Data

ircc30

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

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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 tren
ircc30

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

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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
ircc30

Bayesian quantile based multimodel projections with application to Korean Heatwave data

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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 K

Climate Theory Lab.

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