The Seasonal Prediction Model Of East Asian Monsoon Using Sea Surface Temperature And Eurasian Snow Cover

Seasonal prediction of the East Asian Monsoon(EAM) is deemed as a scientific challenge. 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 East Asian Summer Monsoon (EASM) firstly. Results show that two SST modes shows closely connection with decaying and developing phase of mega-El Nino Southern Oscillation (mega-ENSO), respectively. A PLS prediction model is constructed using the two leading PLS modes and 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 on EASM ,prediction. These indicate that mega-ENSO may provide a critical predictability source for the EASM strength. The model simulation skill comparison also emphasizes the significance of mega-ENSO in dynamical model simulation of the EASM.Secondly, three seasonal prediction models for the East Asian winter monsoon(EAWM) are established using three leading PLS modes of the Eurasian snow cover(ESC), the first leading mode of SST and the four leading modes of the combination of ESC and SST in preceding autumn, respectively. PLS1 mode for the ESC features significantly anomalous snow cover in Siberia and Tibetan Plateau regions. The ESC PLS2 mode corresponds to large areas of snow cover anomalies in the central Siberia, whereas PLS3 mode a meridional seesaw pattern of ESC. The SST PLS1 mode basically exhibits an ENSO developing phase in equatorial central eastern Pacific and significant SST anomalies in North Atlantic. After a 35-yr training period, three physical-based seasonal prediction models are constructed. All of the PLS models show higher skills than CanCM3/4. The PLS model based on combination of the ESC and SST exhibits the best hindcast skill among the three models, indicating that this PLS model may provide another practical tool for the EAWM. In addition, the relative contribution of the ESC and SST is also examined by assessing the hindcast skills of the other two PE models constructed solely by the ESC or SST, indicating the importance of ESC in EAWM seasonal prediction.