the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Sep 2025
Intraseasonal modulation of Sea Surface Temperatures in the North Tropical Atlantic by African Easterly Waves
Abstract. The sea surface temperature (SST) variability in the North Tropical Atlantic plays a crucial role in the regional climate by modulating the Intertropical Convergence Zone (ITCZ) and influencing precipitation, convective systems, and tropical cyclones. While atmospheric synoptic-scale intraseasonal variability in this region is dominated by African Easterly Waves (AEWs), their impact on SST remains poorly understood. This study investigates the modulation of SST by AEWs using a regional configuration of a coupled ocean-atmosphere model and PIRATA mooring air-sea observations. Results reveal a significant AEWs signature in SST anomalies, with temperature fluctuations exceeding ±0.5 °C. A heat budget analysis shows that AEWs mainly influence SST through modulation of the latent heat flux, shortwave radiation, and vertical mixing. The contribution of the ocean mixing and that of the air-sea fluxes appear of similar order, likely reflecting the influence of near-inertial currents. The dominant 3–5-day AEWs exhibit a stronger impact than their 6–9-day counterparts. These findings highlight the role of AEWs in driving SST variability and mixed-layer dynamics, underscore the importance of accurately representing them in coupled climate models, and call for further investigation into their influence on the mean and seasonal upper-ocean state.
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- AC1: 'Comment on egusphere-2025-4429', Marc Kakante Mendy, 01 Oct 2025 reply
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RC1: 'Comment on egusphere-2025-4429', Anonymous Referee #1, 12 Nov 2025
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In their study „Intraseasonal modulation of Sea Surface Temperatures in the North Tropical Atlantic by African Easterly Waves“, the authors investigate how meridional wind anomalies at time scales of 2-10 days in the Mauritania upwelling region impact the surface ocean and mixed layer heat budget in the tropical North Atlantic. They find that these wind fluctuations that are associated with African Easterly Waves (AEWs) modulate sea surface temperatures through changes in atmospheric fluxes (in particular, latent heat flux and shortwave radiation) as well as through changes in vertical mixing. The study is primarily based on model output, but a substantial part of the manuscript focuses on the validation of the model output with in-situ and satellite observations as well as reanalysis data. Overall, the study shows some interesting results and, in my opinion, could be a useful contribution toward a better understanding of air-sea interactions in the Mauritania upwelling region. However, I find several major concerns which should be considered before publication.
Major concerns:
- The validation of the model output makes up a substantial part of the results and half of the shown figures (Figs. 1-5). While I agree that it is important to examine how well the model reproduces SST, horizontal surface winds, and vertical profiles of horizontal winds, this takes up space that could be used for more in-depth analysis (see below for some suggestions).
- It would be useful to include a figure showing a map with the inertial period at each grid point and the period of peak wind variability to assess how close the observed wind variability is to inertial period and in which regions this relation is most prominent. This could strengthen the authors claim of near-inertial currents having an influence on mixed layer dynamics which is an interesting question that could be examined further this way.
- The description of the AEW index in section 4 is not quite clear to me. It would be more useful to include a formula to clearly describe which variable and which region is used.
- In my opinion, Fig. 7 is not very convincing. Why did the authors choose only one year to show the relationship between SST and meridional winds? Why are 2–10-day meridional wind fluctuations so similar to the original meridional wind time series while SST shows substantial differences when bandpass-filtered? More discussion would be helpful here.
- In Figs. 8 and 9, a very locally defined AEW index (basically at the mooring location) is used to examine large-scale changes in winds and SST over the entire tropical North Atlantic. How useful is this regression in case of such a rapidly changing index?
- It seems trivial that the 3–5-day fluctuations have a stronger impact on ocean surface variables because of the vertical pattern of the wind fluctuations (i.e., closer to the surface; Fig. 5) compared to the 6-9-day fluctuations?
- The authors should provide a more thorough discussion of the mixed layer heat budget based on the model output and available studies using in-situ measurements (Foltz et al., 2003; Hummels et al., 2014). This could strengthen the claim that vertical mixing plays a role.
- What is the temporal variability of the relation between AEWs and SST over the examined time period? How large are year-to-year changes that make it hard to quantify a more distinct relation between AEWs and SST?
Minor points:
- The region in the tropical Atlantic north of the equator has been named tropical North Atlantic (TNA) in most publications. I think this should be changed in the manuscript which often uses “North Tropical Atlantic”. In fact, even the authors sometimes refer to this region as tropical North Atlantic (e.g., line 125). It should be uniform throughout the manuscript.
- Lines 17-19: In which region are AEWs close to the inertial periods? This could be shown. See major concern 2
- Lines 23-26: I don’t think key points are required for Ocean Science? Ignore this comment if this has changed.
- Lines 111-113: I wonder how useful it is to validate the model output with a reanalysis product (ERA5) that is used to initialize the atmospheric model? Wouldn’t a comparison with independent data be more useful?
- Line 234: Which region is meant here? This should be defined clearly. The way it is written here is too vague.
- Figure 5e: How to distinguish between near-surface wind variability in the 3–5-day band from AEWs and the African westerly jet? Or is there interaction between these?
Specific comments:
Abstract:
- Line 14: Please define PIRATA or keep it more general in the abstract. For instance, by saying “moored surface buoys”.
1 Introduction:
- Lines 42-44: African Easterly Waves propagate from east to west.
- Line 51: I assume the authors mean “zonal wavelengths”?
2 Data and methodological approach:
- Line 88: Do the authors mean “air-sea” instead of “air-heat”?
- Line 124: Here the surface air temperature at 1m above sea level is meant, correct?
- Line 125: Please also mention the more up to date reference for the PIRATA buoy network: Bourlès et al. (2019)
3 Evaluation of the coupled model:
- Line 154: Typically, this upwelling region is referred to as “Mauritania upwelling”.
- Lines 157-159: It seems that the model also underestimates the magnitude of the Atlantic Cold Tongue. Here, a more quantitative comparison could be useful to validate the model output.
- Figure 1: Why using the time period 2007-2021 and not the full time series since 2001? I don’t see an explanation for using the shorter time period. In Figure 2 the full time series is used.
- Lines 172-174: But satellite SST data are provided as daily averages (i.e. some of the high-frequency variability is averaged out), whereas ERA5 and PIRATA data are available at higher frequencies (3-hourly and hourly). It would be interesting to look at an exemplary season and compare the time series of ERA5, PIRATA, and model output. Because even the PIRATA buoy north of the Cape Verde islands which is closest to the high SST STD off Africa shows reduced variability compared to regions outside of the high variability area.
- Figure 2: It is interesting that ERA5 and OISST produce the same climatology but very different standard deviation (as a function of calendar month). What could be the reason(s) for this? Larger swings in OISST around the same mean values in both products?
- Lines 193-194: I don’t follow this. Why does higher SST STD in OI-SST imply biases from satellite measurements? It seems that most of the model output validation simply depends on whether the comparisons are really between comparable variables (skin temperature vs. SST)?
- Lines 208-209: The winds north of the equator (10°N-15°N) do not cross the equator. The southeasterly trade winds cross the equator and are deflected to the right north of the equator. Please clarify this sentence.
- Figure 4: It should be noted (and discussed why) that the model exhibits the highest deviations from all other products during July to September (Fig. 4a) which is the time of the year when AEWs are investigated.
4 Ocean surface response to AEWs:
- Figure 7b: I believe “2-10jrs” is the French version of “2-10 days”. Please replace.
- Lines 289-290: It says 2015 in the text, but 2001 on the x axis in Figure 7. Please clarify which period is shown here.
5 The ocean mixed layer heat balance:
- Lines 373-375: Shouldn’t a deepening of the mixed layer depth imply warming and not cooling of the mixed layer?
- Lines 386-387: Is this really significant from a statistical point of view? Otherwise, the authors should be careful with using the phrase “significant”.
References:
- Bourlès, B., Araujo, M., McPhaden, M. J., Brandt, P., Foltz, G. R., Lumpkin, R., et al. (2019). Pirata: A sustained observing system for tropical Atlantic climate research and forecasting. Earth and Space Science, 6(4), 577–616. https://doi.org/10.1029/2018EA000428
- Foltz, G. R., Grodsky, S. A., Carton, J. A., & McPhaden, M. J. (2003). Seasonal mixed layer heat budget of the tropical Atlantic Ocean. Journal of Geophysical Research, 108(C5), 3146. https://doi.org/10.1029/2002JC001584
- Hummels, R., Dengler, M., Brandt, P., & Schlundt, M. (2014). Diapycnal heat flux and mixed layer heat budget within the Atlantic Cold Climate Dynamics, 43, 3179–3199. https://doi.org/10.1007/s0038201423396
Citation: https://doi.org/10.5194/egusphere-2025-4429-RC1
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Dear all,
Please note that Figure 9 in the submitted version contained an error. The correct figure and caption are provided below.
Thank you.