Skip to main content
SpringerLink
Log in

Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012

  • Published:
Chinese Geographical Science Aims and scope Submit manuscript

Abstract

As is well known, clouds impact the radiative budget, climate change, hydrological processes, and the global carbon, nitrogen and sulfur cycles. To understand the wide-ranging effects of clouds, it is necessary to assess changes in cloud cover at high spatial and temporal resolution. In this study, we calculate global cloud cover during the day and at night using cloud products estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Results indicate that the global mean cloud cover from 2003 to 2012 was 66%. Moreover, global cloud cover increased over this recent decade. Specifically, cloud cover over land areas (especially North America, Antarctica, and Europe) decreased (slope =–0.001, R2 = 0.5254), whereas cloud cover over ocean areas (especially the Indian and Pacific Oceans) increased (slope = 0.0011, R2 = 0.4955). Cloud cover is relatively high between the latitudes of 36°S and 68°S compared to other regions, and cloud cover is lowest over Oceania and Antarctica. The highest rates of increase occurred over Southeast Asia and Oceania, whereas the highest rates of decrease occurred over Antarctica and North America. The global distribution of cloud cover regulates global temperature change, and the trends of these two variables over the 10-year period examined in this study (2003–2012) oppose one another in some regions. These findings are very important for studies of global climate change.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Ackerman S A, Strabala K I, Paul M W et al., 1999. Discriminating clear sky from clouds with modis. Journal of Geophysical Research, 103(D24): 32141–32157. doi: 10.1029/1998JD 200032

    Article  Google Scholar 

  • Cho H, Yang P, Kattawar G W et al., 2008. Depolarization ratio and attenuated backscatter for nine cloud types: analyses based on collocated CALIPSO lidar and MODIS measurements. Optics Express, 16: 3931–3948. doi:10.1364/OE.16.003931

    Article  Google Scholar 

  • Frey R A, Ackerman S A, Liu Y H et al., 2008. Cloud detection with MODIS. Part I: improvements in the MODIS cloud mask for collection 5. Journal of Atmosphere and Oceanic Technology, 25(7): 1057–1072. doi:10.1175/2008JTECHA1052.1

    Google Scholar 

  • Gao B C, Kaufman Y J, 1995. Selection of the 1.375–?m MODIS channel for remote sensing of cirrus clouds and stratospheric aerosols from space. Journal of the Atmospheric Sciences, 52(23): 4231–4237. doi:10.1109/IGARSS.2008.4780159

    Article  Google Scholar 

  • Hahn C J, Warren S G, 1999. Extended edited synoptic cloud reports from ships and land stations over the globe, 1952–1996. NDP026C, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN, 71. Available at: http://cdiac.esd.ornl.gov/epubs/ndp/ndp026c/ndp026c.html

    Book  Google Scholar 

  • Hirakata M, Okamoto H, Hagihara Y et al., 2014. Comparison of global and seasonal characteristics of cloud phase and horizontal ice plates derived from CALIPSO with MODIS and ECMWF. Journal of Atmosphere and Oceanic Technology, 31: 2114–2130. doi: http://dx.doi.org/10.1175/JTECH–D–13–00245.1

    Article  Google Scholar 

  • Holz R E, Ackerman S A, Nagle F W et al., 2008. Global Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP. Journal of Geophysical Research: Atmospheres, 113(D8): D00A19. doi: 10.1029/2008JD009837

    Book  Google Scholar 

  • Jethva H, Torres O, Waquet F et al., 2014. How do A–train sensors intercompare in the retrieval of above cloud aerosol optical depth? A case study–based assessment. Geophysical Research Letters, 41: 186–192. doi: 10.1002/2013GL058405

    Article  Google Scholar 

  • Mao K B, Li Z L, Chen J M et al., 2016. Global vegetation change analysis based on MODIS data in recent twelve years. High Technology Letters, 22(4): 343–349. doi:10.3773/j.issn. 10066748.2016.04.001

    Google Scholar 

  • Mao K B, Ma Y, Tan X L et al., 2017b. Global surface temperature change analysis based on MODIS data in recent twelve years. Advances in Space Research, 59(2): 503–512. doi: 10. 1016/j.asr.2016.11.007

    Article  Google Scholar 

  • Mao Kebiao, Chen Jingming, Li Zhaoliang et al., 2017a. Global water vapor content decreases from 2003 to 2012: an analysis based on MODIS Data. Chinese Geographical Science, 27(1): 1–7. doi: 10.1007/s11769–017–0841–61

    Article  Google Scholar 

  • Moore III B, Gates W L, Mata L J et al., 2001. Advancing Our Understanding. In: Houghton J T et al. (eds.). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 769–786.

    Google Scholar 

  • Norris J R, 1999. On trends and possible artifacts in global ocean cloud cover between 1952 and 1995. Journal of Climate, 12(6): 1864–1870. doi: 10.1175/1520–0442(1999)012<1864: OTAPAI>2.0.CO;2

    Article  Google Scholar 

  • Norris J R, Wild M, 2007. Trends in aerosol radiative effects over Europe inferred from observed cloud cover, solar ‘dimming’, and solar ‘brightening’. Journal of Geophysical Research: Atmospheres, 112(D8): D08214. doi: 10.1029/2006JD007794

    Book  Google Scholar 

  • Platnick S, King M D, Ackerman S A et al., 2003. The MODIS cloud products: algorithms and examples from terra. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 459–473. doi: 10.1109/TGRS.2002.808301

    Article  Google Scholar 

  • Rahmstorf S, Coumou D, 2011. Increase of extreme events in a warming world. Proceedings of the National Academy of Sciences of the United States of America, 108(44): 17905–17909. doi: 10.1073/pnas.1101766108

    Article  Google Scholar 

  • Solomon S, Rosenlof K H, Portmann R W et al., 2010. Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science, 327(5970): 1219–1223. doi: 10.1126/science.1182488

    Article  Google Scholar 

  • Steven P, Michael D K, Steven A A, et al., 2003. The MODIS cloud products: algorithms and examples from Terra. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 459–473. doi: 10.1109/TGRS.2002.808301

    Article  Google Scholar 

  • Xia L, Zhao F, Chen L et al., 2018. Performance comparison of the MODIS and the VIIRS 1.38µm cirrus cloud channels using libRadtran and CALIOP data. Remote Sensing of Environment, 206: 363–374. doi: https://doi.org/10.1016/j.rse.2017.12.040

    Article  Google Scholar 

  • Xia L, Zhao F, Ma Y et al., 2015. An improved algorithm for the detection of cirrus clouds in the Tibetan Plateau using VIIRS and MODIS data. Journal of Atmosphere and Oceanic Technology, 32 (11): 2125–2129. doi:10.1175/JTECH–D–15–0063.1

    Google Scholar 

Download references

Acknowledgements

The authors thank the following persons for their help with this study: Anthony Arguez and Scott Stephens at NOAA’s National Climatic Data Center.

Author information

Authors and Affiliations

  1. National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Kebiao Mao, Zijin Yuan, Zhiyuan Zuo & Chunyu Gao

  2. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Research, Chinese Academy of Science and Beijing Normal University, Beijing, 100086, China

    Kebiao Mao & Tongren Xu

  3. Hydrometeorology and Remote Sensing Laboratory, University of Oklahoma, Norman, 73072, USA

    Xinyi Shen

Authors
  1. Kebiao Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zijin Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiyuan Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tongren Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinyi Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chunyu Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kebiao Mao.

Additional information

Foundation item: Under the auspices of the National Key Project of China (No. 2018YFC1506602, 2018YFC1506502), National Natural Science Foundation of China (No. 41571427), the Anhui Natural Science Foundation (No. 1808085MF195), Open Fund of State Key Laboratory of Remote Sensing Science (No. OFSLRSS201708)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mao, K., Yuan, Z., Zuo, Z. et al. Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012. Chin. Geogr. Sci. 29, 306–315 (2019). https://doi.org/10.1007/s11769-019-1030-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11769-019-1030-6

Keywords

Search

Navigation