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Recent Advances in Dynamical Extra-Seasonal to Annual Climate Prediction at IAP/CAS


doi: 10.1007/BF02915572

  • Recent advances in dynamical climate prediction at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly,the second generation of the IAP dynamical climate prediction system (IAP DCP-Ⅱ) has been described,and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980-1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore, IAP DCP-Ⅱ has been applied to the real-time prediction of summer rainfall anomalies over China since 1998, and the verification results show that IAP DCP-Ⅱ can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998-2002). Meanwhile, an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction, along with studies on the influences from surface boundary conditions (e.g., land surface characteristics, sea surface temperature).Certain conclusions have been reached, such as, the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies, and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally, several practical techniques (e.g., ensemble technique, correction method, etc.), which lead to the increase of the prediction skill for summer rainfall anomalies over China, have also been illustrated. The paper concludes with a list of critical requirements needed for the further improvement of dynamical seasonal climate prediction.
  • [1] CHEN Hong, LIN Zhaohui, 2006: A Correction Method Suitable for Dynamical Seasonal Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 425-430.  doi: 10.1007/s00376-006-0425-3
    [2] 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
    [3] Chuan GAO, Xinrong WU, Rong-Hua ZHANG, 2016: Testing a Four-Dimensional Variational Data Assimilation Method Using an Improved Intermediate Coupled Model for ENSO Analysis and Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 875-888.  doi: 10.1007/s00376-016-5249-1
    [4] LI Fang, LIN Zhongda, 2015: Improving Multi-model Ensemble Probabilistic Prediction of Yangtze River Valley Summer Rainfall, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 497-504.  doi: 10.1007/s00376-014-4073-8
    [5] Chujie GAO, Gen LI, 2023: Enhanced Seasonal Predictability of Spring Soil Moisture over the Indo-China Peninsula for Eastern China Summer Precipitation under Non-ENSO Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1632-1648.  doi: 10.1007/s00376-023-2361-x
    [6] Wu Aiming, Ni Yunqi, 1999: A Hybrid Coupled Ocean-Atmosphere Model and ENSO Prediction Study, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 405-418.  doi: 10.1007/s00376-999-0019-y
    [7] Ben TIAN, Hong-Li REN, 2022: Diagnosing SST Error Growth during ENSO Developing Phase in the BCC_CSM1.1(m) Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 427-442.  doi: 10.1007/s00376-021-1189-5
    [8] Lu ZHOU, Rong-Hua ZHANG, 2022: A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 889-902.  doi: 10.1007/s00376-021-1368-4
    [9] Maeng-Ki KIM, Yeon-Hee KIM, 2010: Seasonal Prediction of Monthly Precipitation in China Using Large-Scale Climate Indices, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 47-59.  doi: 10.1007/s00376-009-8014-x
    [10] Xiaoran DONG, Yafei NIE, Jinfei WANG, Hao LUO, Yuchun GAO, Yun WANG, Jiping LIU, Dake CHEN, Qinghua YANG, 2024: Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3380-y
    [11] Shuai HU, Tianjun ZHOU, Bo WU, Xiaolong CHEN, 2023: Seasonal Prediction of the Record-Breaking Northward Shift of the Western Pacific Subtropical High in July 2021, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 410-427.  doi: 10.1007/s00376-022-2151-x
    [12] Zhiyi ZHOU, Juan LI, Haishan CHEN, Zhiwei ZHU, 2023: Seasonal Prediction of Extreme High-Temperature Days in Southwestern China Based on the Physical Precursors, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1212-1224.  doi: 10.1007/s00376-022-2075-5
    [13] Changyu ZHAO, Haishan CHEN, Shanlei SUN, 2018: Evaluating the Capabilities of Soil Enthalpy, Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 445-456.  doi: 10.1007/s00376-017-7006-5
    [14] ZHANG Shuwen, LI Deqin, QIU Chongjian, 2011: A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 195-206.  doi: 10.1007/s00376-010-9200-6
    [15] Xu Qun, 1995: Analysis of Causes and Seasonal Prediction of the Severe Floods in Yangtze / Huaihe Basins during Summer 1991, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 215-224.  doi: 10.1007/BF02656834
    [16] Huang Ronghui, Li Xu, Yuan Chongguang, Lu Riyu, Moon Sung-Euii, Kim Ung-Jun, 1998: Seasonal Prediction Experiments of the Summer Droughts and Floods during the Early 1990’s in East Asia with Numerical Models, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 433-446.  doi: 10.1007/s00376-998-0025-5
    [17] ZHANG Shuwen, LI Haorui, ZHANG Weidong, QIU Chongjian, LI Xin, 2005: Estimating the Soil Moisture Profile by Assimilating Near-Surface Observations with the Ensemble Kalman Filter (EnKF), ADVANCES IN ATMOSPHERIC SCIENCES, 22, 936-945.  doi: 10.1007/BF02918692
    [18] SONG Yaoming, GUO Weidong, ZHANG Yaocun, 2009: Numerical Study of Impacts of Soil Moisture on the Diurnal and Seasonal Cycles of Sensible/Latent Heat Fluxes over Semi-arid Region, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 319-326.  doi: 10.1007/s00376-009-0319-2
    [19] REN Zhihua, LI Mingqin, 2007: Errors and Correction of Precipitation Measurements in China, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 449-458.  doi: 10.1007/s00376-007-0449-3
    [20] KE Zongjian, DONG Wenjie, ZHANG Peiqun, WANG Jin, ZHAO Tianbao, 2009: An Analysis of the Difference between the Multiple Linear Regression Approach and the Multimodel Ensemble Mean, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1157-1168.  doi: 10.1007/s00376-009-8024-8

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Manuscript History

Manuscript received: 10 May 2004
Manuscript revised: 10 May 2004
通讯作者: 陈斌, bchen63@163.com
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Recent Advances in Dynamical Extra-Seasonal to Annual Climate Prediction at IAP/CAS

  • 1. International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100080,International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100080,International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100080,International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100080,International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100080,International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100080,International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100080

Abstract: Recent advances in dynamical climate prediction at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly,the second generation of the IAP dynamical climate prediction system (IAP DCP-Ⅱ) has been described,and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980-1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore, IAP DCP-Ⅱ has been applied to the real-time prediction of summer rainfall anomalies over China since 1998, and the verification results show that IAP DCP-Ⅱ can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998-2002). Meanwhile, an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction, along with studies on the influences from surface boundary conditions (e.g., land surface characteristics, sea surface temperature).Certain conclusions have been reached, such as, the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies, and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally, several practical techniques (e.g., ensemble technique, correction method, etc.), which lead to the increase of the prediction skill for summer rainfall anomalies over China, have also been illustrated. The paper concludes with a list of critical requirements needed for the further improvement of dynamical seasonal climate prediction.

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