• ADVANCES IN ATMOSPHERIC SCIENCES, 2017, 34(12): 1395-1403
    doi: 10.1007/s00376-017-6324-y.
    Contrasting the Skills and Biases of Deterministic Predictions for the Two Types of El Niño
    Fei ZHENG1,2,, Jin-Yi YU3


    The tropical Pacific has begun to experience a new type of El Niño, which has occurred particularly frequently during the last decade, referred to as the central Pacific (CP) El Niño. Various coupled models with different degrees of complexity have been used to make real-time El Niño predictions, but high uncertainty still exists in their forecasts. It remains unknown as to how much of this uncertainty is specifically related to the new CP-type El Niño and how much is common to both this type and the conventional Eastern Pacific (EP)-type El Niño. In this study, the deterministic performance of an El Niño-Southern Oscillation (ENSO) ensemble prediction system is examined for the two types of El Niño. Ensemble hindcasts are run for the nine EP El Niño events and twelve CP El Niño events that have occurred since 1950. The results show that (1) the skill scores for the EP events are significantly better than those for the CP events, at all lead times; (2) the systematic forecast biases come mostly from the prediction of the CP events; and (3) the systematic error is characterized by an overly warm eastern Pacific during the spring season, indicating a stronger spring prediction barrier for the CP El Niño. Further improvements to coupled atmosphere-ocean models in terms of CP El Niño prediction should be recognized as a key and high-priority task for the climate prediction community.

    Key words: ENSO; EP El Niño; CP El Niño; prediction skill; systematic bias; spring prediction barrier;
    摘要: El Niño作为全球最显著的年际变化信号,对全球气候和环境均有着重要影响。近年来,因其最大的年际异常信号主要集中在中太平洋区域,且出现频率明显增加,一种新的中太平洋型(CP型)El Niño现象引起了广泛的关注。过去研究主要集中在区分新的CP型El Niño事件与传统的东太平洋型(EP型)El Niño事件对全球各个区域不同的影响,但是对于两类El Niño事件预测技巧存在的差异及其原因并未给出较好的解释。中国科学院大气物理研究所郑飞研究员和美国加州大学欧文分校Jin-Yi Yu教授,基于大气所ENSO集合预测系统检验1950年以来的El Niño事件预测水平,合作发现两类El Niño事件在预测技巧上存在着明显差异,即系统对CP型El Niño事件的预测能力相比EP型事件明显偏弱。进一步的分析发现,CP型事件预测技巧偏弱有两个可能的原因制约:(1)模型的预测偏差主要来源于其对CP型El Niño事件的预测;(2)这一预测偏差主要体现为对CP型El Niño事件预测的春季暖偏差,同时也与CP型事件的“春季预报障碍”更严重紧密联系。
    关键词: ENSO ; EP El Niño ; CP El Niño ; 预测能力 ; 偏差 ; 春季预报障碍
    1. Introduction

    As the most striking interannual variability in the tropical Pacific, El Niño-Southern Oscillation (ENSO) has been intensively studied for several decades. Based on the profound effects of ENSO on environmental and socioeconomic activities worldwide, understanding the changes in ENSO's characteristics remains important and challenging (McPhaden et al., 2006; Ashok and Yamagata, 2009). Currently, due to improved observations (e.g., McPhaden et al., 1998) and modeling techniques (Delecluse et al., 1998), the ability to predict ENSO has become markedly better over the past few decades (Latif et al., 1998; Jin et al., 2008). Indeed, ENSO forecasts have reached the stage where skillful predictions can be made 6-12 months in advance. Several operational centers have used climate models to routinely make ENSO predictions in real time (Latif et al., 1998; Kirtman et al., 2001). However, the skill with respect to sea surface temperature (SST) forecasts in the equatorial Pacific is strongly model-dependent and widely divergent across various prediction systems (Jin et al., 2008; Barnston et al., 2012). Broadly, there is still room for improvement in ENSO prediction (Barnston et al., 2012).

    It has been demonstrated that the real-time ENSO prediction skill over the past decade is obviously lower than that in the 1980s and 1990s (Barnston et al., 2012). For example, the correlation between observations and ENSO hindcasts over a nine-year sliding window has an average value of 0.65 during 1981-2010 at a six-month lead time, but decreases to 0.42 for 2002-11 period (Barnston et al., 2012). One possible reason for the shift in the ENSO prediction skill is because a different type of El Niño——as compared to the canonical eastern Pacific (EP) El Niño (McPhaden et al., 2011; Yu et al., 2012)——emerged in the 2000s. For this type of El Niño, the maximum anomalous SST is mostly confined to the central Pacific, and is thus referred to as the central Pacific (CP) El Niño (Yu and Kao, 2007; Kao and Yu, 2009; Yu et al., 2010; Zheng et al., 2014b).

    The limited predictability may be attributable to factors such as errors in oceanic initial conditions, state-dependent stochastic forcing, or model errors (e.g., Moore and Kleeman, 1996; Karspeck et al., 2006; Duan and Zhao, 2015). However, a systematic examination of climate models' performances in predicting the two types of El Niño has been less well explored, and it remains controversial as to whether their predictabilities are distinct in different state-of-the-art climate models (e.g., Jeong et al., 2012; Yang and Jiang, 2014; Imada et al., 2015; Luo et al., 2016). Based on version 2 of the National Centers for Environmental Prediction Climate Forecast System, (Yang and Jiang, 2014) compared the model skill in using the El Niño Modoki index (EMI) and ño3 index to predict the two types of El Niño, and showed that the EMI was more persistent and predictable than the ño3 index during boreal summer and autumn. On the contrary, (Jeong et al., 2012) and (Luo et al., 2016) found EP events to be more predictable than CP events when adopting the coupled climate prediction multi-model ensemble suite of the Asia-Pacific Economic Cooperation Climate Center. Using version 5 of the Model for Interdisciplinary Research on Climate, (Imada et al., 2015) also found that CP El Niño has limited predictability and a shorter lead time for prediction compared to EP El Niño.

    In the present study, we use the ensemble prediction system (EPS) developed at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (Zheng et al., 2006, 2007, 2009a; Zheng and Zhu, 2010a, 2016), to investigate the predictability of the two types of El Niño. Hindcast experiments are carried out with the EPS for 21 major El Niño events observed since 1950. The predictability of the ensemble-mean forecasts is validated and compared with respect to EP and CP El Niño. The common forecast biases for the two types of El Niño are identified and contrasted throughout the different phases of the El Niño lifecycle.

    2. Model and datasets

    The IAP ENSO EPS has three main components: an intermediate coupled model (ICM), an air-sea coupled data assimilation system, and a stochastic model-error model. The ICM was developed by (Keenlyside and Kleeman, 2002) and (Zhang et al., 2005), and consists of a dynamical ocean model, an SST anomaly model that empirically parameterizes the temperature of subsurface water entrained into the mixed layer based on sea level anomalies, and a statistical wind stress model. The dynamical component of the ICM is described in detail by (Keenlyside and Kleeman, 2002). All coupled model components exchange simulated anomaly fields, such as the wind stress in the atmosphere and the SST in the ocean, once a day. The air-sea coupled data assimilation system (Zheng and Zhu, 2008, 2010a, 2015) uses an ensemble Kalman filter (EnKF) approach to minimize the errors in both the atmospheric and oceanic initial conditions by assimilating available atmosphere and ocean observations simultaneously into the ICM (please refer to the electronic supplementary material for details). A stochastic error model (Zheng et al., 2009a; Zheng and Zhu, 2016) is embedded within the ICM to perturb the modeled SST anomaly field randomly by adding error terms to the right-hand-sides of the model equations. This stochastic error model is designed to account for the temporal evolution of the forecasted uncertainties in the SST anomaly field (Zheng et al., 2006, 2009a, b; Feng et al., 2015; Zheng and Zhu, 2016). The performance of the prediction system is documented in (Zheng and Zhu, 2016), in which a 20-year retrospective forecast comparison shows that a good forecast skill of the EPS with a prediction lead time of up to one year is possible. Therefore, this EPS system is suitable for examining the predictability of the two types of El Niño.

    Because of the need to initialize the model for a long period (1950-2012) to predict the CP and EP El Niño events, the observational data available to us in this study only include version 3b of the Extended Reconstructed SST (Smith et al., 2008) dataset (horizontal resolution: 2°), and wind stress data obtained as the ensemble mean of a 24-ensemble member ECHAM4.5 simulation (Röckner et al., 1996; please refer to the online supplementary file for details). The SST data from 1950 to 2012 are used to select the EP and CP El Niño events for the hindcast experiments (for details, see section 3.1). For the hindcast experiments, the model's spin-up and data assimilation cycle are described in detail in the online supplementary file.

    3. Deterministic prediction skill for the two types of El Niño
    3.1. Selection of EP and CP El Niño events

    The El Niño events are selected in this study based on the National Oceanic and Atmospheric Administration (NOAA) criterion that the Ocean Niño Index (ONI) [i.e., the three-month running mean of SST anomalies in the Niño3.4 region (5°N-5°S, 120°-170°W)] must be greater than or equal to 0.5°C for a period of at least five consecutive overlapping seasons. A total of 21 events are identified based on this criterion and are listed in Table 1. Similar to previous studies (e.g., Yu et al., 2012; Yu and Kim, 2013), we then determine the type of these 21 El Niño events based on a consensus of three identification methods, which are: the EMI method of (Ashok et al., 2007); the cold tongue (CT) and warm pool (WP) index method of (Ren and Jin, 2011); and the pattern correlation (PTN) method of (Yu and Kim, 2013). With the EMI method, an El Niño event is considered to be CP-type when the value of the December-January-February (DJF)-averaged EMI is equal to or greater than 0.7 standard deviations. With the CT/WP method, an El Niño event is classified as CP-type (EP-type) when the DJF-averaged value of the WP index is greater (less) than the averaged value of the CT index. Using the PTN method, the El Niño type is determined based on whether the spatial pattern of El Niño SST anomalies resembles more closely the typical SST anomaly pattern of EP- or CP-type El Niño. According to the consensus listed in Table 1, nine of the twenty-one major El Niño events are classified to be EP-type, and the other twelve are CP-type.

    Table 1. The major El Niño events since 1950, as identified by the NOAA ONI, and their type identified by the majority consensus of three methods.
    El Niño years method method method Consensus
    1951/52 EP EP MIX EP
    1953/54 EP CP EP EP
    1957/58 CP EP CP CP
    1958/59 CP CP MIX CP
    1963/64 CP CP MIX CP
    1965/66 CP EP CP CP
    1968/69 CP CP CP CP
    1969/70 EP EP CP EP
    1972/73 EP EP MIX EP
    1976/77 EP EP MIX EP
    1977/78 CP CP CP CP
    1982/83 EP EP EP EP
    1986/87 EP EP MIX EP
    1987/88 EP CP CP CP
    1991/92 CP EP MIX CP
    1994/95 CP CP CP CP
    1997/98 EP EP EP EP
    2002/03 CP CP CP CP
    2004/05 CP CP CP CP
    2006/07 EP EP MIX EP
    2009/10 CP CP CP CP

    Table 1. The major El Niño events since 1950, as identified by the NOAA ONI, and their type identified by the majority consensus of three methods.

    3.2. Deterministic prediction skill

    Next, the EPS is used to perform retrospective ensemble forecasts for the 21 major El Niño events. A unique index (i.e., ño3.4 index), rather than the ño3 and EMI indices used in previous studies (e.g., Yang and Jiang, 2014, Luo et al., 2016), is adopted to evaluate the skill of the EPS in forecasting the two types of El Niño. A 12-month hindcast is initialized each month during the period 1950-2010 with 100 ensemble members. Figure 1 shows the deterministic retrospective forecast results for the strongest EP El Niño (i.e., the 1997/98 event) and the strongest CP El Niño (i.e., the 2009/10 event) since 1950. Here, the strength of the El Niño event is measured by the peak value of the ño3.4 SST anomalies. The 1997/98 El Niño is also the strongest EP type El Niño event during the past century (McPhaden and Yu, 1999; Picaut et al., 2002), and the 2009/10 El Niño event is considered the largest CP-type El Niño event in the historical record (e.g., Yu et al., 2012). For both events, the EPS system can successfully predict their onset and development as early as 12 months in advance, albeit with errors still existing in the forecasts of their magnitudes.

    The anomaly correlations and root-mean-square errors (RMSEs) between the observed and ensemble-mean-predicted SST anomalies in the ño3.4 region are shown in Fig. 2 as a function of lead time. The hindcasts for the both types of El Niño have generally high skill at all lead times. The correlations are greater than 0.93 for the one-month lead time and remain above 0.6 even for the twelve-month lead time. The skill scores of the hindcast are significantly higher for the EP events than for the CP events at all lead times. There is a distinct difference between the EP and CP events in skill score beyond the two-month lead time. At the nine-month lead time, the correlation coefficient for the EP events is still above 0.8, and is approximately 0.2 higher than that for the CP events. The RMSEs of the hindcast for both the EP and CP events remain smaller than 0.75°C up to the nine-month lead time. Beyond the nine-month lead time, the RMSEs of the hindcast are about 0.1°C-0.15°C smaller for the CP events than for the EP events. This is because the CP events have a relatively weak signal compared to the EP events (Zheng et al., 2014b; Fang et al., 2015), and thus they are more affected by atmospheric noise; this prevents the development of oceanic signals that can be used for prediction (e.g., Imada et al., 2015).

    Fig.1. Deterministic predictions for the largest (a) EP and (b) CP El Niño since 1950. The thick black curves are the observed ño3.4 SST anomalies, and the thin curves of red, green, blue and orange are the ensemble-mean predictions starting 12, 9, 6 and 3 months, respectively, before the peak of each El Niño.

    Fig.2. Anomaly correlations (upper panel) and RMSEs (lower panel) between the observed and predicted SST anomalies in the ño3.4 region as a function of lead time. Values are shown separately for all El Niño events (dot-dashed lines), EP El Niño events only (lines with solid circles), and CP El Niño events only (lines with open circles). The results are obtained as the means of the ensemble hindcasts made for the El Niño events during the period 1950-2012.

    Fig.3. (a-d) Horizontal distribution of the anomaly correlations between observed and ensemble-mean-forecasted SST anomalies for the EP El Niño events at a lead time of (a) 3 months, (b) 6 months, (c) 9 months and (d) 12 months. The contour interval is 0.2, and the shaded areas represent correlation coefficients above 0.5 with 0.1 interval. (e-h) As in (a-d) but for the prediction of CP El Niño events.

    The horizontal distributions of the anomaly correlations and RMSEs between the observed and predicted SST anomalies at lead times of three, six, nine and twelve months are displayed in Figs. 3 and 4. Geographically, the performance of this system is particularly good in the central-eastern equatorial Pacific (Zhang et al., 2005; Zheng and Zhu, 2015). Also, for predicting the EP events, the correlation is above 0.8 in the central basin and above 0.7 in the eastern equatorial Pacific at the three-month lead time (Fig. 3a). As the lead time increases, the correlation drops first and fastest in the eastern basin. However, even at the six-month lead time (Fig. 3b), the skill does not drop much in the central basin (the correlation remains greater than 0.7) and there is only a slight decrease in the eastern basin. At a twelve-month lead time (Fig. 3d), correlations larger than 0.7 can still be found over a sizeable region of the central Pacific, but the correlation drops below 0.6 east of 100°W. Consistent with Fig. 2, there is an obvious decrease in the correlations for the prediction of the CP events over the entire region. The correlations for the CP events are approximately 0.1-0.2 lower than those for the EP events at all lead times. The difference is particularly significant in the central and eastern Pacific (Fig. 3). At the same time, the forecasted errors in the EP El Niño predictions are approximately 0.2°C larger than those in the CP El Niño predictions over the eastern Pacific at all lead times (Fig. 4).

    Fig.4. As in Fig. 3, but for the RMSEs. Contour interval is 0.2°C, and the shaded areas represent RMSEs larger than 0.5°C with 0.1°C interval.

    3.3. Systematic error

    In coupled prediction systems, climate drift is still a significant problem. In some cases, systematic model biases can be much larger than the anomalies to be predicted (e.g., Schneider et al., 2003; Zhang et al., 2005). Typically, the systematic errors can be identified by averaging the differences between the predicted and observed physical fields over all ensemble members. In Fig. 5, we show the systematic errors of the IAP ENSO EPS in the ensemble-mean prediction of equatorial Pacific SST anomalies as a function of initial calendar month. When the systematic errors are calculated from the predictions for all 21 El Niño events (the top row), the errors are characterized by a warm bias in the eastern basin and a cold bias in the central part of the basin. The largest warm bias (close to 0.5°C) occurs in May with the predictions starting from January and April. When stratifying the systematic errors by the predictions of the EP El Niño events (middle row) and the CP El Niño events (bottom row), it is noticeable that the systematic errors are significantly smaller for the EP El Niño predictions than for the CP El Niño predictions. The systematic errors in the El Niño predictions are mostly related to predicting CP El Niño events. The warm bias might be caused by the model's deficiency in simulating the thermocline feedback over the equatorial eastern Pacific during the CP El Niño events. Moreover, the obvious systematic errors in forecasting the CP El Niño events also contribute to the forecast errors in the CP El Niño predictions (Fig. 4). The weak systematic errors in the EP El Niño predictions also indicate the errors in forecasting the EP El Niño events are mainly stochastic (Zheng et al., 2016).

    Fig.5. Systematic errors of the predicted SST anomalies along the equator from the IAP ENSO EPS at different lead times. Results are shown for predictions starting in January (first column), April (second column), July (third column), and October (fourth column); and for the predictions for all the El Niño events (top row), the EP El Niño events only (middle row), and the CP El Niño events only (bottom row). Contour interval is 0.1°C, and the shaded areas represent biases larger (smaller) than 0.3°C (-0.3°C).

    3.4. Seasonality of the prediction skill

    The robust center of high systematic error in May over the eastern Pacific might indicate that CP El Niño events are more difficult to predict through the spring season. Errors in observations can easily lead to a spring prediction barrier (SPB; Webster and Yang, 1992) in the prediction of CP events due to the larger forecast bias (Zheng and Zhu, 2010b). To examine the seasonality of the prediction skill in a deterministic sense, Fig. 6 displays the anomaly correlations for the ensemble-mean forecast calculated as a function of the initial month and lead time. As shown in many previous studies, the skill in predicting SST anomalies depends sensitively on the initial month (e.g., Latif et al., 1998; Jin et al., 2008; Zheng and Zhu, 2010b). For example, as shown in Fig. 6, the correlation is relatively low for the predictions initialized before and even during the spring season, and is significantly higher for the predictions initialized thereafter. Moreover, the SPB can be characterized as a decay in the anomaly correlation skill of ENSO forecasts made before and during the spring being much more obvious and rapid than those made after spring (e.g., Webster and Yang, 1992; Zhang et al., 2005). Our results further indicate that a stronger SPB exists for CP El Niño predictions than for EP El Niño predictions. The ensemble-mean forecasts for the EP El Niño events have higher anomaly correlation coefficients than those for the CP El Niño events at all lead times and initial months. In particular, the decline in correlation skill for the CP El Niño predictions initialized before and during spring is much more rapid than that for the EP El Niño predictions.

    The different speeds of decline in the correlations of the EP and CP El Niño events are likely related to the different seasonal evolution of the EP and CP events (Kao and Yu, 2009; Yeh et al., 2014). While both types of El Niño reach their maximum amplitude during boreal winter, the onset of the positive SST anomalies for EP events typically happens in the eastern Pacific during spring, but for CP events this usually takes place from the eastern subtropics into the tropical central Pacific during summer. Due to the difference in evolution, SST anomalies tend to be more persistent from spring to summer for EP events than for CP events. Consequently, EP El Niño tends to have a high persistence prediction skill across the spring season and a weak SPB, whereas CP El Niño tends to have a more rapid decline in persistence prediction skill and a strong SPB.

    Fig.6. Anomaly correlations in the ño3.4 region as a function of lead time and start month for the ensemble-mean forecasts performed using the IAP ENSO EPS: (a) for the EP El Niño events; (b) for the CP El Niño events.

    4. Conclusions and discussion

    It has been noticed that CP-type El Niño events have occurred more frequently in recent decades, and that this type of El Niño may be generated by a mechanism distinct from that of the traditional EP-type El Niño (Yu et al., 2010, 2017). In this study, the deterministic prediction skill of the IAP ENSO EPS is examined separately for EP- and CP-type El Niño. The prediction skill is found to be lower for CP-type El Niño. Beyond a three-month lead time, the prediction skill is consistently higher for predicting EP events (0.1-0.2 higher in terms of the correlation coefficient) than CP events. Also, the system produces overly warm SSTs in the eastern basin during the spring season when predicting CP El Niño events. This bias indicates that the SST anomalies in the prediction model may be too sensitive to wind forcing. This oversensitivity may be because most El Niño prediction models have been designed, tuned and tested to capture the thermocline dynamics of the traditional EP-type El Niño. These thermocline dynamics are suggested to be less important for CP El Niño, and thus may manifest as a systematic error when predicting such events. Our results indicate that further improvements in El Niño prediction can be realized if we can improve the performance of coupled atmosphere-ocean models in predicting CP El Niño, which should be recognized as a key and high-priority task for the climate prediction community.

    Efforts to improve the ability of the IAP ENSO EPS in predicting CP El Niño are currently in progress. For example, the current version of the system does not consider the physical processes involving freshwater flux and salinity variability over the tropical Pacific (Zheng and Zhang, 2012; Zheng et al., 2014a), and the effects of salinity are not included in the current model. Because El Niño events, especially CP events, can be modulated by the interannually varying salinity (Zhu et al., 2014; Zheng and Zhang, 2015), such effects need to be considered. Using data assimilation methods in the initialization system to include more accurate salinity information may also help improve CP El Niño forecasts (Zheng and Zhu, 2010a, 2015). Moreover, as demonstrated in previous studies (Chen et al., 2004; Zheng et al., 2009a, 2016; Wang et al., 2010; Barnston et al., 2012), strong decadal variations exist in the predictability of ENSO, with the most recent decade having the lowest predictability among the past six. Because of the relatively low reliability of the data in the 1950s-70s compared to recent decades, it is better to assess the predictability of the two types of El Niño by comparing the difference in prediction skill between the recent and previous decades by including as many El Niño events as possible.

    Acknowledgements. The authors wish to thank the two anonymous reviewers for their very helpful comments and suggestions. This work was supported by the National Program for Support of Top-notch Young Professionals, and the National Natural Science Foundation of China (Grant No. 41576019). J.-Y. YU was supported by the US National Science Foundation (Grant No. AGS-150514).

    Electronic supplementary material: Supplementary material is available in the online version of this article at http://dx.doi.org/10.1007/s00376-017-6324-y.


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    The nonlinear forcing singular vector (NFSV) approach is used to identify the most disturbing tendency error of the Zebiak–Cane model associated with El Ni09o predictions, which is most potential for yielding aggressively large prediction errors of El Ni09o events. The results show that only one NFSV exists for each of the predictions for the predetermined model El Ni09o events. These NFSVs cause the largest prediction error for the corresponding El Ni09o event in perfect initial condition scenario. It is found that the NFSVs often present large-scale zonal dipolar structures and are insensitive to the intensities of El Ni09o events, but are dependent on the prediction periods. In particular, the NFSVs associated with the predictions crossing through the growth phase of El Ni09o tend to exhibit a zonal dipolar pattern with positive anomalies in the equatorial central-western Pacific and negative anomalies in the equatorial eastern Pacific (denoted as “NFSV1”). Meanwhile, those associated with the predictions through the decaying phase of El Ni09o are inclined to present another zonal dipolar pattern (denoted as “NFSV2”), which is almost opposite to the NFSV1. Similarly, the linear forcing singular vectors (FSVs), which are computed based on the tangent linear model, can also be classified into two types “FSV1” and “FSV2”. We find that both FSV1 and NFSV1 often cause negative prediction errors for Ni09o-3 SSTA of the El Ni09o events, while the FSV2 and NFSV2 usually yield positive prediction errors. However, due to the effect of nonlinearities, the NFSVs usually have the western pole of the zonal dipolar pattern much farther west, and covering much broader region. The nonlinearities have a suppression effect on the growth of the prediction errors caused by the FSVs and the particular structure of the NFSVs tends to reduce such suppression effect of nonlinearities, finally making the NFSV-type tendency error yield much large prediction error for Ni09o-3 SSTA of El Ni09o events. The NFSVs, compared to the FSVs, are more applicable in describing the most disturbing tendency error of the Zebiak–Cane model since they consider the effect of nonlinearities. The NFSV-type tendency errors may provide information concerning the sensitive areas where the model errors are much more likely to yield large prediction errors for El Ni09o events. If the simulation skills of the states in the sensitive areas can be improved, the ENSO forecast skill may in turn be greatly increased.
    DOI:10.1007/s00382-014-2369-0      URL     [Cited within:1]
    [7] Fang X.-H., F. Zheng, and J. Zhu, 2015: The cloud radiative effect when simulating strength asymmetry in two types of El Niño events using CMIP5 models.J. Geophys. Res.,120(6),4357-4369,doi: 10.1002/2014JC010683.
    Abstract It has been suggested that the strength asymmetry of the Bjerknes feedback is responsible for the pronounced amplitude asymmetry between eastern Pacific (EP) and central Pacific (CP) El Ni09o events. Detailed analyses have indicated that this strength asymmetry is mainly derived from the weaker sensitivity of the zonal sea level pressure (SLP) anomaly to that of the diabatic heating anomaly during the development phase of CP El Ni09o events, which mainly results from the large cancelation induced by the negative sea surface temperature (SST)-cloud thermodynamic feedback that negates the positive dynamical feedback. This study validates these conclusions by using historical runs of 20 models involved in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Our results suggest that the CMIP5 models generally depict the asymmetry in amplitude between the two types of El Ni09o events well, which is consistent with successfully simulating the strength asymmetry of the Bjerknes feedback. As observed during both types of El Ni09o events, variations in the total cloud amount and shortwave radiation also indicated that the cloud-radiative effect is an important factor that causes amplitude asymmetry between CP and EP El Ni09o events. However, the CMIP5 models are severely biased when capturing realistic CP El Ni09o structures, namely few models can simulate the significantly weaker warming anomalies in the EP relative to the CP.
    DOI:10.1002/2014JC010683      URL     [Cited within:1]
    [8] Feng L. S., F. Zheng, J. Zhu, and H. W. Liu., 2015: The role of stochastic model error perturbations in predicting the 2011/12 double-dip La ña.SOLA,11,65-69,doi: 10.2151/sola.2015-014.
    ABSTRACT The 2011/12 La Ni09a, referred to as a double-dip La Ni09a event with following a previous La Ni09a event, was not well predicted by most climate models when starting from early-mid 2011. Based on a developed El Ni09o-Southern Oscillation (ENSO) ensemble prediction system, this paper investigates the key predictors for the 2011/12 La Ni09a, to determine which conditions favor a double-dip La Ni09a event up to one year in advance. The key predictors were isolated in a 100-member ensemble hindcast experiment. Results show that continuous easterly surface winds and persistent subsurface cold conditions preceded the second-year cooling in mid-2011. And a significant difference can be viewed between the best and the worst ensemble forecasts arose from the stochastic model-error perturbations. The detailed comparisons between the best and the worst ensemble forecasts further illustrate that the stochastic model-error perturbations play a significant role in improving the prediction skills of the best ensemble members during the 2011/12 12-month forecast process, through capturing the transition of sea surface temperature (SST) over the tropical Pacific (i.e., from a warm condition to a cold condition) in boreal spring.
    DOI:10.2151/sola.2015-014      URL     [Cited within:1]
    [9] Imada Y., H. Tatebe, M. Ishii, Y. Chikamoto, M. Mori, M. Arai, M. Watanabe, and M. Kimoto, 2015: Predictability of two types of El Niño assessed using an extended seasonal prediction system by MIROC.Mon. Wea. Rev.,143,4597-4617,doi: 10.1175/MWR-D-15-0007.1
    ABSTRACT Predictability of El Nino-Southern Oscillation (ENSO) is examined using ensemble hindcasts made with a seasonal prediction system based on the atmosphere and ocean general circulation model, the Model for Interdisciplinary Research on Climate, version 5 (MIROC5). Particular attention is paid to differences in predictive skill in terms of the prediction error for two prominent types of El Nino: the conventional eastern Pacific (EP) El Nino and the central Pacific (CP) El Nino, the latter having a maximum warming around the date line. Although the system adopts ocean anomaly assimilation for the initialization process, it maintains a significant ability to predict ENSO with a lead time of more than half a year. This is partly due to the fact that the system is little affected by the spring prediction barrier, because increases in the error have little dependence on the thermocline variability. Composite analyses of each type of El Nino reveal that, compared to EP El Ninos, the ability to predict CP El Ninos is limited and has a shorter lead time. This is because CP El Ninos have relatively small amplitudes, and thus they are more affected by atmospheric noise; this prevents development of oceanic signals that can be used for prediction.
    DOI:10.1175/MWR-D-15-0007.1      URL     [Cited within:3]
    [10] Jeong, H.-I., Coauthors, 2012: Assessment of the APCC coupled MME suite in predicting the distinctive climate impacts of two flavors of ENSO during boreal winter.Climate Dyn.,39,475-493,doi: 10.1007/s00382-012-1359-3.
    Forecast skill of the APEC Climate Center (APCC) Multi-Model Ensemble (MME) seasonal forecast system in predicting two main types of El Ni脙卤o-Southern Oscillation (ENSO), namely canonical (or cold tongue) and Modoki ENSO, and their regional climate impacts is assessed for boreal winter. The APCC MME is constructed by simple composite of ensemble forecasts from five independent coupled ocean-atmosphere climate models. Based on a hindcast set targeting boreal winter prediction for the period 1982-2004, we show that the MME can predict and discern the important differences in the patterns of tropical Pacific sea surface temperature anomaly between the canonical and Modoki ENSO one and four month ahead. Importantly, the four month lead MME beats the persistent forecast. The MME reasonably predicts the distinct impacts of the canonical ENSO, including the strong winter monsoon rainfall over East Asia, the below normal rainfall and above normal temperature over Australia, the anomalously wet conditions across the south and cold conditions over the whole area of USA, and the anomalously dry conditions over South America. However, there are some limitations in capturing its regional impacts, especially, over Australasia and tropical South America at a lead time of one and four months. Nonetheless, forecast skills for rainfall and temperature over East Asia and North America during ENSO Modoki are comparable to or slightly higher than those during canonical ENSO events.
    DOI:10.1007/s00382-012-1359-3      URL     [Cited within:2]
    [11] Jin, E. K., Coauthors, 2008: Current status of ENSO prediction skill in coupled ocean-atmosphere models.Climate Dyn.,31(6),647-664,doi: 10.1007/s00382-008-0397-3.
    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 Ni o 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.
    DOI:10.1007/s00382-008-0397-3      URL     [Cited within:2]
    [12] Kao H.-Y., J.-Y. Yu, 2009: Contrasting eastern-Pacific and central-Pacific types of ENSO.J. Climate,22,615-632,doi: 10.1175/2008JCLI2309.1.
    In this study, I identify and contrast two types of El Nino Southern Oscillation (ENSO): one located in the eastern Pacific near the South American coast (i.e. EP-ENSO), and the other in the central Pacific close to the date line (i.e. CP-ENSO). The EP-ENSO possesses the properties of the canonical ENSO and is related to thermocline variations. It is characterized by basin-wide surface and subsurface evolution, coupled with Southern Oscillation and dominated by 2 to 4 year timescale. In contrast, the CP-ENSO is characterized by in-situ evolution and local atmosphere-ocean coupling, and is likely driven by atmospheric forcing. From an upper-ocean heat budget analysis, the CP-ENSO is found to be related to the trade wind forcing associated with the variations of the northern subtropical high. Wind-induced surface heat flux forcing first warms up the upper ocean temperature in the Northeastern Subtropical Pacific. The SST anomalies later spread toward the central equatorial Pacific through heat-flux forcing and vertical advection processes, are further enhanced by zonal advection, and eventually terminate by surface heat flux. The budget results suggest a possible interaction pathway between the north-eastern subtropics and central equatorial Pacific. The CP-ENSO is dominated by a quasi-biennial (藴2.5 yr) periodicity that is also found in subtropical high and Asian-Australian monsoon variability. The possible linkage between CP-ENSO and monsoon variability is demonstrated by an Indian Ocean-decoupled experiment using a coupled GCM. The biennial CP-ENSO in the model is significantly reduced when the Indian Ocean coupling is turned off to weaken the biennial monsoon variability. This study suggests the existence of a distinct CP-ENSO that is a result of interactions among Asian-Australian monsoon, northern subtropical Pacific and central equatorial Pacific.
    DOI:10.1175/2008JCLI2309.1      URL     [Cited within:2]
    [13] Karspeck A. R., A. Kaplan, and M. A. Cane, 2006: Predictability loss in an intermediate ENSO model due to initial error and atmospheric noise.J. Climate,19(15),3572-3588,doi: 10.1175/JCLI3818.1.
    The seasonal and interannual predictability of ENSO variability in a version of the Zebiak - Cane coupled model is examined in a perturbation experiment. Instead of assuming that the model is "perfect," it is assumed that a set of optimal initial conditions exists for the model. These states, obtained through a nonlinear minimization of the misfit between model trajectories and the observations, initiate model forecasts that correlate well with the observations. Realistic estimates of the observational error magnitudes and covariance structures of sea surface temperatures, zonal wind stress, and thermocline depth are used to generate ensembles of perturbations around these optimal initial states, and the error growth is examined. The error growth in response to subseasonal stochastic wind forcing is presented for comparison.In general, from 1975 to 2002, the large-scale uncertainty in initial conditions leads to larger error growth than continuous stochastic forcing of the zonal wind stress fields. Forecast ensemble spread is shown to depend most on the calendar month at the end of the forecast rather than the initialization month, with the seasons of greatest spread corresponding to the seasons of greatest anomaly variance. It is also demonstrated that during years with negative ( and rapidly decaying) Nino-3 SST anomalies ( such as the time period following an El Nino event), there is a suppression of error growth. In years with large warm ENSO events, the ensemble spread is no larger than in moderately warm years. As a result, periods with high ENSO variance have greater potential prediction utility.In the realistic range of observational error, the ensemble spread has more sensitivity to the initial error in the thermocline depth than to the sea surface temperature or wind stress errors. The thermocline depth uncertainty is the principal reason why initial condition uncertainties are more important than wind noise for ensemble spread.
    DOI:10.1175/JCLI3818.1      URL     [Cited within:1]
    [14] Keenlyside N., R. Kleeman, 2002: Annual cycle of equatorial zonal currents in the Pacific. J. Geophys. Res., 107,3093, doi: 10.1029/2000JC000711.
    [1] Observational (Tropical Atmosphere-Ocean array) data on the annual cycle of upper ocean zonal currents on the equator are analyzed using a simple dynamical ocean model in order to investigate underlying dynamics. The model, by treating linear and nonlinear terms semi-independently, allows a separation of various linear and nonlinear effects. The model focuses on linear dynamics of low-order baroclinic modes. By realistically simulating the vertical structure of annual cycle, the model shows that linear dynamics determines the vertical and meridional structure of the annual cycle. Nonlinearity is weak and only important in the undercurrent, where it provides a simple mechanism for the annual cycle: mean meridional advection of the annual cycle north of the equator onto the equator, with the boreal springtime surge in the undercurrent being a direct result of a surge centered at 2degreesN. Model results show that annual variations in zonal currents are out of phase across the equator, surging in the corresponding spring. This behavior is a response to trade wind variations, which are also equatorially antisymmetric, and is generated by the second meridional mode Rossby wave.
    DOI:10.1029/2000JC000711      URL     [Cited within:2]
    [15] Kirtman B. P., J. Shukla, M. Balmaseda, N. Graham, C. Penland , Y. Xue, and S. Zebiak, 2001: Current status of ENSO forecast skill: A report to the Climate Variability and Predictability (CLIVAR) Working Group on Seasonal to Interannual Prediction. WCRP Informal Report No. 23/01,31pp.
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    [16] Latif, M., Coauthors, 1998: A review of the predictability and prediction of ENSO.J. Geophys. Res.,103(C7),14 375-14 393,doi: 10.1029/97JC03413.
    A hierarchy of El Ni o-Southern Oscillation (ENSO) prediction schemes has been developed during the Tropical Ocean-Global Atmosphere (TOGA) program which includes statistical schemes and physical models. The statistical models are, in general, based on linear statistical techniques and can be classified into models which use atmospheric (sea level pressure or surface wind) or oceanic (sea surface temperature or a measure of upper ocean heat content) quantities or a combination of oceanic and atmospheric quantities as predictors. The physical models consist of coupled ocean-atmosphere models of varying degrees of complexity, ranging from simplified coupled models of the "shallow water" type to coupled general circulation models. All models, statistical and physical, perform considerably better than the persistence forecast in predicting typical indices of ENSO on lead times of 6 to 12 months. The TOGA program can be regarded as a success from this perspective. However, despite the demonstrated predictability, little is known about ENSO predictability limits and the predictability of phenomena outside the tropical Pacific. Furthermore, the predictability of anomalous features known to be associated with ENSO (e.g., Indian monsoon and Sahel rainfall, southern African drought, and off-equatorial sea surface temperature) needs to be addressed in more detail. As well, the relative importance of different physical mechanisms (in the ocean or atmosphere) has yet to be established. A seasonal dependence in predictability is seen in many models, but the processes responsible for it are not fully understood, and its meaning is still a matter of scientific discussion. Likewise, a marked decadal variation in skill is observed, and the reasons for this are still under investigation. Finally, the different prediction models yield similar skills, although they are initialized quite differently. The reasons for these differences are also unclear.
    DOI:10.1029/97JC03413      URL     [Cited within:1]
    [17] Luo J.-J., C.-X. Yuan, W. Sasaki, Y. Masumoto, T. Yamagata, J.-Y. Lee, and S. Masson, 2016: Current status of intraseasonal-seasonal-to-interannual prediction of the Indo-Pacific climate. The Indo-Pacific Climate Variability and Predictability, T. Yamagata, and S. Behera, Eds., The World Scientific Publisher, doi: 10.1142/9789814696623_0003.
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    [18] McPhaden M. J., X. R. Yu, 1999: Equatorial waves and the 1997-98 El Niño.Geophys. Res. Lett.,26,2961-2964,doi: 10.1029/1999GL004901.
    DOI:10.1029/1999GL004901      URL     [Cited within:1]
    [19] McPhaden, M. J., Coauthors, 1998: The tropical ocean-global atmosphere observing system: A decade of progress.J. Geophys. Res.,103,14 169-14 240,doi: 10.1029/97JC02906.
    A major accomplishment of the recently completed Tropical Ocean-Global Atmosphere (TOGA) Program was the development of an ocean observing system to support seasonal-to-interannual climate studies. This paper reviews the scientific motivations for the development of that observing system, the technological advances that made it possible, and the scientific advances that resulted from the availability of a significantly expanded observational database. A primary phenomenological focus of TOGA was interannual variability of the coupled ocean-atmosphere system associated with El Ni o and the Southern Oscillation (ENSO).Prior to the start of TOGA, our understanding of the physical processes responsible for the ENSO cycle was limited, our ability to monitor variability in the tropical oceans was primitive, and the capability to predict ENSO was nonexistent. TOGA therefore initiated and/or supported efforts to provide real-time measurements of the following key oceanographic variables: surface winds, sea surface temperature, subsurface temperature, sea level and ocean velocity. Specific in situ observational programs developed to provide these data sets included the Tropical Atmosphere-Ocean (TAO) array of moored buoys in the Pacific, a surface drifting buoy program, an island and coastal tide gauge network, and a volunteer observing ship network of expendable bathythermograph measurements. Complementing these in situ efforts were satellite missions which provided near-global coverage of surface winds, sea surface temperature, and sea level. These new TOGA data sets led to fundamental progress in our understanding of the physical processes responsible for ENSO and to the development of coupled ocean-atmosphere models for ENSO prediction.
    DOI:10.1029/97JC02906      URL     [Cited within:]
    [20] McPhaden M. J., S. E. Zebiak, and M. H. Glantz, 2006: ENSO as an integrating concept in earth science.Science,314,1740-1745,doi: 10.1126/science.1132588.
    Abstract The El Ni01±o-Southern Oscillation (ENSO) cycle of alternating warm El Ni01±o and cold La Ni01±a events is the dominant year-to-year climate signal on Earth. ENSO originates in the tropical Pacific through interactions between the ocean and the atmosphere, but its environmental and socioeconomic impacts are felt worldwide. Spurred on by the powerful 1997-1998 El Ni01±o, efforts to understand the causes and consequences of ENSO have greatly expanded in the past few years. These efforts reveal the breadth of ENSO's influence on the Earth system and the potential to exploit its predictability for societal benefit. However, many intertwined issues regarding ENSO dynamics, impacts, forecasting, and applications remain unresolved. Research to address these issues will not only lead to progress across a broad range of scientific disciplines but also provide an opportunity to educate the public and policy makers about the importance of climate variability and change in the modern world.
    DOI:10.1126/science.1132588      PMID:17170296      URL     [Cited within:1]
    [21] McPhaden M. J., T. Lee, and D. McClurg, 2011: El Niño and its relationship to changing background conditions in the tropical Pacific Ocean. Geophys. Res. Lett., 38,L15709, doi: 10.1029/2011GL048275.
    This paper addresses the question of whether the increased occurrence of central Pacific (CP) versus Eastern Pacific (EP) El Ni09os is consistent with greenhouse gas forced changes in the background state of the tropical Pacific as inferred from global climate change models. Our analysis uses high-quality satellite and in situ ocean data combined with wind data from atmospheric reanalyses for the past 31 years (1980-2010). We find changes in background conditions that are opposite to those expected from greenhouse gas forcing in climate models and opposite to what is expected if changes in the background state are mediating more frequent occurrences of CP El Ni09os. A plausible interpretation of these results is that the character of El Ni09o over the past 31 years has varied naturally and that these variations projected onto changes in the background state because of the asymmetric spatial structures of CP and EP El Ni09os.
    DOI:10.1029/2011GL048275      URL     [Cited within:1]
    [22] Moore A. M., R. Kleeman, 1996: The dynamics of error growth and predictability in a coupled model of ENSO.Quart. J. Roy. Meteor. Soc.,122,1405-1446,doi: 10.1002/ qj.49712253409.
    The singular vectors of the coupled system were computed using the tangent-linear coupled model and its adjoint. The singular-value spectrum was found to be dominated by one singular vector at all times of the year. The potential for error growth in the coupled model, measured in terms of the growth of energy of the dominant singular vector, is found to vary seasonally, being greatest during the boreal spring. These seasonal variations are associated with the seasonal cycle in SST. During boreal spring and early summer, the SST in the central Pacific is at its maximum, at which time conditions are most favourable for error growth. Springtime is also the time of the predictability barrier for ENSO. The potential for error growth is also influenced by the ENSO cycle itself. The results suggest that error growth will be enhanced during the onset of El Ni09o and suppressed during the onset of La Ni09a, which indicates that El Ni09o may be less predictable than La Ni09a.
    DOI:10.1002/qj.49712253409      URL     [Cited within:1]
    [23] Picaut J., E. Hackert, A. J. Busalacchi, R. Murtugudde, and G. S. E. Lagerloef, 2002: Mechanisms of the 1997-1998 El Niño-La ña, as inferred from space-based observations. J. Geophys. Res., 107,3037, doi: 10.1029/2001JC000850.
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    [24] Ren H.-L., F.-F. Jin, 2011: ño indices for two types of ENSO. Geophys. Res. Lett., 38,L04704, doi: 10.1029/2010 GL046031.
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    [25] Röckner, E., Coauthors, 1996: The atmospheric general circulation model ECHAM-4: Model description and simulation of present-day climate. Report No. 218, Max-Planck-Institut für Meteorologie, Hamburg, 90pp.
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    [26] Schneider E. K., B. P. Kirtman, D. G. DeWitt, A. Rosati, L. Ji, and J. J. Tribbia, 2003: Retrospective ENSO forecasts: Sensitivity to atmospheric model and ocean resolution.Mon. Wea. Rev.,131,3038-3060,doi: 10.1175/1520-0493(2003)131<3038: REFSTA>2.0.CO;2.
    Results are described from a series of 40 retrospective forecasts of tropical Pacific SST, starting 1 January and 1 July 1980-99, performed with several coupled ocean-atmosphere general circulation models sharing the same ocean model-the Modular Ocean Model version 3 (MOM3) OGCM-and the same initial conditions. The atmospheric components of the coupled models were the Center for Ocean-Land-Atmosphere Studies (COLA), ECHAM, and Community Climate Model version 3 (CCM3) models at T42 horizontal resolution, and no empirical corrections were applied to the coupling. Additionally, the retrospective forecasts using the COLA and ECHAM atmospheric models were carried out with two resolutions of the OGCM. The high-resolution version of the OGCM had 1℃ horizontal resolution (1/3℃ meridional resolution near the equator) and 40 levels in the vertical, while the lower-resolution version had 1.5℃ horizontal resolution (1/2℃ meridional resolution near the equator) and 25 levels. The initial states were taken from an ocean data assimilation performed by the Geophysical Fluid Dynamics Laboratory (GFDL) using the high-resolution OGCM. Initial conditions for the lower-resolution retrospective forecasts were obtained by interpolation from the GFDL ocean data assimilation. The systematic errors of the mean evolution in the coupled models depend strongly on the atmospheric model, with the COLA versions having a warm bias in tropical Pacific SST, the CCM3 version a cold bias, and the ECHAM versions a smaller cold bias. Each of the models exhibits similar levels of skill, although some statistically significant differences are identified. The models have better retrospective forecast performance from the 1 July initial conditions, suggesting a spring prediction barrier. A consensus retrospective forecast produced by taking the ensemble average of the retrospective forecasts from all of the models is generally superior to any of the individual retrospective forecasts. One reason that averaging across models appears to be successful is that the averaging reduces the effects of systematic errors in the structure of the ENSO variability of the different models. The effect of reducing noise by averaging ensembles of forecasts made with the same model is compared to the effects from multimodel ensembling for a subset of the cases; however, the sample size is not large enough to clearly distinguish between the multimodel consensus and the single-model ensembles. There are obvious problems with the retrospective forecasts that can be connected to the various systematic errors of the coupled models in simulation mode, and which are ultimately due to model error (errors in the physical parameterizations and numerical truncation). These errors lead to initial shock and a spring variability barrier that degrade the retrospective forecasts.
    DOI:10.1175/1520-0493(2003)1312.0.CO;2      URL     [Cited within:1]
    [27] Smith T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA's historical merged land-ocean surface temperature analysis (1880-2006).J. Climate,21,2283-2296,doi: 10.1175/2007JCLI2100.1.
    DOI:10.1175/2007JCLI2100.1      URL     [Cited within:1]
    [28] Wang W. Q., M. Y. Chen, and A. Kumar, 2010: An assessment of the CFS real-time seasonal forecasts.Wea. Forecasting,25,950-969,doi: 10.1175/2010WAF2222345.1.
    This study assesses the real-time seasonal forecasts for 2005-08 with the current National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The forecasts are compared with retrospective forecasts (or hindcasts) for 1981-2004 to examine the consistency of the forecast system, and with the Atmospheric Model Intercomparison Project (AMIP) simulations forced with observed sea surface temperatures (SSTs) to contrast the realized skill against the potential predictability due to the specification of the observed sea surface temperatures. The analysis focuses on the forecasts of SSTs, 2-m surface air temperature (T2M), and precipitation. The CFS forecasts maintained a good level of prediction skill for SSTs in the tropical Pacific, the western Indian Ocean, and the northern Atlantic. The SST forecast skill is within the range of hindcast skill levels calculated with 4-yr windows, which can vary greatly associated with the interannual El Ni o昐outhern Oscillation (ENSO) variability. Overall, the SST forecast skill over the globe is comparable to the average of the hindcast skill. For the tropical eastern Pacific, however, the forecast skill at lead times longer than 2 months is less than the average hindcast skill due to the relatively weaker ENSO variability during the forecast period (2005-08). The forecasts and hindcasts show a similar level of precipitation skill over most of the globe. For T2M, the spatial distribution of skill differs substantially between the forecasts and hindcasts. In particular, the T2M skill of the forecasts for the Northern Hemisphere during its warm seasons is lower than that of the hindcasts. Comparison with the AMIP simulations shows similar levels of precipitation skill over the tropical Pacific. Over the tropical Indian Ocean, the CFS forecasts show a substantially higher level of skill than the AMIP simulations for a large part of the period. This conforms with the results from previous studies that while interannual variability in the tropical Pacific atmosphere is slaved to the underlying SST anomalies, specification of SSTs (as for the AMIP simulations) in the Indian Ocean may lead to incorrect simulation of the atmospheric variability. Over the tropical Atlantic, the precipitation skill of both the CFS forecasts and AMIP simulations is low, suggesting that SSTs have less control over the atmospheric anomalies and the predictability is low. The analysis reveals several deficiencies in the current CFS that need to be corrected for improved seasonal forecasting. For example, the CFS tends to consistently forecast larger ENSO amplitude and delayed transition between the ENSO phases. Forecasts of T2M also have a strong cold bias in Northern Hemisphere mid-to high latitudes during warm seasons. This error is due to initial soil moisture anomalies, which appear to be too wet compared with two other observational analyses. The strong impacts of soil moisture on the seasonal forecasts, and large discrepancies among the soil moisture analyses, call for more accurate specification of soil moisture. Furthermore, average forecast SST and T2M anomalies for 2005-08 show a cold bias over the entire globe, indicating that the model is unable to maintain the observed long-term warming trend.
    DOI:10.1175/2010WAF2222345.1      URL     [Cited within:1]
    [29] Webster P. J., S. Yang, 1992. Monsoon and ENSO: Selectively interactive systems. Quart. J. Roy. Meteor. Soc., 118, 877- 926.
    A longer-period context for the anomalous summer monsoon circulation fields was sought. Based on the summer monsoon index, annual cycles for the years in which there were strong and weak monsoon seasons were composited. Large-scale coherent differences were apparent in the circulation fields over most of the globe including south Asia and the tropical Indian Ocean as far as the previous winter and spring. Although the limited data period renders the absoluteness of the conclusions difficult to confirm, the results indicate that the variable monsoon (and hence the signal in the Pacific Ocean trade regime) are immersed in a larger scale and slowly evolving circulation system. Based on the observation that the monsoon and the Walker circulation appear to be in quadrature, it is proposed that these two circulations are selectively interactive. During the springtime, the rapidly growing monsoon dominates the near-equatorial Walker circulation. During autumn and winter, the monsoon is weakest with convection fairly close to the equator; the Walker circulation is then strongest and may dominate the winter monsoon. During the summer the monsoon may dominate. Numerical experiments are proposed to test both propositions.
    DOI:10.1002/qj.49711850705      URL     [Cited within:2]
    [30] Yang S., X. W. Jiang, 2014: Prediction of eastern and central Pacific ENSO events and their impacts on East Asian climate by the NCEP Climate Forecast System.J. Climate,27,4451-4472,doi: 10.1175/JCLI-D-13-00471.1.
    AbstractThe eastern Pacific (EP) El Ni o揝outhern Oscillation (ENSO) and the central Pacific (CP) ENSO exert different influences on climate. In this study, the authors analyze the hindcasts of the NCEP Climate Forecast System, version 2 (CFSv2), and assess the skills of predicting the two types of ENSO and their impacts on East Asian climate. The possible causes of different prediction skills for different types of ENSO are also discussed.CFSv2 captures the spatial patterns of sea surface temperature (SST) related to the two types of ENSO and their different climate impacts several months in advance. The dynamical prediction of the two types of ENSO by the model, whose skill is season dependent, is better than the prediction based on the persistency of observed ENSO-related SST, especially for summer and fall. CFSv2 performs well in predicting EP ENSO and its impacts on the East Asian winter monsoon and on the Southeast Asian monsoon during its decaying summer. However, for both EP ENSO and CP ENSO, the ...
    DOI:10.1175/JCLI-D-13-00471.1      URL     [Cited within:3]
    [31] Yeh S.-W., J.-S. Kug, and S.-I. An, 2014: Recent progress on two types of El Niño: Observations, dynamics, and future changes. Asia-Pac. J. Atmos. Sci., 50, 69- 81.
    The climate community has made significant progress in observing, understanding and predicting El Ni09o and Southern Oscillation (ENSO) over the last 30 years. In spite of that, unresolved questions still remain, including ENSO diversity and extreme events, decadal modulation, predictability, teleconnection, and the interaction of ENSO with other climate phenomena. In particular, the existence of a different type of El Ni09o from the conventional El Ni09o has been proposed. This type of El Ni09o has occurred more frequently during the recent decades and received a great attention in the climate community. This review provides recent progresses on dynamics, decadal variability and future projection of El Ni09o, with a focus on the two types of El Ni09o.
    DOI:10.1007/s13143-014-0028-3      URL     [Cited within:1]
    [32] Yu J. Y., H. Y. Kao, 2007: Decadal changes of ENSO persistence barrier in SST and ocean heat content indices: 1958-2001. J. Geophys. Res., 112,D13106, doi: 10.1029/2006 JD007654.
    Decadal changes of El Ni o-Southern Oscillation (ENSO) persistence barriers in various indices of sea surface temperature (SST) and ocean heat content (OHC) are examined in this study using observations and ocean data assimilation products for the period 19582001. It is found that the SST indices in the eastern and central equatorial Pacific exhibit very different decadal barrier variability. The variability is large for the eastern Pacific SST indices (NINO1+2 and NINO3) whose persistence barriers shifted abruptly in 1976/1977 and 1989/1990. In contrast, the central Pacific SST indices (NINO3.4 and NINO4) experienced little decadal barrier variability and have had their persistence barriers fixed in spring in the past four decades. The zonal mean OHC index averaged over the equatorial Pacific shows decadal barrier changes similar to those in the eastern Pacific SST indices and always leads the NINO3 SST barrier by about one season. It is noticed that the SST persistence barrier appeared first in the eastern Pacific before 1976/1977, first in the central Pacific between 1976/1977 and 1989/1990, and almost simultaneous in both the eastern and central Pacific after 1989/1990. These timings coincide with the westward propagating, eastward propagating, and standing pattern of ENSO SST anomalies observed in these three periods. These results suggest that ENSO SST anomalies in the equatorial Pacific can be considered to consist of two different processes: a central Pacific process whose phase transition (such as onset) and barrier always happen in spring, and an eastern Pacific process whose phase transition and barrier change from decade to decade and are influenced by changes in the mean thermocline depth along the equatorial Pacific.
    DOI:10.1029/2006JD007654      URL     [Cited within:]
    [33] Yu J.-Y., S. T. Kim, 2013: Identifying the types of major El Niño events since 1870.International Journal of Climatology,33,2105-2112,doi: 10.1002/joc.3575.
    This study develops a pattern correlation method to determine the type of major El Ni09o events since 1870 from a reconstructed sea surface temperature dataset. Different from other identification methods, this method allows an El Ni09o event to be of the Central-Pacific (CP) type, the Eastern-Pacific (EP) type, or the Mixed type (i.e. the both types coexist). Application of this method to the 39 major El Ni09o events identified by the Ocean Ni09o Index during the period 1870–2010 results in 8 events that are categorized to be of the EP type, 16 of the CP type, and 15 of Mixed type. Before the 1910s, the El Ni09o events are mostly of the EP type, but are mostly the CP type after 2000, while in between both types occurred. The consistencies and inconsistencies between the El Ni09o types identified by this method and other three methods, which have been proposed recently for El Ni09o-type classification, are examined and discussed. All four methods consistently identify the El Ni09o events occurring in the following years to be of the EP types: 1876–1877, 1881, 1884–1885, 1895–1896, 1896–1897, 1918–1919, 1982–1983, and 1997–1998; and the events occurring in the following years to be of the CP type: 1968–1969, 1977–1978, 1994–1995, 2004–2005, and 2009–2010. It is evident that the characteristics of the EP type of El Ni09o are more robust in the 19th century and the early part of the 20th century, whereas the characteristics of the CP type of El Ni09o is more robust in the late 20th century and the early 21st century. The list of the El Ni09o types produced by this study can be used for selecting El Ni09o events to further study the dynamics and climate impacts of the EP, CP, and Mixed types of El Ni09o. Copyright 08 2012 Royal Meteorological Society
    DOI:10.1002/joc.3575      URL     [Cited within:2]
    [34] Yu J.-Y., H.-Y. Kao, and T. Lee, 2010: Subtropics-related Interannual sea surface temperature variability in the central equatorial Pacific.J. Climate,23,2869-2884,doi: 10.1175/2010JCLI3171.1.
    Interannual sea surface temperature (SST) variability in the central equatorial Pacific consists of a component related to eastern Pacific SST variations (called Type-1 SST variability) and a component not related to them (called Type-2 SST variability). Lead–lagged regression and ocean surface-layer temperature balance analyses were performed to contrast their control mechanisms. Type-1 variability is part of the canonical, which is characterized by SST anomalies extending from the South American coast to the central Pacific, is coupled with the Southern Oscillation, and is associated with basinwide subsurface ocean variations. This type of variability is dominated by a major 4–5-yr periodicity and a minor biennial (2–2.5 yr) periodicity. In contrast, Type-2 variability is dominated by a biennial periodicity, is associated with local air–sea interactions, and lacks a basinwide anomaly structure. In addition, Type-2 SST variability exhibits a strong connection to the subtropics of both hemispheres, particularly the Northern Hemisphere. Type-2 SST anomalies appear first in the northeastern subtropical Pacific and later spread toward the central equatorial Pacific, being generated in both regions by anomalous surface heat flux forcing associated with wind anomalies. The SST anomalies undergo rapid intensification in the central equatorial Pacific through ocean advection processes, and eventually decay as a result of surface heat flux damping and zonal advection. The southward spreading of trade wind anomalies within the northeastern subtropics-to-central tropics pathway of Type-2 variability is associated with intensity variations of the subtropical high. Type-2 variability is found to become stronger after 1990, associated with a concurrent increase in the subtropical variability. It is concluded that Type-2 interannual variability represents a subtropical-excited phenomenon that is different from the conventional ENSO Type-1 variability.
    DOI:10.1175/2010JCLI3171.1      URL     [Cited within:1]
    [35] Yu J.-Y., Y. H. Zou, S. T. Kim, and T. Lee, 2012: The changing impact of El Niño on US winter temperatures. Geophys. Res. Lett., 39,L15702, doi: 10.1029/2012GL052483.
    In this study, evidence is presented from statistical analyses, numerical model experiments, and case studies to show that the impact on US winter temperatures is different for the different types of El Nino. While the conventional Eastern-Pacific El Nino affects winter temperatures primarily over the Great Lakes, Northeast, and Southwest US, the largest impact from Central-Pacific El Nino is on temperatures in the northwestern and southeastern US. The recent shift to a greater frequency of occurrence of the Central-Pacific type has made the Northwest and Southeast regions of the US most influenced by El Ni o. It is shown that the different impacts result from differing wave train responses in the atmosphere to the sea surface temperature anomalies associated with the two types of El Ni o.
    DOI:10.1029/2012GL052483      URL     [Cited within:2]
    [36] Yu J.-Y., X. Wang, S. Yang, H. Paek, and M. Chen, 2017: The changing El Niño-Southern Oscillation and associated climate extremes.Climate Extremes: Patterns and Mechanisms,S. Wang et al.,Eds., John Wiley & Sons, Inc., Hoboken, NJ, USA, 1-38 pp,doi: 10.1002/9781119068020.ch1.
    Summary The El Ni o揝outhern Oscillation (ENSO) is one of the most powerful climate phenomena that produce profound global impacts. Extensive research since the 1970s has resulted in a theoretical framework capable of explaining the observed properties and impacts of the ENSO and predictive models. However, during the most recent two decades there have been significant changes observed in the properties of ENSO that suggest revisions are required in the existing theoretical framework developed primarily for the canonical ENSO. The observed changes include a shift in the location of maximum sea surface temperature variability, an increased importance in the underlying dynamics of coupled ocean-atmosphere process in the subtropical Pacific, and different remote atmospheric teleconnection patterns that give rise to distinct climate extremes. The causes of these recent changes in ENSO are still a matter of debate but have been attributed to both global warming and natural climate variability involving interactions between the Pacific and Atlantic oceans. The possible future changes of ENSO properties have also been suggested using climate model projections.
    DOI:10.1002/9781119068020.ch1      URL     [Cited within:]
    [37] Zhang R.-H., S. E. Zebiak, R. Kleeman, and N. Keenlyside, 2005: Retrospective El Niño forecasts using an improved intermediate coupled model.Mon. Wea. Rev.,133,2777-2802,doi: 10.1175/MWR3000.1.
    A new intermediate coupled model (ICM) is presented and employed to make retrospective predictions of tropical Pacific sea surface temperature (SST) anomalies. The ocean dynamics is an extension of the McCreary baroclinic modal model to include varying stratification and certain nonlinear effects. A standard configuration is chosen with 10 baroclinic modes plus two surface layers, which are governed by Ekman dynamics and simulate the combined effects of the higher baroclinic modes from 11 to 30. A nonlinear correction associated with vertical advection of zonal momentum is incorporated and applied (diagnostically) only within the two surface layers, forced by the linear part through nonlinear advection terms. As a result of these improvements, the model realistically simulates the mean equatorial circulation and its variability. The ocean thermodynamics include an SST anomaly model with an empirical parameterization for the temperature of subsurface water entrained into the mixed layer (T
    DOI:10.1175/MWR3000.1      URL     [Cited within:4]
    [38] Zheng F., J. Zhu, 2008: Balanced multivariate model errors of an intermediate coupled model for ensemble Kalman filter data assimilation. J. Geophys. Res., 113,C07002, doi: 10.1029/2007JC004621.
    [1] The ensemble Kalman filter (EnKF) depends on a set of ensemble forecasts to calculate the background error covariances. Without model error perturbations and the inflation of forecast ensembles, the spread of the ensemble forecasts can collapse rapidly. There are several ways to generate model perturbations, i.e., perturbations in model parameters/parameterizations, perturbations in the forcing fields of the model and adding some error terms to the right-hand side of the model equations. In this paper, we focus on the “adding model error terms” approach, which utilizes a first-order Markov chain model. This approach is suitable to those unforced models, such as the coupled atmosphere-ocean models. However, for a multivariate model, the balance between different model variables could be an important issue in building its model-error model. In this paper, we focus on building a balanced error model for an intermediate coupled model for El Ni09o–Southern Oscillation (ENSO) predictions. A simple approach to build such a model-error model is proposed on the basis of the multivariate empirical orthogonal functions method. EnKF data assimilation experiments with different configurations of multivariate model error treatments (no model errors, unbalanced and balanced model errors) are performed using realistic sea surface temperature (SST) and sea level (SL) observations. Results show that it is necessary to develop balanced, multivariate model-error models in order to successfully assimilate both SST and SL observations. The hindcasts initialized from these different assimilation experiment results also demonstrate that the balanced model errors can yield more balanced initial conditions that lead to improved predictions of ENSO events.
    DOI:10.1029/2007JC004621      URL     [Cited within:1]
    [39] Zheng F., J. Zhu, 2010a: Coupled assimilation for an intermediated coupled ENSO prediction model.Ocean Dyn.,60,1061-1073,doi: 10.1007/s10236-010-0307-1.
    The value of coupled assimilation is discussed using an intermediate coupled model in which the wind stress is the only atmospheric state which is slavery to model sea surface temperature (SST). In the coupled assimilation analysis, based on the coupled wind搊cean state covariance calculated from the coupled state ensemble, the ocean state is adjusted by assimilating wind data using the ensemble Kalman filter. As revealed by a series of assimilation experiments using simulated observations, the coupled assimilation of wind observations yields better results than the assimilation of SST observations. Specifically, the coupled assimilation of wind observations can help to improve the accuracy of the surface and subsurface currents because the correlation between the wind and ocean currents is stronger than that between SST and ocean currents in the equatorial Pacific. Thus, the coupled assimilation of wind data can decrease the initial condition errors in the surface/subsurface currents that can significantly contribute to SST forecast errors. The value of the coupled assimilation of wind observations is further demonstrated by comparing the prediction skills of three 12-year (19972008) hindcast experiments initialized by the ocean-only assimilation scheme that assimilates SST observations, the coupled assimilation scheme that assimilates wind observations, and a nudging scheme that nudges the observed wind stress data, respectively. The prediction skills of two assimilation schemes are significantly better than those of the nudging scheme. The prediction skills of assimilating wind observations are better than assimilating SST observations. Assimilating wind observations for the 2007/2008 La Ni a event triggers better predictions, while assimilating SST observations fails to provide an early warning for that event.
    DOI:10.1007/s10236-010-0307-1      URL     [Cited within:1]
    [40] Zheng F., J. Zhu, 2010b: Spring predictability barrier of ENSO events from the perspective of an ensemble prediction system.Global and Planetary Change,72,108-117,doi: 10.1016/j.gloplacha.2010.01.021.
    Based on an ENSO (El Ni o-Southern Oscillation) ensemble prediction system (EPS), the seasonal variations in the predictability of ENSO are examined in both a deterministic and a probabilistic sense. For the deterministic prediction skills, the skills of the ensemble-mean are sensitive to the month in which the forecast was initiated. The anomaly correlations decrease rapidly during the Northern Hemisphere (NH) spring, and the root mean square (RMS) errors have the largest values and the fastest growth rates initialized before and during the NH spring. However, the probabilistic predictions based on the verification methods of the relative operating character (ROC) curve and area both show that there are no strong seasonal variations for the two extreme (warm and cold) ENSO events. For the near-normal events, the seasonal variations of the probabilistic skills are much more obvious, and the ROC areas of the ensemble forecasts made in the spring are clearly smaller than those of the ensemble forecasts that began during other seasons.At the same time, the probabilistic prediction skills of the EPS for all three events that only consider the initial perturbations are also clearly sensitive to the initial months. This was indicated by the fact that the most rapid decrease of the ROC area skill occurs as the hindcasts proceed through the spring season. A further signal-to-noise ratio analysis reveals that potential sources of the predictability barrier in the probabilistic skills for the EPS are namely that the spring is the period when stochastic initial error effects can be expected to strongly degrade forecast skill, and that small predicted signals can render the system noisier by further limiting the predictability. However, reasonable considerations of the model-error perturbations during the ensemble forecast process can alleviate the barrier caused by initial uncertainties through coordinately simulating the seasonal variations of the forecast uncertainty in order to significantly improve the probabilistic prediction skills and then to disorder the seasonal predictability related to the SPB.
    DOI:10.1016/j.gloplacha.2010.01.021      URL     [Cited within:2]
    [41] Zheng F., R.-H. Zhang, 2012: Effects of interannual salinity variability and freshwater flux forcing on the development of the 2007/08 La ña event diagnosed from Argo and satellite data.Dyn. Atmos. Oceans,57,45-57,doi: 10.1016/ j.dynatmoce.2012.06.002.
    Oceanic salinity and its related freshwater flux (FWF) forcing in the tropical Pacific have been of increased interest recently due to their roles in the El Ni o-Southern Oscillation (ENSO), the global climate and water cycle. A comprehensive data analysis is performed to illustrate the significant effects of interannual salinity variability and FWF forcing during the 2007/08 La Ni a event using three-dimensional temperature and salinity fields from Argo profiles, and some related fields derived from the Argo and satellite-based data, including the mixed layer depth (MLD), heat flux, freshwater flux, and buoyancy flux ( Q B ). It is demonstrated that during the developing phase of 2007/08 La Ni a, a negative FWF anomaly and its associated positive sea surface salinity (SSS) anomaly in the western-central basin act to increase oceanic density and de-stabilize the upper ocean. At the same time, the negative FWF anomaly tends to reduce a positive Q B anomaly and deepen the mixed layer (ML). These related oceanic processes act to strengthen the vertical mixing and entrainment of subsurface water at the base of ML, which further enhance cold sea surface temperature (SST) anomalies associated with the La Ni a event, a demonstration of a positive feedback induced by FWF forcing.
    DOI:10.1016/j.dynatmoce.2012.06.002      URL     [Cited within:]
    [42] Zheng F., R.-H. Zhang, 2015: Interannually varying salinity effects on ENSO in the tropical Pacific: A diagnostic analysis from Argo.Ocean Dynamics,65(5),691-705,doi: 10.1007/s10236-015-0829-7.
    In this paper, three-dimensional temperature and salinity fields from Argo profiles are used to diagnose the interannual variations of some related upper oceanic fields in the tropical Pacific, with a focus on interannually varying salinity effects on the El Ni09o-Southern Oscillation (ENSO) events. It is clearly demonstrated that the salinity field plays a significantly large role in modulating the density and mixed layer (ML) over the western-central tropical Pacific. In particular, the contribution of interannually varying salinity to the interannual variations in density, ML, and stratification is surprisingly larger than that of interannually varying temperature. Over the entire region west of the dateline, the salinity effects are not limited to the surface but are clearly seen below the ML as represented in density and stratification fields. Furthermore, the mechanism for how the anomalous salinity field is modulating the ENSO cycle is investigated and explained through the El Ni09o (2009–2010) and La Ni09a (2010–2011) cases. Evidently, salinity field is shown to exert a significant influence on interannual variability as it directly affects the vertical mixing and entrainment at the base of the ML, the processes important to sea surface temperature (SST) in the equatorial regions.
    DOI:10.1007/s10236-015-0829-7      URL     [Cited within:]
    [43] Zheng F., J. Zhu, 2015: Roles of initial ocean surface and subsurface states on successfully predicting 2006-2007 El Niño with an intermediate coupled model.Ocean Science,11,187-194,doi: 10.5194/os-11-187-2015.
    The 2006-2007 El Ni09o event, an unusually weak event, was predicted by most models only after the warming in the eastern Pacific had commenced. In this study, on the basis of an El Ni09o prediction system, roles of the initial ocean surface and subsurface states on predicting the 2006-2007 El Ni09o event are investigated to determine conditions favorable for predicting El Ni09o growth and are isolated in three sets of hindcast experiments. The hindcast is initialized through assimilation of only the sea surface temperature (SST) observations to optimize the initial surface condition, only the sea level (SL) data to update the initial subsurface state, or both the SST and SL data. Results highlight that the hindcasts with three different initial states can all successfully predict the 2006-2007 El Ni09o event 1 year in advance and that the hindcast initialized by both the SST and SL data performs best. A comparison between the various sets of hindcast results further demonstrates that successful prediction is more significantly affected by the initial subsurface state than by the initial surface condition. The accurate initial surface state can trigger the easier prediction of the 2006-2007 El Ni09o, whereas a more reasonable initial subsurface state can contribute to improving the prediction in the growth of the warm event.
    DOI:10.5194/os-11-187-2015      URL     [Cited within:1]
    [44] Zheng F., J. Zhu, 2016: Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model.Climate Dyn.,47,3901-3915,doi: 10.1007/s00382-016-3048-0.
    How to design a reliable ensemble prediction strategy with considering the major uncertainties of a forecasting system is a crucial issue for performing an ensemble forecast. In this study, a new stochastic perturbation technique is developed to improve the prediction skills of El Ni o-Southern Oscillation (ENSO) through using an intermediate coupled model. We first estimate and analyze the model uncertainties from the ensemble Kalman filter analysis results through assimilating the observed sea surface temperatures. Then, based on the pre-analyzed properties of model errors, we develop a zero-mean stochastic model-error model to characterize the model uncertainties mainly induced by the missed physical processes of the original model (e.g., stochastic atmospheric forcing, extra-tropical effects, Indian Ocean Dipole). Finally, we perturb each member of an ensemble forecast at each step by the developed stochastic model-error model during the 12-month forecasting process, and add the zero-mean perturbations into the physical fields to mimic the presence of missing processes and high-frequency stochastic noises. The impacts of stochastic model-error perturbations on ENSO deterministic predictions are examined by performing two sets of 21-year hindcast experiments, which are initialized from the same initial conditions and differentiated by whether they consider the stochastic perturbations. The comparison results show that the stochastic perturbations have a significant effect on improving the ensemble-mean prediction skills during the entire 12-month forecasting process. This improvement occurs mainly because the nonlinear terms in the model can form a positive ensemble-mean from a series of zero-mean perturbations, which reduces the forecasting biases and then corrects the forecast through this nonlinear heating mechanism.
    DOI:10.1007/s00382-016-3048-0      URL     [Cited within:3]
    [45] Zheng F., J. Zhu, R.-H. Zhang, and G.-Q. Zhou, 2006: Ensemble hindcasts of SST anomalies in the tropical Pacific using an intermediate coupled model. Geophys. Res. Lett., 33,L19604, doi: 10.1029/2006GL026994.
    Ensemble hindcasts of sea surface temperature (SST) anomalies in the tropical Pacific are studied using an intermediate coupled model (ICM), in which an ensemble Kalman filter (EnKF) data assimilation system is implemented to provide the initial ensemble. A linear, first-order Markov stochastic model is adopted to represent model errors. Parameters in the stochastic model are estimated by comparing observation-minus-forecast values over 30 years. Twelve-month, 120 ensemble hindcasts are performed over the period 1995-2004, each with 100 ensemble members. This ensemble technique provides a simple method of extending the standard ICM forecasts to the probabilistic domain. The results show that the prediction skill of the ensemble mean is better than that of one single deterministic forecast using the same ICM. For the probabilistic perspective, those ensemble forecasts have their ensembles following observed SST anomaly variations well.
    DOI:10.1029/2006GL026994      URL     [Cited within:2]
    [46] Zheng F., J. Zhu, and R.-H. Zhang, 2007: The impact of altimetry data on ENSO ensemble initializations and predictions. Geophys. Res. Lett., 34,L13611, doi: 10.1029/2007GL030451.
    The El Ni o/Southern Oscillation (ENSO) predictions strongly depend on the accuracy and dynamical consistency of the coupled initial conditions. Based on the proposed ensemble Kalman filter (EnKF), a new initialization scheme for the ENSO ensemble prediction system (EPS) was designed and tested in an intermediate coupled model (ICM). The inclusion of this scheme in the ICM leads to substantial improvements in ENSO prediction skill via the successful assimilation of both observed sea surface temperature (SST) and TOPEX/Poseidon/Jason-1 (T/P/J) altimeter data into the initial ensemble conditions. Comparisons with the original ensemble hindcast experiment show that the ensemble prediction skills were significantly improved out to a 12-month lead time by improving sea level (SL) initial conditions for better parameterization of subsurface thermal effects. It is clearly demonstrated that improvement in forecast skill can result from the multivariate and multi-observational ensemble data assimilation.
    DOI:10.1029/2007GL030451      URL     [Cited within:]
    [47] Zheng F., J. Zhu, H. Wang, and R.-H. Zhang, 2009a: Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles.Adv. Atmos. Sci.,26(2),359-372,doi: 10.1007/s00376-009-0359-7.
    基于一个中间的联合模型(ICM ) ,一个概率的整体预言系统(EPS ) 被开发了。整体 Kalman 过滤器(EnKF ) 数据吸收途径被用于产生起始的整体条件,和一个线性、一阶的 Markov 链 SST 异例错误模型被嵌进 EPS 提供模型错误不安。在这研究,我们在 120 年时期上执行 ENSO 回顾的预报 1886 2005 由仅仅吸收历史性的 SST 异例观察用与 100 个整体成员一起并且与起始的条件的 EPS 获得了。由检验回顾的整体预报和可得到的观察,确认结果证明 EPS 的整体平均数的技巧比用一样的 ICM 的一张单个确定的预报的大,与两个的不同改进,关联和根意味着在整体平均数之间的平方( RMS )错误在12月的预言时期上的后部的演员组和确定的计划。整体平均数的 RMS 错误是比在 12 个月的一铅时间的确定的预报的小的几乎 0.2 ° C。 EPS 的概率的技巧与在为三个不同 ENSO 状态操作特征(巨鸟)曲线的亲戚下面跟随 SST 观察井,和区域的预言的整体也高(温暖的事件,冷事件,并且中立事件)都超过 0.55 在外面到 12 月铅时间。然而, EPS 的确定、概率的预言技巧出现一内部十的变化。为确定的技巧,在有高技巧迟了第 19 世纪并且在里面中间迟了第 20 世纪(它由于训练时期的模型包括某人工的技巧) ,并且低技巧在从 1906 ~ 1961 的时期期间。为概率的技巧,为三个不同 ENSO 状态,仍然有一类似内部 ENSO 概率的可预测性的十的变化在时期期间 1886 2005。在有高技巧迟了从 1886 ~ 1905 的第 19 世纪,和到在 1910 50 年代,技巧与时间在以外反弹并且增加直到 2000 年代附近的最少技巧的衰落。
    DOI:10.1007/s00376-009-0359-7.      URL     [Cited within:1]
    [48] Zheng F., H. Wang, and J. Zhu, 2009b: ENSO ensemble prediction: Initial error perturbations vs.model error perturbations. Chinese Science Bulletin,54,2516-2523,doi: 10.1007/s11434-009-0179-2.
    Based on our developed ENSO (El Ni o-Southern Oscillation) ensemble prediction system (EPS), the impacts of stochastic initial-error and model-error perturbations on ENSO ensemble predictions are examined and discussed by performing four sets of 14-a retrospective forecast experiments in both a deterministic and probabilistic sense. These forecast schemes are differentiated by whether they considered the initial or model stochastic perturbations. The comparison results suggest that the stochastic model-error perturbations, which are added into the modeled physical fields to mainly represent the uncertainties of the physical model, have significant, positive impacts on improving the ensemble prediction skills during the entire 12-month forecast process. However, the stochastic initial-error perturbations have relatively small impacts on the ensemble prediction system, and its impacts are mainly focusing on the first 3-month predictions.
    DOI:10.1007/s11434-009-0179-2      URL     [Cited within:]
    [49] Zheng F., R.-H. Zhang, and J. Zhu, 2014a: Effects of interannual salinity variability on the barrier layer in the western-central equatorial Pacific: A diagnostic analysis from Argo.Adv. Atmos. Sci.,31(3),532-542,doi: 10.1007/s00376-013-3061-8.
    In this paper, interannual variations in the barrier layer thickness(BLT) are analyzed using Argo three-dimensional temperature and salinity data, with a focus on the effects of interannually varying salinity on the evolution of the El Nino–Southern Oscillation(ENSO). The interannually varying BLT exhibits a zonal seesaw pattern across the equatorial Pacific during ENSO cycles. This phenomenon has been attributed to two different physical processes. During El Nino(La Nina),the barrier layer(BL) is anomalously thin(thick) west of about 160°E, and thick(thin) to the east. In the western equatorial Pacific(the western part: 130°–160°E), interannual variations of the BLT indicate a lead of one year relative to those of the ENSO onset. The interannual variations of the BLT can be largely attributed to the interannual temperature variability, through its dominant effect on the isothermal layer depth(ILD). However, in the central equatorial Pacific(the eastern part: 160°E–170°W), interannual variations of the BL almost synchronously vary with ENSO, with a lead of about two months relative to those of the local SST. In this region, the interannual variations of the BL are significantly affected by the interannually varying salinity, mainly through its modulation effect on the mixed layer depth(MLD). As evaluated by a one-dimensional boundary layer ocean model, the BL around the dateline induced by interannual salinity anomalies can significantly affect the temperature fields in the upper ocean, indicating a positive feedback that acts to enhance ENSO.
    DOI:10.1007/s00376-013-3061-8      URL     [Cited within:]
    [50] Zheng F., X.-H. Fang, J.-Y. Yu, and J. Zhu, 2014b: Asymmetry of the Bjerknes positive feedback between the two types of El Niño.Geophys. Res. Lett.,41,7651-7657,doi: 10.1002/2014GL062125.
    to the pronounced amplitude asymmetry for the central Pacific (CP) and eastern Pacific (EP) types of El Ni o, an asymmetry in the strength of the Bjerknes positive feedback is found between these two types of El Ni o, which is manifested as a weaker relationship between the zonal wind anomaly and the zonal gradient of sea surface temperature (SST) anomaly in the CP El Ni o. The strength asymmetry mainly comes from a weaker sensitivity of the zonal gradient of sea level pressure (SLP) anomaly to that of diabatic heating anomaly during CP El Ni o. This weaker sensitivity is caused by (1) a large cancelation induced by the negative SST-cloud thermodynamic feedback to the positive dynamical feedback for CP El Ni o, (2) an off-equator shift of the maximum SLP anomalies during CP El Ni o, and (3) a suppression of the mean low-level convergence when CP El Ni o events occur more often.
    DOI:10.1002/2014GL062125      URL     [Cited within:2]
    [51] Zheng F., X.-H. Fang, J. Zhu, J.-Y. Yu, and X.-C. Li, 2016: Modulation of Bjerknes feedback on the decadal variations in ENSO predictability.Geophys. Res. Lett.,43,12 560-12 568,doi: 10.1002/2016GL071636.
    Abstract Clear decadal variations exist in the predictability of the El Ni o揝outhern Oscillation (ENSO), with the most recent decade having the lowest ENSO predictability in the past six decades. The Bjerknes Feedback (BF) intensity, which dominates the development of ENSO, has been proposed to determine ENSO predictability. Here we demonstrate that decadal variations in BF intensity are largely a result of the sensitivity of the zonal winds to the zonal sea level pressure (SLP) gradient in the equatorial Pacific. Furthermore, the results show that during low ENSO predictability decades, zonal wind anomalies over the equatorial Pacific are more linked to SLP variations in the off-equatorial Pacific, which can then transfer this information into surface temperature and precipitation fields through the BF, suggesting a weakening in the ocean-atmosphere coupling in the tropical Pacific. This result indicates that more attention should be paid to off-equatorial processes in the prediction of ENSO.
    DOI:10.1002/2016GL071636      URL     [Cited within:2]
    [52] Zhu J. S., B. H. Huang, R.-H. Zhang, Z.-Z. Hu, Arun Kumar, M. A. Balmaseda, L. Marx, and J. L. Kinter III, 2014: Salinity anomaly as a trigger for ENSO events. Scientific Reports, 4, 6821, doi: 10.1038/srep06821.
    [Cited within:1]
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    Key words
    EP El Niño
    CP El Niño
    prediction skill
    systematic bias
    spring prediction barrier

    Fei ZHENG
    Jin-Yi YU