# Weakened interannual Tropical Atlantic variability in CMIP6 historical simulations

Laura Sobral Verona, Paulo Silva, Ilana Wainer, Myriam Khodri

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Laura Sobral Verona, Paulo Silva, Ilana Wainer, Myriam Khodri. Weakened interannual Tropical Atlantic variability in CMIP6 historical simulations. Climate Dynamics, 2023, 10.1007/s00382-023-06696-9. hal-04004609

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# Weakened Interannual Variability in the Tropical Atlantic

**Laura Sobral Verona** (✉ verona.laura@usp.br)

University of Sao Paulo: Universidade de Sao Paulo https://orcid.org/0000-0002-7565-2009

**Paulo Silva**

University of Sao Paulo: Universidade de Sao Paulo

**Ilana Wainer**

University of Sao Paulo: Universidade de Sao Paulo

**Myriam Khodri**

LOCEAN: Laboratoire d'Oceanographie et du Climat Experimentations et Approches Numeriques

## Research Article

**Keywords:** Atlantic Zonal Mode, Atlantic Meridional Mode, hydroclimate, 34 ITCZ, Tropical Atlantic Variability, CMIP6

**Posted Date:** December 6th, 2021

**DOI:** https://doi.org/10.21203/rs.3.rs-1108528/v1

**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License.
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Weakened interannual variability in the
Tropical Atlantic

Laura Sobral Verona¹*†, Paulo Silva¹*†, Ilana Wainer¹ and Myriam Khodri²

1\* Oceanographic Institute, University of São Paulo, São Paulo, Brazil.

²LOCEAN-IPSL, Sorbonne University, Paris, France.

*Corresponding author(s). E-mail(s): verona.laura@usp.br; paulo2.silva@usp.br;

Contributing authors: wainer@usp.br; mkhodri@gmail.com ;

†These authors contributed equally to this work.

Abstract

Climate variability in the Tropical Atlantic is complex with strong
ocean-atmosphere coupling, where the sea surface temperature (SST)
variability impacts the hydroclimate of the surrounding continents. We
observe a decrease in the variability of the Tropical Atlantic after 1970
in both CMIP6 models and observations. Most of the Tropical Atlantic
interannual variability is explained by its equatorial (Atlantic Zonal
Mode, AZM) and meridional (Atlantic Meridional Mode, AMM) modes
of variability. The observed wind relaxation after 1970 in both the equa-
torial and Tropical North Atlantic (TNA) plays a role in the decreased
variability. Concerning the AZM, a widespread warming trend is observed
in the equatorial Atlantic accompanied by a weakening trend of the
trade winds. This drives a weakening in the Bjerknes Feedback by deep-
ening the thermocline in the eastern equatorial Atlantic and increasing
the thermal damping. Even though individually the TNA and Tropical
South Atlantic (TSA) show increased variability, the observed asym-
metric warming in the Tropical Atlantic and relaxed northeast trade
winds after the 70s play a role in decreasing the AMM variability. This
configuration leads to positive Wind-Evaporation-SST (WES) feedback,
increasing further the TNA SST, preventing AMM from changing phases
as before 1970. Associated with it, the African Sahel shows a positive
precipitation trend and the Intertropical Convergence Zone tends to
shift northward, which acts on maintaining the increased precipitation.

**Keywords:** Atlantic Zonal Mode, Atlantic Meridional Mode, hydroclimate, ITCZ, Tropical Atlantic Variability, CMIP6
# 1 Introduction

Climate variability in the Tropical Atlantic region is complex and significantly different from that in the Pacific. It is not dominated by a single mode of variability like the El Niño-Southern Oscillation (ENSO) in the tropical Pacific, but instead different mechanisms with similar importance are at play (Sutton et al, 2000). Sea surface temperature (SST) variability in this region has important impacts on the climate of the surrounding continents (Lübbecke et al, 2018), influencing the meridional displacement of the Intertropical Convergence Zone (ITCZ) and the hydrological cycle (Carton et al, 1996; Giannini et al, 2003; Back and Bretherton, 2009; Tokinaga and Xie, 2011), hurricane activity (Vimont and Kossin, 2007; Patricola et al, 2014; Zhang et al, 2017) as well as impacting the marine ecosystems and fish availability (Binet et al, 2001; Subramaniam et al, 2013). A strong ocean-atmosphere coupling is present, where ocean currents, upwelling, and SST patterns are linked to pressure systems and the onset of the West African Monsoon (Cabos et al, 2019) and Northeast Brazil (NEB) precipitation (Nobre and Shukla, 1996).

The main modes of interannual variability in the Tropical Atlantic are the equatorial (zonal) and meridional modes, represented by the first two empirical orthogonal functions (EOFs) of detrended SST anomalies (Deser et al, 2010). The Atlantic Zonal Mode (AZM) or Atlantic Niño is an equatorial zonal mode akin to the Pacific El Niño, which is characterized by an equatorial cold tongue with typical SST amplitude of approximately 0.5°C, frequency of 2-3 years and the strongest amplitude during June, July and August (JJA) (Zebiak, 1993; Deser et al, 2010). However, unlike its Pacific counterpart, this mode has been suggested to be a modulation of the seasonal cycle (Burls et al, 2012), as well as being more concentrated around the equator and in the eastern side of the basin than the ENSO (Zebiak, 1993).

The dynamics underlying the Atlantic Zonal Mode is in parts similar to that of the Pacific El Niño (Ding et al, 2010), so that the Bjerknes Feedback (Bjerknes, 1969) observed in the Tropical Pacific is also present in the Tropical Atlantic (Keenlyside and Latif, 2007). In its positive phase, anomalous warming in the eastern Atlantic coupled with weaker trade winds is associated with anomalous eastward currents bringing warmer waters from the west as well as weaker upwelling in the eastern equatorial region and deeper thermocline, which combined act to enhance the initial positive anomaly, closing the feedback cycle (Bjerknes, 1969; Chang et al, 2006; Keenlyside and Latif, 2007; Deppenmeier et al, 2016; Dippe et al, 2019; Silva et al, 2021). The opposite happens in its negative phase.

On the other hand, although ocean dynamics is essential to explain the equatorial SST variations, air-sea fluxes control the off-equatorial SST anomalies for the AZM (Martín-Rey et al, 2019). Other mechanisms have also been discussed to generate the equatorial Atlantic SST variability, besides the Bjerknes Feedback, for example, the role of equatorward advection of north tropical Atlantic subsurface temperature anomalies (Richter et al, 2013) and equatorial deep jets (Brandt et al, 2011). It has also been discussed that a large part
of the variability of this mode is due to thermodynamic processes (68±23% of the variance) (Nnamchi et al, 2015), but other studies (e.g. Lübbecke et al, 2018, and references therein) conclude that despite the significant contribution of thermodynamics to the variability, the dynamics and in particular the Bjerknes Feedback plays the main role.

The AZM is tightly coupled to West African rainfall variability. The leading EOF of precipitation during JJA displays an anomaly pattern that is the strongest along the northern coast of the Gulf of Guinea (Chang et al, 2006). A warming in the equatorial Atlantic in this season leads to a weaker West African Monsoon by decreasing the sea level pressure gradient between the equator and the Saharan heat low (Losada et al, 2010). A decrease in rainfall variability over the coast of Guinea has also been associated with a weakening of the zonal mode (Tokinaga and Xie, 2011).

The second mode of Atlantic SST variability is known as the Atlantic Meridional Mode (AMM). It is represented by an interhemispheric dipole pattern characterized by cross-equatorial SST anomaly gradient, whose nodal line is displaced slightly north of the equator (Servain, 1991; Xie and Carton, 2004; Chang et al, 2006; Doi et al, 2009; Cabos et al, 2019). Its strongest amplitude occurs during the equatorial warm season (March, April and May, MAM) with SST anomalies of 0.3 to 0.5°C in the subtropics (Carton et al, 1996; Xie and Carton, 2004; Deser et al, 2010; Cabos et al, 2019). This mode shows variability from interannual to decadal scales (Carton et al, 1996; Chang et al, 1997; Amaya et al, 2017). The meridional SST gradient is also connected to the ITCZ displacement into the warmer hemisphere, therefore affecting the precipitation pattern over South America, in particular in NEB (Nobre and Shukla, 1996; Xie and Carton, 2004; Chang et al, 2006; Deser et al, 2010).

The AMM shows pronounced coupling of the ocean-atmosphere through thermodynamic feedbacks (Carton et al, 1996). An anomalous cross-equatorial SST gradient pointing north drives a southwesterly surface wind anomaly reducing the predominant trade winds over the warmer region (the northern Tropical Atlantic). This decreases the evaporative cooling at the surface, causing it to warmer further (Chang et al, 1997; Chiang and Vimont, 2004; Mahajan et al, 2010; Amaya et al, 2017). In contrast, the other pole shows surface cooling, related to trade winds intensification. The SST changes are linked to changes in surface heat fluxes, characterizing a positive wind-evaporation-SST (WES) feedback (Xie and Philander, 1994; Chang et al, 1997, 2006; Amaya et al, 2017) that acts to reinforce the anomalous cross-equatorial SST gradient. In addition, results from satellite analysis during a strong AMM event in 2009 point to the importance of the wind-induced upwelling that could also act as positive feedback by further increasing the SST gradient (Foltz et al, 2012). While the WES positive feedback preferentially amplifies the meridional SST gradient, the Bjerknes Feedback is the dynamical mechanism counteracting the thermodynamic air-sea feedback (Chang et al, 2006; Deser et al, 2010).
4 *Weakened interannual variability in the Tropical Atlantic*

Some studies have addressed changes in Atlantic SST variability that could be related to mean state changes and weakening of the feedback mechanisms. It has been shown that the interannual variability of Atlantic SST zonal gradient has decreased by 48+/-13(11)% in variance for 1960-1999 (Tokinaga and Xie, 2011). This indicates a significant weakening of the AZM due to a weaker Bjerknes Feedback related to mean state changes. A decrease in variability of the AZM after 2000 has also been linked to a weakening in the Bjerknes Feedback due to weaker thermocline feedback and stronger thermal damping (Silva et al, 2021). Results presented in (Prigent et al, 2020) agree with the AZM weakening by showing a decrease of more than 30% in the eastern equatorial Atlantic SST variability during March, June and July between 1982-1999 and 2000-2017. The Bjerknes Feedback is also pointed to have weakened after 2000 together with an increased net heat flux damping (Prigent et al, 2020).

It has been suggested that changes in the equatorial Atlantic SST vari-
ability might be related to changes in the Atlantic Meridional Overturning
Circulation (AMOC) associated with the Atlantic Multidecadal Oscillation
(AMO) (Prigent et al, 2020). An increase in the equatorial Atlantic SST vari-
ability has been shown during AMO negative phase, related to an increase in
the AZM amplitude of about 120% compared to positive AMO (Martín-Rey
et al, 2018). It is hypothesized (Prigent et al, 2020) that the change in AMO
phase at the beginning of 1990 from negative to positive might have played
a role in the AZM variance decrease after the 2000s. Conversely, it has also
been argued that there is no significant correlation between AMO and SST
equatorial variability, at least from 1900 to 2000 (Tokinaga and Xie, 2011).
The authors suggest instead a relation to the hemisphere asymmetric cooling
effect from anthropogenic aerosols that causes weakening in the meridional
and zonal SST gradients. It is also discussed that changes in the amplitude
and strength of the surface wind associated with the AMM development could
modulate the equatorial SST, which would also affect the AZM (Martín-Rey
and Lazar, 2019).

Mean state changes in the Tropical Atlantic could play an important role in the variability modes. It has been shown that the Tropical Atlantic SST positive trend since the 1950s plays a role in the decrease in the AZM variability (Tokinaga and Xie, 2011). On the other hand, recently a lack of surface warming has been identified in the Atlantic cold tongue from 1979 to 2018, which has been linked to decreased wind stress forcing and subsequent thermocline shoaling (Nnamchi et al, 2020). This agrees with results shown in (Prigent et al, 2020) that also observe significant warming from 1982-1999 to 2000-2017 mainly north of the equator, but not in the eastern equatorial Atlantic. In this study, we aim to understand how the Tropical Atlantic SST leading modes of variability changed since the beginning of XX century and the related changes in the precipitation over South America and Africa in the CMIP6 models and observations.
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# 2 Methods

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## 2.1 Observations and reanalysis

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As reference data sets for SST, we use the NOAA Extended Reconstruction Sea Surface Temperature version 5 (ERSSTTv5) (Huang et al, 2017), available from 1854-2014. We also use the monthly precipitation analysis of the Global Precipitation Climatology Project (GPCP) (Adler et al, 2018) (1979-2014) and surface wind stress from the NCEP Climate Forecast System Reanalysis (CFSR) (Saha et al, 2010).

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## 2.2 Models

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We use monthly single-member historical simulations from 37 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Eyring et al, 2016), that were available to us at the time of this analysis, for the period ranging from 1900 to 2015. These simulations are forced with common data sets that are largely based on observations. These include evolving, externally imposed forcings such as solar variability, volcanic aerosols, and changes in atmospheric composition (greenhouse gases and aerosols) caused by human activities. The data is available in the Earth System Grid Federation (ESGF) website.

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In this study, a multimodel framework is used in which one ensemble member from each of 37 models is chosen. The models and ensemble members used are summarized in Table 1. All model outputs were remapped to a 1°x1° horizontal resolution before the analyses.

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## 2.3 Data Analysis

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We estimate the Tropical Atlantic variability modes AZM and AMM based on index-driven metrics, that assumes the pole of variability does not change its position. As in (Tokinaga and Xie, 2011) the AZM is represented by an equatorial zonal SST difference index defined as the difference between the averaged SST anomaly over the ATL3 region (3°N-3°S, 20°W-0°, (Zebiak, 1993)) and WATL region (3°N-3°S, 25°W-45°W), defined in Fig. 1a. This index, hereafter referred to as AZM, is highly correlated ($r=+0.93$) with the Atlantic Niño index defined by the ATL3 region (Tokinaga and Xie, 2011). The AMM is defined as the difference between the SST anomaly average of Tropical North Atlantic (TNA, 15°N-5°N and 50°W-20°W) and Tropical South Atlantic (TSA, 5°S-15°S and 20°W-10°E) as defined in (Doi et al, 2009), illustrated in Fig. 1b. The spatial patterns of both modes are acquired by regressing the index into SST anomaly field. To evaluate changes in the mode's long-term variability, we compute a 31-year moving standard deviation of the index series.

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The ITCZ position proxy is estimated as in (Pontes et al, 2020b). It is a position index based on weighted mean latitude where precipitation is greater than 50% of its maximum in the zonal mean. Double-ITCZ biases may be included if the associated precipitation is greater than 50% of the considered

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6 Weakened interannual variability in the Tropical Atlantic

207 maximum (Pontes et al, 2020b). We considered the ITCZ position over the
208 Tropical Atlantic for the average from 40°W to 20°W.

209 All CMIP6 results are shown for the multimodel mean (MMM) of the
210 37 models (Table 1) with 90% confidence interval. We consider at least 60%
211 agreement among CMIP models. For the linear trends, we consider the MMM
212 significant where at least 60% of the models are significant, at least at the 90%
213 level.

<table><caption>**Table 1** Models and the respective ensemble members used. The letters *r*,*i*,*p* and *f* stand for *realization*, *initialization method*, *physics* and *forcing*, respectively.</caption><thead><tr><th>Models</th><th>Ens. Member</th><th>Institution</th><th>Reference</th></tr></thead><tbody><tr><td>ACCESS-CM2</td><td>rliilp1fl</td><td>CSIRO-ARCCSS</td><td>Kiss et al (2020)</td></tr><tr><td>ACCESS-ESM1-5</td><td>rliilp1fl</td><td>CSIRO</td><td>Ziehn et al (2020)</td></tr><tr><td>AWI-CM-1-1-MR</td><td>rliilp1fl</td><td>AWI</td><td>Semmler et al (2018)</td></tr><tr><td>AWI-ESM-1-1-LR</td><td>rliilp1fl</td><td>AWI</td><td>Contzen et al (2021)</td></tr><tr><td>BCC-CSM2-MR</td><td>rliilp1fl</td><td>BCC</td><td>Wu et al (2019)</td></tr><tr><td>BCC-ESM1</td><td>rliilp1fl</td><td>BCC</td><td>Xiao-Ge et al (2019)</td></tr><tr><td>CESM2</td><td>rliilp1fl</td><td>NCAR</td><td>Lauritzen et al (2018)</td></tr><tr><td>CESM2-WACCM</td><td>rliilp1fl</td><td>NCAR</td><td>Liu et al (2018)</td></tr><tr><td>CIESM</td><td>rliilp1fl</td><td>THU</td><td>Lin et al (2020)</td></tr><tr><td>CMCC-CM2-HR4</td><td>rliilp1fl</td><td>CMCC</td><td>Cherchi et al (2019)</td></tr><tr><td>CMCC-CM2-SR5</td><td>rliilp1fl</td><td>CMCC</td><td>Cherchi et al (2019)</td></tr><tr><td>CMCC-ESM2</td><td>rliilp1fl</td><td>CMCC</td><td>Cherchi et al (2019)</td></tr><tr><td>CNRM-CM6-1</td><td>rliilp1f2</td><td>CNRM-CERFACS</td><td>Voldoire et al (2019)</td></tr><tr><td>CNRM-CM6-1-HR</td><td>rliilp1f2</td><td>CNRM-CERFACS</td><td>Voldoire et al (2019)</td></tr><tr><td>CNRM-ESM2-1</td><td>rliilp1f2</td><td>CNRM-CERFACS</td><td>Séférían et al (2019)</td></tr><tr><td>CanESM5</td><td>rliilp1fl</td><td>CCCma</td><td>Swart et al (2019)</td></tr><tr><td>CanESM5-CanOE</td><td>rliilp2fl</td><td>CCCma</td><td>Swart et al (2019)</td></tr><tr><td>E3SM-1-0</td><td>rliilp1fl</td><td>E3SM-Project</td><td>Golaz et al (2019)</td></tr><tr><td>E3SM-1-1</td><td>rliilp1fl</td><td>E3SM-Project</td><td>Golaz et al (2019)</td></tr><tr><td>E3SM-1-1-ECA</td><td>rliilp1fl</td><td>E3SM-Project</td><td>Burrows et al (2020)</td></tr><tr><td>EC-Earth3</td><td>rliilp1fl</td><td>EC-Earth-Consortium</td><td>Wyser et al (2020)</td></tr><tr><td>EC-Earth3-AerChem</td><td>rliilp1fl</td><td>EC-Earth-Consortium</td><td>Döscher et al (2021)</td></tr><tr><td>EC-Earth3-CC</td><td>rliilp1fl</td><td>EC-Earth-Consortium</td><td>Döscher et al (2021)</td></tr><tr><td>EC-Earth3-Veg</td><td>rliilp1fl</td><td>EC-Earth-Consortium</td><td>Wyser et al (2020)</td></tr><tr><td>EC-Earth3-Veg-LR</td><td>rliilp1fl</td><td>EC-Earth-Consortium</td><td>Döscher et al (2021)</td></tr><tr><td>FGOALS-g3</td><td>rliilp1fl</td><td>CAS</td><td>Li et al (2020)</td></tr><tr><td>GISS-E2-1-G</td><td>rliilp1fl</td><td>NASA-GISS</td><td>Kelley et al (2020)</td></tr><tr><td>GISS-E2-1-H</td><td>rliilp1fl</td><td>NASA-GISS</td><td>Kelley et al (2020)</td></tr><tr><td>INM-CM5-0</td><td>rliilp1fl</td><td>INM</td><td>Volodin et al (2019)</td></tr><tr><td>IPSL-CM6A-LR</td><td>rliilp1fl</td><td>IPSL</td><td>Boucher et al (2020)</td></tr><tr><td>IPSL-CM6A-LR-INCA</td><td>rliilp1fl</td><td>IPSL</td><td>Boucher et al (2020)</td></tr><tr><td>MPI-ESM-1-2-HAM</td><td>rliilp1fl</td><td>HAMMOZ-Consortium</td><td>Tegen et al (2019)</td></tr><tr><td>MPI-ESM1-2-HR</td><td>rliilp1fl</td><td>DKRZ</td><td>Müller et al (2018)</td></tr><tr><td>NorCPM1</td><td>rliilp1fl</td><td>NCC</td><td>Bethke et al (2021)</td></tr><tr><td>NorESM2-LM</td><td>rliilp1fl</td><td>NCC</td><td>Seland et al (2020)</td></tr><tr><td>NorESM2-MM</td><td>rliilp1fl</td><td>NCC</td><td>Seland et al (2020)</td></tr><tr><td>UKESM1-0-LL</td><td>rliilp1f2</td><td>MOHC</td><td>Sellar et al (2019)</td></tr></tbody></table>

3 Results

215 The majority of the CMIP6 models represent the Tropical Atlantic variability
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level for at least 60% of the models (Fig. 1a and b). The AZM is defined by the characteristic warm region on the eastern equatorial Atlantic (Xie and Carton, 2004; Deser et al, 2010). It also shows a negative SST anomaly that surrounds the equatorial warm tongue. The AMM typical pattern is also represented by the CMIP6 MMM, which shows a positive SST gradient pointing to the Northern Hemisphere (Xie and Carton, 2004; Amaya et al, 2017; Deser et al, 2010). The wind pattern points to the warmer region for both modes. This means a wind relaxation in the equatorial region for the positive phase of the AZM and over the warm pole of the AMM (Xie and Carton, 2004; Deser et al, 2010; Amaya et al, 2017; Silva et al, 2021).

Although some equatorial Atlantic SST and wind biases still show the same pattern as in previous model generations (Richter and Xie, 2008), results from Richter and Tokinaga (2020) show a slight improvement in the Tropical Atlantic SST representation from CMIP5 to CMIP6 models. Despite the little improvement in the model's mean state, many CMIP6 models reproduce well the Tropical Atlantic SST variability, correctly capturing the amplitude and seasonality of both AZM and AMM (Richter and Tokinaga, 2020). In addition, it is suggested that there is a relatively weak link between mean state biases and the quality of the simulated variability (Prodhomme et al, 2019).

The precipitation pattern associated with each mode is also represented in Fig. 1a and b. For both modes the precipitation is associated with the warmer regions, this characterizes the precipitation pattern in the African Sahel for the AZM (Chang et al, 2006; Deser et al, 2010). The AMM is mostly related to the precipitation pattern in the NEB and Amazon (Nobre and Shukla, 1996; Deser et al, 2010). The resultant precipitation pattern is connected also to the ITCZ displacement into the warmer hemisphere (Nobre and Shukla, 1996; Chiang et al, 2002).

After 1970, the AZM and AMM present a decrease in variability, shown by the 31-year moving standard deviation in Fig. 1c and d. The CMIP6 MMM agrees with ERSSTv5 data set. The reduction in variability for the AZM has already been identified for observations from 1960 to 1999 (Tokinaga and Xie, 2011). Furthermore, the AZM weakening has also been shown after the 2000s in observations and reanalyses (Prigent et al, 2020) and for an ensemble of 7 reanalyses products (Silva et al, 2021). The AZM variability reduction is related to the Bjerkens Feedback weakening due to weaker thermocline feedback (Tokinaga and Xie, 2011) and stronger thermal damping (Silva et al, 2021), with the main contribution from the increased latent heat flux (Prigent et al, 2020). There is a strong coupling between the wind pattern and the Tropical Atlantic SST gradients. The weakened Bjerknes Feedback is identified to be responsible for the decrease in the AZM variability, in connection to a relaxation of the trade winds (Tokinaga and Xie, 2011), as well as its changes in response to the SST anomalies (Prigent et al, 2020).
8 Weakened interannual variability in the Tropical Atlantic

<figure><img src="image_2.png" /><figcaption>**Fig. 1** Spatial pattern of the Tropical Atlantic variability modes (a) AZM for JJA and (b) AMM for MAM from 1900 to 2015. Shading represents the index correlation for the SST anomaly (°C °C⁻¹) and the contour lines represent the index regressed against precipitation anomaly (mm day⁻¹ °C⁻¹), both only shown for regions where correlations are significant at the 95% for at least 60% of the models. Vectors represent wind stress anomaly regressed against each mode index (N m⁻² °C⁻¹), only shown where at least one of the wind components present significant correlation at 95% for at least 60% of the models. The black delimited boxes represent regions used for the index calculation (see Section 2). (c) and (d) 31-year running standard deviation for each mode for ERSSTv5 and for CMIP6 MMM. Shading indicates the 90% confidence interval for the MMM. Dots indicate where at least 60% of the models agree on the sign of the 1st derivative</figcaption></figure>

To better understand the annual variability of each mode for the SST and the related wind stress pattern, we compute the annual cycle of the regression of surface wind stress and SST anomalies against the AZM index in JJA (in the zonal section between 40°W-10°E averaged over 4°N-4°S, box a in Fig. 2d) and the AMM index in MAM (in the meridional section between 15°S-15°N averaged over 25°W-15°W, box b in Fig. 2d). The results are shown in Figs. 2a and b, respectively.

The mode's annual variability is well represented by the CMIP6 MMM, as shown in Fig. 2. As it is observed in Fig. 2a, the AZM pattern in the SST becomes evident in JJA, with a zonal dipole with high positive correlations in the east and negative in the west. The AZM for the CMIP6 MMM shows greater variability during JJA, a delay of one month compared to the ERSSTv5 data set that represents the greatest AZM variability during MJJ (Fig. 2c). Deser et al (2010) show AZM the highest variability during MJJ obtained from the first empirical orthogonal function based on HadISST data set during 1900–2008. For the CMIP6 and CMIP5 data sets, Richter and Tokinaga (2020)
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also show a small delay in the variability of the zonal mode compared to ERA-5 reanalysis. As the ERSSTv5, the ERA-5 standard deviation peaks in June, while for the CMIP6 and CMIP5 MMM the peak occurs one month later, as shown here, during July. As observed in Fig. 2c, there is a large similarity between the magnitude of the zonal mode's variability represented by the CMIP6 MMM and ERSSTv5.

A strong positive correlation with zonal wind stress is observed in the preceding season (MAM), which suggests that the variability of the AZM is influenced by the variability of surface winds in the western side of the basin one season before. This is consistent with the observed positive correlation between MAM wind stress in the western equatorial Atlantic and SST variability in JJA in the eastern side (Richter et al, 2013). This peak in wind variability is delayed by one month in the CMIP6 models (peak in May Fig. 2c) when compared to the ERA-5 reanalysis (peak in April), although this does not change the peak in variability in MAM (Richter and Tokinaga, 2020).

For the AMM (Fig. 2c) the greatest variability in ERSSTv5 occurs during MAM, agreeing with Deser et al (2010). However, this peak is not as clear as that observed for the AZM in JJA. The CMIP6 does not represent as well the climatological change in monthly variability for the AMM. The standard deviation from January to June is almost the same, after that there is a slight decrease in variability. The spatial SST pattern of the AMM appears in MAM for the CMIP6 MMM (Fig. 2b). Richter and Xie (2008) point that the zonal wind variability peaks one month earlier (January) than the SST (February), which is consistent with the forcing of AMM variability by trade wind variations. There is not a clear lag between the wind and the SST pattern in the northern Tropical Atlantic in our results; however, a region of negative correlations starts to develop in JJA between 0° and 5°S, which could be explained by the WES feedback, being a result of the wind forcing observed in the preceding season.

The AMM is dominated by the TNA variability (Amaya et al, 2017), thus looking only at the northern pole, the AMM variability in ERA-5 is highest from February through May (Richter and Tokinaga, 2020), similar to that represented by ERSSTv5 (Fig. 2c). However, as shown in Fig. 2c, the CMIP6 AMM variability underestimates that of ERRSSTv5. Similarly, it has also been found that AMM variability represented by CMIP6 underestimates that of ERA-5 (Richter and Tokinaga, 2020). The authors suggest this to be related to biases in the mixed layer depth in CMIP models.
<figure><img src="image_3.png" /><figcaption>**Fig. 2** (a) Regression of annual Tropical Atlantic variability modes index against SST and wind stress anomalies for each calendar month. Stippling indicates regions where correlations are significant at the 95% level for at least 60% of the models. Vectors are shown only where the correlations are significant at the 95% for at least 60% of the models, for at least one of the wind components. In (a) AZM (JJA) we use the meridional mean in 4°N-4°S and in (b) AMM (MAM) we use the zonal mean in 25°W-15°W. Vector units in (a) and (b) are N m⁻² °C⁻¹. (c) Monthly standard deviations of the AZM and AMM modes for ERSSTv5 and for CMIP6 MMM with 90% confidence interval. All results are for the period of 1900 to 2015. (d) Boxes used in (a) and (b)</figcaption></figure>

Given the clear relation between wind stress and the Tropical Atlantic SST pattern shown in Fig. 2a and b, we look for changes that could explain the decrease in the variability of the modes. For the wind pattern related to the AZM we use box 1 in Fig. 3a that presents winds from the southeast in the climatology. In the anomaly time series (Fig. 3b) we observe wind stress anomalies that are typically from the southeast up to 1970, then the anomalies reverse to the northwest from 1970 to 2000, and after that, the anomalies start to rotate to the south again. This period from 1970 to 2000 where the wind over the AZM region relaxes (northwest anomalies) is consistent with the period that we observe the reduction in the long-term variability (Fig. 1c). The weakening of the trade winds has been suggested to be related to the AZM decrease in variability in connection with deepening of thermocline and intensified warming in the eastern equatorial Atlantic (Tokinaga and Xie, 2011), weaker thermocline feedback, and stronger thermal damping (Silva et al, 2021). The increase in the thermal damping seems to lead the Tropical Atlantic SST changes (Prigent et al, 2020).
The same behavior is observed for the AMM, where the climatological wind pattern is from the northeast (Fig. 3a box 2), in the time series the anomalies agree with this predominant direction up to 1980. After 1980, the wind stress anomaly over the TNA also shows relaxation up to 2015, with wind stress anomalies from the southwest (Fig. 3c), the same period in which we observe a decrease in AMM variability (Fig. 1d). The wind relaxation over the TNA normally is associated with an increase in SST anomalies via positive WES feedback (Chang et al, 1997; Amaya et al, 2017). This may account for the AMM decreased variability after 1980, especially related to AMM negative phases. During these phases, the TNA shows negative SST anomalies (opposite in the TSA), the wind relaxation in this region acts in the opposite direction of the positive WES feedback. Therefore, the wind anomaly would imply a decrease in the interhemispheric SST gradient during negative phases.

The associated precipitation also changes its behavior during 1900 to 2015 for the African Sahel (Fig. 3a box 3) and NEB (Fig. 3a box 4), in agreement with the observational data set (Fig. 3d and e). The precipitation over the Sahel increases significantly after the 1970s at the 90% level for at least 60% of the CMIP6 models. Both data sets agree on the positive trend since 1970 and 1980, respectively 0.0037 mm day⁻¹ year⁻¹ for CMIP6 and 0.0087 mm day⁻¹ year⁻¹ for observations. The NEB time series shows a larger amplitude compared to the Sahel precipitation and presents a decrease in precipitation over time, enhanced after the 1970s. The CMIP6 models agree in the direction of the change over NEB (shown by the dots that represent the agreement in the sign of the first derivative); however, only 7 of the 37 models present a significant negative trend at the 90% level. Conversely, a decrease in precipitation over the Sahel accompanied by an increase in the equatorial West Africa and Amazon are reported in Tokinaga and Xie (2011), with similar trends observed for 2000-2017 in Prigent et al (2020).
<figure><img src="image_4.png" /><figcaption>**Fig. 3** (a) CMIP6 MMM wind stress annual average for 1900 to 2015. (b) and (c) Vector time series of wind stress anomaly from 1900 to 2015 for each predominant season for the Tropical Atlantic variability modes at the black delimited boxes in (a), for AZM average between 4°N-4°S, 40°W-10°E (box 1) and for AMM mean between 5°N-15°N, 50°W-15°W (box 2). The black vectors show where 60% or more models agree on the sign for at least one of the wind stress components, for the gray arrows there is no agreement among models. The precipitation time series over (d) Sahel (box 3) and (e) NEB (box 4) for 1900 to 2015 for the CMIP6 MMM and GPCP data set. Shading indicates the 90% confidence interval for the MMM. Dots indicate where at least 60% of the models agree on the sign of the 1st derivative. In (d) the continuous line indicate the trend from 1970 (for CMIP6 MMM) and from 1980 (for GPCP) to 2015</figcaption></figure>

Thus, the decrease in the variability of the modes appears to be related to the long-term wind relaxation after the 1970s, which impacts the hydroclimate associated with the South America and African continents, in particular the NEB and Sahel regions. To investigate further, we examine the monthly spatial long-term trends from 1970 to 2015 for the Tropical Atlantic SST, wind stress, and precipitation over land (Fig. 4).

We show that there is a relative agreement between CMIP6 MMM and observations trends. For both data sets, a similar pattern emerges from the SST trend, the northern Tropical Atlantic presents a larger positive trend (~2×10⁻² °C year⁻¹) while for the southern Atlantic the SST trend is smaller
(~0.9 × 10⁻² °C year⁻¹). This trend after the 1970s is probably related to the regime shift around the 1980s, but with slightly different times around the world, that can represent a major change in the Earth's biophysical systems (Reid et al, 2016). From the mid-1970s onward, increased warming over the Northern Hemisphere (1.0°C) compared to 0.6°C in the southern basin is observed (Servain et al, 2014). The Northern Hemisphere warming also corroborates previous results that show significant MJJ warming for 2000-2017 (Prigent et al, 2020). The SST positive trend over the equatorial region contributes to the observed weakening of the AZM variability by decreasing its zonal gradient.

The increase in the Tropical Atlantic SST is associated with the increase in the variability for each pole of the AMM (TNA and TSA, Supplementary Fig. 1). The TNA warming results in its increased variability under global warming, which is influenced by an increase in ENSO variability and teleconnections (Yang et al, 2021). It is worth noting, however, that other studies (Hu et al, 2013; Lübbecke and McPhaden, 2014) identify a decrease in ENSO variability after 2000, so the link between the increase in TNA variability and ENSO is not clear. We observe warming also in the TSA after 1970, slightly smaller than the TNA (Fig. 4a,b and Supplementary Fig. 2), in association with increased variability in this region. Even though individually each pole of the AMM shows an increase in variability, the meridional asymmetric warming in the Tropical Atlantic (Fig. 4a and b) plays a role in decreasing AMM long-term variability (Fig. 1d).

The warming is associated with the southwest wind stress trend (Fig. 4a and b) located mostly over the equatorial region of the Northern Hemisphere (TNA box in Fig. 1b) for the CMIP6 MMM, which characterizes a relaxation of the northeast trade winds. Approximately the same pattern of wind relaxation is shown by the CFSR trends; however, the observational data set presents a westward trend farther north (~15°N) as well as a small trend in the Southern Hemisphere (~5°S). The long-term relaxation of the winds in the Northern Hemisphere is probably coupled with the increased warming in this region (Tokinaga and Xie, 2011). The intensified interhemispheric SST gradient together with the wind relaxation in the equatorial Northern Hemisphere contribute to the decrease in AMM long-term variability. The SST positive trend is reinforced by the positive WES feedback resultant from the wind relaxation, which acts on reducing the surface latent heat flux, increasing further the SST (Xie and Carton, 2004; Amaya et al, 2017). Considering the 1970-2015 trends, the configuration of increased SST and weakened winds in TNA is maintained, therefore decreasing AMM variability. The same pattern of increased SST over the Northern Hemisphere and weakening of the north-east trade winds was identified to be related to decreased AMM variability for the mid-Pliocene (Pontes et al, 2020a).

Both SST and wind stress trends can modify the precipitation in the Tropi-cal region. CMIP6 MMM and GPCP show a positive trend in the precipitation over the Sahel. We suggest that the increased precipitation over the Sahel is
connected with the strong SST warming in the Northern Hemisphere, considering the positive correlation between SST and precipitation found in the Tropical Atlantic, also apparent over the subtropical North Pacific (Xie et al, 2010). In contrast, a positive trend in the precipitation over West Africa and Guinea coast and a negative trend over the Sahel has been shown (Tokinaga and Xie, 2011). These differences are probably related to the stronger SST positive trend over the Atlantic cold tongue region, which is not observed here. There is a positive trend for the precipitation over the Amazon only shown by the GPCP data set, which corroborates with previous results (Tokinaga and Xie, 2011). However, the CMIP6 MMM does not present any consistent trend over South America, which has been pointed to as a region of precipitation biases in CMIP models (Richter and Xie, 2008; Tian and Dong, 2020).

We observe a positive trend in the ITCZ position proxy based on the related precipitation pattern (Pontes et al, 2020b) of 0.022° year⁻¹ that is significant at the 90% level for the observations (Fig. 4c). The CMIP6 MMM also shows a significant positive trend of 0.010° year⁻¹ that is represented by 46% of the models (17/37). It is consistent with the increased SST positive trend over the Northern Hemisphere that serves as an attractor for the ITCZ shift to the warmer region (Schneider et al, 2014; Mamalakis et al, 2021). The ITCZ northward trend explains the observed positive trend in the precipitation over the Sahel (Fig. 4a,b and 3d). It was expected a negative trend in the NEB precipitation, however, it was neither consistent among CMIP6 models nor for the observational data set (Fig. 3e and 4). The precipitation trend and ITCZ northward shift are also consistent with the AMM reduced variability. Given the constant anomalous warming and wind relaxation in the Northern Hemisphere related to the decreased AMM variability, the ITCZ shifts northward following the increased SST (Fig. 4c). As consequence, the precipitation pattern is also displaced northward, resulting in increased precipitation over the African Sahel (Fig. 4a) and decreased precipitation over the NEB (not consistent among CMIP6 models).

The ITCZ tends to follow the warmer oceanic region, but the results change when considering different periods and different ITCZ position proxies. A southward displacement of the ITCZ because of the intensified SST warming in the eastern equatorial Atlantic has been suggested (Tokinaga and Xie, 2011), based on the increased cloud cover over this region from 1960 to 2000. Differently, using as a proxy of the ITCZ position the latitude where the meridional component of the wind stress along 30°W equals zero, no significant long-term change in the ITCZ position from 1964 to 2012 has been observed (Servain et al, 2014). From 2000 to 2017, an increase in precipitation over the western African coast, northeastern Brazil, and north of the equator has been detected (Prigent et al, 2020), related to the northward shift of the ITCZ position. The ITCZ trend shown here by observations and the CMIP6 MMM is consistent with the SST positive long-term trend intensified over the northern basin and the increase in Sahel precipitation.
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The changes in Tropical Atlantic variability may be modulated by the AMO behavior during the last 50 years (Prigent et al., 2020). After the 1970s, the AMO index changes its phase from negative to positive (Frajka-Williams et al., 2017), which matches the period of decreased modes variability. During positive AMO, the AZM displays smaller amplitudes when compared to its amplitude during negative AMO (Martín-Rey et al., 2018). It is also pointed that AMO may show causal influence on the AMM on decadal time scales (Vimont and Kossin, 2007; Veiga et al., 2020). Moreno-Chamarro et al. (2020) observed a link between AMOC, AMO, and ITCZ variability. The authors identified a change in AMOC's cross-equatorial heat transport that induces AMO phase change (from negative to positive). This leads to a northward ITCZ shift, as observed in our results, to compensate the energy imbalance across the hemispheres. Therefore, changes in AMOC and AMO phasing could modify AMM variability.
16 Weakened interannual variability in the Tropical Atlantic

<figure><img src="image_5.png" /><figcaption>**Fig. 4** Trends for SST, wind stress and precipitation for (a) ERSSTv5, CFSR and GPCP respectively of *monthly series* from 1979 to 2010 and for (b) CMIP6 MMM. Trends are only shown where significant at the 95% level for observations and reanalysis and where at least 60% of the models show significant trends at the 95% level for the CMIP6 MMM. Note that (a) and (b) present different scales for precipitation and vectors. (c) ITCZ position proxy based on *annual* precipitation for CMIP6 MMM (dark blue) and GPCP (light blue). Shading indicates the 90% confidence interval for the CMIP6 MMM. Dots indicate where at least 60% of the models agree on the sign of the 1st derivative</figcaption></figure>

4 Summary and conclusions

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We rely on monthly historical simulations from 37 CMIP6 models (Eyring et al, 2016) as well as reconstructions and reanalyses for SST, precipitation,
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and surface wind stress to investigate changes in the Tropical Atlantic modes of variability in 1900-2015 and their connection with precipitation in the surrounding continents. Both the AZM and AMM show a decrease in variability after 1970.

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In agreement with Richter et al (2013), our results show a strong positive correlation between MAM zonal winds stress and the JJA time series of the AZM, suggesting that changes in wind stress variability in the preceding season could influence variability in SST in the eastern equatorial Atlantic, which peaks in JJA (Zebiak, 1993; Deser et al, 2010). For the AMM, on the other hand, we do not observe a clear lag with respect to wind stress, with the largest correlation between the mode and the wind occurring in MAM, the season when the mode peaks (Deser et al, 2010).

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Considering the coupling of the modes with the surface wind, we observe a relaxation in both the equatorial Atlantic region and the northern Tropical Atlantic after 1970. Concerning the AZM, a widespread warming trend is observed in the equatorial Atlantic, accompanied by the weakening trend of the trade winds. This resonates with other studies that discuss the weaker trades and warmer equatorial Atlantic driving a weakening in the Bjerknes Feedback by deepening the thermocline in the eastern equatorial Atlantic (Tokinaga and Xie, 2011; Silva et al, 2021). The warmer sea surface also leads to stronger thermal damping (Prigent et al, 2020; Silva et al, 2021), further weakening the Bjerknes Feedback and reducing the AZM variability.

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Regarding the AMM, the relaxation of the northeastern trade winds contributes to a decrease in the amplitude of AMM negative phases by weakening the WES feedback. We identified a warming trend and increased variability for each pole, TNA and TSA, of the AMM. Yang et al (2021) suggest TNA variability is associated with global warming and increased ENSO variability, although this contrasts with other studies (e.g., Hu et al, 2013; Lübbecke and McPhaden, 2014) that show a decrease in ENSO variability after 2000. The TNA and TSA show increased variability individually, but the asymmetric warming associated with the northeast trade winds relaxation after 1970 maintains the positive WES feedback, thus leading to weakened AMM variability. This was also identified for the mid-Pliocene (Pontes et al, 2020a), an important parallel given it was a period of significantly warmer climate than at present, with similar atmospheric carbon dioxide concentrations.

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Associated with SST and trade wind, a positive precipitation trend over the Sahel is also observed in both the simulations and the observations. Positive precipitation trends over the Amazon and negative over central South America are also present in the observations but are not significant in the CMIP6 multimodel mean. The results suggest that the southwest wind anomaly in the TNA (relaxation of the northeasterly trade winds) carries more humidity into the Sahel region, resulting in positive precipitation trends. Consistent with the decreased variability in the AMM, the ITCZ tends to shift northward, which helps to maintain the increased precipitation over the African Sahel (Chiang et al, 2002; Deser et al, 2010).
18 Weakened interannual variability in the Tropical Atlantic

These variability changes may be modulated by the AMO phase changing, from negative to positive, after the 1970s (Prigent et al, 2020; Frajka-Williams et al, 2017). The AMO phase change induced by AMOC's change in cross-equatorial heat transport also leads to ITCZ northward shift (Moreno-Chamarro et al, 2020), which is consistent with our results for AMM and AZM decreased variability. Further studies are needed to investigate the role of AMO or other remote mechanisms that could modulate the observed and simulated shift in Tropical Atlantic Variability in the last decades.

**Acknowledgments.** This study was supported in part by the Grants FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo): 2020/08490-0; 2021/04596-1; 2018/14789-9; CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico): 301726/2013-2, 405869/20134; CNPq.MCT.INCT.CRIOSFERA 573720/2008-8, by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and CAPES 88887.314387/2019-00. This work was undertaken in the framework of the French L-IPSL LABEX and the IPSL Climate Graduate School EUR.

534 Declarations

**Conflict of interest:** The authors declare that they have no conflict of interests.

**Authors' contributions:** LV, PS and IW designed the study. LV and PS performed the analysis and wrote the manuscript draft. All authors contributed to the interpretation of the results and to the improvement of the manuscript.

**Data availability:** The datasets generated during and/or analysed during the current study are available in the Earth System Grid Federation (ESGF) repository [https://esgf-node.llnl.gov/search/cmip6/]. Other data sets (ERSSTv5, GPCP and CFSR) are available in Climate Data Guide repository [https://climatedataguide.ucar.edu/].

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