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    Causality Analysis of ENSO’s Global Impacts on Climate Variables based on Data-driven Analytics and Climate Model Simulation

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    CT2018Team4.pdf (36.30Mb)
    Links to Files
    http://hpcf-files.umbc.edu/research/papers/CT2018Team4.pdf
    Permanent Link
    http://hdl.handle.net/11603/19275
    Collections
    • UMBC Faculty Collection
    • UMBC Joint Center for Earth Systems Technology (JCET)
    • UMBC Physics Department
    • UMBC Student Collection
    Metadata
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    Author/Creator
    Song, Hua
    Tian, Jing
    Huang, Jingfeng
    Wang, Jianwu
    Zhang, Zhibo
    Type of Work
    16 pages
    Text
    technical reports
    Citation of Original Publication
    Hua Song et al., Causality Analysis of ENSO’s Global Impacts on Climate Variables based on Data-driven Analytics and Climate Model Simulation, http://hpcf-files.umbc.edu/research/papers/CT2018Team4.pdf
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Subjects
    UMBC High Performance Computing Facility(HPCF)
    Abstract
    Numerous studies have indicated that El Niño and the Southern Oscillation (ENSO) could have determinant impacts on remote weather and climate using the conventional correlation-based methods, which however cannot identify the cause-and-effect of such linkage and ultimately determine a direction of causality. This study employs the Vector Auto-Regressive (VAR) model estimation method with the long-term observational sea surface temperature (SST) data and the NCEP/NCAR reanalysis data to demonstrate the Granger causality between ENSO and other climate attributes. Results showed that ENSO as the modulating factor can result in abnormal surface temperature, pressure, precipitation and wind circulation remotely, not vice versa. We also carry out the global climate model sensitivity simulations using the parallel computing techniques to double confirm the causality relations between ENSO and abnormal events in remote regions. Our statistical and climate model-based analyses may enrich our current understanding on the occurrences of extreme events worldwide caused by different ENSO strengths through teleconnections.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.