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    System Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies

    Source: Monthly Weather Review:;2007:;volume( 135 ):;issue: 010::page 3541
    Author:
    Zhang, S.
    ,
    Harrison, M. J.
    ,
    Rosati, A.
    ,
    Wittenberg, A.
    DOI: 10.1175/MWR3466.1
    Publisher: American Meteorological Society
    Abstract: A fully coupled data assimilation (CDA) system, consisting of an ensemble filter applied to the Geophysical Fluid Dynamics Laboratory?s global fully coupled climate model (CM2), has been developed to facilitate the detection and prediction of seasonal-to-multidecadal climate variability and climate trends. The assimilation provides a self-consistent, temporally continuous estimate of the coupled model state and its uncertainty, in the form of discrete ensemble members, which can be used directly to initialize probabilistic climate forecasts. Here, the CDA is evaluated using a series of perfect model experiments, in which a particular twentieth-century simulation?with temporally varying greenhouse gas and natural aerosol radiative forcings?serves as a ?truth? from which observations are drawn, according to the actual ocean observing network for the twentieth century. These observations are then assimilated into a coupled model ensemble that is subjected only to preindustrial forcings. By examining how well this analysis ensemble reproduces the ?truth,? the skill of the analysis system in recovering anthropogenically forced trends and natural climate variability is assessed, given the historical observing network. The assimilation successfully reconstructs the twentieth-century ocean heat content variability and trends in most locations. The experiments highlight the importance of maintaining key physical relationships among model fields, which are associated with water masses in the ocean and geostrophy in the atmosphere. For example, when only oceanic temperatures are assimilated, the ocean analysis is greatly improved by incorporating the temperature?salinity covariance provided by the analysis ensemble. Interestingly, wind observations are more helpful than atmospheric temperature observations for constructing the structure of the tropical atmosphere; the opposite holds for the extratropical atmosphere. The experiments indicate that the Atlantic meridional overturning circulation is difficult to constrain using the twentieth-century observational network, but there is hope that additional observations?including those from the newly deployed Argo profiles?may lessen this problem in the twenty-first century. The challenges for data assimilation of model systematic biases and evolving observing systems are discussed.
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      System Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4229522
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    • Monthly Weather Review

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    contributor authorZhang, S.
    contributor authorHarrison, M. J.
    contributor authorRosati, A.
    contributor authorWittenberg, A.
    date accessioned2017-06-09T17:28:47Z
    date available2017-06-09T17:28:47Z
    date copyright2007/10/01
    date issued2007
    identifier issn0027-0644
    identifier otherams-86011.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4229522
    description abstractA fully coupled data assimilation (CDA) system, consisting of an ensemble filter applied to the Geophysical Fluid Dynamics Laboratory?s global fully coupled climate model (CM2), has been developed to facilitate the detection and prediction of seasonal-to-multidecadal climate variability and climate trends. The assimilation provides a self-consistent, temporally continuous estimate of the coupled model state and its uncertainty, in the form of discrete ensemble members, which can be used directly to initialize probabilistic climate forecasts. Here, the CDA is evaluated using a series of perfect model experiments, in which a particular twentieth-century simulation?with temporally varying greenhouse gas and natural aerosol radiative forcings?serves as a ?truth? from which observations are drawn, according to the actual ocean observing network for the twentieth century. These observations are then assimilated into a coupled model ensemble that is subjected only to preindustrial forcings. By examining how well this analysis ensemble reproduces the ?truth,? the skill of the analysis system in recovering anthropogenically forced trends and natural climate variability is assessed, given the historical observing network. The assimilation successfully reconstructs the twentieth-century ocean heat content variability and trends in most locations. The experiments highlight the importance of maintaining key physical relationships among model fields, which are associated with water masses in the ocean and geostrophy in the atmosphere. For example, when only oceanic temperatures are assimilated, the ocean analysis is greatly improved by incorporating the temperature?salinity covariance provided by the analysis ensemble. Interestingly, wind observations are more helpful than atmospheric temperature observations for constructing the structure of the tropical atmosphere; the opposite holds for the extratropical atmosphere. The experiments indicate that the Atlantic meridional overturning circulation is difficult to constrain using the twentieth-century observational network, but there is hope that additional observations?including those from the newly deployed Argo profiles?may lessen this problem in the twenty-first century. The challenges for data assimilation of model systematic biases and evolving observing systems are discussed.
    publisherAmerican Meteorological Society
    titleSystem Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies
    typeJournal Paper
    journal volume135
    journal issue10
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR3466.1
    journal fristpage3541
    journal lastpage3564
    treeMonthly Weather Review:;2007:;volume( 135 ):;issue: 010
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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