Equatorial pacific SSTA-related decadal variations of potential predictability of ENSO and interannual climate

Summary Based on analysis of NCEP reanalysis data and SST indices of the recent 50 years, decadal changes of the potential predictability of ENSO and interannual climate anomalies were investigated. Autocorrelation of Nino3 SST anomalies (SSTA) and correlation between atmospheric anomalies fields and Nino3 SSTA exhibit obvious variation in different decades, which indicates that Nino3 SSTA-related potential predictability of ENSO and interannual climate anomalies has significant decadal changes. Time around 1977 is not only a shift point of climate on the interdecadal time scale but also a catastrophe point of potential predictability of ENSO and interannual climate. As a whole, ENSO and the PNA pattern in boreal winter are more predictable in 1980s than in 1960s and 1970s, while the Nino3 SSTA-related potential predictability of the Indian monsoon and the East Asian Monsoon is lower in 1980s than in 1960s and 1970s. Received October 19, 1999 Revised December 30, 1999     

The retrospective prediction of El Niño-southern oscillation from 1881 to 2000 by a hybrid coupled model: (I) Sea surface temperature assimilation with ensemble Kalman filter

In this study, we assimilated sea surface temperature (SST) data of the past 120 years into an oceanic general circulation model (OGCM) for El Niño-southern oscillation (ENSO) retrospective predictions using ensemble Kalman filter (EnKF). It was found that the ensemble covariance matrix in EnKF can act as a time-variant transfer operator to project the SST corrections onto the subsurface temperatures effectively when initial perturbations of ensemble were constructed using vertically coherent random fields. As such the increments of subsurface temperatures can be obtained via the transfer operator during assimilation cycles. The results show that the SST assimilation improves the model simulation skills significantly, not only for the SST anomalies over the whole assimilated domain, but also for the subsurface temperature anomalies of the upper 100 m over the tropical Pacific off the equator. Along the equator, the improvement of the assimilation is confined within the mixing layer because strong upwelling motions there prevent the downward transfer of SST information. The retrospective prediction skills of ENSO over the past 120 years from 1881 to 2000 were significantly improved by the SST assimilation at all leads of 1–12 months, especially for the 3–6 months leads, compared with those initialized by the control run without assimilation. The skilful predictions by the assimilation allow us to further study ENSO predictability using this coupled model.     

INTERDECADAL VARIATION OF THE RELATIONSHIP BETWEEN ENSO AND SUMMER INTERANNUAL CLIMATE VARIABILITY IN CHINA

Interdecadal variation of the relationships between ENSO and the summer interannual climate variability in China is investigated by using techniques of sliding correlation analysis with the tropical Pacific SSTA and the observed surface air temperature and precipitation from stations in China. The results indicate that there are stable and robust relations that the Northern China is relatively dry during the developing phase of ENSO while the Yangtze River valley is relatively wet during the decaying phase of ENSO. On the other hand, interdecadal variations of the relations are also found in other regions. Over the time both prior to the Pacific decadal climate shift (before the late 1970s) and after it (after the late 1970s), during the developing phases of ENSO the summer precipitation anomaly in South China changed from below to above normal, whereas that in Northeast China changed from above to below normal; the summer surface air temperature anomaly in North and Northeast China changed from cooling to warming, whereas that in South China changed to cooling; during the decaying phases of ENSO the North China changed from wetter to dryer while the Huai River valley changed from dryer to normal; North China, Yangtze River valley and South China tend to be warmer. Based on the composite analysis of the NCAR/NCEP reanalyze datasets, significant differences existing in ENSO-related atmospheric circulation anomaly in East Asia during pre- and post-shift periods may be responsible for the interdecadal variation of relationships between ENSO and surface air temperature and precipitation in China.     

Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles

Based on an intermediate coupled model (ICM), a probabilistic ensemble prediction system (EPS) has been developed. The ensemble Kalman filter (EnKF) data assimilation approach is used for generating the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts over the 120 year period 1886–2005 using the EPS with 100 ensemble members and with initial conditi...     

An empirical model of decadal ENSO variability

This paper assesses potential predictability of decadal variations in the El Ni?o/Southern Oscillation (ENSO) characteristics by constructing and performing simulations using an empirical nonlinear stochastic model of an ENSO index. The model employs decomposition of global sea-surface temperature (SST) anomalies into the modes that maximize the ratio of interdecadal-to-subdecadal SST variance to define low-frequency predictors called the canonical variates (CVs). When the whole available SST time series is so processed, the leading canonical variate (CV-1) is found to be well correlated with the area-averaged SST time series which exhibits a non-uniform warming trend, while the next two (CV-2 and CV-3) describe secular variability arguably associated with a combination of Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) signals. The corresponding ENSO model that uses either all three (CVs 1–3) or only AMO/PDO-related (CVs 2 and 3) predictors captures well the observed autocorrelation function, probability density function, seasonal dependence of ENSO, and, most importantly, the observed interdecadal modulation of ENSO variance. The latter modulation, and its dependence on CVs, is shown to be inconsistent with the null hypothesis of random decadal ENSO variations simulated by multivariate linear inverse models. Cross-validated hindcasts of ENSO variance suggest a potential useful skill at decadal lead times. These findings thus argue that decadal modulations of ENSO variability may be predictable subject to our ability to forecast AMO/PDO-type climate modes; the latter forecasts may need to be based on simulations of dynamical models, rather than on a purely statistical scheme as in the present paper.     

Prediction skill of monthly SST in the North Atlantic Ocean in NCEP Climate Forecast System version 2

This work evaluates the skill of retrospective predictions of the second version of the NCEP Climate Forecast System (CFSv2) for the North Atlantic sea surface temperature (SST) and investigates the influence of El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) on the prediction skill over this region. It is shown that the CFSv2 prediction skill with 0–8 month lead displays a “tripole”-like pattern with areas of higher skills in the high latitude and tropical North Atlantic, surrounding the area of lower skills in the mid-latitude western North Atlantic. This “tripole”-like prediction skill pattern is mainly due to the persistency of SST anomalies (SSTAs), which is related to the influence of ENSO and NAO over the North Atlantic. The influences of ENSO and NAO, and their seasonality, result in the prediction skill in the tropical North Atlantic the highest in spring and the lowest in summer. In CFSv2, the ENSO influence over the North Atlantic is overestimated but the impact of NAO over the North Atlantic is not well simulated. However, compared with CFSv1, the overall skills of CFSv2 are slightly higher over the whole North Atlantic, particularly in the high latitudes and the northwest North Atlantic. The model prediction skill beyond the persistency initially presents in the mid-latitudes of the North Atlantic and extends to the low latitudes with time. That might suggest that the model captures the associated air-sea interaction in the North Atlantic. The CFSv2 prediction is less skillful than that of SSTA persistency in the high latitudes, implying that over this region the persistency is even better than CFSv2 predictions. Also, both persistent and CFSv2 predictions have relatively low skills along the Gulf Stream.     

TYPICAL ANOMALOUS WIND STRESS PATTERNS IN TROPICAL PACIFIC ASSOCIATED WITH EL NINO/SOUTHERN OSCILLATION

Based on years of month-to-month observations of sea surface temperature anomaly (SSTA) and wind stress anomaly, typical wind stress patterns in the tropical Pacific associated with ENSO are Studied with the techniques of linear regression and EOF analysis. The anomalous field, which is linearly correlated with ENSO, is found to be varying at low frequencies on the temporal scale and to be in four typical patterns of distribution horizontally.Pattern 1 is of the easterly anomaly and wind stress divergence in the equatorial region east of the date line. Pattern 2 is of the westerly anomaly and wind sttess convergence in the equatorial region east of the date line. Pattern 3 is of the westerly anomaly and wind stress convergence south of the Equator but east of the data line, with the easterly anomaly west of it. Pattern 4 is of the weak easerly anomaly east 160°W and the westerly anomaly west of 160°W. Wind stress fields linearly independent of ENSO are of a high-frequency process with a typical pattern off the Equator that has a large horizontal amplitude. Using an ocean anomaly-forcing model with the regressed wind stress anomaly field that is associated with ENSO, principal signals of ENSO are reproduced. It indicates the fundamental nature of the typical wind field anomaly patterns revealed for the genesis of El Nino.     

ENSO空间形态变异对ENSO-IOD关系年代际减弱的可能作用

基于1979—2017年哈得来中心的逐月海表温度资料（HadISST）和美国环境预报中心/美国大气研究中心（NCEP/NCAR）提供的大气环流再分析数据,研究了北半球秋季厄尔尼诺和南方涛动（ENSO）与印度洋偶极子（IOD）关系的年代际变化特征及其可能原因。结果显示,ENSO-IOD关系存在显著的年代际变化:21世纪初期前,二者呈显著正相关,厄尔尼诺（拉尼娜）年秋季常对应显著的印度洋偶极子正（负）异常;21世纪初期以后,ENSO-IOD关系显著减弱,统计上不显著。进一步研究表明,ENSO空间型态的变异对于该关系的年代际减弱起重要作用。在第二时段（2004—2017年）,中部型厄尔尼诺事件和东部型拉尼娜事件频发,中部型厄尔尼诺事件与印度洋偶极子强度的关系与其纬向位置密切相关,与其强度的线性关系较弱;而东部型拉尼娜事件,由于海温距平位置偏东（位于赤道东太平洋冷舌区）,在偏低的气候态海温作用下,其引起的大气局地响应很弱,对印度洋的遥强迫作用较弱,因而对印度洋偶极子强度的影响也较弱。在二者的共同作用下,ENSO-IOD的关系发生了年代际减弱。      

CMIP6年代际气候预测计划（DCPP）概况与评述

年代际气候预测计划（DCPP）是第六次国际耦合模式比较计划（CMIP6）的子计划之一,其目标是利用多模式开展气候系统年代际预测、可预测性和变率机制研究。DCPP设计了3组试验,即年代际回报试验、预报试验以及理解年代际变率机制和可预测性的敏感性试验。目前有21个模式拟参与DCPP计划,其中包括5个来自中国的模式。DCPP将推动解决气候系统从年际到年代际尺度预测相关的多项科学问题,评估当前气候预测系统预报技巧,挖掘潜在可预报性,研究长时间尺度气候变率形成机制,提供对科学和社会有用的预测产品。     

Relationship between ENSO and Winter Rainfall over Southeast China and Its Decadal Variability

In this study,the relationship between El Nin o-Southern Oscillation(ENSO) and winter rainfall over Southeast China(SC) is demonstrated based on instrumental and reanalysis data.The results show that ENSO and SC winter rainfall(ENSO-SC rainfall) are highly correlated and intimately coupled through an anomalous high pressure over the northwestern Pacific.In mature phase,El Nin o(La Nin a) events can cause more(less) rainfall over SC in winter.Due to the persistence and spring barrier of ENSO,SC winter rainfall has potential predictability of about half a year ahead with ENSO as a predictor.Besides,the ENSO-SC rainfall relationship exhibits decadal variability,closer before the early 1970s(0.47) and after the early 1990s(0.76),but weaker(0.12) between these times.In different periods,atmospheric teleconnection patterns have large differences and the predictability of SC winter rainfall also changes dramatically.For the most recent 20 years,the ENSO-SC rainfall relationship is closest and the prediction of SC winter rainfall anomalies based on ENSO is most creditable.In addition,the causes and mechanisms of the decadal modulation of the relationship between ENSO and SC winter rainfall need to be further studied.     

Possible causes for the persistence barrier of SSTA in the South China Sea and the vicinity of Indonesia

The persistence barrier refers to the lag correlation of sea surface temperature anomalies (SSTA) showing a rapid and significant decline in a specific season, regardless of the starting month. This implies that there is a decrease in forecast skill for SSTA in this specific season. This paper investigates the possible causes for the persistence barrier of SSTA in the South China Sea (SCS) and its adjacent regions from the perspective of interannual-interdecadal time scales. The results show that the persistence barrier of SSTA exists not only in the SCS, but also in the vicinity of Indonesia south of the equator. The SCS barrier occurs around October--November, while the occurrence of the barrier in the Indonesia region is around November--December. For these two regions, the occurrence of the persistence barrier is closely associated with the interdecadal variability of SSTA, as well as the interannual variability. The persistence barriers in the SCS and the Indonesia region do not exist alone if the interdecadal variability is not considered, because SSTA have a short memory of less than 4 months, regardless of the starting month. Moreover, the influence of the interdecadal variability of SSTA on the persistence barrier of SSTA in the SCS and the Indonesia region may be associated with SSTA in the Indian Ocean and the western Pacific, but is not closely associated with the Pacific Decadal Oscillation. However, compared with the spring persistence barrier (SPB) of ENSO, the close relationship between the persistence barriers in the SCS and the Indonesia region and the interdecadal variability is unique, since the ENSO SPB is not significantly affected by such variability. In addition, although the persistence barriers in both the SCS and the Indonesia region are quite obvious in strong ENSO cases, the interdecadal variability of SSTA also plays a non-negligible role in this relationship.     

ENSO与中国夏季年际气候异常关系的年代际变化

利用热带太平洋海表温度和中国降水和气温站点观测资料,通过滑动相关分析,揭示了ENSO与中国夏季年际气候异常关系的年代际变化事实。结果表明:ENSO与中国夏季年际气候异常的关系既有稳定的方面,又存在年代际变化特征。稳定的关系表现在:处于发展阶段的ENSO事件往往造成华北夏季降水偏少;处于衰减阶段的ENSO事件则易引起长江流域及江南地区夏季降水偏多。而二者关系的年代际变化表现在:1970年代中后期,处于发展阶段的ENSO事件引起的夏季降水异常在华南地区由偏少变为偏多,东北地区则由偏多变为偏少,而江淮地区偏多的现象不再明显,华北和东北夏季气温异常也由偏冷转变为偏暖,而华南则有偏冷趋势;处于衰减阶段的ENSO事件引起的夏季降水异常在华北地区由偏多变为偏少,江淮地区降水由偏少变为正常甚至偏多,华北夏季气温异常则由偏冷变为偏暖,长江流域和华南也有偏暖趋势。利用NCEP/NCAR再分析资料合成分析表明,在不同的年代际背景下ENSO引起的东亚中高纬度大气环流异常型发生了明显改变是ENSO和降水气温关系发生年代际变化的原因。     

全球海温异常年代际分量的方差贡献及其与中国气候异常的相关

将全球海洋海表温度异常 (SSTA) 分解为年代际、年际变化两部分, 用方差分析法给出年代际变化分量显著的洋区及其季节变化, 用奇异值分解 (SVD) 方法分析了它与中国冬温 (冬季平均气温)、夏雨 (夏季总降水量) 年代际异常分量的同期和时滞相关联系的时空结构。结果表明, 无论冬、夏, SSTA的方差构成中, 年代际变化分量方差均较年际变化分量显著; SSTA年代际分量正异常时, 中国 (以北方和东部为主) 易出现一致的冬温正异常, 长江流域、华南夏雨易出现正异常, 而华北夏雨易出现负异常; 反之亦然。     

基于神经网络奇异谱分析的ENSO指数预测

采用奇异谱分析方法研究了ENSO指数及相关序列, 结果表明奇异谱分析能很好的对原始序列进行信噪分离, 增大了ENSO指数的可预报性.在此基础上, 提出了人工神经网络和奇异谱分析相结合的ENSO指数预测方法, 进行了不同因子组合的预报试验, 预报效果明显优于持续性预报, 超前4季的Nio 3区、 Nio 4区预报相关系数仍高于0.5.     

Possible influence of western North Pacific monsoon on TC activity in mid-latitudes of East Asia

The interannual variation of East Asia summer monsoon (EASM) rainfall exhibits considerable differences between early summer [May–June (MJ)] and peak summer [July–August (JA)]. The present study focuses on peak summer. During JA, the mean ridge line of the western Pacific subtropical High (WPSH) divides EASM domain into two sub-domains: the tropical EA (5°N–26.5°N) and subtropical-extratropical EA (26.5°N–50°N). Since the major variability patterns in the two sub-domains and their origins are substantially different, the Part I of this study concentrates on the tropical EA or Southeast Asia (SEA). We apply the predictable mode analysis approach to explore the predictability and prediction of the SEA peak summer rainfall. Four principal modes of interannual rainfall variability during 1979–2013 are identified by EOF analysis: (1) the WPSH-dipole sea surface temperature (SST) feedback mode in the Northern Indo-western Pacific warm pool associated with the decay of eastern Pacific El Niño/Southern Oscillation (ENSO), (2) the central Pacific-ENSO mode, (3) the Maritime continent SST-Australian High coupled mode, which is sustained by a positive feedback between anomalous Australian high and sea surface temperature anomalies (SSTA) over Indian Ocean, and (4) the ENSO developing mode. Based on understanding of the sources of the predictability for each mode, a set of physics-based empirical (P-E) models is established for prediction of the first four leading principal components (PCs). All predictors are selected from either persistent atmospheric lower boundary anomalies from March to June or the tendency from spring to early summer. We show that these four modes can be predicted reasonably well by the P-E models, thus they are identified as the predictable modes. Using the predicted PCs and the corresponding observed spatial patterns, we have made a 35-year cross-validated hindcast, setting up a bench mark for dynamic models’ predictions. The P-E hindcast prediction skill represented by domain-averaged temporal correlation coefficient is 0.44, which is twice higher than the skill of the current dynamical hindcast, suggesting that the dynamical models have large rooms to improve. The maximum potential attainable prediction skills for the peak summer SEA rainfall is also estimated and discussed by using the PMA. High predictability regions are found over several climatological rainfall centers like Indo-China peninsula, southern coast of China, southeastern SCS, and Philippine Sea.     

Characteristics of tropical Pacific SST predictability in coupled GCM forecasts using the NCEP CFS

The limits of predictability of El Niño and the Southern Oscillation (ENSO) in coupled models are investigated based on retrospective forecasts of sea surface temperature (SST) made with the National Centers for Environmental Prediction (NCEP) coupled forecast system (CFS). The influence of initial uncertainties and model errors associated with coupled ENSO dynamics on forecast error growth are discussed. The total forecast error has maximum values in the equatorial Pacific and its growth is a strong function of season irrespective of lead time. The largest growth of systematic error of SST occurs mainly over the equatorial central and eastern Pacific and near the southeastern coast of the Americas associated with ENSO events. After subtracting the systematic error, the root-mean-square error of the retrospective forecast SST anomaly also shows a clear seasonal dependency associated with what is called spring barrier. The predictability with respect to ENSO phase shows that the phase locking of ENSO to the mean annual cycle has an influence on the seasonal dependence of skill, since the growth phase of ENSO events is more predictable than the decay phase. The overall characteristics of predictability in the coupled system are assessed by comparing the forecast error growth and the error growth between two model forecasts whose initial conditions are 1 month apart. For the ensemble mean, there is fast growth of error associated with initial uncertainties, becoming saturated within 2 months. The subsequent error growth follows the slow coupled mode related the model’s incorrect ENSO dynamics. As a result, the Lorenz curve of the ensemble mean NINO3 index does not grow, because the systematic error is identical to the same target month. In contrast, the errors of individual members grow as fast as forecast error due to the large instability of the coupled system. Because the model errors are so systematic, their influence on the forecast skill is investigated by analyzing the erroneous features in a long simulation. For the ENSO forecasts in CFS, a constant phase shift with respect to lead month is clear, using monthly forecast composite data. This feature is related to the typical ENSO behavior produced by the model that, unlike the observations, has a long life cycle with a JJA peak. Therefore, the systematic errors in the long run are reflected in the forecast skill as a major factor limiting predictability after the impact of initial uncertainties fades out.     

Dynamical seasonal prediction of Southern African summer precipitation

Prediction skill for southern African (16°–33°E, 22°–35°S) summer precipitation in the Scale Interaction Experiment-Frontier coupled model is assessed for the period of 1982–2008. Using three different observation datasets, deterministic forecasts are evaluated by anomaly correlation coefficients, whereas scores of relative operating characteristic and relative operating level are used to evaluate probabilistic forecasts. We have found that these scores for December–February precipitation forecasts initialized on October 1st are significant at 95 % confidence level. On a local scale, the level of prediction skill in the northwestern and central parts of southern Africa is higher than that in northeastern South Africa. El Niño/Southern Oscillation (ENSO) provides the major source of predictability, but the relationship with ENSO is too strong in the model. The Benguela Niño, the basin mode in the tropical Indian Ocean, the subtropical dipole modes in the South Atlantic and the southern Indian Oceans and ENSO Modoki may provide additional sources of predictability. Within the wet season from October to the following April, the precipitation anomalies in December-February are the most predictable. This study presents promising results for seasonal prediction of precipitation anomaly in the extratropics, where seasonal prediction has been considered a difficult task.     

Current status of ENSO prediction skill in coupled ocean–atmosphere models

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.     

ENSO对中国夏季降水可预测性变化的研究

众多研究表明,ENSO对东亚夏季风尤其是中国夏季降水存在很大影响,已成为中国夏季降水首要的预测因子。传统的预测模型认为,当前期ENSO为暖位相状态时,夏季中国主要雨带位置偏南,长江流域降水偏多;反之,当前期ENSO为冷位相状态时,夏季中国主要雨带位置偏北,长江流域降水偏少。基于1951—2003年中国160站月平均降水资料和同时段的NOAA ERSST海表温度资料,讨论了中国夏季降水和前冬Nino3区海温关系的年代际变化。分析结果显示,近20年来二者相关性已大大衰减。作为中国夏季降水的主要预测指标,ENSO的指示意义也相应减弱。在1951—1974年,依据前冬Nino3区SSTA预测夏季降水符号准确率在67%以上的站数有43站,但在1980—2003年,同样准确率的站数只有15站。在前一个研究时段,这43站呈区域性分布于东北地区、黄河和长江流域,但后一个研究时段内的15站分布分散,不利于区域性预测。相关分析结果表明,在20世纪70年代中期之前,当前冬赤道东太平洋海温偏高时,华北和江南南部的多数测站夏季降水偏多,淮河流域降水偏少,同时梅雨开始偏晚。反之,当前冬赤道东太平洋海温偏低时,华北和江南南部夏季降水易偏少,淮河流域降水则偏多,同时梅雨开始偏早。但在20世纪80年代之后,上述对应关系较难成立。因此,在汛期预测业务中参考ENSO的作用时必须充分考虑年代际背景的差异。     

Decadal/interdecadal variations of the ocean temperature and its impacts on climate

Decadal/interdecadal climate variability is an important research focus of the CLIVAR Program and has been paid more attention. Over recent years, a lot of studies in relation to interdecadal climate variations have been also completed by Chinese scientists. This paper presents an overview of some advances in the study of decadal/interdecadal variations of the ocean temperature and its climate impacts, which includes interdecadal climate variability in China, the interdecadal modes of sea surface temperature (SST) anomalies in the North Pacific, and in particular, the impacts of interdecadal SST variations on the Asian monsoon rainfall. As summarized in this paper, some results have been achieved by using climate diagnostic studies of historical climatic datasets. Two fundamental interdecadal SST variability modes (7– 10-years mode and 25–35-years mode) have been identified over the North Pacific associated with different anomalous patterns of atmospheric circulation. The southern Indian Ocean dipole (SIOD) shows a major feature of interdecadal variation, with a positive (negative) phase favoring a weakened (enhanced) Asian summer monsoon in the following summer. It is also found that the China monsoon rainfall exhibits interdecadal variations with more wet (dry) monsoon years in the Yangtze River (South China and North China) before 1976, but vice versa after 1976. The weakened relationship between the Indian summer rainfall and ENSO is a feature of interdecadal variations, suggesting an important role of the interdecadal variation of the SIOD in the climate over the south Asia and southeast Asia. In addition, evidence indicates that the climate shift in the 1960s may be related to the anomalies of the North Atlantic Oscillation (NAO) and North Pacific Oscillation (NPO). Overall, the present research has improved our understanding of the decadal/interdecadal variations of SST and their impacts on the Asian monsoon rainfall. However, the research also highlights a number of problems for future research, in particular the mechanisms responsible for the monsoon long-term predictability, which is a great challenge in climate research.