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    Assessing the Uncertainty in Projecting Local Mean Sea Level from Global Temperature

    Source: Journal of Applied Meteorology and Climatology:;2014:;volume( 053 ):;issue: 009::page 2163
    Author:
    Guttorp, Peter
    ,
    Januzzi, Alex
    ,
    Novak, Marie
    ,
    Podschwit, Harry
    ,
    Richardson, Lee
    ,
    Sowder, Colin D.
    ,
    Zimmerman, Aaron
    ,
    Bolin, David
    ,
    Särkkä, Aila
    DOI: 10.1175/JAMC-D-13-0308.1
    Publisher: American Meteorological Society
    Abstract: he process of moving from an ensemble of global climate model temperature projections to local sea level projections requires several steps. Sea level was estimated in Olympia, Washington (a city that is very concerned with sea level rise because parts of downtown are barely above mean highest high tide), by relating global mean temperature to global sea level; relating global sea level to sea levels at Seattle, Washington; and finally relating Seattle to Olympia. There has long been a realization that accurate assessment of the precision of projections is needed for science-based policy decisions. When a string of statistical and/or deterministic models is connected, the uncertainty of each individual model needs to be accounted for. Here the uncertainty is quantified for each model in the described system and the total uncertainty is assessed in a cascading effect throughout the system. The projected sea level rise over time and its total estimated uncertainty are visualized simultaneously for the years 2000?2100, the increased uncertainty due to each of the component models at a particular projection year is identified, and estimates of the time at which a certain sea level rise will first be reached are made.
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      Assessing the Uncertainty in Projecting Local Mean Sea Level from Global Temperature

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4217231
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    contributor authorGuttorp, Peter
    contributor authorJanuzzi, Alex
    contributor authorNovak, Marie
    contributor authorPodschwit, Harry
    contributor authorRichardson, Lee
    contributor authorSowder, Colin D.
    contributor authorZimmerman, Aaron
    contributor authorBolin, David
    contributor authorSärkkä, Aila
    date accessioned2017-06-09T16:49:59Z
    date available2017-06-09T16:49:59Z
    date copyright2014/09/01
    date issued2014
    identifier issn1558-8424
    identifier otherams-74950.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4217231
    description abstracthe process of moving from an ensemble of global climate model temperature projections to local sea level projections requires several steps. Sea level was estimated in Olympia, Washington (a city that is very concerned with sea level rise because parts of downtown are barely above mean highest high tide), by relating global mean temperature to global sea level; relating global sea level to sea levels at Seattle, Washington; and finally relating Seattle to Olympia. There has long been a realization that accurate assessment of the precision of projections is needed for science-based policy decisions. When a string of statistical and/or deterministic models is connected, the uncertainty of each individual model needs to be accounted for. Here the uncertainty is quantified for each model in the described system and the total uncertainty is assessed in a cascading effect throughout the system. The projected sea level rise over time and its total estimated uncertainty are visualized simultaneously for the years 2000?2100, the increased uncertainty due to each of the component models at a particular projection year is identified, and estimates of the time at which a certain sea level rise will first be reached are made.
    publisherAmerican Meteorological Society
    titleAssessing the Uncertainty in Projecting Local Mean Sea Level from Global Temperature
    typeJournal Paper
    journal volume53
    journal issue9
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/JAMC-D-13-0308.1
    journal fristpage2163
    journal lastpage2170
    treeJournal of Applied Meteorology and Climatology:;2014:;volume( 053 ):;issue: 009
    contenttypeFulltext
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