Sea Surface Temperature Variability Research Articles

The South Atlantic Dipole via multichannel singular spectrum analysis

This study analyzes coupled atmosphere–ocean variability in the South Atlantic Ocean. To do so, we characterize the spatio-temporal variability of annual mean sea-surface temperature (SST) and sea-level pressure (SLP) using Multichannel Singular Spectrum Analysis (M-SSA). We applied M-SSA to ERA5 reanalysis data (1959–2022) of South Atlantic SST and SLP, both individually and jointly, and identified a nonlinear trend, as well as two climate oscillations. The leading oscillation, with a period of 13 years, consists of a basin-wide southwest–northeast dipole and is observed both in the individual variables and in the coupled analysis. This mode is reminiscent of the already known South Atlantic Dipole, and it is probably related to the Pacific Decadal Oscillation and to El Niño–Southern Oscillation in the Pacific Ocean. The second oscillation has a 5-year period and also displays a dipolar structure. The main difference between the spatial structure of the decadal, 13-year, and the interannual, 5-year mode is that, in the first one, the SST cold tongue region in the southeast Atlantic’s Cape Basin is included in the pole closer to the equator. Together, these two oscillatory modes, along with the trend, capture almost 40% of the total interannual variability of the SST and SLP fields, and of their co-variability. These results provide further insights into the spatio-temporal evolution of SST and SLP variability in the South Atlantic, in particular as it relates to the South Atlantic Dipole and its predictability.

Investigating the seasonal SST Predictability in the Northern Tropical Atlantic Ocean in an ensemble prediction system

The present study comprehensively investigates the practical and intrinsic predictability of the sea surface temperature (SST) in the Northern Tropical Atlantic (NTA) based on the 138-year-long coupled hindcasts with a recently developed seasonal ensemble prediction system. This system can yield skillful deterministic predictions for the prominent warm and cold events at least 6 months ahead. Notably, it excels in providing probabilistic predictions for below- and above-normal events rather than for neutral events. The predictability of SST in the NTA undergoes remarkable seasonal variation with two peaks of predictability targeted at April and October regardless of the lead time. Various sources of predictability for these target months are revealed. For the target month of April, the preceding remote forcing from the El Niño–Southern Oscillation (ENSO) in the tropical Pacific Ocean combined with local signal results in the phase locking of the SST variation and seasonality of signal component over the NTA. This ultimately contributes to the high predictability targeted at April. However, From the perspective of potential predictability of the predictability targeted at October, which has been rarely mentioned in previous studies. It is also encouraging that, similar to the Indian Ocean Dipole, ENSO and the signal-to-noise ratio of the system mainly contribute to predictability beyond persistence at long lead times for the spring SST in the NTA. This indicates that potential future ENSO improvements may leave much room for improvement in the current SST prediction in the NTA.

Variations in the Central Mode Water in the North Pacific as a manifestation of the Pacific Decadal Oscillation

The North Pacific Central Mode Water (CMW) is examined from the viewpoint of its volume variations. The volume of the CMW layers thicker than 150m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$150 m$$\\end{document} is used as an index of CMW variations, which successfully represents the year-to-year and decadal variations in the CMW volume. The CMW index shows the variation close to that of the Pacific Decadal Oscillation (PDO). The CMW variation is strongly tied with large-scale, dominant variations in sea surface temperature (SST), surface dynamic height (SDH), and sea surface height (SSH) anomalies in the North Pacific, with a significant correlation with the PDO. Year-to-year and decadal variations of CMW volume in May to July are significantly correlated with the wintertime Aleutian Low and 500hPa\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$hPa$$\\end{document} geopotential height variations, which indicates that the Aleutian Low induces the eastward extension/retreat of the strong winter westerlies and resultant net surface heat flux anomaly over the CMW distribution region. Thus, the CMW volume variation can be regarded as a significant manifestation of the PDO. The SST, SDH, and SSH anomalies are associated with the surface cooling in the northern sector of the CMW distribution region. On the other hand, the SDH and SSH anomalies throughout a year and the SST anomaly in the cold season in the southeastern sector of the CMW region are formed due to the heaving of isopycnal surfaces in the subsurface layer above the CMW in response to its volume variations.

A new, high‐resolution atmospheric dataset for southern New Zealand, 2005–2020

AbstractThe regional climate of New Zealand's South Island is shaped by the interaction of the Southern Hemisphere westerlies with the complex orography of the Southern Alps. Due to its isolated geographical setting in the south‐west Pacific, the influence of the surrounding oceans on the atmospheric circulation is strong. Therefore, variations in sea surface temperature (SST) impact various spatial and temporal scales and are statistically detectable down to temperature anomalies and glacier mass changes in the high mountains of the Southern Alps. To enable future studies on the processes that govern the link between large‐scale SST and local‐scale high‐mountain climate, we utilized dynamical downscaling with the Weather Research and Forecasting (WRF) model to produce a regional atmospheric modelling dataset for the South Island of New Zealand over a 16‐year period between 2005 and 2020. The 2 km horizontal resolution ensures realistic representation of high‐mountain topography and glaciers, as well as explicit simulation of convection. The dataset is extensively evaluated against observations, including weather station and satellite data, on both regional (in the inner domain) and local (on Brewster Glacier in the Southern Alps) scales. Variability in both atmospheric water content and near‐surface meteorological conditions is well captured, with minor seasonal and spatial biases. The local high‐mountain climate at Brewster Glacier, where land use and topographic model settings have been optimized, yields remarkable accuracy on both monthly and daily time scales. The data provide a valuable resource to researchers from various disciplines studying the local and regional impacts of climate variability on society, economies and ecosystems in New Zealand. The model output from the highest resolution model domain is available for download in daily temporal resolution from a public repository at the German Climate Computation Center (DKRZ) in Hamburg, Germany (Kropač et al., 2023; 16‐year WRF simulation for the Southern Alps of New Zealand, World Data Center for Climate (WDCC) at DKRZ [data set]. https://doi.org/10.26050/WDCC/NZ‐PROXY_16yrWRF).

Reconstruction of the marine carbonate system at the Western Tropical Atlantic: trends and variabilities from 20 years of the PIRATA program

The Western Tropical Atlantic Ocean (WTAO) is crucial for understanding CO2 dynamics due to inputs from major rivers (Amazon and Orinoco), substantial rainfall from the Intertropical Convergence Zone (ITCZ), and CO2-rich waters from equatorial upwelling. This study, spanning 1998 to 2018, utilized sea surface temperature (SST) and sea surface salinity (SSS) data from the PIRATA buoy at 8°N 38°W to reconstruct the surface marine carbonate system. Empirical models derived total alkalinity (TA) and dissolved inorganic carbon (DIC) from SSS, with subsequent estimation of pH and fCO2 from TA, DIC, SSS, and SST data. Linear trend analysis showed statistically significant temporal trends: DIC and fCO2 increased at a rate of 0.7 µmol kg-1 year-1 and 1.539 µatm year-1, respectively, and pH decreased at a rate of -0.001 pH units year-1, although DIC did not show any trend after data was de-seasoned. Rainfall analysis revealed distinct dry (July to December) and wet (January to June) seasons, aligning with lower and higher freshwater influence on the ocean surface, respectively. TA, DIC, and pH correlated positively with SSS, exhibiting higher values during the dry season and lower values during the wet season. Conversely, fCO2 correlated positively with SST, showcasing higher values during the wet season and lower values during the dry season. This emphasizes the influential roles of SSS and SST variability in CO2 solubility within the region. Finally, we have analysed the difference between TA and DIC (TA-DIC) as an indicator for ocean acidification and found a decreasing trend of -0.93 ± 0.02 μmol kg-1 year-1, reinforcing the reduction in the surface ocean buffering capacity in this area. All trends found for the region agree with data from other stations in the tropical and subtropical Atlantic Ocean. In conclusion, the use of empirical models proposed in this study has proven to help filling the gaps in marine carbonate system data in the Western Tropical Atlantic.

Southern Hemisphere Circumpolar Wavenumber‐4 Pattern Simulated in SINTEX‐F2 Coupled Model

AbstractInterannual sea surface temperature (SST) variations in the subtropical‐midlatitude Southern Hemisphere are often associated with a circumpolar wavenumber‐4 (W4) pattern. This study is the first attempt to successfully simulate the SST‐W4 pattern using a state‐of‐the‐art coupled model called SINTEX‐F2 and clarify the underlying physical processes. It is found that the SST variability in the southwestern subtropical Pacific (SWSP) plays a key role in triggering atmospheric variability and generating the SST‐W4 pattern during austral summer (December‐February). In contrast, the tropical SST variability has a very limited effect. The anomalous convection and associated divergence over the SWSP induce atmospheric Rossby waves confined in the westerly jet. Then, the synoptic disturbances circumnavigate the subtropical Southern Hemisphere, establishing an atmospheric W4 pattern. The atmospheric W4 pattern has an equivalent barotropic structure in the troposphere, and it interacts with the upper ocean, causing variations in mixed layer depth due to latent heat flux (LHF) anomalies. As incoming climatological solar radiation goes into a thinner (thicker) mixed layer, the shallower (deeper) mixed layer promotes surface warming (cooling). This leads to positive (negative) SST anomalies, developing the SST‐W4 pattern during austral summer. Subsequently, anomalous entrainment due to the temperature difference between the mixed layer and the water below the mixed layer, anomalous LHF, and disappearance of the overlying atmospheric W4 pattern cause the decay of the SST‐W4 pattern during austral autumn. These results indicate that accurate simulation of the atmospheric forcing and the associated atmosphere‐ocean interaction is essential to capture the SST‐W4 pattern in coupled models.

Impacts of the Madden‐Julian Oscillation on Marginal Sea Heatwaves of the Southeastern Tropical Indian Ocean

AbstractMarginal seas of the southeastern tropical Indian Ocean (SETIO), including the South Java coasts and the Timor and Arafura Seas, are hotspots of large‐scale marine heatwaves (MHWs) in austral summer with notable impacts on local ecosystems. Yet, the prediction of these MHWs remains challenging. By analyzing 39 MHWs and 45 marine cold‐spells (MCSs) observed in the summers of 1982–2021, this study reveals a robust modulation effect of the Madden‐Julian Oscillation (MJO) on these events. The results show that 56% of MHWs and 51% of MCSs overlapped with MJO events, and their duration is systematically longer than others. Moreover, MHWs in phases 3–4 and MCSs in phases 6–7 of the real‐time multivariate MJO (RMM) index were nearly 2–3 times stronger in intensity than the others. The passage of MJOs drives prominent intraseasonal sea surface temperature (SST) variability in the SETIO through perturbing surface heat fluxes, which in turn modulates the occurrence of MHWs and MCSs. During RMM phases 1–2 (convective center over of the east of Africa or the central‐western Indian Ocean), the SETIO is controlled by a dry condition, with increased surface insolation and suppressed latent heat release. These anomalous fluxes drive a persistent SST increase, facilitating the emergence of strong and prolonged MHWs in the subsequent RMM phases 3–4. Further analysis suggests an 18% probability for MHWs in the SETIO 12 ± 10 days after Phase 1. Therefore, the MJO contributes to the predictability of MHWs/MCSs of the SETIO, with implications for predicting climate extremes and hazards.

Exploring the Factors Controlling the Annual Range of Amazon Precipitation

Abstract The annual range (AR) of precipitation in the Amazon River basin has increased steadily since 1979. This increase may have resulted from natural variability and/or anthropogenic forcing, such as local land-use changes and global warming, which has yet to be explored. In this study, climate model experiments using the Community Earth System Model, version 2 (CESM2), were conducted to examine the relative contributions of sea surface temperatures (SSTs) variability and anthropogenic forcings to the AR changes in the Amazon rainfall. With CESM2, we design several factorial simulations, instead of actual model projection. We found that the North Atlantic SSTs fluctuation dominantly decreases the precipitation AR trend over the Amazon by −85%. In contrast, other factors, including deforestation and carbon dioxide, contributed to the trend changes, ranging from 25% to 35%. The dynamic component, specifically the tendency of vertical motion, made negative contributions, along with the vertical profiles of moist static energy (MSE) tendency. Seasonal-dependent changes in atmospheric stability could be associated with variations in precipitation. It is concluded that surface ocean warming associated with the North Atlantic natural variability and global warming is the key factor in the increased precipitation AR over the Amazon from 1979 to 2014. The continuous local land-use changes may potentially influence the precipitation AR in the future. Significance Statement The annual range (AR) in precipitation, the difference between wet- and dry-season precipitation, has increased from 1979 to 2014 in the Amazon. This increase may have resulted from global warming, deforestation, and sea surface temperature variability in North Atlantic and Pacific. To explore the role of each of these factors in altering the Amazon precipitation AR, five experiments were designed in the climate model (CESM). Among these experiment results, the effect of North Atlantic SSTs was the strongest. In the future, deforestation, global warming, and different ocean temperature states in the North Atlantic and Pacific may become increasingly influential on the changes in precipitation. Further investigation is needed to ascertain how the AR of precipitation in the Amazon will change.

A key hub for climate systems: deciphering from Southern Ocean sea surface temperature variability

A key hub for climate systems: deciphering from Southern Ocean sea surface temperature variability

Sea Surface Temperature Variability in NHA Trang Bay, Viet Nam: Patterns and Mechanisms

Sea Surface Temperature Variability in NHA Trang Bay, Viet Nam: Patterns and Mechanisms

Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates

Most coupled model simulations substantially overestimate tropical tropospheric warming trends over the satellite era, undermining the reliability of model-projected future climate change. Here we show that the model-observation discrepancy over the satellite era has arisen in large part from multi-decadal climate variability and residual biases in the satellite record. Analyses indicate that although the discrepancy is closely linked to multi-decadal variability in the tropical Pacific sea surface temperatures, the overestimation remains over the satellite era in model simulations forced by observed time-varying sea surface temperatures with a La Niña-like pattern. Regarding moist thermodynamic processes governing tropical tropospheric warming, however, we find a broad model-observation consistency over a post-war period, suggesting that residual biases in the satellite record may contribute to model-observation discrepancy. These results underscore the importance of sustaining an accurate long-term observing system as well as constraining the model representation of tropical Pacific sea surface temperature change and variability.

Temporal variability of sea surface temperature affects marine macrophytes range retractions as well as gradual warming

Record mean sea surface temperatures (SST) during the past decades and marine heatwaves have been identified as responsible for severe impacts on marine ecosystems, but the role of changes in the patterns of temporal variability under global warming has been much less studied. We compare descriptors of two time series of SST, encompassing extirpations (i.e. local extinctions) of six cold-temperate macroalgae species at their trailing range edge. We decompose the effects of gradual warming, extreme events and intrinsic variability (e.g. seasonality). We also relate the main factors determining macroalgae range shifts with their life cycles characteristics and thermal tolerance. We found extirpations of macroalgae were related to stretches of coast where autumn SST underwent warming, increased temperature seasonality, and decreased skewness over time. Regardless of the species, the persisting populations shared a common environmental domain, which was clearly differentiated from those experiencing local extinction. However, macroalgae species responded to temperature components in different ways, showing dissimilar resilience. Consideration of multiple thermal manifestations of climate change is needed to better understand local extinctions of habitat-forming species. Our study provides a framework for the incorporation of unused measures of environmental variability while analyzing the distributions of coastal species.

Assessing the accuracy of MUR high resolution satellite sea surface temperature data

Marine bivalve mollusks are valuable proxies for El Niño-Southern Oscillation (ENSO) events in paleoclimate research, reflecting changes in sea surface temperature (SST) and other ambient marine conditions in their shell chemistry. However, the use of several Peruvian species of bivalves as paleoENSO proxies needs to be established by comparing their shells’ δ18O-derived SST records with instrumental SST measurements. Along the Peruvian coast, the limited spatial coverage of in-situ coastal monitoring stations hinders such comparisons. This study aims to assess the accuracy of the Multi-scale Ultra-high Resolution (MUR) satellite-derived SST dataset as an alternative source of SST information for paleoENSO studies in Peru, which could advance research on Peruvian bivalve species. This study compares MUR satellite data with in-situ measured SST from ten coastal monitoring stations operated by the Instituto del Mar del Perú (IMARPE). The correlation between the datasets is evaluated, and systematic and non-systematic variations are examined. Environmental factors that may impact the accuracy of satellite SST measurements, such as clouds and aerosols, are also investigated. Results show that MUR data generally correlate with overall trends in SST variability captured by in-situ measurements. However, there are regional variations in the accuracy of MUR data, with some areas showing consistent temperature offsets. Differences in measurement methodologies and environmental variables are explored as potential causes of variations between the datasets. Overall, the study demonstrates the potential of using high-resolution satellite SST data for assessing δ18O SST records from bivalves in locations without local SST records. It provides insights into the accuracy and limitations of using MUR data as an alternative source of SST information for paleoENSO studies in coastal Peru.

Variability of the Indian Ocean Dipole post-2100 reverses to a reduction despite persistent global warming

Previous examination of the Indian Ocean Dipole (IOD) response to greenhouse warming shows increased variability in the eastern pole but decreased variability in the western pole before 2100. The opposing response is due to a shallowing equatorial thermocline promoting sea surface temperature (SST) variability in the east, but a more stable atmosphere decreasing variability in equatorial zonal winds that weakens SST variability in the west. Post-2100, how the IOD may change remains unknown. Here we show that IOD variability weakens post-2100 in majority of models under a long-term high emission scenario to 2300. Post-2100, the atmosphere stability increases further and persistent ocean warming arrests or even reverses the eastern Indian Ocean shallowing thermocline. These changes conspire to drive decreased variability in both poles, reducing amplitude of moderate, strong and early-maturing positive IOD events. Our result highlights a nonlinear response of the IOD to long-term greenhouse warming under the high emission scenario.

Eccentricity forcing on tropical ocean seasonality

Abstract. The amount of radiative energy received at the Earth's surface depends on two factors: Earth–Sun distance and sunlight angle. Because of the former, high-eccentricity cycles can induce the appearance of seasons in the tropical ocean. In this paper, we use the Earth system model IPSL-CM5A2 to investigate the response of the low-latitude ocean to variations in Earth's orbit eccentricity. Sea surface temperature (SST) and primary production (PP) were simulated under six precession configurations at high eccentricity and two configurations at low eccentricity, representing extreme configurations observed over the past 1 million years. Results show that high eccentricity leads to increased seasonality in low-latitude mean SST, with an annual thermal amplitude of approximately 2.2 °C (vs. 0.5 °C at low eccentricity). Low-latitude mean PP, which already exhibits inherent seasonality under low-eccentricity conditions, sees its seasonality largely increased under high eccentricity. As a consequence, we show that on long timescales the intensity of SST seasonality exhibits only the eccentricity frequency, whereas that of PP additionally follows precession dynamics. Furthermore, the seasonal variations in both SST and PP at high eccentricities are influenced by the annual placement of the perihelion with its direct impact of radiative energy received in tropical regions. This leads to a gradual and consistent transition of seasons within the calendar. We introduce the concept of “eccentriseasons”, referring to distinct annual thermal differences observed in tropical oceans under high-eccentricity conditions, which shift gradually throughout the calendar year. These findings have implications for understanding low-latitude climate phenomena such as the El Niño–Southern Oscillation (ENSO) and monsoons in the past.

Assessing satellite-derived wind speeds: insights from buoy measurements and analysis of deviation factors

ABSTRACT To evaluate the latest generation of satellite wind speed from AMSR2, ASCAT, GMI, SSMIS, SMAP, and WindSat, buoy measurements were obtained from buoys in coastal and tropical open ocean areas. Root mean square errors were calculated, resulting in values of 1.19 m/s for AMSR2, 1.25 m/s for ASCAT, 1.37 m/s for GMI, 1.38 m/s for SSMIS, 2.48 m/s for SMAP, and 1.18 m/s for WindSat. Numerous data pairs fall above the 45-degree line, mainly due to decreased buoy ac-curacy during high winds. Factors contributing to this include underestimation of buoy readings due to rough sea states and wind profile distortion. Satellite biases exhibit unimodal fluctuations over a one-year cycle, aligning with annual sea surface temperature variations. Statistical effects in extreme wind speed range cause abnormal fluctuations in statistics. Higher latitudes experi-ence larger deviations due to rough sea state. Ocean currents affect satellite wind speed observa-tions, particularly when aligned with wind direction. Coastal wind speed gradients primarily influenced from land contamination like rugged mountainous terrain along the West Coast of North America. Coastal upwelling also contributes to bias, with the west coast showing higher bias. A global comparison between SMAP and WindSat retrieval wind speeds demonstrates SMAP’s poor ability to monitor general wind speeds and validates the satellite-buoy experiment.

Regional Variation in Extratropical North Atlantic Air‐Sea Interaction 1960–2020

AbstractAir‐sea interaction in late boreal winter is studied over the extratropical North Atlantic (NA) during 1960–2020 by examining the relationship between sea‐surface temperature (SST) and total turbulent heat flux (THF). The two quantities are positively correlated on interannual timescales over the central‐midlatitude and subpolar NA, suggesting the atmosphere on average drives SST and THF variability is independent of SST. On decadal timescales and over the central‐midlatitude NA the correlation is negative, suggesting ocean processes on average drive SST and THF variability is sensitive to SST. The correlation is positive over the subpolar NA. There, interannual and decadal THF variability is governed by the North Atlantic Oscillation (NAO). During the major late 20th and early 21st century SST increase in the subpolar NA diminishing oceanic heat loss associated with a weakening NAO was observed. This study suggests that the atmosphere is more sensitive to SST over the central‐midlatitude than subpolar NA.

Spatial and Temporal Variability of Ocean Thermal Energy Resource of the Pacific Islands

A lack of natural resources drives the oil dependency in Pacific Island Countries and Territories (PICTs), hampering energy security and imposing high electricity tariffs in the region. Nevertheless, the Western Equatorial Pacific is known for its large Sea Surface Temperature (SST) and deep-sea water (DSW) temperature difference favorable for harvesting thermal energy. In this study, we selected 18 PICTs in the western Equatorial Pacific to estimate Annual Energy Production (AEP) for a 1 MW class Ocean Thermal Energy Conversion (OTEC) plant. We combined the DSW temperature from the mean in situ Argo profiles and 1 km resolution satellite SST data to estimate the thermal energy resource resolving the fine features of the island coastline. Furthermore, the twenty-year-long SST dataset was used to analyze the SST variability. The analysis showed that Equatorial islands and Southern islands have the highest inter-annual variability due to El Nino Southern Oscillation (ENSO). The power density varied from 0.26 to 0.32 W/m2 among the islands, with the lowest values found for the southernmost islands near the South Equatorial Countercurrent. Islands within the South Equatorial Current, Equatorial Undercurrent, and North Equatorial Countercurrent showed the highest values for both power density and gross power. Considering a 1 MW class OTEC plant, Annual Energy Production (AEP) in 2022 varied from 7 GWh to 8 GWh, with relatively low variability among islands near the Equator and in low latitudes. Considering the three variables, AEP, SST variability, and distance from the shore, Nauru is a potential candidate for OTEC, with a net power of 1.14 MW within 1 km from the shore.

Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability

AbstractWe use neural networks and large climate model ensembles to explore predictability of internal variability in sea surface temperature (SST) anomalies on interannual (1–3 years) and decadal (1–5 and 3–7 years) timescales. We find that neural networks can skillfully predict SST anomalies at these lead times, especially in the North Atlantic, North Pacific, Tropical Pacific, Tropical Atlantic and Southern Ocean. The spatial patterns of SST predictability vary across the nine climate models studied. The neural networks identify “windows of opportunity” where future SST anomalies can be predicted with more certainty. Neural networks trained on climate models also make skillful SST predictions in reconstructed observations, although the skill varies depending on which climate model the network was trained. Our results highlight that neural networks can identify predictable internal variability within existing climate data sets and show important differences in how well patterns of SST predictability in climate models translate to the real world.

The Atlantic Niño Mode: A Thermodynamic or a Dynamic Phenomenon?

AbstractThe Atlantic Niño is an important source of the year‐to‐year variability of the tropical Atlantic, consisting in an irregular oscillation of the Sea Surface Temperature (SST) in the eastern tropical Atlantic. The physical mechanism underlying this oscillation is topic of debate. Some theories, known as dynamical, suggest that the Atlantic Niño is driven by internal wave dynamics. Other theories, termed thermodynamic, propose that the eastern tropical Atlantic SST variability is caused by thermodynamic processes associated with heat fluxes at the ocean surface and/or heat transport by oceanic currents. Here, we examine the SST variability from 1940 to 2022 and find that it can be described in terms of two spatial modes: one in the central and the other in eastern tropical Atlantic. However, focusing on the period from 1993 to 2022, these patterns behave differently. In 1993–2009 they concur in determining the eastern tropical Atlantic SST variability, while in 2010–2022 they act in opposite direction. Therefore, we use Sea Surface Height data and heat fluxes advected by oceanic currents to explore the relative contribution of wave dynamics and heat advection to the eastern tropical Atlantic SST fluctuation. We show that, between 1993 and 2010, the eastern tropical Atlantic SST variability is mainly determined by heat advected from the south by horizontal currents, while between 2010 and 2022 it can be explained in terms of wave dynamics. This finding suggests that the two spatial patterns determining the eastern tropical Atlantic SST variability, are two distinct physical phenomena forced by different physical mechanisms.