Volume 29 Issue 2
Apr.  2019
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MAO Kebiao, YUAN Zijin, ZUO Zhiyuan, XU Tongren, SHEN Xinyi, GAO Chunyu. Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012[J]. Chinese Geographical Science, 2019, 20(2): 306-315. doi: 10.1007/s11769-019-1030-6
Citation: MAO Kebiao, YUAN Zijin, ZUO Zhiyuan, XU Tongren, SHEN Xinyi, GAO Chunyu. Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012[J]. Chinese Geographical Science, 2019, 20(2): 306-315. doi: 10.1007/s11769-019-1030-6

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

doi: 10.1007/s11769-019-1030-6
Funds:  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)
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  • Corresponding author: MAO Kebiao
  • Received Date: 2017-11-16
  • Publish Date: 2019-04-01
  • 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.
  • [1] Ackerman S A, Strabala K I, Paul M W et al., 1999. Discriminating clear sky from clouds with modis. Journal of Geophysical Re-search, 103(D24):32141-32157. doi:10.1029/1998JD 200032
    [2] 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. Op-tics Express, 16:3931-3948. doi: 10.1364/OE.16.003931
    [3] 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 Technol-ogy, 25(7):1057-1072. doi: 10.1175/2008JTECHA1052.1
    [4] Gao B C, Kaufman Y J, 1995. Selection of the 1.375-μm 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
    [5] 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
    [6] 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
    [7] 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
    [8] 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
    [9] 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
    [10] 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
    [11] 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
    [12] 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.
    [13] 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
    [14] 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:At-mospheres, 112(D8):D08214. doi: 10.1029/2006JD007794
    [15] 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
    [16] Rahmstorf S, Coumou D, 2011. Increase of extreme events in a warming world. Proceedings of the National Academy of Sci-ences of the United States of America, 108(44):17905-17909. doi: 10.1073/pnas.1101766108
    [17] Solomon S, Rosenlof K H, Portmann R W et al., 2010. Contribu-tions of stratospheric water vapor to decadal changes in the rate of global warming. Science, 327(5970):1219-1223. doi: 10.1126/science.1182488
    [18] 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):
    [19] 459-473. doi: 10.1109/TGRS.2002.808301
    [20] 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
    [21] 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 Tech-nology, 32 (11):2125-2129. doi: 10.1175/JTECH-D-15-0063.1
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Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012

doi: 10.1007/s11769-019-1030-6
Funds:  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)
    Corresponding author: MAO Kebiao

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.

MAO Kebiao, YUAN Zijin, ZUO Zhiyuan, XU Tongren, SHEN Xinyi, GAO Chunyu. Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012[J]. Chinese Geographical Science, 2019, 20(2): 306-315. doi: 10.1007/s11769-019-1030-6
Citation: MAO Kebiao, YUAN Zijin, ZUO Zhiyuan, XU Tongren, SHEN Xinyi, GAO Chunyu. Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012[J]. Chinese Geographical Science, 2019, 20(2): 306-315. doi: 10.1007/s11769-019-1030-6
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