Journal of Meteorological Research

, Volume 31, Issue 4, pp 654–664 | Cite as

Observational characteristics of cloud radiative effects over three arid regions in the Northern Hemisphere



Cloud–radiation processes play an important role in regional energy budgets and surface temperature changes over arid regions. Cloud radiative effects (CREs) are used to quantitatively measure the aforementioned climatic role. This study investigates the characteristics of CREs and their temporal variations over three arid regions in central Asia (CA), East Asia (EA), and North America (NA), based on recent satellite datasets. Our results show that the annual mean shortwave (SW) and net CREs (SWCRE and NCRE) over the three arid regions are weaker than those in the same latitudinal zone of the Northern Hemisphere. In most cold months (November–March), the longwave (LW) CRE is stronger than the SWCRE over the three arid regions, leading to a positive NCRE and radiative warming in the regional atmosphere–land surface system. The cold-season mean NCRE at the top of the atmosphere (TOA) averaged over EA is 4.1 W m–2, with a positive NCRE from November to March, and the intensity and duration of the positive NCRE is larger than that over CA and NA. The CREs over the arid regions of EA exhibit remarkable annual cycles due to the influence of the monsoon in the south. The TOA LWCRE over arid regions is closely related to the high-cloud fraction, and the SWCRE relates well to the total cloud fraction. In addition, the relationship between the SWCRE and the low-cloud fraction is good over NA because of the considerable occurrence of low cloud. Further results show that the interannual variation of TOA CREs is small over the arid regions of CA and EA, but their surface LWCREs show certain decreasing trends that correspond well to their decreasing total cloud fraction. It is suggested that combined studies of more observational cloud properties and meteorological elements are needed for indepth understanding of cloud–radiation processes over arid regions of the Northern Hemisphere.

Key words

arid region cloud fraction cloud radiative effects 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The authors would like to thank the reviewers for their valuable comments.


  1. Adler, R. F., G. J. Huffman, A. Chang, et al., 2003: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 1147–1167, doi: 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0. CO;2.CrossRefGoogle Scholar
  2. 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
  3. Bony, S., R. Colman, V. M. Kattsov, et al., 2006: How well do we understand and evaluate climate change feedback processes. J. Climate, 19, 3445–3482, doi: 10.1175/JCLI3819.1.CrossRefGoogle Scholar
  4. Boucher, O., D. Randall, P. Artaxo, et al., 2013: Clouds and aerosols. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T. F., D. Qin, G.-K. Plattner, et al., Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 571–658.Google Scholar
  5. Online Link Cesana, G., and H. Chepfer, 2012: How well do climate models simulate cloud vertical structure? A comparison between CALIPSO-GOCCP satellite observations and CMIP5 models Geophys. Res. Lett., 39, L20803, doi: 10.1029/2012GL053153.Google Scholar
  6. Cess, R. D., M. H. Zhang, B. A. Wielicki, et al., 2001: The influence of the 1998 El Niño upon cloud–radiative forcing over the Pacific warm pool. J. Climate, 14, 2129–2137, doi: 10.1175/1520-0442(2001)014<2129:TIOTEN>2.0.CO;2.CrossRefGoogle Scholar
  7. Chen, Y. H., H. T. Bai, J. P. Huang, et al., 2008: Comparison of cloud radiative forcing on the atmosphere–earth system over northwestern China with respect to typical geo-topographic regions. China Environ. Sci., 28, 97–101. (in Chinese)Google Scholar
  8. Chepfer, H., S. Bony, D. Winker, et al., 2010: The GCM-oriented CALIPSO cloud product (CALIPSO-GOCCP). J. Geophys. Res., 115, D00H16, doi: 10.1029/2009JD012251.CrossRefGoogle Scholar
  9. Dai, A. G., 2013: Increasing drought under global warming in observations and models. Nature Climate Change, 3, 52–58, doi: 10.1038/nclimate1633.CrossRefGoogle Scholar
  10. Dee, D. P., S. M. Uppala, A. J. Simmons, et al., 2011: The ERAInterim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi: 10.1002/qj.v137.656.CrossRefGoogle Scholar
  11. Doelling, D. R., N. G. Loeb, D. F. Keyes, et al., 2013: Geostationary enhanced temporal interpolation for CERES flux products. J. Atmos. Oceanic Technol., 30, 1072–1090, doi: 10.1175/JTECH-D-12-00136.1.CrossRefGoogle Scholar
  12. Flato, G., J. Marotzke, B. Abiodun, et al., 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T. F., D. Qin, G.-K. Plattner, et al., Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 741–866.Google Scholar
  13. Online Link 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, doi: 10.1175/1520-0442 (1992)005<1281:TEOCTO>2.0.CO;2.CrossRefGoogle Scholar
  14. Hartman, D. L., L. A. Moy, and Q. Fu, 2001: Tropical convection and the energy balance at the top of the atmosphere. J. Climate, 14, 4495–4511, doi: 10.1175/1520-0442(2001)014<44 95:TCATEB>2.0.CO;2.CrossRefGoogle Scholar
  15. Huang, J. P., X. D. Guan, and F. Ji, 2012: Enhanced cold-season warming in semi-arid regions. Atmos. Chem. Phys., 12, 5391–5398, doi: 10.5194/acp-12-5391-2012.CrossRefGoogle Scholar
  16. Huang, J. P., M. X. Ji, Y. K. Xie, et al., 2016: Global semi-arid climate change over last 60 years. Climate Dyn., 46, 1131–1150, doi: 10.1007/s00382-015-2636-8.CrossRefGoogle Scholar
  17. Kiehl, J. T., 1994: On the observed near cancellation between longwave and shortwave cloud forcing in tropical regions. J. Climate, 7, 559–565, doi: 10.1175/1520-0442(1994)007<0559: OTONCB>2.0.CO;2.CrossRefGoogle Scholar
  18. Lauer, A., and K. Hamilton, 2012: 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
  19. Li, Y., D. W. J. Thompson, and S. Bony, 2015: The influence of atmospheric cloud radiative effects on the large-scale atmospheric circulation. J. Climate, 28, 7263–7278, doi: 10.1175/JCLI-D-14-00825.1.CrossRefGoogle Scholar
  20. Liu, R. J., L. Zhang, H. B. Wang, et al., 2011: Cirrus cloud measurement using lidar over semi-arid areas. Chinese J. Atmos. Sci., 35, 863–870, doi: 10.3878/j.issn.1006-9895.2011.05.06. (in Chinese)Google Scholar
  21. Liu, Y. G., W. Wu, M. P. Jensen, et al., 2011: Relationship between cloud radiative forcing, cloud fraction and cloud albedo, and new surface-based approach for determining cloud albedo. Atmos. Chem. Phys., 11, 7155–7170, doi: 10.5194/acp-11-7155-2011.CrossRefGoogle Scholar
  22. Loeb, N. G., B. A. Wielicki, D. R. Doelling, et al., 2009: Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748–766, doi: 10.1175/2008JCLI2637.1.CrossRefGoogle Scholar
  23. Ma, Z. G., and C. B. Fu, 2007: Evidence of the global drying trend during the latter half of the 20th century and its relationship with large-scale climate background. Sci. China Earth Sci., 50, 776–788, doi: 10.1007/s11430-007-0036-6.CrossRefGoogle Scholar
  24. Mace, G. G., S. Benson, K. L. Sonntag, et al., 2006: Cloud radiative forcing at the atmospheric radiation measurement program climate research facility. 1: Technique, validation, and comparison to satellite-derived diagnostic quantities. J. Geophys. Res., 111, D11S90, doi: 10.1029/2005JD005921.Google Scholar
  25. Meehl, G. A., T. F. Stocker, W. D. Collins, et al., 2007: Global Climate Projections. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Solomon, S., D. Qin, M. Manning, et al., Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 747–846.Google Scholar
  26. Online Link Min, M., P. C. Wang, J. R. Campbell, et al., 2010: Midlatitude cirrus cloud radiative forcing over China. J. Geophys. Res., 115, D20210, doi: 10.1029/2010JD014161.CrossRefGoogle Scholar
  27. Minnis, P., S. Sun-Mack, Y. Chen, et al., 2011: CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data. Part II: Examples of average results and comparisons with other data. IEEE Trans. Geosci. Remote Sensing, 49, 4401–4430, doi: 10.1109/TGRS.2011.2144602.CrossRefGoogle Scholar
  28. Ramanathan, V., R. D. Cess, E. F. Harrison, et al., 1989: Cloud-radiative forcing and climate: Results from the earth radiation budget experiment. Science, 243, 57–63, doi: 10.1126/science. 243.4887.57.CrossRefGoogle Scholar
  29. Randall, D. A., R. A. Wood, S. Bony, et al., 2007: Climate models and their evaluation. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Solomon, S., D. Qin, M. Manning, et al., Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 589–662.Google Scholar
  30. Online Link Sassen, K., and J. R. Campbell, 2001: A midlatitude cirrus cloud climatology from the facility for atmospheric remote sensing. Part I: Macrophysical and synoptic properties. J. Atmos. Sci., 58, 481–496, doi: 10.1175/1520-0469(2001)058<0481:AMCCCF> 2.0.CO;2.CrossRefGoogle Scholar
  31. Stubenrauch, C. J., W. B. Rossow, S. Kinne, et al., 2013: Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX radiation panel. Bull. Amer. Meteor. Soc., 94, 1031–1049, doi: 10.1175/BAMS-D-12-00117.1.CrossRefGoogle Scholar
  32. Trenberth, K. E., J. T. Fasullo, and J. Kiehl, 2009: Earth’s global energy budget. Bull. Amer. Meteor. Soc., 90, 311–323, doi: 10.1175/2008BAMS2634.1.CrossRefGoogle Scholar
  33. Wang, J., L. Zhang, J. P. Huang, et al., 2013: Macrophysical and optical properties of midlatitude cirrus clouds over a semi-arid area observed by micro-pulse lidar. J. Quant. Spectros. Radiative Transfer, 122, 3–12, doi: 10.1016/j.jqsrt.2013.02.006.CrossRefGoogle Scholar
  34. Wild, M., D. Folini, C. Schär, et al., 2013: The global energy balance from a surface perspective. Climate Dyn., 40, 3107–3134, doi: 10.1007/s00382-012-1569-8.CrossRefGoogle Scholar
  35. Wu, G. X., Y. Liu, X. Zhu, et al., 2009: Multi-scale forcing and the formation of subtropical desert and monsoon. Annales Geophysicae, 27, 3631–3644, doi: 10.5194/angeo-27-3631-2009.CrossRefGoogle Scholar
  36. Yin, Z. Y., H. L. Wang, and X. D. Liu, 2014: A comparative study on precipitation climatology and interannual variability in the lower midlatitude East Asia and central Asia. J. Climate, 27, 7830–7848, doi: 10.1175/JCLI-D-14-00052.1.CrossRefGoogle Scholar
  37. Zhang, L. X., and T. J. Zhou, 2015: Drought over East Asia: A Review. J. Climate, 28, 3375–3399, doi: 10.1175/JCLI-D-14-00259.1.CrossRefGoogle Scholar
  38. Zhao, S. Y., H. Zhang, S. Feng, et al., 2015: Simulating direct effects of dust aerosol in arid and semi-arid regions using an aerosol–climate coupled system. Int. J. Climatol., 35, 1858–1866, doi: 10.1002/joc.2015.35.issue-8.CrossRefGoogle Scholar
  39. Zhao, T. B., L. Chen, and Z. G. Ma, 2014: Simulation of historical and projected climate change in arid and semi-arid areas by CMIP5 models. Chinese Sci. Bull,. 59, 412–429, doi: 10.1007/s11434-013-0003-x.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.State Key Laboratory of Loess and Quaternary Geology, Institute of Earth EnvironmentChinese Academy of SciencesXi’anChina
  3. 3.Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric SciencesLanzhou UniversityLanzhouChina

Personalised recommendations