Intraseasonal wind bursts in the tropical Pacific are believed to affect the evolution and diversity of El Niño events. In particular, the occurrence of two strong westerly wind bursts (WWBs) in early 2014 apparently pushed the ocean–atmosphere system toward a moderate to strong El Niño—potentially an extreme event according to some climate models. However, the event’s progression quickly stalled, and the warming remained very weak throughout the year. Here, we find that the occurrence of an unusually strong basin-wide easterly wind burst (EWB) in June was a key factor that impeded the El Niño development. It was shortly after this EWB that all major Niño indices fell rapidly to near-normal values; a modest growth resumed only later in the year. The easterly burst and the weakness of subsequent WWBs resulted in the persistence of two separate warming centers in the central and eastern equatorial Pacific, suppressing the positive Bjerknes feedback critical for El Niño. Experiments with a climate model with superimposed wind bursts support these conclusions, pointing to inherent limits in El Niño
predictability. Furthermore, we show that the spatial structure of the easterly burst matches that of the observed decadal trend in wind stress in the tropical Pacific, suggesting potential links between intraseasonal wind bursts and decadal climate variations.El Niño, the warm phase of the El Niño–Southern Oscillation (ENSO), is characterized by anomalously warm water appearing in the central and eastern equatorial Pacific every 2–7 years, driven by tropical ocean–atmosphere interactions with far-reaching global impacts (recent reviews are in refs.
1–
3). These interactions and El Niño development involve several important feedbacks, including the positive Bjerknes feedback [zonal wind relaxation leads to the reduction of the zonal sea surface temperature (SST) gradient and further wind relaxation] (
4). Since the year 2000, there has been a shift in the observed properties of El Niño, including its magnitude, frequency, and spatial structure of temperature anomalies (
5,
6). For example, El Niño events occurred more frequently than during the previous two decades, but all were weak, and none reached the extreme magnitude of the 1982 and 1997 events. Concurrently, the rise of global mean surface temperature has slowed down with the so-called global warming hiatus (
7–
9). The stalled development of the 2014 El Niño presents a showcase to explore the relevant connection and mechanisms of these changes.At the beginning of 2014, many in the scientific community anticipated that a moderate to strong El Niño could develop by the end of the year (
10–
14) (). In March, the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center announced an “El Niño watch” based on predictions made by dynamical and statistical models (
12), attracting attention of the general public. Admittedly, these predictions encompassed large uncertainties because of the stochastic nature of the tropical climate system (
15–
17). In May, the National Aeronautics and Space Administration (NASA) suggested that 2014 could potentially rival the strongest on-record event of 1997/19998 (), while acknowledging the large existing uncertainty (
14); their projection was supported by satellite observations of strong Kelvin waves evident in sea surface height (SSH) (). The spread of spring forecast plumes from some climate models, for example that of the European Centre for Medium-Range Weather Forecasts (ECMWF), included the possibility of a failed El Niño () but only as a low-probability outcome involving unusual instances of weather noise. The observed development fell near the limit of these forecast possibilities after June and July, and eventually, the 2014 warm event barely qualified as El Niño ().
Open in a separate windowEl Niño development in (
A and
C) 2014 and (
B and
D) 1997. (
A and
B) Evolution of the Niño3, Niño4, and Niño3.4 indices; the first two indices describe SST anomalies (in degrees Celsius) in the eastern and central equatorial Pacific, respectively, whereas the last index covers the region in between. (
C and
D) Variation in the zonal wind stress indices. These indices are obtained by averaging wind stress anomalies (in 10
−2 newtons per meter
2) in the equatorial Pacific zonally and between 5 °S and 5 °N and then selecting negative (blue; easterly anomalies), positive (red; westerly anomalies), or full values (black) (
Materials and Methods). The spatial averaging is intended to take into account both the magnitude and the fetch of the wind bursts. During 2014, two early year WWBs were followed by an exceptional EWB in June (highlighted by pink and blue, respectively). This easterly burst apparently led to a rapid decrease of the Niño indices (
A). In contrast, the 1997 El Niño exhibited persistent westerly wind activity throughout the year. The graphs start on January 1.
Open in a separate windowSpatiotemporal evolution of the 2014 El Niño. (
A–
D) Hovmöller diagrams for anomalies in (
A) SST, (
B) zonal wind stress, (
C) SSH, and (
D) surface zonal currents in the equatorial Pacific. Time goes downward. The SSH and surface velocity plots highlight the eastward propagating downwelling Kelvin waves, especially pronounced early in the year, and a strong upwelling Kelvin wave midyear. (
E and
F) El Niño development in 2014 (black line) compared with several historical (
E) EP and (
F) CP events. The diagrams show the position of the Warm Pool Eastern Edge (degrees of longitude) vs. the Niño3 SST (degrees Celsius) for different months of the year. The Warm Pool Eastern Edge is defined as the position of the 29 °C isotherm at the equator. Numbers indicate monthly averages (1, January; 2, February, etc.). The light vertical line marks the Dateline. In 2014, both the warm pool displacement and Niño3 SST anomalies were exceptionally large during May (month 5), were similar to those in 1997 and 1982 (the strongest events of the 20th century), and then, rapidly decreased by August (month 8).
Open in a separate windowThe El Niño spring forecasts of the Niño3.4 index from the European Centre for Medium-Range Weather Forecasts (ECMWF). Red lines show 50 ensemble members of the forecast plume initiated in March of 2014; the black dotted line indicates the observed Niño3.4 index. The observed development fell outside the forecast plume in June and July and remained beyond the typical forecast spread after that. Adapted from ref.
13.The question then arises as to which dynamic factors controlled the temporal and spatial development in the tropical Pacific in 2014. This warm event began with a rapid growth, such that, in early June, all major Niño indices (
Materials and Methods) along the equator were nearly identical to those during the same time of 1997 (). A substantial warming also developed along the Peruvian coast (). Then, the event’s progression slowed down or even reversed. By year end, the equatorial warming barely exceeded 1 °C, but the SST anomaly stretched uncharacteristically across the entire equatorial Pacific almost uniformly ( and ). Accordingly, the major goal of this study is to investigate this unusual development, identify the main factors that impeded this event, and explore its broad implications.
Open in a separate windowThe June of 2014 EWB in satellite-based data. (
A) The spatial structure of anomalies in surface winds (vectors; in meters per second) and SST (colors; in degrees Celsius) on June 12, 2014, when the burst was strongest. (
B) Daily vs. weekly mean values of the zonal wind stress index (10
−2 newtons per meter
2) for the period 1988–2014. The blue cross marks the peak value of the June of 2014 EWB. The wind stress index is defined as anomalous zonal wind stress averaged in the equatorial Pacific zonally and between 5 °S and 5 °N (
Materials and Methods). Black circles are for the year 2014, red circles are for all El Niño years before 2014, and gray circles are for all other years (La Niña or neutral). Note that the June of 2014 EWB appears strongest in the satellite record for not only daily data but also, weekly averaged values, which confirms that the observations are robust.
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