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    Seasonal Forecasts of the Pan-Arctic Sea Ice Extent Using a GCM-Based Seasonal Prediction System

    Source: Journal of Climate:;2013:;volume( 026 ):;issue: 016::page 6092
    Author:
    Chevallier, Matthieu
    ,
    Salas y Mélia, David
    ,
    Voldoire, Aurore
    ,
    Déqué, Michel
    ,
    Garric, Gilles
    DOI: 10.1175/JCLI-D-12-00612.1
    Publisher: American Meteorological Society
    Abstract: n ocean?sea ice model reconstruction spanning the period 1990?2009 is used to initialize ensemble seasonal forecasts with the Centre National de Recherches Météorologiques Coupled Global Climate Model version 5.1 (CNRM-CM5.1) coupled atmosphere?ocean general circulation model. The aim of this study is to assess the skill of fully initialized September and March pan-Arctic sea ice forecasts in terms of climatology and interannual anomalies. The predictions are initialized using ?full field initialization? of each component of the system. In spite of a drift due to radiative biases in the coupled model during the melt season, the full initialization of the sea ice cover on 1 May leads to skillful forecasts of the September sea ice extent (SIE) anomalies. The skill of the prediction is also significantly high when considering anomalies of the SIE relative to the long-term linear trend. It confirms that the anomaly of spring sea ice cover in itself plays a role in preconditioning a September SIE anomaly. The skill of predictions for March SIE initialized on 1 November is also encouraging, and it can be partly attributed to persistent features of the fall sea ice cover. The present study gives insight into the current ability of state-of-the-art coupled climate systems to perform operational seasonal forecasts of the Arctic sea ice cover up to 5 months in advance.
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      Seasonal Forecasts of the Pan-Arctic Sea Ice Extent Using a GCM-Based Seasonal Prediction System

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    contributor authorChevallier, Matthieu
    contributor authorSalas y Mélia, David
    contributor authorVoldoire, Aurore
    contributor authorDéqué, Michel
    contributor authorGarric, Gilles
    date accessioned2017-06-09T17:07:31Z
    date available2017-06-09T17:07:31Z
    date copyright2013/08/01
    date issued2013
    identifier issn0894-8755
    identifier otherams-79759.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4222574
    description abstractn ocean?sea ice model reconstruction spanning the period 1990?2009 is used to initialize ensemble seasonal forecasts with the Centre National de Recherches Météorologiques Coupled Global Climate Model version 5.1 (CNRM-CM5.1) coupled atmosphere?ocean general circulation model. The aim of this study is to assess the skill of fully initialized September and March pan-Arctic sea ice forecasts in terms of climatology and interannual anomalies. The predictions are initialized using ?full field initialization? of each component of the system. In spite of a drift due to radiative biases in the coupled model during the melt season, the full initialization of the sea ice cover on 1 May leads to skillful forecasts of the September sea ice extent (SIE) anomalies. The skill of the prediction is also significantly high when considering anomalies of the SIE relative to the long-term linear trend. It confirms that the anomaly of spring sea ice cover in itself plays a role in preconditioning a September SIE anomaly. The skill of predictions for March SIE initialized on 1 November is also encouraging, and it can be partly attributed to persistent features of the fall sea ice cover. The present study gives insight into the current ability of state-of-the-art coupled climate systems to perform operational seasonal forecasts of the Arctic sea ice cover up to 5 months in advance.
    publisherAmerican Meteorological Society
    titleSeasonal Forecasts of the Pan-Arctic Sea Ice Extent Using a GCM-Based Seasonal Prediction System
    typeJournal Paper
    journal volume26
    journal issue16
    journal titleJournal of Climate
    identifier doi10.1175/JCLI-D-12-00612.1
    journal fristpage6092
    journal lastpage6104
    treeJournal of Climate:;2013:;volume( 026 ):;issue: 016
    contenttypeFulltext
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