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    Diagnosing Sources of U.S. Seasonal Forecast Skill

    Source: Journal of Climate:;2006:;volume( 019 ):;issue: 013::page 3279
    Author:
    Quan, X.
    ,
    Hoerling, M.
    ,
    Whitaker, J.
    ,
    Bates, G.
    ,
    Xu, T.
    DOI: 10.1175/JCLI3789.1
    Publisher: American Meteorological Society
    Abstract: In this study the authors diagnose the sources for the contiguous U.S. seasonal forecast skill that are related to sea surface temperature (SST) variations using a combination of dynamical and empirical methods. The dynamical methods include ensemble simulations with four atmospheric general circulation models (AGCMs) forced by observed monthly global SSTs from 1950 to 1999, and ensemble AGCM experiments forced by idealized SST anomalies. The empirical methods involve a suite of reductions of the AGCM simulations. These include uni- and multivariate regression models that encapsulate the simultaneous and one-season lag linear connections between seasonal mean tropical SST anomalies and U.S. precipitation and surface air temperature. Nearly all of the AGCM skill in U.S. precipitation and surface air temperature, arising from global SST influences, can be explained by a single degree of freedom in the tropical SST field?that associated with the linear atmospheric signal of El Niño?Southern Oscillation (ENSO). The results support previous findings regarding the preeminence of ENSO as a U.S. skill source. The diagnostic methods used here exposed another skill source that appeared to be of non-ENSO origins. In late autumn, when the AGCM simulation skill of U.S. temperatures peaked in absolute value and in spatial coverage, the majority of that originated from SST variability in the subtropical west Pacific Ocean and the South China Sea. Hindcast experiments were performed for 1950?99 that revealed most of the simulation skill of the U.S. seasonal climate to be recoverable at one-season lag. The skill attributable to the AGCMs was shown to achieve parity with that attributable to empirical models derived purely from observational data. The diagnostics promote the interpretation that only limited advances in U.S. seasonal prediction skill should be expected from methods seeking to capitalize on sea surface predictors alone, and that advances that may occur in future decades could be readily masked by inherent multidecadal fluctuations in skill of coupled ocean?atmosphere systems.
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      Diagnosing Sources of U.S. Seasonal Forecast Skill

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    contributor authorQuan, X.
    contributor authorHoerling, M.
    contributor authorWhitaker, J.
    contributor authorBates, G.
    contributor authorXu, T.
    date accessioned2017-06-09T17:02:01Z
    date available2017-06-09T17:02:01Z
    date copyright2006/07/01
    date issued2006
    identifier issn0894-8755
    identifier otherams-78256.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4220905
    description abstractIn this study the authors diagnose the sources for the contiguous U.S. seasonal forecast skill that are related to sea surface temperature (SST) variations using a combination of dynamical and empirical methods. The dynamical methods include ensemble simulations with four atmospheric general circulation models (AGCMs) forced by observed monthly global SSTs from 1950 to 1999, and ensemble AGCM experiments forced by idealized SST anomalies. The empirical methods involve a suite of reductions of the AGCM simulations. These include uni- and multivariate regression models that encapsulate the simultaneous and one-season lag linear connections between seasonal mean tropical SST anomalies and U.S. precipitation and surface air temperature. Nearly all of the AGCM skill in U.S. precipitation and surface air temperature, arising from global SST influences, can be explained by a single degree of freedom in the tropical SST field?that associated with the linear atmospheric signal of El Niño?Southern Oscillation (ENSO). The results support previous findings regarding the preeminence of ENSO as a U.S. skill source. The diagnostic methods used here exposed another skill source that appeared to be of non-ENSO origins. In late autumn, when the AGCM simulation skill of U.S. temperatures peaked in absolute value and in spatial coverage, the majority of that originated from SST variability in the subtropical west Pacific Ocean and the South China Sea. Hindcast experiments were performed for 1950?99 that revealed most of the simulation skill of the U.S. seasonal climate to be recoverable at one-season lag. The skill attributable to the AGCMs was shown to achieve parity with that attributable to empirical models derived purely from observational data. The diagnostics promote the interpretation that only limited advances in U.S. seasonal prediction skill should be expected from methods seeking to capitalize on sea surface predictors alone, and that advances that may occur in future decades could be readily masked by inherent multidecadal fluctuations in skill of coupled ocean?atmosphere systems.
    publisherAmerican Meteorological Society
    titleDiagnosing Sources of U.S. Seasonal Forecast Skill
    typeJournal Paper
    journal volume19
    journal issue13
    journal titleJournal of Climate
    identifier doi10.1175/JCLI3789.1
    journal fristpage3279
    journal lastpage3293
    treeJournal of Climate:;2006:;volume( 019 ):;issue: 013
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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