(c),(e),(g),(i) Ctl; (b),(d),(f),(h),( j) NoEnt; (k),(m),(o),(q),(s) AS; and (l),(n),(p),(r),(t) ASRH: (a),(b),(k),(l) precipitation (mm day−1); (c),(d),(m),(n) probability density of cloud-top height [contour interval (CI): 7% (100 hPa)−1]; (e),(f),(o),(p) detrainment mass flux (CI: 0.001 kg m−2 s−1); (g),(h),(q),(r) relative humidity (CI: 5%); and (i),(j),(s),(t) temperature anomaly from the climatological average (CI: 0.5K). Values >7 in (c),(d),(m),(n); >0.003 in (e),(f),(o),(p); >70 in (g),(h),(q),(r); and >0 in (i),(j),(s),(t) are shaded. The time evolution of the events is shown in (a),(b),(k),(l) and the left-hand panels in (c)–(j) and (m)–(t). The right-hand panels in (c)–(j) and (m)–(t) show the average from day −20 to day +20.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-13-00701.1

The composite variable precipitation, probability density of the cloud top, detrainment mass flux, relative humidity, and temperature anomalies from climatological average are shown in Fig. 9. The probability density is shown in units of percent per 100 hPa. For example, the value of 10% (100 hPa)−1 at 300 hPa observed in Figs. 9c,d indicates that approximately 1.4 cloud types have top heights at approximately 250–350 hPa because an ensemble of 14 cloud types is considered in each grid in the model (see section 2). Note that the vertical integrals of the probability density are below 100% because some cloud types do not have enough buoyancy for the given profiles and are not allowed to occur.

In Ctl (Figs. 9a,c,e,g,i), shallow convection and congestus with cloud tops at approximately 850 and 600 hPa, respectively, are dominant, and deep convection and precipitation are mainly suppressed before the events. Large detrainment mass flux is identified around 600 hPa. Dry layers with relative humidity less than 70% are located in the free troposphere at 800–250 hPa. In this model, the relative humidity at 700–600 hPa gradually increases as the events develop because of the cumulus detrainment from the congestus, whereas horizontal convergence of moisture is negative at 600 hPa (not shown). Temperature also increases around day +0. Precipitation rapidly increases from day −4, as deep convection with a cloud top at approximately 400–100 hPa appears, and reaches a maximum of 37 mm day−1 at day +0. The events then decay after day +4 as the midtropospheric humidity decreases. These results suggest that cumulus detrainment increases midtropospheric humidity, and the increase in humidity in turn causes deepening of the convection. This feedback process will be further verified by sensitivity analyses described below. The deep convection maximum is separated from that of the congestus because of the latent heating of fusion, as suggested in previous studies (Zipser 2003; Jensen and Del Genio 2006). The trimodal structure (shallow convection, congestus, and deep convection) is consistent with the structure shown by Johnson et al. (1999).

On the other hand, deep convection with a cloud top at 200 hPa is always dominant in the NoEnt (Figs. 9b,d,f,h,j), and only a small amount of shallow convection and congestus are identified. Minor cumulus detrainment is observed in the midtroposphere, leading to a smaller increase in midtropospheric humidity. The mean maximum precipitation of the top 90 events at day +0 is 12 mm day−1, which is very small compared with the mean obtained during the Ctl.

As for the AS (Figs. 9k,m,o,q,s), although congestus and some variations in midtropospheric humidity are identified, they are not as large as those identified in the Ctl. Correspondingly, maximum precipitation of 18 mm day−1 at day +0 is relatively small, which is more comparable with the results of the NoEnt than those of the Ctl. Deep convection is always identified, and therefore, the transition from congestus to deep convection is unclear. The AS entrainment rate in the lower troposphere was smaller than the Ctl entrainment rate, suggesting that entrainment in the lower troposphere is more important for the transition and occurrence of large precipitation events.

The cloud top in the ASRH is similar to that in the NoEnt; deep convection is always dominant, and only little shallow convection and congestus exist. However, the ASRH shows an increase in tropospheric humidity as the events develop. As cumulus detrainment in the midtroposphere is small compared with the Ctl, this moistening is partially due to horizontal advection (not shown).

We perform additional sensitivity analyses to explore the feedback process between humidity and cloud-top height as well as the transition from congestus to deep convection in the Ctl. In the model, the cloud top of the cumulus ensemble is diagnosed at every time step in the cumulus scheme using the instantaneous input variables, such as the grid average (environmental) humidity and temperature. In this sensitivity test, we substitute each instantaneous input variable in the free troposphere (above the cloud base) with the climatological average variable, one by one. The composites of cloud-top PDFs rediagnosed using climatological humidity and climatological temperature are shown in Fig. 10. A composite of the substituted relative humidity is shown in Fig. 10c, which indicates only small seasonal variations of the climatological average in the free troposphere. Note that this is an offline convective scheme calculation; thus, the rediagnosed clouds do not influence the next time step. Although this is an unrealistic test, this helps us understand behavior of the cumulus scheme under a different environment.

Fig. 10.
Fig. 10.

(a),(b) As in Fig. 9c, but for the cloud top diagnosed using climatological humidity in (a) and temperature in (b). (c) As in Fig. 9g, but for a composite of the climatological humidity.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-13-00701.1

The cloud top diagnosed using climatological humidity shows only shallow convection and congestus, and deep convection is mainly suppressed, even around the event (Fig. 10a). This is a clear difference from the Ctl (Fig. 9c), supporting the conclusion that the increase in midtropospheric humidity, which is absent in the climatological average (Fig. 10c), is a critical factor in the deepening of convection for the events in the Ctl. In contrast, using the climatological temperature further deepens the cloud top (Fig. 10b) because the substituted climatological environment is cooler (Fig. 9t) and the convective parcel is more buoyant.

4. Summary and discussion

This study examines the role of convective entrainment in precipitation variations over tropical oceans using numerical MIROC5 experiments. The entrainment rate in the Ctl, utilizing a new entrainment scheme proposed by Chikira and Sugiyama (2010), directly varies with parcel buoyancy and inversely varies with the square of the updraft speed, and about half of the buoyancy-generated energy is consumed by entrainment. The entrainment in the AS and ASRH is constant for each cloud type, as in the original Arakawa and Schubert (1974) scheme, and is significantly smaller in the lower troposphere than in the Ctl (Fig. 1). The only difference between the AS and ASRH is that the ASRH employs empirical suppression of cumulus convection when the relative humidity over the cloud layer is below 80% (Emori et al. 2001). We also perform the NoEnt with zero entrainment to understand the role of entrainment more clearly.

The Ctl satisfactorily reproduces the observed precipitation distributions over tropical oceans, including the southeastward extending SPCZ with realistic spatial variations. The NoEnt and AS overestimate precipitation over subsidence regions and underestimate precipitation over ascending regions, resulting in spatial variations that are too small. The precipitation overestimation over the southeastern Pacific corresponds to the double ITCZ bias. Similarly, the temporal variations are satisfactorily simulated in Ctl and are underestimated in the NoEnt and AS. In the Ctl, congestus gradually moistens the midtroposphere, and large deep convection precipitation events occur once sufficient moisture is available. Such large precipitation events are absent in the NoEnt and AS, and relatively weak precipitation occurs more frequently. A comparison of the spatial and temporal variations as well as the weighted precipitation intensity PDFs between the experiments and the TRMM observations verifies that the Ctl is a more realistic experiment. The behaviors of AS and NoEnt are very similar to each other and different from observations and the Ctl. Since the difference between the Ctl and the AS is the entrainment rate, especially in the lower troposphere for congestus and deep convection, the latter is suggested to be important for the spatial and temporal variations of precipitation. The behavior of ASRH is somewhat complicated; while the spatial variations are smaller than in the observations, the temporal variations are excessive.

Comparing the Ctl to the NoEnt and the AS, we conclude that convective entrainment enhances the magnitudes of spatial and temporal precipitation variations. This is attributed to stronger entrainment in the Ctl that increases free tropospheric moistening by congestus and strengthens the coupling of convection with free tropospheric humidity. When the deep convection is more active over the western Pacific than over the eastern Pacific, the associated diabatic heating drives the large-scale circulation with the dry descending branch and the moist ascending branch; thus, the convection is further enhanced over the western Pacific and suppressed over the eastern Pacific. The congestus play a role in the free troposphere moistening over the western Pacific. The diabatic heating that drives the large-scale circulation mostly corresponds to the latent heating, whereas the radiative cooling strengthens the subsidence over the eastern Pacific. Although the ASRH empirically suppresses convection over the dry regions, it does not produce congestus because the entrainment is small. This suggests that the coupling of convection and humidity in the ASRH is stronger than that in the NoEnt and AS but not as strong as that in the Ctl. The importance of congestus is also suggested by Chikira (2010). Here, we further discussed its role in the feedback processes. Similarly, the precipitation time series in the Ctl shows suppressed periods with a dry midtroposphere and large deep convection precipitation events when sufficient moisture is available with the moistening by congestus. However, in the NoEnt and AS, deep convection occurs easily even where (when) the midtroposphere is very dry, and relatively weak precipitation occurs more frequently. This is consistent with Oueslati and Bellon (2013), who showed that frequency of weak-to-moderate ascending regimes and that of subsidence regimes are over- and underestimated, respectively, in models with the double ITCZ bias.

We observed that the coupling of convection and tropospheric humidity is essential in the transition from shallow to deep convection in the Ctl. Detrainment from shallower convection increases midtropospheric humidity, and the humidity increase in turn causes deepening of the convection by decreasing the buoyancy of the entrainment. This deepening convection by midtropospheric moistening is consistent with the results of previous studies (e.g., Maloney and Hartmann 2001; Del Genio et al. 2012; Hirons et al. 2013).

This study showed the spatial and temporal variations in precipitation are more realistic in the Ctl and suggested the importance of entrainment rate in the lower troposphere for congestus and deep convection. However, there are still many important issues left for the future work. For example, some previous studies using CRM results have suggested that downdraft-driven cold pools are important for decreasing entrainment (Kuang and Bretherton 2006; Khairoutdinov and Randall 2006; Del Genio and Wu 2010). Downdrafts from shallower convection create cold pools, and the associated gust fronts produce larger convective plumes with less entrainment, which develop into deep convection. Furthermore, Kim et al. (2011) suggested that changing rain reevaporation rate in the convective scheme has similar impacts when changing entrainment rate. These processes should be examined in future.

Acknowledgments

This study was supported by the Green Network of Excellence Program of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan, and by the Environment Research and Technology Development Fund (2A-1201) of the Ministry of the Environment, Japan. We also acknowledge the Program for Risk Information on Climate Change from MEXT. The Grid Analysis and Display System was used to plot the figures.

REFERENCES

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

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

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

    • Search Google Scholar
    • Export Citation
  • Bechtold, P., , N. Semane, , P. Lopez, , J.-P. Chaboureau, , A. Beljaars, , and N. Bormann, 2014: Representing equilibrium and nonequilibrium convection in large-scale models. J. Atmos. Sci., 71, 734753, doi:10.1175/JAS-D-13-0163.1.

    • Search Google Scholar
    • Export Citation
  • Bellucci, A., , S. Gualdi, , and A. Navarra, 2010: The double-ITCZ syndrome in coupled general circulation models: The role of large-scale vertical circulation regimes. J. Climate, 23, 11271145, doi:10.1175/2009JCLI3002.1.

    • Search Google Scholar
    • Export Citation
  • Chikira, M., 2010: A cumulus parameterization with state-dependent entrainment rate. Part II: Impact on climatology in a general circulation model. J. Atmos. Sci., 67, 21942211, doi:10.1175/2010JAS3317.1.

    • Search Google Scholar
    • Export Citation
  • Chikira, M., 2014: Eastward-propagating intraseasonal oscillation represented by Chikira–Sugiyama cumulus parameterization. Part II: Understanding moisture variation under weak temperature gradient balance. J. Atmos. Sci., 71, 615639, doi:10.1175/JAS-D-13-038.1.

    • Search Google Scholar
    • Export Citation
  • Chikira, M., , and M. Sugiyama, 2010: A cumulus parameterization with state-dependent entrainment rate. Part I: Description and sensitivity to temperature and humidity profiles. J. Atmos. Sci., 67, 21712193, doi:10.1175/2010JAS3316.1.

    • Search Google Scholar
    • Export Citation
  • Chikira, M., , and M. Sugiyama, 2013: Eastward-propagating intraseasonal oscillation represented by Chikira–Sugiyama cumulus parameterization. Part I: Comparison with observation and reanalysis. J. Atmos. Sci., 70, 39203939, doi:10.1175/JAS-D-13-034.1.

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

  • Del Genio, A. D., , and J. Wu, 2010: The role of entrainment in the diurnal cycle of continental convection. J. Climate, 23, 27222738, doi:10.1175/2009JCLI3340.1.

    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., , Y. Chen, , D. Kim, , and M.-S. Yao, 2012: The MJO transition from shallow to deep convection in CloudSat/CALIPSO data and GISS GCM simulations. J. Climate, 25, 37553770, doi:10.1175/JCLI-D-11-00384.1.

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

    • Search Google Scholar
    • Export Citation
  • de Rooy, W. C., and et al. , 2013: Entrainment and detrainment in cumulus convection: An overview. Quart. J. Roy. Meteor. Soc., 139, 119, doi:10.1002/qj.1959.

    • Search Google Scholar
    • Export Citation
  • de Szoeke, S. P., , and S.-P. Xie, 2008: The tropical eastern Pacific seasonal cycle: Assessment of errors and mechanisms in IPCC AR4 coupled ocean–atmosphere general circulation models. J. Climate, 21, 25732590, doi:10.1175/2007JCLI1975.1.

    • Search Google Scholar
    • Export Citation
  • Emori, S., , T. Nozawa, , A. Numaguti, , and I. Uno, 2001: Importance of cumulus parameterization for precipitation simulation over East Asia in June. J. Meteor. Soc. Japan, 79, 939947, doi:10.2151/jmsj.79.939.

    • Search Google Scholar
    • Export Citation
  • Esbensen, S., 1978: Bulk thermodynamic effects and properties of small tropical cumuli. J. Atmos. Sci., 35, 826837, doi:10.1175/1520-0469(1978)035<0826:BTEAPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Grant, A., , and A. Brown, 1999: A similarity hypothesis for shallow-cumulus transports. Quart. J. Roy. Meteor. Soc., 125, 19131936, doi:10.1002/qj.49712555802.

    • 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, doi:10.1002/qj.49712757104.

    • Search Google Scholar
    • Export Citation
  • Gregory, D., , and P. R. Rowntree, 1990: A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Wea. Rev., 118, 14831506, doi:10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hirons, L., , P. Inness, , F. Vitart, , and P. Bechtold, 2013: Understanding advances in the simulation of intraseasonal variability in the ECMWF model. Part II: The application of process-based diagnostics. Quart. J. Roy. Meteor. Soc., 139, 14271444, doi:10.1002/qj.2059.

    • Search Google Scholar
    • Export Citation
  • Hirota, N., , and M. Takahashi, 2012: A tripolar pattern as an internal mode of the East Asian summer monsoon. Climate Dyn., 39, 22192238, doi:10.1007/s00382-012-1416-y.

    • Search Google Scholar
    • Export Citation
  • Hirota, N., , and Y. N. Takayabu, 2012: Inter-model differences of future precipitation changes in CMIP3 and MIROC5 climate models. J. Meteor. Soc. Japan, 90A, 307316, doi:10.2151/jmsj.2012-A16.

    • Search Google Scholar
    • Export Citation
  • Hirota, N., , and Y. N. Takayabu, 2013: Reproducibility of precipitation distribution over the tropical oceans in CMIP5 multi-climate models compared to CMIP3. Climate Dyn.,41, 2909–2920, doi:10.1007/s00382-013-1839-0.

  • Hirota, N., , Y. N. Takayabu, , M. Watanabe, , and M. Kimoto, 2011: Precipitation reproducibility over tropical oceans and its relationship to the double ITCZ problem in CMIP3 and MIROC5 climate models. J. Climate, 24, 48594873, doi:10.1175/2011JCLI4156.1.

    • Search Google Scholar
    • Export Citation
  • Holloway, C. E., , and J. D. Neelin, 2009: Moisture vertical structure, column water vapor, and tropical deep convection. J. Atmos. Sci., 66, 16651683, doi:10.1175/2008JAS2806.1.

    • Search Google Scholar
    • Export Citation
  • Jensen, M. P., , and A. D. Del Genio, 2006: Factors limiting convective cloud-top height at the ARM Nauru Island climate research facility. J. Climate, 19, 21052117, doi:10.1175/JCLI3722.1.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., , T. M. Rickenbach, , S. A. Rutledge, , P. E. Ciesielski, , and W. H. Schubert, 1999: Trimodal characteristics of tropical convection. J. Climate, 12, 23972418, doi:10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M., , and D. Randall, 2006: High-resolution simulation of shallow-to-deep convection transition over land. J. Atmos. Sci., 63, 34213436, doi:10.1175/JAS3810.1.

    • Search Google Scholar
    • Export Citation
  • Kim, D., , A. H. Sobel, , E. D. Maloney, , D. M. Frierson, , and I.-S. Kang, 2011: A systematic relationship between intraseasonal variability and mean state bias in AGCM simulations. J. Climate, 24, 55065520, doi:10.1175/2011JCLI4177.1.

    • Search Google Scholar
    • Export Citation
  • Kim, D., , A. H. Sobel, , A. D. Del Genio, , Y. Chen, , S. J. Camargo, , M.-S. Yao, , M. Kelley, , and L. Nazarenko, 2012: The tropical subseasonal variability simulated in the NASA GISS general circulation model. J. Climate, 25, 46414659, doi:10.1175/JCLI-D-11-00447.1.

    • Search Google Scholar
    • Export Citation
  • Kuang, Z., , and C. S. Bretherton, 2006: A mass flux scheme view of a high-resolution simulation of a transition from shallow to deep cumulus convection. J. Atmos. Sci., 63, 18951909, doi:10.1175/JAS3723.1.

    • Search Google Scholar
    • Export Citation
  • Lin, C., 1999: Some bulk properties of cumulus ensembles simulated by a cloud-resolving model. Part II: Entrainment profiles. J. Atmos. Sci., 56, 37363748, doi:10.1175/1520-0469(1999)056<3736:SBPOCE>2.0.CO;2.

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

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

  • Maloney, E. D., , and D. L. Hartmann, 2001: The sensitivity of intraseasonal variability in the NCAR CCM3 to changes in convective parameterization. J. Climate, 14, 20152034, doi:10.1175/1520-0442(2001)014<2015:TSOIVI>2.0.CO;2.

    • 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, doi:10.1175/1520-0493(1965)093<0769:SCOAGC>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mechoso, C. R., and et al. , 1995: The seasonal cycle over the tropical Pacific in coupled ocean–atmosphere general circulation models. Mon. Wea. Rev., 123, 28252838, doi:10.1175/1520-0493(1995)123<2825:TSCOTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., , C. Covey, , B. McAvaney, , M. Latif, , and R. J. Stouffer, 2005: Overview of the Coupled Model Intercomparison Project. Bull. Amer. Meteor. Soc., 86, 8993, doi:10.1175/BAMS-86-1-89.

    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., , and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys., 20, 851875, doi:10.1029/RG020i004p00851.

    • Search Google Scholar
    • Export Citation
  • Miura, H., , T. Maeda, , and M. Kimoto, 2012: A comparison of the Madden–Julian oscillation simulated by different versions of the MIROC climate model. SOLA, 8, 165169, doi:10.2151/sola.2012-040.

    • Search Google Scholar
    • Export Citation
  • Möbis, B., , and B. Stevens, 2012: Factors controlling the position of the intertropical convergence zone on an aquaplanet. J. Adv. Model. Earth Syst.,4, M00A04, doi:10.1029/2012MS000199.

  • Nakanishi, M., , and H. Niino, 2004: An improved Mellor–Yamada level-3 model with condensation physics: Its design and verification. Bound.-Layer Meteor., 112, 131, doi:10.1023/B:BOUN.0000020164.04146.98.

    • Search Google Scholar
    • Export Citation
  • Oueslati, B., , and G. Bellon, 2013: Convective entrainment and large-scale organization of tropical precipitation: Sensitivity of the CNRM-CM5 hierarchy of models. J. Climate, 26, 29312946, doi:10.1175/JCLI-D-12-00314.1.

    • Search Google Scholar
    • Export Citation
  • Pan, D.-M., , and D. D. A. Randall, 1998: A cumulus parameterization with a prognostic closure. Quart. J. Roy. Meteor. Soc., 124, 949981, doi:10.1002/qj.49712454714.

    • Search Google Scholar
    • Export Citation
  • Philander, S. G. H., , D. Gu, , D. Halpern, , G. Lambert, , N.-C. Lau, , T. Li, , and R. C. Pacanowski, 1996: Why the ITCZ is mostly north of the equator. J. Climate, 9, 29582972, doi:10.1175/1520-0442(1996)009<2958:WTIIMN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Redelsperger, J., , D. Parsons, , and F. Guichard, 2002: Recovery processes and factors limiting cloud-top height following the arrival of a dry intrusion observed during TOGA COARE. J. Atmos. Sci., 59, 24382457, doi:10.1175/1520-0469(2002)059<2438:RPAFLC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sekiguchi, M., , and T. Nakajima, 2008: A k-distribution-based radiation code and its computational optimization for an atmospheric general circulation model. J. Quant. Spectrosc. Radiat. Transfer, 109, 27792793, doi:10.1016/j.jqsrt.2008.07.013.

    • Search Google Scholar
    • Export Citation
  • Siebesma, A., , and J. Cuijpers, 1995: Evaluation of parametric assumptions for shallow cumulus convection. J. Atmos. Sci., 52, 650666, doi:10.1175/1520-0469(1995)052<0650:EOPAFS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Simpson, J., , and V. Wiggert, 1969: Models of precipitating cumulus towers. Mon. Wea. Rev., 97, 471489, doi:10.1175/1520-0493(1969)097<0471:MOPCT>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Song, X., , and G. Zhang, 2009: Convection parameterization, tropical pacific double ITCZ, and upper-ocean biases in the NCAR CCSM3. Part I: Climatology and atmospheric feedback. J. Climate, 22, 42994315, doi:10.1175/2009JCLI2642.1.

    • Search Google Scholar
    • Export Citation
  • Takata, K., , S. Emori, , and T. Watanabe, 2003: Development of the minimal advanced treatments of surface interaction and runoff. Global Planet. Change, 38, 209222, doi:10.1016/S0921-8181(03)00030-4.

    • Search Google Scholar
    • Export Citation
  • Takayabu, Y. N., , S. Shige, , W.-K. Tao, , and N. Hirota, 2010: Shallow and deep latent heating modes over tropical oceans observed with TRMM PR spectral latent heating data. J. Climate, 23, 20302046, doi:10.1175/2009JCLI3110.1.

    • Search Google Scholar
    • Export Citation
  • Takemi, T., , O. Hirayama, , and C. Liu, 2004: Factors responsible for the vertical development of tropical oceanic cumulus convection. Geophys. Res. Lett., 31, L11109, doi:10.1029/2004GL020225.

    • Search Google Scholar
    • Export Citation
  • Takemura, T., , T. Nozawa, , S. Emori, , T. Y. Nakajima, , and T. Nakajima, 2005: Simulation of climate response to aerosol direct and indirect effects with aerosol transport-radiation model. J. Geophys. Res., 110, D02202, doi:10.1029/2004JD005029.

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

    • 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.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., , L. Zhou, , and K. Hamilton, 2007: Effect of convective entrainment/detrainment on the simulation of the tropical precipitation diurnal cycle. Mon. Wea. Rev., 135, 567585, doi:10.1175/MWR3308.1.

    • Search Google Scholar
    • Export Citation
  • Watanabe, M., , S. Emori, , M. Satoh, , and H. Miura, 2009: A PDF-based hybrid prognostic cloud scheme for general circulation models. Climate Dyn., 33, 795816, doi:10.1007/s00382-008-0489-0.

    • Search Google Scholar
    • Export Citation
  • Watanabe, M., and et al. , 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 63126335, doi:10.1175/2010JCLI3679.1.

    • Search Google Scholar
    • Export Citation
  • Watanabe, M., , M. Chikira, , Y. Imada, , and M. Kimoto, 2011: Convective control of ENSO simulated in MIROC. J. Climate, 24, 543562, doi:10.1175/2010JCLI3878.1.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M., , and G. N. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber–frequency domain. J. Atmos. Sci., 56, 374399, doi:10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., and et al. , 2007: A regional ocean–atmosphere model for eastern Pacific climate: Towards reducing tropical biases. J. Climate, 20, 15041522, doi:10.1175/JCLI4080.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., , and X. Song, 2010: Convection parameterization, tropical Pacific double ITCZ, and upper-ocean biases in the NCAR CCSM3. Part II: Coupled feedback and the role of ocean heat transport. J. Climate, 23, 800812, doi:10.1175/2009JCLI3109.1.

    • Search Google Scholar
    • Export Citation
  • Zipser, E. J., 2003: Some views on “hot towers” after 50 years of tropical field programs and two years of TRMM data. Cloud Systems, Hurricanes, and the Tropical Rainfall Measuring Mission (TRMM): A Tribute to Dr. Joanne Simpson, Meteor. Monogr., No 29, Amer. Meteor. Soc., 75116, doi:10.1175/0065-9401(2003)029<0049:CSVOHT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
Save