# Stronger Westerly Wind Bursts in a Warming Climate:
The Effects of the Pacific Warming Pattern, the
Madden-Julian Oscillation, and Tropical Cyclones

Yu Liang, Alexey Fedorov

► To cite this version:

Yu Liang, Alexey Fedorov. Stronger Westerly Wind Bursts in a Warming Climate: The Effects of the Pacific Warming Pattern, the Madden-Julian Oscillation, and Tropical Cyclones. Journal of Climate, 2024, 37, pp.6029-6045. 10.1175/JCLI-D-23-0681.1. insu-04821263

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# Stronger Westerly Wind Bursts in a Warming Climate: The Effects of the Pacific Warming Pattern, the Madden-Julian Oscillation, and Tropical Cyclones

YU LIANG<sup>a</sup> AND ALEXEY V. FEDOROV<sup>a,b</sup>

(Manuscript received 13 November 2023, in final form 21 July 2024, accepted 12 August 2024)

**ABSTRACT:** Westerly wind bursts (WWBs) in the western-central equatorial Pacific are critical in El Niño-Southern Oscillation (ENSO) dynamics. Understanding how they may change with global warming has important implications for future projections of El Niño. In this study, we investigate how the enhanced eastern equatorial Pacific warming pattern, emerging in future climate projections, can influence WWB characteristics in an atmospheric general circulation model, CAM6. Changes in three main factors affecting WWBs—El Niño-conditions mean westerly wind stress anomalies, the Madden-Julian oscillation (MJO), and tropical cyclones (TCs)—are analyzed. We find that during El Niño onset (December-April), the WWB wind stress intensity remains largely unchanged but WWBs shift westward by about 20° of longitude. In contrast, during El Niño development (May-November), the WWB intensity increases by 41% or 79% in two warming scenarios considered [shared socioeconomic pathways (SSP5-8.5) and SSP2-4.5, respectively], which is mainly caused by a higher frequency of TC occurrence in the central tropical Pacific within 15°N/S. Further, we find that westerly wind speed anomalies associated with El Niño and the MJO also increase during El Niño development. However, as trade winds weaken in the central-eastern equatorial Pacific, the mean strength of the resulting westerly wind stress anomalies does not change much, and no significant eastward shift of WWBs is observed. Thus, our atmospheric model simulations suggest a strong TC-driven increase in WWB wind stress anomalies during El Niño development and hence a more important role of TCs in WWB generation, which in a coupled system could lead to stronger ENSO events, enhanced TC-ENSO coupling, and even greater intensification of WWBs.

**KEYWORDS:** ENSO; Madden-Julian oscillation; Tropical cyclones; Climate change; Climate variability

## 1. Introduction

El Niño-Southern Oscillation (ENSO) is the dominant interannual mode of the climate system with huge impacts on global climate, weather, and ecosystems (see a brief review by McPhaden et al. 2020). Its warm phase, El Niño, for example, often causes drought in Australia and Maritime Continent in the tropical Pacific, while bringing excessive rainfall to the central Pacific and the west coast of South America (Ropelewski and Halpert 1987). Outside the tropical Indo-Pacific, El Niño also affects the East Asian monsoon (Wang et al. 2000), North Atlantic hurricanes (Gray 1984), and U.S. precipitation (Ropelewski and Halpert 1987) through atmospheric teleconnections.

Extensive efforts have been directed to understand potential changes of El Niño and the underlying dynamics [see a most recent review by Cai et al. (2021)]. Although the majority of coupled general circulation models (GCMs) agree that ENSO rainfall variability will increase (Power et al. 2013),

and ENSO SST variability may increase as well by the end of the twenty-first century (e.g., Cai et al. 2022; Heede and Fedorov 2023b), the latter conclusion remains under debate (e.g., Chen et al. 2017b; Beobide-Arsuaga et al. 2021; Cai et al. 2018; Wengel et al. 2021). The disagreement arises from the fact that ENSO SST amplitude is determined by competing positive and negative feedbacks (e.g., zonal advective and thermocline feedbacks, thermodynamic damping, etc.), whose strength depends on the mean state of the tropical Pacific and whose response to global warming vary broadly across models (Fedorov and Philander 2001; Philip and Van Oldenborgh 2006; Fedorov and Philander 2000; Collins et al. 2010; Guilyardi et al. 2009, 2012; Zhao and Fedorov 2020). These feedbacks keep ENSO close to a neutrally stable regime with atmospheric noise important for maintaining a continual oscillation (Fedorov and Philander 2001, 2000; Philander and Fedorov 2003; Fedorov 2002, 2010; Jin et al. 2006; Philip and Van Oldenborgh 2006; Collins et al. 2010; Fedorov et al. 2020).

Coupled GCMs generally disagree on the relative importance, magnitude, or even the direction of the change to these and other related processes under global warming (e.g., L. Chen et al. 2015; Chen et al. 2017b; Beobide-Arsuaga et al. 2021; Cai et al. 2018; Wengel et al. 2021; Fredriksen et al. 2020; Ferrett and Collins 2019; Heede and Fedorov 2023b). For example, Cai et al. (2018) emphasize that the increased upper-ocean stratification would strengthen ocean-wind coupling and favor stronger El Niño. Wengel et al. (2021) find that ENSO variability weakens with global warming due to

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-23-0681.s1.

Liang's current affiliation: Scripps Institution of Oceanography, University of California, San Diego, San Diego, California.

*Corresponding author:* Yu Liang, yu.liang@yale.edu, yul257@ucsd.edu

<footnote><sup>a</sup> Department of Earth and Planetary Sciences, Yale University, New Haven, Connecticut</footnote>

<footnote><sup>b</sup> LOCEAN/IPSL, Sorbonne University, Paris, France</footnote>
a stronger damping by latent heat fluxes in a high-resolution model simulation. Heede and Fedorov (2023b) find that ENSO strengthening is related to the future weakening of the Walker cell, but the connection is relatively modest, while the ENSO linear stability does not necessarily explain the simulated changes.

One critical but often overlooked process in El Niño projections is westerly wind bursts (WWBs) (Collins et al. 2010; Lopez et al. 2022)—episodes of strong westerly wind anomalies on the western-central equatorial Pacific with the duration of several days to a few weeks (Harrison and Vecchi 1997; Fedorov 2002; Seiki and Takayabu 2007; Puy et al. 2016; Liang and Fedorov 2021, among others). Those WWBs are often observed during the onset and development stages of El Niño, contributing to the event growth through zonal advective and thermocline feedbacks (McPhaden 1999; Lengaigne et al. 2002; Fedorov et al. 2003, 2015; Hu and Fedorov 2019; Yu and Fedorov 2020). A modeling study finds that removing nearly all WWBs could reduce ENSO amplitude in a coupled GCM by 22% (Yu and Fedorov 2022), in agreement with simple ENSO models showing that a larger amplitude of stochastic forcing (including WWBs) results in a larger ENSO amplitude (e.g., Jin et al. 2020). These studies highlight the critical role of WWBs in the ENSO dynamics, which needs to be accounted for in ENSO projections.

WWBs are usually associated with the Madden-Julian oscillation (MJO) (e.g., Zhang 2005; McPhaden 1999; Zhang and Gottschalck 2002; Seiki and Takayabu 2007; Puy et al. 2016; Liang and Fedorov 2021; Liang et al. 2021a,b; Liang and Fedorov 2022) and tropical cyclones (TCs) (e.g., Hartten 1996; Fedorov et al. 2015; Lian et al. 2018a, 2019; Liang and Fedorov 2021; Liang et al. 2021a). A recent study by Liang and Fedorov (2021) has found that during the onset and development stages of El Niño, about 90% of WWBs are generated by westerly wind anomalies associated with the MJO, TCs, or both, highlighting their importance in WWB generation. Other studies suggest that WWBs can also be caused by equatorial Rossby waves (Puy et al. 2016), cold surges (Chu 1988; Chongyin 1990; Yu and Rienecker 1998), and southerly wind anomalies from the Australian continent (Chen and Wu 2000; Xu and Chan 2001).

Note that the details of the MJO-WWB relationship have been intensely debated. Chiodi et al. (2014), for example, have found that MJO events did not significantly affect the likelihood of WWB occurrence, as the increased occurrence of WWBs during the MJO westerly phase is counteracted by the reduced occurrence in the MJO easterly phase. Here, however, we emphasize the role of MJO westerlies in generating WWBs, rather than focusing on how the overall WWB occurrence statistics is related to different phases of the MJO.

A robust feature of climate GCMs in the recent Coupled Model Intercomparison Project, phase 6 (CMIP6) under different warming scenarios is that the models exhibit an enhanced warming in the eastern equatorial Pacific but a weaker warming over the Pacific warm pool. This eastern equatorial Pacific (EP) mean warming pattern (El Niño like) typically develops by the end of the twenty-first century or even earlier (e.g., Heede and Fedorov 2021), although a small number of studies (e.g., Koyama et al. 2017) do suggest a La Niña-like pattern. The recent

decadal trends have also been La Niña-like (e.g., Heede and Fedorov 2023a; Watanabe et al. 2024 and references therein), but the transition to the eastern equatorial Pacific warming pattern is still widely expected (e.g., Heede et al. 2020; Heede and Fedorov 2021; Ying et al. 2022). This El Niño-like warming pattern has been shown to shift the location of TCs in the western North Pacific eastward (Murakami et al. 2011); how it affects TC-related WWBs, however, has been unexplored.

Meanwhile, although MJO precipitation variability is projected to strengthen significantly by the end of the twenty-first century, changes of MJO wind variability, important for WWBs, remain uncertain in climate GCMs, ranging across different studies from a small increase to even a decrease (e.g., Bui and Maloney 2018; Maloney et al. 2019; Bui and Maloney 2019), which may be related to differences in the projected warming pattern (Maloney and Xie 2013). Recently, Lopez et al. (2022) have found that WWBs may intensify with global warming in the CESM1 Large Ensemble, and El Niño amplitude also increases. Heede and Fedorov (2023b) also argued that increases in atmospheric noise might explain the strengthening of ENSO under different warming scenarios. However, whether the WWB intensification is due to the strengthening of El Niño or vice versa is hard to decipher in the coupled system.

In this study, we employ atmospheric-only experiments with the Community Atmospheric Model 6 (CAM6) forced by SSTs derived from a coupled model simulation under two different warming scenarios to investigate how the characteristics of WWBs will change under global warming. Additionally, we dissect the SST warming pattern into a globally uniform pattern and an EP-enhanced pattern, enabling us to differentiate between their respective effects. The use of an atmospheric model, rather than a coupled model, is intentional since it allows us to isolate the impact of mean SST changes on WWBs, a challenge often encountered in a coupled setting where ENSO can affect WWBs as well. Our analysis extends to changes in the MJO and TCs during El Niño events, which are essential components for WWB generation. We then discuss how the simulated changes of WWB characteristics could affect El Niño projections. The rest of this paper is organized as follows: in section 2, we describe the numerical experiments and methods. In section 3, we present our experiment results and analyses, which are followed by the conclusion and discussion (section 4).

## 2. Numerical experiments and methods

### a. Numerical experiments

The numerical model we use is the atmospheric component of CESM2, CAM6 (Danabasoglu et al. 2020). Compared to CESM1, CESM2 has a considerably improved representation of the MJO (e.g., Hung et al. 2013; Ahn et al. 2020); the previous version of this atmospheric model, CAM4, has also been shown to capture well the main characteristics of WWBs and their relations to the MJO and ENSO (Lian et al. 2018b). We choose a horizontal grid of 0.9° × 1.25° (f09_f09_mg17) and a hybrid sigma-pressure system with 32 levels. As we will show, this resolution is sufficient to simulate realistic warm-core cyclonic convective disturbances that in many ways resemble the observed TCs and TCs simulated by higher-resolution models (e.g., Bell et al. 2014).
<figure><img src="image_2.png" /><figcaption>FIG. 1. (a),(c) CESM2 ensemble mean SST linear warming trends following SSP5-8.5 and SSP2-4.5, respectively (estimated for 2015–2100). (b),(d) As in (a) and (c), but with the average warming rate for the same period in the tropical Indo-Pacific subtracted. Note the enhanced eastern EP warming pattern in both scenarios. (e) Oceanic Niño-3.4 index (3-month running mean of SST anomalies averaged in 5°N–5°S, 120°–170°W) based on the imposed SST forcing during the 38 years of CAM6 model integration. For three ensemble members used in each perturbation experiment, this yields 27 El Niño events in total.</figcaption></figure>

We conduct five sets of atmospheric ensemble experiments, each having three ensemble members. For the control experiment, CAM6 is forced by monthly SSTs and sea ice concentrations of years 1000–1037 (cf. Fig. 1e) obtained from the CMIP6 CESM2 preindustrial control experiment (r1i1p1f1). We do not use the observed SSTs to force the model because the MJO propagation across the Maritime Continent (e.g., Ahn et al. 2020) can be better simulated when the SST forcing derived from CESM2 is used (Fig. S1 in the online supplemental material). This is important for WWB generation in the western–central equatorial Pacific. The better simulation of MJO eastward propagation may be due to the current experiments that simulate a steeper background meteorological moisture gradient (Fig. S2), which is important for MJO propagation across the Maritime Continent (Kang et al. 2020).

To obtain boundary conditions (i.e., modified SSTs) for the warming experiments, we first calculate the ensemble-mean SST linear warming trend in CESM2 (Fig. 1a), estimated for 2015–2100 in experiments following shared socioeconomic pathway 5 (SSP5-8.5) (O'Neill et al. 2016). Monthly SST data from five ensemble members are used, namely, r(1,2,4,10,11)i1p1f1 in the CMIP6 dataset. This yields a global-mean warming rate of about 4.5 K century⁻¹. The tropical Indo-Pacific (30°E–70°W, 30°N–30°S) has a slightly higher mean warming rate of about 4.9 K century⁻¹; the differential SST warming rates in the tropical Indo-Pacific (SST<sub>diff</sub>) are then calculated by subtracting the 4.9 K century⁻¹ from the linear warming rate at each grid point, as shown in Fig. 1b. We find that in CESM2 under the SSP5-8.5 scenario, the warming rate difference between the warm pool

and the eastern equatorial Pacific is as large as 4 K century⁻¹, which leads to the development of a strong EP warming pattern.

Following the above computations, we approximate the SST change by the end of the twenty-first century as the SST linear warming trend (in units of K year⁻¹) multiplied by 100 years. The SST boundary conditions for the perturbation warming experiment (hereafter the 4.5KEP experiment) are then constructed by adding a 4.5-K globally uniform warming and SST<sub>diff</sub> in the tropical Indo-Pacific to the Control SSTs. The imposed SST perturbation is constant in time. The CO₂ concentration is set at 4× the preindustrial level (i.e., 1138.8 ppmv), and we neglect sea ice changes at high latitudes. This representation of the global SST change helps isolate the effects of the uniform warming and EP-enhanced differential warming patterns. Accordingly, two more ensemble experiments are conducted with the following SST boundary conditions: The 4.5-K experiment is the control SSTs plus the 4.5-K uniform warming with 4× the preindustrial CO₂ level, and the EP experiment is the control SSTs plus SST<sub>diff</sub> in the tropical Indo-Pacific and the preindustrial CO₂ concentration (see Table 1).

Finally, we conduct a 2KEP ensemble experiment for which the SST boundary conditions are constructed in a similar way as for the 4.5KEP experiment, except that ensemble members following SSP2-4.5 are used. For these computations, we utilize four ensemble members r(1,4,10,11)i1p1f1 in the CMIP6 dataset. For this scenario, we estimate the global-mean linear warming rate of about 2 K century⁻¹ and set the CO₂ concentration at about 2× the preindustrial level (600 ppmv). Note that the differential warming pattern in the tropical Indo-
<table><caption>TABLE 1. A summary of five atmospheric ensemble experiments forced with fixed SSTs within CAM6. The SST<sub>ctrl</sub> refers to the control SSTs obtained from the CESM2 control simulation, and SST<sub>diff</sub> and SST<sub>diff2</sub> refer to the differential SST warming rate in the tropical Indo-Pacific obtained from CESM2 SSP5-8.5 and SSP2-4.5 simulations, respectively (see Figs. 1b,d).</caption><thead><tr><th>Name</th><th>SST boundary conditions</th><th>Ensemble size</th><th>Length of integration for each ensemble member (years)</th></tr></thead><tbody><tr><td>Control</td><td>SST<sub>ctrl</sub></td><td>3</td><td>38</td></tr><tr><td>4.5KEP</td><td>SST<sub>ctrl</sub> + 4.5K + SST<sub>diff</sub></td><td>3</td><td>38</td></tr><tr><td>4.5K</td><td>SST<sub>ctrl</sub> + 4.5K</td><td>3</td><td>38</td></tr><tr><td>EP</td><td>SST<sub>ctrl</sub> + SST<sub>diff</sub></td><td>3</td><td>38</td></tr><tr><td>2KEP</td><td>SST<sub>ctrl</sub> + 2K + SST<sub>diff2</sub></td><td>3</td><td>38</td></tr></tbody></table>

Pacific is different from that of the 4.5KEP experiment. This set of experiments is mainly used to test the sensitivity of WWB changes to different warming scenarios. Note that by construction, ENSO SST anomalies in the perturbation experiments are the same as the control ones; they only differ in the SST climatology used.

### b. El Niño events

El Niño events are defined if a 3-month running mean of SST anomalies averaged in the Niño-3.4 region (5°N–5°S, 120°–170°W) exceeds 0.5°C for a minimum of five consecutive months. Previous studies have shown that El Niño evolution is typically phase locked to the seasonal cycle (e.g., Rasmusson and Carpenter 1982; Hirst 1986)—events start in boreal winter and early spring, rapidly develop in summer and fall, reach peak in winter, and decay in the following spring. Accordingly, following Liang and Fedorov (2021), we define December (−1) to April (0) as the onset stage of El Niño and May (0) to November (0) as the development stage of El Niño. The 0 and −1 indicate the year when El Niño events peak and the year before that, respectively. In this study, we only examine changes of WWB characteristics during the onset and development stages of El Niño, as they directly affect the El Niño peak amplitude (Yu and Fedorov 2020). Note that during the 38 years of integration in each ensemble member, there are nine El Niño events (Fig. 1e), which all peak in winter, yielding 27 events in total for each experiment. Among the nine El Niño events, one belongs to the consecutive type, which attains two SST peaks in consecutive winters (years 1023–25). For convenience, we only analyze the first year of this El Niño event.

### c. WWBs

To identify WWBs, we basically follow the approach of Liang and Fedorov (2021), except that here we use surface zonal wind stress anomalies instead of wind speed anomalies. This is because wind stress describes momentum transfer from the atmosphere to the ocean, which directly affects the zonal advective and

thermocline feedback, making wind stress anomalies more important for El Niño development (e.g., Jin et al. 2006).

We first compute a 5-day running-mean daily climatology of the surface zonal wind stress $\tau_x$ averaged between 3°N and 3°S and then calculate daily anomalies relative to this climatology. We then use three criteria to identify WWBs: 1) equatorially averaged $\tau_x$ anomalies exceed a 0.04-Pa threshold, which is about 1.5 times the standard deviation calculated over the central equatorial Pacific (Yu and Fedorov 2020); 2) the area that satisfies the wind stress criterion extends zonally for at least 10° of longitude; and 3) the first two criteria are met for at least 5 days. Here, we do not merge two adjacent WWBs, and no WWBs last for more than 45 days in all experiments. Note that according to this definition, WWBs not only mainly describe intraseasonal wind anomalies but also include anomalies on interannual and seasonal time scales. Similar definitions have also been used in many previous studies (e.g., Harrison and Vecchi 1997; Seiki and Takayabu 2007; Yu and Fedorov 2020). Later, when investigating the causes of changes in WWB properties, we will focus on El Niño conditions mean westerly wind anomalies, as well as wind anomalies associated with the MJO and TCs, which correspond to three major factors affecting WWBs on different time scales: seasonal to interannual, intraseasonal, and synoptic, respectively.

Examples of WWBs in each experiment are shown in Fig. 2. One noticeable model bias is that the simulated WWBs are weaker than the observations (cf. Liang and Fedorov 2021; Yu and Fedorov 2020), especially during the onset stage of El Niño. Model’s WWBs during El Niño onset also seem to be displaced westward. Similar issues were reported for CAM4 simulations (Lian et al. 2018b), which may be due to a generally weaker wind variability of the simulated MJO or the lack of ocean-atmosphere coupling.

To estimate the strength of WWBs during each El Niño event, we define an integrated WWB intensity index $Amp_{WWB}$ specified in Eq. (1), for the onset and development stages, respectively. This index reflects the integration of equatorially averaged zonal wind stress anomalies over longitude and time within the WWB region (cf. Fig. 2), similar to the approach of Chen et al. (2017a). Consequently, 27 different values of $Amp_{WWB}$ are obtained for the El Niño onset stage, then for the development stage, and for each experiment. We further define the corresponding mean longitude of WWBs $lon_{WWB}$ in Eq. (2) where $lon(x, t)$ denotes the longitude of zonal wind stress anomalies within the WWB region. The $lon(x, t)$ is a function of longitude and time. Note that during some El Niño events, WWBs are absent during the onset or development stage, in which case $lon_{WWB}$ cannot be calculated:

$$ Amp_{WWB} = \sum_{WWBs} \int_{lon} \int_{t} \tau_{xano}(x, t) dx dt, \quad (1) $$

$$ lon_{WWB} = \sum_{WWBs} \int_{lon} \int_{t} \tau_{xano}(x, t) lon(x, t) dx dt / Amp_{WWB}. \quad (2) $$

To elucidate the relationship between WWBs and the MJO, we further calculate the central date $day_0$ for each individual
<figure><img src="image_3.png" /><figcaption>FIG. 2. Hovmöller diagrams of equatorially averaged (3°N–3°S) surface zonal wind stress anomalies in the (a) control, (b) 2KEP, (c) 4.5KEP, (d) 4.5 K, and (e) EP experiments in an El Niño year (year 1001; see Fig. 1c). Wind stress anomalies are calculated relative to the 5-day running mean daily climatology of each experiment. Yellow patches circled by red contours (corresponding to the 0.04-Pa threshold) indicate individual WWBs. For each panel, only one ensemble member is used. The 2KEP corresponds to SSP2-4.5, and 4.5KEP corresponds to SSP5-8.5. The 4.5 K and EP experiments also correspond to SSP5-8.5.</figcaption></figure>

WWB event, as done in Liang and Fedorov (2021). The day(x, t) denotes the date of zonal wind stress anomalies within the WWB region:

$$day_0 = \int_{\text{lon}} \int_{t} \tau_{\text{xano}}(x, t) \text{day}(x, t) \, dx \, dt / \int_{\text{lon}} \int_{t} \tau_{\text{xano}}(x, t) \, dx \, dt. \quad (3)$$

### d. MJO events

To extract the MJO signal, we first apply bandpass filtering to daily wind speeds at 200 and 850 hPa and to outgoing long-wave radiation (OLR) to retain only eastward-propagating signals with zonal wavenumbers 1–5 and periods 30–95 days (following Wheeler and Kiladis 1999). We then conduct an empirical orthogonal function (EOF) analysis of the combined fields of the filtered 850-hPa zonal wind (U850hPa), 200-hPa zonal wind (U200hPa), and OLR, as described by Wheeler and Hendon (2004), who used the covariance matrix of averaged around the equator fields (15°N–15°S) in the eigenvector computation. The leading two EOFs (e.g., Fig. S3), which appear as a pair, describe large-scale eastward-propagating MJO events. Note that in Wheeler and Hendon (2004), intraseasonal signals are obtained by subtracting low-frequency signals related to ENSO, the seasonal cycle, and other long-term trends from the raw fields, while here it is done by applying bandpass filtering as in many other studies (e.g., Maloney and Hartmann 1998; Slingo et al. 1999; Kessler 2001).

Further, the location and amplitude of MJO events can be determined by the corresponding principal component pair (PC1, PC2). As done in Wheeler and Hendon (2004), the (PC1, PC2) phase space is divided into eight sectors, each corresponding to a particular location of MJO convective activity in the Indo-Pacific. The MJO is considered active if $\sqrt{PC1^2 + PC2^2} > 1$; otherwise, it is considered inactive. In Figs. S4 and S5, we show composite horizontal structures of

the active MJO in the control experiment in January–March and in June–August, respectively, which closely resemble the observed MJO structures (cf. Wheeler and Hendon 2004). The MJO phases in the warming experiments are defined in the same way by the first two EOF modes derived from the corresponding warming experiments, which are different from the method of Wang et al. (2020). As shown in Fig. 3, WWBs tend to occur in the westerly phase of the MJO, especially in phases 6 and 7, in both the control and 4.5KEP experiments.

Subsequently, we will compare the MJO composite structure in phase 7. Note that this is different from Liang and Fedorov (2021) who compared the MJO composite structure on daywesterly, which was defined as the date when average surface westerly wind anomalies of the MJO reached the maximum within 120°E–180°, 15°N–15°S; this generally corresponds to RMM phases 6 and 7. The current method, as will be shown, can better depict the eastward shift of the MJO convection for the EP-enhanced warming pattern.

Note that the MJO in April and November has mixed wintertime and summertime features (not shown); therefore, when comparing the composite MJO structures during the onset and development stages of El Niño between the control and perturbation experiments, we exclude these 2 months. Moreover, since WWBs are defined via surface wind stress anomalies, to assess the MJO’s direct impacts on WWBs, we also apply bandpass filtering to extract the MJO surface vector wind stress signals, as done for the 850-hPa vector wind speeds.

### e. Cyclonic convective disturbances (model’s TCs)

To identify cyclonic convective disturbances that resemble TCs in the atmospheric model, we follow an algorithm for TC detection discussed in Walsh et al. (2007). We first calculate vorticity at 1000 hPa for each model grid point and then find
<figure><img src="image_4.png" /><figcaption>FIG. 3. The number of WWBs during different phases of the MJO in the (a),(b) control and (c),(d) 4.5KEP experiments. The left column corresponds to the onset stage of El Niño (December–April), and the right column corresponds to the development stage (May–November). In both experiments, WWBs preferentially occur in the westerly phase of the MJO, particularly during phases 6 and 7. “IA” indicates that the MJO is inactive.</figcaption></figure>

grid points whose cyclonic vorticity shows a local maximum within a radius corresponding to four grid points (about 400 km). If this maximum vorticity is greater than $4 \times 10^{-5} \text{ s}^{-1}$, this grid point becomes a candidate for the center of a cyclonic convective disturbance, which we subject to two more tests: 1) the maximum wind speed at 1000 hPa exceeds $14.5 \text{ m s}^{-1}$ within the same 400-km radius from this grid point and 2) the considered cyclonic disturbance has a warm core; that is, average temperature anomalies at 700, 500, and 300 hPa at this grid point, estimated relative to the background mean temperature within a band 1200 km to the east and to the west, and 400 km to the north and to the south, exceed 2/3 K (Walsh et al. 2004).

Note that the surface wind speed threshold of $14.5 \text{ m s}^{-1}$ used for the model horizontal grid spacing corresponds to about $17.5 \text{ m s}^{-1}$ in the observations (Walsh et al. 2007), which is the typical threshold for tropical storms. Hereafter, for brevity, we call the identified warm-core cyclonic convective disturbances simply as TCs; as we will show later, these simulated cyclones respond to El Niño conditions in a way very similar to the observed TCs. WWBs are considered to be related to TC occurrence if a TC is present within $15^\circ$ north or south of the equator and within the longitudinal range of the WWB for at least 1 day. We have reduced the threshold of 3 days used in Liang and Fedorov (2021) to 1 day because TCs are generally weaker and less persistent in the current numerical experiments than in observations. Table 2 indicates that during the onset and development stages of El Niño, a

large fraction of WWBs, ranging from 44% to 75%, are related to TCs, which is slightly lower than in the observations (Liang and Fedorov 2021).

## 3. Results and analyses

### a. Changes of WWB characteristics during El Niño onset

We first examine changes of WWB characteristics during the onset stage of El Niño (December–April, Fig. 4). Compared to the control, a modest decrease of $\text{Amp}_{\text{WWB}}$ by 8% (not statistically significant) is seen in the 4.5KEP experiment. At the same time, $\text{lon}_{\text{WWB}}$ shifts on average by about $19^\circ$ westward at a 95% significance level. The small change of $\text{Amp}_{\text{WWB}}$ appears to result from competing effects of the 4.5-K uniform warming and the EP-enhanced differential SST change: the average $\text{Amp}_{\text{WWB}}$ increases by 47% in the 4.5-K experiment but decreases by 61% in the EP experiment.

Changes to WWBs in the 2KEP experiment are similar to those in the 4.5KEP experiment. Therefore, for brevity, we will concentrate only on the 4.5KEP experiment throughout the remainder of section 3a. To better understand the causes of the WWB changes, we will next investigate how El Niño composite seasonal-mean westerly wind anomalies and westerly wind anomalies associated with the MJO and TCs, the three major ingredients of WWBs, are affected by the EP-enhanced warming pattern.

Figure 5 shows the composites of El Niño–conditions mean SST, precipitation, surface wind speed, and wind stress anomalies
<table><caption>TABLE 2. The number of WWBs associated with TCs, as well as those not related to TCs, in the control, 2KEP, and 4.5KEP experiments during the onset (December–April) and development (May–November) stages of El Niño.</caption><thead><tr><th></th><th>Onset, with TCs</th><th>Onset, no TCs</th><th>Development, with TCs</th><th>Development, no TCs</th></tr></thead><tbody><tr><td>Control</td><td>26</td><td>24</td><td>78</td><td>100</td></tr><tr><td>2KEP</td><td>30</td><td>10</td><td>130</td><td>99</td></tr><tr><td>4.5KEP</td><td>30</td><td>11</td><td>107</td><td>62</td></tr></tbody></table>

during El Niño onset in the control and 4.5KEP experiments. A “blob” of warm SST is found in the western–central equatorial Pacific, possibly related to the Pacific meridional modes (Chang et al. 2007), which resembles the observed SST pattern during the onset of El Niño but with a smaller magnitude (e.g., Vecchi and Harrison 2000; Liang and Fedorov 2021). In the control experiment, positive precipitation and equatorial westerly wind anomalies develop to the west of the warm SST anomalies, contributing to WWB generation (Liang et al. 2021a). In the 4.5KEP experiment, similar anomalous precipitation and surface wind patterns are observed (Figs. 5d–f) but with slightly larger magnitudes (cf. Fig. S6), which should increase Amp<sub>WWB</sub>. We find that the precipitation and wind response are also stronger in the 4.5-K and EP experiments (Fig. S7) than that in the control, suggesting that the 4.5-K uniform warming and the EP-enhanced differential SST change both contribute to the enhanced positive precipitation and westerly wind anomalies in the 4.5KEP experiment.

We next compare the MJO horizontal structures (Fig. 6), whose surface westerly wind anomalies are the major source

of Amp<sub>WWB</sub> on intraseasonal time scales (e.g., Zhang and Gottschalck 2002). The composite MJO structure is compared in its phase 7 (Wheeler and Hendon 2004), when the MJO surface westerly wind stress anomalies are located in the western–central equatorial Pacific. For reference, the composite MJO structures during phase 8 are also shown (Fig. S8). Compared to the control, the convective MJO in the 4.5KEP experiment shifts eastward and has stronger OLR and slightly stronger equatorial westerly wind speed anomalies. Westerly wind stress anomalies, however, are slightly weaker on the equator, which should contribute to the reduction of Amp<sub>WWB</sub>.

The opposite direction of changes of the MJO westerly wind speed and wind stress anomalies is likely due to the weakening of equatorial trade winds (Fig. S9c), since to the leading order, the MJO westerly wind stress anomalies τ<sub>MJO</sub> are approximately proportional to V<sub>speed</sub> × u<sub>MJO</sub>, where V<sub>speed</sub> is the background wind speed and u<sub>MJO</sub> is the MJO westerly wind speed anomalies. Later, we will see that the weakening of trade winds also has profound impacts on WWB characteristics during El Niño development. For reference, the

<figure><img src="image_5.png" /><figcaption>FIG. 4. (top) Histograms of the seasonally integrated WWB intensity (Amp<sub>WWB</sub>) across 27 El Niño events in the (a) control, (b) 2KEP, (c) 4.5KEP, (d) 4.5 K, and (e) EP experiments for the onset stage (December–April). Seasonally integrated WWB intensities are binned, so that bin<sub>1</sub> = 31.5 Pa × degree × day, whose unit comes from integrating surface westerly wind stress anomalies over longitude and time. (bottom) Histograms of the corresponding WWB mean longitude (lon<sub>WWB</sub>). During the onset stage of some El Niño events, WWBs are absent and therefore lon<sub>WWB</sub> is not defined. The average Amp<sub>WWB</sub> and lon<sub>WWB</sub> changes relative to the control are shown in the upper-right corner of each panel. The changes are significant at the 95% confidence level in (e), (g), (h), and (i) using the two-sample Student's t test. Note the partially compensating effects of the 4.5-K uniform warming and the EP differential warming pattern on the WWB intensity.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>FIG. 5. Composite anomalies of (a) precipitation, (b) U1000hPa winds, and (c) surface zonal wind stress (color shading) in the control experiment for the onset stage of 27 El Niño events (December–April averages are shown). Red contour lines in (a) indicate composite SST anomalies during this period with a contour interval of 0.2 K; the solid and dashed lines start at 0.1 and −0.1 K, respectively. Arrows indicate surface wind speed vector anomalies in (b) and wind stress vector anomalies in (c). (d)–(f) The same fields, but for the 4.5KEP experiment. The anomalous fields are calculated relative to the climatological seasonal mean fields in each experiment.</figcaption></figure>

composite MJO structure during phase 3 is also shown in Fig. S10. The equatorial easterly wind stress anomalies of the MJO are slightly weaker in the 4.5KEP experiment than in the control, suggesting a slightly weaker modulation of WWBs by the MJO under global warming.

The MJO horizontal structures in the 4.5-K and EP experiments are also shown in Figs. S11 and S12. The MJO OLR and wind speed variability increase in the 4.5-K experiment but decrease in the EP experiment. The direction of the changes appears to agree with that of the background precipitation within

<figure><img src="image_7.png" /><figcaption>FIG. 6. Composite horizontal structures of the MJO in the (a),(d) control and (b),(e) 4.5KEP experiments during the onset stage (December–March) for the same El Niño events as used in previous figures. MJO phase 7 (Wheeler and Hendon 2004) is plotted when WWBs tend to occur. Color shading indicates the bandpass-filtered (zonal wave-numbers 1–5, periods 30–95 days) OLR anomalies, and arrows indicate 850-hPa wind vector speed anomalies in (a) and (b) and surface wind stress vector anomalies in (d) and (e). (c), (f) Differences (significant at the 95% confidence interval) in the MJO horizontal structures between the 4.5KEP experiment and the control, i.e., (b) and (e) minus (a) and (d). To compare the strength of the MJO westerly wind anomalies, we calculate the average MJO westerly wind anomalies in a 40°-longitude-long band within 3°N–3°S [red boxes in (a), (b), (d), and (e)], where the average MJO westerly wind anomalies attain maximum. Despite slightly stronger westerly wind speed anomalies in the equatorial far-western Pacific, MJO westerly wind stress anomalies are weaker in the 4.5KEP experiment than those in the control, which is unfavorable for WWBs. Note that we have excluded April from the composites because the MJO in April has both wintertime and summertime features.</figcaption></figure>
the South Pacific convergence zone (SPCZ) (Figs. S9d,e), as suggested by Rushley et al. (2019). It is likely that the competing effects of the 4.5-K uniform warming and the EP-enhanced differential SST warming cause only modest changes of the MJO wind speed variability in the 4.5KEP experiment, highlighting the sensitivity of the MJO to the warming pattern (Maloney and Xie 2013).

Finally, we compare anomalous TC activity between the control and 4.5KEP experiments (Fig. 7), as TCs' eastward and equatorward shifts have been suggested to be critical for WWB generation in the observations (Lian et al. 2018a; Liang and Fedorov 2021). For illustration purposes, in Figs. S13a–c, we show locations of all winter-season (December–April) TCs as well as those only during the onset stage of El Niño events in the control experiment and calculate the anomalous frequency of TC occurrence. The same fields but for the observations (1979–2016) are also shown for reference (Figs. S13d–f). Although the simulated TC density is smaller than that of the observations, CAM6 still captures the climatological TC distributions in winter and the response of TCs to El Niño onset: TC activity in the western North Pacific is enhanced near the equator (within 15°N), which should contribute to WWB generation. In the southwest Pacific (centered around 150°E) though, the model fails to capture the enhanced TCs, which has been suggested important for the WWBs during El Niño onset (Liang and Fedorov 2021).

Nonetheless, a clear eastward shift of the enhanced TC occurrence is seen in both the western North Pacific and South Pacific in the 4.5KEP experiment (Figs. 7b,c) compared to the control. The overall enhanced frequency of TC occurrence (yellow-red blocks) in the western Pacific remains largely unchanged, consistent with the changes in the 4.5-K and EP experiments (Fig. S14), which should have minimal effects on the simulated change of $Amp_{WWB}$. Nevertheless, we note that a higher percentage of WWBs are associated with TCs in the 4.5KEP and 2KEP experiments, as compared to the control (Table 2). As the background trade winds relax (Fig. S9c), the eastward-displaced TCs and MJO could not generate strong enough westerly wind stress anomalies in the central equatorial Pacific. In particular, WWBs that sometimes occur to the east of the date line in the control experiment disappear in the 4.5KEP experiment (cf. Figs. 2a,c and Fig. S15), which manifests as a westward shift of WWBs.

## b. Changes of WWB characteristics during El Niño development

We now turn to changes of WWB characteristics during the development stage (May–November) of El Niño (Fig. 8). Compared to the control, significant increases of $Amp_{WWB}$ are observed in the 4.5KEP (by 41%) and EP (by 118%) experiments, with no significant change in the 4.5-K experiment. Intriguingly, in the 2KEP experiment, the average $Amp_{WWB}$ increases by 79%, nearly twice the increase seen in the 4.5KEP experiment, despite the milder uniform and EP-enhanced differential warming (cf. Fig. 1). In the 2KEP experiment, a significantly greater number of WWBs also occur, as compared to the control (Table 2). This suggests a nonlinear

<figure><img src="image_8.png" /></figure>

FIG. 7. Anomalous frequency of TC occurrence during El Niño onset in the (a) control and (b) 4.5KEP experiments, calculated relative to the climatological frequency in each experiment computed between December and April and (c) the difference between the two experiments. The occurrence of TCs is counted within each 10° longitude × 5° latitude grid box, and the units used are 1 TC day per grid box per winter season (December–April). Note a small decrease in the anomalous frequency of TC occurrence in the western North Pacific but an increase in east of Indonesia and Australia in (c). The reduced TC occurrence in the central South Pacific in (a) and (b) is not relevant to WWBs and is mainly related to the enhanced TC activity there during the El Niño peak and decay stages, which is included in the climatological TC frequency calculation.

WWB response to warming, which we will explore further in the subsequent analyses. In addition, the $lon_{WWB}$ shifts westward by 8° longitude in the 4.5-K experiment and eastward by 7° longitude in the EP experiment, while changes in $lon_{WWB}$ are not significant in the 4.5KEP and 2KEP experiments. It appears that the EP-enhanced warming pattern is particularly favorable for the intensification of WWBs during El Niño development, which we shall also investigate in more detail below.

We first examine the composites of mean precipitation and surface wind anomalies in the control, 4.5KEP, and 2KEP experiments during the El Niño development stage (Fig. 9); differences between the control and the perturbation experiments are shown in Fig. S16 for reference. As during the El Niño onset stage, positive precipitation and westerly wind speed anomalies are enhanced near the equator in the 4.5KEP experiment compared to the control. This enhancement appears to arise from the combined effects of the 4.5-K uniform warming and EP-enhanced differential warming patterns, as shown by the enhanced anomalies in both experiments (Fig. S17). We note the eastward shift of positive precipitation and westerly
<figure><img src="image_9.png" /><figcaption>FIG. 8. As in Fig. 4, but for the development stage (May–November) of El Niño. The bin₂ = 114.6 Pa × degree × day. The changes are significant at the 90% significance level or above in (b), (c), (e), (i), and (j).</figcaption></figure>

wind speed anomalies in the 4.5KEP experiment; this is because the EP-enhanced warming pattern increases the tropics relative SST (e.g., Johnson and Xie 2010) in the central-eastern equatorial Pacific, making deep convection easier to occur there.

Another interesting finding is that although westerly wind speed anomalies averaged between 3°N and 3°S, 150°E–150°W in the 4.5KEP experiment increase by 9% relative to the control, the average westerly wind stress anomalies remain the same due to the weakened trade winds on the equator (Fig. S9h). This suggests that the intensification of WWBs during El Niño development in the 4.5KEP experiment is not directly related to the composite seasonal-mean wind stress

changes. In contrast, both the westerly wind speed and wind stress anomalies in the 2KEP experiment increase by 16% and 11%, respectively, which in part explains the larger $Amp_{WWB}$ compared to the 4.5KEP experiment.

We next compare the MJO horizontal structures at phase 7 (Fig. 10), which are now located mostly to the north of the equator. Compared to the control, the MJO OLR anomalies in the 4.5KEP experiment are much stronger, and the MJO westerly wind speed anomalies also strengthen. Examining the MJO structures in the 4.5 K and EP experiments (Figs. S18 and S19), we find that the 4.5-K uniform warming enhances both OLR and wind speed variability; the EP-enhanced differential

<figure><img src="image_10.png" /><figcaption>FIG. 9. As in Fig. 5, but for the development stage of El Niño (May–November averages are shown). The composite mean surface zonal wind speed anomalies averaged between 3°N and 3°S, 150°E–150°W increase by about 9% in (e) relative to (b), but the mean surface zonal wind stress anomalies averaged within the same region do not change between (c) and (f) (both are 0.016 Pa). In contrast, zonal wind speed anomalies in (h) and wind stress anomalies in (i) increase by 16% and 11% compared to (b) and (c), respectively.</figcaption></figure>
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>FIG. 10. As in Fig. 6, but for the composite MJO structure during the development stage (May–October) of El Niño. Although the MJO westerly wind speed anomalies increase in the 4.5KEP experiment compared to the control, average MJO westerly wind stress anomalies do not show significant changes. We exclude November here because the MJO in November has both wintertime and summertime features.</figcaption></figure>

SST change, on the other hand, only enhances MJO OLR variability but does not increase the magnitude of MJO equatorial westerly wind speed anomalies. This suggests that the strengthening of MJO wind speed variability in the 4.5KEP experiment is mainly due to the uniform warming effect. Nonetheless, the strength of MJO westerly wind stress anomalies does not change significantly, which again should be due to the weakened trade winds in the equatorial Pacific. The composite MJO horizontal structure at phase 3, at the same time, exhibits slightly weaker easterly wind stress anomalies in the central Pacific in the 4.5KEP experiment than the control (Fig. S20). Changes of the MJO in the 2KEP experiment are overall consistent with those in the 4.5KEP experiment (Fig. S21). Therefore, changes in MJO variability should have minimal effects on the amplified $Amp_{WWB}$ in the 4.5KEP and 2KEP experiments.

Finally, we compare anomalous TC activity between the control, 4.5KEP, and 2KEP experiments (Fig. 11). In the control, a more frequent TC occurrence is seen almost across the entire tropical Pacific during El Niño development, except the reduction in the Philippine Sea and along the west coast of North America. This generally agrees with the observed anomalous TC occurrence (Fig. S22f) and many other studies (e.g., Wang et al. 2000; Camargo et al. 2007), except that in the observations, the enhanced TC activity in the North Pacific is closer to the equator. Nevertheless, a clear eastward shift by more than 10° longitude of the enhanced TC occurrence is seen in the 4.5KEP and 2KEP experiments compared to the control (Fig. 11), which may not be surprising given the eastward shift of the seasonal-mean precipitation and westerly wind anomalies (cf. Fig. 9), as well as the MJO. The extent of

<figure><img src="image_13.png" /><figcaption>FIG. 11. (a)–(c) As in Fig. 7, but for the El Niño development stage. (d), (e) The results for the 2KEP experiment. Note the general increase in the anomalous TC occurrence frequency in the western and central tropical Pacific in the 4.5KEP and 2KEP experiments, which favors stronger WWB intensity.</figcaption></figure>
the TC's eastward shift is much larger than that of $lon_{WWB}$ (only 4° and 0°, respectively), which should again be due to the weakened trade winds in the central equatorial Pacific.

Moreover, in the regions where WWBs tend to occur, the average enhanced frequency of TC occurrence is much larger in the 4.5KEP experiment as compared to the control. For example, in the control, the average enhanced frequency of TC occurrence between 140°E and 160°W, 15°N–15°S is 0.39 TC day per 10° longitude × 5° latitude grid box per season (May–November), while it is 0.55 in the band between 150°E and 150°W, 15°N–15°S in the 4.5KEP experiment. Note we shift the region for averaging 10° eastward, because TCs are displaced more eastward in the 4.5KEP experiment. This is an increase by about 40%; zonally shifting the two averaging regions of the same size 10° westward or eastward at the same time yields an increase of 21% and 69%, respectively. The same calculation yields an increase of 77%, 79%, and 89% for the 2KEP experiment. We also find that, similar to the onset stage, a higher percentage of WWBs are associated with TCs in the full 4.5KEP (63%) and 2KEP (57%) experiments, when compared to the control (44%).

These results suggest that the greater increase of the frequency of TC occurrence during El Niño development and the enhanced role of TCs in WWB generation are robust features of the 4.5KEP and 2KEP experiments, which should significantly contribute to the increased $Amp_{WWB}$. Furthermore, the nonlinear response of the anomalous TC frequency to the warming is another major contributor to the nonlinear WWB response. In addition, examining the anomalous frequency of TC occurrence in the 4.5 K and EP experiments (Fig. S23), we find that the EP-enhanced differential warming pattern plays a major role in increasing the anomalous occurrence of TCs in the central tropical Pacific in the 4.5KEP experiment. We will discuss the possible causes for the increased anomalous frequency of TC occurrence in section 4.

## 4. Summary and discussion

In this study, we investigate potential changes of WWB characteristics under global warming using CAM6 atmospheric experiments. The goal of using atmospheric-only experiments (rather than coupled experiments) is to isolate the effects of warming patterns on WWBs, since in a coupled setting, it is difficult to establish causality, i.e., whether WWBs change because ENSO changes or vice versa.

Eastern equatorial Pacific SST warming patterns derived from CESM2 under two scenarios SSP5-8.5 and SSP2-4.5 are utilized, i.e., 4.5KEP and 2KEP, together with more idealized patterns—a uniform warming and an EP-enhanced differential SST change in the Indo-Pacific (no mean warming). Note that in the 2KEP experiment, not only the magnitude of uniform warming is weaker than that of the 4.5KEP experiment but the EP-enhanced differential SST change is also weaker. Five experiments, each consisting of three ensemble members, are conducted to understand the effects of the EP-enhanced warming pattern on WWBs. We find that imposing the EP-enhanced warming results in several dynamical effects: 1) during the onset stage of El Niño (December–April),

the mean longitude of WWBs ($lon_{WWB}$) shifts westward by 19° and 17° of longitude in the 4.5KEP and 2KEP experiments, respectively; there are no significant changes in the integrated WWB intensity ($Amp_{WWB}$); 2) during the development stage of El Niño (May–November), the average $Amp_{WWB}$ increases by 41% and 79% in the 4.5KEP and 2KEP experiments, respectively, while the average $lon_{WWB}$ does not change significantly; and 3) a higher percentage of WWBs are associated with TCs in the 4.5KEP and 2KEP experiments, as compared to the control, suggesting a more important role of TCs in WWB generation.

The results suggest that under global warming, WWBs may strengthen considerably during El Niño development, which could favor stronger El Niño events, given the critical role of WWBs in El Niño dynamics (e.g., Fedorov 2002; Fedorov et al. 2003; Lengaigne et al. 2004; Lopez et al. 2013; D. Chen et al. 2015; Fedorov et al. 2015; Hu and Fedorov 2019; Yu and Fedorov 2020, 2022; Jin et al. 2020; Heede and Fedorov 2023b). Moreover, the response of WWBs to warming may be nonlinear, i.e., WWB intensity increases more in the 2KEP experiment than in the 4.5KEP experiment.

The implication of the potential westward shift of WWBs during El Niño onset, as suggested in the current study, however, needs to be further investigated, although previous studies (e.g., Hayashi and Watanabe 2019) have suggested that different WWB longitudes during El Niño onset do not necessarily affect the evolution of El Niño events. On the other hand, WWBs that occur one after another during El Niño evolution are known to shift eastward as part of the Bjerknes feedback (e.g., Yu and Fedorov 2022), so that their location may still play a role in the overall dynamics.

To better understand the causes of these WWB changes, we qualitatively analyze three major factors affecting WWBs, including the El Niño conditions mean equatorial westerly wind stress anomalies, MJO westerly wind stress anomalies, and TC activity near the equator (with 15°N/S). It appears that the weakened background trade winds in the 4.5KEP and 2KEP experiments, due to the EP-enhanced warming pattern, have large influences on all three WWB sources. This is because westerly wind stress anomalies to the leading order can be approximated as a product of westerly wind speed anomalies and the background mean wind speed. The weakened trade winds, especially in the central equatorial Pacific (Figs. S9g,h), therefore tend to reduce WWB wind stress anomalies and WWB intensity and prevent WWBs from shifting eastward.

Note that other processes such as convectively coupled Rossby waves (Puy et al. 2016), cold surges (Chu 1988; Chongyin 1990; Yu and Rienecker 1998), and southerly wind anomalies from the Australian continent (Chen and Wu 2000; Xu and Chan 2001) could also contribute to WWB generation, which would need to be addressed by future studies. Also note that for brevity, throughout the paper, we refer to the warm-core cyclonic convective disturbances that resemble TCs in the model as TCs. However, one needs to keep in mind that the relatively coarse resolution models (i.e., 0.9° × 1.25°) cannot simulate category 4–5 TCs (Murakami et al. 2015; Roberts et al. 2018), which may explain why the simulated WWBs are
generally weaker and not as tightly linked to TCs than in the observations. However, as shown in Figs. S13 and S22, the modulation of TC occurrence by El Niño is relatively well simulated in our experiments, in agreement with previous GCM studies (e.g., Wang et al. 2014; Han et al. 2016), which is important for WWB generation (Lian et al. 2018a; Liang and Fedorov 2021).

We find that during the onset stage of El Niño (December–April), the seasonal-mean positive precipitation, westerly wind speed, and wind stress anomalies in the 4.5KEP and 2KEP (not shown) experiments are stronger than those in the control, which by themselves should increase $Amp_{WWB}$. During the development stage of El Niño (May–November), the precipitation and westerly wind speed anomalies are also enhanced in the 4.5KEP and 2KEP experiments. However, as the climatological trade winds weaken, westerly wind stress anomalies averaged in the western–central equatorial Pacific (150°E–150°W, 3°N–3°S) remain largely unchanged in the 4.5KEP experiment and increase by a less extent in the 2KEP experiment. Moreover, the center of the westerly wind stress anomalies shifts westward in the 4.5KEP experiment, whose implications for El Niño projections remain to be explored. Such a westward shift of the seasonal mean westerly wind stress anomalies is also observed in CMIP6 CESM2-coupled experiments under the SSP5-8.5 scenario (not shown).

When analyzing changes of the MJO in the 4.5KEP experiment, we also pay attention to how they are affected by the 4.5-K uniform warming and the EP-enhanced differential SST change, respectively. We find that the 4.5-K uniform warming enhances the MJO OLR and wind speed variability during both the onset and development stages of El Niño. The EP-enhanced differential SST change, however, reduces the MJO variability during El Niño onset but enhances the MJO OLR variability with little effect on the strength of the MJO westerly wind speed anomalies during El Niño development. Similar to the 4.5-K experiment, the MJO precipitation and wind speed variabilities both increase in the 4.5KEP experiment during El Niño evolution. Yet, due to the relaxation of trade winds, the strength of the MJO westerly wind stress anomalies decreases during El Niño onset, which may cause a slight reduction of $Amp_{WWB}$, and remains largely unchanged during El Niño development. The MJO changes in the 2KEP experiment are, in general, consistent with those in the 4.5KEP experiment. It is important to note that our analysis has primarily focused on effects of the changes in the strength of MJO winds. However, variations in the zonal extent of MJO wind anomalies could also influence future changes in $Amp_{WWB}$.

Changes in TC activity are found to be critical for the changing WWB characteristics, especially during El Niño development. During the onset stage of El Niño, the increased frequency of TC occurrence in the western North Pacific and South Pacific relative to the climatology overall does not change much in the 4.5KEP and 2KEP experiments compared to the control. Despite the eastward shift of the enhanced TC activity, $lon_{WWB}$ does not follow, which is likely due to the weakening of equatorial trade winds. In contrast, during the development stage of El Niño, the average enhanced frequency of TC occurrence in the WWB region increases

considerably, by about 40% in the 4.5KEP experiment and by 80% in the 2KEP experiment. A higher percentage of WWBs are also associated with TCs.

The latter result can be related to two factors. First, the low-level relative vorticity associated with the stronger westerly wind speed anomalies is stronger in the perturbation experiments (Figs. 9e,h), which favors a stronger TC genesis (e.g., Wang and Chan 2002; Emanuel and Nolan 2004; Camargo et al. 2007). Second, TCs have a longer life span: since more TCs are generated in the southeast quadrant of the western North Pacific (Figs. 11b,d; also see, e.g., Murakami et al. 2011), they tend to travel longer over the warm ocean before making landfall or encountering colder midlatitude water (Wang and Chan 2002). The strongly enhanced and eastward-shifted anomalous TC activity should play a critical role in the amplified and slightly eastward-shifted WWBs during the development stage of El Niño.

Note that for the current atmospheric GCM-type experiment setting, the imposed ENSO-related SST anomalies are fixed across experiments; however, the characteristic pattern of El Niño anomalies as well as their amplitude will most likely change under global warming. Thus, the question of how the identified changes in WWB characteristics will impact future ENSO remains to be explored in an atmosphere-ocean coupled setting.

We would also like to note that there exists a large intermodel spread of the east-west Pacific warming rate difference in CMIP6 (Heede and Fedorov 2021), and CESM2 is at the higher end. Considering the critical role of the EP-enhanced warming pattern in modifying WWB characteristics, one could examine the potential changes of WWBs using warming patterns derived from different CMIP6 models for a comprehensive comparison of WWB projections. For example, during El Niño onset, the marginal increase in the MJO wind speed variability in the 4.5KEP experiment relative to the control could turn into a decrease when using other models. Overall, the results of the current study highlight the importance of correctly projecting the tropical Pacific warming pattern under global warming.

Finally, we would like to note that although CESM2 simulations of ENSO and the MJO have improved considerably as compared to CESM previous versions (Danabasoglu et al. 2020), there still exist model biases in simulating, for example, the correct amplitude of the MJO or the amplitude and location of WWBs. Consequently, projections of WWBs and El Niño changes may benefit from higher-resolution model simulations, which could reduce some of the model mean-state biases (e.g., the cold tongue bias; see Wengel et al. 2021; Wang et al. 2022).

*Acknowledgments.* The authors acknowledge the comments from three anonymous reviewers and the editor, which helped improve this manuscript. Yu Liang acknowledges the computing resources provided by the Yale center for research computing. This research has been supported in part by grants to A. V. F. from NASA (80NSSC18K1396 and 80NSSC21K0558), NOAA (NA20OAR4310377), and DOE (DE-SC0023134).
Additional support has been provided by ARCHANGE project (ANR-18-MPGA-0001, France). Both authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Yu Liang. The first draft of the manuscript was written by Yu Liang, and both authors commented on previous versions of the manuscript. Both authors read and approved the final manuscript.

*Data availability statement.* CESM2 data in CMIP6 used for the boundary conditions of the four AMIP-type experiment can be obtained at https://esgf-node.llnl.gov/projects/cmip6/. The CESM2 code can be accessed at https://www.cesm.ucar.edu/models/cesm2/. The dataset on which this paper is based is too large to be retained or publicly archived with available resources. Documentation and methods used to support this study are available from Yu Liang at Yale University.

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