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    ircc30

    Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections

    Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: application to the AMOC and temperature time series Projections accounting for model trend and variability skill Roman Olson1,2,3, Soon-Il An1,*, Yanan Fan4 and Jason P. Evans5 1Department
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    최근 활동:
    2019년 3월 27일
    ircc30

    Changes in ENSO Activity during the Last 6,000 Years Modulated by Background Climate State

    Author : Soon-Il An, Seul-Hee Lim and Sang-Yun Jun   Title : Changes in ENSO Activity during the Last 6,000 Years Modulated by Background Climate State. Data Link: https://drive.google.com/drive/folders/1AGW7YyC5-3XZ8pRxCTa50UhB4hTntM7A How to download data : Open this link at the new windo
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    최근 활동:
    2018년 11월 08일
    ircc30

    Bayesian quantile based multimodel projections with application to Korean Heatwave data

    Yanan. Fan1, Roman Olson2, Jason P. Evans3, Jong-Soo Shin2, and Soon-Il An2 1 School of Mathematics and Statistics, UNSW, Australia 2 Department of Atmospheric Sciences, Yonsei University, South Korea 3 Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, UNSW, Au
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    최근 활동:
    2018년 11월 08일

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