Journal of Meteorological Research

, Volume 30, Issue 2, pp 156–168 | Cite as

Advances in studies of cloud overlap and its radiative transfer in climate models



The latest advances in studies on the treatment of cloud overlap and its radiative transfer in global climate models are summarized. Developments with respect to this internationally challenging problem are described from aspects such as the design of cloud overlap assumptions, the realization of cloud overlap assumptions within climate models, and the data and methods used to obtain consistent observations of cloud overlap structure and radiative transfer in overlapping clouds. To date, there has been an appreciable level of achievement in studies on cloud overlap in climate models, demonstrated by the development of scientific assumptions (e.g., e-folding overlap) to describe cloud overlap, the invention and broad application of the fast radiative transfer method for overlapped clouds (Monte Carlo Independent Column Approximation), and the emergence of continuous 3D cloud satellite observation (e.g., CloudSat/CALIPSO) and cloud-resolving models, which provide numerous data valuable for the exact description of cloud overlap structure in climate models. However, present treatments of cloud overlap and its radiative transfer process are far from complete, and there remain many unsettled problems that need to be explored in the future.

Key words

cloud overlap climate model parameterization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ackerman, T. P., and G. M. Stokes, 2003: The atmospheric radiation measurement program. Phys. Today, 56, 38–44.CrossRefGoogle Scholar
  2. Barker, H. W., 2008a: Overlap of fractional cloud for radiation calculations in GCMs: A global analysis using CloudSat and CALIPSO data. J. Geophys. Res., 113, D00A01.Google Scholar
  3. Barker, H. W., 2008b: Representing cloud overlap with an effective decorrelation length: An assessment using CloudSat and CALIPSO data. J. Geophys. Res., 113, D24205.CrossRefGoogle Scholar
  4. Barker, H. W., G. L. Stephens, and Q. Fu, 1999: The sensitivity of domain-averaged solar fluxes to assumptions about cloud geometry. Quart. J. Roy. Meteor. Soc., 125, 2127–2152.CrossRefGoogle Scholar
  5. 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, 3102–3122.CrossRefGoogle Scholar
  6. Bergman, J. W., and P. J. Rasch, 2002: Parameterizing vertically coherent cloud distributions. J. Atmos. Sci., 59, 2165–2182.CrossRefGoogle Scholar
  7. Browning, K. A., A. K. Betts, P. R. Jonas, et al., 1993: The GEWEX cloud system study (GCSS). Bull. Amer. Meteor. Soc., 74, 387–399.CrossRefGoogle Scholar
  8. Bu Lingbing, Qin Yanqiu, Wu Fang, et al., 2014: Analysis of cirrus properties based on micro-pulse lidar and millimeter wave cloud radar. High Power Laser Part. Beams, 26, 297–302. (in Chinese)Google Scholar
  9. Cheng, A., and K. M. Xu, 2011: Improved low-cloud simulation from a multiscale modeling framework with a third-order turbulence closure in its cloudresolving model component. J. Geophys. Res., 116, D14101.CrossRefGoogle Scholar
  10. Chou, M.-D., M. J. Suarez, C.-H. Ho, et al., 1998: Parameterizations for cloud overlapping and shortwave single-scattering properties for use in general circulation and cloud ensemble models. J. Climate, 11, 202–214.CrossRefGoogle Scholar
  11. Collins, W. D., 2001: Parameterization of generalized cloud overlap for radiative calculations in general circulation models. J. Atmos. Sci., 58, 3224–3242.CrossRefGoogle Scholar
  12. Fu Yunfei, Cao Aiqin, Li Tianyi, et al., 2012: Climatic characteristics of the storm top altitude for the convective and stratiform precipitation in summer Asia based on measurements of the TRMM precipitation radar. Acta Meteor. Sinica, 70, 436–451. (in Chinese)Google Scholar
  13. Harshvardhan, R. Davies, D. A. Randall, et al., 1987: A fast radiation parameterization for atmospheric circulation models. J. Geophys. Res., 92, 1009–1016.CrossRefGoogle Scholar
  14. Hogan, R. J., and A. J. Illingworth, 2000: Deriving cloud overlap statistics from radar. Quart. J. Roy. Meteor. Soc., 128, 2903–2909.CrossRefGoogle Scholar
  15. Hogan, R. J., and A. J. Illingworth, 2003: Parameterizing ice cloud inhomogeneity and the overlap of inhomogeneities using cloud radar data. J. Atmos. Sci., 60, 756–767.CrossRefGoogle Scholar
  16. Huang, J. P., P. Minnis, B. Lin, et al., 2005: Advanced retrievals of multilayered cloud properties using multispectral measurements. J. Geophys. Res., 110, D15S18.CrossRefGoogle Scholar
  17. Huang Xingyou, Xia Junrong, Bu Lingbing, et al., 2013: Comparison and analysis of cloud base height measured by ceilometer, infrared cloud measuring system and cloud radar. Chinese J. Quant. Electron., 30, 73–78. (in Chinese)Google Scholar
  18. Huo Juan and Lü Daren, 2009: Simulations of inhomogeneous cloud and its effects on radiative distribution of atmosphere with a 3D radiative transfer model. Chinese J. Atmos. Sci., 33, 168–178. (in Chinese)Google Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. Jing Xianwen, Zhang Hua, and Guo Pinwen, 2009: A study of the effect of sub-grid cloud structure on global radiation in climate models. Acta Meteor. Sinica, 67, 1058–1068. (in Chinese)Google Scholar
  22. Jing Xianwen and Zhang Hua, 2012: Application and evaluation of McICA cloud-radiation framework in the AGCM of the National Climate Center. Chinese J. Atmos. Sci., 36, 945–958. (in Chinese)Google Scholar
  23. Kato, S., S. Sun-Mack, W. 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.CrossRefGoogle Scholar
  24. Li, J., 2000: Accounting for overlap of fractional cloud in infrared radiation. Quart. J. Roy. Meteor. Soc., 126, 3325–3342.CrossRefGoogle Scholar
  25. Li, J., 2002: Accounting for unresolved clouds in a 1D infrared radiative transfer model. Part I: Solution for radiative transfer, including cloud scattering and overlap. J. Atmos. Sci., 59, 3302–3320.CrossRefGoogle Scholar
  26. Li, J., S. Dobbie, P. Räisänen, et al., 2005: Accounting for unresolved clouds in a 1D solar radiative-transfer model. Quart. J. Roy. Meteor. Soc., 131, 1607–1629.CrossRefGoogle Scholar
  27. Li, J., J. Huang, K. Stamnes, et al., 2015: A global survey of cloud overlap based on CALIPSO and CloudSatmeasurements. Atmos. Chem. Phys., 15, 519–536.CrossRefGoogle Scholar
  28. Li Jiming, Huang Jianping, Yi Yuhong, et al., 2009: Analysis of vertical distribution of cloud in East Asia by space-based lidar data. Chinese J. Atmos. Sci., 33, 698–707. (in Chinese)Google Scholar
  29. Li Xiaofan, Li Tingting, and Lou Lingyun, 2014: Effects of doubled carbon dioxide on rainfall responses to radiative processes of water clouds. J. Meteor. Res., 28, 1114–1126, doi:  10.1007/s13351-014-4043-1.CrossRefGoogle Scholar
  30. Liang, X. Z., and W. C. Wang, 1997: Cloud overlap effects on general circulation model climate simulations. J. Geophys. Res., 102, 11039–11047.CrossRefGoogle Scholar
  31. Liou, K. N., 1992: Radiation and Cloud Processes in the Atmosphere. Oxford University Press, New York, 504 pp.Google Scholar
  32. Liu Liping, Zheng Jiafeng, Ruan Zheng, et al., 2015: Comprehensive radar observations of clouds and precipitation over the Tibetan Plateau and preliminary analysis of cloud properties. J. Meteor. Res., 29, 546–561, doi:  10.1007/s13351-015-4208-6.CrossRefGoogle Scholar
  33. Lü Qiaoyi, Li Jiming, Wang Tianhe, et al., 2015: Cloud radiative forcing induced by layered clouds and associated impact on the atmospheric heating rate. J. Meteor. Res., 29, 779–792, doi:  10.1007/s13351-015-5078-7.CrossRefGoogle Scholar
  34. Mace, G. G., and S. Benson-Troth, 2002: Cloud-layer overlap characteristics derived from long-term cloud radar data. J. Climate, 15, 2505–2515.CrossRefGoogle Scholar
  35. Manabe, S., and R. F. Strickler, 1964: Thermal equilibrium of the atmosphere with a convective adjustment. J. Atmos. Sci., 21, 361–385.CrossRefGoogle Scholar
  36. McFarlane, S. A., J. H. Mather, and T. P. Ackerman, 2007: Analysis of tropical radiative heating profiles: A comparison of models and observations. J. Geophys. Res., 112, D14218.CrossRefGoogle Scholar
  37. Morcrette, J.-J. and Y. Fouquart, 1986: The overlapping of cloud layers in shortwave radiation parameterizations. J. Atmos. Sci., 43, 321–328.CrossRefGoogle Scholar
  38. Morcrette, J.-J., and C. Jakob, 2000: The response of the ECMWF model to changes in the cloud overlap assumption. Mon. Wea. Rev., 128, 1707–1732.CrossRefGoogle Scholar
  39. Morcrette, J.-J., H. W. Barker, J. S. Cole, et al., 2008: Impact of a new radiation package, McRad, in the ECMWF Integrated Forecasting System. Mon. Wea. Rev., 136, 4773–4798.CrossRefGoogle Scholar
  40. Mrowiec, A. A., C. Rio, A. M. Fridlind, et al., 2012: Analysis of cloud-resolving simulations of a tropical mesoscale convective system observed during TWPICE: Vertical fluxes and draft properties in convective and stratiform regions. J. Geophys. Res., 117, D19201.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. Neale, R. B., C. C. Chen, G. Andrew, et al., 2010: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note TN-486, 274pp. [Available online at m5−desc.pdf].Google Scholar
  43. Neggers, R. A. J., T. Heus, and P. Siebesma, 2011: Overlap statistics of cumuliform boundary-layer cloud fields in large-eddy simulations. J. Geophys. Res., 116, D21202.CrossRefGoogle Scholar
  44. Oreopoulos, L., and M. Khairoutdinov, 2003: Overlap properties of clouds generated by a cloud-resolving model. J. Geophys. Res., 108, 4479.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. Peng Jie, Zhang Hua, and Shen Xinyong, 2013: Analysis of vertical structure of clouds in East Asia with CloudSat data. Chinese J. Atmos. Sci., 37, 91–100. (in Chinese)Google Scholar
  47. Peng, J., H. Zhang, and Z. Q. Li, 2014: Temporal and spatial variations of global deep cloud systems based on CloudSat and CALIPSO satellite observations. Adv. Atmos. Sci., 31, 593–603.CrossRefGoogle Scholar
  48. Pincus, R., H. W. Barker, and J. J. Morcrette, 2003: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. J. Geophys. Res., 108, 4376.CrossRefGoogle Scholar
  49. Pincus, R., C. Hannay, S. A. Klein, et al., 2005: Overlap assumptions for assumed probability distribution function cloud schemes in large-scale models. J. Geophys. Res., 110, D15S09.CrossRefGoogle Scholar
  50. Pincus, R., R. Hemler, and S. A. Klein, 2006: Using stochastically-generated subcolumns to represent cloud structure in a large-scale model. Mon. Wea. Rev., 134, 3644–3656.CrossRefGoogle Scholar
  51. Räisänen, P., 1998: Effective longwave cloud fraction and maximum-random overlap of clouds: A problem and a solution. Mon. Wea. Rev., 126, 3336–3340.CrossRefGoogle Scholar
  52. Räisänen, P., and H. W. Barker, 2004: Evaluation and optimization of sampling errors for the Monte Carlo independent column approximation. Quart. J. Roy. Meteor. Soc., 130, 2069–2085.CrossRefGoogle Scholar
  53. 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.CrossRefGoogle Scholar
  54. Räisänen, P., and H. Järvinen, 2010: Impact of cloud and radiation scheme modifications on climate simulated by the ECHAM5 atmospheric GCM. Quart. J. Roy. Meteor. Soc., 136, 1733–1752.CrossRefGoogle Scholar
  55. Randall, D., M. Khairoutdinov, A. Arakawa, et al., 2003: Breaking the cloud parameterization deadlock. Bull. Amer. Meteor. Soc., 84, 1547–1564.CrossRefGoogle Scholar
  56. 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.Google Scholar
  57. Shonk, J. K. P., and R. J. Hogan, 2010: Effect of improving representation of horizontal and vertical cloud structure on the earth’s global radiation budget. Part II: The global effects. Quart. J. Roy. Meteor. Soc., 136, 1205–1215.Google Scholar
  58. Stephens, G. L., N. B. Wood, and P. M. Gabriel, 2004: An assessment of the parameterization of subgridscale cloud effects on radiative transfer. Part I: Vertical overlap. J. Atmos. Sci., 61, 715–732.CrossRefGoogle Scholar
  59. 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.CrossRefGoogle Scholar
  60. Tian, L., and J. A. Curry, 1989: Cloud overlap statistics. J. Geophys. Res., 94, 9925–9935.CrossRefGoogle Scholar
  61. Tompkins, A. M., and L. Feudale, 2009: Seasonal ensemble predictions of West African monsoon precipitation in the ECMWF system 3 with a focus on the AMMA special observing period in 2006. Wea. Forecasting, 25, 768–788.CrossRefGoogle Scholar
  62. Tompkins, A. M., and F. Di Giuseppe, 2015: An interpretation of cloud overlap statistics. J. Atmos. Sci., 72, 2877–2889.CrossRefGoogle Scholar
  63. Wang Fang and Ding Yihui, 2005: An evaluation of cloud radiative feedback mechanism in climate models. Adv. Earth Sci., 20, 207–215. (in Chinese)Google Scholar
  64. Wang Hui, Luo Yali, and Zhang Renhe, 2011a: Analyzing seasonal variation of clouds over the Asian monsoon regions and the Tibetan Plateau region using Cloud-Sat/CALIPSO data. Chinese J. Atmos. Sci., 35, 1117–1131. (in Chinese)Google Scholar
  65. Wang Shuaihui, Han Zhigang, Yao Zhigang, et al., 2011b: An analysis of cloud types and macroscopic characteristics over China and its neighborhood based on the CloudSat data. Acta Meteor. Sinica, 69, 883–899. (in Chinese)Google Scholar
  66. 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.CrossRefGoogle Scholar
  67. Weare, B. C., 2001: Effect of cloud overlap on radiative feedback. Climate Dyn., 17, 143–150.CrossRefGoogle Scholar
  68. Wu Juxiu, Wei Ming, Hang Xin, et al., 2014a: The first observed cloud echoes and microphysical parameter retrievals by China’s 94-GHz cloud radar. J. Meteor. Res., 28, 430–443, doi:  10.1007/s13351-014-3083-x.CrossRefGoogle Scholar
  69. Wu Juxiu, Wei Ming, and Zhou Jie, 2014b: Echo and capability analysis of 94-GHz cloud radars. Acta Meteor. Sinica, 72, 402–416. (in Chinese)Google Scholar
  70. Wu, X. Q., and X. Z. Liang, 2005: Radiative effects of cloud horizontal inhomogeneity and vertical overlap identified from a monthlong cloud-resolving model simulation. J. Atmos. Sci., 62, 4105–4112.CrossRefGoogle Scholar
  71. Xu, K. M., R. T. Cederwall, L. J. Donner, et al., 2002: An intercomparison of cloud-resolving models with the atmospheric radiation measurement summer 1997 intensive observation period data. Quart. J. Roy. Meteor. Soc., 128, 593–624.CrossRefGoogle Scholar
  72. Yang Bingyun, Zhang Hua, Peng Jie, et al., 2014: Analysis on global distribution characteristics of cloud microphysical and optical properties based on the CloudSat data. Plateau Meteor., 33, 1105–1118. (in Chinese)Google Scholar
  73. Yin Jinfang, Wang Donghai, Zhai Guoqing, et al., 2013: Observational characteristics of cloud vertical profiles Continent of over the East Asia from the Cloudsat data. Acta Meteor. Sinica, 27, 26–39. (in Chinese)CrossRefGoogle Scholar
  74. 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., 118, 7733–7749.Google Scholar
  75. Zhang Hua and Jing Xianwen, 2010: Effect of cloud overlap assumptions in climate models on modeled Earth-atmosphere radiative fields. Chinese J. Atmos. Sci., 34, 520–532. (in Chinese)Google Scholar
  76. Zhang Hua, Peng Jie, Jing Xianwen, et al., 2013a: The features of cloud overlapping in eastern Asia and their effect on cloud radiative forcing. Sci. China: Earth Sci., 43, 523–535. (in Chinese)Google Scholar
  77. Zhang Hua, Peng Jie, Jing Xianwen, et al., 2013b: The features of cloud overlapping in eastern Asia and their effect on cloud radiative forcing. Sci. China: Earth Sci., 56, 737–747.CrossRefGoogle Scholar
  78. Zhang, H., X. Jing, and J. Li, 2014: Application and evaluation of a new radiation code under McICA scheme in BCC−AGCM2.0.1. Geosci. Model Dev., 7, 737–754.CrossRefGoogle Scholar
  79. Zhang Hua, Yang Bingyun, Peng Jie, et al., 2015: The characteristics of cloud microphysical properties in East Asia with the CloudSat dataset. Chinese J. Atmos. Sci., 39, 235–248. (in Chinese)Google Scholar
  80. Zhou Tian, Huang Zhongwei, Huang Jianping, et al., 2013: Study of vertical distribution of cloud over Loess Plateau based on a ground-based lidar system. J. Arid Meteor., 31, 246–253. (in Chinese)Google Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Hua Zhang (张华)
    • 1
    • 2
  • Xianwen Jing (荆现文)
    • 1
    • 2
  1. 1.Laboratory for Climate Studies, National Climate CenterChina Meteorological AdministrationBeijingChina
  2. 2.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science & TechnologyNanjingChina

Personalised recommendations