Based on an empirical orthogonal function （EOF） analysis of the monthly NCEP Optimum Interpolation Sea Surface Temperature （OISST） data in the South China Sea （SCS） after removing the climatological mean and tre...Based on an empirical orthogonal function （EOF） analysis of the monthly NCEP Optimum Interpolation Sea Surface Temperature （OISST） data in the South China Sea （SCS） after removing the climatological mean and trends of SST, over the period of January 1982 to October 2003, the corresponding TCF correlates best with the Dipole Mode Index （DMI）, Nino1＋2, Nino3.4, Nino3, and Niflo4 indices with time lags of 10, 3, 6, 5, and 6 months, respectively. Thus, a statistical hindcasts in the prediction model are based on a canonical correlation analysis （CCA） model using the above indices as predictors spanning from 1993/1994 to 2003/2004 with a 1-12 month lead time after the canonical variants are calculated, using data from the training periods from January 1982 to December1992. The forecast model is successful and steady when the lead times are 1-12 months. The SCS warm event in 1998 was successfully predicted with lead times from 1-12 months irrespective of the strength or time extent. The prediction ability for SSTA is lower during weak ENSO years, in which other local factors should be also considered as local effects play a relatively important role in these years. We designed the two forecast models： one using both DMI and Nino indices and the other using only Nino indices without DMI, and compared the forecast accuracies of the two cases. The spatial distributions of forecast accuracies show different confidence areas. By turning off the DMI, the forecast accuracy is lower in the coastal areas off the Philippines in the SCS, suggesting some teleconnection may occur with the Indian Ocean in this area. The highest forecast accuracies occur when the forecast interval is five months long without using the DMI, while using both of Nino indices and DMI, the highest accuracies occur when the forecast interval time is eight months, suggesting that the Nino indices dominate the interannual variability of SST anomalies in the SCS. Meanwhile the forecast accuracy is evaluated over an independent test period of more than 11 years （1993/94 to October 2004） by comparing the model performance with a simple prediction strategy involving the persistence of sea surface temperature anomalies over a 1-12 month lead time （the persisted prediction）. Predictions based on the CCA model show a significant improvement over the persisted prediction, especially with an increased lead time （longer than 3 months）. The forecast model performs steadily and the forecast accuracy, i.e., the correlation coefficients between the observed and predicted SSTA in the SCS are about 0.5 in most middle and southern SCS areas, when the thresholds are greater than the 95% confidence level. For all 1 to 12 month lead time forecasts, the root mean square errors have a standard deviation of about 0.2. The seasonal differences in the prediction performance for the 1-12 month lead time are also examined.展开更多
With correlation analysis and factor analysis methods, the effects of preceding Pacific SSTs on subtropical high indexes of main raining seasons are discussed. The results of correlation analysis show that the effects...With correlation analysis and factor analysis methods, the effects of preceding Pacific SSTs on subtropical high indexes of main raining seasons are discussed. The results of correlation analysis show that the effects of SSTs on five subtropical high indexes differ in seasons and regions. The variation of SSTs mostly affects the area and intensity indexes of the subtropical high, followed by the western ridge index, and the effect on the ridge line index is more remarkable than on the north boundary index. The results of factor analysis reveals that the first common factor of SST of each season reflected mainly the inverse relation of SSTs variation between the central and eastern part of equatorial Pacific and the western Pacific, which correlates better with the subtropical high indexes in the main raining seasons than other common factors of SST. The analysis of interdecadal variation indicated that the variation of SSTs was conducive to the emergence of the La Ni?a event before the end of 1970s, such that in the summer the subtropical high is likely to be weaker and smaller and located eastward and northward. After the 1980s, the opposite characteristics prevailed.展开更多
The interdecadal change in the interannual variability of the South China Sea summer monsoon(SCSSM)intensity and its mechanism are investigated in this study.The interannual variability of the low-level circulation of...The interdecadal change in the interannual variability of the South China Sea summer monsoon(SCSSM)intensity and its mechanism are investigated in this study.The interannual variability of the low-level circulation of the SCSSM has experienced a significant interdecadal enhancement around the end of the 1980s,which may be attributed to the interdecadal changes in the evolution of the tropical Indo-Pacific sea surface temperature(SST)anomalies and their impacts on the SCSSM.From 1961 to 1989,the low-level circulation over the South China Sea is primarily affected by the SST anomalies in the tropical Indian Ocean via the mechanism of Kelvin-wave-induced Ekman divergence.While in 1990 to 2020,the impacts of the summer SST anomalies in the Maritime Continent and the equatorial central to eastern Pacific on the SCSSM are enhanced,via anomalous meridional circulation and Mastuno-Gill type Rossby wave atmospheric response,respectively.The above interdecadal changes are closely associated with the interdecadal changes in the evolution of El Niño–Southern Oscillation(ENSO)events.The interdecadal variation of the summer SST anomalies in the developing and decaying phases of ENSO events enhances the influence of the tropical Indo-Pacific SST on the SCSSM,resulting in the interdecadal change in the interannual variability of the SCSSM.展开更多
This article examines the off season rainfall in northern coast Tanzania(NCT)including Zanzibar which occurred in January and February 2020(JF).Like the JF rainfalls of 2001,2004,2010,2016 and 2018,the JF(2020)rainfal...This article examines the off season rainfall in northern coast Tanzania(NCT)including Zanzibar which occurred in January and February 2020(JF).Like the JF rainfalls of 2001,2004,2010,2016 and 2018,the JF(2020)rainfall was more unique in damages including loss of lives,properties and infrastructures.The study used the NCEP/NCAR reanalysis data to examine the cause of uniqueness of JF rainfall in 2001,2004,2010,2016,2018 and 2020 over NCT and Zanzibar.These datasets include monthly mean u,v wind at 850,700,500,and 200 mb;SSTs,mean sea level pressure(MSLP)anomalies,Dipole Mode Index(DMI),and monthly rainfall from NCT and Zanzibar stations.Datasets were processed and calculated into long term,seasonal,and monthly averages,indeed,Precipitation Index(PI)was calculated.Correlation analysis between the rainfall(December to January),SST,DMI and 850 mb wind vectors;and long-term percentage contribution of investigated parameters was calculated.Results revealed significant positive and negative correlations between JF rainfall,SSTs and DMI.Moreover,JFs of 2004 and 2016 had higher rainfalls of 443 mm with percentage contribution of up to 406%,while January and February,2020 had the highest of 269.1 and 101.1mm in Zanzibar and 295 and 146.1 mm over and NCT areas,with highest January long-term rainfall contribution of 356%in Zanzibar and 526%over NCT.The DJF(2019/20)had the highest rainfall record of 649.5 mm in Zanzibar contributing up to 286%,while JF 2000 rainfall had a good spatial and temporal distribution over most NCT areas.JF,2020 rainfall had impacts of more than 20 people died in Lindi and several infrastructures including Kiyegeya Bridge in Morogoro were damaged.Conclusively,more research works on understanding the dynamics of wet and dry JF seasons should be conducted.展开更多
Winter North Atlantic Oscillation(NAO)indexes from observations based on various winter durations are compared.Results show that there are significant differences in the interannual and decadal variations of these NAO...Winter North Atlantic Oscillation(NAO)indexes from observations based on various winter durations are compared.Results show that there are significant differences in the interannual and decadal variations of these NAO indexes.For the same data source,a different definition of winter duration can lead to different signs of NAO index in some years,which mainly appear to be in the period of decadal phase transition.The different winter durations induce different cycles of the observation-based NAO.The longer the winter duration,the stronger the decadal variation.The NAO defined by different winter durations also can generate different descriptions of the NAO action centers,including the position and movement.The longer the winter duration,the more southerly action centers appear to be.The movement of the action centers affects not only site-based NAO indexes but also those defined by other methods,such as empirical orthogonal function(EOF)analysis.The length of time used in EOF analysis has a great influence on the spatial pattern of the NAO mode,which results in a considerable difference between the corresponding NAO indexes.Regardless of which NAO index is used,the NAO-related SST anomalies show the same tripole pattern.The longer the winter duration,the more significant the relationship between the NAO and SST affected by the timescale of sea-air interaction.展开更多
基金Supported by National Natural Science Foundation of China (No. 40706011)the Key Program of Knowledge Innovation Project of Chinese Academy of Sciences (No. KZCX1-YW-12)+2 种基金the National Science Foundation of China (Nos. 405201 and 40074)the International Cooperative Program of the Ministry of Science and Technology (No. 2006DFB21630)by the Open Foundation of Key Laboratory of Marine Science and Numerical Modeling (MASNUM)
文摘Based on an empirical orthogonal function （EOF） analysis of the monthly NCEP Optimum Interpolation Sea Surface Temperature （OISST） data in the South China Sea （SCS） after removing the climatological mean and trends of SST, over the period of January 1982 to October 2003, the corresponding TCF correlates best with the Dipole Mode Index （DMI）, Nino1＋2, Nino3.4, Nino3, and Niflo4 indices with time lags of 10, 3, 6, 5, and 6 months, respectively. Thus, a statistical hindcasts in the prediction model are based on a canonical correlation analysis （CCA） model using the above indices as predictors spanning from 1993/1994 to 2003/2004 with a 1-12 month lead time after the canonical variants are calculated, using data from the training periods from January 1982 to December1992. The forecast model is successful and steady when the lead times are 1-12 months. The SCS warm event in 1998 was successfully predicted with lead times from 1-12 months irrespective of the strength or time extent. The prediction ability for SSTA is lower during weak ENSO years, in which other local factors should be also considered as local effects play a relatively important role in these years. We designed the two forecast models： one using both DMI and Nino indices and the other using only Nino indices without DMI, and compared the forecast accuracies of the two cases. The spatial distributions of forecast accuracies show different confidence areas. By turning off the DMI, the forecast accuracy is lower in the coastal areas off the Philippines in the SCS, suggesting some teleconnection may occur with the Indian Ocean in this area. The highest forecast accuracies occur when the forecast interval is five months long without using the DMI, while using both of Nino indices and DMI, the highest accuracies occur when the forecast interval time is eight months, suggesting that the Nino indices dominate the interannual variability of SST anomalies in the SCS. Meanwhile the forecast accuracy is evaluated over an independent test period of more than 11 years （1993/94 to October 2004） by comparing the model performance with a simple prediction strategy involving the persistence of sea surface temperature anomalies over a 1-12 month lead time （the persisted prediction）. Predictions based on the CCA model show a significant improvement over the persisted prediction, especially with an increased lead time （longer than 3 months）. The forecast model performs steadily and the forecast accuracy, i.e., the correlation coefficients between the observed and predicted SSTA in the SCS are about 0.5 in most middle and southern SCS areas, when the thresholds are greater than the 95% confidence level. For all 1 to 12 month lead time forecasts, the root mean square errors have a standard deviation of about 0.2. The seasonal differences in the prediction performance for the 1-12 month lead time are also examined.
文摘With correlation analysis and factor analysis methods, the effects of preceding Pacific SSTs on subtropical high indexes of main raining seasons are discussed. The results of correlation analysis show that the effects of SSTs on five subtropical high indexes differ in seasons and regions. The variation of SSTs mostly affects the area and intensity indexes of the subtropical high, followed by the western ridge index, and the effect on the ridge line index is more remarkable than on the north boundary index. The results of factor analysis reveals that the first common factor of SST of each season reflected mainly the inverse relation of SSTs variation between the central and eastern part of equatorial Pacific and the western Pacific, which correlates better with the subtropical high indexes in the main raining seasons than other common factors of SST. The analysis of interdecadal variation indicated that the variation of SSTs was conducive to the emergence of the La Ni?a event before the end of 1970s, such that in the summer the subtropical high is likely to be weaker and smaller and located eastward and northward. After the 1980s, the opposite characteristics prevailed.
基金Program of National Science Foundation of China(42175018,42088101)Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies(2020B1212060025)。
文摘The interdecadal change in the interannual variability of the South China Sea summer monsoon(SCSSM)intensity and its mechanism are investigated in this study.The interannual variability of the low-level circulation of the SCSSM has experienced a significant interdecadal enhancement around the end of the 1980s,which may be attributed to the interdecadal changes in the evolution of the tropical Indo-Pacific sea surface temperature(SST)anomalies and their impacts on the SCSSM.From 1961 to 1989,the low-level circulation over the South China Sea is primarily affected by the SST anomalies in the tropical Indian Ocean via the mechanism of Kelvin-wave-induced Ekman divergence.While in 1990 to 2020,the impacts of the summer SST anomalies in the Maritime Continent and the equatorial central to eastern Pacific on the SCSSM are enhanced,via anomalous meridional circulation and Mastuno-Gill type Rossby wave atmospheric response,respectively.The above interdecadal changes are closely associated with the interdecadal changes in the evolution of El Niño–Southern Oscillation(ENSO)events.The interdecadal variation of the summer SST anomalies in the developing and decaying phases of ENSO events enhances the influence of the tropical Indo-Pacific SST on the SCSSM,resulting in the interdecadal change in the interannual variability of the SCSSM.
文摘This article examines the off season rainfall in northern coast Tanzania(NCT)including Zanzibar which occurred in January and February 2020(JF).Like the JF rainfalls of 2001,2004,2010,2016 and 2018,the JF(2020)rainfall was more unique in damages including loss of lives,properties and infrastructures.The study used the NCEP/NCAR reanalysis data to examine the cause of uniqueness of JF rainfall in 2001,2004,2010,2016,2018 and 2020 over NCT and Zanzibar.These datasets include monthly mean u,v wind at 850,700,500,and 200 mb;SSTs,mean sea level pressure(MSLP)anomalies,Dipole Mode Index(DMI),and monthly rainfall from NCT and Zanzibar stations.Datasets were processed and calculated into long term,seasonal,and monthly averages,indeed,Precipitation Index(PI)was calculated.Correlation analysis between the rainfall(December to January),SST,DMI and 850 mb wind vectors;and long-term percentage contribution of investigated parameters was calculated.Results revealed significant positive and negative correlations between JF rainfall,SSTs and DMI.Moreover,JFs of 2004 and 2016 had higher rainfalls of 443 mm with percentage contribution of up to 406%,while January and February,2020 had the highest of 269.1 and 101.1mm in Zanzibar and 295 and 146.1 mm over and NCT areas,with highest January long-term rainfall contribution of 356%in Zanzibar and 526%over NCT.The DJF(2019/20)had the highest rainfall record of 649.5 mm in Zanzibar contributing up to 286%,while JF 2000 rainfall had a good spatial and temporal distribution over most NCT areas.JF,2020 rainfall had impacts of more than 20 people died in Lindi and several infrastructures including Kiyegeya Bridge in Morogoro were damaged.Conclusively,more research works on understanding the dynamics of wet and dry JF seasons should be conducted.
基金supported jointly by the National Key Research and Development Program of China [grant number 2016YFB02008001]the National Natural Science Foundation of China [grant number 41530426]
文摘Winter North Atlantic Oscillation(NAO)indexes from observations based on various winter durations are compared.Results show that there are significant differences in the interannual and decadal variations of these NAO indexes.For the same data source,a different definition of winter duration can lead to different signs of NAO index in some years,which mainly appear to be in the period of decadal phase transition.The different winter durations induce different cycles of the observation-based NAO.The longer the winter duration,the stronger the decadal variation.The NAO defined by different winter durations also can generate different descriptions of the NAO action centers,including the position and movement.The longer the winter duration,the more southerly action centers appear to be.The movement of the action centers affects not only site-based NAO indexes but also those defined by other methods,such as empirical orthogonal function(EOF)analysis.The length of time used in EOF analysis has a great influence on the spatial pattern of the NAO mode,which results in a considerable difference between the corresponding NAO indexes.Regardless of which NAO index is used,the NAO-related SST anomalies show the same tripole pattern.The longer the winter duration,the more significant the relationship between the NAO and SST affected by the timescale of sea-air interaction.
