# Air-sea feedback in the northeastern tropical Atlantic in boreal summer at intraseasonal time-scales

Mamadou Thiam, Gaëlle de Coëtlogon, Malick Wade, Mamadou Sarr, Bouya Diop

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

## To cite this version:

Mamadou Thiam, Gaëlle de Coëtlogon, Malick Wade, Mamadou Sarr, Bouya Diop. Air-sea feedback in the northeastern tropical Atlantic in boreal summer at intraseasonal time-scales. Quarterly Journal of the Royal Meteorological Society, 2025, 151 (770), pp.e4982. 10.1002/qj.4982. hal-05022560

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RESEARCH ARTICLE

# Air-sea feedback in the northeastern tropical Atlantic in boreal summer at intraseasonal time-scales

Mamadou Thiam<fnref n="1" />,<fnref n="2" /> | Gaëlle de Coetlogon<fnref n="2" /> | Malick Wade<fnref n="1" /> | Mamadou Sarr<fnref n="1" />

Correspondence

Mamadou Thiam, Laboratoire des Sciences de l'Atmosphère et des Océans—Matériaux, Énergie et Dispositifs (LSAO-MED), Université Gaston Berger, Saint-Louis, Sénégal.
Email: thiam.mamadou9@ugb.edu.sn

Funding information

LEFE project of the French National Centre for Scientific Research (CNRS), the Laboratoire Mixte International ECLAIRS2 and the French Embassy in Senegal

ABSTRACT

This study analyzes air-sea interaction in the northeast tropical Atlantic (NETA) at intraseasonal time-scales, focusing on the feedback between sea-surface temperature (SST) and surface winds in July, August, and September, using observational data from 2009 to 2020 (Reynolds SST and advanced scatterometer surface winds) and European Centre for Medium-Range Weather Forecasts Reanalysis v5 data from 2000 to 2020. The analysis reveals that anomalies in the northeastern trade winds over the NETA, often associated with an African easterly wave, result in warmer SST when winds weaken and cooler SST when they intensify. In turn, this SST anomaly modulates surface wind anomalies, generating a negative feedback loop that lasts for 2–3 weeks. A secondary positive feedback loop at the northern edge of the SST anomaly contributes to this prolonged persistence and results in a slow northward migration of the coupled SST-sea-level-pressure gradient pattern. A warm SST anomaly between 5° N and 15° N therefore alters the propagating pattern of easterly waves by creating a negative pressure anomaly, enhancing moisture transport convergence off the West African coast and intensifying precipitation. It potentially contributes to the seasonal northward migration of the intertropical convergence zone from early to mid boreal summer. This study highlights the importance of better understanding these air-sea interaction processes and their representation in operational forecasting models to improve weather forecasts in the western Sahel region.

KEYWORDS

air-sea coupling, atmospheric circulation, intraseasonal variability, northeastern tropical Atlantic, precipitation, SST-surface wind feedback, western Sahel precipitation

1 | INTRODUCTION

Summer precipitation in the Sahel exhibits significant spa-
tiotemporal variability under the influence of the West
African monsoon. This complex system relies on multi-
ple atmospheric components, from the lower layers to the
free troposphere, that interact to generate summer rainfall.

The population of this region depends heavily on this
precipitation for essential needs such as water, agriculture,
and livestock (Sultan *et al.*, 2005). It is therefore imperative
to better understand the factors modulating this variability
to improve climate forecasts.

Previous studies have emphasized the role of land
albedo parametrization in global coupled models for

This is an open access article under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Quarterly Journal of the Royal Meteorological Society* published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.

<footnote n="1">¹Laboratoire des Sciences de l'Atmosphère et des Océans - Matériaux, Énergie et Dispositifs (LSAO-MED), Université Gaston Berger, Saint-Louis, Senegal</footnote>

<footnote n="2">²Laboratoire Atmosphère et Observations Spatiales (LATMOS), Sorbonne Université, Paris, France</footnote>
representing boreal summer tropical rainfall climatology, with subtropical deserts playing a key role through heat low mechanisms (Terray et al., 2018). However, sea-surface temperature (SST) in the equatorial Pacific, Mediterranean, and Indian Ocean has also been shown to strongly influence Sahelian rainfall variability across time-scales ranging from intraseasonal to longer periods (Biasutti et al., 2008; Diatta & Fink, 2014; Flaounas et al., 2012; Fontaine et al., 2010; Gaetani et al., 2017; Gian-nini et al., 2003; Janicot et al., 2011; Mutton et al., 2024; Rodríguez-Fonseca et al., 2011; Rowell, 2003; Sylla et al., 2013; Vizy & Cook, 2002). SST influences deep convection through latent heat release, thereby promoting precipita-tion development in tropical regions. In turn, this modifies ocean conditions by affecting turbulent heat and momen-tum fluxes, primarily through evaporation and surface wind stress. SST and surface winds therefore form a cou-pled system that it is crucial to accurately represent in climate models for reliable predictions of tropical climate dynamics and their variability (Xie, 2024).

However, these models frequently exhibit a thermal bias in the eastern tropical Atlantic, simulating an intertropical convergence zone (ITCZ) located farther south than observed in boreal spring and summer (Harlaß et al., 2018; Mohino et al., 2019; Siongco et al., 2015). Even atmospheric global climate models forced with observed SSTs display this bias (Richter & Xie, 2008), suggesting that the models do not accurately represent the surface wind response to SST anomalies and the underlying ocean-atmosphere coupling mechanisms.

Beyond large-scale atmospheric responses of the Matsuno-Gill type to midtropospheric convective heat-ing (Gill, 1980), local mechanisms explain SST influences on atmospheric circulation, particularly in regions with strong SST gradients (Small et al., 2008; Xie, 2004). One such mechanism is that of Lindzen and Nigam (LN) (Lindzen & Nigam, 1987), which explains surface wind responses to SST gradients via hydrostatic adjustment of sea-level-pressure (SLP) gradients. For instance, a warm SST anomaly releases heat flux, warming the overlying air, which rises and creates a low-pressure zone. Conversely, in cooler areas, the air cools, becomes denser, and a high-pressure zone develops. This process generates a pressure gradient force directed from high- to low-pressure zones that influences wind direction: surface winds tend to slow down when blowing from warm to colder waters, and they accelerate when blowing from cold to warmer ones. Surface winds can also respond to SST anomalies through changes in stratification and vertical mixing of horizon-tal momentum, as proposed by Sweet et al. (1981) and Wallace et al. (1989) (hereafter referred to as the SW mech-anism for Sweet and Wallace). Warm SST destabilizes the atmospheric boundary layer, increasing turbulent mixing

in this layer. This enhances the vertical flux of horizontal momentum from stronger winds aloft to weaker surface winds, thereby strengthening the latter. Conversely, a cold SST stabilizes the atmospheric column and reduces vert-cal mixing, decoupling surface winds from stronger winds in the free troposphere, weakening the wind near the ocean surface.

In the tropical Atlantic, these processes result in a zonal equatorial coupled mode, based on the Bjerknes feedback and similar to the El Niño-Southern Oscillation in the equatorial Pacific (albeit weaker) that dominates the seasonal cycle and interannual variability (Chang et al., 1997; Zebiak, 1993). In the meridional direction, SST anomalies modulate the position of the ITCZ during boreal spring and summer, subsequently influencing pre-cipitation in surrounding regions (Fontaine et al., 1999; Hastenrath & Heller, 1977; Hisard, 1980; Lamb, 1978). Interhemispheric SST gradients in the tropical Atlantic affect surface pressure gradients and meridional wind components, thereby modulating the ITCZ and associated precipitation regimes (Folland et al., 1986; Hastenrath & Greischar, 1993; Lamb, 1978). Numerical modeling studies (Bates, 2008; Wu et al., 2005; Wu & Liu, 2002), as well as observational analyses (Amaya et al., 2017), have confirmed that SST anomalies in the northeastern tropical Atlantic (NETA) can organize interhemispheric SST dipoles during boreal spring and summer, primarily via a coupled wind-evaporation-SST (WES) feedback. Karnauskas (2022) proposed a simple model in which two mechanisms (LN and SW) combine to explain the significance of the WES feedback in the tropical Atlantic. Furthermore, Kataoka et al. (2019) demonstrated that coupling the variable oceanic mixed-layer depth with winds and SST more than doubles the growth rate of the WES mode and enhances the equatorward propagation of coupled disturbances. At the seasonal scale, Diakhaté et al. (2018) found evidence that the LN mechanism is active in this region and plays a key role in controlling the position of the marine ITCZ edges. Moreover, using a set of regional simulations forced by SST, Wane et al. (2023) showed that positive (negative) SST anomalies in the NETA are associated with increased (decreased) precip-itation in the western Sahel, driven by a strengthening (weakening) of low-level moisture transport toward the continent at the seasonal scale. This mechanism was further investigated using monthly European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) data by Thiam et al. (2024), who proposed that SST warm-ing in the NETA accelerates the West African westerly jet (WAWJ) at 10° N via geostrophic adjustment, potentially explaining the observed increase in precipitation over the western Sahel (Senegal). Their results highlight a signal on the southern edge of the SST anomaly, possibly linked
<figure><img src="image_3.png" /></figure>

to hydrostatic adjustments in the lower atmosphere (see their fig. 9b).

However, none of these studies investigated the SST-surface wind feedback at the time-scales relevant to the LN and SW processes; that is, from a few hours to a few days. In the Gulf of Guinea, studies of this kind have identified a feedback between SST and surface winds—negative near the Equator and positive between the Equator and the Guinean coast (5° N)—involving both the SW and LN mechanisms (de Coëtlogon et al., 2010; de Coëtlogon et al., 2014). This feedback significantly influences precipitation (Leduc-Leballeur et al., 2013) and drives the northward migration of rainfall from the Gulf of Guinea to its northern coast in May–June (Meynadier et al., 2016). It is therefore pertinent to investigate similar SST-surface wind coupling mechanisms in the NETA during spring and early boreal summer, when the ITCZ attains its northernmost position. The goal of this study is to investigate the mechanisms of this coupling using daily resolution data, enabling detailed monitoring of the processes involved at synoptic and intraseasonal time-scales.

The structure of the article is as follows: Section 2 describes the data used, Section 3 outlines the data processing and analysis methods, Section 4 presents the results and discusses their implications, and Section 5 summarizes the main conclusions of the study.

## 2 | DATA

### 2.1 | Reynolds SST

The National Oceanic and Atmospheric Administration' (NOAA's) Daily Optimum Interpolation Sea Surface Temperature version 2.1, commonly referred to as Reynolds SST after Reynolds et al. (2007), will be used in this article. This dataset combines ocean temperature observations from satellite platforms and in-situ measurements to generate global fields. The primary data source is the advanced very high-resolution radiometer (AVHRR) satellite instrument, offering broad temporal coverage from late 1981 to 2023 and a spatial resolution of 0.25° × 0.25°.

We utilized daily data from the period 2009–2020, available at NOAA's National Centers for Environmental Information (https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/).

### 2.2 | ASCAT wind

The advanced scatterometer (ASCAT) from the operational meteorological satellite MetOp is also used. Managed by the European Organization for the

Exploitation of Meteorological Satellites, ASCAT's main objective is to measure the speed and direction of winds at 10 m above the ocean surface. These data have been available since 2009. We used daily data with the highest spatial resolution (25 km or 0.25° × 0.25°). The data can be downloaded at https://podaac.jpl.nasa.gov/dataset/ASCATA_L2_25KM_CDR#.

### 2.3 | Reanalyses using ERA5

Reanalyses are an essential complement to observational data, helping to overcome limitations found in satellite or in-situ observations, such as limited spatial coverage (especially in coastal regions) and instrumental constraints. Here, we used daily data from ERA5 Hersbach et al. (2023). Produced by the Copernicus Climate Change Service (C3S), ERA5 data are available on a horizontal grid of 0.25° × 0.25° and cover 37 vertical levels ranging from 1,000 hPa to 1 hPa. The parameters used include SST, precipitation, SLP, zonal wind u and meridional wind v at 10 m high (hereafter “surface wind”), geopotential height Z, and specific humidity q.

Note that ERA5 SST is derived from the Hadley Centre Sea Ice and Sea Surface Temperature version 2 dataset for the period from 1979 to August 2007, and the daily Operational Sea Surface Temperature and Ice Analysis product from September 2007 to the present (2023). It closely matches the Reynolds observational product (Yang et al., 2021). We used the period 2009–2020 for comparisons with observational data, but the whole period 2000–2020 was used for a more precise analysis of coupling mechanisms.

## 3 | METHODS

### 3.1 | Removing the seasonal cycle

To remove the seasonal cycle and longer time-scales, we applied a high-pass Lanczos filter, based on the Fourier transform of the sinc function (Duchon, 1979). All parameters (daily atmospheric data as well as SST) are high-pass filtered with a cut-off frequency of 1/90 days, and the remaining signals are referred to as “intraseasonal anomalies”. The intraseasonal standard deviation (std) was calculated from the July, August, and September (JAS) daily intraseasonal anomalies as follows:

$$ \text{std} = \sqrt{\frac{1}{N} \sum_{i=1}^{N} X_{i}^{2}}, $$

where $X_i$ is the high-pass-filtered data in JAS.
<figure><img src="image_4.png" /></figure>

Note that our results remain unchanged when we apply a slightly shorter frequency of 1/60 days.

## 3.2 | Low-level moisture transport

The specific humidity q and horizontal winds $U = (u, v)$ are available between 1,000 hPa and 850 hPa every 25 hPa, which correspond to the seven lower pressure levels in ERA5. Since most of the humidity is located in this layer over the ocean, the calculation of moisture transport is made by summing, from daily data, $q \times U$ over these seven levels. The resulting moisture transport magnitude has units of kg·m$^{-1}$·s$^{-1}$, with a zonal ($HT_x$) and a meridional ($HT_y$) component.

The divergence of moisture transport $\partial HT_x/\partial x + \partial HT_y/\partial y$ is then calculated using a classical centered scheme, where i and j represent the indices of grid points, and $\delta x$ and $\delta y$ represent the zonal and meridional grid-point lengths respectively:

$$ \nabla \cdot \mathbf{H}\mathbf{T}(i, j) = \frac{\mathbf{H}\mathbf{T}_x(i+1, j) - \mathbf{H}\mathbf{T}_x(i-1, j)}{2 \delta x} + \frac{\mathbf{H}\mathbf{T}_y(i, j+1) - \mathbf{H}\mathbf{T}_y(i, j-1)}{2 \delta y}. \quad (1) $$

Note that a similar centered scheme is used to compute the meridional gradient of SST, noted thereafter $dSST/dy$, as well as the meridional gradient of SLP, noted $dSLP/dy$.

## 3.3 | Maximum covariance analysis

Maximum covariance analysis (MCA) is considered a generalization of principal component analysis for two or more different parameters (Bretherton et al., 1992). It is used here to identify the most dominant mode of covariability between SST (matrix X in the following formula) and surface wind (matrix Y) anomalies. The X and Y matrices cover the same temporal range with N rows (using observational data from July 1 to September 30 over the period 2009–2020), and the columns correspond to different spatial grid points (organized in any order). The covariance matrix between the two variables is calculated as follows:

$$ C_{XY} = \frac{1}{N} X^T Y. \quad (2) $$

An eigenvalue decomposition is applied to the cross-covariance matrix $C_{XY}$:

$$ C_{XY} = PDQ^T. \quad (3) $$

The eigenvectors P and Q contain the maximum covariant modes between X and Y, and D is a diagonal matrix whose

values indicate the variance represented by each mode, sorted in decreasing order. A time series a (i.e., of length N) is then obtained by performing linear regression (see Section 3.4 for detail) of SST anomalies onto the leading SST eigenmode (first column of P), and a time series b by performing linear regression of zonal (or meridional) wind anomalies onto the leading zonal (or meridional) wind eigenmode (first column of Q). a and b are called expansion coefficients of P and Q. The squared covariance fraction (SCF) corresponds to the first eigenvalue (first value in D) divided by the total variance (sum of all diagonal values in D) and measures the importance of the mode. R, the correlation between a and b, can also be calculated, providing additional information on the mode. We present a “homogeneous” spatial pattern for SST and a “heterogeneous” spatial pattern for wind speed, corresponding respectively to the linear regression of X and Y onto the same time series a.

To assess the robustness of a given mode, 200 Monte Carlo simulations were performed: the same decomposition is applied while randomly permuting the rows of matrix Y, and R and SCF values are retained. If the temporal coherence between the two fields is altered, there is indeed no reason to expect a high SCF and/or R. Therefore, a mode is considered significant if more than 95% of the 200 SCF test values are lower than the actual SCF.

Note that the modes are presented with an arbitrarily chosen sign (+1 or -1). Being a linear tool for statistical analysis, all conclusions from the MCA remain the same if we consider the maps of first-mode anomalies with the opposite sign, representing the opposite fluctuation of the parameters.

## 3.4 | Correlation and linear regression

For independent and identically distributed samples of random parameters $X_i$ and $Y_i$ for $i = 1$ to $n$, the Pearson correlation is calculated using the following estimator Von Storch and Zwiers (1999):

$$ \hat{R} = \frac{\sum_{i=1}^{n} (X_i - \bar{X})(Y_i - \bar{Y})}{\sqrt{\left(\sum_{i=1}^{n} (X_i - \bar{X})^2\right) \left(\sum_{i=1}^{n} (Y_i - \bar{Y})^2\right)}} \quad (4) $$

where $\bar{X} = (1/n) \sum_{i=1}^{n} X_i$ and $\bar{Y} = (1/n) \sum_{i=1}^{n} Y_i$.

Then, we apply the least-squares estimation of the slope of the simple linear regression, as described in Von Storch and Zwiers (1999):

$$ \hat{s} = \frac{\sum_{i=1}^{n} (X_i - \bar{X})(Y_i - \bar{Y})}{\sqrt{\sum_{i=1}^{n} (X_i - \bar{X})^2}}. \quad (5) $$
The resulting field $\hat{s}$ represents the variation of $Y$ associated with a one standard deviation fluctuation of $X$. For example, if $X$ represents an index and $Y$ SST anomalies, $\hat{s}$ indicates the variation of SST in degrees Celsius associated with an increase of one standard deviation in the index.

Note that if there is causality between the correlated variables, a significant signal will be detected, but not vice versa. To distinguish between correlations indicating a genuine physical relationship and those occurring by chance, a commonly used “P value” is 0.05, suggesting a 5% probability that a correlation exceeds the threshold by pure coincidence (i.e., without a physical basis). Assuming atmospheric parameters are independent from one day to the next, implying one degree of freedom per day in the time series, the correlation threshold is set using a Student's t test.

Note, in addition, that the same remarks previously made for the MCA stand for the linear regression: the modes are presented for an arbitrarily chosen choice of sign (+1 or -1), and all conclusions remain the same for the contrary fluctuation of parameters.

## 3.5 | Time lag

MCA or linear regression can incorporate a time lag between $X$ and $Y$ to identify signals in $Y$ that precede or lag behind fluctuations in $X$. For instance, with $X$ describing fluctuations from July 1 to September 30 between 2000 and 2020, performing linear regression on $Y$, or an MCA with $Y$, “lag 0” refers to the regression of $Y$ anomalies at the same time (July 1–September 30). “Lag -1” indicates the regression or MCA with $Y$ 1 day prior (i.e., between June 30 and September 29). “Lag -2” refers to 2 days prior (between June 29 and September 28); and so on.

# 4 | RESULTS AND DISCUSSIONS

## 4.1 | Climatology and variability

We first present the climatology of the NETA region in JAS over the period 2009–2020 (Figure 1). Since ASCAT wind data are only available from 2009 to 2020, all other datasets were restricted to this period for consistency in this figure only. Figure 1a–h shows the JAS means and std of Reynolds SST, ASCAT surface wind, and ERA5 reanalyses. The WAWJ is a key component of the atmospheric circulation in this region; thus, the zonal component of the surface wind is shaded in Figure 1a,b. The zonal wind means from ERA5 and ASCAT are broadly comparable in Figure 1a,b, as are their std (Figure 1c,d). The correlation

of zonal winds, averaged over the region outlined by the black frame, is 0.74, meaning that they share almost half of their variance. In both observations and ERA5, the zonal wind std is maximal (around $3 \text{ m} \cdot \text{s}^{-1}$) in the region of the marine ITCZ, between 8° N and 15° N (Figure 1c,d); that is, slightly north of where the mean zonal wind is maximum. However, ERA5 slightly underestimates the zonal wind variability compared with the ASCAT product, especially off the Senegal coast between 10° N and 15° N, in the WAWJ region. The fit between zonal surface wind in reanalyses and observations is therefore very good, except for this small bias, which is also documented in Campos et al. (2022).

The SST average in the north tropical Atlantic shows values exceeding 28°C along the African coasts between 10° N and 15° N for both datasets (Figure 1e,f). However, it reaches a minimum near Cape Blanc, where SSTs drop below 24°C due to coastal upwelling, with its southern boundary situated there during JAS. This region exhibits large SST gradients, making it the location of maximum std of Reynolds SST anomalies, which peak above 1°C near Cape Blanc (Figure 1g). A similar pattern is observed in ERA5, albeit with slightly lower std values (Figure 1h).

On average, the low-level moisture transport shows a dominant southwestward direction over the ocean through the eastern branch of the Azores anticyclone north of 15° N. Further south and east of 30° W, it transports humidity toward the West African continent, with a maximum between 8° N and 13° N via the WAWJ (Figure 1i). The std of moisture transport magnitude anomalies is at its maximum in a zonal band located between 8° N and 12° N and between 32° W and 19° W, as indicated by the black dashed frame. This maximum exceeds $60 \text{ kg} \cdot \text{m}^{-1} \cdot \text{s}^{-1}$, a notably high value representing approximately half of the mean moisture transport (Figure 1j). This exact location also corresponds to a maximum of interannual variability related to the Senegal JAS precipitation (Thiam et al., 2024, their fig. 7b).

Precipitation reaches its maximum where the maritime trade winds converge, around 10° N, forming the marine ITCZ (black contours in Figure 1i). The highest precipitation occurs east of 20° W, near the West African coast, precisely in the region where zonal winds attain their maximum intensity. Notably, the precipitation std is also highest in the WAWJ region, coinciding with the regional maximum of moisture transport variability (Figure 1j).

In summary, the mean and std of wind, and to a lesser extent SST, help explain the strong variability of moisture transport entering West Africa south of Senegal in the WAWJ region. This variability is primarily driven by the high surface wind std in this area, where winds converge on average (ITCZ), combined with notable SST
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**FIGURE 1** (a, b, e, f, i)
Averages of the raw parameters and (c, d, g, h, j) intraseasonal standard deviations (std) of their intraseasonal anomalies for July–September (JAS), 2009–2020. (a, c) Advanced scatterometer (ASCAT) and (b, d) European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) zonal wind (m·s⁻¹), (e, g) Reynolds and (f, h) ERA5 sea-surface temperature (SST; °C), and ERA5 (i) average moisture transport and (j) its std (kg·m⁻¹·s⁻¹). Black contours in (i) and (j) represent JAS average precipitation (PPT) and its std respectively. The black dashed frame in (a) and (b) indicates the West African westerly jet region, the area of maximum std in low-level moisture transport. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

variability, although the latter is less pronounced than in the upwelling region off Cape Blanc. These results are consistent with the findings of Pu and Cook (2012) and Lélé *et al.* (2015), who demonstrated that moisture transport fluctuations in this region are primarily governed by dynamic processes. Therefore, surface wind and SST are key parameters to investigate for a better understanding of air–sea coupling and its influence on precipitation.

## 4.2 | MCA between SST and surface wind

There is good agreement between wind and SST in observations and ERA5, but analyzing the observational

data may provide deeper insights owing to the larger SST variance observed in the observations (Figure 1g) compared with ERA5 reanalysis (Figure 1h). With the influence of SST on low-level atmospheric circulation becoming evident when considering observational data (Small *et al.*, 2008; Xie, 2004), we chose to perform an MCA between daily observed SST anomalies (Reynolds product) and wind velocity (ASCAT product) anomalies from July 1 to September 30 over the period 2009–2020, from lag –15 days (i.e., the wind leads the SST by up to 15 days) to lag +15 days (the wind lags the SST). These anomalies are considered in a reduced region; that is, between 5° N and 15° N: including latitudes north of 15° N did, in fact, cause the MCA to be dominated by the huge SST variability
present in the southern edge of the coastal upwelling, off Cape Blanc (which is beyond the scope of this study). In addition, winds in August and September exhibit greater variance than in July (not shown), but the latter is still included in the MCA as our objective is to identify coherent structures over the longest possible period.

The SCF is clearly largest when the atmosphere leads the SST by a few days (negative lags), with a maximum at lag -6 (blue profile, Figure 2a), far exceeding all Monte Carlo tests (plotted as blue dots). These highly significant modes illustrate a predominantly atmosphere-to-ocean forcing, with the corresponding SST and surface wind anomaly patterns shown when the wind leads by 6 days: a warm SST anomaly centered in the WAWJ region (Figure 2b) and strong northeasterly anomalies (Figure 2c). The shading indicates a negative wind speed anomaly south of 10-14° N and a positive anomaly further north; the wind anomalies are directed opposite to the mean westerly wind around 10° N but are aligned with the mean northeasterlies to the north (Figure 2c). This weakens the southwesterlies around 10° N by more than 0.3 m·s⁻¹ and leads to the formation of a warm SST anomaly of up to 0.5°C within 1 week. This result is in agreement with previous findings in the region and was expected, since SST mainly results from a balance between incoming solar heating and cooling through surface latent heat flux (Foltz & McPhaden, 2006; Wang et al., 2017): a reduction in surface wind speed decreases the latent heat flux, thereby creating a warm SST anomaly. A strong vertical gradient in specific humidity can also increase the loss of latent heat flux.

When the atmosphere follows the SST (at positive lags), the SCF becomes weaker and loses its significance between lags 5 and 8, but the modes become significant after lag 9 for over 2 weeks. At lag 3, the SST map shows a positive anomaly similar to the one at lag -6, albeit slightly weaker (Figure 2d). The wind map now shows northeasterly anomalies north of 10° N and westerly anomalies south of 8-10° N, both corresponding to positive speed anomalies (Figure 2e). The change in anomaly direction between 5° N and 8° N after lag 0 may indicate a negative feedback between the warm SST anomaly and the surface winds along its southern edge, whereas the persistence of positive wind anomalies north of 10-12° N suggests a positive feedback.

The spatial patterns of the modes at lags -6 and +3 are shown, but all lags from -15 to +15 were assessed. They all displayed a first mode for SST anomalies similar to Figure 2b,d. As mentioned previously, the SCF decreases sharply after reaching its maximum at lag -6, and the correlation coefficient *R* peaks near lag 0. Its symmetrical pattern around lag 0 suggests a significant response of surface winds to the SST anomaly generated by the

initial wind fluctuations (Figure 2a). This highlights the need for a more detailed examination of the feedbacks involved from the perspective of the SST mode. Indeed, intraseasonal variability in this region is predominantly driven by African easterly waves propagating along the southern edge of the midtropospheric African easterly jet (Diedhiou *et al.*, 1999; Mekonnen *et al.*, 2006; Núñez Ocasio *et al.*, 2020; Viltard *et al.*, 1997): using an atmospheric parameter-based index may not be ideal since its variance would be probably dominated by these waves. Therefore, the wind-SST interaction is best investigated using a linear lagged regression on an index calculated by averaging SST anomalies in the WAWJ region (between 8-12° N and 32-19° W), called SSTW (SST anomaly in the WAWJ region).

## 4.3 | Linear lagged regressions onto the SSTW index

Lagged linear regressions allow one to filter synoptic events from the perspective of the SST anomaly. Figure 3 shows SLP (black contours) and *dSLP/dy* (colors, top row), surface wind speed (colors, middle row), and SST (black contours) and *dSST/dy* (colors, third row) anomalies at various lags, ranging from 6 days before the SST anomaly fully develops to 6 days after.

At lag -6, there is a broad positive pressure anomaly (high) in the whole region, peaking between 15° N and 20° N over the ocean (top row). A negative pressure anomaly (low) is seen around 10° W in the heat low region over the continent. At lag -3, the oceanic high has shifted slightly westward. And at lag 0, the next low-pressure front appears over the continent along the coast north of 14° N. This westward-moving pressure dipole suggests a propagating wave consistent with the pattern of the African easterly waves. A longitude-lag diagram of the SLP linear regression averaged between 9° N and 15° N indeed highlights westward propagations speeds ranging from 5 to 8 m·s⁻¹ (not shown), which are characteristic of an easterly wave (Diedhiou *et al.*, 1999; Viltard *et al.*, 1997).

However, two factors disrupt this pattern over the ocean at lag 3: a significant pressure drop across most of the NETA, centered over the WAWJ region, and a sharp increase in the northwest. The pressure drop aligns with the warm SST anomaly (Figure 3, bottom row): there is clear colocalization of the pressure low at lags 3 and 6 with the SST maximum at lags 0 and 3 around 10°N between 20°W and 30°W. The meridional SLP gradient adjusts negatively to the meridional SST gradient, as predicted by the LN mechanism: the negative *dSST/dy* anomaly at lag 0 between 10°N and 15°N (Figure 3, bottom row) likely reinforces the strong positive *dSLP/dy* anomaly at the
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>FIGURE 2 Maximum covariance analysis between sea-surface temperature (SST) and surface wind anomalies using observations (Reynolds and advanced scatterometer [ASCAT]): (a) squared covariance fraction (SCF; blue line) and R (red line). Patterns at (b, c) lag -6 and (d, e) lag +3: (b, d) homogeneous maps (SST anomaly, °C) and (c, e) heterogeneous maps (surface wind anomaly, m·s⁻¹). The blue and red dots in (a) indicate SCF and R values obtained with 200 Monte Carlo tests. Only the first modes are shown. The black dashed frame indicates the West African westerly jet region. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>FIGURE 3 Linear regression of European Centre for Medium-Range Weather Forecasts Reanalysis v5 reanalyses on the SSTW index (index of sea-surface temperature [SST] anomaly in the West African westerly jet region) for lags -6 to +6 days. Top row: *dSLP/dy* (colors, $10^{-3} \text{ hPa} \cdot \text{m}^{-1}$) and sea-level pressure (SLP, contours, hPa). Middle row: 10 m wind speed (colors, $\text{m} \cdot \text{s}^{-1}$) and direction (arrows). Bottom row: *dSST/dy* (colors, $10^{-5} \text{ }^{\circ}\text{C} \cdot \text{m}^{-1}$) and SST (contours, $^{\circ}\text{C}$). Only statistically significant values at the 95% confidence level are shown. The parameters lead the SSTW index at negative lags and lag it at positive lags. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

same latitude (Figure 3, top row), possibly explaining the persistence of northeastern wind anomalies from lag -6 to lag 0 (Figure 3, middle row). In addition, the positive *dSST/dy* anomaly between 5 and $10^\circ\text{N}$ (Figure 3, bottom row) can account for the negative *dSLP/dy* after lag 0 at the same latitude (top row), leading to complete reversal of wind anomalies south of $10^\circ\text{N}$ (Figure 3, middle row). The SW mechanism also probably occurs, explaining why the persistent weak wind anomaly before lag 0 eventually diminishes after lag 0: the warm SST anomaly accelerates the surface wind just above, thereby strongly reducing the negative wind anomalies after lag 0.

The results presented here clearly highlight a combination of positive and negative feedback loops between SST anomalies and surface winds, which vary depending on the location of the SST anomaly and the time lag. To better understand them, we examine regressions averaged over the longitude band $32^\circ\text{W}-19^\circ\text{W}$ (highlighted by the black dashed frame in Figure 2). This approach provides a continuous view of the different feedbacks, showing lags from -15 to +15 days in a time-latitude diagram.

The linear regressions of ERA5 SST and surface wind anomalies are plotted in Figure 4b. The warm SST anomaly (black contours) forms before lag 0, reaches a maximum of about $0.25^\circ\text{C}$, at lag 0 between $5^\circ\text{N}$ and $15^\circ\text{N}$

by construction, and then dissipates within a week. This warming is partly explained by a positive turbulent heat flux anomaly at the sea surface (mostly latent, with the convention of positive vertical flux corresponding to the atmosphere warming the ocean), observed around $10^\circ\text{N}$ before lag 0 (Figure 4c). The heat flux anomaly is induced by the concomitant weakening of surface wind speed. Since, on average, the wind is eastward to northeastward in the SSTW region, southwestward wind anomalies mean weaker wind, and therefore less evaporation (i.e., cooling effect) at the ocean surface, and hence the warm anomaly. The short-wave radiative heat flux also contributes to the formation and subsequent damping of the warm SST anomaly, exhibiting positive anomalies at negative lags and negative anomalies at positive lags (not shown). Its magnitude, though slightly smaller than that of the latent heat flux anomaly, closely mirrors the precipitation anomaly pattern discussed in Section 4.4—negative prior to SST warming and positive thereafter—likely reflecting a response to cloud cover. Another possible mechanism for the SST warming is a reduction of vertical oceanic mixing following the surface wind weakening (Foltz & McPhaden, 2008).

The surface wind response to the warm anomaly can be observed before lag 0. Indeed, the timing of the SW effect
<figure><img src="image_11.png" /><figcaption>**FIGURE 4** Lagged linear regression on SSTW (sea-surface temperature [SST] anomaly in the West African westerly jet region) index of surface wind speed (colors, m·s⁻¹) with direction (arrows) and SST (black contours, °C), using (a) advanced scatterometer (ASCAT) observations or (b) ERA5 data. (c) Turbulent surface heat flux (colors, J·m⁻²) and SST anomaly (black contours). (d) dSLP/dy (colors, 10⁻⁵ Pa·m⁻¹; SLP: sea-level pressure) and correlation coefficient between dSST/dy anomaly and SSTW index (black contours). Only statistically significant values at a 95% confidence level are plotted. The parameters lead the SSTW index at negative lags and follow it at positive lags. Heat fluxes are positive downward (from the atmosphere to the ocean). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

(vertical destabilization) has been previously suggested to be very short (from a few minutes to a few hours; de Coët-logon *et al.* (2014)). Its signature can then manifest before lag 0, as soon as the growing anomaly is warm enough to create a significant surface wind response. This can, for example, explain why the decrease of the wind anomaly between lags -3 and 0 is faster than the growing phase of the anomaly, roughly between lags -10 and -4. Overall, this effect may contribute to the reversal of the wind anomalies from negative to positive lags within 2 weeks.

Additionally, *dSST/dy* coefficient correlations are the largest about 2° to 3° apart from the SST anomaly center;

that is, about 200–300 km (Figure 4d, black contours). In response, we then expect to get *dSLP/dy* anomalies that are positive in the north (*dSST/dy* < 0), thereby sustaining southwestward wind anomalies, and negative in the south of the warm anomaly (*dSST/dy* > 0), thereby sustaining northeastward wind anomalies. As the original wind anomalies are southwestward, the LN mechanism feedback would then be positive in the north and negative in the south of the warm anomaly. The positive feedback theoretically leads to a longer persistence on the north side of the anomaly compared with the south side, where the feedback is negative. It is also observed in the heat flux
anomalies in Figure 4c: the positive anomaly is located around 10°N between lags -9 and -2, when it creates the warm SST anomaly, whereas the negative flux anomaly between lags 3 and 6 is centered slightly further south, around 8°N: the dampening of the warm SST anomaly through the negative heat flux feedback is stronger on the southern side of the anomaly. As a result, the SST anomaly tends to propagate northward; the effect is slow, but the warm SST anomaly peaks around 10°N at lag -6 and around 12-13°N at lag 8; that is, 200-300 km further north in about 2 weeks (Figure 4b or c, black contours). We can also note that the negative *dSLP/dy* anomaly south of 10°N is hardly significant here, whereas the positive *dSLP/dy* anomaly between 10°N and 14°N at negative lags is very intense (Figure 4d, shading): the negative feedback in the south rapidly dampens the original positive pressure gradient anomaly, whereas the positive feedback in the north sustains it.

The resulting slow northward migration of the warm SST anomaly is clearly seen in the *dSST/dy* coefficient correlation (Figure 4d, black contours): the minimum negative value is located around 11-12°N at lag -9 and shifts to approximately 15-16°N at lag +6, indicating a northward displacement of about 400 km over roughly 2 weeks. Notably, the two poles of opposite *dSST/dy* anomalies at lag 0 are situated at 7-9°N and 13°N, separated by about 500 km: the southern pole would reach the latitude of the northern pole after approximately 2-3 weeks. This time frame aligns with the interval between the minimum and maximum surface wind speed anomalies at 10°N, as shown in Figure 4a,b. Consequently, a full cycle would likely span 4-6 weeks. Therefore, it is plausible that the positive feedback mechanism along the northern edge of the SST anomaly drives a 4- to 6-week periodic pattern, with a spatiotemporal structure shown in Figure 4.

Interestingly, a significant *dSLP/dy* anomaly is found at lags +2 to +5 north of 15°N, which could be created by underlying negative *dSST/dy* anomalies; even if their pattern appears quite noisy (since their calculation by differentiation doubles the noise of the level in the time series), they are significant. Therefore, the southwestward surface wind anomalies north of 15°N between lags 0 and 3 could be partly controlled by the coupled positive feedback between the SST gradients and the SLP gradients in the north side of the warm SST anomaly, leading to acceleration of the northeasterlies. Their immediate cooling impact on the SST is seen via surface heat fluxes: a negative anomaly is found between 20°N and 25°N at the same lags. However, the reason for their sudden change in their northward propagation speed is not clear. Moreover, distinguishing this response from the typical eastward wave train and extratropical variance is challenging and should be investigated with a nonlinear statistical tool.

A similar linear regression was performed using SST and surface wind in observational datasets (Figure 4a). The SST anomaly is slightly warmer (0.3°C), and the deceleration of the wind preceding the SST is less pronounced (-0.5 to -0.4 m·s⁻¹ instead of -0.6 to -0.5 m·s⁻¹). However, the signals at positive lags are strictly similar, confirming that ERA5 parameters accurately capture the characteristics of the coupling between SST and surface wind, thereby increasing confidence in our results.

In addition, we observe a weak but significant warm SST anomaly propagating from the subtropical North Atlantic to the warm SST anomaly at 10°N before lag 0 (Figure 4a), together with significant wind anomalies, suggesting that another coupled SST-wind surface mechanism could occur between these two regions as well. However, the signals are too weak here to lead to a clear conclusion about the teleconnection between the North Atlantic and the north tropical Atlantic. Instead, Section 4.4 focuses on how the regional ocean-atmosphere coupling possibly affects atmospheric conditions in the western Sahel.

## 4.4 | Impact of the NETA mode on moisture transport and precipitation

In this section, we examine the influence of ocean-atmosphere coupling in the WAWJ region on moisture transport and its divergence, as well as on precipitation in NETA and West Sahel. The linear regression of integrated moisture transport between 1,000 and 850 hPa (in the lower atmosphere) and precipitation on the SSTW index are presented in Figure 5.

The transport anomalies (Figure 5, left column) closely resemble those of surface wind shown in Figure 3. At lags -6 and -3, a strong negative anomaly is observed in NETA with a weaker extension over the continent (colors), maximized between 8°N and 14°N over the ocean. This indicates a decrease in moisture transported to the continent via the WAWJ and, as expected, a decrease in precipitation in the same region (Figure 5, right panel). At lag 0, as discussed in Section 4.3, the signal reverses in response to pressure gradients opposed to the north and south of the warm SST anomaly centered around 10°N (Figure 3, bottom row) and surface wind anomalies become positive, inducing an acceleration of the moisture transport toward the continent via the WAWJ, with a maximum at lag 6 (Figure 5, left column).

This links the warm SST anomaly to the increase in precipitation over the ocean slightly south of Senegal's latitude in the WAWJ region (Figure 3, right column). It is also noteworthy that there is a cyclonic signal in the heat low region at lag 3 that could correspond to an atmospheric adjustment to the warm SST anomaly, but also to
<figure><img src="image_12.png" /></figure>

FIGURE 5 Linear regression on the SSTW (sea-surface temperature [SST] anomaly in the West African westerly jet region) index in European Centre for Medium-Range Weather Forecasts Reanalysis v5 reanalyses, from July 1 to September 30, 2000–2020, for lags -6 to +6 (days): moisture transport magnitude (colors, kg·m⁻¹·s⁻¹, left panel) and its direction (arrows), and precipitation (PPT, mm·day⁻¹, right panel). Only statistically significant values at the 95% confidence level are plotted. The black contours on the right panel represent the mean precipitation at the respective lags. The parameters lead the SSTW index at negative lags and follow it at positive lags. [Colour figure can be viewed at wileyonlinelibrary.com]

a coincident trough of tropical easterly waves. It induces moisture inflow north of 15°N at lags 3 and 6, but with a weak impact on precipitation. Conversely, south of 15°N, the moisture inflow seems to increase precipitation with weak but significant positive anomalies at positive lags in the western Sahel (Figure 5, right column).

To further clarify the link between SST, moisture transport divergence, and precipitation, we conducted a time-altitude linear regression of the vertical atmospheric column averaged in the WAWJ region, defined as

32°W–19.5°W, 8°N–12°N, on the SSTW index. Figure 6 displays anomalies of moisture transport divergence resulting from this linear regression (shading).

The main signal is found below 700 hPa, with a positive divergence anomaly of moisture transport at negative lags, explaining the precipitation deficit (Figure 6b, red contours). It changes sign as the warm anomaly develops in the WAWJ region (Figure 6b, black contours). The low-level moisture transport at negative lags becomes convergent after lag -3, obviously forced by the SST signal.
<figure><img src="image_13.png" /></figure>

**FIGURE 6** Time-pressure diagrams of linear regressions on SSTW (sea-surface temperature [SST] anomaly in the West African westerly jet region), 32°W–19.5°W, 8°N–12°N, July to September, 2000–2020 in European Centre for Medium-Range Weather Forecasts Reanalysis v5: (a) divergence of moisture transport (colors, $10^{-8} \text{ kg} \cdot \text{m}^{-2} \cdot \text{s}^{-1}$) and (b) SST (black line, °C) and precipitation (PPT, red line, mm·day$^{-1}$). Only values significant at the 95% confidence level are plotted. The parameters lead the SSTW index at negative lags and follow it at positive lags. [Colour figure can be viewed at wileyonlinelibrary.com]

<figure><img src="image_14.png" /></figure>

**FIGURE 7** Schematic illustration of the double feedback mechanism depending on the direction of the mean wind (large black arrows on the left) and wind anomalies (dashed black arrows at the top). The resulting sea-surface temperature (SST) anomaly is either (a, d) cold or (b, c) warm. The Lindzen–Nigam mechanism then creates a wind response, directed from the cold or toward the warm. The feedback is positive in the north and negative in the south of the anomalies when the wind average is southerly, and the contrary when it is northerly; hence the direction of propagation of the coupled SST wind anomalies in large blue or red arrows. [Colour figure can be viewed at wileyonlinelibrary.com]

The variability of precipitation in the WAWJ region in ERA5 is therefore confirmed to be significantly influenced by the NETA SST-surface wind feedback through low-level moisture transport divergence. This, in turn, significantly impacts precipitation in the WAWJ region and the western Sahel (Figure 5, right column): the positive precipitation anomalies are indeed significant in

the continental western Sahel region and the southwest part of Senegal.

5 | CONCLUSIONS

This work documents the air-sea interaction in the NETA on the intraseasonal time-scale. After filtering
<figure><img src="image_15.png" /></figure>

the parameters intraseasonally and keeping the summer period from July 1 to September 30, it starts by analyzing the daily SST and surface wind from observations and ERA5 reanalysis data over the available observation period (2009–2020). The mechanism of this feedback in the NETA is then investigated using ERA5 daily reanalyses over the 2000–2020 period.

An MCA between intraseasonal anomalies of SST and surface wind observations highlights a predominant atmosphere-to-ocean forcing: a northeasterly wind anomaly in the NETA, caused by the passage of a high-pressure system (easterly wave), generates a warm SST anomaly in less than a week (or a cold anomaly if the wave is in its “low-pressure” phase). However, when the atmosphere lags the SST, significant covariant modes are still found, indicating a feedback effect of the SST anomaly on the surface wind in the same region; the SST anomaly seems to reverse the surface wind anomalies in 2–3 weeks south of 10°N (negative feedback), and sustain them north of 12–13°N (positive feedback).

Lagged linear regressions on an SST index averaged over the WAWJ region (32°W–19.5°W, 8°N–12°N) indicate that these feedbacks operate through the LN mechanism, with anomalies in the meridional SST gradient altering the meridional pressure gradient over a surprisingly large scale. Between 10°N and 15°N, the negative SST gradient anomaly sustains the positive SLP gradient anomaly beyond lag 0, whereas south of 10°N the positive SST gradient anomaly reverses the SLP gradient anomaly from positive to negative after lag 0. The positive feedback increases the persistence of the SST anomaly at its northern edge, whereas the negative feedback dampens it at the southern edge. This likely explains both the prolonged persistence of the SST anomaly and its slow northward migration (about 300 km over 2 weeks). The rate of this migration probably leads to the inversion of the initial wind anomaly after approximately 2–3 weeks, suggesting a periodicity of the pattern on the order of 4–6 weeks. This dual feedback structure contributes, first, to the strong persistence of the SST anomaly and, second, to the slow northward propagation of SST gradient and surface wind anomalies. Consequently, it may play a role in the seasonal northward migration of the ITCZ over the NETA region during July and August.

Figure 7 illustrates the mechanism of the double opposite feedbacks as discussed previously. Assuming a mean pressure gradient force directed northward in the Northern Hemisphere, which drives the mean westernlies/southwesterlies that dominate the WAWJ region in JAS, a negative wind anomaly will allow the SST to warm up (Figure 7b). This warming lowers the pressure and induces anomalous pressure gradient forces directed toward the center of the warm anomaly; the northern

force sustains the northeasterlies, whereas the southern force dampens them. Conversely, if the wind anomaly is positive (Figure 7a), the cold SST anomaly resulting from the strengthened wind will induce pressure-gradient force anomalies directed outward from the anomaly, sustaining the southwestern wind anomalies to the north and damping them to the south. On the other hand, if the mean wind is directed southward (Figure 7c,d), the same wind anomalies result in similar but opposite feedbacks, causing the propagation of the anomalies toward the south. In short, the direction of the mean wind will generally indicate the direction of propagating SST/wind anomalies, leaving aside the deviation due to the Coriolis effect. This conclusion holds true in the Southern Hemisphere as well.

Note that this periodic pattern is weak compared with synoptic atmospheric wave patterns. The NETA mode is embedded in these waves, whose variance dominates for periods of 2–6 days, and its variability is fed by these waves. But the positive feedback mechanism north of the SST anomaly drives its long persistence, and the spatial pattern of a 6-week periodicity. It discretely but significantly influences intraseasonal atmospheric variability in the NETA and western Sahel on time-scales ranging from a few days to several weeks, since it is detected using an MCA, and may account for the regional peak of moisture transport variance observed around 10°N in the WAWJ region discussed in Section 4.1. These results are in perfect agreement with Amaya et al. (2017), who concluded that variability in the north tropical Atlantic is primarily driven by atmospheric forcing on the ocean, but identified signs of active ocean-atmosphere coupling around 10°N. Moreover, the anomalous SLP pattern identified here perfectly matches the signal found in Thiam et al. (2024), which used monthly data to investigate atmospheric and oceanic signals preceding a precipitation anomaly in Senegal. Therefore, the NETA coupled mode emphasized here probably has a significant influence on the seasonal cycle of precipitation in the West Sahel due to its persistence over a few weeks.

A negative pressure signal is also observed in the North Atlantic at negative lags; this could indicate a possible feedback between fluctuations of the North Atlantic Oscillation (NAO) and extratropical SST, with the signal potentially propagating to the NETA. This hypothesis requires further investigation, as Czaja and Frankignoul (2002) found an atmospheric signal at the origin of the NETA SST anomaly related to the NAO, whereas Wu and Liu (2002) suggested that the NAO has little effect on the variability of the tropical Atlantic, which is mainly dominated by regional ocean-atmosphere interaction.

Understanding the formation of the SST anomaly at the center of the ocean-atmosphere coupling mechanism
in the NETA is very important. Previous studies suggest that a warm anomaly is primarily created by decreased surface evaporation due to weaker winds (Foltz & McPhaden, 2008). In order to test this hypothesis, we plan to conduct a heat budget analysis to examine SST variations and the processes controlling them within the mixed layer: turbulent surface heat fluxes (latent and sensible), radiative fluxes (solar and infrared), horizontal or vertical advection by mean oceanic currents, entrainment (when the mixed layer deepens), and diffusion (mostly vertical). It will be particularly important to explore the role of oceanic processes such as entrainment or vertical mixing, given that the mixed-layer depth is often poorly represented in climate models for this region (Polo et al., 2015; Treguier et al., 2023).

In conclusion, we have demonstrated in this study the existence of an air-sea interaction phenomenon between 5°N and 15°N that potentially influences the intraseasonal and synoptic variability of wind and moisture transport in the NETA, with a significant impact on precipitation in Senegal and the western Sahel. Further investigations, such as numerical experiments with regional atmospheric circulation models forced by an SST anomaly similar to the one found here, are needed to evaluate its importance. If confirmed, improving the representation of its mechanism in weather forecast models could contribute to improve seasonal to interannual precipitation forecasts for the western Sahel.

## ACKNOWLEDGEMENTS

We extend our sincere gratitude to the two anonymous reviewers for the time and expertise they dedicated to the thorough review of this article. This study benefited from the IPSL Data and Computing Center ESPRI, which is supported by CNRS, SU, CNES, and École Polytechnique. This research was supported by the LEFE project of the French National Centre for Scientific Research (CNRS) and the Laboratoire Mixte International ECLAIRS2. It also benefited from the support of the French Embassy in Senegal.

## CONFLICT OF INTEREST STATEMENT

The authors have no conflicts of interest to declare.

## DATA AVAILABILITY STATEMENT

Data—Version 2.1 of NOAA's Daily Optimum Interpolation Sea Surface Temperature since September 2009 (Reynolds et al., 2007) is available at https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/.

ASCAT winds have been available since 2009 and can be downloaded from https://podaac.jpl.nasa.gov/dataset/ASCATA_L2_25KM_CDR.

Daily ERA5 data on simple levels and pressure levels from 1940 to the present are also available (Hersbach *et al.*, 2023).

## ORCID

Mamadou Thiam https://orcid.org/0000-0003-0857-3009

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**How to cite this article:** Thiam, M., de Coetlogon, G., Wade, M., Sarr, M. & Diop, B. (2025) Air-sea feedback in the northeastern tropical Atlantic in boreal summer at intraseasonal time-scales. *Quarterly Journal of the Royal Meteorological Society*, e4982. Available from: https://doi.org/10.1002/qj.4982