1The 2023 extreme coastal El Niño: Atmospheric and air-sea coupling mechanisms

2

Qihua Peng1, Shang-Ping Xie1*, Gino A. Passalacqua2, Ayumu Miyamoto1, and Clara Deser3

3Affiliations:

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1

5

92093, USA

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Save The Waves Coalition, Santa Cruz, California 95060, USA

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3

National Center for Atmospheric Research, Boulder, CO, USA

8*Correspondence author. Email: sxie@ucsd.edu

Scripps Institution of Oceanography, University of California San Diego, La Jolla, California

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Abstract: In the boreal spring of 2023, an extreme coastal El Niño struck the coastal regions of

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Peru and Ecuador, causing devastating rainfalls, flooding, and record dengue outbreaks.

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Observations and ocean model experiments reveal that northerly alongshore winds and westerly

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wind anomalies in the eastern equatorial Pacific, initially associated with a record-strong Madden-

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Julian Oscillation and cyclonic disturbance off Peru in March, drove the coastal warming through14

suppressed coastal upwelling and downwelling Kelvin waves. Atmospheric model simulations

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indicate that the coastal warming in turn favors the observed wind anomalies over the far eastern

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tropical Pacific by triggering atmospheric deep convection. This implies a positive feedback

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between the coastal warming and the winds, which further amplifies the coastal warming. In May,18

the seasonal background cooling precludes deep convection and the coastal Bjerknes feedback,19

leading to the weakening of the coastal El Niño. This coastal El Niño is rare but predictable at one

20month lead, which is useful to protect lives and properties.

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Teaser: The 2023 coastal El Niño was triggered by atmospheric wind perturbations and amplified

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by coastal Bjerknes feedback.

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INTRODUCTION24

Due to the strong upwelling of cold subsurface water, deep convection is largely suppressed25

off Peru. Consequently, the Peruvian coastal region receives merely ~5 cm of annual rainfall (1),26rendering it one of the driest places on Earth. In March-April 2023, an extreme coastal warming

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event, one of the strongest in the last four decades (figs. S1 and S2), occurred along the coast of28

Peru and Ecuador. This extreme coastal El Niño caused widespread flooding and the worst

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recorded dengue outbreak in Peruvian history, leading to more than 300 deaths (2, 3). The arid

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Sechura desert in northern Peru was submerged under a vast lake known as Lake La Niña, a31

phenomenon observed only during extreme El Niño events. Tens of thousands of homes were2

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destroyed by the flooding. Additionally, this event caused the lowest chlorophyll-a (Chl-a)33

concentrations in the coastal regions since MODIS satellite measurements began in 2002 (fig. S3),

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with impacts on marine ecosystems and fisheries (4).

35This type of phenomenon, distinguished by its distinct evolution, spatial pattern, dynamics,

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and impacts from basin-scale El Niño events, is categorized as a coastal El Niño (5-7). Similar

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extreme events occurred in 1925 (7), and 2017 (6, 8, 9). Some studies show that intraseasonal

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downwelling Kelvin waves (6, 10, 11) trigger the coastal El Niño, and coastal Bjerknes feedback

39(6) further amplifies it. Other studies suggest that surface heat flux anomalies (9), reduced40

atmospheric stability due to central Pacific cooling (7), or extratropical circulation anomalies in41

the Southern Hemisphere (8, 12), play an important role in driving the coastal El Niño. However,42

the rarity of extreme coastal El Niño events in modern instrumental records hinders a

43comprehensive understanding of the ocean-atmosphere dynamics involved. The occurrence of the44

2023 coastal El Niño event, with extensive observations (some in real-time), presents a valuable

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opportunity to further investigate crucial dynamical processes of coastal El Niño. Indeed, the 2023

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event exhibits several unique features including unusual atmospheric perturbations in the far

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eastern Pacific and strong subsurface temperature anomalies along the equator that leads to the48

rapid growth of a basin-scale El Niño. These features highlight the need for a close look into ocean-49

atmospheric processes that produced the 2023 coastal El Niño.

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Here we examine the evolution and mechanisms of the 2023 extreme coastal El Niño by using

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a variety of observations. We further utilize comprehensive ocean (OGCM) and atmospheric

52(AGCM) general circulation models to reveal the underlying oceanic and atmospheric dynamics.

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In addition to confirming the coastal Bjerknes feedback that amplifies the coastal El Niño, our54results indicate that atmospheric internal variability in the far eastern Pacific helps trigger, and is3

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also amplified by, the extreme coastal El Niño. Both observations and numerical experiments

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reveal that the seasonal cooling of background SST decouples the coastal ocean and atmosphere57

from May onwards, ultimately leading to the decay of the coastal El Niño.

58

RESULTS59

Evolution of the 2023 coastal El Niño

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The year of 2023 opened with the tropical Pacific Ocean in a La Niña state. The onset of the61

coastal warming was first observed in mid-February: weak warming signals initially manifested

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in locations away from the coastline together with weak northwesterly winds over the equatorial

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southeastern Pacific Ocean (Figs. 1 and S4). In early March, a burst of westerly wind anomalies

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took place in the eastern equatorial Pacific (Fig. 2A), concomitant with strong northerly wind65

anomalies off Ecuador and Peru (Fig. 2D). The SST warming signal shifted to coastal regions and66

greatly intensified through March and April (Fig. 2C). The coastal El Niño peaked at the end of67

April with a monthly maximum SST anomaly of +4°C in the Coastal region of South America

68(CSA, averaged over 85 °W–80 °W, 10 °S–0°), while the central equatorial Pacific sustained

69

neutral conditions during this period. Monthly coastal SST warming in 2023 was the strongest at

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most coastal stations since 2000 (fig. S2), comparable to the extreme warming in 1983, and 1998

71

(fig. S1A). Positive sea level anomalies (SLA) exceeding 10 cm were observed along the equator72

and CSA (Figs. 2C and S4B), coinciding with strong subsurface warming (exceeding 5°C) above73

80 m in the coastal region (fig. S5). During this period, the coastal warming was accompanied by74

heavy rainfall (Figs. 1 and 2D) and the strongest v10 anomalies off Peru since 1982 (fig. S1B).

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The warming signals started to decay in May. The alongshore winds relaxed to their76

climatological state, accompanied by a return to normal rainfall amounts (Figs. 2 and S4). In June,4

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a basin-scale El Niño emerged, with westerly wind anomalies over the central-western equatorial

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Pacific. This evolution evokes similarities with the Rasmusson and Carpenter (RC) El Niño

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composite (13), which has been rarely observed during the satellite era. During the 1950s and the80

mid-1970s, warm SST anomalies first appeared off the coast of South America and then developed81

westward into basin-scale El Niños (13). After the 1976-77 climate regime shift, this pattern82

reversed, with coastal warming often trailing the basin-wide peak (e.g., 1983, 1998) (fig. S1A).

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This change coincides with a reversal in equatorial SST anomaly propagation (9, 14), possibly due

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to altered background state (15-17). Whether the 2023 event signifies a resurgence of RC

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composite-type El Niño awaits confirmation through continued observations in the future. As the862023 coastal warming after May is closely linked to basin-scale El Niño with relatively minor

87

effects in the far eastern Pacific, hereafter we focus on the atmospheric and oceanic dynamics

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during March-May.

89

Ocean dynamics

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We first conduct a mixed layer heat budget analysis for the 2023 coastal El Niño to reveal the91

underlying physical processes. Figure S6A shows that the thermodynamical processes, particularly

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the shortwave radiation and latent heat flux (fig. S6E), primarily dampened the 2023 coastal El

93

Niño during March-May. Figure S6A also highlight that vertical advection drives the coastal SST

94$ 𝒛 , EK) dominating (fig.
warming throughout March and April, with the Ekman feedback (−𝒘! 𝑻

95

S6C). Specifically, the northerly alongshore wind anomalies strongly suppress upwelling in the96

coastal region (fig. S6D), thus raising CSA SST. This weakened upwelling coincided with record-97

low Chl-a since measurements began in 2002 (fig. S3). Moreover, the thermocline feedback98$ 𝑻!𝒛 , TH) term is also important for coastal SST increase in April. After May, the thermocline
(−𝒘

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feedback becomes dominant (figs. S6C), and the CSA warming is primarily caused by basin-scale100

wave dynamics. The meridional advection term is an additional positive contributor to the coastal

101

warming during April-May. In the CSA region, the northerly wind anomalies drive anomalous

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southward currents (fig. S6D), resulting in warm advection along the coast.

103

Observations show that the 2023 coastal El Niño is accompanied by strong northerly

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alongshore wind anomalies in the southeastern Pacific, and westerly (easterly) wind anomalies

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along the eastern (central and western) Pacific during February-April (Figs. 1 and 2A). We conduct

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three OGCM experiments denoted as 𝝉!𝐂𝐨𝐚𝐬𝐭 , 𝝉!𝐄𝐄𝐏 , and 𝝉!𝐖𝐄𝐏 , to quantify the relative importance107

of wind anomalies over the coastal region, the eastern equatorial Pacific, and the central-western108

equatorial Pacific, respectively (see Materials and Methods). The control run (CTRL), a hindcast

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run forced by the full forcings (see Materials and Methods), captures the key characteristics of the110

2023 coastal El Niño. Specifically, the simulated warming signals in the CTRL simulation are

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largely confined to the coastal regions during March-April (fig. S7), along with a weak La Niña

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state in the central Pacific, consistent with observations. The simulated CSA SST anomalies show

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that weak positive anomalies began in mid-February, followed by a rapid intensification during

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March and April. The anomalies reached their peak amplitude of approximately 4°C in late April

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and then decayed in May (Fig. 3A), similar to observations. In addition, the CTRL run successfully

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simulates the Kelvin waves, including the upwelling Kevin waves in January and February and117

downwelling Kelvin waves from March to June (Fig. 2B). Overall, the good model/observation

118

agreement provides confidence in the subsequent numerical experiments to examine key factors

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responsible for the 2023 coastal El Niño.

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Fig. 3B shows that the coastal wind stress anomalies dominate the 2023 coastal El Niño,

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contributing approximately +2.5°C SST warming. Specifically, the northerly alongshore wind6

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anomalies strongly suppress the coastal upwelling and raise SST there, consistent with the heat123budget results (fig. S6C). In addition, the coastal wind anomalies excite downwelling coastal

124

Kelvin waves (Fig. 3B), which depress the thermocline off Peru and raise SST there. The simulated

125&
CSA warming in 𝜏!"#$%
amplifies in March, peaks at the end of April, and declines in May due to126

the dissipation of the alongshore wind anomalies, much as in observations and the CTRL run.

127

The wind stress anomalies over the eastern equatorial Pacific (90 ˚ W- 130˚ W, 5˚ S-5˚ N;128!
𝜏,,) cause sizable warming (~1.5°C) along the coast (Fig. 3C). These westerly wind anomalies

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drive downwelling equatorial Kelvin waves, which propagate eastward along the equatorial

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waveguide. Upon arriving at the east coast, the signals split to the south and then propagate along131

the coast of South America, leading to strong coastal warming through thermocline feedback in

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April-May. Our heat budget analysis also underscores the importance of the thermocline feedback133′
in driving coastal warming during April, consistent with the results from the 𝜏''(experiment and134

confirming that the EEP westerly wind anomalies contribute to the coastal El Niño (10). Despite

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the weakening of the EEP westerly wind anomalies after May (Fig. 2A), the positive SST

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anomalies in the eastern Pacific persist (Fig. 3C). This persistence is likely due to the time lag

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associated with Kelvin wave propagation.

138$
The central-western equatorial Pacific wind anomalies (𝜏!"#) have different impacts on the139

coastal El Niño. Prior to March, easterly wind anomalies prevail over the central-western Pacific140(Fig. 2A), exciting weak upwelling Kelvin waves that cool SST along the coast (Fig. 3D). From

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March onwards, a series of westerly wind bursts occur west of 160°E, driving equatorial

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downwelling Kelvin waves (18, 19) (Fig. 2A). It takes 3 months for these waves to propagate into143

the coastal region, contributing to the coastal warming after June (Fig. 3D). The dominant role of144

thermocline feedback arising from basin-scale wave dynamics after June is further confirmed by

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the heat budget analysis (fig. S6C). By this time, coastal warming is primarily due to the well-146

known basin-scale ENSO dynamics (13, 20, 21), which differ from those growing the coastal El

147

Niño. Thus, wind stress variations east of 130oW cause the coastal El Niño to grow while wind148

anomalies to the west contribute to the persistence of the coastal warming.

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Atmospheric response and coastal Bjerknes feedback150

To explore the atmospheric response to coastal warming, we performed a 10-member AGCM

151

experiment (“AGlobal”) forced by globally observed SSTs (see Materials and Methods). The

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AGlobal ensemble mean, which averages out atmospheric internal variability, represents the153

atmospheric (e.g., precipitation and surface wind) responses induced by SST anomalies. The

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AGlobal experiment broadly captures observed atmospheric anomalies in the far southeastern

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Pacific during March and April, including deep convection (together with heavy rainfall) (Figs.

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1F and 1H), sizable westerly anomalies over the EEP, and strong alongshore northerly wind157

anomalies over the southeastern Pacific (Fig. 1, right panels). This indicates that a substantial

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portion of the atmospheric response is driven by the 2023 SST anomalies. To further isolate the159

impacts of the 2023 coastal warming, we conducted the “ACoast2023” experiment. In this160

experiment, observed SST anomalies are prescribed only over the CSA region, while the remaining

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ocean is set to its climatological values (see Materials and Methods). The ACoast2023 successfully

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reproduces key features for the 2023 event, including heavy rainfall off Peru, the strong northerly

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alongshore winds, and the deep meridional overturning cell in the far eastern Pacific with164

ascent/descent in the southern/northern hemisphere, closely resembling both the observations and165

the AGlobal experiment (figs. S8 and S9). Therefore, the atmospheric responses in the far eastern

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Pacific are predominantly driven by the 2023 coastal warming.

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Taken together, the above OGCM and AGCM results imply positive feedback between the168

coastal warming and wind anomalies over the far eastern Pacific Ocean. Northerly (westerly)169

wind anomalies over the coastal (EEP) region cause anomalous coastal SST warming. Once the170

coastal SSTs rise above the convective threshold, the convective anomalies cause a Matsuno-Gill

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response (22, 23) with anomalous northerly (westerly) winds over the coastal (EEP) region. These172

wind anomalies in turn intensify the coastal warming (6). Additionally, the Intertropical

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Convergence Zone (ITCZ) and wind anomalies across the Pacific Ocean are coupled with basin-

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scale ENSO (24, 25). The preceding basin-scale La Niña could destabilize the ITCZ through175

reduced tropospheric stability (7) and positive feedback that strengthens (weakens) the ITCZ south

176(north) of the equator (6, 26, 27), favoring northerly coastal wind anomalies.

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Unlike the equatorial Bjerknes feedback during basin-scale ENSO (28), the coastal Bjerknes

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feedback in this region operates within a specific time window: it is only active during February-

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March-April (FMA) and plays a crucial role in the phase-locking behavior of extreme coastal El

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Niño events. Specifically, during FMA when SSTs off Peru reach their annual maximum and are181

close to the convective threshold, coastal warming can cause deep convection and thus activate the182

coastal Bjerknes feedback. In other seasons, however, background SST is too low for deep

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convection south of the equator even with large coastal warming, which decouples the coastal

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warming and eastern Pacific wind anomalies. As a result, the extreme coastal El Niño peak phase185

is locked to FMA, in contrast to basin-scale El Niño. In 2023, the coastal Bjerknes feedback was

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active during March and April, manifested by strong wind and rainfall anomalies over the187

southeastern Pacific (Figs. 1 and 2). These strong atmospheric responses contrast with the188

considerably weak responses observed in this region within the RC composite. After May, this189

positive feedback became inactive due to the background SST cooling (fig. S4), explaining why

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the 2023 coastal El Niño and especially the atmospheric anomalies started to decay in May.

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The activation of this coastal Bjerknes feedback is a key source of predictability of the 2023

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coastal El Niño. At the end of February, the seasonal background warming, the suppressed

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upwelling, and the arrival of the first downwelling Kelvin wave pulse (Figs. 2A and S4) work194

together to raise SSTs off Peru to exceed the convective threshold, activating the coastal Bjerknes

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feedback. Seasonal forecast models from the North American Multi-Model Ensemble (NMME)

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successfully captured this coastal Bjerknes feedback, which explains why the NMME started to197

predict heavy rainfall over the CSA region when initialized on March 1st, and April 1st (fig. S10).

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Role of atmospheric perturbations

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SST-forced wind anomalies as in the AGlobal ensemble-mean last for a season or longer

200(figs. S11I and S11J). Superimposed on these slow SST-forced wind variations are higher-

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frequency wind anomalies over the far eastern Pacific. In early March, a patch of strong westerly

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wind anomalies appeared over the EEP, mostly associated with the 30-90 day MJO (Figs. 4B and203

S11E). Indeed, the March 2023 MJO index was in phase 8 and the strongest since 1974 (Fig. 4A).

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A phase-8 MJO features westerly wind anomalies at 850 hPa in the eastern equatorial Pacific, with

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suppressed (active) convection over the maritime continent (South America). The band-pass (30-206

90 day) filtered results indicate that the phase 8 MJO event creates strong u10 anomalies of ~2 m/s

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over the EEP and v10 anomalies of -1.5 m/s over the coastal region during the first half of March

208(figs. S11E and S11F) (29). Additionally, during 6-20 March 2023, a highly unusual tropical209

depression system dubbed “Cyclone Yaku” developed off Peru centered at ~8oS (fig. S12A). Such210

a cyclonic depression-like system is exceptionally rare there due to cool background SSTs. This211

low-pressure system induces 4.5 (3.7) m/s northerly alongshore (westerly) winds over the coastal

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region (EEP) in mid-March (figs. S11C, S11D, and S12A). The wind perturbations associated with
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the MJO and tropical depression system favor coastal warming by suppressing coastal upwelling214

and exciting downwelling Kelvin waves.

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Yaku was accompanied by considerable rainfall so it is conceivable that eastern Pacific216

atmospheric variability such as Yaku could be energized by coastal warming and deep convection.

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We investigate this possibility by comparing the March-April ensemble standard deviation (a218measure of internal atmospheric variability) from AGlobal in 2023 with the climatological value219

during 2010-2022. In support of the hypothesis, Table 1 shows that the coastal warming in March-

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April 2023 strengthens the ensemble standard deviation of rainfall and v10 in CSA by 123% and221

64%, respectively, relative to their climatological values. The ACoast2023 experiment also yields

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similar results.

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To explore what drives internal variability of alongshore winds off Peru, we have calculated

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the wind and precipitation regressions against the ensemble spread in CSA v10 during March-

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April 2023 in AGlobal. A Yaku-like cyclonic circulation off Peru emerges with increased rainfall

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near the center and westerly wind anomalies on the equator (fig. S13A, sign reversed). Indeed,227

ensemble member 10 produces a Yaku-like tropical depression off Peru (fig. S12B). The northerly

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wind anomalies off Peru act to strengthen the coastal warming. The regression against CSA v10229

ensemble spread during 2010-2022 in AGlobal exhibits similar pattern, albeit with much-reduced

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magnitudes (not shown). This implies positive feedback between the coastal warming and internal

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atmospheric variability such as Yaku-like coastal tropical depression systems.

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The record-strong phase 8 MJO during March 5-13 features broad westerly anomalies on the233

equator that impinge on the steep Andes mountains (Fig. 4C). At low levels, the orographic barrier

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forces poleward-propagating Kelvin waves with poleward flows west of the Andes. Cyclone Yaku

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develops on the cyclonic region of the topographically forced flow off Peru, aided by convective
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heating induced by coastal warming. Likewise, the intraseasonal easterly phase during the second

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half of February corresponds to a southerly surge off Peru at 10oS (figs. S11E and S11F). A

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regression analysis for the 30-90 day observed EEP u10 during 2012-2023 confirms the funnel-

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like flow pattern off CSA (Figs. 4D and S13B). Further research is necessary to investigate the240

connection between the MJO and v10 variability off Peru, as both are important forcings of coastal

241

El Niño.

242

Summary and discussion

243

We have investigated the evolution and coupled mechanisms of the 2023 extreme coastal El

244

Niño using observations and GCM experiments. In early March, a record-strong MJO drives

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westerly wind anomalies over the eastern equatorial Pacific, and a rare low-pressure system Yaku

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develops off Peru with anomalous northerly alongshore winds. Our OGCM and heat budget

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anlaysis results show that these anomalous winds are crucial in driving the 2023 coastal El Niño

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by suppressing coastal upwelling and deepening the thermocline depth via downwelling Kelvin249

waves (6, 10, 11).

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The AGCM results further show that the SST warming drives deep convection south of the251

equator, strengthening the coastal northerly and EEP westerly anomalies. The intensified wind252

anomalies further amplify the coastal warming, indicative of coupled positive feedback. This253

coastal Bjerknes feedback is important in intensifying, sustaining, and predicting the 2023 coastal

254

El Niño. In May, the background seasonal cooling inhibits deep convection and the coastal

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Bjerknes feedback, causing the coastal El Niño to decay. We further identify a novel positive256

feedback between coastal warming and internal atmospheric variability such as Yaku-like cyclones

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off Peru. Our analysis suggests that Yaku is part of the strong phase-8 MJO, induced by the258

orographic effect of the high Andes and amplified by convective heating over warm coastal waters.
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Given the rarity of extreme coastal El Niño, the 2023 case provides a valuable opportunity to test

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ocean-atmospheric processes involved.

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Coastal SST off Peru has exhibited a warming trend since the mid-2010s (fig. S2), possibly262

related to the fact that the two most extreme coastal El Niño events since 1925 both occurred after

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2017. The increase in the frequency and intensity of coastal El Niño took place despite a La Niña-

264

like pattern of SST change over the recent three decades. Anthropogenic global warming (30) is

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likely to affect the characteristics of coastal El Niño events. The El Niño-like warming pattern266

projected by climate models reduces the barrier to deep convection and strengthens the coastal

267

Bjerknes feedback off Peru and Ecuador, favoring increased frequency and intensity of extreme

268

coastal El Niño events (6). With more frequent occurrences of coastal El Niño, the compounded269

heavy rainfall would result in greater damages in a warmer climate.

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MATERIALS AND METHODS

271

Observational and reanalysis datasets.

272

We utilized the daily and monthly NOAA Optimum Interpolation Sea Surface Temperature273

version 2 dataset (OISSTv2) during 1982–2023 (31), daily wind velocity derived from the Cross-274

Calibrated Multi-Platform Version 2.0 (CCMP V2.0) from Remote Sensing Systems (32), and275

daily Sea Level Anomalies (SLA) from the Copernicus Marine and Environment Monitoring

276

Service (CMEMS). All datasets mentioned above are at 0.25° × 0.25° resolution. The daily air-sea

277

fluxes are derived from the ERA5 reanalysis data (33). We employed the daily 0.1° × 0.1°

278

precipitation spanning 2001–2023 from the Global Precipitation Measurement (GPM) (34) and279

monthly 2.5° × 2.5° rainfall from Global Precipitation Climatology Project during 1979–2023

280(GPCP) (35). Before the analysis, we eliminated the long-term trend within these datasets. The

281

ocean temperature, mixed layer depth, currents, and monthly sea level anomalies are obtained from282

the NCEP Global Ocean Data Assimilation System. The Real-time Multivariate MJO indices283(RMM1 and RMM2) are used to track the MJO activities. We also use monthly 0.1° × 0.1° MODIS

284

Chlorophyll-a Concentration data (36) to investigate the marine ecological response. The in-situ

285

coastal SST data are obtained from the Instituto del Mar del Peru (IMARPE).

286

OGCM experiments.

287

We utilized the MIT General Circulation Model (MITgcm) in this study. The model is configured288

to a Lat–Lon–Cap (LLC270) grid, with a horizontal resolution of 1/3 ˚ in the zonal direction and289

1/9˚ in the meridional direction at low and high latitudes, stretching to 1/3˚ at mid-latitudes. The

290

model has 50 vertical layers, with layer thickness gradually increasing from 5 m near the surface291

to 456 m in the deep ocean. The diffusion and mixing parameters of the model are identical to292

those used in previous studies (37, 38). In the hindcast run (hereafter CTRL), the MITgcm was
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integrated forward in time from 1 January 2000 to 1 July 2023, forced by 6-hourly realistic wind294

stress, wind speed, downward shortwave and longwave radiations, precipitation, and 2-m air

295

temperature and humidity from the JRA55-do product (39).

296

As thermodynamics primarily act to damp the 2023 coastal El Niño (figs. S6A and S6C), here297

we mainly focus on the underlying dynamic process forced by anomalous wind stress. To

298

investigate the relative importance of wind stress anomalies over the coastal region (75˚ W- 90˚

299

W, 20˚ S-5˚ N), the eastern equatorial Pacific (EEP, 90 ˚ W- 130˚ W, 5˚ S-5˚ N) and the central-300

western Pacific Ocean (CWP, 120 ˚ E- 130˚ W, 5˚ S-5˚ N), we conduct 3 sensitive experiments

301&(Table. S1). In the coastal wind run (𝜏Coast), we retained time-varying wind stress over the coastal

302

region but daily climatological wind stress outside this region with a 3° buffer region where the303

strength of the wind stress anomalies is linearly reduced, and all the other forcings are fixed to304&
their daily climatological values. The eastern equatorial Pacific (𝜏EEP) [central-western Pacific

305

&
&
wind run (𝜏CWP)] is similar to the 𝜏Coast
except that only the time-varying wind stress in the eastern

306

&
&
&
(central-western) equatorial Pacific is retained. The solutions, 𝜏Coast, 𝜏EEP, and 𝜏CWP
, thus isolate307

the dynamic effects of wind stress anomalies over the coastal region, EEP, and CWP, respectively.

308

AGCM Experiments.

309

To evaluate the effects of coastal warming, we performed two experiments. In the “AGlobal” run,310

we force the Geophysical Fluid Dynamics Laboratory (GFDL) AM4.0 (40) with 1/4º observed

311

daily OISST (41) from January 1, 2010, to June 30, 2023. The model resolution is approximately

312

100 km with 33 levels in the vertical. The AGlobal is radiatively forced by historical (until 2014)313

and Shared Socio-economic Pathway 2-4.5 (SSP245) scenarios

314Intercomparison Project phase 6 (CMIP6) (42). The AGlobal experiments have 10 members, each315with slightly different initial conditions. The ensemble mean of the simulations is analyzed to15

in Coupled Model316

assess the atmospheric response to 2023 SST anomalies across the global ocean, and the spread

317(standard deviation) indicates the uncertainty from internal variability, prominently reflecting the318

influence of MJO and weather-scale perturbation in tropical regions. The coastal run

319(“ACoast2023”) is similar to AGlobal; however, starting from January 1st, 2023, it retains only320

realistic SST in the coastal region ( 90°W–coast, 15°S–equator, with 5° linear tapering zones

321

outside this region), while employing climatological SST in other regions. The results thus isolate

322

the atmospheric responses to 2023 coastal SST anomalies.

323

Ocean mixed layer heat budget.

324

Here we use a mixed layer heat budget (26) to assess the relative contribution of ocean dynamics

325

and thermodynamics to the 2023 coastal El Niño.

326

𝑇/! = −(u𝑇0 )! − (v𝑇1 )! − (w𝑇2 )! + (327

where u, v, and T indicate the mixed layer averaged zonal current, meridional current, and ocean

328

temperature, respectively. w is the vertical velocity at the bottom of the mixed layer. The Qnet is329

the surface net heat flux, and the Qpen is the shortwave radiation transmitted through the bottom of330

the mixed layer depth. ρ and cp are the density and specific heat capacity of seawater,

331

respectively; and H is the mean mixed layer depth (here we use the monthly climatological mixed

332

layer depth). R is the residual term. The vertical advection term [−(𝐰𝑻𝒛 )! ] could be further

333$𝒛 , EK),$ 𝑻!𝒛 , TH), the Ekman feedback (−𝒘! 𝑻
decomposed into the thermocline feedback (−𝒘334

and the nonlinear term ( −𝒘! 𝑻!𝒛 ). Here the overbar indicates the climatological value, and335

the prime indicates the anomalies. More details can be found in (26).

3!"# 43$"!
56& 7

16

)! + 𝑅,(1)

336

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337
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18

434

Acknowledgments: We extend our gratitude to J.M. Wallace for engaging in insightful discussions. We thank435

the two anonymous reviewers for their constructive comments and suggestions, which improved the study. The

436

National Center for Atmospheric Research (NCAR) is sponsored by the National Science Foundation.

437

Additionally, we would like to acknowledge the high-performance computing support provided by Cheyenne.

438

Funding: Q.P. and S.-P.X. are supported by the National Science Foundation (AGS 1637450). S.-P.X. is439

additionally supported by NASA (80NSSC22M0010). A.M. is supported in part by the Japanese Ministry of440

Education, Culture, Sports, Science and Technology (MEXT) programs for the advanced studies of climate

441

change projection (JPMXD0722680395). C.D. is supported by NCAR.

442

Author contributions: Q.P., S.P.X., and G. A. P. designed the study. Q.P., S.P.X., C. D., G. A. P., and A. M.

443

carried out the analysis. Q.P., and A. M. performed the numerical experiments. Q.P., and S.P.X. wrote the paper444with input from all the authors.

445

Competing interests: The authors declare that they have no competing interests.

446

Data

447

https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html;

448

https://www.remss.com/measurements/ccmp; CMEMS SLA at https://marine.copernicus.eu/access-data; ERA5

449

reanalysis

450

at https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDL_06/summary?keywords=GPM;

451

https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html;

452

https://psl.noaa.gov/data/gridded/data.gpcp.html;

453

http://www.bom.gov.au/climate/mjo/graphics/rmm.74toRealtime.txt; the MODIS Chlorophyll-a Concentration

454

data

455

http://www.imarpe.gob.pe/imarpe/index2.php?id_seccion=I0178030200000000000000;

456

AM4.0 outputs at https://zenodo.org/records/10437145. All other data needed to evaluate the conclusions in

457

this paper are present in the paper and/or the Supplementary Materials.

458

Supplementary Materials

andmaterials

data

at

459

Table S1

460

figs. S1 to S13

availability:

the

OISSTv2

atdatasetisCCMPavailable

V2.0

at

https://www.copernicus.eu/en;

https://www.earthdata.nasa.gov/;

realtime
the

19

GPM
GODAS

GPCP

the

in-situ

at

at
at

MJO

index

SST
the

at

data

at

MITgcm

and

461
462

Fig. 1. The spatiotemporal distribution of the 2023 coastal El Niño and associated463

atmospheric conditions. (Left panels) Observed SST (℃, color shading), 10-m wind (m/s,464vectors; values below 0.5 m/s not shown), and rainfall anomalies (line contours with an interval of465

2 mm/day; positive values in green and negative values in brown) during (A) January, (C)466

February, (E) March, (G) April , and (I) May. (Right panels) Same as (Left panels) but for the467

anomalous wind and rainfall obtained from the AGlobal experiment (see Materials and Methods468

for details).

20

469
470

Fig. 2. Evolution of the 2023 coastal El Niño. Longitude-time Hovmöller diagram of u10471

anomalies (line contours with an interval of 2 m/s; positive black and negative grey) as well as (A)472

observed SLA (cm, color shading) and (B) simulated SLA from the OGCM CTRL run (cm, color

473

shading). All meridionally averaged over 2oS-2oN.

474

color shading) and SLA (line contours with an interval of 2 cm; positive black and negative grey),475

and (D) rainfall (mm/day, color shading) and v10 anomalies (line contours with an interval of 1

476m/s, positive black and negative grey) zonally averaged over 80oW-85oW. The dashed line

477

indicates the approximate time when the coastal rainfall and wind anomaly signals disappear.

21

Latitude-time evolution of (C) SSTA (℃,

478
479

Fig. 3. Latitude-time evolution of the 2023 extreme coastal El Niño in OGCM experiments.

480

Latitude-time Hovmöller diagrams of coastal (80oW-85oW) SST anomalies (color shading; oC)

481

and SLA (contours with an interval of 2 cm; positive black, zero omitted and negative grey) from

482

&
&
&
the OGCM (A) CTRL, (B) 𝜏!"#$%, (C) 𝜏''(and (D) 𝜏!)(simulations.

22

483
484

Fig. 4. Weather to intraseasonal timescale perturbations during the 2023 event. (A) The

485

Wheeler-Hendon phase diagram for June 1974 to July 2023. The 2023 MJO index is highlighted

486

in color, with February in magenta, March in red, and April in blue. (B) Hovmöller diagrams of487

equatorial (5°S−5°N) 200 hPa velocity potential (ϕ, color shading, m2/s), and 850 hPa zonal wind488

anomalies (line contours with an interval of 1 m/s; positive black and negative grey). (C) Observed489

30-90 band-filtered anomalies of rainfall (mm/day; color shading ) and 10-m wind (m/s, vectors),

490

averaged during March 5-13, 2023. (D) Correlation (color shading) of observed v10 with eastern

491

Pacific (EP, 85°W-100°W, 2°S-2°N) averaged u10; also shown are 10-m wind regressions

492(vectors) onto EP u10 (30-90 days band-filtered anomalies used for 2012-2023).
23

493

Table 1. Standard deviation of ensemble spread in CSA v10, rainfall and EP u10 in AGlobal494

experiments. Monthly anomalies are first calculated, followed by the computation of the495

standard deviation from the squared variance averaged for March and April.

Variable

Climatological2023

fractional difference(2010-2022)
CSA v10

0.42

0.69

+64%

CSA rainfall

1.59

3.52

+123%

EP u10

0.78

1.08

+38%

496

24