The initial errors that induce a significant “spring predictability barrier” for El Niño events and their implications for target observation: results from an earth system model
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The National Center for Atmospheric Research Community Earth System Model is used to study the “spring predictability barrier” (SPB) problem for El Niño events from the perspective of initial error growth. By conducting perfect model predictability experiments, we obtain two types of initial sea temperature errors, which often exhibit obvious season-dependent evolution and cause a significant SPB when predicting the onset of El Niño events bestriding spring. One type of initial errors possesses a sea surface temperature anomaly (SSTA) pattern with negative anomalies in the central–eastern equatorial Pacific, plus a basin-wide dipolar subsurface temperature anomaly pattern with negative anomalies in the upper layers of the eastern equatorial Pacific and positive anomalies in the lower layers of the western equatorial Pacific. The other type consists of an SSTA component with positive anomalies over the southeastern equatorial Pacific, plus a large-scale zonal dipole pattern of the subsurface temperature anomaly with positive anomalies in the upper layers of the eastern equatorial Pacific and negative anomalies in the lower layers of the central–western equatorial Pacific. Both exhibit a La Niña-like evolving mode and cause an under-prediction for Niño-3 SSTA of El Niño events. For the former initial error type, the resultant prediction errors grow in a manner similar to the behavior of the growth phase of La Niña; while for the latter initial error type, they experience a process that is similar to El Niño decay and transition to a La Niña growth phase. Both two types of initial errors cause negative prediction errors of Niño-3 SSTA for El Niño events. The prediction errors for Niño-3 SSTA are mainly due to the contribution of initial sea temperature errors in the large-error-related regions in the upper layers of the eastern tropical Pacific and/or in the lower layers of the western tropical Pacific. These regions may represent ‘‘sensitive areas’’ for El Niño–Southern Oscillation (ENSO) predictions, thereby providing information for target observations to improve the forecasting skill of ENSO.
KeywordsEl Niño events Spring predictability barrier Initial errors Target observation
El Niño–Southern Oscillation (ENSO) describes the extreme sea surface warming events that occur in the eastern tropical Pacific Ocean accompanied by large-scale atmospheric circulation anomalies (Philander 1983, 1990). Although ENSO originates and develops mainly in the tropical Pacific, it is capable of bringing climate variability to various parts of the globe through teleconnection and resulting in serious societal and economic consequences (Bjerknes 1969; Ropelewski and Halpert 1987; Hoerling et al. 1997; Trenberth et al. 1998). Accordingly, we as a research community are continually motivated to improve the forecasting skill of ENSO.
With the development of ENSO-related theories, observing systems and numerical models, there has been encouraging progress in our understanding and prediction of ENSO (Wang and Picaut 2004; Wang and Fiedler 2006), and generally it is skillfully predictable with a 1-year lead time in hindcast experiments (Chen and Cane 2008; Jin et al. 2008). However, considerable uncertainties still exist in realistic ENSO predictions; in particular, the influence of the so-called “spring predictability barrier” (SPB) for El Niño (Latif et al. 1994, 1998; Kirtman et al. 2002; Luo et al. 2005, 2008; Jin et al. 2008). This barrier manifests as a sharp drop-off in the monthly persistence of observed oceanic and atmospheric index associated with ENSO across boreal spring, and in numerical prediction models it appears as a sudden decrease (or increase) of the anomaly correlation coefficient (ACC) (or RMSE: root-mean-square error), regardless of the starting month (Webster and Yang 1992; Webster 1995; Torrence and Webster 1998; Luo et al. 2008). From the perspective of error growth, a “significant SPB” refers to the phenomenon that ENSO forecasting has a large prediction error; and in particular, a prominent error growth occurs during boreal spring when the prediction is made before and throughout that spring (Mu et al. 2007a, b; Duan et al. 2009; Duan and Wei 2012).
Agreement regarding the cause of the SPB has yet to be reached, although considerable efforts have been made in studying this phenomenon. Some studies argue that the SPB is an intrinsic characteristic of ENSO forecasting. Because the signal-to-noise ratio for SST tends to be lowest in spring, even additional observations cannot change the fact of the low signal in spring (Xue et al. 1994; Samelson and Tziperman 2001). Others, meanwhile, believe that the SPB arises from the growth of initial errors. Chen et al. (1995, 2004) suggest that ENSO predictions depend more on the initial conditions than on unpredictable noise, and hence the predictability of ENSO across spring can be greatly enhanced through improving the initialization. Moore and Kleeman (1996) investigated the season-dependent evolutions of initial errors related to SPB by using the linear singular vector (LSV) method, and Xue et al. (1997a, b) also applied LSV to ENSO predictability studies.
Mu et al. (2007a) demonstrated that the SPB may be a result of the combined effect of the climatological annual cycle, the El Niño event itself and the initial error pattern. In terms of the third factor, Mu et al. (2007b) used the Zebiak–Cane model (ZC model; Zebiak and Cane 1987) along with the conditional nonlinear optimal perturbation (CNOP) approach (Mu et al. 2003) to explore the initial errors that cause a significant SPB. Yu et al. (2009, 2012) further recognized two kinds of CNOP-type initial errors, which show a large-scale zonal dipolar pattern for the sea surface temperature anomaly (SSTA) component and a basin wide deepening or shoaling along the equator for the thermocline depth anomaly, and similar CNOP-like initial errors also exist in realistic ENSO predictions (Duan et al. 2009; Duan and Wei 2012). All these studies attempt to reveal the initial error that induces a significant SPB for El Niño events most probably, and identify the location in which additional observations should be a priority for improving the El Niño forecast skill. However, the results were obtained from the ZC model, which is an anomaly coupled model of intermediate-complexity and only considers the interannual variability of the tropical Pacific. In particular, they focused on the SSTA component of the initial errors and did not consider the role of subsurface temperatures in yielding the SPB, due to the limitation of the simplicity of the ZC model.
In fact, subsurface processes play an important role not only in the evolution of the ENSO life cycle, but also in ENSO predictions. On the one hand, observations show that the movement of upper-ocean warm water in the equatorial Pacific is closely related to ENSO events. The buildup of warm water volume (WWV) in the equatorial Pacific is a necessary precondition for the development of ENSO (Wyrtki 1975, 1985; Cane et al. 1986; Zebiak and Cane 1987; Zebiak 1989; Jin 1997a). Ramesh and Murtugudde (2013) stated that subsurface processes can be a fundamental driver for the onset of ENSO, whereas the SSTA follows later, serving as the surface manifestation of the subsurface temperature anomaly. Zelle et al. (2004) showed that there are two pathways for the thermocline depth anomaly leading to SST anomalies. One is the local “upwelling pathway” in which the SSTA is directly related to thermocline depth anomalies over the eastern equatorial Pacific, and the other “wind coupling pathway” provides a remote coupling through wave dynamics. Moreover, most theoretical ENSO oscillator models emphasize the importance of oceanic wave propagation processes associated with the upper-ocean heat content (OHC) anomalies (Suarez and Schopf 1988; Battisti and Hirst 1989; Weisberg and Wang 1997; Picaut et al. 1997). In particular, Jin (1997a, b) proposed that it is the phase lag between the zonal mean thermocline depth over the entire equatorial Pacific and the SST anomaly in the eastern Pacific that leads to an oscillation of ENSO. Generally, the predictability of ENSO mainly comes from the oceanic memory associated with subsurface temperature anomalies along the equatorial thermocline (Zebiak 1989). Variations in the equatorial WWV anomalies or the heat content anomalies of the equatorial Pacific precede ENSO SSTA variability by two to three seasons, so that they can serve as reliable predictors of Niño-3 SST (Meinen and Mcphaden 2000; Hasegawa and Hanawa 2003). Consistent with the phase relationship, there is a winter prediction barrier for the WWV (or OHC) anomalies, rather than a spring barrier. As a consequence, accurate initialization for sea level heights or OHC and correct prediction of subsurface signals can help to reduce the SPB (Clarke and Van Gorder 2003; Mcphaden 2003; Yu and Kao 2007; Luo et al. 2005, 2008).
In the above context, the following questions arise: What kinds of initial errors often cause the SPB in a more complex earth system model (ESM)? What is the dynamical mechanism responsible for error growth? In particular, what is the role of initial subsurface temperature errors in the occurrence of SPB? Motivated by these questions, in this study we use an ESM to explore the initial errors that cause a significant SPB for El Niño events, and suggest a possible mechanism responsible for the initial error growth by performing perfect model predictability experiments.
The remainder of this paper is organized as follows: First, in Sect. 2, we introduce the ESM, followed by the experimental strategy in Sect. 3. In Sect. 4, two types of initial errors that often yield a significant SPB for El Niño forecasts are identified. Then, in Sect. 5, we provide dynamical explanations for the season-dependent evolution of prediction errors caused by the two types of initial errors. In Sect. 6, we reveal some implications of the initial errors associated with the SPB in terms of target observations for El Niño events; and then to close, we summarize the study and offer further discussion in Sect. 7.
2 The community earth system model
The model used in this study is the Community Earth System Model (CESM), supported by the National Center for Atmospheric Research (NCAR). The CESM, superseding its previous version of the Community Climate System Model (CCSM4), is a fully-coupled ESM that includes ocean, atmosphere, land, sea ice, and land ice components, interacting together through a central flux coupler, which can provide state-of-the-art simulations of the Earth’s past, present and future climate states. The CESM uses one of three alternatives as its atmospheric component: either the Community Atmosphere Model (CAM), the high-top atmosphere Whole Atmosphere Community Climate Model (WACCM), or the CAM with chemistry (CAM-CHEM) model. The Community Atmosphere Model version 4 (CAM4), used in this study, has a finite-volume (FV) dynamical core with 26 vertical layers. The horizontal resolution is 0.9° (longitude) × 1.25° (latitude) upon the regular longitude–latitude grid. The model configuration is described in detail in Neale et al. (2012). The ocean component is based on the Parallel Ocean Program version 2 (POP2) of the Los Alamos National Laboratory, which has 60 vertical levels varying from 10 m below the surface to a depth of 250 m. It uses spherical coordinates in the Southern Hemisphere, and a displaced pole grid in the Northern Hemisphere. The horizontal resolution is approximately 1° (longitude) × 0.27° (latitude) at the equator, with the domain ranging from 79°S to 89°N. Further details on the ocean component can be found in Smith et al. (2010). The CAM4 and POP2 are coupled through the version 7 coupler (CPL7) (Craig et al. 2012) together with the Community Land Model version 4.0 (CLM4) (Oleson et al. 2010), the Los Alamos sea ice model, referred to as the Community Ice CodE version 4.0 (CICE4) (Hunke and Lipscomb 2008), and a dynamic ice sheet model known as Glimmer-CISM (Rutt et al. 2009; Lipscomb et al. 2013). More details of the CESM model configuration and its simulation of the climate system are given in Hurrell et al. (2013). The model demonstrates some biases in the tropical Pacific interannual variability, such as a westward displacement (compared to observations) of the location of maximum warming (Capotondi 2013) and an underestimation of ENSO asymmetry (Zhang and Sun 2014). However, the fundamental characteristics of modeled El Niño generally compare well with observations (Bellenger et al. 2014).
3 Experimental strategy
The initial errors are superimposed on the initial sea temperature fields of the six “true state” El Niño events. Considering that the dominant period of El Niño is about 3 years, the initial errors are generated by taking the differences between the sea temperature of the “true state” El Niño events at the start month, and that in each month of the 3 years preceding each El Niño year, which may be responsible for much ergodic initial errors. For example, when the start month is October(−1), the first initial error is determined by subtracting the sea temperature of October(−1) from that of September(−1); when September(−1) is changed to August(−1), the sea temperature difference between these 2 months will be the second initial error, and so forth. Therefore, at each start month, we have 36 different initial error patterns to be superimposed on the initial values of each El Niño event. In total, there are 432 predictions for the two start months of the six El Niño events. Several studies have used the ZC model to examine the initial errors that cause a significant SPB for El Niño (Mu et al. 2007b; Duan et al. 2009; Yu et al. 2009). However, the ZC model is a simple model and cannot depict the evolution of subsurface temperature anomalies, which are important for the onset of El Niño events. In this study, we adopt the CESM, which is a complex earth system model and can explore the role of subsurface temperature anomalies in the occurrence of an SPB. In the numerical experiments, the initial errors of the sea temperature fields cover the region (20.19°S–20.05°N, 130.44°E–84.49°W); and to explore the role of subsurface processes, the initial errors extend from the surface to 165 m depth, which is approximately the bottom of the thermocline over the western equatorial Pacific. Quite a few studies emphasized the importance of the spatial structure of initial errors in yielding SPB (Xue et al. 1994; Duan et al. 2009; Yu et al. 2012); furthermore, we found that the initial errors being normalized often cause an initial shock phenomenon that characterized as a rapid growth of errors within a short time after the beginning of predictions, which may be due to the dynamical unbalance among different levels of the upper ocean temperature field induced by normalized initial errors. It is therefore pointed out that the magnitudes of the initial errors are not constrained uniformly in numerical experiments and the spatial pattern of initial errors is mainly emphasized.
4 Two types of initial errors that often cause the SPB for El Niño events
5 Dynamical mechanisms of error growth related to the SPB for El Niño events
Having demonstrated in Sect. 4 the existence of two types of initial errors that often cause predictions of El Niño onset to yield a significant SPB in the CESM model, and that these errors induce a large prediction error for Niño-3 SSTA (specifically, both error types cause El Niño events to be under-predicted), we therefore naturally ask: why do the errors cause a negative prediction error of Niño-3 SSTA for El Niño events? That is to say, what is the dynamical mechanism underpinning the two types of SPB-related initial errors? Duan et al. (2009) and Yu et al. (2009) identified two initial errors of opposite sign that are most likely to evolve into El Niño-like and La Niña-like modes, respectively, in the ZC model (Zebiak and Cane 1987). We therefore also ask: is the behavior of the SPB-related error growth in the CESM similar to El Niño and La Niña events? To address these questions, we explore the time-dependent evolution of the prediction errors caused by the two types of SPB-related initial errors.
For the type-2 initial errors, the initial positive SSTA error is confined to the southeast of the equatorial Pacific, where a weak westerly anomaly occurs only over the central tropical Pacific (Fig. 9a2, b2), and consequently the Bjerknes feedback process fails to establish. However, the large negative subsurface temperature anomalies lift the thermocline of the western equatorial Pacific (Fig. 9c2) and generate upwelling Kelvin waves that propagate eastward, carrying cold water with them and causing a negative SSTA in the eastern Pacific. The negative SSTA will then induce zonal easterly wind anomalies in the central Pacific, leading to the warm SSTA error and westerly anomalies to decay and disappear gradually (Fig. 9a2, b2). Once the warm SSTA error disappears and the negative SSTA error subsequently occurs over the eastern equatorial Pacific, the cooling error will be further intensified through the easterlies and anomalous upwelling due to the Bjerknes positive feedback mechanism, also underestimating the El Niño events. In short, for the earlier development of the type-2 errors, the negative feedback associated with equatorial waves travelling from the western equatorial Pacific has a first-order effect; while once a negative SST anomaly over the eastern equatorial Pacific occurs, the Bjerknes positive feedback becomes the leading factor.
The results thus far described have demonstrated that two types of SPB-related initial errors exist for El Niño events in the CESM model, and that they cause significantly large prediction errors (see Sect. 4). In particular, it is apparent that the negative prediction errors for Niño-3 SSTA caused by the type-1 initial errors grow to be large in the eastern equatorial Pacific (see region A in Fig. 6), mainly due to the contribution of cold water from the subsurface layers of the eastern equatorial Pacific (see region B in Fig. 6). Therefore, if we reduce the initial errors in regions A and B, the resultant prediction errors for Niño-3 SSTA should be greatly decreased. For the type-2 initial errors, the resultant negative prediction errors for Niño-3 SSTA first originate from the lower layers of the western equatorial Pacific (see region C in Fig. 6), and then grow to be large in the Niño-3 region. It is therefore reasonable to suggest that the El Niño predictions may also be sensitive to the initial errors of sea temperature in region C. Since the prediction errors for Niño-3 SSTA are sensitive to the initial errors in regions A, B, and C, implementing additional observations in these regions may be superior to doing so in other regions for improving the prediction skill of El Niño. This argument is related to an observing strategy named as “target observation”.
Target observation is an observing strategy whose development began after the 1990s. To better predict an event at a future time t1 (called the verification time) in a focused area (called the verification area), additional observations are deployed at time t2 (called the targeted time; t2 < t1) in some key areas (generally called the “sensitive area”), where additional observations are expected to have a considerable impact on the forecasts in the verification area (Snyder 1996; Mu 2013). A key problem in target observation is the determination of the “sensitive area” where additional observations are expected to yield a better forecast than observations taken in other regions. Furthermore, given the high cost of observations, a focus on the “sensitive area” may represent an economical and efficient strategy aimed at improving the prediction skill of El Niño events. From the definition of target observation, it is inferred that the above regions A, B, and C may represent the sensitive areas for El Niño predictions.
In this study, we use the NCAR’s CESM to investigate the initial errors that often cause a significant SPB for El Niño events, under the assumption that the model physics are perfect. From the predictions bestriding spring during the growth phase of El Niño events, two types of initial errors are identified that have significant season-dependent evolutions, with the significant growth occurring in the AMJ or JAS season and large prediction error related to the significant SPB for El Niño events. One of the error types possesses an SSTA component with negative anomalies in the central–eastern equatorial Pacific, plus a basin-wide dipolar pattern in the subsurface temperature anomaly with negative anomalies in the upper layers of the eastern equatorial Pacific and positive anomalies in the lower layers of the western equatorial Pacific. The other type consists of an SSTA component with positive anomalies in the southeastern equatorial Pacific and a large-scale zonal dipole pattern of the subsurface temperature anomaly, with positive anomalies in the upper layers of the eastern equatorial Pacific and negative anomalies in the lower layers of the central–western equatorial Pacific.
In spite of the different patterns of the two types of SPB-related initial errors, both cause El Niño events to be under-predicted. Specifically, both types of SPB-related initial errors exhibit a typical La Niña-like evolving mode, causing a large negative prediction error for El Niño events, despite presenting different behaviors in their early stages of the error growth. In one case, the initial errors grow directly in a manner similar to the growth behavior of a La Niña event; in the other case, the errors initially exhibit a rapid decay of the El Niño-like mode, and then a quick transition to a typical La Niña-like evolving mode. Further investigation suggests that there are two competing factors affecting the SSTA error in the eastern equatorial Pacific. One factor is the Bjerknes positive feedback mechanism, the coupling of the equatorial zonal wind anomalies and changes in SST owing to equatorial upwelling in the eastern Pacific, which causes an intensification of the initial SSTA error. The second factor is the negative feedback mechanism associated with an eastward Kelvin wave from the western tropical Pacific, which leads to weakening of the initial SSTA error over the eastern equatorial Pacific. For the type-1 initial errors, the Bjerknes positive feedback mechanism plays a leading role throughout the error growth; while for the development of the type-2 errors, the negative feedback initially has a first-order effect, and the role of Bjerknes feedback is moderate, but once a negative SST anomaly over the eastern equatorial Pacific occurs, the Bjerknes feedback becomes dominant and the negative feedback is negligible. Finally, a La Niña-like mode is then quickly established.
Both type-1 and -2 SPB-related initial errors induce large negative prediction errors of Niño-3 SSTA for El Niño events. Results show that the large prediction errors are mainly attributed to the growth of the initial sea temperature errors in several key regions of the tropical Pacific, which are also consistent with the regions bearing large initial errors. For the type-1 initial errors, the errors in the upper layer of the eastern equatorial Pacific (i.e. regions A and B in Fig. 6) tend to make a greater contribution to the final prediction error of Niño-3 SSTA; while for the type-2 initial errors, the prediction errors for Niño-3 SSTA mainly originate from the initial sea temperature errors in the lower layer of the western equatorial Pacific (i.e. region C in Fig. 6). In particular, when the initial errors in regions A and B for the type-1 initial errors and region C for the type-2 initial errors are eliminated, without changing the initial errors in other regions, the resultant predictions errors normalized by related region’s volume are more significantly reduced than those by only removing initial errors outside of A, B, and C regions. The regions A, B, and C may therefore represent the “sensitive area” for target observation of El Niño predictions. That is to say, if we implement the additional observation in these regions and assimilate them to the initial fields, the El Niño forecasting skill could be greatly improved, as compared to doing so in other regions.
Yu et al. (2009) recognized two types of initial errors using the simple ZC model. These two types of initial errors are of almost opposite sign and cause a significant SPB for El Niño events. One type possesses an SSTA pattern with negative anomalies in the central–western equatorial Pacific, positive anomalies in the eastern equatorial Pacific, and a thermocline depth anomaly pattern with positive anomalies along the equator; while the other type possesses patterns that are almost opposite to those of the former type. To facilitate the following discussion, we refer to the former type as type-A initial errors and the latter type as type-B initial errors (also see Duan et al. 2009). Yu et al. (2009) illustrated that the type-A and -B initial errors possess dynamic behavior similar to El Niño and La Niña events, and are explained by the Bjerknes positive feedback mechanism. Furthermore, Mu et al. (2014) demonstrated that the precursory perturbations that are most likely to develop into El Niño or La Niña events bear a strong resemblance with the initial errors that induce a significant SPB with the ZC model. This indicates that initial anomalies with the structure of type-A and -B initial errors also act as precursory disturbances for El Niño and La Niña events, respectively (Duan and Wei 2012; Zhang et al. 2014). In the present study, we also obtain two types of SPB-related initial errors (i.e. the type-1 and type-2 initial errors in Fig. 6) for El Niño predictions in the CESM, both of which include not only the SST component but also the subsurface temperature component. However, the type-1 and -2 initial errors in this study are asymmetrical in pattern, which is not the case in the type-A and -B initial errors in the ZC model. Actually, the type-1 initial errors show negative anomalies in both surface and subsurface layers of the eastern equatorial Pacific, and suggest a pattern comprising negative SST anomalies and a shoaling thermocline in the eastern equatorial Pacific. This pattern mirrors that presented by the type-B initial errors in the ZC model. In spite of asymmetrical pattern, both the type-1 and -2 initial errors exhibit a growth behavior similar to the La Niña evolving mode. Similarly, the initial anomalies with the structure of type-1 and -2 initial errors could be a precursory disturbance for La Niña events. However, unlike type-1 initial errors that grow directly starting from a cold phase, the type-2 initial errors experience a decaying period of positive SST anomalies in the eastern equatorial Pacific due to a negative feedback process associated with wave dynamics, and then a transition to a cold phase, followed quickly by growth into a La Niña-like mode because of Bjerknes positive feedback. Therefore, if we regard the initial anomaly with type-2 initial error structure as a precursory disturbance for La Niña, it is clearly preceding the initial anomaly with the type-1 initial error structure (similar to type-B in the ZC model). In other words, the initial anomaly with type-2 initial error structure may present a much earlier signal for the occurrence of La Niña events, favoring prediction of La Niña events with a much longer lead time. Besides, considering the onset of El Niño and La Niña often occurs in spring due to the fast growth of anomalies in this season (Wang and Fang 1996), we infer that the predictions for a neutral year may also yield SPB phenomenon because of the effect of type-1 and -2 errors.
To investigate the spatial characteristics of initial errors that cause a significant SPB for El Niño events, we chose the sea temperature differences between a particular month and the start month in the tropical Pacific as initial errors. Therefore, this strategy may not guarantee that the constructed initial errors cover all kinds of realistic initial error patterns. That is to say, the sensitive areas identified in this paper may not consist of all sensitive areas for target observation associated with El Niño predictions. Especially, mounting evidences suggest that El Niño changes and its predictability can also be due to the influence from outside of the tropical Pacific. For example, within the tropic, the variability in the Indian Ocean sector [e.g. Indian Ocean dipole (IOD); Saji et al. 1999] can influence El Niño through both the Indonesian Throughflow and Walker circulation, and the initial conditions of the tropical Atlantic have a stronger impact on the predictability of El Niño (Frauen and Dommenget 2012; Keenlyside et al. 2013; Zhou et al. 2015). While, outside of the tropic, SST in the North Pacific, South Atlantic, and South Indian Oceans can offer important source of predictability for El Niño (Boschat et al. 2013). In this sense, these regions may also contain the “sensitive area” for target observations for El Niño predictions.
In this study, we emphasize the role of oceanic initial error pattern in leading to a significant SPB for El Niño events. Some studies also suggested the annual cycle plays a significant role in occurrence of the SPB (Webster and Yang 1992; Moore and Kleeman 1996; Thompson and Battisti 2001). Especially, recently, basing on a damped and noise driven conceptual model, Levine and McPhaden (2015) examined the effectiveness of the annual cycle in producing the SPB and found that only inclusion of the annual cycle of ENSO growth rate gives rise to the SPB phenomenon. And others put an emphasis on the ENSO event itself as well (Samelson and Tziperman 2001). With a conceptual ENSO model, Mu et al. (2007a) combined above arguments and stated that the SPB may result from combined effect of the annual cycle, the El Niño event itself and the initial error pattern. In particular, Duan et al. (2009) further stressed that even with the annual cycle, there are still some initial errors that can induce SPB while others cannot. Then, in the present study, we just explored the characteristics of those initial errors that induce a significant SPB for El Niño events. Apparently, in addition to the influence from ocean, the prediction skill of El Niño events may also be affected by atmospheric states. With an intermediated coupled model, Zheng and Zhu (2010) have shown that better representation of zonal wind stress anomalies through coupled assimilation can reduce SST forecast errors through improve accuracy of ocean currents. Moreover, increasing studies highlight the potential impact of atmospheric stochastic forcing [e.g. westerly wind burst (WWB)] on the diversity and predictability of El Niño events (Hu et al. 2014; Chen et al. 2015). Particularly, Lopez and Kirtman (2014) indicated the SPB is due to the presence of WWB because significant WWB activities can contribute to a rapid drop off in signal-to-noise ratio of coupled system in spring. Actually, comparison between these studies associated with atmospheric effects and the present study proposes a new question to the SPB: is the SPB more sensitive to ocean state or variations in atmosphere? That is to say, ones should further explore sensitive variables for ENSO predictions and then the associated target observation, which may be much important for improving ENSO forecast skill.
The SPB may also affect the prediction of La Niña. In this paper, we did not pay attention to it because main characteristics of La Niña events, for example, phase locking, cannot be well modeled by the CESM control simulation. Yu et al. (2009) adopted the simple ZC model (Zebiak and Cane 1987) and demonstrated that the SPB-related initial errors associated with the predictions bestriding spring in the growth phase of El Niño have patterns for SSTA and thermocline depth anomaly components similar to those across spring in the decay phase of El Niño. However, ones do not know whether or not the results from the ZC model are applied to complex coupled GCMs. Therefore, to make it much clear, ones should further explore this question by using CESM or other coupled GCMs.
In addition, it is well-known that there are two types of El Niño events. One type consists of canonical El Niño events, which have their maximum SST anomaly center located in the eastern equatorial Pacific attached to the coast of South America (Rasmusson and Carpenter 1982), and has been referred to as ‘‘Eastern Pacific El Niño’’ (EP-El Niño) (Kao and Yu 2009). The other type is often called ‘‘Central Pacific El Niño’’ (CP-El Niño) (Kao and Yu 2009), in which warm SST is mainly concentrated in the central Pacific and it propagation is weaker and less clear (Ashok et al. 2007; Kao and Yu 2009; Kug et al. 2009). In the present study, we pay more attention on the SPB problem of the EP-El Niño events and identified two types of initial errors that often cause a significant SPB and explored corresponding dynamical mechanisms for error growth and further revealed their implications for target observation of EP-El Niño predictions. However, recent studies have shown that the CP-El Niño events have become more frequent and common than the EP-El Niño during the late twentieth century, especially after the 1990s (Ashok et al. 2007; Kao and Yu 2009; Kug et al. 2009). Unlike the EP-El Niño events, the evolution of CP-El Niño events are mainly due to the zonal advective feedback rather than the thermocline feedback, which may be linked to the shift of the relationship between SST anomalies and WWV anomalies. Since 2000, the lead time between WWV and SST anomalies has decreased from 2 to 3 seasons to only one season (McPhaden 2012). Consequently, for the CP-El Niño, whether there exists the SPB phenomenon? If so, what kind of initial error will be the most likely to cause a significant SPB, and whether it is the same to that of EP-El Niño or not? In fact, the relevant work is currently underway within a simple coupled model and will hopefully be reported in the future, expecting to provide useful information for identifying and predicting two different flavors of El Niño.
This work was jointly sponsored by the National Basic Research Program of China (Grant No. 2012CB955200), the National Public Benefit (Meteorology) Research Foundation of China (Grant No. GYHY201306018), and the National Natural Science Foundation of China (Grant Nos. 41230420 and 41176013).
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