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    Decadal Variation of Predictability of the Indian Ocean Dipole during 1880–2017 Using an Ensemble Prediction System

    Source: Journal of Climate:;2022:;volume( 035 ):;issue: 017::page 5759
    Author:
    Xunshu Song
    ,
    Youmin Tang
    ,
    Xiaojing Li
    ,
    Ting Liu
    DOI: 10.1175/JCLI-D-21-0848.1
    Publisher: American Meteorological Society
    Abstract: In this study, we investigate both the decadal variation of the Indian Ocean dipole (IOD) prediction skill and possible sources of this decadal variation. We use an ensemble long-term retrospective forecast experiment covering 1880–2017 that utilizes the Community Earth System Model (CESM). We find that the decadal variation of the IOD prediction skill is significant and that it varies with the lead time. We also find that the decadal variation of the IOD prediction skill for the target season of boreal autumn determines that for all initial conditions, regardless of the lead months. For short lead times, the decadal variations of the IOD strength and of the IOD precursor in the initial month of July are the major factors influencing the IOD prediction skill. This occurs because the IOD events are in the developmental phase, and the stronger IOD signal in the initial conditions leads to better predictions. For long lead times, the decadal variation of remote forcing by El Niño–Southern Oscillation (ENSO) and the ENSO precursor signal in the IOD influence the IOD prediction skill more significantly than do the strengths of the ENSO or the IOD. In addition, the analysis also indicated that the period with a low ENSO–IOD relationship has low predictability, not only because the ENSO little influence on IOD but also because the model biasedly overestimates the ENSO–IOD relationship.
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      Decadal Variation of Predictability of the Indian Ocean Dipole during 1880–2017 Using an Ensemble Prediction System

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    contributor authorXunshu Song
    contributor authorYoumin Tang
    contributor authorXiaojing Li
    contributor authorTing Liu
    date accessioned2023-04-12T18:45:31Z
    date available2023-04-12T18:45:31Z
    date copyright2022/09/01
    date issued2022
    identifier otherJCLI-D-21-0848.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4290197
    description abstractIn this study, we investigate both the decadal variation of the Indian Ocean dipole (IOD) prediction skill and possible sources of this decadal variation. We use an ensemble long-term retrospective forecast experiment covering 1880–2017 that utilizes the Community Earth System Model (CESM). We find that the decadal variation of the IOD prediction skill is significant and that it varies with the lead time. We also find that the decadal variation of the IOD prediction skill for the target season of boreal autumn determines that for all initial conditions, regardless of the lead months. For short lead times, the decadal variations of the IOD strength and of the IOD precursor in the initial month of July are the major factors influencing the IOD prediction skill. This occurs because the IOD events are in the developmental phase, and the stronger IOD signal in the initial conditions leads to better predictions. For long lead times, the decadal variation of remote forcing by El Niño–Southern Oscillation (ENSO) and the ENSO precursor signal in the IOD influence the IOD prediction skill more significantly than do the strengths of the ENSO or the IOD. In addition, the analysis also indicated that the period with a low ENSO–IOD relationship has low predictability, not only because the ENSO little influence on IOD but also because the model biasedly overestimates the ENSO–IOD relationship.
    publisherAmerican Meteorological Society
    titleDecadal Variation of Predictability of the Indian Ocean Dipole during 1880–2017 Using an Ensemble Prediction System
    typeJournal Paper
    journal volume35
    journal issue17
    journal titleJournal of Climate
    identifier doi10.1175/JCLI-D-21-0848.1
    journal fristpage5759
    journal lastpage5771
    page5759–5771
    treeJournal of Climate:;2022:;volume( 035 ):;issue: 017
    contenttypeFulltext
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