Diverse NPMM conditions deviate the 2023/24 El Niño from the 1997/1998 and 2015/2016 extreme El Niño events

Lin, Yong-Fu; Chen, Mengyan; Liu, Lingling; Zheng, Fei; Ding, Ruiqiang; Wang, Xin; Wu, Chau-Ron; Lo, Min-Hui; Hsu, Huang-Hsiung; Chen, Jiepeng; Lee, Ting-Hui; Yu, Jin-Yi

doi:10.1038/s41612-025-01013-z

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Published: 31 March 2025

Diverse NPMM conditions deviate the 2023/24 El Niño from the 1997/1998 and 2015/2016 extreme El Niño events

Yong-Fu Lin^1,2,
Mengyan Chen³,
Lingling Liu⁴,
Fei Zheng⁵,
Ruiqiang Ding⁶,
Xin Wang³,
Chau-Ron Wu^2,7,8,
Min-Hui Lo⁹,
Huang-Hsiung Hsu⁸,
Jiepeng Chen³,
Ting-Hui Lee⁹ &
…
Jin-Yi Yu¹

npj Climate and Atmospheric Science volume 8, Article number: 133 (2025) Cite this article

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Abstract

The 2023/24 El Niño commenced with an exceptionally large warm water volume in the equatorial western Pacific, comparable to the extreme 1997/98 and 2015/16 events, but did not develop into a super El Niño. This study highlights the critical role of contrasting Northern Pacific Meridional Mode (NPMM) conditions in this divergence. Warm NPMM conditions during the 1997/98 and 2015/16 events created a positive zonal sea surface temperature (SST) gradient in the equatorial western-central Pacific and enhanced Madden-Julian Oscillation (MJO) propagation, driving sustained westerly wind bursts (WWBs) and downwelling Kelvin waves that intensified both events. In contrast, the cold NPMM during 2023/24 induced a negative SST gradient and suppressed MJO activity, resulting in weaker WWBs and limited eastward wave activity, preventing the event from reaching super El Niño intensity. A 2,200-year CESM1 pre-industrial simulation corroborates these observational findings, underscoring the importance of NPMM interference in improving El Niño intensity predictions.

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Introduction

At the onset of 2023, the equatorial Pacific exhibited an exceptionally large warm water volume, a phenomenon largely attributed to the recharging process associated with the preceding triple-dip La Niña event^1,2. This unusual accumulation of heat content sparked widespread anticipation of an extreme El Niño event for 2023/24^3,4. Some forecasts even projected the possibility of an event surpassing the intensity of the 1997/98 and 2015/16 extreme El Niños, given that the observed warm water volume exceeded that of those years by approximately 50% (Fig. S1). Moreover, this volume was notably four times higher than the average recorded for other El Niño events from 1980 to 2024. However, defying these expectations, the 2023/24 El Niño, while undeniably strong, failed to attain the extreme intensities (with ONI > 2.15°C, corresponding to 2.5 standard deviations of ONI) witnessed during the 1997/98 and 2015/16 events (Fig. 1a). This unexpected outcome challenges our understanding of El Niño dynamics, highlighting the complexity of factors influencing El Niño-Southern Oscillation (ENSO) events.

**Fig. 1: Comparison of ONI and NPMM indices, and seasonal evolution of SST and surface wind anomalies during 1997/98, 2015/16, and 2023/24 El Niños.**

The development and intensity of El Niño events are governed by a complex interplay of oceanic and atmospheric processes. Key factors include the warm water volume^5,6,7, Westerly Wind Bursts (WWBs)^{8,9,10,11,12,13,14,15,16}, oceanic Kelvin waves in the equatorial Pacific^17,18, interbsin interaction (with Indian and Atlantic oceans)^19,20, and even tropical cyclones²¹. Among these, the warm water volume serves as an energy reservoir, providing the necessary heat content for large-scale ocean-atmosphere interactions that can lead to extreme El Niño events^5,6,7. WWBs can trigger the eastward spreading of the warm water volume by generating downwelling Kelvin waves. These waves propagate eastward, deepening the thermocline and further warming the eastern Pacific sea surface temperatures (SSTs), thus contributing to the development of El Niño conditions^17,18. However, the relationship between these factors and El Niño development is not always straightforward. For instance, in 2014, conditions appeared favorable with substantial heat content and significant WWB activity generating downwelling Kelvin waves, yet a strong El Niño failed to materialize²². Understanding such exceptions requires consideration of the various WWB sources, which include the Madden-Julian Oscillation (MJO)^23,24,25, the North Pacific Meridional Mode (NPMM)^{26,27,28,29,30}, the large-scale equatorial SST field in the equatorial western Pacific^31,32,33,34, East Asian winter monsoon³⁵, and tropical cyclone^21,36,37.

A key distinction among the 1997/98, 2015/16, and 2023/24 El Niño events lies their associated NPMM conditions. The NPMM, driven by the Seasonal Footprinting Mechanism³⁸, is characterized by variations in SSTs and surface wind patterns in the subtropical North Pacific, plays a critical role in initiating and modulating El Niño events^{39,40,41,42,43,44,45,46,47}. The 1997/98 and 2015/16 El Niños coincided with warm NPMM conditions (Fig. 1b), marked by positive SST anomalies in the northeastern Pacific (Fig. 1c, d). In contrast, the 2023/24 El Niño developed under a cold NPMM condition (Fig. 1b, e). This unique characteristic is further highlighted in the phase-plane analysis by Hong et al. ⁴⁸, which examined the trajectory of ENSO and PMM of tropical Pacific SST for El Niño events since 1979. Their analysis revealed that most El Niños typically initiate with weak PMM and evolve clockwise. The 2023/24 El Niño, however, exhibited an unusual counterclockwise evolution, with PMM developing into a strong negative value during its development stage. This cold NPMM was a remnant feature of the preceding 2020–2022 La Niña⁴⁹. This triple-dip La Niña was notably long-lasting and strong, classified as a Central Pacific (CP) type of ENSO⁵⁰. CP La Niña events can excite atmospheric Rossby waves that propagate to the northeastern Pacific, triggering cold NPMM conditions⁵¹. They can also induce strong descending motion in the equatorial central Pacific, leading to intense easterly winds in the western Pacific and westerly wind anomalies in the equatorial eastern Pacific. As a result, while the easterly wind anomaly accumulates a significant warm water volume in the western equatorial Pacific, the westerly wind anomaly in the equatorial eastern Pacific contributes to a coastal El Niño in spring 2023⁵² (Fig. 1e). Both conditions were crucial role in influencing the 2023/24 El Niño.

This study investigates whether the varying NPMM conditions are the primary factor responsible for the differing peak intensities observed across these three El Niño events, despite their comparable initial warm water volumes. We focus on how the distinct NPMM conditions associated with each event influence atmospheric and oceanic conditions in the equatorial central Pacific. Specifically, we examine how NPMM conditions modulate WWB activities and the subsequent effects on El Niño properties. Through this analysis, we aim to elucidate the unique characteristics of the 2023/24 El Niño and enhance our understanding of the diverse manifestations of extreme El Niño events, potentially improving our ability to predict and comprehend future occurrences.

Results

Diverse NPMM, WWB, and Oceanic Kelvin Wave conditions during the onset stages of the three El Niño events

The propagation of downwelling Kelvin waves is a crucial process in the onset and growth of El Niño, driven by changes in zonal winds across the equatorial Pacific^17,18. Fig. 2a–c depict the evolution of thermocline depth changes (∆D20, where ∆D20(t) = D20(t) - D20(t-1), with t representing time in months) across the equatorial Pacific (5°S‒5°N) during the 1997/98, 2015/16, and 2023/24 El Niños. This metric can more effectively visualize the propagation of oceanic waves, with positive values indicating downwelling waves.

**Fig. 2: Spatiotemporal evolution of oceanic wave propagations and their relationship with WWB and MJO during 1997/98, 2015/16, and 2023/24 El Niños.**

During the 1997/98 El Niño, three major episodes of downwelling Kelvin wave propagation (red shading in Fig. 2a) occurred in January, March-April, and August-September of 1997. The timing of these wave events coincided with active WWBs (purple contours in Fig. 2a). This correspondence is further corroborated by the overlapping elevated WWB index values, which quantify WWB activity in the region (refer to Data Sets and “Methods”), during these three periods of downwelling Kelvin wave propagation (Fig. 2d). Additionally, we note that these periods were preceded by elevated NPMM intensities in January–February and May–July 1997 (Fig. 1b, c), with a one- to two-month lead time. While the first NPMM peak (January–February 1997) did not significantly deviate from the spread of average ENSO events (Fig. 1b), the presence of a local maximum NPMM value during this period suggests that NPMM conditions may have played a key role in triggering the WWBs. The one- to two-month lag between the NPMM index and the WWB index reflects the time required for the NPMM influence to propagate from the subtropics to the tropics and for equatorial Pacific ocean–atmosphere interactions to develop²⁹.

The 2015/16 El Niño exhibited similarities to the 1997/98 event, with three episodes of Kelvin wave propagation occurring in January-March, July-August, and September-October of 2015 (Fig. 2b). The wave events were also accompanied concurrently by intensified WWB activities (Fig. 2b, e) and preceded by elevated NPMM index values (Fig. 1b). The 1997/98 and 2015/16 events together indicate a possibility that the NPMM can influence WWB and ocean Kelvin wave activities to affect the El Niño development.

This possibility is further supported by what was observed during the 2023/24 El Nino event, where a cold NPMM condition occurred in contrast to the warm NPMM conditions during the 197/98 and 2015/16 El Nino event. Accompanied with the cold NPMM, WWB activities were suppressed and the development of downwelling Kelvin waves was limited (Fig. 2c, f). Only one weak episode of downwelling Kelvin wave was observed initiated from the equatorial central Pacific during October-December of 2023, coincided with elevated WWB activity. Another weak episode of downwelling Kelvin wave was identified in February-March of 2023, but this episode was initialized not from equatorial central Pacific but closer to the South American coast and was not accompanied by WWB event, as indicated by the near-negative WWB index during this period (Fig. 2f). Instead, this downwelling Kelvin wave was associated with a Coastal El Niño⁵² and is likely triggered by the cold NPMM at the beginning of 2023. During boreal spring, the cold NPMM led to cold SST anomalies in the equatorial central Pacific, which in turn generated anomalous westerly winds over the eastern Pacific (Fig. 2c). These winds triggered the first downwelling Kelvin wave (Fig. 2c), which propagated toward the South American coast and set the stage for the development of a Coastal El Niño⁵² (Fig. 1e). This downwelling Kelvin wave was then reflected from the South American coast, becoming a westward-propagating downwelling Rossby wave around April 2023 (Figs. 2c and S2). The reflected Rossby wave reached the International Dateline around October 2023 (Figs. S2 and S3), elevating SSTs there (Fig. 3c). This warming process established a positive zonal SST gradient over the WWB generation region (135°E–180°E) (Fig. S4), potentially creating conditions that facilitated the generation of a WWB in fall (September-October-November; SON) 2023 (Fig. 2f). Although weak, this second episode of downwelling Kelvin wave played a crucial role in maintaining and amplifying the El Niño, which peaked during the winter (November-December-January, NDJ) of 2023/24 (Fig. 1a).

**Fig. 3: Spatiotemporal evolution of equatorial Pacific SST anomalies and their zonal gradients during 1997/98, 2015/16, and 2023/24 El Niños.**

Relationship between Zonal SSTA Gradient and WWB activity in the Equatorial Western-Central Pacific

We notice that stronger westerly wind anomalies over the equatorial western-central Pacific during the onset stages (March⁰-April⁰-May⁰; MAM⁰) of the 1997–98 El Niño (Fig. 1c) and 2015–16 El Niño (Fig. 1d) were accompanied by a positive zonal SST gradient, characterized by warm SSTAs in the equatorial central Pacific and cold SSTAs in the equatorial western Pacific. Here, the year in which the El Niño occurs is designated as year 0, while the year immediately preceding the onset of the El Niño is designated as year -1. In contrast, the onset stages of the 2023–24 El Niño featured relatively weak westerly wind anomalies and a negative zonal SST gradient, with cold SSTAs in the equatorial central Pacific and warm SSTAs in the equatorial western Pacific. The contrasting zonal SST gradients in the equatorial western-central Pacific between the 2023–24 El Niño and the earlier El Niño events are evident in the evolution of SSTAs averaged along the equatorial Pacific (5°S–5°N) (Fig. 3). This evolution shows that the contrasting SST gradients persisted from January to July of the events. These reversed zonal SST gradients between the 2023–24 El Niño and the 1997–98 and 2015–16 events likely played a critical role in the reduced activity of WWB events during the 2023–24 El Niño.

To investigate whether the zonal SST gradient modulates WWB activity, we analyzed the relationship between the monthly occurrence frequency of WWB events and the zonal SST gradient using data from 1952 to 2023. In this analysis, WWB events were defined as instances where the zonal wind anomaly averaged over the WWB region exceeded one standard deviation. The monthly WWB occurrence frequencies were then stratified based on the corresponding zonal SST gradients (Fig. 4a). The results show that during months with positive SST gradients in the equatorial western-central Pacific, WWB events occur more frequently, with a mean occurrence rate of 0.26 per month (maximum: 1.0 per month). In contrast, during months with negative SST gradients, the WWB frequency drops significantly to 0.08 per month (maximum: 0.65 per month). A broader correlation analysis between the zonal SST gradients and WWB occurrence frequencies during the same onset season (MAM) reveals a significant correlation coefficient of 0.58 (P < 0.01) (Fig. 4b), further supporting the conclusion that zonal SST gradients in the equatorial western-central Pacific play a critical role in modulating WWB activity.

**Fig. 4: Relationship between WWB, zonal SST gradient, and NPMM condition.**

We further analyzed the relationships between spring (MAM) NPMM, zonal SST gradients, and WWB occurrences during 1952–2023 (Fig. 4c–d). Fig. 4c shows a scatter plot of the normalized zonal SST gradient versus the normalized NPMM index, while Fig. 4d compares the normalized spring NPMM index with the spring WWB event frequency. The analysis revealed significant correlations, with correlation coefficients of 0.54 between NPMM and zonal SST gradients, and 0.38 between NPMM and WWB frequency (P < 0.01 for both). These results, represented by the linear regression lines in Fig. 4c, d, confirm that the NPMM can modulate zonal SST gradients in the WWB region, which in turn affects WWB events.

Relationship between NPMM and Zonal SSTA Gradient in the Equatorial Western-Central Pacific

Figure 1c–e indicates that the contrasting zonal SST gradients in the equatorial western-central Pacific during the three El Niño events are closely linked to their differing NPMM conditions. The warm SSTAs in the equatorial central Pacific during the onset (MAM season) of the 1997–98 and 2015–16 El Niños were associated with warm SSTAs in the northeastern Pacific, characteristic of the warm NPMMs that developed in the preceding winter season (December^-1-January⁰-February⁰; DJF^-1). Conversely, the cold SSTAs in the equatorial central Pacific during the 2023–24 El Niño were connected to a cold NPMM that also developed in the preceding winter.

To further explore the relationship between the NPMM and El Niño SSTAs in the equatorial central Pacific, we analyzed the latitude-time evolution of SSTAs in the subtropical-to-tropical Pacific (5°N–25°N) during these three El Niño events (Fig. S5). To better illustrate the meridional propagation of the SST footprint, SSTA values were zonally averaged over the eastern North Pacific region (black box in Fig. 1c).

For the 1997–98 El Niño, although SST footprinting was relatively weak, Fig. S5a reveals a distinct episode of warm NPMM propagation from the subtropics to the equatorial central Pacific during January–March 1997. This warm NPMM was induced by weakened trade winds in the subtropical northeastern Pacific, leading to a southward SST footprint extending into the tropical Pacific. The weakened trade winds associated with the NPMM redistributed oceanic heat content along the equatorial Pacific via the trade wind charging (TWC) mechanism⁵³. This process resulted in warm SSTAs in the equatorial central Pacific. Through the combined effects of the TWC and seasonal footprinting mechanisms, the warm NPMM established favorable conditions for the development of warm SSTAs in the equatorial central Pacific, as evident in the MAM 1997 SSTA pattern (Fig. 1c). The resulting increase in SSTs in the equatorial central Pacific (Fig. 3a and S5a) strengthened the zonal SST gradient (Fig. 3d), promoting the generation of westerly wind anomalies in the western-central Pacific (Fig. 2a).

Similarly, during the 2015–16 El Niño, the warm NPMM influenced the equatorial central Pacific through episodes of equatorial SST spreading from November 2014 to April 2015 and from May to October 2015 (Fig. S5b). This spreading contributed to the establishment of a positive zonal SST gradient in the equatorial western-central Pacific (Fig. 3e), which, in turn, supported enhanced WWB activity in the region (Fig. 2e).

In contrast, the cold NPMM during the 2023–24 El Niño introduced cold SSTAs into the equatorial central Pacific through an equatorial spreading episode in January–February 2023 (Fig. S5c). This process established a negative zonal SST gradient in the equatorial western-central Pacific during the onset stages of the 2023–24 El Niño (Fig. 3f), creating unfavorable conditions for WWB formation and significantly reducing their occurrence.

MJO activities during the three El Niño events

As mentioned, the MJO is an important source of WWB events^23,24,25. Using Outgoing Longwave Radiation (OLR) anomalies to quantify MJO activity, we observe notable differences in MJO behavior between the 2023–24 El Niño and the 1997–98 and 2015–16 El Niños (Fig. 2). During the 1997–98 and 2015–16 El Niños, the warm NPMM appears to have created favorable conditions that enhanced the eastward propagation of the MJO, allowing it to extend into the central Pacific region (Fig. 2a, b). The evolution of the MJO index (see Data and Methods), as shown in Fig. 2d, e, further highlights the coincidence of elevated MJO index values with major episodes of enhanced westerly wind anomalies (Fig. 2d, e) and the generation of downwelling Kelvin waves (Fig. 2a, b).

In contrast, during the 2023–24 El Niño, the few MJO events propagating from the Indian Ocean into the Pacific during January–July 2023 failed to penetrate deeply into the central and eastern Pacific (Fig. 2c). The MJO index remained relatively weak throughout the 2023–24 El Niño, reflecting limited activity. The cold NPMM conditions during this event likely created an environment that hindered MJO propagation into the equatorial central Pacific (Fig. 2c), resulting in fewer and weaker WWBs (Fig. 2f).

The comparisons among these three El Niño events suggest that NPMM conditions may modulate the eastward propagation of MJO into the equatorial western-central Pacific, thereby influencing WWB events and, consequently, the onset and development of El Niño. However, the interaction between equatorial Pacific SST patterns and MJO propagation is complex, as already pointed out by previous studies^54,55. Different El Niño patterns can exert distinct effects on MJO behavior, with La Niña conditions being particularly unfavorable for the MJO’s eastward propagation. Based on this understanding, the lingering La Niña conditions from winter 2022/23 may have played a significant role in suppressing MJO propagation.

Linking the unique cold NPMM condition of the 2023/2024 El Niño to the preceding triple-dip La Niña

Why did the 2023/2024 El Niño coincide with a cold NPMM condition, in contrast to the warm NPMM conditions that accompanied the 1997/1998 and 2015/2016 El Niños? Fang and Yu⁵¹ suggested that the central locations of ENSO SSTAs, such as those centered in the eastern Pacific (EP) versus the central Pacific (CP), can influence the occurrence of warm or cold NPMM conditions. Prior to the 1997/98 El Niño, the preceding year experienced an EP-type La Niña (Fig. 1c). According to Fang and Yu⁵¹, this type of La Niña can trigger a Gill-type response^56,57, leading to southwesterly wind anomalies in the Northeastern Pacific, which in turn fosters the development of a warm NPMM. The case of the 2015/16 El Niño, it was preceded by a CP-type El Niño during 2014 (Fig. 1d), which can induce an atmospheric Rossby wave response and feedback into the North Pacific to force changes in atmospheric circulation over the Hawaiian region, weakening the northeasterly trade winds in the Northeastern Pacific and also contributing to warm NPMM development^51,58.

In contrast, the 2023/24 El Niño was preceded by a CP-type La Niña in 2022 (Fig. 1e). This can induce a Gill-type response further westward than the EP-type La Niña that preceded the 1997/98 El Niño, triggering northeasterly wind anomalies in the Northeastern Pacific and leading to the formation of a cold NPMM. Therefore, while the triple-dip La Niña of 2020–2023 resulted in an unprecedented accumulation of warm water volume in the equatorial western Pacific, favoring the development of an extreme El Niño, the more CP-oriented ___location of this triple-dip La Niña triggered a cold NPMM condition. The cold NPMM condition hindered the 2023/2024 El Niño from developing into an extreme event.

We conducted a correlation analysis to examine further the relationship between the preceding-year winter (November^-1-December^-1-January⁰; NDJ^-1) zonal ___location (ZL) index of ENSO (see Data Sets and Methods) and the developing-year spring (MAM⁰) NPMM and for all El Niño events during 1952–2024 (Fig. 5a). The ENSO ___location is defined as the Niño 4 index minus the Niño 1 + 2 index. When the ZL index value is negative, it indicates an ENSO type that is either an EP El Niño or a CP La Niña, both of which, according to Fang and Yu⁵¹, favor the development of a cold NPMM. In contrast, a positive value indicates that the ENSO type is either a CP El Niño or an EP La Niña, which favor inducing a warm NPMM. Our correlation analysis reveals a significant positive correlation of R = 0.53 (P < 0.01), consistent with Fang and Yu’s⁵¹ findings. The different NPMM conditions observed during the 1997/98, 2015/16, and 2023/24 El Niños align with this positive correlation and can all be explained by their preceding-year ENSO conditions.

**Fig. 5: Relationship between NPMM condition, ENSO ___location, and ONI during El Niño events.**

Linking preceding ENSO Patterns, NPMM and warm water volume to El Niño intensity

Since the preceding year’s ENSO ___location can affect the NPMM phase in the developing year of an El Niño event, and the NPMM phase can affect the intensity of that El Niño event, there may be potential for using the preceding-year ENSO ___location to forecast El Niño intensity. We explored this possibility in a scatter plot that displays information about the preceding winter (NDJ^-1) ZL index of ENSO, the developing spring (MAM⁰) NPMM condition, and peak winter (November⁰-December⁰-January¹, NDJ⁰) El Niño intensity for all El Niño events during 1952–2024 (Fig. 5b). The figure generally shows a positive correlation between the preceding winter ENSO ___location and peak winter El Niño intensity. However, this correlation does not reach the level of statistical significance.

We further separated the El Niño events into two groups: moderate El Niño, defined as ONI < 1.5°C (blue dots in Fig. 5b), and strong El Niño, defined as ONI > 1.5°C (red circles in Fig. 5b). The size of the circle in the figure indicates the NPMM intensity, while the filled (open) circles indicate the positive (negative) phase of the NPMM. The strong El Niño group (both the filled and open red circles) clearly shows that positive ZL index values of the preceding-year ENSO favor producing a positive NPMM condition, and vice versa for negative ZL index values, consistent with our expectations. Furthermore, the figure shows that a majority of strong El Niño events with a warm NPMM condition (3 out of the 5 filled red circles) developed into super El Niños (with ONI > 2.15°C, corresponding to 2.5 standard deviations of ONI). This includes the 1982/83 super El Niño event, which also exhibited positive NPMM. Although the 1982/83 event showed larger NPMM values (0.83 STD) than the 1997/98 event (0.59 STD), its El Niño intensity (ONI = 2.23 °C) was weaker (Fig. 5a, b). This difference can be attributed to the preceding winter’s WWV, which was significantly smaller in 1982/83 (0.43 × 10¹⁴ m³) compared to 1997/98 (1 × 10¹⁴ m³) (Figure not shown). This suggests that NPMM played an important role in the 1982/83 El Niño evolution, even if the WWV is relatively small.

In contrast, the two events that did not develop into super El Niños had very small NPMM intensities, implying that the NPMM likely played a less important role in influencing the El Niño evolution. For the four strong El Niño events with cold NPMM conditions (i.e., the four open red circles), each had a peak El Niño intensity lower than most of the strong El Niños with positive NPMM conditions. The correlations are less robust for the moderate El Niño group. Our results suggest that NPMM phase information is particularly influential and useful for predicting whether a strong El Niño event is likely to become a super El Niño.

To further validate these findings, we analyzed a 2200-year preindustrial simulation from the Community Earth System Model, version 1 (CESM1). This simulation provided a substantially larger sample size of 504 El Niño events. The model analysis strongly supports our observational findings. Fig. 5c demonstrates a significant correlation between NPMM and ZL indices, consistent with observational results (Fig. 5a). We also separated the El Niño events into two groups: moderate El Niño, defined as ONI < 1.5°C (blue dots in Fig. 5d), and strong El Niño, defined as ONI > 1.5°C (red circles in Fig. 5d) in CESM1 preindustrial simulation. In the model’s strong El Niño group, positive ZL index values from the preceding-year ENSO favor positive NPMM conditions (Fig. 5d), matching our observational findings (Fig. 5b). Importantly, we identified 126 strong El Niño events associated with positive NPMM conditions, compared to only 23 during negative NPMM conditions.

We then examined how Ocean Heat Content (OHC, a proxy for WWV) and NPMM together influenced ENSO intensity (Fig. 6). using CESM1 preindustrial simulation (Fig. 6). During periods when both OHC (OHC > 0) and NPMM are positive (NPMM > 0.5 standard deviations), the mean El Niño intensity reaches approximately 1.6°C, which is about 60% stronger than El Niño events occurring during negative NPMM conditions (NPMM < -0.5 standard deviations, approximately 1.0°C). In the positive NPMM region, El Niño events can reach intensities up to 3.5°C, while in the negative NPMM region, the maximum intensity observed was 1.9°C. These results collectively indicate that positive NPMM conditions facilitate the development of strong El Niño events. Moreover, preceding-year ENSO condition information appears particularly valuable for predicting the potential intensification of strong El Niño events into super El Niños.

**Fig. 6: Relationship between the NPMM index, Ocean Heat Content (OHC), and El Niño intensity in CESM1.**

Previous studies, such as Chen et al. ⁵⁹, have highlighted that the intensity of preceding-year ENSO conditions, particularly La Niña-like SSTAs, can significantly influence subsequent El Niño development. Complementing these findings, our analysis reveals that the spatial pattern of the preceding-year ENSO—specifically the central ___location of the SSTAs—also plays a critical role in modulating subsequent El Niño evolution through its impact on NPMM conditions.

Conclusion and discussion

This study provides a comparative analysis of the 2023/2024 El Niño event with the extreme El Niño events of 1997/1998 and 2015/2016, revealing significant differences in their onset mechanisms, evolution, and intensity. Our findings underscore the complex interplay of atmospheric and oceanic factors in shaping El Niño events, with a particular emphasis on the crucial roles of the NPMM, MJO, and preceding ENSO conditions.

The 2023/2024 El Niño displayed a distinctive onset mechanism, primarily influenced by a cold NPMM condition, in stark contrast to the warm NPMM conditions that amplified the 1997/1998 and 2015/2016 El Niños. This cold NPMM condition hindered the development of strong WWBs and weakened the propagation of downwelling Kelvin waves, resulting in a milder El Niño event. Additionally, our analysis highlights the importance of preceding ENSO conditions in shaping the NPMM phase and subsequent El Niño development. The differing ENSO phases before each event—EP-type La Niña (1997/1998), CP-type El Niño (2015/2016), and CP-type La Niña (2023/2024)—led to distinct atmospheric responses and NPMM patterns, which influenced the intensity and characteristics of the subsequent El Niño events.

The study also emphasizes the role of other factors, such as the MJO and coastal El Niño events along with the associated Bjerknes feedback, in modulating El Niño intensities. The interactions among these components illustrate the multifaceted nature of El Niño dynamics and highlight the need for a comprehensive approach to understanding and predicting these events. Our findings advocate for improved modeling and observation of the NPMM, MJO, and preceding ENSO conditions, along with their interactions with other climate phenomena, to enhance our ability to accurately forecast El Niño events.

Data sets and methods

Data sets

This study employed a diverse array of datasets to examine SST, atmospheric, and oceanic variables. The research utilized monthly SST data from the Extended Reconstructed Sea Surface Temperature (ERSST) version 5⁶⁰, wind field data from the NCEP/NCAR reanalysis 1⁶¹, outgoing longwave radiation (OLR) data from the NOAA Climate Data Record⁶², and oceanic variables from the NCEP Global Ocean Data Assimilation System (GODAS)⁶³. These datasets varied in their temporal coverage, spatial resolution, and specific variables measured. The ERSST data spanned from 1854 to the present with a 2° × 2° resolution, while the NCEP/NCAR reanalysis covered 1948 to present at 1.875° × 1.875° resolution. The OLR data ranged from January 1979 to the present, and the GODAS data covered 1980 to the present with a 0.333° × 1° resolution. To analyze trends, anomalies were calculated by removing linear trends and subtracting monthly climatology from the respective analysis periods.

CESM1 preindustrial simulation

To mitigate the challenge posed by the limited number of ENSO events in observational data, our analysis was extended by incorporating a 2200-year preindustrial simulation generated by the Community Earth System Model, version 1 (CESM1)⁶⁴. We focused on the model years 400 to 2200 for this study. Prior research has shown that the CESM1 model effectively simulates key ENSO features, such as reoccurrence frequencies, amplitude ranges, spatial structures, and temporal evolutions^46,65,66,67. The anomalies in the model simulations were calculated by subtracting the climatological values for each calendar month after removing linear trends.

Climate indices

In this study, five climate indices were utilized: the oceanic Niño index (ONI), zonal ___location (ZL) index of ENSO, Warm Water Volution (WWV) index, the northern Pacific Meridional Mode (NPMM) index, westerly wind burst (WWB) index, and Madden-Julian Oscillation (MJO) index.

ONI

This index was calculated as the averaged SSTAs within the Niño 3.4 region (5°N-5°S and 170-120°W) and was used to represent the intensity of ENSO.

ZL index

This index was utilized to determine whether the maximum center of an ENSO event was located more towards the equatorial eastern or central Pacific, and was calculated as the SSTA difference between a equatorial central Pacific region (5°N-5°S, 160°E-150°W, i.e. Niño 4 region) and a tropical eastern Pacific region (0°-10°S, 90°W-80°W, i.e. Niño 1 + 2 region). A more positive (negative) value of the ZL index indicates that the El Niño event was located more toward the equatorial central (eastern) Pacific, and the La Niña was located more toward the equatorial eastern (central) Pacific⁴⁷.

Warm water volume (WWV) index

This index was introduced by Meinen and McPhaden⁵, and is a commonly used metric for assessing heat content in the equatorial Pacific. It measures the volume of water above the 20 °C isotherm across the entire Pacific region, spanning from 120°E to 80°W and between 5°S and 5°N.

NPMM index

This index is defined as the SSTA averaged over the subtropical northeastern Pacific region (i.e., 15-25°N and 150-120°W)⁴⁹.

WWB index

This index was defined as the daily zonal surface wind anomaly averaged over 135°E-180°E and 5°S-5°N, which is the region where WWBs typically occur (see Fig. 2)⁶⁸. The WWB event is defined as when the zonal wind anomaly average in the WWB region exceeds 1 standard deviations⁶⁹.

MJO index

The MJO events analyzed in this study are characterized using large-scale OLR anomalies within the tropical region (10°S–10°N). To isolate the key features of the MJO, we applied a two-dimensional time–space fast Fourier transform, focusing on large-scale zonal wavenumbers (1–5) and intraseasonal variability within the 20–100 day range^70,71,72,73. Only eastward-propagating signals were retained for further analysis, ensuring a targeted examination of the MJO’s characteristic propagation. The MJO index is constructed by taking the filtered daily OLR (as described above), averaging it over the region 135°E-180°E, and multiplying by -1. This index allows us to verify MJO propagation into the westerly wind burst region (135°E-180°E).

Identifying El Niño events

An El Niño event is defined as occurring when the three-month running mean of the Oceanic Niño Index (ONI) reaches or exceeds 0.5°C during the peak winter months (NDJ⁰). Based on our established criterion, we identified a total of 15 events that occurred between 1980 and 2023. These events are featured in Figs. 1a, b, 2d–f, S1, and S4. The limited timeframe for these figures is due to the availability of WWV and GODAS data, which only spans from 1980 to 2023. However, for our analysis presented in Fig. 5a, b, we were able to include a broader range of 26 El Niño events, covering the period from 1952 to 2023. This extended analysis was made possible by using the ONI and ERSST data, which offers a longer historical record. The complete chronology of El Niño years is: 1953/54, 1957/58, 1958/59, 1963/64, 1965/66, 1968/69, 1969/70, 1972/73, 1976/77, 1977/78, 1979/80, 1982/83, 1986/87, 1987/88, 1991/92, 1994/95, 1997/98, 2002/03, 2004/05, 2006/07, 2009/10, 2014/15, 2015/16, 2018/19, 2019/2020, and 2023/24. In our analysis, “Other El Niños” refers to all events excluding the 1997/98, 2015/16, and 2023/24 episodes, which are the focus of our comparative study due to their distinctive characteristics.

El Niño events in the CESM1 preindustrial simulation were identified when the Niño-3.4 index exceeded 0.57°C (0.5 standard deviations) during winter months (November-December-January). Using this criterion, we identified 504 El Niño events between model years 400 and 2200.

Significance test of composite and correlation analyses

To assess the statistical significance of composite analysis results, we use two-tailed p values derived from Student’s t-tests. The 95% confidence interval (CI) is calculated using the following formula⁷⁴:

$${\rm{CI}}(95 \% )=({{\rm{STD}}}_{{\rm{sample}}}/\sqrt{{\rm{N}}-1})\times 1.96$$

where STD_sample is the sample standard deviation, and N is the number of sample used in the composites. The factor 1.96 corresponds to the z-score for a 95% confidence level under a normal distribution, representing the range within which we can be 95% confident that the true population parameter lies.

For correlation analysis, we also use two-tailed p values from Student’s t test. However, the effective number of degrees of freedom is adjusted to account for the autocorrelation of the time series of the tested variables.

Data availability

No datasets were generated or analysed during the current study. The NCEP/NCAR R1 data are available from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. The GODAS data are available from https://psl.noaa.gov/data/gridded/data.godas.html. The WWV index data can be accessed at https://www.pmel.noaa.gov/elNiño/upper-ocean-heat-content-and-enso. The NOAA CDR data are available from https://www.ncei.noaa.gov/data/outgoing-longwave-radiation-daily/access/. The ONI index data can be accessed at https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php. The CESM1 simulation, which covers a period of 2200 years, was conducted by the CESM project team and is available via the Earth System Grid (https://www.earthsystemgrid.org/).

Code availability

Relevant codes required to replicate the results presented in this study can be obtained from the authors upon request.

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Acknowledgements

This research was supported by the Climate and Large-Scale Dynamics Program of the U. S. National Science Foundation under Grants AGS-2109539.

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Authors and Affiliations

Department of Earth System Science, University of California, Irvine, CA, USA
Yong-Fu Lin & Jin-Yi Yu
Research Center for Critical Issues, Academia Sinica, Taipei, Taiwan
Yong-Fu Lin & Chau-Ron Wu
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
Mengyan Chen, Xin Wang & Jiepeng Chen
Key Laboratory of Ocean Observation and Forecasting and Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Lingling Liu
Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Fei Zheng
Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing, China
Ruiqiang Ding
Department of Earth Sciences, National Taiwan Normal University, Taipei, Taiwan
Chau-Ron Wu
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
Chau-Ron Wu & Huang-Hsiung Hsu
Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
Min-Hui Lo & Ting-Hui Lee

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Yong-Fu Lin
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Contributions

Y.F.L. and J.Y.Y. developed the initial concept for the study, and drafted the paper. Y.F.L. was responsible for executing the simulations, analyzing the data, and preparing all figures. All authors contributed to the data interpretation, discussion of the dynamics, and improvement of this paper.

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Correspondence to Jin-Yi Yu.

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Lin, YF., Chen, M., Liu, L. et al. Diverse NPMM conditions deviate the 2023/24 El Niño from the 1997/1998 and 2015/2016 extreme El Niño events. npj Clim Atmos Sci 8, 133 (2025). https://doi.org/10.1038/s41612-025-01013-z

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Received: 12 October 2024
Accepted: 18 March 2025
Published: 31 March 2025
DOI: https://doi.org/10.1038/s41612-025-01013-z