The sea surface temperature （SST） in the In- dian Ocean affects the regional climate over the Asian continent mostly through a modulation of the monsoon system. It is still difficult to provide an a priori indication of the seasonal variability over the Indian Ocean. It is widely recognized that the warm and cold events of SST over the tropical Indian Ocean are strongly linked to those of the equatorial eastern Pacific. In this study, a statistical prediction model has been developed to predict the monthly SST over the tropical Indian Ocean. This model is a linear regression model based on the lag relationship between the SST over the tropical Indian Ocean and the Nino3.4 （5°S-5°N, 170°W-120°W） SST Index. The pre- dictor （i.e., Nino3.4 SST Index） has been operationally predicted by a large size ensemble E1 Nifio and the Southern Oscillation （ENSO） forecast system with cou- pled data assimilation （Leefs_CDA）, which achieves a high predictive skill of up to a 24-month lead time for the equatorial eastern Pacific SST. As a result, the prediction skill of the present statistical model over the tropical In- dian Ocean is better than that of persistence prediction for January 1982 through December 2009.
El Nio events in the central equatorial Pacific (CP) are gaining increased attention,due to their increasing intensity within the global warming context.Various physical processes have been identified in the climate system that can be responsible for the modulation of El Nio,especially the effects of interannual salinity variability.In this work,a comprehensive data analysis is performed to illustrate the effects of interannual salinity variability using surface and subsurface salinity fields from the Met Office ENSEMBLES (EN3) quality controlled ocean dataset.It is demonstrated that during the developing phase of an El Nio event,a negative sea surface salinity (SSS) anomaly in the western-central basin acts to freshen the mixed layer (ML),decrease oceanic density in the upper ocean,and stabilize the upper layers.These related oceanic processes tend to reduce the vertical mixing and entrainment of subsurface water at the base of the ML,which further enhances the warm sea surface temperature (SST) anomalies associated with the El Nio event.However,the effects of interannually variable salinity are much more significant during the CP-El Nio than during the eastern Pacific (EP) El Nio,indicating that the salinity effect might be an important contributor to the development of CP-El Nio events.
ZHENG Fei 1,WAN Li-Ying 2,and WANG Hui 3 1 International Center for Climate and Environment Science (ICCES),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China 2 Key Laboratory of Research on Marine Hazards Forecasting,National Marine Environmental Forecasting Center,Beijing 100081,China 3 National Meteorological Center,Beijing 100081,China
Weather and climate in East China are closely related to the variability of the western Pacific subtropical high(WPSH), which is an important part of the Asian monsoon system. The WPSH prediction in spring and summer is a critical component of rainfall forecasting during the summer flood season in China. Although many attempts have been made to predict WPSH variability, its predictability remains limited in practice due to the complexity of the WPSH evolution. Many studies have indicated that the sea surface temperature(SST) over the tropical Indian Ocean has a significant effect on WPSH variability. In this paper, a statistical model is developed to forecast the monthly variation in the WPSH during the spring and summer seasons on the basis of its relationship with SST over the tropical Indian Ocean. The forecasted SST over the tropical Indian Ocean is the predictor in this model, which differs significantly from other WPSH prediction methods. A 26-year independent hindcast experiment from 1983 to 2008 is conducted and validated in which the WPSH prediction driven by the combined forecasted SST is compared with that driven by the persisted SST. Results indicate that the skill score of the WPSH prediction driven by the combined forecasted SST is substantial.
