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    The 3–4-Week MJO Prediction Skill in a GFDL Coupled Model

    Source: Journal of Climate:;2015:;volume( 028 ):;issue: 013::page 5351
    Author:
    Xiang, Baoqiang
    ,
    Zhao, Ming
    ,
    Jiang, Xianan
    ,
    Lin, Shian-Jiann
    ,
    Li, Tim
    ,
    Fu, Xiouhua
    ,
    Vecchi, Gabriel
    DOI: 10.1175/JCLI-D-15-0102.1
    Publisher: American Meteorological Society
    Abstract: ased on a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the Madden?Julian oscillation (MJO) prediction skill in boreal wintertime (November?April) is evaluated by analyzing 11 years (2003?13) of hindcast experiments. The initial conditions are obtained by applying a simple nudging technique toward observations. Using the real-time multivariate MJO (RMM) index as a predictand, it is demonstrated that the MJO prediction skill can reach out to 27 days before the anomaly correlation coefficient (ACC) decreases to 0.5. The MJO forecast skill also shows relatively larger contrasts between target strong and weak cases (32 versus 7 days) than between initially strong and weak cases (29 versus 24 days). Meanwhile, a strong dependence on target phases is found, as opposed to relative skill independence from different initial phases. The MJO prediction skill is also shown to be about 29 days during the Dynamics of the MJO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (DYNAMO/CINDY) field campaign period. This model?s potential predictability, the upper bound of prediction skill, extends out to 42 days, revealing a considerable unutilized predictability and a great potential for improving current MJO prediction.
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      The 3–4-Week MJO Prediction Skill in a GFDL Coupled Model

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4223962
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    contributor authorXiang, Baoqiang
    contributor authorZhao, Ming
    contributor authorJiang, Xianan
    contributor authorLin, Shian-Jiann
    contributor authorLi, Tim
    contributor authorFu, Xiouhua
    contributor authorVecchi, Gabriel
    date accessioned2017-06-09T17:12:05Z
    date available2017-06-09T17:12:05Z
    date copyright2015/07/01
    date issued2015
    identifier issn0894-8755
    identifier otherams-81006.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4223962
    description abstractased on a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the Madden?Julian oscillation (MJO) prediction skill in boreal wintertime (November?April) is evaluated by analyzing 11 years (2003?13) of hindcast experiments. The initial conditions are obtained by applying a simple nudging technique toward observations. Using the real-time multivariate MJO (RMM) index as a predictand, it is demonstrated that the MJO prediction skill can reach out to 27 days before the anomaly correlation coefficient (ACC) decreases to 0.5. The MJO forecast skill also shows relatively larger contrasts between target strong and weak cases (32 versus 7 days) than between initially strong and weak cases (29 versus 24 days). Meanwhile, a strong dependence on target phases is found, as opposed to relative skill independence from different initial phases. The MJO prediction skill is also shown to be about 29 days during the Dynamics of the MJO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (DYNAMO/CINDY) field campaign period. This model?s potential predictability, the upper bound of prediction skill, extends out to 42 days, revealing a considerable unutilized predictability and a great potential for improving current MJO prediction.
    publisherAmerican Meteorological Society
    titleThe 3–4-Week MJO Prediction Skill in a GFDL Coupled Model
    typeJournal Paper
    journal volume28
    journal issue13
    journal titleJournal of Climate
    identifier doi10.1175/JCLI-D-15-0102.1
    journal fristpage5351
    journal lastpage5364
    treeJournal of Climate:;2015:;volume( 028 ):;issue: 013
    contenttypeFulltext
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    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian