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Volume 26 Issue 2

Mar.  2009

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2009 >  26(2): 359-372

Ensemble Hindcasts of ENSO Events over the Past 120 Years Using a Large Number of Ensembles

  • 1.

    International Center for Climate and Environment Science (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry ( LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;National Meteorological Center, Beijing 100081;Earth System Science Interdisciplinary Center ( ESSIC), University of Maryland, College Park, Maryland, USA


doi: 10.1007/s00376-009-0359-7

  • Based on an intermediate coupled model (ICM), a probabilistic ensemble prediction system (EPS) has been developed. The ensemble Kalman filter (EnKF) data assimilation approach is used for generating the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts over the 120 year period 1886--2005 using the EPS with 100 ensemble members and with initial conditions obtained by only assimilating historic SST anomaly observations. By examining the retrospective ensemble forecasts and available observations, the verification results show that the skill of the ensemble mean of the EPS is greater than that of a single deterministic forecast using the same ICM, with a distinct improvement of both the correlation and root mean square (RMS) error between the ensemble-mean hindcast and the deterministic scheme over the 12-month prediction period. The RMS error of the ensemble mean is almost 0.2oC smaller than that of the deterministic forecast at a lead time of 12 months. The probabilistic skill of the EPS is also high with the predicted ensemble following the SST observations well, and the areas under the relative operating characteristic (ROC) curves for three different ENSO states (warm events, cold events, and neutral events) are all above 0.55 out to 12 months lead time. However, both deterministic and probabilistic prediction skills of the EPS show an interdecadal variation. For the deterministic skill, there is high skill in the late 19th century and in the middle-late 20th century (which includes some artificial skill due to the model training period), and low skill during the period from 1906 to 1961. For probabilistic skill, for the three different ENSO states, there is still a similar interdecadal variation of ENSO probabilistic predictability during the period 1886--2005. There is high skill in the late 19th century from 1886 to 1905, and a decline to a minimum of skill around 1910--50s, beyond which skill rebounds and increases with time until the 2000s.
  • [1] ZHENG Fei, ZHU Jiang, Rong-Hua ZHANG, ZHOU Guangqing, 2006: Improved ENSO Forecasts by Assimilating Sea Surface Temperature Observations into an Intermediate Coupled Model, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 615-624.  doi: 10.1007/s00376-006-0615-z
    [2] LI Gang*, LI Chongyin, TAN Yanke, and BAI Tao, 2014: The Interdecadal Changes of South Pacific Sea Surface Temperature in the Mid-1990s and Their Connections with ENSO, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 66-84.  doi: 10.1007/s00376-013-2280-3
    [3] KANG Xianbiao, HUANG Ronghui, WANG Zhanggui, ZHANG Rong-Hua, 2014: Sensitivity of ENSO Variability to Pacific Freshwater Flux Adjustment in the Community Earth System Model, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1009-1021.  doi: 10.1007/s00376-014-3232-2
    [4] Sijia LI, Yuan WANG, Huiling YUAN, Jinjie SONG, Xin XU, 2016: Ensemble Mean Forecast Skill and Applications with the T213 Ensemble Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1297-1305.  doi: 10.1007/s00376-016-6155-2
    [5] 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
    [6] 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
    [7] DING Yihui, LIU Yiming, SHI Xueli, LI Qingquan, LI Qiaoping, LIU Yan, 2006: Multi-Year Simulations and Experimental Seasonal Predictions for Rainy Seasons inChina byUsing a Nested Regional ClimateModel (RegCM NCC) Part II: The Experimental Seasonal Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 487-503.  doi: 10.1007/s00376-006-0323-8
    [8] WANG Zhiren, WU Dexing, CHEN Xue'en, QIAO Ran, 2013: ENSO Indices and Analyses, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1491-1506.  doi: 10.1007/s00376-012-2238-x
    [9] Xinyi XING, Xianghui FANG, Da PANG, Chaopeng JI, 2024: Seasonal Variation of the Sea Surface Temperature Growth Rate of ENSO, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 465-477.  doi: 10.1007/s00376-023-3005-x
    [10] Xiaofei WU, Jiangyu MAO, 2019: Decadal Changes in Interannual Dependence of the Bay of Bengal Summer Monsoon Onset on ENSO Modulated by the Pacific Decadal Oscillation, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1404-1416.  doi: 10.1007/s00376-019-9043-8
    [11] Yuanhai FU, Zhongda LIN, Tao WANG, 2021: Simulated Relationship between Wintertime ENSO and East Asian Summer Rainfall: From CMIP3 to CMIP6, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 221-236.  doi: 10.1007/s00376-020-0147-y
    [12] Yadi LI, Xichen LI, Juan FENG, Yi ZHOU, Wenzhu WANG, Yurong HOU, 2024: Uncertainties of ENSO-related Regional Hadley Circulation Anomalies within Eight Reanalysis Datasets, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 115-140.  doi: 10.1007/s00376-023-3047-0
    [13] Jingrui YAN, Wenjun ZHANG, Suqiong HU, Feng JIANG, 2024: Different ENSO Impacts on Eastern China Precipitation Patterns in Early and Late Winter Associated with Seasonally-Varying Kuroshio Anticyclonic Anomalies, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-023-3196-1
    [14] FENG Juan*, CHEN Wen, 2014: Interference of the East Asian Winter Monsoon in the Impact of ENSO on the East Asian Summer Monsoon in Decaying Phases, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 344-354.  doi: 10.1007/s00376-013-3118-8
    [15] Fei ZHENG, Jianping LI, Ruiqiang DING, 2017: Influence of the Preceding Austral Summer Southern Hemisphere Annular Mode on the Amplitude of ENSO Decay, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 1358-1379.  doi: 10.1007/s00376-017-6339-4
    [16] Shangfeng CHEN, Linye SONG, Wen CHEN, 2019: Interdecadal Modulation of AMO on the Winter North Pacific Oscillation−Following Winter ENSO Relationship, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1393-1403.  doi: 10.1007/s00376-019-9090-1
    [17] Fei ZHENG, Jin-Yi YU, 2017: Contrasting the Skills and Biases of Deterministic Predictions for the Two Types of El Niño, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 1395-1403.  doi: 10.1007/s00376-017-6324-y
    [18] Xiaoxuan ZHAO, Riyu LU, 2020: Vertical Structure of Interannual Variability in Cross-Equatorial Flows over the Maritime Continent and Indian Ocean in Boreal Summer, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 173-186.  doi: 10.1007/s00376-019-9103-0
    [19] Ning JIANG, Congwen ZHU, 2021: Seasonal Forecast of South China Sea Summer Monsoon Onset Disturbed by Cold Tongue La Niña in the Past Decade, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 147-155.  doi: 10.1007/s00376-020-0090-y
    [20] Xiaomeng SONG, Renhe ZHANG, Xinyao RONG, 2019: Influence of Intraseasonal Oscillation on the Asymmetric Decays of El Niño and La Niña, ADVANCES IN ATMOSPHERIC SCIENCES, , 779-792.  doi: 10.1007/s00376-019-9029-6
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    • Manuscript History

    Manuscript received: 10 March 2009
    Manuscript revised: 10 March 2009
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    Ensemble Hindcasts of ENSO Events over the Past 120 Years Using a Large Number of Ensembles

    • 1. International Center for Climate and Environment Science (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry ( LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;National Meteorological Center, Beijing 100081;Earth System Science Interdisciplinary Center ( ESSIC), University of Maryland, College Park, Maryland, USA
    • Manuscript received: 2009-03-10
    • Manuscript revised: 2009-03-10

    Abstract: Based on an intermediate coupled model (ICM), a probabilistic ensemble prediction system (EPS) has been developed. The ensemble Kalman filter (EnKF) data assimilation approach is used for generating the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts over the 120 year period 1886--2005 using the EPS with 100 ensemble members and with initial conditions obtained by only assimilating historic SST anomaly observations. By examining the retrospective ensemble forecasts and available observations, the verification results show that the skill of the ensemble mean of the EPS is greater than that of a single deterministic forecast using the same ICM, with a distinct improvement of both the correlation and root mean square (RMS) error between the ensemble-mean hindcast and the deterministic scheme over the 12-month prediction period. The RMS error of the ensemble mean is almost 0.2oC smaller than that of the deterministic forecast at a lead time of 12 months. The probabilistic skill of the EPS is also high with the predicted ensemble following the SST observations well, and the areas under the relative operating characteristic (ROC) curves for three different ENSO states (warm events, cold events, and neutral events) are all above 0.55 out to 12 months lead time. However, both deterministic and probabilistic prediction skills of the EPS show an interdecadal variation. For the deterministic skill, there is high skill in the late 19th century and in the middle-late 20th century (which includes some artificial skill due to the model training period), and low skill during the period from 1906 to 1961. For probabilistic skill, for the three different ENSO states, there is still a similar interdecadal variation of ENSO probabilistic predictability during the period 1886--2005. There is high skill in the late 19th century from 1886 to 1905, and a decline to a minimum of skill around 1910--50s, beyond which skill rebounds and increases with time until the 2000s.

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