Journal of Meteorological Research

, Volume 29, Issue 5, pp 779–792 | Cite as

Cloud radiative forcing induced by layered clouds and associated impact on the atmospheric heating rate

  • Qiaoyi Lü (吕巧谊)
  • Jiming Li (李积明)
  • Tianhe Wang (王天河)
  • Jianping Huang (黄建平)


A quantitative analysis of cloud fraction, cloud radiative forcing, and cloud radiative heating rate (CRH) of the single-layered cloud (SLC) and the multi-layered cloud (MLC), and their differences is presented, based on the 2B-CLDCLASS-LIDAR and 2B-FLXHR-LIDAR products on the global scale. The CRH at a given atmospheric level is defined as the cloudy minus clear-sky radiative heating rate. The statistical results show that the globally averaged cloud fraction of the MLC (24.9%), which is primarily prevalent in equatorial regions, is smaller than that of the SLC (46.6%). The globally averaged net radiative forcings (NET CRFs) induced by the SLC (MLC) at the top and bottom of the atmosphere (TOA and BOA) and in the atmosphere (ATM) are–60.8 (–40.9),–67.5 (–49.6), and 6.6 (8.7) W m-2, respectively, where the MLC contributes approximately 40.2%, 42.4%, and 57% to the NET CRF at the TOA, BOA, and in the ATM, respectively. The MLC exhibits distinct differences to the SLC in terms of CRH. The shortwave CRH of the SLC (MLC) reaches a heating peak at 9.75 (7.5) km, with a value of 0.35 (0.60) K day-1, and the differences between SLC and MLC transform from positive to negative with increasing altitude. However, the longwave CRH of the SLC (MLC) reaches a cooling peak at 2 (8) km, with a value of–0.45 (–0.42) K day-1, and the differences transform from negative to positive with increasing altitude. In general, the NET CRH differences between SLC and MLC are negative below 7.5 km. These results provide an observational basis for the assessment and improvement of the cloud parameterization schemes in global models.

Key words

single-layered cloud multi-layered cloud cloud fraction cloud radiative forcing cloud radiative heating rate 


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  1. Behrangi, A., T. Kubar, and B. H. Lambrigtsen, 2012: Phenomenological description of tropical clouds using CloudSat cloud classification. Mon. Wea. Rev., 140, 3235–3249.CrossRefGoogle Scholar
  2. Cess, R. D., G. L. Potter, J. P. Blanchet, et al., 1989: Interpretation of cloud-climate feedback as produced by 14 atmospheric general circulation models. Science, 245, 513–516.CrossRefGoogle Scholar
  3. Chen, C., and W. R. Cotton, 1987: The physics of the marine stratocumulus-capped mixed layer. J. Atmos. Sci., 44, 2951–2977.CrossRefGoogle Scholar
  4. Chen, T., W. B. Rossow, and Y. C. Zhang, 2000: Radiative effects of cloud-type variations. J. Climate, 13, 264–286.CrossRefGoogle Scholar
  5. Christensen, M. W., G. Carrió, G. L. Stephens, et al., 2013: Radiative impacts of free-tropospheric clouds on the properties of marine stratocumulus. J. Atmos. Sci., 70, 3102–3118.CrossRefGoogle Scholar
  6. Chen Yonghang, Huang Jianping, Ge Jinming, et al., 2006: Cloud properties and its relation to precipitation over Northwest China. Acta Meteor. Sinica, 20, 23–30.Google Scholar
  7. Chen Yonghang, Bai Hongtao, Huang Jianping, et al., 2008: Comparison of cloud radiative forcing on the earth-atmosphere system over northwestern China with respect to typical geo-topography regions. China Environ. Sci., 28, 97–101. (in Chinese)Google Scholar
  8. Ding Xiaodong, Huang Jianping, Li Jiming, et al., 2012: Study on cloud vertical structure feature over Northwest China based on active satellite remote sensing and its influence on precipitation enhancement. J. Arid Meteor., 30, 529–538. (in Chinese)Google Scholar
  9. Fu Yunfei, 2014: Cloud parameters retrieved by the bispectral reflectance algorithm and associated applications. J. Meteor. Res., 28, 965–982.CrossRefGoogle Scholar
  10. Fung, I. Y., D. E. Harrison, and A. A. Lacis, 1984: On the variability of the net longwave radiation at the ocean surface. Rev. Geophys., 22, 177–193.CrossRefGoogle Scholar
  11. Hartmann, D. L., V. Ramanathan, A. Berroir, et al., 1986: Earth radiation budget data and climate research. Rev. Geophys., 24, 439–468.CrossRefGoogle Scholar
  12. Hartmann, D. L., M. E. Ockert-Bell, and M. L. Michelsen, 1992: The effect of cloud type on earth’s energy balance: Global analysis. J. Climate, 5, 1281–1304.CrossRefGoogle Scholar
  13. Haynes, J. M., C. Jakob, W. B. Rossow, et al., 2011: Major characteristics of southern ocean cloud regimes and their effects on the energy budget. J. Atmos. Sci., 24, 5061–5080.Google Scholar
  14. Haynes, J. M., T. H. V. Haar, T. L’Ecuyer, et al., 2013: Radiative heating characteristics of earth’s cloudy atmosphere from vertically resolved active sensors. Geophys. Res. Lett., 40, 624–630.CrossRefGoogle Scholar
  15. Henderson, D., T. L’Ecuyer, G. Stephens, et al., 2013: A multisensor perspective on the radiative impacts of clouds and aerosols. J. Appl. Meteor. Climatol., 52, 853–871.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, 1275–1287.Google Scholar
  17. Huang, J. P., B. Lin, P. Minnis, et al., 2006a: Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia. Geophys. Res. Lett., 33, L19802, doi: 10.1029/2006GL026561.CrossRefGoogle Scholar
  18. Huang, J. P., P. Minnis, B. Lin, et al., 2006b: Determination of ice water path in ice-over-water cloud systems using combined MODIS and AMSR-E measurements. Geophys. Res. Lett., 33, 1522–1534.Google Scholar
  19. Huang, J. P., P. Minnis, B. Lin, et al., 2006c: Possible influences of Asian dust aerosols on cloud properties and radiative forcing observed from MODIS and CERES. Geophys. Res. Lett., 33, L06824, doi: 10.1029/2005GL024724.Google Scholar
  20. Hu, Y. X., S. Rodier, K. M. Xu, et al., 2010: Occurrence, liquid water content, and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements. J. Geophys. Res., 115, D00H34, doi: 10.1029/2009JD012384.
  21. L’Ecuyer, T. S., 2007: Level 2 fluxes and heating rates product process description and interface control document, ver. 5.1, CloudSat Data Processing Center, Fort Collins, Colorado, Scholar
  22. L’Ecuyer, T. S., N. B. Wood, T. Haladay, et al., 2008: Impacts of clouds on atmospheric heating based on the R04 CloudSat fluxes and heating rates data set. J. Geophys. Res., 113, D00A15, doi: 10.1029/2008JD009951.Google Scholar
  23. Li, J. M., Y. H. Yi, P. Minnis, et al., 2011: Radiative effect differences between multi-layered and singlelayer clouds derived from CERES, CALIPSO, and CloudSat data. J. Quant. Spectrosc. Radiat. Transfer, 112, 361–375.CrossRefGoogle Scholar
  24. Li, J. M., J. P. Huang, K. Stamnes, et al., 2015: A global survey of cloud overlap based on CALIPSO and CloudSat measurements. Atmos. Chem. Phys., 15, 519–536.CrossRefGoogle Scholar
  25. 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
  26. Liang, X. Z., and X. Q. Wu, 2005: Evaluation of a GCM subgrid cloud-radiation interaction parameterization using cloud-resolving model simulations. Geophys. Res. Lett., 32, 347–354.Google Scholar
  27. Liou, K. N., and Q. L. Zheng, 1984: A numerical experiment on the interactions of radiation, clouds and dynamic processes in a general circulation model. J. Atmos. Sci., 41, 1513–1536.CrossRefGoogle Scholar
  28. Liu Jingjing, Chen Bin, and Huang Jianping, 2014: Discrimination and validation of clouds and dust aerosol layers over the Sahara desert with combined CALIOP and IIR measurements. J. Meteor. Res., 28, 185–198.CrossRefGoogle Scholar
  29. Mace, G. G., D. Vane, G. Stephens, et al., 2007: Level 2 radar-lidar GEOPROF product version 1.0 process description and interface control document. JPL, Pasadena, USA, 1–20.Google Scholar
  30. Min, Q. L., T. H. Wang, C. N. Long, et al., 2008: Estimating fractional sky cover from spectral measurements. J. Geophys. Res., 113, D20208, doi: 10.1029/2008JD010278.CrossRefGoogle Scholar
  31. Minnis, P., J. P. Huang, B. Lin, et al., 2007: Ice cloud properties in ice-over-water cloud systems using Tropical Rainfall Measuring Mission (TRMM) visible and infrared scanner and TRMM Microwave Imager data. J. Geophys. Res., 112, 541–553.Google Scholar
  32. 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
  33. 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
  34. Randall, D. A., J. A. Coakley ffixJr., D. H. Lenschow, et al., 1984: Outlook for research on subtropical marine stratification clouds. Bull. Amer. Meteor. Soc., 65, 1290–1301.CrossRefGoogle Scholar
  35. Ritter, B., and J. F. Geleyn, 1992: A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon. Wea. Rev., 120, 303–325.CrossRefGoogle Scholar
  36. Rossow, W. B., and A. A. Lacis, 1990: Global, seasonal cloud variations from satellite radiance measurements. Part II: Cloud properties and radiative effects. J. Climate, 3, 1204–1253.CrossRefGoogle Scholar
  37. Slingo, A., 1990: Sensitivity of the earth’s radiation budget to changes in low clouds. Nature, 343, 49–51.CrossRefGoogle Scholar
  38. Stephens, G. L., P. M. Gabriel, and P. T. Partain, 2001: Parameterization of atmospheric radiative transfer. Part I: Validity of simple models. J. Atmos. Sci., 58, 3391–3409.CrossRefGoogle Scholar
  39. Su, J., J. P. Huang, Q. Fu, et al., 2008: Estimation of Asian dust aerosol effect on cloud radiation forcing using Fu-Liou radiative model and CERES measurements. Atmos. Chem. Phys., 8, 2763–2771.CrossRefGoogle Scholar
  40. Tian, L., and J. A. Curry, 1989: Cloud overlap statistics. J. Geophys. Res., 94, 9925–9935.CrossRefGoogle Scholar
  41. Trenberth, K. E., and J. T. Fasullo, 2010: Simulation of present-day and twenty-first-century energy budgets of the southern oceans. J. Climate, 23, 440–454.CrossRefGoogle Scholar
  42. 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
  43. Wang Fang, Ding Yihui, and Xu Ying, 2007: Cloud and radiation processes simulated by a coupled atmosphere-ocean model. Acta Meteor. Sinica, 21, 397–408.Google Scholar
  44. Wang Hongqi and Zhao Gaoxiang, 1994: Cloud and radiation (I): Cloud climatology and radiative effects of clouds. Scientia Atmospheric Sinica, 18, 910–921. (in Chinese)Google Scholar
  45. Wang, J. H., W. B. Rossow, and Y. C. Zhang, 2000: Cloud vertical structure and its variations from a 20-yr global rawinsonde dataset. J. Climate, 13, 3041–3056.CrossRefGoogle Scholar
  46. Wang, L. K., and A. E. Dessler, 2006: Instantaneous cloud overlap statistics in the tropical area revealed by ICESat/GLAS data. Geophys. Res. Lett., 33, 292–306.Google Scholar
  47. Wang Shuaihui, Han Zhigang, Yao Zhigang, et al., 2011: Analysis of vertical structure of clouds in China and the surrounding with CloudSat data. Plateau Meteorology, 30, 38–52. (in Chinese)Google Scholar
  48. Wang, T. H., and Q. L. Min, 2008: Retrieving optical depths of optically thin and mixed-phase clouds from MFRSR measurements. J. Geophys. Res., 113, D19203, doi: 10.1029/2008JD009958.
  49. Wang Tianhe and Huang Jianping, 2009: A method for estimating optical properties of dusty cloud. Chinese Optics Letters, 7, 368–372.CrossRefGoogle Scholar
  50. Wang, W. C., J. P. Huang, P. Minnis, et al., 2010: Dusty cloud properties and radiative forcing over dust source and downwind regions derived from A-Train data during the Pacific Dust Experiment. J. Geophys. Res., 115, D00H35, doi: 10.1029/2010JD014109.
  51. Wang, Z., and K. Sassen, 2007: Level 2 cloud scenario classification product process description and interface control document, Ver. 5.0, CloudSat Data Processing Center, Fort Collins, Colorado, 50 pp, Scholar
  52. Yuan, T. L., and L. Oreopoulos, 2013: On the global character of overlap between low and high clouds. Geophys. Res. Lett., 40, 5320–5326.CrossRefGoogle Scholar
  53. Zhang Hua and Jing Xianwen, 2010: Effect of cloud overlap assumptions in climate models on modeled earth-atmosphere radiative field. Chinese J. Atmos. Sci., 34, 520–532. (in Chinese)Google Scholar
  54. Zhang Hua, Ma Jinghui, and Zheng Youfei, 2010: Modeling study of the global distribution of radiative forcing by dust aerosol. Acta Meteor. Sinica, 24, 558–570.Google Scholar
  55. Zhang Hua, Peng Jie, Jing Xianwen, et al., 2013: The features of cloud overlapping in eastern Asia and their effect on cloud radiative forcing. Sci. China (Earth Science), 56, 737–747. (in Chinese)CrossRefGoogle Scholar
  56. 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
  57. Zhao Gaoxiang and Wang Hongqi, 1994: Cloud and radiation (II): Cloud and cloud radiation parameterizations in general circulation models. Chinese J. Atmos. Sci., 18, 933–958. (in Chinese)Google Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Qiaoyi Lü (吕巧谊)
    • 1
  • Jiming Li (李积明)
    • 1
  • Tianhe Wang (王天河)
    • 1
  • Jianping Huang (黄建平)
    • 1
  1. 1.Department of Atmospheric SciencesLanzhou UniversityLanzhouChina

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