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The Interannual Variability and Predictability in a Global Climate Model


doi: 10.1007/s00376-997-0073-2

  • The interannual variabilities of the climatological simulation (V1) and the AMIP (Atmospheric Model intercomparison Project) simulation (V2) by the IAP 9-Level Atmospheric General Circulation Model are studied and discussed in this paper. Based on the analysis of ratio of variability (R) of above two simulations the predictability of the model on the interannual climate variation are studied as well. Results show that V2 is bigger than V1 generally and V2 is more comparable to the real variability of the atmosphere, the major difference of VI and V2 is in the tropics, for temperature and geopotential height the predictability is higher in the tropics while in the extra-tropics there is almost no predictability and the predictability is bigger in higher level thin in lower level. The predictability for precipitation is generally low in the globe, and generally the predictability is high in the tropical eastern Pacific for the lower level. This study suggests that the possible way of increasing the model predictability is the improvement of land surface process modelling and the inclusion of the interannual variations of the land surface conditions (snow cover, albedo, soil moisture, etc.) as the forcing factor for climate modelling and prediction.
  • [1] Se-Hwan YANG, LI Chaofan, and LU Riyu, 2014: Predictability of Winter Rainfall in South China as Demonstrated by the Coupled Models of ENSEMBLES, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 779-786.  doi: 10.1007/s00376-013-3172-2
    [2] BEI Naifang, Fuqing ZHANG, 2014: Mesoscale Predictability of Moist Baroclinic Waves: Variable and Scale-dependent Error Growth, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 995-1008.  doi: 10.1007/s00376-014-3191-7
    [3] LI Fei, WANG Huijun, 2012: Predictability of the East Asian Winter Monsoon Interannual Variability as Indicated by the DEMETER CGCMS, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 441-454.  doi: 10.1007/s00376-011-1115-3
    [4] LIU Xiangwen, WU Tongwen, YANG Song, LI Qiaoping, CHENG Yanjie, LIANG Xiaoyun, FANG Yongjie, JIE Weihua, NIE Suping, 2014: Relationships between Interannual and Intraseasonal Variations of the Asian-Western Pacific Summer Monsoon Hindcasted by BCC_CSM1.1(m), ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1051-1064.  doi: 10.1007/s00376-014-3192-6
    [5] ZHI Hai, WANG Panxing, DAN Li, YU Yongqiang, XU Yongfu, ZHENG Weipeng, 2009: Climate-Vegetation Interannual Variability in a Coupled Atmosphere-Ocean-Land Model, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 599-612.  doi: 10.1007/s00376-009-0599-6
    [6] Ya GAO, Huijun WANG, Dong CHEN, 2017: Interdecadal Variations of the South Asian Summer Monsoon Circulation Variability and the Associated Sea Surface Temperatures on Interannual Scales, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 816-832.  doi: 10.1007/ s00376-017-6246-8
    [7] Li Wei, Yu Rucong, Zhang Xuehong, 2001: Impacts of Sea Surface Temperature in the Tropical Pacific on Interannual Variability of Madden-Julian Oscillation in Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 429-444.  doi: 10.1007/BF02919322
    [8] Hai ZHI, Rong-Hua ZHANG, Fei ZHENG, Pengfei LIN, Lanning WANG, Peng YU, 2016: Assessment of Interannual Sea Surface Salinity Variability and Its Effects on the Barrier Layer in the Equatorial Pacific Using BNU-ESM, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 339-351.  doi: 10.1007/s00376-015-5163-y
    [9] SHENG Li, LIU Shuhua, Heping LIU, 2010: Influences of Climate Change and Its Interannual Variability on Surface Energy Fluxes from 1948 to 2000, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1438-1452.  doi: 10.1007/s00376-010-9215-z
    [10] WEI Na, LI Ying, 2013: A Modeling Study of Land Surface Process Impacts on Inland Behavior of Typhoon Rananim (2004), ADVANCES IN ATMOSPHERIC SCIENCES, 30, 367-381.  doi: 10.1007/s00376-012-1242-5
    [11] Wang Huijun, 2000: The Interannual Variability of East Asian Monsoon and Its Relationship with SST in a Coupled Atmosphere-Ocean-Land Climate Model, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 31-47.  doi: 10.1007/s00376-000-0041-6
    [12] Liu Jingmiao, Ding Yuguo, Zhou Xiuji, Wang Jijun, 2002: Land Surface Hydrology Parameterization over Heterogeneous Surface for the Study of Regional Mean Runoff Ratio with Its Simulations, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 89-102.  doi: 10.1007/s00376-002-0036-6
    [13] XUE Feng, ZENG Qingcun, HUANG Ronghui, LI Chongyin, LU Riyu, ZHOU Tianjun, 2015: Recent Advances in Monsoon Studies in China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 206-229.  doi: 10.1007/s00376-014-0015-8
    [14] CHEN Xiao, YAN Youfang, CHENG Xuhua, QI Yiquan, 2013: Performances of Seven Datasets in Presenting the Upper Ocean Heat Content in the South China Sea, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1331-1342.  doi: 10.1007/s00376-013-2132-1
    [15] WANG Huijun, FAN Ke, SUN Jianqi, LI Shuanglin, LIN Zhaohui, ZHOU Guangqing, CHEN Lijuan, LANG Xianmei, LI Fang, ZHU Yali, CHEN Hong, ZHENG Fei, 2015: A Review of Seasonal Climate Prediction Research in China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 149-168.  doi: 10.1007/s00376-014-0016-7
    [16] Yang Xiaosong, Lin Zhaohui, Dai Yongjiu, Guo Yufu, 2001: Validation of IAP94 Land Surface Model over the Huaihe River Basin with HUBEX Field Experiment Data, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 139-154.  doi: 10.1007/s00376-001-0009-1
    [17] Yunyun LIU, Zeng-Zhen HU, Renguang WU, Xing YUAN, 2022: Causes and Predictability of the 2021 Spring Southwestern China Severe Drought, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1766-1776.  doi: 10.1007/s00376-022-1428-4
    [18] Zhiyong MENG, Eugene E. CLOTHIAUX, 2022: Contributions of Fuqing ZHANG to Predictability, Data Assimilation, and Dynamics of High Impact Weather: A Tribute, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 676-683.  doi: 10.1007/s00376-021-1362-x
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    [20] YUAN Yuan, C. L. Johnny CHAN, ZHOU Wen, LI Chongyin, 2008: Decadal and Interannual Variability of the Indian Ocean Dipole, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 856-866.  doi: 10.1007/s00376-008-0856-0

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Manuscript History

Manuscript received: 10 October 1997
Manuscript revised: 10 October 1997
通讯作者: 陈斌, bchen63@163.com
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The Interannual Variability and Predictability in a Global Climate Model

  • 1. LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100080,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100080,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100080

Abstract: The interannual variabilities of the climatological simulation (V1) and the AMIP (Atmospheric Model intercomparison Project) simulation (V2) by the IAP 9-Level Atmospheric General Circulation Model are studied and discussed in this paper. Based on the analysis of ratio of variability (R) of above two simulations the predictability of the model on the interannual climate variation are studied as well. Results show that V2 is bigger than V1 generally and V2 is more comparable to the real variability of the atmosphere, the major difference of VI and V2 is in the tropics, for temperature and geopotential height the predictability is higher in the tropics while in the extra-tropics there is almost no predictability and the predictability is bigger in higher level thin in lower level. The predictability for precipitation is generally low in the globe, and generally the predictability is high in the tropical eastern Pacific for the lower level. This study suggests that the possible way of increasing the model predictability is the improvement of land surface process modelling and the inclusion of the interannual variations of the land surface conditions (snow cover, albedo, soil moisture, etc.) as the forcing factor for climate modelling and prediction.

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