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 of Atmospheric Sciences, Yonsei University, Seoul, South Korea 2Center for Climate Physics, Institute for Basic Science, Busan, South Korea 3Pusan National University, Busan, South Korea 4School of Mathematics and Statistics, UNSW Australia, Sydney, NSW, Australia 5Climate Change Research Centre and ARC Centre for Excellence in Climate Extremes, UNSW Australia, Sydney, NSW, Australia Download: https://drive.google.com/open?id=15KIPFx1gkNy0PIhNO7vVcNAhPP1iNJvR

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, Australia Corresponding author: Y. Fan, Y.Fan@unsw.edu.au Download: https://drive.google.com/open?id=1DbstE03xye0PhleEbdNs7m07ln3NGDUe

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 window.