Studying Of The Interannual And Decadal Predictability Of SST Based On The Statistical Method

The climate system is complex and nonlinear, and predictability (unpredictability) is its inherent property. Studies have shown that interannual and decadal predictability of the climate system mainly comes from oceans. As an important component of the climate system, sea surface temperature (SST) of ocean is a significant factor of measuring the mean and variability of climate. Therefore, it is important to study the interannual and decadal predictability of SST, which can provide a reference for predicting the future climate change.Predictability of climatic variables is definited as the ratio between variance of predictable parts and total variance. Similar to empirical orthogonal function (EOF) decomposition, climatic variables could be decomposed into a linear combination of predictable components and spatial structure according to the principle of maximum predictability. In this paper, in order to investigate the areas where exist interannual and decadal predictability, interannual predictable component and predictability of SST is studied by using the extended reconstructed sea surface temperature (ERSST) data from the National Oceanic and Atmospheric Administration (NOAA), and decadal predictable components and predictability of SST is analyzed by using the output of pre-industrial experiment of GFDL model-CM3.By analyzing the observed monthly SST, it is informed that predictability of global SST is 3 months. Predictability of the first predictable component is more than 2 years and the spatial pattern is shown as abnormal warming in North Pacific and North Atlantic, which characters a long period fluctuation similar to AMO of SST climatology. The predictability of the second and third predictable component is about 6 months. Interannual predictability of SST is mainly in the tropical Pacific Ocean. It is shown that predictability of the tropical Pacific SST is 4 months and predictable components have similar spatial pattern of ENSO, which presents as abnormal warming of equatorial Pacific SST. Besides, the second predictable component is related better to Nino3 index. In a word, predictability of tropical Pacific SST comes from ENSO.By analyzing the pre-industrial yearly SST, it is informed that forecast skill of the global SST is maintained at about 0.55 when the lead time is 1 year. It is found that the decadal predictability is mainly in the middle and high latitudes. The predictable components of the North Pacific and North Atlantic have predictability of more than 5 years and show significant decadal variability. Furthermore, the second predictable component of North Pacific is related to the Pacific Decadal Osciallion(PDO)and the second predictable component of North Atlantic is related to the Atlantic Multi-decadal Osciallion (AMO)In summary, the interannual predictability of SST is mainly in tropical Pacific ocean, which is related to ENSO, while the decadal predictability of SST is mainly in the middle and high latitudes, such as the North Pacific, North Atlantic. Decadal predictability has relation to the Pacific Decadal Oscillation (PDO) and the Atlantic Multi-decadal Oscillation (AMO).