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Spatiotemporal analysis of information entropy of the global temperature

Feng Ai-Xia Wang Qi-Guang Huang Yan Gong Zhi-Qiang

Spatiotemporal analysis of information entropy of the global temperature

Feng Ai-Xia, Wang Qi-Guang, Huang Yan, Gong Zhi-Qiang
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  • Based on the concept of entropy in information theory, the entropy of meteorological elements is determined and used to analyze the uncertainty of the global temperature field anomaly from the climate state (1971—2000) on different time and spatial scales. It is found that the temperature climate entropy (CE) possesses a zonal distribution, increases from tropics to mid-high latitudes and has an obvious difference between the ocean region and the continent, thereby being able to distinguish the climatic zones properly. The temperature CE in low-mid troposphere increases with altitude increasing, while in extratropical the situation retains above 300 hPa but below 300 hPa the situation is reversed, and this feature is more obvious in high latituderegions. On the whole, the temperature monthly entropy (ME) is obviously dependent on season change: it is smallest in summer and largest in winter. Besides, there exists a distinguishable interdecadal period. Different monthly ME values from low atmosphere to high atmosphere each have an obvious five -to-ten year quasi-period oscillation. All the spatiotemporal characteristics and their relationships with annual temperature range verify the usefulness of the entropy in meteorology, and it is an effective method to measure the uncertainty of the meteorological elements.
    • Funds:
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    Chou J F, 1997 Bull. Chin. Acad. Sci. 5 325 (in Chinese)[丑纪范 1997 中国科学院院刊 5 325]

    [2]

    Feng G L, Dong W J 2004 Chin. Phys. 13 413

    [3]

    Feng G L, Gong Z Q, Dong W J, Li J P 2005 Acta Phys. Sin. 54 5494 (in Chinese)[封国林、龚志强、董文杰、李建平 2005 物理学报 54 5494]

    [4]

    Feng G L, Dong W J, Gong Z Q, Hou W, Wan S Q, Zhi R 2006 Nonlinear theories and methods on spatial-temporal distribution of the observational data (Beijing: Metrological Press) (in Chinese)[封国林、董文杰、龚志强、侯威、万仕全、支 蓉 2006 观测数据非线性时空分布理论和方法(北京:气象出版社)]

    [5]

    Feng G L, Gao X Q, Dong W J, Li J P 2008 Chaos Soli. Fract. 37 487

    [6]

    Feng G L, Gong Z Q, Zhi R, Zhang D Q 2008 Chin. Phys. B 17 2745

    [7]

    Li J P, Gao L 2006 Chin. J. Atmos. Sci. 30 834 (in Chinese) [李建平、高 丽 2006 大气科学 30 834]

    [8]

    Li J P, Wang X L 2003 Adv. Atmos. Sci. 20 661

    [9]

    Li J P, Chou J F 1997 Acta Meteor. Sin. 11 57

    [10]

    Li J P, Zeng Q C, Chou J F 2000 Sci. China. (E) 30 550 (in Chinese)[李建平、曾庆存、丑纪范 2000中国科学 (E辑) 30 550]

    [11]

    Dai X G, Wang P, Chou J F 2004 Pro. Nat. Sci. 14 73

    [12]

    Gong Z Q, Zhou L, Zhi R, Feng G L 2008 Acta Pyhs. Sin. 57 5351 (in Chinese)[龚志强、周 磊、支 蓉、封国林 2008 物理学报 57 5351]

    [13]

    Ding Y H, Li Q Q, Li W J, Luo Y, Zhang P Q, Zhang Z Q, Shi X L, Liu Y M, Wang L N 2004 Acta Meteor. Sin. 62 598 (in Chinese) [丁一汇、李清泉、李维京、罗 勇、张培群、张祖强、史学丽、刘一鸣、王兰宁 2004 气象学报 62 598 ]

    [14]

    Liu H B, Zhang D L, Wang B 2006 Climatic Environ. Res. 11 649 (in Chinese)[刘鸿波、张大林、王 斌 2006气候与环境研究 11 649 ]

    [15]

    Shannon C E 1948 Bell Sys. Tech. J. 27 379

    [16]

    Zhang X W 1981 Weather forecasting using information analysis (Beijing: Science Press) (in Chinese) (in Chinese) [张学文 1981 气象预告问题的信息分析 (北京:科学出版社)]

    [17]

    Zhang X W, Ma L 1992 Entropy Meteorology (Beijing: Meteorology Press)(in Chinese)[张学文、马 力 1992 熵气象学(北京:气象出版社)]

    [18]

    Leung L Y, North G R 1990 J. Climate 3 5

    [19]

    Zhang J G, Liu X R 2000 Advan. Water Sci. 11 133 (in Chinese)[张继国、刘新仁 2000 水科学进展 11 133]

    [20]

    Delsole T 2004 J. Atmos. Sci. 61 2425

    [21]

    Zhao W T, Chen X, Zhao P Z 1988 Entropy and cross Science: Information Entropy in Meteorology (Beijing: Meteorology Press) p127—130 (in Chinese)[赵文桐、陈 霞、赵佩章 1988 信息熵于气象熵,熵与交叉科学(北京:气象出版社)第127—130页]

    [22]

    Wang J Y, Fu Z T, Zhang L, Liu S D 2005 Plate. Meteor. 24 38 (in Chinese)[汪景烨、付遵涛、张 霖、刘式达 2005 高原气象 24 38]

    [23]

    Wang Q G, Zhang Z P 2008 Acta Phys. Sin. 57 1976 (in Chinese)[王启光、张增平 2008 物理学报57 1976]

    [24]

    Feng G L,Wang Q G, Hou W, Gong Z Q, Zhi R 2009 Acta. Phys. Sin. 58 4 (in Chinese)[封国林、王启光、侯 威、龚志强、支 蓉 2009 物理学报 58 4]

    [25]

    Hao C Y, WU S H, Li S C 2007 Geo. Graphi. Res. 26 46 (in Chinese)[郝成元、吴绍洪、李双成 2007 地理研究 26 46]

    [26]

    Zhang Z S, Gong Z Q, Zhi R, Feng G L, Hu J G 2011 Chin. Phys. B 20

    [27]

    Li J P, Ding R Q 2008 Chin. J. Atmos Sci. 32 975 (in Chinese)[李建平、丁瑞强 2008 大气科学 32 975]

    [28]

    Ding R Q, Li J P 2009 Acta Meteor. Sin. 67 343 (in Chinese)[丁瑞强、李建平 2009 气象学报 67 343]

    [29]

    Zhi R, Gong Z Q, Feng G L, Zhou L 2010 Acta Meteor. Sin. 24 150

    [30]

    Feng G L, Gong Z Q, Zhi R, Zhang D Q 2008 Chin. Phys. B 17 2745

    [31]

    Gong Z Q, Wang X J, Zhi R, Feng G L 2009 Acta. Phys. Sin. 58 4342(in Chinese)[龚志强、王晓娟、支 蓉、封国林2009 物理学报 58 4342]

    [32]

    Wang Y, Shi N, Gu J Q, Feng G L, Zhang L B 2006 Chin. J. Atmos Sci. 30 162(in Chinese)[王 颖、施 能、顾俊强、封国林、张立波 2006 大气科学 30 162]

    [33]

    Shi N 2005 Chin. Phys. 14 844

  • [1]

    Chou J F, 1997 Bull. Chin. Acad. Sci. 5 325 (in Chinese)[丑纪范 1997 中国科学院院刊 5 325]

    [2]

    Feng G L, Dong W J 2004 Chin. Phys. 13 413

    [3]

    Feng G L, Gong Z Q, Dong W J, Li J P 2005 Acta Phys. Sin. 54 5494 (in Chinese)[封国林、龚志强、董文杰、李建平 2005 物理学报 54 5494]

    [4]

    Feng G L, Dong W J, Gong Z Q, Hou W, Wan S Q, Zhi R 2006 Nonlinear theories and methods on spatial-temporal distribution of the observational data (Beijing: Metrological Press) (in Chinese)[封国林、董文杰、龚志强、侯威、万仕全、支 蓉 2006 观测数据非线性时空分布理论和方法(北京:气象出版社)]

    [5]

    Feng G L, Gao X Q, Dong W J, Li J P 2008 Chaos Soli. Fract. 37 487

    [6]

    Feng G L, Gong Z Q, Zhi R, Zhang D Q 2008 Chin. Phys. B 17 2745

    [7]

    Li J P, Gao L 2006 Chin. J. Atmos. Sci. 30 834 (in Chinese) [李建平、高 丽 2006 大气科学 30 834]

    [8]

    Li J P, Wang X L 2003 Adv. Atmos. Sci. 20 661

    [9]

    Li J P, Chou J F 1997 Acta Meteor. Sin. 11 57

    [10]

    Li J P, Zeng Q C, Chou J F 2000 Sci. China. (E) 30 550 (in Chinese)[李建平、曾庆存、丑纪范 2000中国科学 (E辑) 30 550]

    [11]

    Dai X G, Wang P, Chou J F 2004 Pro. Nat. Sci. 14 73

    [12]

    Gong Z Q, Zhou L, Zhi R, Feng G L 2008 Acta Pyhs. Sin. 57 5351 (in Chinese)[龚志强、周 磊、支 蓉、封国林 2008 物理学报 57 5351]

    [13]

    Ding Y H, Li Q Q, Li W J, Luo Y, Zhang P Q, Zhang Z Q, Shi X L, Liu Y M, Wang L N 2004 Acta Meteor. Sin. 62 598 (in Chinese) [丁一汇、李清泉、李维京、罗 勇、张培群、张祖强、史学丽、刘一鸣、王兰宁 2004 气象学报 62 598 ]

    [14]

    Liu H B, Zhang D L, Wang B 2006 Climatic Environ. Res. 11 649 (in Chinese)[刘鸿波、张大林、王 斌 2006气候与环境研究 11 649 ]

    [15]

    Shannon C E 1948 Bell Sys. Tech. J. 27 379

    [16]

    Zhang X W 1981 Weather forecasting using information analysis (Beijing: Science Press) (in Chinese) (in Chinese) [张学文 1981 气象预告问题的信息分析 (北京:科学出版社)]

    [17]

    Zhang X W, Ma L 1992 Entropy Meteorology (Beijing: Meteorology Press)(in Chinese)[张学文、马 力 1992 熵气象学(北京:气象出版社)]

    [18]

    Leung L Y, North G R 1990 J. Climate 3 5

    [19]

    Zhang J G, Liu X R 2000 Advan. Water Sci. 11 133 (in Chinese)[张继国、刘新仁 2000 水科学进展 11 133]

    [20]

    Delsole T 2004 J. Atmos. Sci. 61 2425

    [21]

    Zhao W T, Chen X, Zhao P Z 1988 Entropy and cross Science: Information Entropy in Meteorology (Beijing: Meteorology Press) p127—130 (in Chinese)[赵文桐、陈 霞、赵佩章 1988 信息熵于气象熵,熵与交叉科学(北京:气象出版社)第127—130页]

    [22]

    Wang J Y, Fu Z T, Zhang L, Liu S D 2005 Plate. Meteor. 24 38 (in Chinese)[汪景烨、付遵涛、张 霖、刘式达 2005 高原气象 24 38]

    [23]

    Wang Q G, Zhang Z P 2008 Acta Phys. Sin. 57 1976 (in Chinese)[王启光、张增平 2008 物理学报57 1976]

    [24]

    Feng G L,Wang Q G, Hou W, Gong Z Q, Zhi R 2009 Acta. Phys. Sin. 58 4 (in Chinese)[封国林、王启光、侯 威、龚志强、支 蓉 2009 物理学报 58 4]

    [25]

    Hao C Y, WU S H, Li S C 2007 Geo. Graphi. Res. 26 46 (in Chinese)[郝成元、吴绍洪、李双成 2007 地理研究 26 46]

    [26]

    Zhang Z S, Gong Z Q, Zhi R, Feng G L, Hu J G 2011 Chin. Phys. B 20

    [27]

    Li J P, Ding R Q 2008 Chin. J. Atmos Sci. 32 975 (in Chinese)[李建平、丁瑞强 2008 大气科学 32 975]

    [28]

    Ding R Q, Li J P 2009 Acta Meteor. Sin. 67 343 (in Chinese)[丁瑞强、李建平 2009 气象学报 67 343]

    [29]

    Zhi R, Gong Z Q, Feng G L, Zhou L 2010 Acta Meteor. Sin. 24 150

    [30]

    Feng G L, Gong Z Q, Zhi R, Zhang D Q 2008 Chin. Phys. B 17 2745

    [31]

    Gong Z Q, Wang X J, Zhi R, Feng G L 2009 Acta. Phys. Sin. 58 4342(in Chinese)[龚志强、王晓娟、支 蓉、封国林2009 物理学报 58 4342]

    [32]

    Wang Y, Shi N, Gu J Q, Feng G L, Zhang L B 2006 Chin. J. Atmos Sci. 30 162(in Chinese)[王 颖、施 能、顾俊强、封国林、张立波 2006 大气科学 30 162]

    [33]

    Shi N 2005 Chin. Phys. 14 844

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  • Received Date:  24 November 2010
  • Accepted Date:  19 December 2010
  • Published Online:  15 September 2011

Spatiotemporal analysis of information entropy of the global temperature

  • 1. (1)Department of Atmospheric and Sciences Lanzhou University, Lanzhou 730000, China; (2)Department of Atmospheric and Sciences Lanzhou University, Lanzhou 730000, China;Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China; (3)Institute of Climate System, Chinese Academy of Meteorological Science, Beijing 100081, China; (4)Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081, Chi

Abstract: Based on the concept of entropy in information theory, the entropy of meteorological elements is determined and used to analyze the uncertainty of the global temperature field anomaly from the climate state (1971—2000) on different time and spatial scales. It is found that the temperature climate entropy (CE) possesses a zonal distribution, increases from tropics to mid-high latitudes and has an obvious difference between the ocean region and the continent, thereby being able to distinguish the climatic zones properly. The temperature CE in low-mid troposphere increases with altitude increasing, while in extratropical the situation retains above 300 hPa but below 300 hPa the situation is reversed, and this feature is more obvious in high latituderegions. On the whole, the temperature monthly entropy (ME) is obviously dependent on season change: it is smallest in summer and largest in winter. Besides, there exists a distinguishable interdecadal period. Different monthly ME values from low atmosphere to high atmosphere each have an obvious five -to-ten year quasi-period oscillation. All the spatiotemporal characteristics and their relationships with annual temperature range verify the usefulness of the entropy in meteorology, and it is an effective method to measure the uncertainty of the meteorological elements.

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