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    Forecast Skill and Predictability of Observed Atlantic Sea Surface Temperatures

    Source: Journal of Climate:;2012:;volume( 025 ):;issue: 014::page 5047
    Author:
    Zanna, Laure
    DOI: 10.1175/JCLI-D-11-00539.1
    Publisher: American Meteorological Society
    Abstract: n empirical statistical model is constructed to assess the forecast skill and the linear predictability of Atlantic Ocean sea surface temperature (SST) variability. Linear inverse modeling (LIM) is used to build a dynamically based statistical model using observed Atlantic SST anomalies between latitudes 20°S and 66°N from 1870 to 2009. LIM allows one to fit a multivariate red-noise model to the observed annually averaged SST anomalies and to test it. Forecast skill is assessed and is shown to be O(3?5 yr). After a few years, the skill is greatly reduced, especially in the subpolar region. In the stable dynamical system determined by LIM, skill of annual average SST anomalies arises from four damped eigenmodes. The four eigenmodes are shown to be relevant in particular for the optimal growth events of SST variance, with a pattern reminiscent of the low-frequency mode of variability, and in general for the predictability and variability of Atlantic SSTs on interannual time scales. LIM might serve as a useful benchmark for interannual and decadal forecasts of SST anomalies that are based on numerical models.
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      Forecast Skill and Predictability of Observed Atlantic Sea Surface Temperatures

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    contributor authorZanna, Laure
    date accessioned2017-06-09T17:05:17Z
    date available2017-06-09T17:05:17Z
    date copyright2012/07/01
    date issued2012
    identifier issn0894-8755
    identifier otherams-79191.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4221943
    description abstractn empirical statistical model is constructed to assess the forecast skill and the linear predictability of Atlantic Ocean sea surface temperature (SST) variability. Linear inverse modeling (LIM) is used to build a dynamically based statistical model using observed Atlantic SST anomalies between latitudes 20°S and 66°N from 1870 to 2009. LIM allows one to fit a multivariate red-noise model to the observed annually averaged SST anomalies and to test it. Forecast skill is assessed and is shown to be O(3?5 yr). After a few years, the skill is greatly reduced, especially in the subpolar region. In the stable dynamical system determined by LIM, skill of annual average SST anomalies arises from four damped eigenmodes. The four eigenmodes are shown to be relevant in particular for the optimal growth events of SST variance, with a pattern reminiscent of the low-frequency mode of variability, and in general for the predictability and variability of Atlantic SSTs on interannual time scales. LIM might serve as a useful benchmark for interannual and decadal forecasts of SST anomalies that are based on numerical models.
    publisherAmerican Meteorological Society
    titleForecast Skill and Predictability of Observed Atlantic Sea Surface Temperatures
    typeJournal Paper
    journal volume25
    journal issue14
    journal titleJournal of Climate
    identifier doi10.1175/JCLI-D-11-00539.1
    journal fristpage5047
    journal lastpage5056
    treeJournal of Climate:;2012:;volume( 025 ):;issue: 014
    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
    yabeshDSpacePersian