Seasonal prediction of the East Asian summer monsoon with a partial-least square model

Abstract

Seasonal prediction of the East Asian summer monsoon (EASM) strength is probably one of the most challenging and crucial issues for climate prediction over East Asia. In this paper, a statistical method called partial-least square (PLS) regression is utilized to uncover principal sea surface temperature (SST) modes in the winter preceding the EASM. Results show that the SST pattern of the first PLS mode is associated with the turnabout of warming (or cooling) phase of a mega-El Nino/Southern Oscillation (mega-ENSO) (a leading mode of interannual-to-interdecadal variations of global SST), whereas that of the second PLS mode leads the warming/cooling mega-ENSO by about 1 year, signaling precursory conditions for mega-ENSO. These indicate that mega-ENSO may provide a critical predictability source for the EASM strength. Based on a 40-year training period (1958–1997), a PLS prediction model is constructed using the two leading PLS modes and 3-month-lead hindcasts are performed for the validation period of 1998–2013. A promising skill is obtained, which is comparable to the ensemble mean of versions 3 and 4 of the Canadian Community Atmosphere Model (CanCM3/4) hindcasts from the newly developed North American Multi-model Ensemble Prediction System regarding the interannual variations of the EASM strength. How to improve dynamical model simulation of the EASM is also examined through comparing the CanCM3/4 hindcast (1982–2010) with the 106-year historical run (1900–2005) by the Second Generation Canadian Earth System Model (CanESM2). CanCM3/4 exhibits a high skill in the EASM hindcast period 1982–2010 during which it also has a better performance in capturing the relationship between the EASM and mega-ENSO. By contrast, the simulation skill of CanESM2 is quite low and it is unable to reproduce the linkage between the EASM and mega-ENSO. All these results emphasize importance of mega-ENSO in seasonal prediction and dynamical model simulation of the EASM.