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    A Theory for the Limitation of ENSO Predictability Due to Stochastic Atmospheric Transients

    Source: Journal of the Atmospheric Sciences:;1997:;Volume( 054 ):;issue: 006::page 753
    Author:
    Kleeman, Richard
    ,
    Moore, Andrew M.
    DOI: 10.1175/1520-0469(1997)054<0753:ATFTLO>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: It is argued that a major fundamental limitation on the predictability of the El Niño?Southern Oscillation phenomenon is provided by the stochastic forcing of the tropical coupled ocean?atmosphere system by atmospheric transients. A new theoretical framework is used to analyze in detail the sensitivity of a skillful coupled forecast model to this stochastic forcing. The central concept in this analysis is the so-called stochastic optimal, which represents the spatial pattern of noise most efficient at causing variance growth within a dynamical system. A number of interesting conclusions are reached. (a) Sensitivity to forcing is greatest during the northern spring season and prior to warm events. (b) There is little sensitivity to meridional windstress noise. (c) A western Pacific dipole pattern in heat flux noise is most efficient in forcing eastern Pacific SST variance. An estimate of the actual wind stress stochastic forcing is obtained from recent ECMWF analyses and it is found that ?unavoidable? error growth within the model due to this stochastic forcing saturates at approximately 0.5°C in the NINO3 region with very rapid error growth during the first 6 months. The noise projects predominantly onto the first stochastic optimal and, in addition, around 95% of the error growth can be attributed to stochastic forcing with a strong synoptic character.
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      A Theory for the Limitation of ENSO Predictability Due to Stochastic Atmospheric Transients

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4158344
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    contributor authorKleeman, Richard
    contributor authorMoore, Andrew M.
    date accessioned2017-06-09T14:34:23Z
    date available2017-06-09T14:34:23Z
    date copyright1997/03/01
    date issued1997
    identifier issn0022-4928
    identifier otherams-21949.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4158344
    description abstractIt is argued that a major fundamental limitation on the predictability of the El Niño?Southern Oscillation phenomenon is provided by the stochastic forcing of the tropical coupled ocean?atmosphere system by atmospheric transients. A new theoretical framework is used to analyze in detail the sensitivity of a skillful coupled forecast model to this stochastic forcing. The central concept in this analysis is the so-called stochastic optimal, which represents the spatial pattern of noise most efficient at causing variance growth within a dynamical system. A number of interesting conclusions are reached. (a) Sensitivity to forcing is greatest during the northern spring season and prior to warm events. (b) There is little sensitivity to meridional windstress noise. (c) A western Pacific dipole pattern in heat flux noise is most efficient in forcing eastern Pacific SST variance. An estimate of the actual wind stress stochastic forcing is obtained from recent ECMWF analyses and it is found that ?unavoidable? error growth within the model due to this stochastic forcing saturates at approximately 0.5°C in the NINO3 region with very rapid error growth during the first 6 months. The noise projects predominantly onto the first stochastic optimal and, in addition, around 95% of the error growth can be attributed to stochastic forcing with a strong synoptic character.
    publisherAmerican Meteorological Society
    titleA Theory for the Limitation of ENSO Predictability Due to Stochastic Atmospheric Transients
    typeJournal Paper
    journal volume54
    journal issue6
    journal titleJournal of the Atmospheric Sciences
    identifier doi10.1175/1520-0469(1997)054<0753:ATFTLO>2.0.CO;2
    journal fristpage753
    journal lastpage767
    treeJournal of the Atmospheric Sciences:;1997:;Volume( 054 ):;issue: 006
    contenttypeFulltext
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