YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • AMS
    • Journal of Climate
    • View Item
    •   YE&T Library
    • AMS
    • Journal of Climate
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Further Exploring and Quantifying Uncertainties for Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4)

    Source: Journal of Climate:;2015:;volume( 029 ):;issue: 009::page 3119
    Author:
    Huang, Boyin
    ,
    Thorne, Peter W.
    ,
    Smith, Thomas M.
    ,
    Liu, Wei
    ,
    Lawrimore, Jay
    ,
    Banzon, Viva F.
    ,
    Zhang, Huai-Min
    ,
    Peterson, Thomas C.
    ,
    Menne, Matthew
    DOI: 10.1175/JCLI-D-15-0430.1
    Publisher: American Meteorological Society
    Abstract: he uncertainty in Extended Reconstructed SST (ERSST) version 4 (v4) is reassessed based upon 1) reconstruction uncertainties and 2) an extended exploration of parametric uncertainties. The reconstruction uncertainty (Ur) results from using a truncated (130) set of empirical orthogonal teleconnection functions (EOTs), which yields an inevitable loss of information content, primarily at a local level. The Ur is assessed based upon 32 ensemble ERSST.v4 analyses with the spatially complete monthly Optimum Interpolation SST product. The parametric uncertainty (Up) results from using different parameter values in quality control, bias adjustments, and EOT definition etc. The Up is assessed using a 1000-member ensemble ERSST.v4 analysis with different combinations of plausible settings of 24 identified internal parameter values. At the scale of an individual grid box, the SST uncertainty varies between 0.3° and 0.7°C and arises from both Ur and Up. On the global scale, the SST uncertainty is substantially smaller (0.03°?0.14°C) and predominantly arises from Up. The SST uncertainties are greatest in periods and locales of data sparseness in the nineteenth century and relatively small after the 1950s. The global uncertainty estimates in ERSST.v4 are broadly consistent with independent estimates arising from the Hadley Centre SST dataset version 3 (HadSST3) and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2). The uncertainty in the internal parameter values in quality control and bias adjustments can impact the SST trends in both the long-term (1901?2014) and ?hiatus? (2000?14) periods.
    • Download: (3.708Mb)
    • Show Full MetaData Hide Full MetaData
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Further Exploring and Quantifying Uncertainties for Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4)

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4224118
    Collections
    • Journal of Climate

    Show full item record

    contributor authorHuang, Boyin
    contributor authorThorne, Peter W.
    contributor authorSmith, Thomas M.
    contributor authorLiu, Wei
    contributor authorLawrimore, Jay
    contributor authorBanzon, Viva F.
    contributor authorZhang, Huai-Min
    contributor authorPeterson, Thomas C.
    contributor authorMenne, Matthew
    date accessioned2017-06-09T17:12:40Z
    date available2017-06-09T17:12:40Z
    date copyright2016/05/01
    date issued2015
    identifier issn0894-8755
    identifier otherams-81147.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4224118
    description abstracthe uncertainty in Extended Reconstructed SST (ERSST) version 4 (v4) is reassessed based upon 1) reconstruction uncertainties and 2) an extended exploration of parametric uncertainties. The reconstruction uncertainty (Ur) results from using a truncated (130) set of empirical orthogonal teleconnection functions (EOTs), which yields an inevitable loss of information content, primarily at a local level. The Ur is assessed based upon 32 ensemble ERSST.v4 analyses with the spatially complete monthly Optimum Interpolation SST product. The parametric uncertainty (Up) results from using different parameter values in quality control, bias adjustments, and EOT definition etc. The Up is assessed using a 1000-member ensemble ERSST.v4 analysis with different combinations of plausible settings of 24 identified internal parameter values. At the scale of an individual grid box, the SST uncertainty varies between 0.3° and 0.7°C and arises from both Ur and Up. On the global scale, the SST uncertainty is substantially smaller (0.03°?0.14°C) and predominantly arises from Up. The SST uncertainties are greatest in periods and locales of data sparseness in the nineteenth century and relatively small after the 1950s. The global uncertainty estimates in ERSST.v4 are broadly consistent with independent estimates arising from the Hadley Centre SST dataset version 3 (HadSST3) and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2). The uncertainty in the internal parameter values in quality control and bias adjustments can impact the SST trends in both the long-term (1901?2014) and ?hiatus? (2000?14) periods.
    publisherAmerican Meteorological Society
    titleFurther Exploring and Quantifying Uncertainties for Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4)
    typeJournal Paper
    journal volume29
    journal issue9
    journal titleJournal of Climate
    identifier doi10.1175/JCLI-D-15-0430.1
    journal fristpage3119
    journal lastpage3142
    treeJournal of Climate:;2015:;volume( 029 ):;issue: 009
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian