Journal of Meteorological Research

, Volume 32, Issue 2, pp 233–245 | Cite as

Improving Representation of Tropical Cloud Overlap in GCMs Based on Cloud-Resolving Model Data

  • Xianwen Jing
  • Hua Zhang
  • Masaki Satoh
  • Shuyun Zhao
Special Collection on Aerosol-Cloud-Radiation Interactions


The decorrelation length (Lcf) has been widely used to describe the behavior of vertical overlap of clouds in general circulation models (GCMs); however, it has been a challenge to associate Lcf with the large-scale meteorological conditions during cloud evolution. This study explored the relationship between Lcf and the strength of atmospheric convection in the tropics based on output from a global cloud-resolving model. Lcf tends to increase with vertical velocity in the mid-troposphere (w500) at locations of ascent, but shows little or no dependency on w500 at locations of descent. A representation of Lcf as a function of vertical velocity is obtained, with a linear regression in ascending regions and a constant value in descending regions. This simple and dynamic-related representation of Lcf leads to a significant improvement in simulation of both cloud cover and radiation fields compared with traditional overlap treatments. This work presents a physically justifiable approach to depicting cloud overlap in the tropics in GCMs.

Key words

cloud overlap decorrelation length cloud-resolving model Nonhydrostatic Icosahedral Atmospheric Model (NICAM) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anderson, G. P., S. A. Clough, F. X. Kneizys, et al., 1986: AFGL atmospheric constituent profiles (0.120 km). AFGL Tech. Rep., AFGL-TR-86-0110, Bedford, MA, Air Force Geophys. Lab., 1–43.Google Scholar
  2. Barker, H. W., 2008: Representing cloud overlap with an effective decorrelation length: An assessment using CloudSat and CALIPSO data. J. Geophys. Res., 113, D24205, doi: 10.1029/2008JD010391.CrossRefGoogle Scholar
  3. Barker, H. W., and P. Räisänen, 2005: Radiative sensitivities for cloud structural properties that are unresolved by conventional GCMs. Quart. J. Roy. Meteor. Soc., 131, 3103–3122, doi: 10.1256/qj.04.174.CrossRefGoogle Scholar
  4. Barker, H. W., B. A. Wiellicki, and L. Parker, 1996: A parameterization for computing grid-averaged solar fluxes for inhomogeneous marine boundary layer clouds. Part II: Validation using satellite data. J. Atmos. Sci., 53, 2304–2316, doi: 10.1175/1520-0469(1996)053<2304:APFCGA>2.0.CO;2.Google Scholar
  5. Barker, H. W., G. L. Stephens, P. Partain, et al., 2003: Assessing 1D atmospheric solar radiative transfer models: Interpretation and handling of unresolved clouds. J. Climate, 16, 2676–2699, doi: 10.1175/1520-0442(2003)016<2676:ADASRT>2.0.CO;2.CrossRefGoogle Scholar
  6. Bergman, J. W., and P. J. Rasch, 2002: Parameterizing vertically coherent cloud distributions. J. Atmos. Sci., 59, 2165–2182, doi: 10.1175/1520-0469(2002)059<2165:PVCCD>2.0.CO;2.CrossRefGoogle Scholar
  7. Bodas-Salcedo, A., M. J. Webb, S. Bony, et al., 2011: COSP: Satellite simulation software for model assessment. Bull. Am. Meteor. Soc., 92, 1023–1043, doi: 10.1175/2011BAMS2856.1.CrossRefGoogle Scholar
  8. Bony, S., K.-M. Lau, and Y. C. Sud, 1997: Sea surface temperature and large-scale circulation influences on tropical greenhouse effect and cloud radiative forcing. J. Climate, 10, 2055–2077, doi: 10.1175/1520-0442(1997)010<2055:SSTALS>2.0.CO;2.CrossRefGoogle Scholar
  9. Bony, S., B. Stevens, D. M. W. Frierson, et al., 2015: Clouds, circulation and climate sensitivity. Nature Geosci., 8, 261–268, doi: 10.1038/ngeo2398.CrossRefGoogle Scholar
  10. Collins, W. D., 2001: Parameterization of generalized cloud overlap for radiative calculations in general circulation models. J. Atmos. Sci., 58, 3224–3242, doi: 10.1175/1520-0469(2001)058<3224:POGCOF>2.0.CO;2. DiCrossRefGoogle Scholar
  11. Giuseppe, F., 2005: Sensitivity of one-dimensional radiative biases to vertical cloud-structure assumptions: Validation with aircraft data. Quart. J. Roy. Meteor. Soc., 131, 1655–1676, doi: 10.1256/qj.03.129.CrossRefGoogle Scholar
  12. Di Giuseppe, F., and A. M. Tompkins, 2015: Generalizing cloud overlap treatment to include the effect of wind shear. J. Atmos. Sci., 72, 2865–2876, doi: 10.1175/JAS-D-14-0277.1.CrossRefGoogle Scholar
  13. GEWEX Cloud System Science Team, 1993: The GEWEX cloud system study (GCSS). Bull. Amer. Meteor. Soc., 74, 387–400, doi: 10.1175/1520-0477(1993)074<0387:TGCSS>2.0.CO;2.CrossRefGoogle Scholar
  14. Grabowski, W. W., 1998: Toward cloud resolving modeling of large-scale tropical circulations: A simple cloud microphysics parameterization. J. Atmos. Sci., 55, 3283–3298, doi: 10.1175/1520-0469(1998)055<3283:TCRMOL>2.0.CO;2.CrossRefGoogle Scholar
  15. Hogan, R. J., and A. J. Illingworth, 2000: Deriving cloud overlap statistics from radar. Quart. J. Roy. Meteor. Soc., 126, 2903–2909, doi: 10.1002/qj.49712656914.CrossRefGoogle Scholar
  16. Ichikawa, H., H. Masunaga, Y. Tsushima, et al., 2012: Reproducibility by climate models of cloud radiative forcing associated with tropical convection. J. Climate, 25, 1247–1262, doi: 10.1175/JCLI-D-11-00114.1.CrossRefGoogle Scholar
  17. Inoue, T., M. Satoh, H. Miura, et al., 2008: Characteristics of cloud size of deep convection simulated by a global cloud resolving model over the western tropical Pacific. J. Meteor. Soc. Japan, 86A, 1–15, doi: 10.2151/jmsj.86A.1.CrossRefGoogle Scholar
  18. Inoue, T., M. Satoh, Y. Hagihara, et al., 2010: Comparison of high-level clouds represented in a global cloud system-resolving model with CALIPSO/CloudSat and geostationary satellite observations. J. Geophys. Res., 115, D00H22, doi: 10.1029/2009JD012371.CrossRefGoogle Scholar
  19. Jin, Z. H., T. P. Charlock, W. L. Jr. Smith, et al., 2004: A parameterization of ocean surface albedo. Geophys. Res. Lett., 31, L22301, doi: 10.1029/2004GL021180.CrossRefGoogle Scholar
  20. Jing, X. W., H. Zhang, J. Peng, et al., 2016: Cloud overlapping parameter obtained from CloudSat/CALIPSO dataset and its application in AGCM with McICA scheme. Atmos. Res., 170, 52–65, doi: 10.1016/j.atmosres.2015.11.007.CrossRefGoogle Scholar
  21. Kato, S., S. Sun-Mack, M. F. Miller, et al., 2010: Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles. J. Geophys. Res., 115, D00H28, doi: 10.1029/2009JD012277.CrossRefGoogle Scholar
  22. Lauer, A., and K. Hamilton, 2013: Simulating clouds with global climate models: A comparison of CMIP5 results with CMIP3 and satellite data. J. Climate, 26, 3823–3845, doi: 10.1175/JCLI-D-12-00451.1.CrossRefGoogle Scholar
  23. Li, J., J. Huang, K. Stamnes, et al., 2015: A global survey of cloud overlap based on CALIPSO and CloudSat measurements. Atmos. Chem. Phys., 15, 519–536, doi: 10.5194/acp-15-519-2015.CrossRefGoogle Scholar
  24. Li, J. D., Y. M. Liu, and G. X. Wu, 2009: Cloud radiative forcing in Asian monsoon region simulated by IPCC AR4 AMIP models. Adv. Atmos. Sci., 26, 923–939, doi: 10.1007/s00376-009-8111-x.CrossRefGoogle Scholar
  25. Liang, S. L., 2001: Narrowband to broadband conversions of land surface albedo. I: Algorithms. Remote Sens. Environ., 76, 213–238, doi: 10.1016/S0034-4257(00)00205-4.Google Scholar
  26. Liang, X. Z., and W. C. Wang, 1997: Cloud overlap effects on general circulation model climate simulations. J. Geophys. Res., 102, 11039–11047, doi: 10.1029/97JD00630.CrossRefGoogle Scholar
  27. Liang, X. Z., and X. Q. Wu, 2005: Evaluation of a GCM subgrid cloud-radiation interaction parameterization using cloudresolving model simulations. Geophys. Res. Lett., 32, L06801, doi: 10.1029/2004GL022301.Google Scholar
  28. Mace, G. G., and S. Benson-Troth, 2002: Cloud-layer overlap characteristics derived from long-term cloud radar data. J. Climate, 15, 2505–2515, doi: 10.1175/1520-0442(2002)015<2505:CLOCDF>2.0.CO;2.CrossRefGoogle Scholar
  29. Marchand, R., G. G. Mace, T. Ackerman, et al., 2008: Hydrometeor detection using CloudSat—An earth-orbiting 94-GHz cloud radar. J. Atmos. Oceanic Technol., 25, 519–533, doi: 10.1175/2007JTECHA1006.1.CrossRefGoogle Scholar
  30. Masunaga, H., M. Satoh, and H. Miura, 2008: A joint satellite and global cloud-resolving model analysis of a Madden–Julian Oscillation event: Model diagnosis.. J. Geophys. Res., 113, D17210, doi: 10.1029/2008JD009986.CrossRefGoogle Scholar
  31. Miura, H., M. Satoh, T. Nasuno, et al., 2007: A Madden–Julian oscillation event realistically simulated by a global cloudresolving model. Science, 318, 1763–1765, doi: 10.1126/science.1148443.CrossRefGoogle Scholar
  32. Morcrette, J. J., and Y. Fouquart, 1986: The overlapping of cloud layers in shortwave radiation parameterizations. J. Atmos. Sci., 43, 321–328, doi: 10.1175/1520-0469(1986)043<0321:TOOCLI>2.0.CO;2.CrossRefGoogle Scholar
  33. Nakanishi, M., and H. Niino, 2006: An improved Mellor-Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397–407, doi: 10.1007/s10546-005-9030-8.CrossRefGoogle Scholar
  34. Naud, C. M., A. Del Genio, G. G. Mace, et al., 2008: Impact of dynamics and atmospheric state on cloud vertical overlap. J. Climate, 21, 1758–1770, doi: 10.1175/2007JCLI1828.1.CrossRefGoogle Scholar
  35. Oreopoulos, L., and M. Khairoutdinov, 2003: Overlap properties of clouds generated by a cloud-resolving model. J. Geophys. Res., 108, 4479, doi: 10.1029/2002JD003329.CrossRefGoogle Scholar
  36. Oreopoulos, L., D. Lee, Y. C. Sud, et al., 2012: Radiative impacts of cloud heterogeneity and overlap in an atmospheric general circulation model. Atmos. Chem. Phys., 12, 9097–9111, doi: 10.5194/acp-12-9097-2012.CrossRefGoogle Scholar
  37. Peng, J., H. Zhang, and X. Y. Shen, 2013: Analysis of vertical structure of clouds in East Asia with CloudSat data. Chinese J. Atmos. Sci., 37, 91–100, doi: 10.3878/j.issn.1006-9895.2012.11188. (in Chinese)Google Scholar
  38. Räisänen, P., 1998: Effective longwave cloud fraction and maximumrandom overlap of clouds: A problem and a solution. Mon. Wea. Rev., 126, 3336–3340, doi: 10.1175/1520-0493(1998)126<3336:ELCFAM>2.0.CO;2.CrossRefGoogle Scholar
  39. Räisänen, P., H. W. Barker, M. F. Khairoutdinov, et al., 2004: Stochastic generation of subgrid-scale cloudy columns for large-scale models. Quart. J. Roy. Meteor. Soc., 130, 2047–2067, doi: 10.1256/qj.03.99.CrossRefGoogle Scholar
  40. Randall, D., M. Khairoutdinov, A. Arakawa, et al., 2003: Breaking the cloud parameterization deadlock. Bull. Amer. Meteor. Soc., 84, 1547–1564, doi: 10.1175/BAMS-84-11-1547.CrossRefGoogle Scholar
  41. Sato, T., H. Miura, M. Satoh, et al., 2009: Diurnal cycle of precipitation in the tropics simulated in a global cloud-resolving model. J. Climate, 22, 4809–4826, doi: 10.1175/2009JCLI2890.1.CrossRefGoogle Scholar
  42. Satoh, M., T. Matsuno, H. Tomita, et al., 2008: Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations. J. Comput. Phys., 227, 3486–3514, doi: 10.1016/ Scholar
  43. Satoh, M., T. Inoue, and H. Miura, 2010: Evaluations of cloud properties of global and local cloud system resolving models using CALIPSO and CloudSat simulators. J. Geophys. Res., 115, D00H14, doi: 10.1029/2009JD012247.CrossRefGoogle Scholar
  44. Satoh, M., H. Tomita, H. Yashiro, et al., 2014: The non-hydrostatic icosahedral atmospheric model: Description and development. Progress in Earth and Planetary Science, 1, 18, doi: 10.1186/s40645-014-0018-1.CrossRefGoogle Scholar
  45. Shonk, J. K. P., R. J. Hogan, J. M. Edwards, et al., 2010: Effect of improving representation of horizontal and vertical cloud structure on the Earth’s global radiation budget. Part I: Review and parametrization. Quart. J. Roy. Meteor. Soc., 136, 1191–1204, doi: 10.1002/qj.647.Google Scholar
  46. Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18, 237–273, doi: 10.1175/JCLI-3243.1.CrossRefGoogle Scholar
  47. Stephens, G. L., D. G. Vane, S. Tanelli, et al., 2008: CloudSat mission: Performance and early science after the first year of operation. J. Geophys. Res., 113, D00A18, doi: 10.1029/2008JD009982.CrossRefGoogle Scholar
  48. Tian, L., and J. A. Curry, 1989: Cloud overlap statistics. J. Geophys. Res., 94, 9925–9935, doi: 10.1029/JD094iD07p09925.CrossRefGoogle Scholar
  49. Tomita, H., and M. Satoh, 2004: A new dynamical framework of nonhydrostatic global model using the icosahedral grid. Fluid Dyn. Res., 34, 357–400, doi: 10.1016/j.fluiddyn.2004.03.003.CrossRefGoogle Scholar
  50. Tompkins, A. M., and F. Di Giuseppe, 2015: An interpretation of cloud overlap statistics. J. Atmos. Sci., 72, 2877–2889, doi: 10.1175/JAS-D-14-0278.1.CrossRefGoogle Scholar
  51. Wang, X. C., Y. M. Liu, and Q. Bao, 2016: Impacts of cloud overlap assumptions on radiative budgets and heating fields in convective regions. Atmos. Res., 167, 89–99, doi: 10.1016/j.atmosres.2015.07.017.CrossRefGoogle Scholar
  52. Wu, X. Q., and X.-Z. Liang, 2005a: Radiative effects of cloud horizontal inhomogeneity and vertical overlap identified from a monthlong cloud-resolving model simulation. J. Atmos. Sci., 62, 4105–4112, doi: 10.1175/JAS3565.1.CrossRefGoogle Scholar
  53. Wu, X. Q., and X.-Z. Liang, 2005b: Effect of subgrid cloud-radiation interaction on climate simulations. Geophys. Res. Lett., 32, L24806, doi: 10.1029/2005GL024432.CrossRefGoogle Scholar
  54. Wu, X. Q., and X. F. Li, 2008: A review of cloud-resolving model studies of convective processes. Adv. Atmos. Sci., 25, 202–212, doi: 10.1007/s00376-008-0202-6.CrossRefGoogle Scholar
  55. Zhang, F., X.-Z. Liang, J. N. Li, et al., 2013: Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects. J. Geophys. Res. Atmos., 118, 7733–7749, doi: 10.1002/jgrd.50604.CrossRefGoogle Scholar
  56. Zhang, H., and X. W. Jing, 2010: Effect of cloud overlap assumptions in climate models on modeled earth–atmosphere radiative fields. Chinese J. Atmos. Sci., 34, 520–532, doi: 10.3878/j.issn.1006-9895.2010.03.06. (in Chinese)Google Scholar
  57. Zhang, H., and X. W. Jing, 2016: Advances in studies of cloud overlap and its radiative transfer in climate models. J. Meteor. Res., 30, 156–168, doi: 10.1007/s13351-016-5164-5.CrossRefGoogle Scholar
  58. Zhang, H., T. Nakajima, G. Y. Shi, et al., 2003: An optimal approach to overlapping bands with correlated k distribution method and its application to radiative calculations. J. Geophys. Res., 108, 4641, doi: 10.1029/2002JD003358.CrossRefGoogle Scholar
  59. Zhang, H., G. Y. Shi, T. Nakajima, et al., 2006a: The effects of the choice of the k-interval number on radiative calculations. J. Quant. Spectro. Rad. Trans., 98, 31–43, doi: 10.1016/j.jqsrt.2005.05.090.CrossRefGoogle Scholar
  60. Zhang, H., T. Suzuki, T. Nakajima, et al., 2006b: Effects of band division on radiative calculations. Opt. Eng., 45, 016002, doi: 10.1117/1.2160521.CrossRefGoogle Scholar
  61. Zhang, H., J. Peng, X. W. Jing, et al., 2013: The features of cloud overlapping in eastern Asia and their effect on cloud radiative forcing. Sci. China Earth Sci., 56, 737–747, doi: 10.1007/s11430-012-4489-x.CrossRefGoogle Scholar
  62. Zhang, H., X. Jing, and J. Li, 2014: Application and evaluation of a new radiation code under McICA scheme in BCC_AG CM2.0.1. Geosci. Model Dev., 7, 737–754, doi: 10.5194/gmd-7-737-2014.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xianwen Jing
    • 1
  • Hua Zhang
    • 1
    • 2
  • Masaki Satoh
    • 3
  • Shuyun Zhao
    • 1
    • 4
  1. 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science & TechnologyNanjingChina
  2. 2.State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijingChina
  3. 3.Atmosphere and Ocean Research InstituteThe University of TokyoKashiwaJapan
  4. 4.Laboratory for Climate StudiesNational Climate Center, China Meteorological AdministrationBeijingChina

Personalised recommendations