Sea surface temperature （SST） variation in the Subei coastal waters, East China, which is important for the ecological environment of the Yellow Sea where Enteromorphaprolifera blooms frequently, is affected by the ...Sea surface temperature （SST） variation in the Subei coastal waters, East China, which is important for the ecological environment of the Yellow Sea where Enteromorphaprolifera blooms frequently, is affected by the East Asian winter monsoon （EAWM）, El Nifio-Southem Oscillation （ENSO）, and Pacific Decadal Oscillation （PDO）. In this study, correlations between climatic events and SST anomalies （SSTA） around the Subei （North Jiangsu Province, East China） Coast from 1981-2012 are analyzed, using empirical orthogonal function （EOF） and correlation analyses. First, a key region was determined by EOF analysis to represent the Subei coastal waters. Then, coherency analyses were performed on this key region. According to the correlation analysis, the EAWM index has a positive correlation with the spring and summer SSTA of the key region. Furthermore, the Nifio3.4 index is negatively correlated with the spring and summer SSTA of the key region 1 year ahead, and the PDO has significant negative coherency with spring SSTA and negative coherency with summer SSTA in the key region 1 year ahead. Overall, PDO exhibits the most significant impact on SSTA of the key region. In the key region, all these factors are correlated more significantly with SSTA in spring than in summer. This suggests that outbreaks ofEnteromorpha prolifera in the Yellow Sea are affected by global climatic changes, especially the PDO.展开更多
Based on the Zebiak-Cane model, the timedependent nonlinear forcing singular vector （NFSV）-type tendency errors with components of 4 and 12 （denoted by NFSV-4 and NFSV-12） are calculated for predetermined El Nifio...Based on the Zebiak-Cane model, the timedependent nonlinear forcing singular vector （NFSV）-type tendency errors with components of 4 and 12 （denoted by NFSV-4 and NFSV-12） are calculated for predetermined El Nifio events and compared with the constant NFSV （denoted by NFSV-1） from their patterns and resultant prediction errors. Specifically, NFSV-1 has a zonal dipolar sea surface temperature anomaly （SSTA） pattern with negative anomalies in the equatorial eastern Pacific and positive anomalies in the equatorial central-western Pa- cific. Although the first few components in NFSV-4 and NFSV-12 present patterns similar to NFSV-1, they tend to extend their dipoles farther westward; meanwhile, the positive anomalies gradually cover much smaller regions with the lag times. In addition, the authors calculate the predic- tion errors caused by the three kinds of NFSVs, and the results indicate that the prediction error induced by NFSV-12 is the largest, followed by the NFSV-4. However, when compared with the prediction errors caused by random tendency errors, the NFSVs generate significantly larger prediction errors. It is therefore shown that the spatial structure of tendency errors is important for producing large prediction errors. Furthermore, in exploring the tendency errors that cause the largest prediction error for E1 Nifio events, the timedependent NFSV should be evaluated.展开更多
基金Supported by the National Basic Research Program of China(973 Program)(No.2010CB950403)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA11020301)+1 种基金the National Natural Science Foundation of China(No.41176018)the Special Fund for Marine Research in the Public Interest(No.201005006)
文摘Sea surface temperature （SST） variation in the Subei coastal waters, East China, which is important for the ecological environment of the Yellow Sea where Enteromorphaprolifera blooms frequently, is affected by the East Asian winter monsoon （EAWM）, El Nifio-Southem Oscillation （ENSO）, and Pacific Decadal Oscillation （PDO）. In this study, correlations between climatic events and SST anomalies （SSTA） around the Subei （North Jiangsu Province, East China） Coast from 1981-2012 are analyzed, using empirical orthogonal function （EOF） and correlation analyses. First, a key region was determined by EOF analysis to represent the Subei coastal waters. Then, coherency analyses were performed on this key region. According to the correlation analysis, the EAWM index has a positive correlation with the spring and summer SSTA of the key region. Furthermore, the Nifio3.4 index is negatively correlated with the spring and summer SSTA of the key region 1 year ahead, and the PDO has significant negative coherency with spring SSTA and negative coherency with summer SSTA in the key region 1 year ahead. Overall, PDO exhibits the most significant impact on SSTA of the key region. In the key region, all these factors are correlated more significantly with SSTA in spring than in summer. This suggests that outbreaks ofEnteromorpha prolifera in the Yellow Sea are affected by global climatic changes, especially the PDO.
基金sponsored by the National Basic Research Program of China (Grant No. 2012CB955202)the National Public Benefit (Meteorology) Research Foundation of China (Grant No. GYHY201306018)the National Natural Science Foundation of China (Grant Nos. 41176013 and 41230420)
文摘Based on the Zebiak-Cane model, the timedependent nonlinear forcing singular vector （NFSV）-type tendency errors with components of 4 and 12 （denoted by NFSV-4 and NFSV-12） are calculated for predetermined El Nifio events and compared with the constant NFSV （denoted by NFSV-1） from their patterns and resultant prediction errors. Specifically, NFSV-1 has a zonal dipolar sea surface temperature anomaly （SSTA） pattern with negative anomalies in the equatorial eastern Pacific and positive anomalies in the equatorial central-western Pa- cific. Although the first few components in NFSV-4 and NFSV-12 present patterns similar to NFSV-1, they tend to extend their dipoles farther westward; meanwhile, the positive anomalies gradually cover much smaller regions with the lag times. In addition, the authors calculate the predic- tion errors caused by the three kinds of NFSVs, and the results indicate that the prediction error induced by NFSV-12 is the largest, followed by the NFSV-4. However, when compared with the prediction errors caused by random tendency errors, the NFSVs generate significantly larger prediction errors. It is therefore shown that the spatial structure of tendency errors is important for producing large prediction errors. Furthermore, in exploring the tendency errors that cause the largest prediction error for E1 Nifio events, the timedependent NFSV should be evaluated.
