# Chile Niño/Niña in the coupled model intercomparison project phases 5 and 6

Emilio Concha<fnref n="1" />☐ · Boris Dewitte<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> · Cristian Martinez-Villalobos<fnref n="4" />,<fnref n="5" /> · Fabien Solmon<fnref n="6" /> · Emilia Sanchez-Gomez<fnref n="1" />

Received: 12 April 2024 / Accepted: 30 August 2024

© The Author(s) 2024

## Abstract

The north and central coast of Chile is influenced by El Niño-Southern Oscillation (ENSO) through oceanic and atmospheric teleconnections. However, it also experiences episodic oceanic warmings off central Chile (30°S) lasting a few months that are not necessarily associated with ENSO. These episodes, called “Chile Niño” events, besides their ecological and socio-economic impacts, have also the potential to influence tropical Pacific variability. Here, we investigate how realistically the models in the Coupled Model Intercomparison Project (CMIP, Phases 5 and 6) simulate Chile Niño/Niña (CN) events, and quantify their changes under anthropogenic forcing. Despite limitations of the global models in simulating realistically coastal upwelling dynamics, we show that they simulate reasonably well the observed spatial pattern, amplitude and seasonal evolution of CN events. They however fail to properly represent the positive skewness from observations. The analysis of a sub-group of models (36) that simulate ENSO realistically reveals that CN events increase in amplitude and variance in the future climate with no changes in their frequency of occurrence. This is interpreted as resulting from compensating effects amongst changes in remote drivers and local feedbacks. In particular, ENSO variance increases while that of the South Pacific Oscillation decreases. Conversely, we found that while the Wind-Evaporation-SST feedback tends to increase and the coupling between mixed-layer depth and SST weakens, favoring the development of CN events, the thermocline and wind-SST feedbacks decrease. However, only the change in the thermocline feedback is correlated to changes in CN variance amongst the models, suggesting a dominant role of local oceanic stratification changes in constraining the sensitivity of CN to global warming.

**Keywords** Coastal Niño/Niña · South eastern Pacific · Air-sea interactions · Climate change · CMIP models

## 1 Introduction

Basin-scale climate modes in the tropics are important drivers of both the ocean and atmospheric circulation near continent boundaries, which impacts socio-ecological systems in the densely populated coastal regions. In the Pacific, the El Niño-Southern Oscillation (ENSO) phenomenon, the most emblematic interannual mode of variability at the global scale, has for instance far reaching consequences on society all around the Pacific (McPhaden et al. 2006; Holbrook et al. 2020a), with damaging impact over the global economy (Liu et al. 2023). Eastern boundary upwelling systems (EBUS) are particularly sensitive to basin-scale tropical modes due to the efficient equatorial oceanic teleconnection that propagates thermocline fluctuations along the coast at high latitudes (Sprintall et al. 2020). In addition, in these systems the process of upwelling maintains a shallow mixed-layer and an on-shoreward sloping thermocline, which favors local air-sea

<figure><img src="image_1.png" /></figure>

<footnote>Emilio Concha
concha@cerfacs.fr</footnote>

<footnote n="1">¹ CECI, Université de Toulouse III, CERFACS/CNRS, Toulouse, France</footnote>

<footnote n="2">² Centro de Estudios Avanzados en Zonas Áridas (CEAZA), Coquimbo, Chile</footnote>

<footnote n="3">³ Center for Ecology and Sustainable Management of Oceanic Islands (ESMOI), Departamento de Biología Marina, Facultad de Ciencias del Mar, Universidad Católica del Norte, Coquimbo, Chile</footnote>

<footnote n="4">⁴ Faculty of Engineering and Science, Universidad Adolfo Ibáñez, Santiago, Chile</footnote>

<footnote n="5">⁵ Data Observatory Foundation, ANID Technology, Center No. DO210001, Santiago, Chile</footnote>

<footnote n="6">⁶ Laboratoire d'Aérologie, Université de Toulouse III, Toulouse, France</footnote>
interactions and the growth of sea surface temperature (SST) anomalies under stochastic forcing. Besides the coastal oceanic wave activity of equatorial origin, eastward propagating extra-tropical atmospheric disturbances in the path of the subtropical jet streams (Rahn and Garreaud 2014) can also act as a noise forcing for local ocean-atmosphere instabilities to develop. As a result, persistent SST anomalies can grow independently of the basin-scale modes. Such SST anomalies have been observed in all EBUS and have been termed "Coastal Niño events" because of their dynamic similarities to their basin-scale counterparts. For instance, the Benguela Niño, consisting in an SST warming off the coast of Angola and Northern South Africa represents such type of events (Shannon et al. 1986; Lübbecke et al. 2010). More recently such coastal warmings have also been documented in other EBUS: Ningaloo Niño in Australia (Feng et al. 2013; Tozuka et al. 2014), Dakar Niño in Senegal (Oettli et al. 2016), California Niño in California (Simpson 1983), and Chile Niño in Chile (Xue et al. 2020). Off Peru, coastal Niño events also take place (Takahashi and Martinez 2019). The growth of these events can be broadly interpreted as resulting from a “coastal Bjerknes feedback” (Kataoka et al. 2014; Yuan and Yamagata 2014; Xue et al. 2020; Oettli et al. 2021) in which anomalous alongshore low level winds impact upwelling processes generating SST anomalies, that in turn can influence the zonal gradients of atmospheric pressure reinforcing the initial wind anomalies. However, since they take place in upwelling systems in which the cool SST favors a shallow Marine Boundary Layer (MBL), SST-low stratus cloud feedback (Clement et al. 2009) may be also important for these events to develop (Xue et al. 2020).

Here we focus on Chile Niño/Niña (CN) events that develop off Central Chile, a region under the direct influence of ENSO through both oceanic and atmospheric teleconnections (Cai et al. 2020; Sprintall et al. 2020). SST variability associated

with CN accounts for up to 40% of the total variance of SST anomalies off the coast of north and central Chile (Fig. 1a) and can thus result in impacts in the marine and terrestrial ecosystems through producing stresses on fauna. For instance, Tongoy bay (30°S), a hotspot of the scallop aquaculture industry in Chile experiences episodes of warming and hypoxic conditions (Ramajo et al. 2019) that might be related to CN events. CN events are also associated with an increase in surface air temperature in the northern and central Chile continental region (Xue et al. 2021), favoring warmer than usual conditions in an already dry climate. The worst wildfires in the Chilean history of austral summer 2017 were preceded by a Chile Niño event (McWethy et al. 2018; Garreaud et al. 2019), and the burned area from wildfires in central Chile between 2004 and 2023 is correlated with CN SST anomalies (Cordero et al. 2024). In addition, CN events are associated with the development of the South Pacific Meridional Mode (Xue et al. 2020; Dewitte et al. 2023) that also yields anomalous conditions at the scale of the South Eastern Pacific with potentially remote impacts. Considering the likely important societal implications of impacts due to CN, Xue et al. (2021) further investigated their predictability in the SINTEX-F2 coupled model (Luo et al. 2005). They showed that they can be predicted up to 3-month lead. However they note that the skill of the SINTEX-F2 system depends on whether or not events co-occur with ENSO. Events not preceded by ENSO are in particular less predictable. Therefore, accurate prediction of CN events remains a challenge.

A related concern is the degree to which climate prediction models are effective in accurately representing CN statistics and their changes in a warmer climate. This is relevant since CN can be considered as Marine Heat Waves (MHWs), i.e. episodes of anomalously warm surface waters in the ocean. Although MHWs are usually considered as warming events lasting a few days (Hobday et al. 2016), several studies define them using monthly mean data (Xu

<figure><img src="image_2.png" /><figcaption>**Fig. 1** a Explained variance of SST anomalies over 1948–2020 from observations, by CN. b Observed monthly SST anomalies in March 1980. Black lines show the zonal sections used in Fig. 3</figcaption></figure>

<figure><img src="image_3.png" /></figure>
et al. 2022; Amaya et al. 2023) and therefore coastal Niños that last several months could also be considered as MHWs. MHWs lasting a few days have drawn the interest of the scientific community in recent decades because they have increased in frequency and duration in most of the global ocean since the early twentieth century (Holbrook et al. 2020b; Oliver et al. 2018), and are also projected to continue increasing globally for the next decades under the influence of climate change (Frölicher et al. 2018; Oliver et al. 2019). Consequently, this raises the question of whether CN events will increase in frequency and amplitude in the future. It is noteworthy that the oceanic region along the coast of Peru and Chile (so-called Humboldt system) is projected to experience significant changes in its mean circulation and variability. Not only ENSO is projected to increase in variability (Cai et al. 2018, 2021) and persistence (Carréric et al. 2020; Lopez et al. 2022) in the future climate, but climate model projections also consistently indicate an increase in mid latitudes upwelling favorable winds in the future associated with a strengthening and poleward shift of the mid-latitude low-pressure systems (Rykaczewski et al. 2015). Note that this is not necessarily the case for tropical EBUS (Oettli et al. 2021). Therefore whether or not CN will increase in frequency and/or intensity in the future climate remains an open question.

Here we tackle this issue taking advantage of the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). As a first step, the skill of the CMIP models in simulating CN variability is evaluated. Despite the persistent warm bias along the coast of Chile in these models, we find that most models simulate realistically the CN pattern, which allows the investigation of their future evolution under anthropogenic forcing using the climate projection scenarios of CMIP5 and 6.

The paper is organized as follows: Sect. 2 describes the data and methods used. Section 3 provides an assessment of the CMIP models' skill in simulating the CN properties. Section 4 is an analysis of the changes in variability associated with CN events due to global warming and its relationship with that of remote drivers. Section 5 discusses the results and provides a conceptual view of competing processes at work to explain the CN property changes, along with concluding remarks.

# 2 Data and methods

## 2.1 Data sets

The observational monthly SST data used is retrieved from the Hadley Center data set (Rayner et al. 2003) which has a 1° resolution and covers the period 1948–2020. The monthly 10-m wind and sea level pressure data is taken from the

National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis 1 (Kalnay et al. 1996) for the same time period. Monthly data of sea water potential temperature is obtained from the GLORYS12V1 product of the CMEMS reanalysis (Lelouche et al. 2021) with a horizontal resolution of 1/12° and 50 vertical levels for the period 1993–2020.

We analyze monthly outputs from 19 CMIP5 models and 43 CMIP6 models. The CMIP5 sub-group of 19 models was selected based on their ability to simulate realistically ENSO diversity (Cai et al. 2018) and to contrast with that of CMIP6 models in order to evaluate if our results are dependent on the generation of coupled models and to increase the ensemble size. We used historical runs (1920–2014) and simulations under greenhouse forcing from the RCP8.5 and SSP5-8.5 scenario (2015–2100). Models’ names and their respective grid size are provided in Table 1. In most diagnostics, we treated the 62 models as a unique ensemble.

## 2.2 Methods

Prior to any analysis, all the model raw data is bilinearly interpolated onto a common global grid of 1°×1.25° (latitude x longitude). Also, all observed and model anomalies are linearly detrended prior to carrying out statistical analyses. The period used for the historical runs is 1920–2014 and for future projections 2015–2100.

We characterize ENSO variability in the tropics using the E and C indices of Takahashi et al. (2011) defined as $E = (PC1 - PC2)/\sqrt{2}$ and $C = (PC1 + PC2)/\sqrt{2}$, where PC1 and PC2 correspond to the first and second normalized principal components, respectively, of the first two EOF (Empirical Orthogonal Function) modes of SST monthly anomalies in the tropical Pacific (12°S-12°N, 120°E-70°W). E and C are orthogonal by construction, therefore their correlation at zero-lag is zero. Here, the E index accounts for El Niño events in the eastern Pacific and most of the extreme El Niño events, whereas the C index accounts for El Niño and La Niña events in the central Pacific. To characterize ENSO probability distribution, we also estimated its asymmetry based on the value of the first coefficient of the quadratic fit of PC2 onto PC1, i.e., $PC2(t) = \alpha \cdot [PC1(t)]^2 + \beta \cdot PC1(t) + \gamma$, which follows Karamperidou et al. (2017). This provides, in particular, a metric for models’ ability to realistically simulate the Eastern Pacific El Niño pattern, which is considered a driver of CN events. The α values of the models are provided in Table 1.

A significant part of the subtropical SST variability off the coast of northern Chile is under the influence of ENSO through both atmospheric and oceanic teleconnections (Taschetto et al. 2020; Sprintall et al. 2020). Therefore, to define the Chile Niño/Niña index (CNI), we remove from the SST anomalies the ENSO influence through linear
<table><caption>**Table 1** Description of the 62 CMIP5 and 6 models used</caption><thead><tr><th>Archives</th><th>Name</th><th>Model center</th><th>ENSO non-linearity</th><th>Grid size (ocean/atmosphere)</th></tr></thead><tbody><tr><td rowspan="24">CMIP5</td><td>BCC-CSM1-1-M</td><td>BCC/China</td><td>-0.21</td><td>360 x 232 / 320 x 160</td></tr><tr><td>CCSM4</td><td>NCAR/United States</td><td>-0.53</td><td>320 x 384 / 288 x 192</td></tr><tr><td>CESM1-BCG</td><td>NSF-DOE-NCAR/United states</td><td>-0.15</td><td>320 x 384 / 288 x 192</td></tr><tr><td>CESM1-CAM5</td><td>NSF-DOE-NCAR/United states</td><td>-0.40</td><td>320 x 384 / 288 x 192</td></tr><tr><td>CMCC-CM</td><td>CMCC/Italy</td><td>-0.17</td><td>182 x 149 / 480 x 240</td></tr><tr><td>CMCC-CMS</td><td>CMCC/Italy</td><td>-0.31</td><td>182 x 149 / 192 x 96</td></tr><tr><td>CMCC-CESM</td><td>CMCC/Italy</td><td>-0.22</td><td>182 x 149 / 96 x 48</td></tr><tr><td>CNRM-CM5</td><td>CNRM-CERFACS/France</td><td>-0.26</td><td>362 x 292 / 256 x 128</td></tr><tr><td>GFDL-CM3</td><td>NOAA-GFDL/United States</td><td>-0.30</td><td>144 x 90 / 240 x 121</td></tr><tr><td>GFDL-ESM2G</td><td>NOAA-GFDL/United States</td><td>-0.03</td><td>360 x 210 / 144 x 90</td></tr><tr><td>GFDL-ESM2M</td><td>NOAA-GFDL/United States</td><td>-0.49</td><td>360 x 200 / 144 x 90</td></tr><tr><td>GISS-E2-H</td><td>NASA-GISS/United States</td><td>-0.27</td><td>144 x 90 / 240 x 121</td></tr><tr><td>GISS-E2-R</td><td>NASA-GISS/United States</td><td>-0.26</td><td>144 x 90 / 144 x 90</td></tr><tr><td>FGOALS-s2</td><td>LASG-IAP/China</td><td>-0.23</td><td>360 x 196 / 128 x 108</td></tr><tr><td>FIO-ESM</td><td>FIO/China</td><td>-0.56</td><td>320 x 384 / 128 x 64</td></tr><tr><td>IPSL-CM5B-LR</td><td>IPSL/France</td><td>-0.13</td><td>182 x 149 / 96 x 96</td></tr><tr><td>MIROC5</td><td>MIROC/Japan</td><td>-0.43</td><td>256 x 128 / 240 x 121</td></tr><tr><td>MPI-ESM-LR</td><td>MPI-M/Germany</td><td>-0.05</td><td>192 x 96 / 240 x 121</td></tr><tr><td>MRI-CGCM3</td><td>MRI/Japan</td><td>-0.19</td><td>320 x 160 / 240 x 121</td></tr><tr><td rowspan="16">CMIP6</td><td>ACCESS-CM2</td><td>CSIRO-ARCCSS/Australia</td><td>-0.08</td><td>360 x 300 / 192 x 144</td></tr><tr><td>ACCESS-ESM1-5</td><td>CSIRO/Australia</td><td>0.01</td><td>360 x 300 / 192 x 145</td></tr><tr><td>AWI-CM-1-1-MR</td><td>AWI/Germany</td><td>0.03</td><td>(unstructured) /384 x 192</td></tr><tr><td>BCC-CSM2-MR</td><td>BCC/China</td><td>-0.05</td><td>360 x 232 / 320 x 160</td></tr><tr><td>CMCC-ESM2</td><td>CMCC/Italy</td><td>-0.51</td><td>362 x 292 / 288 x 192</td></tr><tr><td>CNRM-CM6-1-HR</td><td>CNRM-CERFACS/France</td><td>-0.06</td><td>1442 x 1050 / 720 x 360</td></tr><tr><td>CanESM5</td><td>CCCma/Canada</td><td>-0.03</td><td>360 x 291 / 128 x 64</td></tr><tr><td>CanESM5-CanOE</td><td>CCCma/Canada</td><td>-0.02</td><td>360 x 291 / 128 x 64</td></tr><tr><td>CAMS-CSM1-0</td><td>CAMS/China</td><td>-0.29</td><td>360 x 200 / 320 x 160</td></tr><tr><td>CAS-ESM2-0</td><td>CAS/China</td><td>-0.23</td><td>360 x 196 / 256 x 128</td></tr><tr><td>CMCC-CM2-SR5</td><td>CMCC/Italy</td><td>-0.16</td><td>362 x 292 / 288 x 192</td></tr><tr><td>CNRM-CM6-1</td><td>CNRM-CERFACS/France</td><td>-0.16</td><td>362 x 294 / 256 x 128</td></tr><tr><td>CNRM-ESM2-1</td><td>CNRM-CERFACS/France</td><td>-0.08</td><td>362 x 294 / 256 x 128</td></tr><tr><td>EC-Earth3-CC</td><td>EC-Earth-Consortium</td><td>-0.26</td><td>362 x 292 / 512 x 256</td></tr><tr><td>EC-Earth3</td><td>EC-Earth-Consortium</td><td>-0.13</td><td>362 x 292 / 512 x 256</td></tr></tbody></table>

<figure><img src="image_4.png" /></figure>
<table><caption>**Table 1 (continued)**</caption><tr><td>EC-Earth3-Veg</td><td>EC-Earth-Consortium</td><td>-0.13</td><td>362 x 292 / 512 x 256</td></tr><tr><td>EC-Earth3-Veg-LR</td><td>EC-Earth-Consortium</td><td>-0.26</td><td>362 x 292 / 320 x 160</td></tr><tr><td>FGOALS-f3-L</td><td>CAS/China</td><td>-0.43</td><td>360 x 218 / 288 x 180</td></tr><tr><td>FGOALS-g3</td><td>CAS/China</td><td>-0.08</td><td>360 x 218 / 180 x 80</td></tr><tr><td>GISS-E2-1-H</td><td>NASA-GISS/United States</td><td>-0.39</td><td>144 x 90 / 144 x 90</td></tr><tr><td>GISS-E2-1-G</td><td>NASA-GISS/United States</td><td>-0.46</td><td>144 x 90 / 144 x 90</td></tr><tr><td>GFDL-ESM4</td><td>NOAA-GFDL/United States</td><td>-0.16</td><td>720 x 576 / 288 x 190</td></tr><tr><td>HadGEM3-GC31-LL</td><td>MOHC NERC/United Kingdom</td><td>-0.14</td><td>360 x 330 / 192 x 144</td></tr><tr><td>HadGEM3-GC31-MM</td><td>MOHC/United Kingdom</td><td>-0.19</td><td>1440 x 1205 / 432 x 324</td></tr><tr><td>INM-CM4-8</td><td>INM/Russia</td><td>-0.01</td><td>360 x 180 / 180 x 120</td></tr><tr><td>INM-CM5-0</td><td>INM/Russia</td><td>0.04</td><td>360 x 180 / 180 x 120</td></tr><tr><td>IPSL-CM6A-LR</td><td>IPSL/France</td><td>-0.06</td><td>362 x 332 / 144 x 143</td></tr><tr><td>KACE-1-0-G</td><td>NIMS-KMA/Republic of Korea</td><td>-0.11</td><td>360 x 200 / 192 x 144</td></tr><tr><td>KIOST-ESM</td><td>KIOST/Republic of Korea</td><td>-0.17</td><td>360 x 200 / 192 x 96</td></tr><tr><td>MIROC6</td><td>MIROC/Japan</td><td>-0.33</td><td>360 x 256 / 256 x 128</td></tr><tr><td>MIROC-ES2L</td><td>MIROC/Japan</td><td>-0.42</td><td>360 x 256 / 128 x 64</td></tr><tr><td>MPI-ESM1-2-HR</td><td>MPI-M DWD DKRZ/Germany</td><td>-0.14</td><td>802 x 404 / 384 x 192</td></tr><tr><td>MPI-ESM1-2-LR</td><td>MPI-M AWI DKRZ DWD/Germany</td><td>-0.15</td><td>256 x 220 / 192 x 96</td></tr><tr><td>MRI-ESM2-0</td><td>MRI/Japan</td><td>-0.37</td><td>360 x 363 / 320 x 160</td></tr><tr><td>MCM-UA-1-0</td><td>UA/United States</td><td>0.23</td><td>192 x 80 / 96 x 80</td></tr><tr><td>NESM3</td><td>NUIST/China</td><td>-0.06</td><td>362 x 292 / 192 x 96</td></tr><tr><td>UKESM1-0-LL</td><td>MOHC NERC NIMS-KMA NIWA/United Kingdom, Republic of Korea and New Zealand</td><td>-0.24</td><td>360 x 330 / 192 x 144</td></tr><tr><td>CESM2</td><td>NCAR/United States</td><td>-0.30</td><td>320 x 384 / 288 x 192</td></tr><tr><td>CESM2-WACCM</td><td>NCAR/United States</td><td>-0.24</td><td>320 x 384 / 288 x 192</td></tr><tr><td>E3SM-1-1</td><td>E3SM-Project RUBISCO/United States</td><td>-0.03</td><td>360 x 180 / 360 x 180</td></tr><tr><td>NorESM2-LM</td><td>NCC/Norway</td><td>-0.22</td><td>360 x 385 / 144 x 96</td></tr><tr><td>NorESM2-MM</td><td>NCC/Norway</td><td>-0.17</td><td>360 x 385 / 288 x 192</td></tr><tr><td>TaiESM1</td><td>AS-RCEC/Taiwan</td><td>-0.43</td><td>320 x 384 / 288 x 192</td></tr><tr><td>Observations</td><td>HadISST/NCEP-NCAR</td><td>-0.31</td><td></td></tr></table>

Following Karamperidou et al. (2017) we quantify ENSO non-linearity using the value α of the first coefficient in the quadratic fit PC2(t)=α [PC1(t)]²+β PC1(t)+γ, where PC1 and PC2 are the time series of the first two EOF modes of SST anomalies in the tropical Pacific. Models in blue correspond to the ones that are skillful in simulating the observed ENSO non-linearity and conform the group that is used to study the future variability of Chile Niño/Niña

regression. The CNI is then defined as the principal component of the first EOF mode of the residual SST anomalies over the region (80°W-70°W; 35°S-20°S). This follows Xue et al. (2020), except that here the influence of ENSO is removed using two ENSO indices instead of one, because at least two indices are required to properly describe ENSO diversity (Takahashi et al. 2011; Capotondi et al. 2015). This also eases the interpretation of the CNI variability since it is by construction independent of ENSO at zero lag. This is also required here for an objective comparison of the CMIP models since the ENSO pattern does not necessarily have

the same projection onto the E and C modes amongst the models. The spatial pattern of CN is obtained by linearly regressing the normalized CNI onto the SST anomaly fields.

Since extra-tropical disturbances and their modulation by the South Pacific Anticyclone (Rahn and Garreaud 2014) can act as a noise forcing to CN, we also consider the South Pacific Oscillation (SPO) index as defined by You and Furtado (2017). An EOF analysis is performed onto the sea level pressure (SLP) anomaly fields in the region (10°S-45°S; 160°W-70°W) and the SPO index corresponds to the
normalized principal component of the first EOF mode of
variability.

## 3 Characterization of Chile Niño/Niña in CMIP models

As a first step, we evaluate the models’ skill in accounting for the main features of CN events. Figure 2a and d show the SST spatial pattern for observations and the model ensemble, respectively. It indicates that the models simulate realistically the CN pattern, with comparable magnitude and peak amplitude located in the same latitude range than the observations. Noteworthy the dispersion among the models (blue contours in Fig. 2d) does not peak in the region of maximum amplitude but to the west and in a narrow region along the coast. The mean explained variance of 65% ± 9% is however lower than the observed value of 80%, which reflects dispersion amongst models outside the core region of CN (rectangle in Fig. 2a), as will be seen later on. The spatial correlation (100°W-70°W, 45°S-10°S) between the simulated and the observed patterns is always

larger than 0.65 (Figure S1 in Supplementary Information). The agreement between models and observations is also good in terms of the relationship between winds and CN SST anomalies at zero lag: The maximum wind anomaly magnitude value in the core region of the CN reaches 0.33 m s⁻¹ for both observations and models, and their respective mean values in the CN region are 0.14 m s⁻¹ and 0.22 m s⁻¹. Discrepancy between models and observations are more pronounced in terms of the seasonality of the CNI. Figure 2b and e display the climatological variance of the CNI for observations and the model ensemble. Despite an overall comparable magnitude and seasonality (i.e. peak variance around austral summer), the maximum variance takes place in April in the models, two months after the observed peak in February. Looking at the models individually, we notice that 37 out of 62 (60%) have their peak between February and April (Figure S2) while the rest have it in other months of the year. Additionally, we examined the asymmetry of the CNI as a function of calendar months (Fig. 2c and f). The skewness value for a specific calendar month represents the skewness of the three consecutive months centered around that calendar

<figure><img src="image_5.png" /><figcaption>**Fig. 2** CN characteristics in observations and CMIP models: **a**, **d** Spatial pattern of CN obtained through a linear regression of the normalized CNI index onto SST anomalies (shading) and low-level wind anomalies (arrows) for **a** observations (Feb-1948 to May-2020) and **d** the ensemble mean of the 62 CMIP5/6 models. Explained variability of the first EOF mode is indicated in the map. Blue contours in **d** represent</figcaption></figure>

standard deviation of the SST patterns among the ensemble. **b**, **e** Seasonal variance of the CNI. **c**, **f** Seasonal skewness of the CNI. In **e** and **f** dispersion is shown as vertical segments on the bars. The size of the segment represents plus or minus the standard deviation amongst the ensemble

<figure><img src="image_6.png" /></figure>
month. To quantify the asymmetry we use the normalized skewness (s), defined as:

$$s = \frac{\frac{1}{N} \sum_{i=1}^{N} (x_i - \bar{x})^3}{\sigma^3}$$

where $x_i$ is the $i^{th}$ data point, N is the number of data (3 times the number of years, since we take 3 consecutive months per year for a given calendar month), $\bar{x}$ is the mean and $\sigma$ is the standard deviation. The observed CN has a positive skewness most of the year, which we verified was due to the larger occurrence of Chile Niños than Chile Niñas rather than their difference in amplitude. Peak skewness is reached in austral winter (Fig. 2c). In contrast, the model ensemble fails to reproduce this feature, presenting a weak positive skewness on average most of the year (Fig. 2f). Noteworthy, most CN events in observations occur in austral summer when the skewness is rather low. In order to understand why the CNI skewness is weak in the models, we further analyzed the number of events in observations and in the models. CN events are defined based on the value of the CNI: when the CNI is above $1\sigma$ (below – $1\sigma$) a Chile Niño (Niña) event takes place. The event ends when the CNI values no longer satisfy this condition for at least two consecutive months. We found that there are 4.4 Chile Niño and 3.9 Chile Niña events per decade in the observational record, while for the models there is an average of 4.6 Chile Niño and 4.7 Chile Niña events per decade (See Figure S3). Hence the frequency of occurrence of warm events is similar between observations and models, but the models tend to simulate more (+ 18%) cold events.

To document the time evolution of CN events, we carry out a composite evolution analysis in two zonal sections, at 25°S and 35°S (Fig. 1b) for both warm and cold events (Fig. 3). The peak of amplitude of Chile Niño is comparable to the one in observations, being located also at the same distance from the coast in the 25°S section and attached to the coast at the 35°S section. In the case of the Chile Niña events the peak location is similar to observations, but the models tend to simulate a slightly weaker anomaly. Despite the overall good agreement, a notable discrepancy between the observations and the models is the larger persistence of SST anomalies in the observations compared to the model composite. Warm events last ~2 months more in the observations than in the models. This is more pronounced for Chile Niña events, for which the persistence (~8 months) is more than twice the build-up time (~3 months) in the observations, while the models feature a comparable span of the developing and decaying phases (i.e. ~2 months). This lack of temporal asymmetry between the developing and decaying phases in the models can also explain the weaker skewness of the CNI compared the observations (Fig. 2c, f), e.g., a longer

persistence of strong Chile Niño events may positively skew the distribution.

Xue et al. (2020) showed that the CN growth rate involves a coastal Bjerknes feedback in which shortwave radiation and mixing also play a key role. This indicates that the vertical structure of temperature variability is a key parameter in CN dynamics. We thus inspect the mean vertical structure of temperature of the CN events in the models (Fig. 4). The CMEMS reanalysis (1993–2020) is used as a benchmark for comparison. The latter has a CN pattern that closely resembles the one from HadISST (Figure S4). Similarly to the methodology used to obtain the surface pattern, here we calculated the linear regression of the CNI in a zonal section at 25°S for the reanalysis data and the model ensemble. The comparison indicates a relatively good agreement between models and reanalysis data in terms of the spatial pattern and amplitude, considering that models have significant mean state bias in the coastal region (Dewitte et al. 2023). The peak warming during CN is confined to the first 50 m consistent with the dominant role of heat flux forcing on SST anomalies (Xue et al. 2020). Although the reanalysis data shows a negative anomaly of temperature near the coast (between 50 and 100 m), this is not statistically significant to the 95% level. On the other hand, models display a high dispersion of coastal warming between 10 and 100 m depth (Fig. 4c). This could indicate coastal processes involved in the development of CN events that are not well resolved in some of the models, such as the passing of coastal trapped Kelvin waves (Pizarro et al. 2001). To summarize, the results indicate that the CMIP models have some skill in simulating the main characteristics of CN events, which motivates us to document how they simulate their changes in properties in the future.

# 4 Chile Niño/Niña under global warming

## 4.1 Changes in variance and frequency

Since the definition of CN is tied to ENSO variability, hereafter we select a sub-group of models that realistically simulate ENSO characteristics. In particular, we are interested in models that can simulate the different type of events; consisting, in general, in strong eastern Pacific events and moderate amplitude events that peak in the central equatorial Pacific (Takahashi et al. 2011). This is referred to as ENSO diversity in the ENSO literature (Capotondi et al. 2015). A compact measure of the ENSO diversity has been proposed by Karamperidou et al. (2017), which consists in the value $\alpha$ of the first coefficient in the quadratic fit of PC1 and PC2 time series of the first two EOF modes of SST anomalies in the tropical Pacific (see Methods). Here we thus select a group of 36 models that have a value of $\alpha$ that reaches at
<figure><img src="image_7.png" /><figcaption>**Fig. 3** Composite evolution of SST anomalies for Chile Niño (a, b, e, f) and Chile Niña (c, d, g, h) events in zonal sections at 25°S (top) and 35°S (bottom) for observations (a, c, e, g) and multimodel ensemble (b, d, f, h). Hovmöller diagrams show positive (negative)</figcaption></figure>

values on the y-axis that indicate months after (before) the peak of events. Dots indicate values that are significant to the 95% level based on a t-test. Number of events is indicated in parentheses

least 50% of the observed value of – 0.31 (models in blue font in Table 1).

To study the projected changes in CN we assess the
change in variability of the CNI index. Since the latter is
normalized, the CNI of the RCP8.5/SSP5-8.5 scenario for
each model is scaled by the projection of the spatial pattern
onto that of the historical run. This procedure follows Car-
ric et al. (2020). Figure 5 displays the change in variability
of the scaled CNI (in %) between the future and present
climates. The results indicate that on average there is an
increase in variability of the CNI of 3.2% (last red bar in
Fig. 5). Although relatively small, the lower limit of disper-
sion does not reach zero and hence this increase is statisti-
cally significant based on a bootstrap test (see details in cap-
tion text of Fig. 5). Moreover, 24 out of 36 models (66.7%)
show an increase in the variability of the CNI under the
anthropogenic forcing scenario with a total mean increase
in variability reaching 8% (±2.8%, obtained with the boot-
strap method). Most of the models show a variation inferior
to 12% with the exception of the CESM2-WACCM model,

which reaches a value of nearly 40%. Removing this outlier
from the ensemble mean yields an increase in CNI variance
of 2.2%, which is still statistically significant. When calcu-
lating the change of CNI variance between the present and
future climates for each month of the year it is found that the
increase is very similar throughout the year, therefore this
change is not seasonally dependent (not shown). Notewor-
thy, this increase in variance is associated with the increase
of the amplitude of the CN events rather than the increase in
the number of events. In fact, the frequency of occurrence of
CN does not change between the present and future climates
(not shown).

Considering the relatively low increase in variance
reflecting a rather weak consensus amongst the models,
we separate the ensemble into two groups in order to inter-
pret the dispersion in the change in variance of the CNI.
The two groups consist in the models for which the CNI
variance increase (IVG) or decrease (DVG). Figure 6 dis-
plays the mean difference between the future and present
SST patterns for the 36 models and for these two groups.

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 4** CN pattern in depth, at 25°S, obtained by linearly regressing CNI onto sea water potential temperature anomalies. **a** CMEMS reanalysis (1993–2020), **b** ensemble mean of 55 CMIP5 and 6 models. **c** Dispersion (standard deviation) amongst the models. Black stippling</figcaption></figure>

in **a** indicates values that are significant to the 95% level based on a t-test, and stippling in **b** indicates points where at least 75% of the models agree on the sign

The IVG pattern shows a statistically significant increase in amplitude (+0.05 °C) near the coast, between 25°S and 35°S, a region where also at least 75% of the models agree on the sign of pattern change. On the other hand, the DVG group shows a statistically significant decrease in amplitude in the off-shore oceanic region between 15°S and 25°S, with no statistically significant change in the core region of CN. The peak changes are approximately co-located with the maximum amplitude of the mean historical pattern (black contours in Fig. 6), with the IVG group exhibiting a more coastal mean pattern than the DVG group. This suggests different mechanisms at work in the development of the CN events for the two groups. This will be discussed later in Sect. 5.

## 4.2 Changes in the drivers of Chile Niño/Niña

In order to elucidate what drives the variability changes in CN and interpret the above results, we first analyze the relationship between the CNI and the E index. Although the CNI and the E index are independent at zero lag by construction, an El Niño event can yield warm SST anomalies ~6 months off central Chile after the El Niño peak. This coastal SST warming has been shown to be associated with the development of the South Pacific Meridional Mode (SPMM) in some models (Dewitte et al. 2023). Here we observe that CN tends to be associated with the development of SPMM events (Figure S5), which motivates to investigate the CN-ENSO relationship. The lagged-correlation between the CNI and the E index as a function of calendar month is presented in Fig. 7 for both the model ensemble and the observations. It indicates that the E index tends to be ahead the CNI by ~10 months in austral spring (winter) in the model ensemble (the observations), although such relationship
<figure><img src="image_10.png" /><figcaption>**Fig. 5** Change in variability of the CNI between the present (1920–2014) and future (2015–2100) climate for the 36 selected models. Green (black) font in the x-axis corresponds to CMIP5 (CMIP6) models. The last bar shows the multi-model mean with its corresponding confidence interval as a vertical segment. The confidence interval is obtained using a bootstrap test consisting in randomly</figcaption></figure>

re-sampling 10,000 times the changes in variance amongst the models. 36 models are considered on each re-sampling with repetition allowed. Standard deviation is calculated for each realization and then the standard deviation among these 10,000 values is calculated. This value is used to plot the upper and lower limits of dispersion

<figure><img src="image_11.png" /><figcaption>**Fig. 6** Projected change in CN SST pattern (in °C). Shading corresponds to the projected pattern (SSP585/RCP8.5) minus the historical pattern for **a** the subset of 36 models, **b** IVG (24 models) and **c** DVG (12 models). Black contours show the mean CN pattern for the</figcaption></figure>

historical runs of each group. Black dots indicate where values are significant to the 90% level based on a t-test and where at least 75% of the models agree on the sign of change. Dashed rectangles indicate CN region

results from no more than ~29% of the CN being preceded by an ENSO event, which is diversely simulated by the models (Fig. 8). Note that the model ensemble fails in simulating the positive relationship between CNI and the E index for positive lag (CNI ahead of E) evidenced in the observations.

We now focus on a mid-latitude driver of the CN, the South Pacific Oscillation (SPO) that consists in a meridional dipole of sea level pressure (SLP) anomalies extending from 60°S to 35°S (You and Furtado 2017). Its northern node represents the variability of the South Pacific subtropical

high intensity and the southeasterly trade winds associated to it. Xue et al. (2020) identifies the SPO as a precursor signal of CN. They have shown that a negative (positive) anomaly of SLP forms 2–3 months ahead a Chile Niño (Niña) event, suggesting that the weakened (strengthened) coastal winds trigger this event. To examine the SPO-CN relationship, the lagged-correlation between the CNI and the SPO index is estimated as function of calendar month (Fig. 9). The results indicate that the model ensemble simulates realistically such a lagged relationship as well as its seasonality. In particular,

<figure><img src="image_12.png" /></figure>
<figure><img src="image_13.png" /><figcaption>**Fig. 7** Lagged correlation between CNI and E index as a function of calendar month for **a** observations and **b** ensemble mean of the 36 selected models. Months in the x-axis correspond to CNI months. Dots indicate values that are significant to the 95% level based on a t-test. For **b** black dots also indicate where at least 75% of the models agree on the correlation sign</figcaption></figure>

the peak correlation (c ~ 0.5) between the SPO index and the CNI is reached between March and May with the SPO index preceding CNI by ~2 months. The correlation pattern is slightly slanted towards larger phase difference from May to September. An estimated 29% CN events are preceded by an SPO event (ahead by 0 to 4 months) in the observations, while in the model ensemble this percentage varies between 35 to 55%, which weakly changes with the mean climate conditions (Figure S6).

ENSO and SPO can be thus considered as drivers of the CN events in the models. It is worth noticing that although by construction the E index and CNI are uncorrelated at zero lag, they exhibit a strong linear relationship at ~10 months lag (E ahead of CNI). It is now useful to document ENSO changes in variability from the present and future climates, as well as the SPO changes, in order to interpret the results of Fig. 5. Cai et al. (2018, 2020) showed that the E index increases in variance in the future, which we verified here from our model ensemble. In particular there is a significant increase of 9.2% of the E variance in the 36 selected models here (Fig. 10a) with 70% of the models exhibiting an increase. This does not translate into a comparable increase

in the CNI variance because only about one fourth of the CN events are preceded by an El Niño event. However, it may explain why there are more models that showcase an increase in CNI variance than a decrease. From the 24 models that showcase an increase in CNI variance (red bars in Fig. 5), 17 (71%) evidence an increase in the variance of the E index (red diamonds in Fig. 10a). Additionally, from these 17 models 10 evidence an increase in the frequency of Chile Niño events that are preceded by Eastern Pacific El Niño events (black line in Figure S7), of which 9 also showcase an increase in the occurrence of Eastern Pacific El Niño events (red line in Figure S7). Also, 5 models showcase a decrease in event frequency in both CNI and the E index (Figure S7). While there is no clear linear relationship between changes in variance of the CNI and E indices (c = 0.07), these considerations suggest that the change in CNI variance is linked to the change in the statistics of ENSO. If ENSO is favorable for the development of CN and that the increase in ENSO variance in the future does not translate in a proportional increase in the CNI variance, we may hypothesize that this is due to changes in the other driver of CN, the SPO. Figure 10b displays the changes in variance of the SPO index

<figure><img src="image_14.png" /><figcaption>**Fig. 8** Percentage of CN events preceded by eastern Pacific ENSO events: Histogram for the number of models by percentage for **a** All models, **b** historical runs of the 36 selected models and **c** projected</figcaption></figure>

scenarios (RCP8.5/SSP5-8.5) of the 36 selected models. The black dashed vertical line indicates the value for the observations
**Fig. 9** Lagged correlation between CNI and SPO index as a function of calendar month for **a** observations and **b** ensemble mean of the 36 selected models. Months in the x-axis correspond to CNI months. Dots indicate values that are significant to the 95% level based on a t-test. For **b** black dots also indicate where at least 75% of the models agree on the correlation sign

<figure><img src="image_15.png" /></figure>

**Fig. 10** Change in variability of the **a** E index and **b** SPO index between the present (1920–2014) and future (2015–2100) climate for the 36 selected models. Green (black) font in the x-axis corresponds to CMIP5 (CMIP6) models. Multi-model mean is shown with its corresponding confidence interval as a vertical segment. The confidence interval is obtained using a bootstrap method following the methodology of Cai et al. (2018), as in Fig. 5. Red (blue) diamonds indicate models for which the sign of change in CNI and E/SPO is positive (negative) for both indices

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

between the present and future climates. It indicates that there is a tendency towards a reduction in the variance of the SPO although the consensus among models is low, with 23 (64%) experiencing a decrease in variability. There is no linear relationship between the changes in variance of the CNI index and that of the SPO index amongst the models (c = 0.03), for instance, the CMCC-ESM2 model evidences a large increase in variance for both the SPO and E indices, but has a strong reduction in the CNI variance. Therefore, although we observe a relationship between changes in E

and CNI variability this is not linear and this aspect cannot be explained by changes in the SPO.

Summarizing, amongst the two climate drivers of CN events, only ENSO evidences changes in the future that could explain the increase in CNI variance. However, the increase in CNI variance is not correlated to either that of the E index (c = 0.08) nor that of the SPO index (c = 0.03) amongst the models, which suggests that the change in CNI variability is not only controlled by changes in external forcing but also by alterations in local processes due to changes in the mean conditions. In the following, we document

<figure><img src="image_17.png" /></figure>
changes in the mean conditions controlling key processes of the mixed-layer and marine boundary layer (MBL) budgets during CN.

## 4.3 Changes in local processes

As a starting point, we rely on first principles and focus on few key local processes known to take place and be altered by global warming in the region of interest. Three features of the mean circulation are considered: oceanic stratification, MBL stability and surface winds. So-called feedbacks are evaluated, which is based on simple linear regressions between relevant variables. This approach is commonly used to address processes in a multi-model framework (e.g. Planton et al. (2021) for ENSO).

### 4.3.1 Thermocline feedback

Along the coast of Chile, changes in oceanic stratification (measured as the difference between mean temperature at 5 m and 75 m) are manifested as a significant deepening of the mean thermocline rather than a clear shoaling of the mixed-layer (Figure S8). The former yields a reduction (increase) of the thermocline feedback along the coast (in the open ocean) off Central Chile as diagnosed by the change in the regression coefficient between the CNI and thermocline depth anomalies (Fig. 11a). The results indicate

a zonal dipole pattern of the changes within the CN region, which can be interpreted as resulting from a flattening of the isotherms due to the stronger coastal warming with regards to the off-shore region. This dipole pattern is also evidenced in other quantities, as will be seen, and we thus chose to consider two regions within the CN region accounting for this dipole pattern (see rectangles in Fig. 11). There is thus a reduction of the thermocline feedback in the coastal box, while it increases in the off-shore box (see Table 2). The reduction in the coastal box implies a less effective equatorial oceanic teleconnection through coastal Kelvin wave-induced vertical advection, while the increase in the off-shore box may favor the forcing of SST anomalies by ENSO-induced extra-tropical Rossby waves radiating from the coast or locally forced by the wind (Vega et al. 2003).

### 4.3.2 Mixing feedback

This feedback represents the control of the rate of change in temperature by the climatological surface heat flux under changing mixed layer heat capacity ($\frac{\partial T}{\partial t} = -\frac{\bar{Q}}{\rho c_p \bar{h}} h'$, see Eq. 2 in Xue et al. (2020)). It is estimated as the regression coefficient between mixed-layer depth anomalies and CNI. Xue et al (2020) refers to it as the heat capacity anomaly effect and suggest it is a key process during the development of CN events. We call it here "mixing feedback" because, through

<figure><img src="image_18.png" /><figcaption>**Fig. 11** Mean changes of local physical processes in the ensemble of models between future projections and historical runs (future minus historical). **a** Linear regression coefficient between the CNI and thermocline depth anomalies, **b** linear regression coefficient between the CNI and mixed layer depth anomalies, **c** WESp value and **d** linear regression coefficient between meridional low-level wind anomalies</figcaption></figure>

and CNI. The regression coefficients of **d** have been multiplied by -1. Black dots indicate values that are significant to the 95% level based on a t-test. The black box divided by a segmented line shows the offshore and coastal CN regions used to calculate the averaged values on Table 2
<table><caption>**Table 2** Mean values of local physical processes inside the coastal (75°W-70°W, 35°S-20°S and offshore (80°W-75°W, 35°S-20°S) boxes of the CN region</caption><thead><tr><th rowspan="3"></th><th colspan="2">WES feedback</th><th colspan="2">Mixing feedback</th><th colspan="2">Thermocline feedback</th><th colspan="2">Wind-SST feedback</th><th colspan="2">MBL stability</th></tr><tr><th>WESp (V)</th><th></th><th>β</th><th></th><th>α</th><th></th><th>-η</th><th></th><th>LTS [°C]</th><th></th></tr><tr><th>[W s m<sup>-2</sup>]</th><th>Off-shore</th><th>Coast</th><th>(CNI(t) ≈ β · H<sub>mix</sub>(t))</th><th>[°C m<sup>-1</sup>]</th><th>(CNI(t) ≈ α · H(t))</th><th>[°C m<sup>-1</sup>]</th><th>(V<sub>wind</sub>(t) ≈ η · CNI(t))</th><th>[m s<sup>-1</sup> ° C<sup>-1</sup>]</th><th>Off-shore</th><th>Coast</th></tr></thead><tbody><tr><td>Present<br>(1920–2014)</td><td>10.64</td><td>8.31</td><td>-6.6×10<sup>-3</sup></td><td>-3.2×10<sup>-3</sup></td><td>6×10<sup>-3</sup></td><td>9.9×10<sup>-3</sup></td><td>0.85</td><td>0.44</td><td>-13.02</td><td>-12.11</td></tr><tr><td>Future<br>(2015–2100)</td><td>11.11</td><td>8.88</td><td>-6.1×10<sup>-3</sup></td><td>-0.6×10<sup>-3</sup></td><td>7.4×10<sup>-3</sup></td><td>8.3×10<sup>-3</sup></td><td>0.68</td><td>0.36</td><td>-12.05</td><td>-12.01</td></tr><tr><td>Future – Present<br>(Bold when significant)</td><td>0.47<br>(4.4%)</td><td>0.57<br>(6.9%)</td><td>0.6×10<sup>-3</sup><br>(8.34%)</td><td><strong>2.6×10<sup>-3</sup></strong><br>(<strong>80.74%</strong>)</td><td><strong>1.4×10<sup>-3</sup></strong><br>(<strong>23.54%</strong>)</td><td><strong>-1.6×10<sup>-3</sup></strong><br>(<strong>-16.1%</strong>)</td><td><strong>-0.16</strong><br>(<strong>-19.24%</strong>)</td><td><strong>-0.08</strong><br>(<strong>-18.12%</strong>)</td><td><strong>0.97</strong><br>(<strong>7.45%</strong>)</td><td>0.1<br>(0.84%)</td></tr><tr><td>Correlation among models<br>with change in CNI vari-<br>ance</td><td>0.16</td><td>0.21</td><td>-0.3</td><td>0.29</td><td>0.69</td><td>0.5</td><td>0.35</td><td>0.25</td><td>-0.01</td><td>0.06</td></tr></tbody></table>

Boxes are shown in Fig. 11. The definition of each feedback is provided in the main text of Sect. 4.3. The statistical significance is obtained using a bootstrap method where the values for the models are randomly re-sampled 10.000 times, taking only N models on each re-sampling (N is the closer integer to 80% of the total number of available models) and allowing repetition. This is done for historical and future projections. Standard deviation is calculated for each realization and then the standard deviation among these 10.000 values is calculated. If the interval defined by this standard deviation and the mean value does not overlap between present and future then the difference is considered statistically significant

the regression, it may also encapsulate other processes that are sensitive to the changes in the mixed-layer depth (e.g. vertical diffusivity, vertical entrainment). The results indicate that it is mostly a damping process (i.e. negative regression coefficient) and that it is significantly altered in the future climate in the coastal region with a reduction amounting to 80% (Table 2 and Fig. 11b). Its reduction implies that warm CN events are less damped by this process in the future climate in the coastal domain, which is favorable for the growth of CN.

### 4.3.3 Wind-evaporation-SST (WES) feedback

The WES feedback refers to the mutual reinforcement between winds and SST through evaporative processes and has been shown to be a key mechanism in mid-latitude systems (Xie and Philander 1994; Chang et al. 1997). Its strength is tightly linked to the background surface winds. Off-central Chile, the equatorward winds increase in the future climate (Figure S9), which results from the poleward expansion of the Hadley cell (Lu et al. 2007; Goubanova et al. 2011; Rykaczewski et al. 2015). The increase in meridional winds implies an increase in the WES feedback (Wang 2010). This was confirmed through assessing the sensitivity of latent heat flux to meridional winds, or Wind-Evaporation-SST parameter (WESp) defined as:

$$WESp = \frac{-LH \cdot V}{W^2}$$

where LH is the latent heat, V is the meridional wind, W is the wind speed ($W = \sqrt{u^2 + v^2 + \hat{w}^2}$) and w is a background wind speed used to account for higher frequency variance with a value of w=4 m s⁻¹ (Czaja et al. 2002; Vimont 2010; Martinez-Villalobos and Vimont 2016; Sanchez et al. 2019). WESp is expressed in units of Kg s⁻² m⁻¹. As expected, we find that the WESp increases significantly where the increase in low-level meridional wind is the largest and significant (Figure S9), that is in the region of the Coastal Jet and to the west of the CN region (Fig. 11c). This implies a larger sensitivity of SST to latent heat changes at regional scale in the future climate, which is favorable to the increase in variance of CNI.

### 4.3.4 Wind-SST feedback

Another compact measure of how strongly the atmospheric winds respond to SST anomalies is the wind-SST feedback (Guilyardi 2006) that encapsulates the collective role of the processes discussed above. In coastal upwelling regions it is a damping process due to the dominance of Ekman dynamics (i.e. inverse relationship between along-shore winds and SST anomalies). It is here inferred from the regression coefficient between the CNI and the meridional low-level winds anomalies. For the sake of clarity, the regression coefficient
is multiplied by – 1, so that an enhanced coupling strength implies that it makes it easier for a small SST or wind perturbations to grow and develop as a CN event, and the contrary otherwise. The results, displayed in Fig. 11d and Table 2, indicate that the coupling strength weakens on average off central Chile, with a peak and significant change to the South of the CN core region where the low-level atmospheric coastal jet extends from further south (Garreaud and Muñoz 2005). Over this region (40°S–25°S, 85°W–75°W), 32 out of the 36 models present a mean weaker coupling (not shown) with no clear separation between the IVG and DVG in terms of mean value of the changes. Therefore, the change in coupling strength between the present and future climates in the model ensemble is favorable for the damping of CN variability in the future climate.

Taken together, the results suggest that although changes in the WES and mixing feedbacks will favor the development of events near the coast, coastal Kelvin waves of tropical origin will be a less efficient trigger for them. On the other hand, changes in the WES and thermocline feedbacks will make the offshore region more susceptible to the development of events through atmospheric forcing and Rossby waves. However this will be counterbalanced by a reduction in the Wind-SST feedback. As a summary, we provide the Table 3 that synthesizes the changes in feedbacks and their relationship with the changes in the mean circulation.

## 5 Discussion and concluding remarks

We evaluated how the historical runs of 62 CMIP5/6 models represent the main characteristics of CN. Despite the relatively coarse resolution of the models with regards to their ability to simulate coastal processes, they are found to be skillful in simulating the SST spatial pattern of CN events. Most of the models (62%) also realistically simulate the seasonal evolution of the CNI although 18 models show a shift in the peak of variability up to 2 seasons. The main deficiency of the models regards their tendency to simulate too many Chile Niña events than observed resulting in a weak positive skewness of the CNI compared to observations. This relatively good performance of the CMIP models in simulating CN characteristics contrast with their limited

skill for coastal El Niño events off Peru (Chen and Jin 2023; Martinez-Villalobos et al. 2024).

The good performance of the CMIP models in simulating the CN provided an incentive for investigating its future changes in a sub-group of models (36) that realistically simulate ENSO variability. We find that 24 (12) out of 36 models project an increase (decrease) in the variability of CNI under climate change, resulting in an average 3.2% increase in variance. This increase in variance is associated with the increase in amplitude of the CN events (Fig. 6) rather than the increase in the frequency of the events (not shown). The increase in amplitude/variance of CN events is interpreted as resulting from both changes in external forcing and local processes: (1) Increased ENSO variance is propitious for the enhancement of SST off central Chile during CN events linked to ENSO (one fourth of the events). We also find that equatorial zonal wind anomalies not necessarily linked to ENSO in the western-central equatorial Pacific are enhanced between the present and future climates (not shown), which favors the excitation of CN events by coastal disturbances originating from the equatorial region (e.g. coastal Kelvin waves). On the other hand, the SPO variance is reduced, which reduces one of the sources of noise forcing to CN events through the fluctuations of the mid-latitude pressure systems. The reduced variance (also evidenced in the daily and monthly meridional winds in the CN region (not shown)) can be interpreted as resulting from the increase in MBL stability. In a warming world, there is a tendency for a greater atmospheric warming in subtropical regions above the boundary layer than at the surface (Miller 1997). Warm-above-cool anomalies imply enhanced low-tropospheric stability (LTS) that can reduce the vertical motion and impede convection. The LTS can be measured by the difference between the 700 hPa potential temperature and SST and is usually strong in the southeast Pacific stratus region (> 18 °C; Klein and Hartmann 1993). Consistently, we find that stability is enhanced in the CN region (See Table 2 and Figure S10). (2) Regarding local processes, there are also competing effects due to changes in the mean conditions. Amongst the four local feedbacks diagnosed here (see Sect. 4.2, Table 2), two experience changes favorable to the damping of CN growth: the thermocline feedback along the coast and the wind-SST feedback. The reduced wind-SST

<table><caption>**Table 3** Changes in the strength of feedbacks between the present and future climates and related changes in mean circulation</caption><thead><tr><td>Local process</td><td>Projected changes in strength</td><td>Related changes in mean circulation</td></tr></thead><tbody><tr><td>Thermocline feedback</td><td>Increase (decrease) in the oceanic (coastal) region</td><td>Deepening of thermocline and on-shoreward flattening of isotherms</td></tr><tr><td>Mixing feedback</td><td>Decrease</td><td>Increase in vertical stratification</td></tr><tr><td>WES feedback</td><td>Increase</td><td>Intensification of meridional wind speed</td></tr><tr><td>Wind-SST feedback</td><td>Decrease</td><td>Deepening thermocline and increase in MBL stability</td></tr></tbody></table>
feedback can be interpreted as resulting from the reduction in the thermocline feedback and the increase in MBL stability. It is worth to mention that the mean changes in the SST-low cloud feedback (Xue et al. 2020) were also quantified in 31 models for which cloud cover data was available. Low cloud cover was defined as the values at 680 hPa or higher (average between 680 and 1000 hPa), based on the International Satellite Cloud Climatology Project (ISCCP) definition (Rossow and Schiffer 1999). The results indicate a slight mean increase of this feedback but that is not significant due to the large dispersion amongst the models (not shown).

The reduced thermocline feedback along the coast weakens the equatorial oceanic teleconnection to the central Chile region since wave activity originating from the equatorial region is less effective in inducing mean vertical advection of anomalous temperature. Amongst all the feedbacks, only the change in thermocline feedback correlates significantly to the change in CNI variance ($c = 0.69$ and $c = 0.50$ for the off-shore and coastal boxes, respectively, see Table 2), suggesting the importance of thermal vertical changes in constraining the CN amplitude increase. Interestingly we find that vertical stratification is slightly increased between the present and future climates (Figure S8c) but the changes are not significant. On the other hand, the mean thermocline experiences a significant deepening that is larger along the coast than in the open ocean (Figure S8b). The latter is consistent with the weakening of the thermocline feedback along the coast because it becomes more difficult for thermocline fluctuations to influence SST. Consistently, we also find that the changes in mean thermocline depth are correlated to changes in CNI variance amongst models ($c = 0.4$ for the CN region). This however does not mean that the other feedbacks are not influential but their changes may be more diversely simulated amongst the models with also compensating effects between them. In particular the mean warm SST bias along the coast of Chile in the CMIP models (Dewitte et al. 2023) varies significantly amongst them. Since mean state constrains the balance between feedbacks locally, we may expect a different sensitivity of this balance to mean state changes. This issue would deserve further research, which could be based on the experimentation with a coupled model (e.g. Goubanova et al. 2019), as well as an explicit quantification of the mixed-layer heat budget. Overall, our study contrasts with previous works suggesting that MHWs will increase in intensity and frequency in the future climate (Frölicher et al. 2018; Oliver et al. 2019). For CN, we only find a mild increase in intensity with no increase in frequency, which results from compensating effects in terms of both external remote forcing (i.e. ENSO versus SPO) and local feedbacks. Nonetheless, while no strong changes in CN variability are observed, the increase in mean SST in the future climate will make that the threshold with respect to the baseline climate for a CN to take place will

be reached more often in the future, which still poses challenges to marine ecosystems and coastal communities. In that respect our results may be useful to create a narrative for the local stakeholders that emphasizes the critical role of long-term changes in mean SST rather than of extremes, which can provide guidance for adaptation strategies. Our results also questions if coastal Niño events in other EBUS (e.g., Benguela Niño, and Ningaloo Niño) will undergo comparable changes considering that the other tropical basins are also projected to experience robust changes in variability in a warmer climate (Atlantic: Crespo et al. 2022; Indian ocean: Sun et al. 2022). Inter-hemispheric asymmetry in the response of these coastal events to climate change may be also expected due to differences in equatorial oceanic teleconnections between the two hemispheres and the asymmetry in the climate change SST patterns in the mid-latitude EBUS (Rykaczewski et al. 2015; Watt-Meyer et al. 2019).

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s00382-024-07434-5.

**Acknowledgements** We thank Pr. S.-I. An and Dr. Laurent Teray for stimulating discussions. EC acknowledges support from the Agencia Nacional de Investigación y Desarrollo (ANID)/Programa Formación de Capital Humano Avanzado (PFCHA) (Doctorado Becas Chile/2019-72200310). BD acknowledges support from ANID (Concurso de Fortalecimiento al Desarrollo Científico de Centros Regionales 2020-R20F0008-CEAZA, Anillo Eclipse ACT210071, and Centro de Investigación Oceanográfica en el Pacífico Sur-Oriental COPAS COASTAL FB210021, Fondecyt Regular N°1231174). BD also acknowledges support from the French National program LEFE (Les Enveloppes Fluides et l'Environnement) through the SEPICAF project, the EU H2020 FutureMares project (Theme LC-CLA-06-2019, Grant agreement No 869300) and the CE2COAST project funded by ANR through the 2019 "Joint Transnational Call on Next Generation Climate Science in Europe for Oceans" initiated by JPI Climate and JPI Oceans. CM acknowledges support from Proyecto ANID Fondecyt Postdoctorado código 3200621, Data Observatory Foundation ANID Technology Center No. DO210001 and Fondecyt Regular N°1231174. This work was granted access to the HPC resources of CALMIP supercomputing center under the allocation 2023-1044.

**Funding** Open access funding provided by Université Toulouse III - Paul Sabatier. EC acknowledges support from the Agencia Nacional de Investigación y Desarrollo (ANID)/Programa Formación de Capital Humano Avanzado (PFCHA) (Doctorado Becas Chile/2019-72200310). BD acknowledges support from ANID (Concurso de Fortalecimiento al Desarrollo Científico de Centros Regionales 2020-R20F0008-CEAZA, Anillo Eclipse ACT210071, and Centro de Investigación Oceanográfica en el Pacífico Sur-Oriental COPAS COASTAL FB210021, Fondecyt Regular N°1231174). BD also acknowledges support from the French National program LEFE (Les Enveloppes Fluides et l'Environnement) through the SEPICAF project. CM acknowledges support from Proyecto ANID Fondecyt Postdoctorado código 3200621, Data Observatory Foundation ANID Technology Center No. DO210001 and Fondecyt Regular N°1231174. This work was granted access to the HPC resources of CALMIP supercomputing center under the allocation 2023-1044.

**Data availability** The monthly SST data from HadISST used here is available at https://www.metoffice.gov.uk/hadobs/hadisst/data/downl oad.html. The monthly 10-m winds and sea level pressure fields from
NCEP/NCAR Reanalysis 1 are available at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. The monthly data of sea water potential temperature from the GLORYS12V1 product of the CMEMS reanalysis can be downloaded from https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEARPHY_001_030/services. CMIP5 and 6 data is available through several portals that can be found at https://pcmdi.llnl.gov/mips/cmip5/data-access-getting-started.html and https://pcmdi.llnl.gov/CMIP6/, respectively.

## Declarations

**Conflict of interests** The authors have no relevant financial or non-financial interests to disclose.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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