ENSO influence on the dynamical seasonal prediction of the East Asian Winter Monsoon
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This study examined the forecast skill for the East Asian Winter Monsoon (EAWM) using the following state-of-the-art dynamical seasonal prediction systems: CanCM3, CanCM4, CFSv2, CM2.1, and GEOS-5. To assess the prediction skills and the associations with ENSO teleconnections of these systems, long-term seasonal hindcast data sets for 28 years (1983–2010) were investigated. Observational data indicated that the ENSO–EAWM connection strengthened in a recent period (1997–2010; r = −0.84) compared with that in an earlier period (1983–1996; r = −0.44). For the recent period, a practical level of prediction skill for the EAWM index was retained by CFSv2 and GEOS-5 for the lead time of 2 months or longer, with these models showing a realistic ENSO–EAWM relationship throughout the Western Pacific Warm Pool with east–west dipole anomalies of precipitation induced by ENSO. The prediction skill of the other models was poor, even for lead times of zero to 1 month, with weak ENSO–EAWM relationships and errant north–south dipole anomalies of precipitation associated with ENSO. A large model spread was also found consistently in the CMIP5 AMIP and the Historical simulations by 14 models of the spatial pattern of equatorial Pacific precipitation anomalies associated with ENSO and the effect on the ENSO–EAWM relationship. Based on this study, the accurate prediction of EAWM should be linked with a realistic representation of the convection response in the equatorial Pacific by ENSO and the teleconnection to EAWM.
KeywordsSeasonal prediction East Asian Winter Monsoon ENSO Teleconnection Decadal variability
This study was supported by the Korea Meteorological Administration Research and Development Program under Grant KMIPA 2016-6010. Daehyun Kang is also supported by Fostering Core Leaders of the Future Basic Science Program of the National Research Foundation of Korea (NRF-2014H1A8A1022342). The authors appreciate helpful comments and discussions from Fei-Fei Jin, Ji-Won Kim, and Yoshimitsu Chikamoto.
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