# Equatorial Pacific Cold Tongue Bias Degrades Simulation of ENSO Asymmetry due to Underestimation of Strong Eastern Pacific El Niños

Tobias Bayr, Joke F. Lübbecke, Jérôme Vialard, Mojib Latif

► To cite this version:

Tobias Bayr, Joke F. Lübbecke, Jérôme Vialard, Mojib Latif. Equatorial Pacific Cold Tongue Bias Degrades Simulation of ENSO Asymmetry due to Underestimation of Strong Eastern Pacific El Niños. Journal of Climate, 2024, 37, pp.6167-6182. 10.1175/JCLI-D-24-0071.1. insu-04821262

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# Equatorial Pacific Cold Tongue Bias Degrades Simulation of ENSO Asymmetry due to Underestimation of Strong Eastern Pacific El Niños

TOBIAS BAYRO,ª JOKE F. LÜBBECKE,ª JÉRÔME VIALARD,ᵇ AND MOJIB LATIFª,ᶜ

(Manuscript received 6 February 2024, in final form 24 July 2024, accepted 17 August 2024)

**ABSTRACT:** El Niño-Southern Oscillation (ENSO) exhibits a considerable asymmetry in sea surface temperature anomalies (SSTa), as El Niño events tend to be stronger and centered further east than La Niña events. Here, we analyze ENSO asymmetry in observations and preindustrial control integrations of 32 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Observations indicate a significant link between strong eastern Pacific (EP) El Niño events and the ENSO amplitude and asymmetry. The large CMIP6 database confirms this strong link. Most CMIP6 models suffer from an equatorial Pacific cold SST bias. This cold tongue bias hinders the southward migration of the ITCZ toward the equator over the eastern equatorial Pacific, which is characteristic of strong EP El Niño events in observations. Therefore, many models underestimate the eastern equatorial Pacific rainfall anomalies and simulate a wind stress feedback over the western Pacific that is too weak and too far west. As a result, the cold tongue bias exerts a strong control on the climate models' ability to generate strong EP El Niño events and therefore on the ENSO overall amplitude and asymmetry. We discuss the relevance of these results in view of a potential increase of strong EP El Niño events under global warming.

**SIGNIFICANCE STATEMENT:** El Niño and La Niña are asymmetric, as El Niño events tend to be stronger and further east than La Niña events. Due to an equatorial Pacific cold tongue bias with too cold SSTs, the simulation of ENSO asymmetry is degraded in many climate models participating in CMIP6. Here, we show how the cold tongue bias influences ENSO asymmetry. The cold bias hampers the simulation of strong eastern Pacific El Niños by making it more difficult for SST to exceed the threshold for deep atmospheric convection over the eastern equatorial Pacific. Recent research indicates that climate models with a realistic ENSO asymmetry agree on the projected ENSO under global warming. The results of this study suggest that reducing the cold tongue bias has the potential to enhancing the reliability of future ENSO projections.

**KEYWORDS:** Pacific Ocean; Atmosphere-ocean interaction; ENSO; Nonlinear dynamics; Coupled models; Model errors

## 1. Introduction

El Niño-Southern Oscillation (ENSO) is the dominant tropical climate mode on interannual time scales, with major environmental and socioeconomic impacts in the Pacific region and beyond (McPhaden 1999; McPhaden et al. 2020). El Niño, its warm phase, is associated with higher than normal sea surface temperature (SST) in the central equatorial Pacific (CEP) and/or eastern equatorial Pacific (EEP). The higher SST increases the atmospheric convection over the CEP and weakens the easterlies over the western equatorial Pacific (WEP) (Gill 1980; Philander 1990). The latter in turn leads to a reduction of the thermocline slope across the equatorial Pacific, where a deeper thermocline in the EEP amplifies the initial warming (Neelin et al. 1998).

During La Niña, the cold phase of ENSO, the situation is largely opposite to that during El Niño. There are, however, noticeable differences between El Niño and La Niña. SST anomalies (SSTa) tend to be stronger and located further to the east during El Niño than during La Niña (Hoerling et al. 1997; Kang and Kug 2002; Takahashi et al. 2011; Dommenget et al. 2013; Capotondi et al. 2015; Timmermann et al. 2018). Further, El Niño events can either have peak SSTa in the central Pacific (CP) or eastern Pacific (EP), and EP events tend to have larger amplitudes than CP events (Capotondi et al. 2015; Timmermann et al. 2018), while such diversity is much less marked for La Niña events.

ENSO is thus asymmetric, and the asymmetry is attributed to various nonlinearities in the atmospheric and oceanic feedbacks (see, e.g., the review by An et al. 2020). The amplifying zonal wind feedback is stronger during El Niño than during La Niña (Frauen and Dommenget 2010; Takahashi and Dewitte 2016; Karamperidou et al. 2017), as well as the damping short-wave feedback (Bellenger et al. 2014; Bayr et al. 2021). These atmospheric nonlinearities are largely related to the nonlinearity of deep convection that causes a larger convective response during El Niño than during La Niña (Lloyd et al. 2012; Cai et al. 2014; Geng et al. 2020; Srinivas et al. 2022). Other atmospheric

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-24-0071.s1.

Corresponding author: Tobias Bayr, tbayr@geomar.de

<footnote>ª GEOMAR Helmholtz Centre for Ocean Research Kiel, Wischhofstr, Kiel, Germany</footnote>

<footnote>ᵇ LOCEAN-IPSL, IRD-CNRS-Sorbonne Universités-MNHN, Paris, France</footnote>

<footnote>ᶜ Faculty of Mathematics and Natural Sciences, Christian-Albrechts-University of Kiel, Kiel, Germany</footnote>
nonlinearities also contribute to the ENSO asymmetry such as state-dependent stochastic forcing by westerly wind bursts (Jin et al. 2007; Levine et al. 2016; Hayashi and Watanabe 2017). Oceanic processes also contribute to ENSO asymmetry, for example, nonlinear advection related to tropical instability waves at subseasonal frequencies and nonlinear dynamical heating at low frequency (Jin et al. 2003; Imada and Kimoto 2012; Hayashi and Jin 2017). Other oceanic mechanisms of ENSO asymmetry include a stronger subsurface-surface coupling (Lübbecke and McPhaden 2017) and a larger dynamical ocean response per unit zonal wind stress change due to the eastward-shifted wind feedback during El Niño (Kim et al. 2015; Im et al. 2015). A study with an idealized ENSO model suggests a dominant role of the nonlinearity in atmospheric deep convection that in turn is driving oceanic nonlinearities (Geng et al. 2019). Finally, teleconnections from the tropical Indian and Atlantic Oceans (Okumura and Deser 2010; An and Kim 2018) and the extratropics also may contribute to the ENSO asymmetry (Li et al. 2007; Wu et al. 2010).

Despite the recent progress in understanding and simulating ENSO, most state-of-the-art climate models still underestimate ENSO asymmetry (Guilyardi et al. 2009b; Dommenget et al. 2013; Bellenger et al. 2014; Planton et al. 2021). The causes are under debate, but recent studies have shown that mean-state biases in climate models play an important role. The cold equatorial SST bias (cold tongue bias) hampers a realistic simulation of the ENSO dynamics by shifting the rising branch of the Pacific Walker circulation (PWC) too far to the west (Bayr et al. 2018, 2020; Zhao and Sun 2022). As a consequence, the amplifying wind and the damping heat flux feedbacks are both too weak, giving rise to error compensation and biased ENSO dynamics (Guilyardi et al. 2009b; Lloyd et al. 2009, 2011, 2012; Kim et al. 2014; Bayr et al. 2019, 2021; Jiang et al. 2020, 2021; Planton et al. 2021). Even though the simulation of ENSO became more realistic from CMIP5 to CMIP6, there was on average no significant improvement of the cold tongue bias and ENSO asymmetry (Hayashi et al. 2020; Planton et al. 2021).

The cold tongue bias is a long-standing problem in climate models and caused by several factors including too strong mean easterly zonal wind stress and oceanic equatorial upwelling (Davey et al. 2002; Guilyardi et al. 2009b). Significant biases are already observed in the individual components of the climate models, which are amplified by atmosphere-ocean coupling (Vannière et al. 2013; Zhang et al. 2023). Too little mean surface shortwave radiation due to too large mean cloud cover and optical thickness also contributes to the cold tongue bias (Guilyardi et al. 2009a; Vannière et al. 2013). Further, subtropical wind biases also seem to play a role, as they influence equatorial SSTs via the ocean circulation (Burls et al. 2017; Zhang et al. 2023).

Earlier studies provided indications that the cold tongue strength influences the ENSO asymmetry via the strength of the atmospheric convection during ENSO events (Lloyd et al. 2012; Bellenger et al. 2014; Bayr et al. 2018, 2020; Zhao and Sun 2022). Here, we investigate in more detail the importance of the spatial asymmetry of the atmospheric feedbacks during strong EP El Niños for ENSO asymmetry and how ENSO

asymmetry is degraded in climate models by the cold tongue bias. While amplitude asymmetries of the atmospheric feedbacks are relatively well described in the literature (e.g., Frauen and Dommenget 2010; Lloyd et al. 2012; Bellenger et al. 2014; Takahashi and Dewitte 2016; Karamperidou et al. 2017), spatial asymmetries are harder to measure and less discussed. To assess and quantify them, we use the center of heat index (CHI; Giese and Ray 2011), which represents the amplitude-weighted mean location of a pattern. To elucidate how the equatorial Pacific cold SST bias influences the atmospheric convective response during strong EP El Niño events and degrades ENSO asymmetry, we investigate observations and 500-yr-long preindustrial control (pictl) simulations from CMIP6 models (Eyring et al. 2016). An added value of our paper is also that the ENSO amplitude and asymmetry are controlled by the occurrence rate of strong El Niño events in models, which was already indicated from observations by Santoso et al. (2017). This enables us to clearly link the cold tongue bias with the underestimation of strong El Niño events and therefore with the weaker ENSO asymmetry in CMIP6.

The paper is organized as follows: We describe the data and methods in section 2. We then show the importance of the zonally asymmetric atmospheric convective response for ENSO asymmetry in observations, which further suggests a strong link between ENSO amplitude and asymmetry and strong EP El Niño events (section 3). In section 4, we explain the relationship of the spread in ENSO asymmetry in the CMIP6 models to the equatorial Pacific cold SST bias and the influence of the bias on the ability to simulate a realistic convective response over the EEP and strong EP El Niños. The study is concluded with a summary and a discussion in section 5.

## 2. Data and methods

Observed SSTs for the period 1880–2019 are taken from HadISST (Rayner et al. 2003), ERSST.v5 (Huang et al. 2017), Centennial Observation-Based Estimates of SSTs (COBE-SSTv2) (Ishii et al. 2005), and Kaplan SST (Kaplan et al. 1998). We only use the SST data from 1880 onward, because of the large uncertainties in SST due to sparse tropical Pacific observations before that date (Bunge and Clarke 2009). The atmospheric variables, i.e., zonal wind stress (TAUX) and precipitation (PR) are taken from ERA5 reanalysis over the period 1970–2019 (Hersbach et al. 2020). The subsurface oceanic potential temperature is taken from ORAS5 reanalysis over the period 1970–2019 (Zuo et al. 2019). The thermocline depth is estimated from the depth of the 20°C isotherm (Z20).

Additionally, we use 500 years from 32 preindustrial control (pictl) simulations from the CMIP6 database (see list in Fig. 5). SST, TAUX, and PR from the models are interpolated onto a common 2.5° × 2.5° regular grid.

We use the following common index regions: Niño-3 (90°–150°W and 5°S–5°N), Niño-3.4 (120°–170°W and 5°S–5°N), and Niño-4 (160°E–150°W and 5°S–5°N). Monthly mean values are used in all analyses. For the long datasets of observed SST and the pictl runs, we calculate anomalies with respect to a 30-yr running climatology. For the shorter datasets of atmospheric and oceanic reanalysis data, we use anomalies relative
<figure><img src="image_2.png" /></figure>

FIG. 1. Composites of observed SSTa (a) during El Niño events, (b) during La Niña events (multiplied by −1 for a better comparison), and (c) in the difference between El Niño and La Niña: SSTa in Niño-3.4 > ±1.2 stdv is used as the selection criterion for ENSO months; the numbers given in the header of (a) and (b) are the amplitude and longitude of the CHI and in (c) the east (80°–140°W, 5°S–5°N) minus west (140°E–160°W, 5°S–SN) difference as indicated by the boxes; (d) the 20-yr running standard deviation of SSTa in the Niño-3.4 region; the dashed line is a sixth-order polynomial fit on the 20-yr running standard deviation of the ensemble mean, representing the long-term change in ENSO amplitude; (e) CHI of SSTa for each ENSO month of the ensemble mean; the blue color indicates the LOW ENSO amplitude period 1910–69, while the red color indicates the HIGH ENSO amplitude periods 1880–1909 and 1970–2019; the blue and red lines are the mean over 10° chunks for El Niño and La Niña for LOW and HIGH period, respectively; note that the periods with higher amplitude are plotted first to highlight the differences between HIGH and LOW periods; (f) the ENSO amplitude on the x axis vs the total asymmetry measured by east minus west difference [as shown in (c)] on the y axis in four observational datasets and ensemble mean for 20-yr segments as given in the legend; the black line is the regression line and the correlation coefficient is given as number, with three stars indicating a statistical significant correlation on a 99% confidence level.

to the climatology calculated over the whole period and remove a quadratic trend from the anomalies. Using detrended anomalies from a fixed climatology for all datasets has only minor effects on the results.

ENSO amplitude is measured by the standard deviation (stdv) of the SSTa in the Niño-3.4 region, as the SSTa in the Niño-3.4 region represents both El Niño and La Niña events properly (Trenberth 1997). To calculate the ENSO asymmetry, we consider composites of El Niño and La Niña events (Figs. 1a,b, La Niña composite multiplied by −1), with the monthly SSTa in the Niño-3.4 region exceeding +1.2 stdv as the selection criterion for El Niño months, and <−1.2 stdv for La Niña months (Dommenget et al. 2013). All months are considered for the calculation of the El Niño and La Niña composites as many climate models exhibit a biased ENSO phase locking (Wengel et al. 2018). The sum of the El Niño (+) and La Niña (−) composites is displayed in Fig. 1c, depicting a zonal dipole that exemplifies both the amplitude and spatial asymmetry. A joint measure of the ENSO amplitude and spatial asymmetry, hereafter termed total asymmetry, is calculated from Fig. 1c as the average over the eastern box (140°–80°W, 5°S–5°N) minus the average over the western box (140°E–160°W, 5°S–5°N) (Dommenget et al. 2013).

Separate measures of the spatial and amplitude asymmetry are obtained from the CHI (Giese and Ray 2011). As the CHI does not rely on a fixed box, it can capture zonal shifts of the patterns due to mean state biases in the climate models. The CHI longitude is defined as the center of mass of the SSTa pattern calculated as the average over all longitudes with SSTa exceeding 0.5 stdv of the Niño-3.4 index. The CHI amplitude is the average amplitude over all grid points that exceed 0.5 stdv of the Niño-3.4 index. The amplitude asymmetry is defined as the difference between the CHI amplitude of the El Niño pattern minus the CHI amplitude of the La Niña pattern. The spatial asymmetry is defined as the difference between the CHI longitude of the El Niño pattern minus the CHI longitude of the La Niña pattern.

The same method is applied to calculate the spatial, amplitude, and total asymmetries for TAUX, PR, and Z20. For these variables, a threshold of one-third of the maximum pattern value is used to define the area over which the center and amplitude are calculated. The boxes used to calculate the total asymmetry for each variable in the reanalysis data are given in Table 1.

The ENSO asymmetry in the CMIP6 models calculated by the above method is very similar to that using the nonlinear
<table><caption>TABLE 1. List of the boxes to calculate the total asymmetry of the feedbacks in observations/reanalysis data and CMIP6 models.</caption><thead><tr><th>Variable</th><th>Western box</th><th>Eastern box</th></tr></thead><tbody><tr><td>SST</td><td>140°E–160°W, 5°S–5°N</td><td>140°–80°W, 5°S–5°N</td></tr><tr><td>TAUX</td><td>120°–160°E, 5°S–5°N</td><td>180°–140°W, 8°S–2°N</td></tr><tr><td>PR</td><td>130°–170°E, 5°S–5°N</td><td>180°–140°W, 5°S–5°N</td></tr><tr><td>Z20</td><td>150°E–120°W, 5°S–5°N</td><td>110°–80°W, 10°S–5°N</td></tr></tbody></table>

relation of the principal components of the first and second EOF of equatorial Pacific SSTa (correlation *r* = 0.92, Fig. S1a in the online supplemental material), as used, e.g., in Cai et al. (2015, 2021) or to that calculated by the skewness difference in SSTa between the Niño-3 and Niño-4 region (*r* = 0.88, Fig. S1b). The advantage of our definition is that our method can also be easily applied to other variables like TAUX, PR, or Z20, and amplitude asymmetry and spatial asymmetry can be separated by calculating the CHI of the composite pattern. Further, we note that the observed ENSO asymmetry used here may be lower than that in other studies (e.g., Hayashi et al. 2020), as we calculate ENSO asymmetry over the period 1880–2019, which covers high and low ENSO asymmetry periods, and not only over the high asymmetry period of the last four decades.

We distinguish between EP and CP El Niño events by using an adapted trans-Niño index (TNI; Trenberth and Stepaniak 2001), measuring the normalized SSTa difference between the eastern (120°–80°W, 5°S–5°N) and western Pacific (Niño-4 region). A larger eastern box than in Trenberth and Stepaniak (2001) is used to account for the weaker SST variability close to the South American coast observed in many climate models. A positive TNI indicates EP El Niños and a negative TNI indicates CP El Niños. Strong EP El Niño months are defined as EP El Niño months during which the SSTa in the Niño-3 region is larger than 1.5 K, and the fraction of strong EP El Niño months calculated as the number of strong EP El Niño months divided by the total number of El Niño months including CP and EP El Niños.

To calculate the SST bias in the CMIP6 models, we use relative SST, i.e., SST minus the tropical Pacific mean SST (120°E–80°W, 30°S–30°N). This helps to account for the difference in the mean SST between the observations and pictl runs of the CMIP6 ensemble. Such differences can arise from different greenhouse gas concentrations or model differences. Further, the tropical atmospheric convection is more strongly related to relative SSTs than absolute SSTs (Bayr et al. 2018; Izumo et al. 2020).

### 3. ENSO asymmetry in observations

Recent studies indicate the importance of strong EP El Niños for the observed ENSO asymmetry (see, e.g., the reviews of Capotondi et al. 2015; Santoso et al. 2017; Timmermann et al. 2018; An et al. 2020). Here, special attention is paid to the contribution of the spatially asymmetric atmospheric convective response to ENSO asymmetry.

As pointed out above, on average, El Niño events are stronger and located further east than La Niña events (Figs. 1a,b), as demonstrated by the amplitude and longitude of the CHI, namely, +1.3 K and 129°W for El Niño and −1.0 K and 140°W for La Niña. The sum of the two composites (Fig. 1c) displays a positive pole in the east close to the South American coast and a negative horseshoe-like pattern surrounding the positive pole. ENSO asymmetry is measured by the difference between the eastern and western pole (the two index boxes are indicated in Fig. 1c), as suggested by Dommenget et al. (2013).

The diversity of El Niño and La Niña events is displayed by a scatterplot depicting the amplitude and longitude of the CHI for each El Niño and La Niña month during 1880–2019 (Fig. 1e). We note that Fig. 1e is based on the ensemble mean of the four observational SST datasets, but the picture is very similar in the individual datasets (Fig. S2). Figure 1e exhibits a horn-shaped pattern during El Niño months, with increasing amplitude as the SST pattern moves eastward and a peak around 120°W. This distribution reflects the larger amplitudes of EP relative to CP El Niño events, with the largest amplitudes observed during the strong EP El Niños of 1982–83, 1997–98, and 2015–16 (indicated in Fig. 1e by the triangles, stars, and squares, respectively). The strong EP El Niño of 2015–16 (red squares) had its center around 128°W and a smaller amplitude than the strong EP El Niño of 1997–98, consistent with the smaller PR anomalies over the Niño-3 region in 2015–16 (Santoso et al. 2017). We note that all strong El Niño events are EP events, but not all EP events are strong (e.g., Santoso et al. 2017). The intensity of La Niña events on the other hand weakly depends on the location of their center.

The multidecadal modulation of the ENSO amplitude is associated with a period of generally smaller ENSO amplitude (LOW period: 1910–69 highlighted in blue in Figs. 1d,e) than during the previous and following epoch (HIGH periods: 1880–1910 and 1970–2019 highlighted in red in Figs. 1d,e). HIGH (LOW) periods have been chosen as the decades with a 20 year running standard deviation of Niño-3.4 SSTa being higher (lower) than 0.75 K (Fig. 1d). Events during the LOW period are indicated in blue and those during the HIGH period are in red in Fig. 1e (the blue and red lines are the means over 10° longitude chunks). El Niño events over the CEP have similar amplitudes during the LOW and HIGH periods, but the LOW period is missing the strong, eastward-shifted EP El Niño events (Fig. 1e). The reason behind the increased ENSO amplitude in HIGH is thus the strong EP El Niño events. Consequently, the horn-shaped pattern is well developed during HIGH but less pronounced during LOW. HIGH includes seven strong EP El Niño events (1888–89, 1896–97, 1902–03, 1972–73, 1982–83, 1997–98, and 2015–16), while LOW contains only one (1930–31). The La Niña months in the two HIGH periods exhibit slightly larger amplitudes around 145°W than the LOW period, but the difference is not as pronounced as for the El Niño months.

The ENSO amplitude has a statistically significant correlation (*r* = 0.59) with the ENSO asymmetry across decades (Fig. 1f), suggesting a relation with strong EP El Niño events. For example, the 1980–99 period with the two strongest
<figure><img src="image_3.png" /><figcaption>FIG. 2. As in Figs. 1a-c, but for composites of atmospheric and oceanic variables in ERA5/ORAS5 for the second HIGH period 1970-2019: (a)-(c) zonal wind stress anomalies (TAUXa), (d)-(f) precipitation anomalies (PRa), and (g)-(i) thermocline depth anomalies represented by the 20°C isotherm (Z20a); the composites are normalized by the mean SSTa over Niño-3.4 of all El Niño and La Niña months.</figcaption></figure>

EP El Niños is outstanding in terms of both ENSO asymmetry and amplitude (Fig. 1f). Thus, in observations, epochs with a high ENSO amplitude and asymmetry correspond to decades with more strong EP El Niño events. This suggests that the more frequent occurrence of strong EP El Niño events plays a substantial role in multidecadal variations of the ENSO amplitude and asymmetry.

To relate the SST asymmetries to atmospheric and subsurface ocean processes, we compute El Niño and La Niña composites of zonal wind stress anomalies (TAUXa), precipitation anomalies (PRa), and thermocline depth anomalies (Z20a), utilizing re-analysis products for 1970–2019 (second HIGH period). The composites are normalized by the average Niño-3.4 SSTa to obtain feedbacks (i.e., units of atmospheric/oceanic response per degree Celsius of Niño-3.4 change). During El Niño events, the warm SSTa is associated with an eastward shift of the ascending branch of the PWC with enhanced PR over the CEP and westerly TAUXa over the WEP (Figs. 2a,d). This goes along with a deeper Z20 in the EEP that tends to reinforce the SST warming in the east (Fig. 2g).

During La Niña events, the cold SSTa is associated with a westward shift of the PWC and easterly TAUX anomalies over the WEP (Figs. 2b,e). The thermocline in the EEP is anomalously shallow tending to reinforce the cold SST anomalies in the east (Fig. 2h). The TAUXa, PRa, and Z20a anomalies per unit SSTa change are larger and shifted eastward (by 11°, 27°, and 15°, respectively) for El Niño relative to La Niña (Fig. 2 left and middle columns, respectively). As a result, the difference between El Niño and La Niña exhibits a dipole pattern in all three feedbacks (Figs. 2c,f,i).

To emphasize the relation between ENSO asymmetry and ENSO diversity, we show the diversity of the feedbacks with the CHI in Fig. 3 and then depict separate composites for CP El Niño and strong EP El Niño in Fig. 4. The CHI amplitude

and center of the feedbacks in each El Niño and La Niña month are calculated from ERA5/ORAS5 over the period 1970–2019 (second HIGH period). The CHI of the SSTa (Fig. 3a) is very similar to that depicted for the full period in Fig. 1e. The amplitude and center distribution of the TAUXa is rather different between El Niño and La Niña events. The amplitude distribution for El Niño events exhibits a marked maximum around 160°W, while the distribution for La Niña events exhibits a less-marked minimum around 175°E (Fig. 3b). The PRa also displays remarkable asymmetries (Fig. 3c). The spread in the center and the overall amplitude is much larger for El Niño than for La Niña events. Especially, the strong EP El Niño months stick out with their large amplitude and easternmost centers in PRa, explaining the far eastern centers of TAUXa during these events. The Z20a, too, exhibits a large amplitude asymmetry (Fig. 3d). Here, a saturation effect may be important: the climatological thermocline is shallowest in the east with the ocean surface limiting negative Z20a during La Niña. The strong EP El Niño events clearly stick out regarding Z20, with the largest amplitudes and centers in the far EEP.

We next investigate the spatial differences in the atmospheric anomalies during La Niña, CP El Niño, and strong EP El Niño events using full-field composites (shading in Figs. 4a–f) at the peak phase of the ENSO events [November–February (NDJF)]. During La Niña events, there is a pronounced cold tongue (Fig. 4a), and the ITCZ and the South Pacific convergence zone (SPCZ) are relatively far away from the equator (Fig. 4d). During CP El Niño events, the cold tongue is less well developed (Fig. 4b), and the ITCZ and the SPCZ are closer to the equator, inducing considerable PR over the Niño-4 region (Figs. 4e,k). During strong EP El Niño events, the cold tongue is even weaker (Fig. 4c), with a nearly vanishing meridional SST gradient over the EEP (Fig. 4j), and the ITCZ almost reaches the equator with PR of more than 5 mm day⁻¹
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>FIG. 3. (a) As in Fig. 1e, but for the CHI of observed SSTa for the HIGH period 1970–2019; (b) as in (a), but for TAUX in ERA5; (c) as in (b), but for PR in ERA5; (d) as in (a), but for Z20 in ORAS5; in (b)–(d), a 3-month running mean was applied before center and amplitude calculation.</figcaption></figure>

over the Niño-3 region (Figs. 4f,k). The southward shift of the ITCZ toward the equator during strong EP El Niño events is possible, because the SST crosses the 28°C threshold of deep convection (white line in Fig. 4c) in large parts of the Niño-3 region.

The composites of the SSTa, PRa, and TAUXa for La Niña, CP El Niño, and strong EP El Niño events (contours in Figs. 4a–f and shadings in Figs. 4g–i) demonstrate that there is hardly any spatial asymmetry between La Niña and CP El Niño events, while there is considerable spatial asymmetry between La Niña and strong EP El Niño events. The southward migration of the ITCZ toward the equator in the east (Fig. 4f) causes the eastward shift of the PRa by 36° and TAUXa by 15° during strong EP El Niño relative to La Niña events (Fig. 4i), which in turn supports stronger and more eastward located SSTa (Fig. 4c, Kang and Kug 2002).

In summary, the observations/reanalyses highlight the primary importance of strong EP El Niño events for ENSO amplitude and zonal asymmetry, as the asymmetry between La Niña and CP El Niño events is much weaker. Further, there are hints that the eastward shift of the atmospheric deep convection during strong EP El Niños is essential for their genesis.

## 4. ENSO asymmetry in CMIP6 models

We now relate the biases in the ENSO asymmetry in CMIP6 models to mean-state biases. To this end, we analyze all preindustrial control (pictl) integrations of the CMIP6 models that are 500 years long to obtain a robust measure of ENSO asymmetry given the large internal variability of ENSO.

The CMIP6 models exhibit a large spread in ENSO amplitude ranging from 0.43 to 1.52 K and ENSO asymmetry ranging from -0.08 to 1.09 K (Fig. 5a). The ENSO amplitude and asymmetry are highly correlated in the models ($r$ = 0.82, Fig. 5a), and both quantities strongly depend on the fraction of strong EP El Niño events in the models ($r$ = 0.88 and 0.84, respectively; Figs. 5b,c). On the other hand, if we exclude the strong EP El Niño events from the ENSO asymmetry calculation, i.e., calculating the asymmetry between CP El Niño events and La Niña events, there is no significant correlation between ENSO amplitude and ENSO asymmetry ($r$ = 0.14, not shown), highlighting the importance of strong EP El Niño events in controlling the overall ENSO amplitude and asymmetry. Thus, there is a strong relation between the overall ENSO amplitude, asymmetry, and occurrence rate of strong EP events in the CMIP6 models, in agreement with observations.
<figure><img src="image_8.png" /><figcaption>FIG. 4. As in Fig. 2, but for composites for the months NDJF for La Niña, CP El Niño, and strong EP El Niño in observations and reanalysis data: (a)–(c) for full fields of SST (shading), the 28°C SST isotherm (thick white line) and SST anomalies (contours, ±0.4°C increment); (d)–(f) as in (a)–(c), but for full fields and anomalies of PR (±0.75 mm day⁻¹ increment); (g)–(i) as in (a)–(c), but for TAUX and only the anomalies are shown as shading. The composites of the anomalies in (d)–(i) are normalized by the mean SSTa of the Niño-3.4 region of the specific events, and the black boxes in (a)–(i) mark the Niño-4 and Niño-3 region; (j) the meridional SST gradient *dT/dy* between the region north of Niño-3 (90°–150°W, 5°–10°N) and Niño-3 and the SSTa in Niño-3 for La Niña, CP El Niño, and strong EP El Niño; (k) as in (j), but for the PR in Niño-4, Niño-3.4, and Niño-3 region. The error bars in (j) and (k) represent the 90% quantile of the individual events.</figcaption></figure>

Half of the CMIP6 models underestimate the total ENSO asymmetry, with an asymmetry of less than half of the observed, and we define these models as the LOW subensemble (blue symbols in Fig. 5a). The most realistic models in terms of ENSO amplitude and total asymmetry are defined as the models that have more than half of the observed asymmetry but an amplitude of less than 1.2 K (MEDIUM subensemble, black symbols in Fig. 5a). The models with the largest (and overestimated) ENSO amplitude (>1.2 K) are clustered into the HIGH subensemble.

The differences in ENSO asymmetry between the LOW, MEDIUM, and HIGH subensembles can also be seen in the

composites of SSTa of each subensemble (Fig. S3), where El Niño and La Niña in LOW are nearly symmetric, HIGH has the largest asymmetry, and MEDIUM is in between LOW and HIGH. This holds also for the composites of TAUXa (Fig. S4) and PRa (Fig. S5).

To further elucidate how the strong EP El Niño events determine ENSO asymmetry, we show in Fig. 6 the SSTa composites and CHI of three selected models (CanESM5, MIROC6, and CESM2-WACCM-FV2), one from each subensemble. The CanESM5 model of the WEAK subensemble (number 5 in Fig. 5) has a too weak ENSO amplitude (std = 0.64 K) and has no asymmetry (−0.04 K), and only 4% of the El Niño
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>FIG. 5. (a) ENSO amplitude on the *x* axis vs the total asymmetry of ENSO measured as in Fig. 1c on the *y* axis; (b) as in (a), but for the fraction of strong EP El Niño events on the *y* axis; (c) as in (b), but for the total asymmetry on the *x* axis. The black symbols indicate the most realistic CMIP6 models in terms of ENSO amplitude and asymmetry, i.e., that they have more than half of the observed ENSO asymmetry value and an ENSO amplitude less than 1.2 K (MEDIUM subensemble). The blue symbols indicate the models that have less than half of the observed asymmetry (LOW subensemble), and the red symbols indicate the models with an ENSO amplitude that is higher than 1.2 K (HIGH subensemble). The black line is the regression line, and the value given in the upper-left corner is the correlation for all CMIP6 models, and three stars indicate a statistically significant correlation on the 99% confidence level.</figcaption></figure>

months are strong EP El Niño months. The composites of El Niño and La Niña events are symmetric in terms of CHI amplitude and longitude (Figs. 6a,b), and there is nearly no difference in the SSTa pattern between the two phases (Fig. 6c). The CHI (Fig. 6d) does not display the horn-shaped pattern for El Niño, as this model barely simulates strong EP El Niño events.

The MIROC6 model of the MEDIUM subensemble (number 23 in Fig. 5) has a slightly higher ENSO amplitude than the observations (std = 0.96 K), but a realistic ENSO asymmetry (0.57 K) and a realistic fraction of strong EP El Niños

CMIP6

1: ACCESS-CM2

2: AWI-CM-1-1-MR

3: BCC-CSM2-MR

4: CAMS-CSM1-0

5: CanESM5

6: CESM2

7: CESM2-FV2

8: CESM2-WACCM

9: CESM2-WACCM-FV2

10: CIESM

11: CMCC-CM2-SR5

12: EC-Earth3-Veg-LR

13: FGOALS-f3-L

14: FGOALS-g3

15: GFDL-CM4

16: GFDL-ESM4

17: GISS-E2-1-G

18: GISS-E2-1-H

19: HadGEM3-GC31-MM

20: INM-CM4-8

21: INM-CM5-0

22: IPSL-CM6A-LR

23: MIROC6

24: MPI-ESM1-2-HAM

25: MPI-ESM1-2-HR

26: MPI-ESM1-2-LR

27: MRI-ESM2-0

28: NESM3

29: NorCPM1

30: NorESM2-MM

31: SAM0-UNICON

32: TaiESM1

<figure><img src="image_11.png" /></figure>

(23% of all El Niño months are strong EP El Niño months). The ENSO SSTa composite pattern has a higher amplitude by 0.3 K and a center that is 13° further east during El Niño than during La Niña (Figs. 6e,f), which is similar to observations. The CHI of El Niño has the highest amplitudes for the easternmost centers (Fig. 6h), as in observations, which explains why this model simulates a relative realistic asymmetry (Fig. 6g).

The CESM2-WACCM-FV2 model of the HIGH subensemble (number 9 in Fig. 5) is the model with the largest ENSO amplitude (std = 1.53 K) and asymmetry (1.09 K), and
<figure><img src="image_12.png" /><figcaption>FIG. 6. (a)–(c) As in Figs. 1a–c, but for composites of SSTa of the CanESM5 model as one representative of the LOW subensemble; (d) as in Fig. 1e, but for the CHI of NDJF seasonal mean anomalies of all El Niño and La Niña events of the 500-yr-long pictl run of the CanESM5 model; (e)–(h) as in (a)–(d), but for the MIROC6 model as one representative of the MEDIUM subensemble; (i)–(l) as in (a)–(d), but for the CESM2-WACCM-FV2 model as one representative of the HIGH subensemble. The stars in (d), (h), and (l) represent strong EP El Niño events and the large black dot is the CHI of the El Niño and La Niña composites as shown in the header of the first and second row.</figcaption></figure>

40% of the El Niño events are strong EP El Niño events. The ENSO SSTa composite pattern shows a realistic spatial asymmetry of 11° but a too low amplitude asymmetry of 0.1 K in comparison to observations (Figs. 6i,j). The CHI (Fig. 6l) indicates that this model simulates many strong EP El Niño events with centers in the far east as well as strong La Niña events with the highest amplitudes around 150°W, explaining why this model has the highest amplitude and asymmetry in the investigated ensemble (Fig. 6k).

The other members of the three subensembles exhibit quite similar CHI as the shown examples (Fig. S6): The LOW models have problems in simulating the high amplitudes and easternmost centers of the strong EP El Niño events and have therefore either no or only a weak horn-shaped pattern of the CHI for El Niño. Most MEDIUM models are able to simulate the horn-shaped pattern of the CHI for El Niño events and also have a realistic fraction of strong EP El Niño events. The

HIGH models are also able to simulate the horn-shaped pattern of the CHI for El Niño but tend to have a higher fraction of strong EP El Niño events, and some models also simulate high-amplitude La Niña events.

To further elucidate how the cold SST bias hampers a realistic simulation of ENSO asymmetry, we show the mean-state SST and PR biases in the LOW, MEDIUM, and HIGH subensembles (Fig. 7). The models in the LOW subensemble exhibit the largest cold tongue bias, while the bias is considerably weaker in the MEDIUM and smallest in the HIGH subensemble (Figs. 7a–c). As a result, the largest underestimation of the equatorial PR is observed in the LOW subensemble. The underestimation is less pronounced in the MEDIUM and smallest in the HIGH subensemble (Figs. 7d–f). Although the HIGH subensemble has the most realistic equatorial SST and PR, it has considerably higher SST and PR north and south of the equator relative to the observations, similar to the other subensembles (Figs. 7a–f).
Mean state biases in CMIP6 sub-ensembles

<figure><img src="image_13.png" /></figure>

FIG. 7. Mean-state bias in CMIP6 models in the LOW, MEDIUM, and HIGH subensembles. Bias in (a)–(c) relative SST (relative to the area mean tropical Pacific SST over the region 120°E–80°W, 30°S–30°N) and (d)–(f) PR. The numbers in the header are the bias in Niño-3.4 in SST and in PR over Niño-4 and Niño-3, respectively. The regions are indicated by the boxes; stippling indicates that at least 80% of the models agree on the sign of the biases.

The mean-state biases go along with biases in the signature of strong EP El Niño events in NDJF (Fig. 8). For example, the LOW subensemble still depicts a clear cold tongue during strong EP El Niño events (Fig. 8a), unlike in observations (Fig. 4c). The equatorial PR is too weak and too westward, as the SST crosses the threshold of deep convection only in the western Pacific (thick white line in Fig. 8a). Further, the ITCZ remains north of the Niño-3 region due to a too strong meridional SST gradient over the EEP (Figs. 8d,j,k). As the equatorial convective response is too far west and too weak, the TAUXa feedback is too far west and too weak as well (Fig. 8g).

During strong EP El Niño events, the MEDIUM subensembles has a weaker cold tongue than the LOW subensemble (Fig. 8b), which is still too strong relative to observations. The equatorial PR is still too small and the meridional SST gradient over the EEP is too strong, and the ITCZ is too far north of the equator (Figs. 8b,e,j,k). Moreover, the 28°C threshold of deep convection is crossed only over a small part of the EEP. Nevertheless, the equatorial PR and TAUXa are more eastward and stronger in the MEDIUM than in the LOW subensemble (Figs. 8e,h), likely explaining the higher fraction of strong EP El Niño events in the former (Fig. 5c).

The HIGH subensemble is characterized by a weak cold tongue during strong EP El Niño events, and the 28°C threshold of deep convection is crossed over large parts of the EEP. The meridional SST gradient almost vanishes over the EEP, allowing the ITCZ to migrate southward toward the equator (Figs. 8c,f,j). Both the PR and the TAUXa are relatively far in the east (Figs. 8f,i), and the occurrence of strong EP El Niño events is highest among the three subensembles (Fig. 5c).

The total ENSO asymmetry is strongly related to the zonal asymmetry of the PR feedback ($r = 0.80$, Fig. 9a). The total

ENSO asymmetry is also related to the TAUX-feedback strength in the models ($r = 0.63$, Fig. 9b). Both findings can be explained by the cold SST bias (Figs. 9c,d). First, the bias hampers the southward migration of the ITCZ toward the equator over the EEP, which results in a weaker zonal asymmetry of the PR feedback ($r = 0.62$, Fig. 9c). Second, the cold SST bias causes a too westward location of the rising branch of the mean-state PWC, leaving the TAUX feedback too weak and too far west (Bayr et al. 2018, 2020). This in turn also weakens the oceanic feedbacks and leads to overall biased ENSO dynamics (Guilyardi et al. 2009b, 2020; Bayr et al. 2019, 2021). Both factors together explain the high correlation between the cold SST bias in the Niño-3.4 region and the intermodel spread in total asymmetry of SST ($r = 0.63$, Fig. 9e). In summary, by preventing the development of deep convection over the EEP, the cold tongue bias weakens the wind-SST feedback and shifts it westward, hindering the development of strong EP events and reducing the simulated ENSO asymmetry.

5. Summary and discussion

In this study, we analyze the ENSO asymmetry in observations and climate models. As a novel approach, the center of heat index (CHI) is utilized here to investigate the spatial asymmetries of ENSO atmospheric feedbacks and to explicitly show how the cold tongue bias leads to an underestimated spatial asymmetry of the PR and wind-SST feedback in the climate models. Models with a strong cold tongue bias simulate fewer strong EP El Niño events and underestimate the ENSO asymmetry, and the variation in ENSO asymmetry found across climate models is related to the occurrence of strong EP El Niño events.
Composites of strong EP El Nino in CMIP6 sub-ensembles

<figure><img src="image_14.png" /><figcaption>FIG. 8. As in Fig. 4, but for composites of strong EP El Niños in the LOW, MEDIUM, and HIGH subensemble of the CMIP6 models. The error bars in (j) and (k) depict the 90% quantile of the spread between the individual CMIP6 models within each subensemble.</figcaption></figure>

In more detail, in observations, considerably fewer strong EP El Niño events occurred during the period 1910–69, which was associated with a weaker ENSO asymmetry than during 1880–1909 and 1970–2019 that exhibited a much stronger asymmetry. The observed spatial asymmetry in the atmospheric ENSO feedbacks is related to the eastern Pacific ITCZ’s southward migration toward the equator during EP El Niño events. Specifically, the equatorial convective response linked the southward shift of the ITCZ over the EEP, expressed in terms of the precipitation anomalies, is up to 36° further east during strong EP El Niño events than during La Niña events. La Niña and CP El Niño events are largely mirror images, indicating that ENSO asymmetry is mainly caused by the strong EP El Niño events.

Previous studies (e.g., Santoso et al. 2017) related the occurrence rate of strong El Niño events with ENSO amplitude

and asymmetry in observations, but not in models. The ENSO amplitude and asymmetry diversity in CMIP6 are also related to the fraction of strong EP El Niño events, confirming the observational results. We further investigate the relation between ENSO asymmetry and mean-state biases in CMIP6 models. The ENSO asymmetry is less than half of the observed value in 15 out of the 32 investigated models, which is a long-standing problem in climate models (Dommenget et al. 2013; Capotondi et al. 2015; Timmermann et al. 2018; Guilyardi et al. 2020; Planton et al. 2021). This can partly (r = 0.63) be explained by the equatorial cold SST bias in those models, which causes a too westward position of the rising branch of the mean-state PWC (Guilyardi et al. 2009b, 2020; Bayr et al. 2018, 2019, 2020) and hampers the ITCZ migration toward the equator over the EEP (Cai et al. 2014; Capotondi et al. 2020). These two factors explain the weaker
<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

17: GISS-E2-1-G

18: GISS-E2-1-H

19: HadGEM3-GC31-MM

20: INM-CM4-8

21: INM-CM5-0

22: IPSL-CM6A-LR

23: MIROC6

24: MPI-ESM1-2-HAM

25: MPI-ESM1-2-HR

26: MPI-ESM1-2-LR

27: MRI-ESM2-0

28: NESM3

29: NorCPM1

30: NorESM2-MM

31: SAM0-UNICON

32: TaiESM1

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

FIG. 9. As in Fig. 5, but for (a) the total asymmetry of SST on the x axis vs the spatial symmetry of the PR feedback pattern on the y axis; (b) as in (a), but for the TAUX feedback strength measured by TAUX anomalies of Niño-4 regressed on SSTa of Niño-3.4 on the y axis; (c) as in (a), but for the relative SST bias in the Niño-3.4 region on the x axis (relative to tropical Pacific area mean SST); (d) as in (b), but for the relative SST bias in the Niño-3.4 region on the x axis; (e) as in (c), but for the total asymmetry of SST on the y axis. The black line is the regression line and the value given in the upper-left corner is the correlation value for all CMIP6 models, and the three stars indicate a statistically significant correlation on the 99% confidence level.

and too westward wind-SST feedback in models with a large equatorial cold SST bias, which results in a lower fraction of strong EP El Niño events and a too small ENSO asymmetry.

Nonlinearities in the oceanic feedbacks may be also important to ENSO asymmetry (e.g., Jin et al. 2003; Imada and Kimoto 2012; Im et al. 2015; Hayashi and Jin 2017). As ENSO is a coupled atmosphere-ocean mode, it is difficult to estimate the relative contributions of atmospheric and oceanic processes to ENSO asymmetry from coupled simulations. The study of Geng et al. (2019) with an idealized model suggests that ENSO asymmetry originates from the nonlinearity of the atmospheric deep convection that subsequently induces nonlinearities of the oceanic feedbacks. This is further supported by experiments with uncoupled atmospheric and oceanic general circulation models, which indicate a dominant contribution of the nonlinearity of the atmospheric deep convection to strong El Niño events, and more generally to ENSO asymmetry (Srinivas et al. 2024). This is in line with our results that highlight the strong influence of the cold tongue bias on the nonlinearity of the atmospheric deep convection.

There is a long-lasting discussion about whether there is a relation between the cold tongue strength and ENSO amplitude and asymmetry in observations. The SST over the EEP varies considerably over decadal time scales due to the Pacific decadal oscillation (PDO), and the mean SST was warmer in the period 1980–99 and colder in the period 2000–19 (Latif and Barnett 1994; Mantua and Hare 2002; Power et al. 2021; Latif et al. 2023). The warmer period was that with the largest ENSO amplitude and asymmetry in the instrumental record, while the colder period had a considerable weaker ENSO amplitude and asymmetry (Fig. 1f). The warmer period contains also the two strongest El Niño events on record (1982–83 and 1997–98), while studies suggest that the 2015–16 El Niño could not get as strong due to colder SSTs over the EEP, which hampered the movement of the ITCZ onto the equator (Lübbecke and McPhaden 2014; Hu et al. 2017; Santoso et al. 2017). The colder SSTs over the EEP in the period 2000–19 relative 1980–99 also led to a more northward position of the ITCZ (Fig. S7), consistent with the weaker PR response over the EEP during the 2015–16 El Niño. Further, the atmospheric feedbacks also show a higher asymmetry during the
warmer period and weaker asymmetry during the colder period (Figs. S8–S10). This is similar to the differences between the CMIP6 subensembles with varying cold tongue strengths. Unfortunately, the same analyses as shown in Figs. S7–S10 for the HIGH and LOW time periods during 1880–2019 do not yield significant results, which may be related to the high uncertainty in the observations and reanalysis products before the satellite era.

This study reveals a strong impact of the cold tongue bias on the ability of climate models to simulate strong EP El Niño events and a realistic ENSO asymmetry. Our results have major implications for ENSO projections in a warmer climate: While earlier projections did not show much consensus on future changes in ENSO amplitude (e.g., Latif and Keenlyside 2009; Collins et al. 2010; Cai et al. 2015), a more recent study has shown that when only considering CMIP5 and CMIP6 models with a relatively realistic ENSO asymmetry, an increase in future ENSO amplitude over the EEP under global warming is found in all of the models (see the review by Cai et al. 2021). Further, when focusing only on the latest generation of models (CMIP6), more than 76% of the models predict an increase in ENSO amplitude over the EEP from 1900–99 to 2000–99, independent of the applied IPCC scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, Cai et al. 2022). A similar consensus is found in extended historical simulations with the CMIP6 models, when comparing the ENSO amplitude over the EEP between the periods 1900–60 and 1961–2020 (Cai et al. 2023). This greater consistency concerning the ENSO amplitude change under global warming in the CMIP6 models can be explained by a better representation of the nonlinear ENSO feedbacks and improved Pacific mean state (Cai et al. 2021; Planton et al. 2021). Additionally, recent studies can explain why ENSO amplitude increases over the EEP under global warming, consistent with the results above: Models with a realistic cold tongue strength simulate the strongest mean state warming of SST over the EEP, while models with an overly strong cold tongue simulate the strongest mean state warming farther west due to biased atmospheric and oceanic feedbacks (Li et al. 2016; Ying et al. 2019). The larger mean state warming over the EEP makes a southward shift of the ITCZ onto the equator during El Niño events more likely, explaining the increase of strong EP El Niño events and ENSO amplitude under global warming (Cai et al. 2021). Our study hence suggests that reducing the cold tongue bias should further enhance ENSO projections for the twenty-first century.

**Acknowledgments.** We acknowledge the Hadley Center, NOAA, the Japanese Oceanographic Data Center, the Columbia University, and ECMWF for providing the observational and reanalysis datasets. Further we acknowledge the World Climate Research Program's Working Group on Coupled Modeling, the individual modeling groups of the phase 6 of Coupled Model Intercomparison Project (CMIP6). TB was supported by the Deutsche Forschungsgemeinschaft (DFG) project "Influence of Model Bias on ENSO Projections of the twenty-first Century" through Grant 429334714, and JV was supported by the French Agence Nationale pour la Recherche

(ANR) ARiSE Grant (ANR-18-CE01-0012). The authors have no relevant financial or nonfinancial interests to disclose. All authors contributed to the study conception and design. Data analysis and the first draft of the manuscript were performed by Tobias Bayr. Joke Lübbecke, Jérôme Vialard, and Mojib Latif commented on previous versions of the manuscript. All authors read and approved the final manuscript.

*Data availability statement.* The datasets analyzed during the study are available at HadISST: https://www.metoffice.gov.uk/hadobs/hadisst/, ERSSTv5, COBE-SSTv2, and Kaplan-SST: https://psl.noaa.gov/data/gridded/tables/sst.html, ERA5: https://cds.climate.copernicus.eu/#!/search?text=ERA5%20monthly&type=dataset, ORAS5: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means?tab=overview, and CMIP6: https://esgf-node.llnl.gov/projects/cmip6/.

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