Emergence of the enhanced equatorial Atlantic warming as a fingerprint of global warming

Domain: earth_science
Published: 2025-12-29
Editorial outcome: major_revision

Abstract
The response of tropical sea surface temperature (SST) to global warming plays a crucial role in shaping both global and regional climates, so it receives immense attention and remains being debated. Here, we demonstrate that enhanced equatorial warming (EEW) is a more robust response to global warming than the commonly examined changes in the zonal SST gradient across the tropical Pacific, which is marked by discrepancies between observations and models. EEW is defined as the annual-mean SST warming averaged over 5°S-5°N, relative to the tropical SST warming averaged over 20°S-20°N. By combining observations and climate models, we identify the emergence of EEW in the Atlantic since the 1950s, primarily attributed to greenhouse gas forcing. The formation of EEW is driven by weakened equatorial upwelling, resulting from the slowdown of equatorial zonal winds. The identification of Atlantic EEW as a fingerprint of global warming has important implications for understanding changes in the tropical oceans in a warming climate and the associated impacts.

Introduction
Tropical ocean warming has garnered attention due to its impact on the rate of global warming1,2, regional precipitation changes3, tropical cyclone genesis4, global climate feedbacks5 and many others. Most previous studies have focused on changes in zonal sea surface temperature (SST) gradient in the tropical Pacific6–19, which is a key that underpins the dominant modes of variability in the Pacific Ocean, namely, the El Niño Southern Oscillation (ENSO), Interdecadal Pacific Oscillation (IPO) and Pacific Decadal Oscillation (PDO), that modulate the Walker Circulation on interannual and decadal timescales. Climate models tend to project a weakening of the zonal SST gradient (i.e., an El Niño-like change) and Walker Circulation under greenhouse gas forcing20. However, a discrepancy in the change between observations and models during the past several decades has undermined the future projection, and the reason behind the disparity remains being debated21–24. On the other hand, a more prominent and robust feature of SST response to future warming exhibited by climate models is an enhanced equatorial warming (EEW), characterized by intensified warming near the equator relative to the broader tropical oceans25. The global EEW pattern drives the contraction of tropical convection toward the equator, manifesting as reduced meridional migration of the intertropical convergence zone (ITCZ), which is generally positioned over the highest SSTs26–30. This contraction of tropical convection shortens tropical cyclone seasons31. Regionally, global EEW is associated with an equatorward shift in the East Asian summer monsoon, subtropical circulation such as the subtropical jet, and the Meiyu–Baiu rain band30. In particular, such a meridional SST gradient change in the tropical Atlantic can significantly influence the Walker Circulation and Hadley Circulation32,33, the movement of the ITCZ32,34,35 and precipitation patterns in northern and northeastern Brazil32,35–37. Given the critical role of meridional SST gradient, especially the EEW in global and regional climates, it is essential to elucidate its drivers for understanding and improving predictions of climate change at multiple scales.

Progress has been made in understanding the formation of EEW in response to transient CO2 experiments. For example, the mechanism for global EEW has been linked to the effect of ocean heat uptake38, off-equatorial radiative forcing39, and the EEW in the Pacific has been influenced by reduced evaporative damping25 and the weakening of surface ocean currents at the equator driven by the slowdown of tropical circulation40,41. However, observations show an increase of the equatorial Pacific near-surface currents over the last few decades42–44. Furthermore, the slowdown of global atmospheric circulation and the narrowing of the ITCZ have already been detected in recent decades39,45. Hence, it is urgent to examine whether EEW has already emerged and where it has emerged in the observational records, to determine the relative importance of the different underpinning physical processes. Here, it is found that the EEW has already been detectable in the Atlantic, primarily driven by reduced equatorial upwelling associated with the weakened atmospheric circulation. This warming pattern is expected to exert significant influence on the tropical climate and global atmospheric circulation.

Results
Emergence of enhanced equatorial warming in the Atlantic
We first compare the trends of global EEW and Pacific SST zonal gradient during 1951–2020 based on 36 CMIP6 models under external forcing, including greenhouse gas forcing (GHG), aerosol forcing (AER) and natural forcing (NAT). We select this time period for analysis because the observations are more accurate after 195146. The EEW index is defined as the difference in SST anomalies between 5°S-5°N and 20°S-20°N, while the Pacific SST zonal gradient is defined as the SST difference between the equatorial eastern Pacific (5°S-5°N, 140°W-80°W) and the equatorial western Pacific (5°S-5°N, 140°E-170°W) (See “Definition of SST metrics” in Methods). Although the two indices are highly correlated at the interannual timescale due to internal variability, there is no significant relationship between the long-term trend of the two indices (r = 0.02, Fig. 1a), suggesting that they are independent for longer timescales. The Pacific SST zonal gradient trends exhibit large uncertainty among models, with approximately half of the models exhibiting La Niña-like warming patterns (i.e., strengthening zonal SST gradient), while the other half showing El Niño-like warming patterns (i.e., weakening zonal SST gradient), with the multi-model ensemble mean (MME) close to zero. Instead, EEW trends exhibit positive values based on the MME, with inter-model agreement exceeding 77%. Specifically, there are 17 models simulating a statistically significant EEW trend, but only 3 models with a statistically significant zonal gradient trend (Fig. 1a). Further, the EEW pattern is evident regardless of whether models simulate El Niño-like or La Niña-like warming trends (Fig. 1b, c). In addition, despite the different changes in zonal SST gradient over the tropical Pacific, with La Niña-like warming in observations (Supplementary Fig. 1a) and El Niño-like warming in CMIP6 models (Supplementary Fig. 1b), the EEW trends across the entire tropical ocean are positive for both observations and CMIP6 models, statistically significant based on the MME of CMIP6 models (Fig. 2a). Therefore, EEW is a more robust response to global warming compared to the Pacific zonal SST gradient in the historical period. It’s worth noting that there is a peak of SST warming in the subtropical northeastern Pacific in both El Niño-like and La Niña-like warming patterns, which has been investigated in detail by Geng et al. (2023)47.

Although EEW is a robust signal in the CMIP6 models, the observed EEW during 1951–2020 is not statistically significant when averaged in the whole tropical ocean (Fig. 2a). Given the distinct spatial warming patterns among the three tropical oceans (Supplementary Fig. 1a), we further extend the EEW definition from global scale to individual ocean basins (Fig. 2). Both the Indian Ocean and Atlantic Ocean see statistically significant EEW trends (Fig. 2a). External forcing to the EEW trends is detectable using the optimal fingerprinting (OFP) method with the 5–95% confidence interval of scaling factor significantly above zero (Fig. 2b; see “Methods”). By contrast, EEW in the Pacific Ocean is neither statistically significant nor detectable, likely due to its substantial internal variability, such as the IPO and PDO21,24,48. This is consistent with the discrepancy between the observed SST warming patterns and those driven by external forcing in the Pacific (Supplementary Fig. 1a, b). In contrast, the internal variability, represented as the noise based on the OFP method, is insufficient to mask the EEW trends under external forcing in the Indian and Atlantic Ocean (Fig. 2b). In addition, the EEW trends in the Atlantic are more evident than those across the entire tropical ocean in both the La Niña-like warming and El Niño-like warming in CMIP6 models (Fig. 1b, c). Among the 36 CMIP6 models, the Atlantic exhibits the highest inter-model agreement in positive EEW trends (86%), followed by the Indian Ocean (77%) and the Pacific Ocean (63%) (Supplementary Fig. 2).

Among the external forcings, greenhouse gases (GHGs) predominantly shape the spatial pattern of relative SST trends, with pattern correlation coefficient of 0.70 (Supplementary Fig. 1b, c). This pattern features stronger warming along the equator in the Pacific and Atlantic Oceans, in the subtropical northeastern Pacific, as well as in the western Indian Ocean. In contrast, aerosol forcing produces an opposite effect on the spatial patterns of SST trends, with stronger cooling along the equatorial Pacific and weaker relative warming along the equatorial Atlantic (Supplementary Fig. 1d). This may be due to the effects of reduced aerosol emissions from North America on the SST over North Atlantic49. Analysis of EEW trends in each ocean reveal that GHG forcing dominates the positive trends in the Indian and Atlantic Oceans, though these effects are partially offset by aerosol forcing (Fig. 2a). Furthermore, the impact of GHGs on the EEW trend is detectable in the Indian and Atlantic Oceans (Fig. 2b). The responses of Atlantic EEW to external forcing and GHG-only forcing are closer to the observed trend with scaling factors closer to 1, while the EEW trends in the Indian Ocean are underestimated in models with scaling factors greater than 1. Moreover, the warming pattern in the Indian Ocean does not exhibit enhanced warming along the equator, but features an east-west dipole pattern under external forcing and GHG-only forcing (Supplementary Fig. 1b, c). Therefore, the EEW has emerged in the Atlantic, which can be regarded as a robust response to global warming. We will focus on the mechanism for the EEW in the Atlantic Ocean based on CMIP6 models in the following analysis.

Mechanisms for the enhanced Atlantic equatorial warming
To investigate the mechanism driving Atlantic EEW in recent decades, we conduct a mixed-layer heat budget analysis using 35 CMIP6 models, which have the complete heat flux variables (See “Mixed-layer heat budget analysis” in Methods). Among all atmospheric and oceanic processes, oceanic dynamics (Do) stand out as the prominent driver of EEW trends, with a pronounced warming effect centered along the equator (Fig. 3a, b). By further estimating the contributions of different oceanic processes, both warm advection from weakening of the equatorial upwelling (WT) and changes in the zonal temperature gradient (UT) contribute to the EEW trend (Supplementary Fig. 3a, b, c), with the former term dominating the stronger warming along the Atlantic equator (Supplementary Fig. 3d). Surface net heat flux acts as a counteracting response to EEW, primarily due to reduced shortwave radiation associated with enhanced equatorial convection (Supplementary Fig. 4).

Climatologically, the poleward Ekman transport driven by the trade winds and Kelvin waves can both produce equatorial upwelling in the upper ocean50. According to the ocean thermostat mechanism, equatorial upwelling has a significant influence on tropical SST variations51. In response to global warming, the weakening of ocean currents along the equator, driven by the slowdown of tropical circulation, is a key factor in EEW formation in future projections40,41. In the historical period, the weakening of equatorial upwelling is evident in the upper Atlantic Ocean (0–150 m) based on CMIP6 models, with the most pronounced weakening occurring in the region (3°S-3°N) where the climatological upwelling is the strongest (Fig. 3c–e). Furthermore, the reanalysis data sets indicate that the equatorial upwelling in the Atlantic has weakened since the 1980s, consistent with externally forced experiments (Supplementary Fig. 5). These findings suggest that the weakened equatorial upwelling has contributed to the Atlantic EEW trend in recent decades.

Inter-model relationship further supports this mechanism. Specifically, the linear trend in oceanic dynamics (Do) is linked to the weakening of equatorial upwelling (r = −0.56, Fig. 4a), which in turn is associated with a more pronounced EEW in the Atlantic (Fig. 4b). The weakening of equatorial upwelling occurs in most CMIP6 models, with 90% inter-model agreement. This highlights the critical role of oceanic processes, such as the weakening of equatorial upwelling, in shaping EEW trends for both the multi-model mean and inter-model spread.

Since the equatorial upwelling is driven by zonal winds via poleward Ekman transport and equatorial Kelvin waves50, we examine the inter-model relationship between trends of upwelling and zonal winds. We use the absolute values of zonal wind speed here, with negative values indicating the slowdown of easterlies along the equator. A statistically significant positive relationship (Fig. 4c) suggests that weaker zonal winds lead to reduced equatorial upwelling and vice versa. In addition, zonal wind changes are strongly correlated with EEW trends (Fig. 4d), and the weakened zonal wind has a high inter-model agreement of 75%. These findings confirm the dominant role of weakening equatorial upwelling, driven by the relaxation of trade winds, in the emergence of EEW trends over the Atlantic during recent decades.

A fingerprint of global warming
The above analysis shows that the weakening of trade winds over the Atlantic Ocean has driven the EEW via reducing the upwelling. In the observations, the recent decades have witnessed a significant weakening trend of zonal wind along the equatorial Atlantic (black bar in Fig. 5a). Given that the zonal wind weakening is also a response to surface warming pattern, here we further demonstrate that the wind weakening occurs even without the SST pattern and is thus a driver of EEW. In response to global warming, the weakening of tropical atmospheric circulation, mainly Walker Circulation and Hadley Circulation, required by energetic and hydrological constraints, has been identified as a robust feature in future climate projections40,52. The Walker Circulation slowdown over the Pacific is a clear response to the spatially uniform surface warming without any change in SST gradients53. Thus, the weakening of zonal winds is a regional manifestation of this broader atmospheric circulation slowdown. To confirm this hypothesis, we further analyze three sets of prescribed-SST atmosphere-only experiments from CMIP6 models (See “Models simulations” in Methods). We use AMIPfuture4K−AMIP to isolate the effects of patterned SST warming and AMIPp4K−AMIP to estimate the response to spatially uniform SST warming29,54.

The experiments indicate that zonal wind speed weakens in response to both uniform and patterned SST warming (Fig. 5), consistent across all of the CMIP6 models (Supplementary Fig. 6). The uniform warming simulation without EEW shows zonal wind speed decreases over tropical Atlantic (Fig. 5b). While the SST warming pattern also leads to weakened zonal winds mainly along the equator (Fig. 5c), the magnitude of this weakening is smaller than that under uniform warming (Fig. 5a, b). This difference could be due to the effect of an El Niño-like warming pattern in the Pacific in the AMIPfuture4K experiments that tends to strengthen the trade winds along the equatorial Atlantic, partially offsetting the circulation slowdown that would occur without the SST warming pattern. Therefore, we conclude that EEW has emerged from observations in the Atlantic over recent decades driven by weakened zonal winds as a fingerprint of global warming.

Methods
Observational datasets
In this study, we use three monthly mean sea surface temperature (SST) products to characterize the observed enhanced equatorial warming (EEW) from 1951 to 2020, including (1) HadISSTv1.1 (Hadley Center Sea Ice and Sea Surface Temperature dataset version 1.1) with 1°×1° horizontal resolution55, (2) ERSSTv5 (Extended Reconstructed Sea Surface Temperature version 5) with 2°×2° horizontal resolution56, and (3) COBE-SST2 (Centennial in situ Observation-Based Estimates version 2) with 1°×1° horizontal resolution57. While minor variations exist in EEW strength among these datasets, our main conclusions remain robust across all products. To analyze SST trend patterns, we interpolate the three datasets onto a common 90×180 global grid using bilinear interpolation. We have checked that this interpolation process does not affect the distribution of SST trends in the tropical oceans.

To examine the dominant factors influencing EEW, we use three monthly reanalysis datasets: (1) surface zonal wind from the ERA5 reanalysis (fifth generation ECMWF atmospheric reanalysis of the global climate) during 1940–2023 with 0.25°×0.25° horizontal resolution58, and (2) ocean vertical velocity from the Simple Ocean Data Assimilation (SODA) 3.15.2 during 1980–2020 with 0.5°×0.5° horizontal resolution and 50 vertical levels extending to beyond 5000 m59, and (3) ocean vertical velocity from NCEP Global Ocean Data Assimilation System (GODAS) during 1980–2020 with 1°×0.5° horizontal resolution and 40 vertical levels extending to beyond 5000 m60. The SODA datasets significantly enhance the accuracy, reliability, and applicability of ocean reanalysis through using higher-resolution models, improving assimilation methods, correcting flux bias and richer observational data59. The GODAS datasets assimilates extensive in situ temperature and salinity observations, providing a more accurate description of the ocean’s internal structure, and is continuously updated61.

Models simulations
To investigate the physical mechanisms driving EEW under external forcing, we employ outputs from 38 Coupled Model Intercomparison Project 6 (CMIP6)62 models (Supplementary Data 1) under all-forcing (ALL) scenarios. External forcing refers to factors that can cause climate change outside the climate system, including natural (NAT) forcing, including solar radiation and volcanic eruption, and anthropogenic (ANT) forcing, such as greenhouse gas forcing (GHG) and aerosol forcing (AER).

We use monthly SST, surface heat flux, ocean horizontal and vertical velocity, mixed layer depth (MLD), ocean potential temperature (PT) and sea surface zonal wind data from both the historical and future scenario experiments. There are variations in the number of models used across figures because of the different availability of the required variables among all the models. The number of models and realizations used for each variable is detailed in Supplementary Data 1. To disentangle the contributions of different external forcings, mainly the greenhouse gases (GHGs) and anthropogenic aerosols (AERs), we utilize CMIP6 simulations from the Detection and Attribution Model Intercomparison Project (DAMIP), specifically hist-GHG and hist-AER, to isolate their respective impacts. To quantify the contributions of these forcings to EEW, we utilize monthly SST and sea surface zonal wind of historical (1850–2020) simulations across 10 CMIP6 models under GHG-only and AER-only forcing. The number of realizations per model is provided in Supplementary Data 1. All outputs from CMIP6 models are interpolated to a common 73×144 global grid using bilinear interpolation.

To estimate the noise term (ε) used in the optimal fingerprinting (OFP) method for detecting anthropogenic influence on EEW in observation, we use 386 realizations of 10 large ensembles under all-forcing scenarios and 23 pre-industrial control simulations, with a total simulation length of 14,420 years (Supplementary Data 2). All outputs are interpolated to a common 73×144 global grid using bilinear interpolation.

To examine the response of equatorial zonal wind speed to uniform and patterned SST warming, we analyze monthly sea surface wind data from three sets of Atmospheric Model Intercomparison Project (AMIP), prescribed-SST atmosphere-only experiments based on CMIP6 models (Supplementary Data 1): (1) the standard AMIP simulation, which is driven by observed SSTs, sea ice, and prescribed anthropogenic forcing; (2) AMIP experiments with a globally uniform SST warming of 4K (AMIPp4K); and (3) AMIP experiments with spatially patterned SST warming, derived from the ensemble mean of the coupled CMIP3 model experiments under a 1% per year CO2 increase scenario at the time of CO2 quadrupling, scaled to achieve a global mean SST increase of 4K (AMIPfuture4K). The analysis covers the period from 1979 to 2014. All model outputs are interpolated to a common 73×144 global grid using bilinear interpolation. In this study, the differences between AMIPfuture4K and AMIP represent the response to patterned SST warming, and the differences between AMIPp4K and AMIP represent the response to spatially uniform SST warming. This methodology is widely used in previous studies29,54,63.

Definition of SST metrics
Enhanced equatorial warming (EEW) is measured as the annual-mean SST warming averaged over 5°S-5°N, relative to the tropical SST warming averaged over 20°S-20°N30:
EEW = SSTequator − SSTtropical (1)

The zonal SST gradient across the tropical Pacific is defined as the SST difference between the equatorial eastern Pacific (5°S-5°N, 140°W-80°W) and the equatorial western Pacific (5°S-5°N, 140°E-170°W)18:
SSTgradient = SSTEP − SSTWP (2)

A strengthened zonal gradient of the tropical Pacific SST is described as La Niña-like warming, and the weakened gradient is described as El Niño-like warming.

Optimal fingerprinting detection and attribution
To detect anthropogenic influence on EEW in observation, we apply an optimal fingerprinting (OFP) method using a generalized multivariate linear regression model64,65:
y = β(X − γ) + ε (3)
where y represents observed variations, expressed as the sum of scaled responses to external forcings X and observational noise ε. The externally forced signal X is estimated using a multi-model ensemble mean, with sampling noise γ arising from the limited number of ensemble realizations. The scaling factor β adjusts the forced responses to best match observations. A scaling factor with a 5–95% confidence interval significantly above zero indicates a detectable influence of external forcing. A scaling factor greater than 1 suggests an underestimated response to a specific forcing, and vice versa. To estimate the noise ε, we use two independent internal variability estimates: (1) pre-industrial control simulations, providing 206 samples and (2) the inter-member spread of large ensembles from single-forcing runs, yielding 386 samples. The ensemble spread is derived by subtracting the multi-realization mean from each realization for a specific forcing in each model to eliminate the influence of external forcing. Each sample of a single model is a 70-year EEW sequence reflecting the influence of internal variability. Only large ensembles with at least 10 realizations are considered to ensure reliable internal variability estimates (Supplementary Data 2). In total, 592 samples of noise estimation are obtained, with half used for optimization and the other half for testing.

To enhance the signal-to-noise ratio, we apply a 5-year non-overlapping mean in the detection66. Our study employs a regularized OPF algorithm that does not rely on Empirical Orthogonal Function (EOF) decomposition67 and uses total least squares regression. We conduct one-signal analyses to detect the effects of individual external forcings by regressing observed anomalies onto simulated anomalies under all external forcings (ALL), greenhouse gas forcing (GHG), and anthropogenic aerosol forcing (AER).

Mixed-layer heat budget analysis
The surface heat flux consists of four physical components: shortwave radiation QS, longwave radiation QL, and sensible heat flux QH and latent heat flux QE. The net surface heat flux into the ocean Qnet, which is positive downward, is expressed as:
Qnet = QS + QL + QH + QE (4)

Following Xie et al. (2010)3, the SST tendency is assumed to approach zero when considering interdecadal and longer timescales. Compared to slowly increased SST, the variations in Qnet are one order of magnitude larger, so the ocean transport effect balances the net surface flux to first order. Therefore, the oceanic process effects can be inferred from the net surface heat flux3:
Do = −Qnet (5)
where Do represents the ocean heat transport effect resulting from three-dimensional advection (ADV) and mixing. Since the sub-monthly and sub-grid processes, such as turbulent mixing and mesoscale oceanic eddies, cannot be well resolved by monthly heat budget analysis, we mainly focus on the ADV from horizontal and vertical temperature advection in this study. More importantly, the results clearly suggest the dominant role of the equatorial upwelling change in the total oceanic dynamic effects (Fig. 4a), indicating the minor role of mixing in oceanic dynamic effects on the EEW trend during 1951–2020.

The ADV components of heat budget for the mixed layer can be expressed as follows, based on Jiang et al. (2024)68:
ADV = −[u_a(∂T_c/∂x) + u_c(∂T_a/∂x) + u_a(∂T_a/∂x) + v_a(∂T_c/∂y) + v_c(∂T_a/∂y) + v_a(∂T_a/∂y) + w_a(∂T_c/∂z) + w_c(∂T_a/∂z) + w_a(∂T_a/∂z)] (6)
in which a denotes linear trends during 1951–2020, and c denotes climatology during 1951–2020. The heat budget terms include changes in the temperature gradient (U_T_c, V_T_c, W_T_c), changes in the oceanic current (U_T_a, V_T_a, W_T_a), and their nonlinear interaction (U_T_a, V_T_a, W_T_a). The horizontal advection terms are averaged over the mixed-layer depth. The vertical velocity is calculated at the bottom of the mixed layer.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review: Emergence of the enhanced equatorial Atlantic warming as a fingerprint of global warming This article is highly scientifically relevant because it addresses a current concern: the impacts on planet Earth's climate due to climate change, with an emphasis on anthropogenic influence. A series of numerical experiments using anthropogenic influence as an external forcing are analyzed. The article has significant potential for publication in this journal, but requires some \"minor\" modifications, as listed below. General comments: The title of the manuscript focuses on the equatorial Atlantic Ocean; however, the second sentence of the abstract provides context about the tropical Pacific without presenting any conclusions for this last specific region. The abstract should be rewritten to clearly show the main findings and conclusions of the study and highlight the importance of the research. The introduction lacks an explanation of the importance of Sea Surface Temperature (SST) and the meridional SST gradient in the Tropical Atlantic (commonly referred to as the SST Atlantic dipole) in modulating the ITCZ shift and its impact on precipitation (climate) patterns in the North and Northeast regions of Brazil. This is particularly significant since the most relevant results of the manuscript focus on the Tropical Atlantic, specifically on the weakening of trade winds over the Atlantic Ocean, which drives the EEW due to decreased upwelling. I suggest reading the article below, which highlights the impact of SST gradients on the ITCZ (the battle between ENSO and Atlantic dipole), Hadley, and Walker circulations. This analysis is integral and cannot be separated. Pezzi, L., Cavalcanti, I. The relative importance of ENSO and tropical Atlantic sea surface temperature anomalies for seasonal precipitation over South America: a numerical study. Climate Dynamics 17, 205–212 (2001). https://doi.org/10.1007/s003820000104 Again, in your conclusions section, these current climate mechanisms and already known modulation of tropical circulation (ITCZ, rain patterns, Walker, Hadley) must be contextualized, analyzed and compared again when you analyze your results considering climate change and future scenarios. Specific Comments: #61 Even though it is jargon, and widely used, I do not like the expression \"warm SST\" because it refers to a serious conceptual physical error, where it is being stated that temperatures (which is a simple measurement) are hot, and not surface water. #80 We first compare … Pacific SST gradient zonal gradient … Please, remove the first gradient in this part of the sentence. #95-96 Therefore, EEW is a… This statement is not clear. Why do you state that? You use this idea of external forcing prior to define or mention these external forcings. In this way it is out of context. You need to rephrase/clarify it. #114, 119-120 here you mention external forcings again… is it anthropogenic caused? GHG? Aerosols? This explanation needs to come before, even being in the methodology. #189-190 you state “Specifically, the linear trend in oceanic dynamics (D_o) is linked to the weakening of equatorial upwelling”, however in the methodology #372 you state that D_o “represents the ocean heat transport effect resulting from three-dimensional advection and mixing”. These needs to be better explained in the methodology and in the #189-190, since D_o , does not provide distinctions among vertical and horizontal advection and mixing. #221-223 “… the weakening of tropical atmospheric circulation” this statement is far generic… you must be more precise. Methodology: #308-314 It is not clear how the noise term is calculated. It deserves a better explanation. Why it appears here (#308) and not only concentrate in the section where OFP is described? Additionally, (#311) there is an inconsistency between the number of realizations (382) stated and the total shown in the extended data Table 2 (386). #329 The SST metrics (or gradient calculation) must come with equations.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "In their study „Emergence of the enhanced equatorial Atlantic warming as a fingerprint of global warming“ by Dong et al., the authors combine satellite SST observations, CMIP6 model output and reanalysis data to investigate the factors and mechanisms of enhanced equatorial warming (EEW). EEW is defined as the difference in ocean surface warming rates between the equatorial band (+-5°) and the broader tropics (+-20°). Their analysis suggests that EEW is strongest in the Atlantic which they attribute to reduced equatorial upwelling due to weakened trade winds. In contrast, EEW in the Pacific is not statistically significant and is likely obscured by internal variability associated with interannual to multidecadal climate modes. Based on this, the authors propose that Atlantic EEW provides a more robust indicator of global warming due to anthropogenic external forcing. The manuscript addresses a timely and relevant research topic, and presents potentially interesting results. However, I find the analysis and several of the conclusions unconvincing for multiple reasons (listed below). In particular, the analysis, interpretation, and discussion require substantially greater rigor, especially for a journal such as Nature Communications. For instance, the applied mixed layer heat budget treats ocean dynamics as the residual of net surface heat fluxes without explicitly distinguishing between horizontal/vertical advection, vertical mixing, diffusion and a residual term. Despite this, the authors conclude that equatorial upwelling, as one component of the ocean dynamics term, plays the dominant role. I outline my other major concerns, along with minor points and specific comments below. In its current form, I do not recommend this manuscript for publication in Nature Communications. Major concerns: 1. Lines 91-93: Equatorial SST trends are not different from SST trends north of the equator (Fig. 1b and c). Maybe even smaller for those models that simulate El-Niño like warming. Is that really enough to argue that EEW is evident? What about significance testing und uncertainty assessment? This could help to increase the robustness of the presented results. It could also help backup the claim that “EEW is a more robust response to external forcing” which is a (too) strong conclusion from the presented figure. 2. Extended Fig. 1: SST trends from observations and the CMIP6 ensemble are substantially different for all three basins (Extended Fig. 1a and b). In fact, the observed Pacific cooling trend (Extended Fig. 1a) and the slight warming trend in CMIP6 models (Extended Fig. 1b) lead to about the same EEW trend (Fig. 2a). So even though these datasets represent very different trends, EEW can look the same which makes me wonder how good of a tool EEW can really be? 3. How do the historical changes in equatorial Atlantic upwelling compare with observations and reanalyses? There are studies showing decadal intensification of the EUC (Brandt et al., 2021; https://www.nature.com/articles/s41561-021-00716-1) as part of an acceleration of the Subtropical Cells (Tuchen et al., 2020; https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JC016592) which suggests increased equatorial Atlantic upwelling during that time period. An uncertainty assessment and a more thorough discussion are needed. Also it would be helpful if the authors could justify their choice for SODA. What about other commonly used reanalysis products like ERA5, ORAS5 or even more observation-based products like OSCAR to examine equatorial upwelling through applying the continuity equation to horizontal velocities? 4. In their mixed layer heat budget, the authors define ocean dynamics as the equivalent of the negative net surface heat flux (Fig. 3b is the exact inverse of Extended Fig. 3e). This conceals processes like horizontal advection, vertical mixing, and equatorial upwelling. How can the authors conclude that other ocean dynamic processes than equatorial upwelling do not play a major role? Why simplifying the mixed layer heat budget so drastically and not calculating at least some of the ocean dynamics terms? There also needs to be a thorough explanation and discussion on this simplification. 5. The horizontal resolution of the observational data sets is 1° or 2° and they are eventually interpolated on a 2°x2° grid. Is that resolution really enough to resolve equatorial dynamics and features? How do these datasets compare with higher-resolved data sets such as OI-SST? The CMIP6 models are interpolated on a 2.5°x2.5° global grid which is even coarser than the observational datasets. While this might be ok in off-equatorial regions, horizontal resolution plays a huge role in resolving equatorial dynamics. A discussion on this is required. 6. How does EEW as a response to global warming fit together with the ocean dynamical thermostat theory (Clement et al., 1996; doi: https://journals.ametsoc.org/view/journals/clim/9/9/1520-0442_1996_009_2190_aodt_2_0_co_2.xml)? The ocean dynamical thermostat theory says that under global warming, equatorial upwelling increases and acts as a cooling mechanism near the equator in a warming ocean. However, here the authors present the opposite process (reduced upwelling as a response to global warming). A discussion of their result with respect to Clement et al. (1996) would be helpful. Minor points: • Lines 34-35: In my opinion, EEW should be clearer defined already here in the abstract. The acronym EEW is also not introduced here, when it is first mentioned, but in the next sentence, when it is mentioned again. • Lines 51-53: A reference is needed for this statement. For instance, Latif et al. (2023) doi: https://www.nature.com/articles/s43247-023-00912-4 • Line 70: However, observations show an increase of the equatorial Pacific near-surface currents over the last few decades (England et al., 2014 https://www.nature.com/articles/nclimate2106; Tuchen et al., 2024 https://doi.org/10.1029/2024JC021343; Wang et al., 2024 https://www.nature.com/articles/s41558-023-01915-x). • Throughout the introduction, the three different tropical basins are mixed together. There should be a clearer separation and notification when discussing the different mechanisms and effects for each basin. For example the paragraph starting in line 67. • Lines 81-86: Figure 1a does not show the described EEW index but its trend. Same for the zonal gradient index. Does it matter whether to correlate the index time series of the trends of the index time series? • Figure 1 and Extended Fig. 1: I would suggest to not cut through the Atlantic in these figures, but to change all maps to 30°E-30°E. • In Extended Fig. 1d, aerosol forcing leads to warming on the equator in the Atlantic, but to even more warming off the equator which leads to an overall negative EEW trend in the Atlantic. What is the reason for the regional differences of aerosol forced SST trends? • Lines 116-119: What about internal variability in the Atlantic and Indian Ocean? That could possibly impact (maybe even promote) the EEW trends there. This needs to be discussed. • Figure 4: How is wind defined here? Zonal wind speed or absolute of horizontal wind speed? The negative sign of the wind trend suggests a strengthening of the easterlies at the equator. So, a definition is required. • Can the authors exclude that the weakening of the winds is a consequence of reduced upwelling rather than driving it? If it’s a trend driven largely by changing ocean dynamics, the atmosphere could be responding to that rather than being the initiator. Specific comments: • Line 80: Two times gradient. Remove “gradient” after “SST”. • Lines 80-81: Why this specific time period from 1951-2020? CMIP6 historical simulations and SST from ERSST and HadISST are available for a longer time period. Please provide a motivation/justification for choosing this specific time period. • Lines 81-85: The EEW here is defined for the whole tropical band (including Indian and Atlantic Ocean), but it is only compared to Pacific SST zonal gradient? Is that a meaningful comparison? • Lines 90-91: Looks like 9 out of 36 models show a negative EEW trend, and 7 models are positive, but very close to zero (maybe not even significantly different from zero?). It would be helpful to include the uncertainty of those trends as vertical and horizontal lines to see which ones are statistically different from a zero trend. This would possibly strengthen the claim that “EEW is a robust signal in the CMIP6 models”. • Lines 160-162: There is upwelling due to Ekman in the central equatorial Atlantic and upwelling due to equatorial waves in the eastern equatorial Atlantic. See Wang et al. (2017), doi: https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2016JC012537. Figures: • Figure 1: What is shown by the black dots in b and c? This is not explained in the figure caption."}, "author_response": "1 Response to Reviewer #1\n3 the manuscript. The comments are all valuable and helpful for revising our paper. Based\n4 on these comments, we provide a point-by-point response. The text in black is the\n5 original review comments, and the text in blue is the authors’ response.\n7 impacts on planet Earth's climate due to climate change, with an emphasis on\n8 anthropogenic influence. A series of numerical experiments using anthropogenic\n9 influence as an external forcing are analyzed. The article has significant potential for\n10 publication in this journal, but requires some \"minor\" modifications, as listed below.\n13 second sentence of the abstract provides context about the tropical Pacific without\n14 presenting any conclusions for this last specific region. The abstract should be rewritten\n15 to clearly show the main findings and conclusions of the study and highlight the\n16 importance of the research.\n18 (SST) and the meridional SST gradient in the Tropical Atlantic (commonly referred to\n19 as the SST Atlantic dipole) in modulating the ITCZ shift and its impact on precipitation\n20 (climate) patterns in the North and Northeast regions of Brazil. This is particularly\n21 significant since the most relevant results of the manuscript focus on the Tropical\n23 drives the EEW due to decreased upwelling. I suggest reading the article below, which\n24 highlights the impact of SST gradients on the ITCZ (the battle between ENSO and\n26 be separated.\n28 surface temperature anomalies for seasonal precipitation over South America: a\n29 numerical study. Climate Dynamics 17, 205–212\n30 (2001). https://doi.org/10.1007/s003820000104\n32 known modulation of tropical circulation (ITCZ, rain patterns, Walker, Hadley) must\n33 be contextualized, analyzed and compared again when you analyze your results\n34 considering climate change and future scenarios.\n36 have rewritten the abstract, to emphasize the importance of focusing on more robust\n37 enhanced equatorial warming (EEW) when studying tropical sea surface temperatures\n38 (SST), in contrast to the extensively-focused and hotly-debated zonal gradient of SST\n39 in tropical Pacific. We also clarified the definition of EEW and its emergence in the\n42 meridional SST gradient in the Tropical Atlantic in influencing tropical atmospheric\n43 circulation (Walker circulation and Hadley circulation), regulating the movement of the\n46 and Cavalcanti (2001) and the related references in the revised manuscript.\n49 the importance of weakened trade wind in driving the EEW. They are not conflicting as\n50 tropical ocean and atmosphere are closely coupled with each other. In addition, we\n51 demonstrate that the weakened zonal winds over Atlantic emerge as a robust response\n52 to global warming even under the spatially uniform SST warming by analyzing\n54 global warming. We have added this discussion in the Lines 305-312 of the revised\n55 manuscript.\n57 1. #61 Even though it is jargon, and widely used, I do not like the expression \"warm\n59 that temperatures (which is a simple measurement) are hot, and not surface water.\n61 2. #80 We first compare … Pacific SST gradient zonal gradient … Please, remove the\n62 first gradient in this part of the sentence.\n64 3. #95-96 Therefore, EEW is a… This statement is not clear. Why do you state that?\n68 climate change outside the climate system, including natural (NAT) forcing, such as\n69 solar radiation and volcanic eruption, and anthropogenic (ANT) forcing, such as\n70 greenhouse gas forcing (GHG) and aerosol forcing (AER). We have clarified the\n71 definition of external forcing in Lines 90-91 of the revised manuscript. To give clear\n72 evidences of the statement, we have added that “despite the different changes in zonal\n74 (Extended Data Fig. 1a) and El Niño-like warming in CMIP6 models (Extended Data\n76 observations and CMIP6 models, statistically significant based on the MME of CMIP6\n77 models” in Lines 107-111. Then we have revised “external forcing” into “global\n78 warming” in this sentence as “Therefore, EEW is a more robust response to global\n79 warming compared to the Pacific zonal SST gradient in the historical period”.\n80 4. #114, 119-120 here you mention external forcings again… is it anthropogenic caused?\n83 of anthropogenic forcing (greenhouse gas and aerosols) and natural forcing (solar\n84 radiation and volcanic eruption). We have added the explanation of external forcing\n85 before this sentence in Lines 90-91, as well as in the methodology, Lines 347-350 of\n86 the revised manuscript.\n87 5. #189-190 you state “Specifically, the linear trend in oceanic dynamics (D_o) is linked\n88 to the weakening of equatorial upwelling”, however in the methodology #372 you state\n89 that D_o “represents the ocean heat transport effect resulting from three-dimensional\n90 advection and mixing”. These needs to be better explained in the methodology and in\n91 the #189-190, since D_o , does not provide distinctions among vertical and horizontal\n92 advection and mixing.\n94 the individual components of the ocean dynamic term to determine the dominant role\n95 of the equatorial upwelling. Following Jiang et al. (2024)7, the oceanic advection (ADV)\n96 components of heat budget for the mixed layer can be expressed as:\n97 𝐴𝐷𝑉 =−𝑢 𝜕𝑇 −𝑢 𝜕𝑇 −𝑢 𝜕𝑇 −𝑣 𝜕𝑇 −𝑣 𝜕𝑇 −𝑣 𝜕𝑇 −𝑤 𝜕𝑇 −𝑤 𝜕𝑇 −𝑤 𝜕𝑇 (1),\n⏟ 𝑎 𝜕𝑥 𝑐 ⏟𝑐 𝜕 𝑥 𝑎 ⏟𝑎 𝜕 𝑥 𝑎 ⏟𝑎 𝜕 𝑦 𝑐 ⏟𝑐 𝜕 𝑦 𝑎 ⏟𝑎 𝜕 𝑦 𝑎 ⏟𝑎 𝜕𝑧 𝑐 ⏟𝑐 𝜕 𝑧 𝑎 ⏟𝑎 𝜕 𝑧𝑎\n𝑈𝑎𝑇𝑐 𝑈𝑐𝑇𝑎 𝑈𝑎𝑇𝑎 𝑉𝑎𝑇𝑐 𝑉𝑐𝑇𝑎 𝑉𝑎𝑇𝑎 𝑊𝑎𝑇𝑐 𝑊𝑐𝑇𝑎 𝑊𝑎𝑇𝑎\n98 in which a denotes linear trends during 1951-2020 and c denotes climatology during\n99 1951-2020. The heat budget terms include changes in the temperature gradient (𝑈 𝑇 ,\n𝑐 𝑎\n100 𝑉𝑇 , 𝑊𝑇 ), changes in the oceanic current (𝑈 𝑇 , 𝑉 𝑇 , 𝑊 𝑇 ), and their nonlinear\n𝑐 𝑎 𝑐 𝑎 𝑎 𝑐 𝑎 𝑐 𝑎 𝑐\n101 interaction (𝑈 𝑇 , 𝑉 𝑇 , 𝑊 𝑇 ). The horizontal advection terms are averaged over the\n𝑎 𝑎 𝑎 𝑎 𝑎 𝑎\n102 mixed layer depth. The vertical velocity is calculated at the bottom of the mixed layer.\n104 upwelling (𝑊 𝑇 ) and changes in the zonal temperature gradient (𝑈 𝑇 ) contributes to\n𝑎 𝑐 𝑐 𝑎\n105 the EEW trend (Figs. R1a, b). However, the spatial pattern of 𝑊 𝑇 dominates the\n𝑎 𝑐\n106 stronger warming along the Atlantic equator (Fig. R1d). It confirms that the weakening\n107 of equatorial upwelling dominates the increased EEW in the Atlantic. In the revision,\n109 methods of calculating the oceanic advection components in Lines 436-443 and the\n110 related description about the contribution of ocean advection and the dominance of\n111 weakening of equatorial upwelling in the oceanic dynamics in Lines 188-192 of the\n112 revised manuscript.\n113\n115 the EEW trend. (a) Zonal mean and (b) relative equatorial (5°S-5°N) mean with\n116 the averaged over 20°S to 20°N removed of decomposition of the heat budget terms\n117 into oceanic current change (𝑈 𝑇 , 𝑉 𝑇 , 𝑊 𝑇 ), changes in the temperature gradient\n𝑎 𝑐 𝑎 𝑐 𝑎 𝑐\n118 (𝑈 𝑇 , 𝑉𝑇 , 𝑊𝑇 ) and nonlinear terms (𝑈 𝑇 , 𝑉 𝑇 , 𝑊 𝑇 ) for the Atlantic based on\n𝑐 𝑎 𝑐 𝑎 𝑐 𝑎 𝑎 𝑎 𝑎 𝑎 𝑎 𝑎\n119 21 CMIP6 models. (c, d) The patterns of (c) 𝑈 𝑇 and (d) 𝑊 𝑇 during 1951-2020\n𝑐 𝑎 𝑎 𝑐\n120 based on the average of 21 CMIP6 models in the Atlantic. Units: K month-1 decade-1\n121 6. #221-223 “… the weakening of tropical atmospheric circulation” this statement is\n122 far generic… you must be more precise.\n124 primarily separated into a component related to the zonal-mean circulation, mainly the\n127 7. #308-314 It is not clear how the noise term is calculated. It deserves a better\n128 explanation. Why it appears here (#308) and not only concentrate in the section where\n130 of realizations (382) stated and the total shown in the extended data Table 2 (386).\n132 description of the noise calculation method to avoid confusion here, and only focus on\n133 the model simulations used in this section. The noise calculation method has been\n134 introduced in details in the optimal fingerprinting detection and attribution section. In\n135 fact, the method for calculating noise is to estimate the impact of internal variability.\n137 the time series from pre-industrial control simulations, which have fixed external\n138 forcing at pre-industrial level and are only influenced by internal variability, can be\n139 directly used to estimate the noise. Second, for large ensemble simulations of a single\n140 model, we eliminate the influence of external forcing by subtracting the average of all\n141 members from each member so that we can get the time series only affected by internal\n142 variability. Please see Lines 409-413 of the revised manuscript for details. In addition,\n143 the error in the number of realizations has been corrected as 386 in Line 365. Thanks\n144 for pointing out this typo.\n145 8. #329 The SST metrics (or gradient calculation) must come with equations.\n147 measured as the annual-mean SST warming averaged over 5°S-5°N, relative to the\n148 tropical SST warming averaged over 20°S-20°N9:\n149 𝐸𝐸𝑊 = 𝑆𝑆𝑇 −𝑆𝑆𝑇 (2)\n𝑒𝑞𝑢𝑎𝑡𝑜𝑟 𝑡𝑟𝑜𝑝𝑖𝑐𝑎𝑙\n153 𝑆𝑆𝑇 = 𝑆𝑆𝑇 −𝑆𝑆𝑇 (3)\n𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 𝐸𝑃 𝑊𝑃\n155 manuscript.\n157 1. Pezzi, L. & Cavalcanti, I. The relative importance of ENSO and tropical Atlantic\n158 sea surface temperature anomalies for seasonal precipitation over South America: a\n159 numerical study. Clim. Dyn. 17, 205–212 (2001).\n160 2. Huang, B., Carton, J. A. & Shukla, J. A. numerical simulation ofthe variability in\n161 the tropical Atlantic Ocean, 1980-1988. J. Phys. Oceanogr. 25, 835-854 (1995).\n162 3. Wagner, R. G. Mechanisms controlling variability of the in-terhemispheric sea\n163 surface temperature gradient in the tropical Atlantic. J. Clim. 9, 2010-2019 (1996).\n164 4. Nobre, P. & Shukla, J. Variations of sea surface temperature.wind stress and rainfall\n165 over the tropical Atlantic and South America. J. Clim. 10, 2464-2479 (1996)\n166 5. Chang, P., Ji, L. & Li, H. A decadal variation in the tropical Atlantic Ocean from\n167 thermodynamic air-sea interactions. Nature 385, 516-518 (1997).\n168 6. Souza, E. B. & Nobre, P. Uma revisào sobre o Padrào de Di-polo no Oceano\n170 7. Jiang, F., Seager, R. & Cane, M.A. A climate change signal in the tropical Pacific\n171 emerges from decadal variability. Nat Commun 15, 8291 (2024).\n172 8. Shrestha, S., & Soden, B. J. Anthropogenic weakening of the atmospheric\n173 circulation during the satellite era. Geophys. Res. Lett. 50, e2023GL104784\n174 (2023).\n175 9. Zhou, W., Xie, S. P. & Yang, D. Enhanced equatorial warming causes deep-\n176 tropical contraction and subtropical monsoon shift. Nat. Clim. Chang. 9, 834–839\n177 (2019).\n178 10. Wang Z., Dong, L., Song, F. F., Zhou, T. & Chen, X. Uncertainty in the Past and\n181\n182\n185 the manuscript. The comments are all valuable and helpful for revising our paper. Based\n186 on these comments, we provide a point-by-point response. The text in black is the\n187 original review comments, and the text in blue is the authors’ response.\n189 of global warming” by Dong et al., the authors combine satellite SST observations,\n191 enhanced equatorial warming (EEW). EEW is defined as the difference in ocean surface\n192 warming rates between the equatorial band (+-5°) and the broader tropics (+-20°). Their\n193 analysis suggests that EEW is strongest in the Atlantic which they attribute to reduced\n194 equatorial upwelling due to weakened trade winds. In contrast, EEW in the Pacific is\n195 not statistically significant and is likely obscured by internal variability associated with\n196 interannual to multidecadal climate modes. Based on this, the authors propose that\n198 external forcing.\n200 interesting results. However, I find the analysis and several of the conclusions\n201 unconvincing for multiple reasons (listed below). In particular, the analysis,\n202 interpretation, and discussion require substantially greater rigor, especially for a journal\n203 such as Nature Communications. For instance, the applied mixed layer heat budget\n204 treats ocean dynamics as the residual of net surface heat fluxes without explicitly\n205 distinguishing between horizontal/vertical advection, vertical mixing, diffusion and a\n206 residual term. Despite this, the authors conclude that equatorial upwelling, as one\n207 component of the ocean dynamics term, plays the dominant role.\n209 below. In its current form, I do not recommend this manuscript for publication in Nature\n212 strengthened the scientific argument by integrating more data analysis, conducting a\n213 detailed mixed-layer heat budget analysis to specify the processes of oceanic dynamics,\n214 enhancing the rigor of interpretation, and adding more related discussion and\n215 comparison with previous studies. To address your concerns, we have provided a\n216 decomposition of oceanic dynamic processes to further substantiate our conclusions\n217 that the equatorial upwelling plays the dominant role in driving the EEW. Our point-\n218 by-point responses are shown below.\n220 1. Lines 91-93: Equatorial SST trends are not different from SST trends north of the\n221 equator (Fig. 1b and c). Maybe even smaller for those models that simulate El-Niño\n222 like warming. Is that really enough to argue that EEW is evident? What about\n223 significance testing and uncertainty assessment? This could help to increase the\n224 robustness of the presented results. It could also help backup the claim that “EEW is a\n225 more robust response to external forcing” which is a (too) strong conclusion from the\n226 presented figure.\n228 warming in the subtropical northeastern Pacific, which has been thoroughly explained\n229 by Geng et al. (2023)1. It is contributed by increased shortwave and longwave radiation\n230 into the ocean and associated with enhanced wind-evaporation-SST (WES) feedback.\n233 northeastern Pacific as “It’s worth noting that there is a peak of SST warming in the\n234 subtropical northeastern Pacific in both El Niño-like and La Niña-like warming patterns,\n235 which has been revealed and explained by Geng et al. (2023)” in Lines 113-115.\n237 significant across the entire tropical ocean based on the multi-model ensemble mean\n238 (MME). In addition, there are 28 models exhibit a positive EEW trend (Fig. 1a), with\n239 inter-model agreement exceeding 77%. This indicates that the uncertainty in the model\n240 simulation of EEW is relatively small. In addition, the EEW trends in the Atlantic are\n241 more evident than those across the entire tropical ocean in both the La Niña-like\n242 warming and El Niño-like warming in CMIP6 models (Figs. 1b, c), and can be\n243 detectable in observations (Figure 2). To highlight this conclusion, we have added error\n244 bars to represent inter-model uncertainty in Figure 1a, and calculated the zonal average\n245 for the Atlantic SST as blue lines in Figures 1b, c. Meanwhile, we have added the related\n246 descriptions about the significance and the uncertainty of EEW in Lines 103-105, 106-\n247 111, 146-148 of the revised manuscript.\n248 2. Extended Fig. 1: SST trends from observations and the CMIP6 ensemble are\n249 substantially different for all three basins (Extended Fig. 1a and b). In fact, the observed\n251 (Extended Fig. 1b) lead to about the same EEW trend (Fig. 2a). So even though these\n252 datasets represent very different trends, EEW can look the same which makes me\n253 wonder how good of a tool EEW can really be?\n255 patterns of the observed and models differ significantly in Pacific, with La Niña-like\n256 warming pattern in observations (Fig. R1a) and El Niño-like warming pattern in CMIP6\n257 models (Fig. R1b), both the EEW trends are positive for the entire tropical ocean and\n259 changes include both the effects of external forcing and internal variability, while the\n260 ensemble mean of CMIP6 models mainly reflects the effect of external forcing due to\n261 the suppression of internal variability. Thus, the discrepancy between observed and\n262 simulated Pacific zonal SST gradient change indicates that the observed zonal SST\n263 gradient changes in the Pacific are potentially obscured by internal variability. In\n264 contrast, the EEW trends are positive in both observations and CMIP6 models,\n265 indicating that the EEW is more prone to emerge than SST zonal gradient in response\n266 to external forcing. We have added this discussion as “In addition, despite the different\n267 changes in zonal SST gradient over the tropical Pacific, with La Niña-like warming in\n268 observations (Extended Data Fig. 1a) and El Niño-like warming in CMIP6 models\n269 (Extended Data Fig. 1b), the EEW trends across the entire tropical ocean are positive\n270 for both observations and CMIP6 models, statistically significant based on the multi-\n271 model ensemble mean (MME) of CMIP6 models (Fig. 2a). Therefore, the EEW is a\n272 more robust response to global warming compared to the Pacific zonal SST gradient in\n273 the historical period” in Lines 106-113 of the revised manuscript.\n275 in observations, and the effect of external forcing is detectable (Fig. 2). Therefore, we\n276 suggest EEW in the Atlantic is a more robust tool in response to global warming. We\n277 have clarified this conclusion as “Therefore, the EEW has emerged in the Atlantic,\n278 which can be regarded as a robust response to global warming” in Lines 179-180 of the\n279 revised manuscript.\n280\n282 20°N removed at each grid during 1951-2020 from (a) OBS (HadISST v1.1, ERSST\n283 v5 and COBE-SST 2), (b) all forcing runs (ALL) based on 36 CMIP6 models in Pacific\n284 (140°E-80°W). Stippling indicates the regions where the regression is statistically\n285 significant at the 95% level of confidence based on Student’s t-test. (c) The trend of\n288 trend is not statistically significant at the 95% level of confidence based on Student’s t-\n289 test. Error bars denote one standard deviation from three OBS and 36 CMIP6 models.\n291 3. How do the historical changes in equatorial Atlantic upwelling compare with\n292 observations and reanalyses? There are studies showing decadal intensification of the\n294 part of an acceleration of the Subtropical Cells (Tuchen et al.,\n295 2020; https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JC016592) which\n296 suggests increased equatorial Atlantic upwelling during that time period. An uncertainty\n297 assessment and a more thorough discussion are needed. Also it would be helpful if the\n298 authors could justify their choice for SODA. What about other commonly used\n299 reanalysis products like ERA5, ORAS5 or even more observation-based products like\n301 horizontal velocities\n306 during 1980-2020 based on SODA. This may be the reason for the differences in the\n307 equatorial upwelling changes. The SODA datasets we used in this study significantly\n308 enhance the accuracy, reliability, and applicability of ocean reanalysis through using\n309 higher-resolution models, improving assimilation methods, correcting flux bias and\n310 richer observational data2. In addition, the SODA reanalysis dataset has been used in\n311 numerous previous studies to compare with models as a substitute for observations3-7.\n313 upwelling in the Atlantic based on NCEP Global Ocean Data Assimilation System\n314 (GODAS)8, which provides the variable of vertical velocity during 1980-2020. As\n315 shown in Fig. R2, the equatorial upwelling of the Atlantic exhibits a pronounced\n316 weakening based on GODAS during 1980-2020 (Figs. R2a, b), consistent with the\n317 results based on SODA. Furthermore, in our study, the weakening of the equatorial\n318 upwelling shows high inter-model consistency (91.3%) based on CMIP6 models (the\n319 y-axis of Fig. 4a). Therefore, the weakening of the equatorial upwelling is robust in the\n321 different time periods. We have added the results based on GODAS to Extended Data\n324 datasets in Lines 339-343 of the revised manuscript.\n325\n327 reanalysis. (a) Vertical mean (0-150m) ocean vertical velocity trend (shading, in m day-\n328 1 per decade) over 1980-2020 and climatological vertical velocity (black contours, CI\n329 2 m day-1; solid > 0 m day-1, dashed <0 m day-1; thick 0 m day-1) based on GODAS.\n331 level of confidence based on Student’s t-test. (b) The zonal mean of vertical velocity\n332 trend (red line, in m day-1 per decade) and climatological vertical velocity (black line,\n333 in m day-1) in (a). Blue shading indicates 3°S-3°N.\n334 4. In their mixed layer heat budget, the authors define ocean dynamics as the equivalent\n335 of the negative net surface heat flux (Fig. 3b is the exact inverse of Extended Fig. 3e).\n337 upwelling. How can the authors conclude that other ocean dynamic processes than\n338 equatorial upwelling do not play a major role? Why simplifying the mixed layer heat\n339 budget so drastically and not calculating at least some of the ocean dynamics terms?\n342 slowly increased SST at interdecadal and longer timescales, the variations in 𝑄 are\n𝑛𝑒𝑡\n343 one order of magnitude larger, so the ocean transport effect balances the net surface\n344 flux to first order, which means ocean dynamics as the equivalent of the negative net\n345 surface heat flux. We have added an explanation regarding the simplified heat budget\n346 of the mixed layer in Lines 430-432 of the revised manuscript.\n348 ocean dynamic term to determine the dominant role of the equatorial upwelling.\n350 for the mixed layer can be expressed as:\n351 𝐴𝐷𝑉 =−𝑢 𝜕𝑇 −𝑢 𝜕𝑇 −𝑢 𝜕𝑇 −𝑣 𝜕𝑇 −𝑣 𝜕𝑇 −𝑣 𝜕𝑇 −𝑤 𝜕𝑇 −𝑤 𝜕𝑇 −𝑤 𝜕𝑇 (1),\n⏟ 𝑎 𝜕𝑥 𝑐 ⏟𝑐 𝜕 𝑥 𝑎 ⏟𝑎 𝜕 𝑥 𝑎 ⏟𝑎 𝜕 𝑦 𝑐 ⏟𝑐 𝜕 𝑦 𝑎 ⏟𝑎 𝜕 𝑦 𝑎 ⏟𝑎 𝜕𝑧 𝑐 ⏟𝑐 𝜕 𝑧 𝑎 ⏟𝑎 𝜕 𝑧𝑎\n𝑈𝑎𝑇𝑐 𝑈𝑐𝑇𝑎 𝑈𝑎𝑇𝑎 𝑉𝑎𝑇𝑐 𝑉𝑐𝑇𝑎 𝑉𝑎𝑇𝑎 𝑊𝑎𝑇𝑐 𝑊𝑐𝑇𝑎 𝑊𝑎𝑇𝑎\n352 in which a denotes linear trends during 1951-2020 and c denotes climatology during\n353 1951-2020. The heat budget terms include changes in the temperature gradient (𝑈 𝑇 ,\n𝑐 𝑎\n354 𝑉𝑇 , 𝑊𝑇 ), changes in the oceanic current (𝑈 𝑇 , 𝑉 𝑇 , 𝑊 𝑇 ), and their nonlinear\n𝑐 𝑎 𝑐 𝑎 𝑎 𝑐 𝑎 𝑐 𝑎 𝑐\n355 interaction (𝑈 𝑇 , 𝑉 𝑇 , 𝑊 𝑇 ). The horizontal advection terms are averaged over\n𝑎 𝑎 𝑎 𝑎 𝑎 𝑎\n356 mixed layer depth. The vertical velocity is calculated at the bottom of the mixed layer.\n358 upwelling (𝑊 𝑇 ) and changes in the zonal temperature gradient (𝑈 𝑇 ) contributes to\n𝑎 𝑐 𝑐 𝑎\n359 the EEW trend (Figs. R3a, b). However, the spatial pattern of 𝑊 𝑇 dominates the\n𝑎 𝑐\n360 stronger warming along the Atlantic equator (Fig. R3d). It confirms that the weakening\n361 of equatorial upwelling dominates the increased EEW in the Atlantic. In the revision,\n363 methods of calculating the oceanic advection components in Lines 436-443 and the\n364 related description about the contribution of ocean advection and the dominance of\n365 weakening of equatorial upwelling in the oceanic dynamics in Lines 188-192 of the\n366 revised manuscript.\n367\n369 to EEW trend. (a) Zonal mean and (b) relative equatorial (5°S-5°N) mean with the\n370 averaged over 20°S to 20°N removed of decomposition of the heat budget terms into\n371 oceanic current change (𝑈 𝑇 , 𝑉 𝑇 , 𝑊 𝑇 ), changes in temperature gradient (𝑈 𝑇 ,\n𝑎 𝑐 𝑎 𝑐 𝑎 𝑐 𝑐 𝑎\n372 𝑉𝑇 , 𝑊𝑇 ) and nonlinear terms (𝑈 𝑇 , 𝑉 𝑇 , 𝑊 𝑇 ) for the Atlantic based on 21\n𝑐 𝑎 𝑐 𝑎 𝑎 𝑎 𝑎 𝑎 𝑎 𝑎\n𝑐 𝑎 𝑎 𝑐\n374 on the average of 21 CMIP6 models in the Atlantic. Units: K month-1 decade-1\n375\n376 5. The horizontal resolution of the observational data sets is 1° or 2° and they are\n377 eventually interpolated on a 2°x2° grid. Is that resolution really enough to resolve\n378 equatorial dynamics and features? How do these datasets compare with higher-resolved\n379 data sets such as OI-SST? The CMIP6 models are interpolated on a 2.5°x2.5° global\n380 grid which is even coarser than the observational datasets. While this might be ok in\n381 off-equatorial regions, horizontal resolution plays a huge role in resolving equatorial\n382 dynamics. A discussion on this is required.\n384 important impact on the equatorial dynamics, and the horizontal resolution we used to\n385 explore the emergence of EEW in this study is interpolated on a coarse grid based on\n386 the observation and models. To examine the influence of horizontal resolution on our\n387 results, we have compared the SST trend distribution in tropical oceans between the\n388 original resolution and interpolated resolution for the same datasets. We choose three\n389 observations (OISSTv2, HadISST, COBESST 2) and two CMIP6 models (HadGEM3-\n391 relatively higher horizontal resolutions among CMIP6 models we used. It should be\n392 noted that the SST trends based on OISSTv2 are during 1982-2020, and the other\n393 datasets are during 1951-2020. The results show that interpolating data from higher to\n394 lower horizontal resolution does not affect the primary features of SST trend\n395 distribution along the equator (Fig. R4). This is also true for OISSTv2 with the huge\n396 difference in horizontal resolutions before and after interpolation. Regardless of\n397 resolution, the emergence of EEW in the Atlantic is always robust during 1951-2020,\n398 consistent with our conclusion. We have added the related discussion as “We have\n399 checked that this interpolation process does not affect the distribution of SST trends in\n400 the tropical oceans” in Lines 329-330 of the revised manuscript.\n401\n403 interpolation. (a-e) The relative SST trend patterns with the warming trend averaged\n404 over 20°S to 20°N removed at each grid over 1982-2020 from the (a) OISSTv2 and\n405 over 1951-2020 based on the (b) HadISST, (c) COBESST 2 and 2 CMIP6 models\n406 including (d) HadGEM3-GC31-MM, (e) EC-Earth3 with their original resolutions. (f-\n407 j) As in (a-c), but with uniform horizontal resolution interpolated onto 2.5°×2.5°.\n409 the trend is statistically significant at the 95% level of confidence based on Student’s\n410 t-test. Unit: K per decade.\n411 6. How does EEW as a response to global warming fit together with the ocean\n412 dynamical thermostat theory (Clement et al., 1996,\n413 doi: https://journals.ametsoc.org/view/journals/clim/9/9/1520-\n414 0442_1996_009_2190_aodt_2_0_co_2.xml)? The ocean dynamical thermostat theory\n415 says that under global warming, equatorial upwelling increases and acts as a cooling\n416 mechanism near the equator in a warming ocean. However, here the authors present the\n417 opposite process (reduced upwelling as a response to global warming). A discussion of\n418 their result with respect to Clement et al. (1996) would be helpful.\n421 by equatorial upwelling of cold water, thereby suppressing SST increase in the eastern\n422 tropical Pacific. Clement et al. (1996) focuses on the role of ocean dynamical thermostat\n423 in the zonal SST gradient change in the tropical Pacific, while here we focus on the\n425 results (Fig. 1). The ocean dynamical thermostat theory addresses the role of cold-water\n426 advection from the mean upwelling on the changes in vertical temperature gradient,\n427 which is calculated as 𝑊𝑇 in equation (1) of our response to your Comment #4.\n429 from the weakened upwelling on the mean vertical temperature gradient, which is\n430 calculated as 𝑊 𝑇 in equation (1). The mixed-layer heat budget analysis shows that\n𝑎 𝑐\n431 the weakening equatorial upwelling as a response to global warming dominates the\n435 processes. We have added the discussion about the ocean dynamical thermostat\n436 mechanism and cited Clement et al. (1996) in Lines 196-198 of the revised manuscript.\n438 • Lines 34-35: In my opinion, EEW should be clearer defined already here in the\n439 abstract. The acronym EEW is also not introduced here, when it is first mentioned, but\n440 in the next sentence, when it is mentioned again.\n442 defined as the annual-mean SST warming averaged over 5°S-5°N, relative to the\n443 tropical SST warming averaged over 20°S-20°N. We have added the definition of EEW\n444 and the introduction of acronym in Lines 36-37, 34 of the revised manuscript.\n445 • Lines 51-53: A reference is needed for this statement. For instance, Latif et al. (2023)\n446 doi: https://www.nature.com/articles/s43247-023-00912-4\n448 • Line 70: However, observations show an increase of the equatorial Pacific near-\n449 surface currents over the last few decades (England et al.,\n450 2014 https://www.nature.com/articles/nclimate2106; Tuchen et al.,\n451 2024 https://doi.org/10.1029/2024JC021343; Wang et al.,\n452 2024 https://www.nature.com/articles/s41558-023-01915-x).\n454 references in Lines 78-79.\n455 • Throughout the introduction, the three different tropical basins are mixed together.\n457 mechanisms and effects for each basin. For example the paragraph starting in line 67.\n459 mechanisms and effects for EEW in different tropical basins and global average\n460 throughout the introduction in Lines 61, 64, 74-78 of the revised manuscript.\n461 • Lines 81-86: Figure 1a does not show the described EEW index but its trend. Same\n462 for the zonal gradient index. Does it matter whether to correlate the index time series\n463 of the trends of the index time series?\n465 correlation coefficient between the time series of EEW index and zonal gradient index\n466 based on the observations (OBS, Fig. R5a) and each of the 36 CMIP6 models (Fig.\n468 correlation coefficients statistically significant at the 95% level of confidence for both\n469 observations and 35 CMIP6 models. This indicates that EEW is closely related to the\n470 zonal gradient at interannual timescale, which is dominated by internal variability like\n473 linear trends of EEW and zonal gradient during 1951-2020 are independent with each\n474 other, with correlation coefficient of 0.02 based on 36 CMIP6 models (Fig. 1a). Thus,\n475 they are different phenomena and influenced by different factors at long-term\n476 timescales. We have added the description about the correlation between these two\n477 indices at interannual timescale in Lines 96-97 of the revised manuscript.\n478\n480 interannual timescale. (a) The time series of EEW and the SST zonal gradient in the\n481 tropical Pacific during 1951-2020 from the mean of observations (OBS) including\n483 noted in the upper right corner of the figure. Unit: K. (b) The correlation coefficients\n484 between the time series of EEW and the SST zonal gradient based on each of the 36\n486 95% level of confidence based on Student's t-test.\n487 • Figure 1 and Extended Fig. 1: I would suggest to not cut through the Atlantic in these\n488 figures, but to change all maps to 30°E-30°E.\n491 • In Extended Fig. 1d, aerosol forcing leads to warming on the equator in the Atlantic,\n492 but to even more warming off the equator which leads to an overall negative EEW trend\n493 in the Atlantic. What is the reason for the regional differences of aerosol forced SST\n494 trends?\n496 the tropical oceans has been removed to highlight the spatial pattern of SST trends. As\n497 you mentioned, there are obvious differences in relative SST trends between equator\n498 and off equator under aerosol-only forcing in the Atlantic. According to Booth (2012)10,\n499 the difference is due to the meridional uneven distribution of aerosol emissions in North\n502 shortwave radiation and decreasing SST. However, since the 1980s, significant\n503 reductions in aerosol emissions from North America have produced the opposite effect,\n504 causing the previously cooler regions to warm again, which confirms our findings with\n505 weaker cooling (or stronger warming) off the equator in the North Atlantic compared\n506 to the equatorial Atlantic, resulting in a negative EEW trend. We have added the related\n507 description and cited this reference as “In contrast, aerosol forcing produces an opposite\n508 effect on the spatial patterns of SST trends, with stronger cooling along the equatorial\n510 1d). This may be due to the effects of reduced aerosol emissions from North America\n511 on the SST over North Atlantic49” in Lines 166-170 of the revised manuscript.\n512 • Lines 116-119: What about internal variability in the Atlantic and Indian Ocean?\n514 be discussed.\n516 discussed the impact of internal variability on EEW in the three tropical oceans based\n517 on the noise 𝜀 in the optimal fingerprinting (OFP) method. We calculate the noise 𝜀,\n518 which represents internal variability, during 1951-2020 in the Indian, Pacific and\n520 of large ensembles from single-forcing runs. The ensemble spread is derived by\n521 subtracting the multi-realization mean from each realization for a specific forcing in\n522 each model to eliminate the influence of external forcing. As shown in Figure 2b, the\n523 influence of internal variability (error bar) is large and crosses zero line in the Pacific,\n524 but is relatively small and above zero line in the Indian and Atlantic Oceans. In\n525 particular, the error bar in the Atlantic is smaller than the Indian Ocean, especially under\n527 the highest inter-model consistency in simulating the positive EEW trends in the\n529 strongest in the Pacific, masking the external signals on the EEW trend, while the\n530 impacts of internal variability in the Indian and Atlantic are not strong enough to\n531 obscure the emergence of EEW under external forcing, particularly in the Atlantic. We\n532 have added the related discussion as “In contrast, the internal variability, represented as\n533 the noise based on the OFP method, is insufficient to mask the EEW trends under\n534 external forcing in the Indian and Atlantic Ocean (Fig. 2b)” in Lines 143-146 of the\n535 revised manuscript.\n536 • Figure 4: How is wind defined here? Zonal wind speed or absolute of horizontal wind\n537 speed? The negative sign of the wind trend suggests a strengthening of the easterlies at\n538 the equator. So, a definition is required.\n540 absolute value of zonal wind speed, with negative sign indicating the slowdown of\n541 easterlies at the equator. We have clarified the definition of zonal wind speed as “We\n542 use the absolute values of zonal wind speed here, with negative values showing the\n543 slowdown of easterlies along the equator” in Lines 233-235 as well as in the figure\n544 caption of Figure 4, 5 of the revised manuscript.\n545 • Can the authors exclude that the weakening of the winds is a consequence of reduced\n546 upwelling rather than driving it? If it’s a trend driven largely by changing ocean\n547 dynamics, the atmosphere could be responding to that rather than being the initiator.\n549 (AMIP) experiments (prescribed-SST atmosphere-only) are conducted based on\n551 and prescribed anthropogenic forcing in the period 1979-2014. We utilized these AMIP\n552 experiments to examine the response of atmosphere to SST forcing. Based on the AMIP\n553 experiments, a globally uniform SST warming of 4K is added to observed SST in\n555 increase of 4K is added to observed SST in AMIPfuture4K experiment. Thus, we use\n556 the differences between AMIPfuture4K and AMIP to represent the response to\n557 patterned SST warming and the differences between AMIPp4K and AMIP to represent\n558 the response to spatially uniform SST warming. As shown in the Figure 5, the zonal\n559 wind speed weakens in response to both uniform (Fig. 5b) and patterned SST warming\n560 (Fig. 5c), but the former causes a greater reduction in the zonal wind speed. It means\n561 the weakened zonal wind is a response to global warming even though the SST warming\n562 is spatially uniform in the AGCM experiments without oceanic dynamics. Therefore,\n563 we suggest the weakening of the zonal winds is a driver of reduced upwelling rather\n564 than a consequence of EEW. We have clarified the description of AMIP simulations in\n565 “Model simulations” section, and added the related discussion about the results based\n566 on Fig. 5 in “A fingerprint of global warming” section of the revised manuscript.\n568 • Line 80: Two times gradient. Remove “gradient” after “SST”.\n570 • Lines 80-81: Why this specific time period from 1951-2020? CMIP6 historical\n571 simulations and SST from ERSST and HadISST are available for a longer time period.\n574 observed data has lower uncertainty and higher reliability in this period11. According to\n576 twentieth-century, which is identified as the Second World War SST bias, potentially\n577 owing to varying source data from national fleets with unaccounted transitions in\n578 measurement practice. Meanwhile, as shown in Figure R6, the three observed datasets\n579 we selected (HadISST v1.1, ERSST v5, COBE-SST 2) exhibit significant uncertainty\n580 prior to 1950s. We have added the explanations about the reason selecting time period\n581 as “We selected time period after 1951 to ensure the accuracy of the observations” and\n582 cited this reference in Lines 91-92 of the revised manuscript.\n583\n585 higher reliability. The time series of EEW during 1870-2020 from the observations\n586 (OBS) including (a) HadISST v1.1, ERSST v5, COBE-SST 2 and (b) mean of OBS.\n588 represents the year of 1950.\n589\n590 • Lines 81-85: The EEW here is defined for the whole tropical band (including Indian\n591 and Atlantic Ocean), but it is only compared to Pacific SST zonal gradient? Is that a\n592 meaningful comparison?\n594 in previous studies12, revealing a significant role of EEW in global atmospheric\n595 circulation. However, most previous studies have focused on changes in zonal SST\n596 gradient in the tropical Pacific13-20, despite its significant uncertainty. Therefore, we\n597 compare the two important tropical SST warming patterns, EEW and the zonal SST\n598 gradient in tropical Pacific, to demonstrate the robustness of EEW. Furthermore, we\n599 also define the EEW for each of the three tropical oceans in Figure 2 and Extended Data\n601 • Lines 90-91: Looks like 9 out of 36 models show a negative EEW trend, and 7 models\n602 are positive, but very close to zero (maybe not even significantly different from zero?).\n604 lines to see which ones are statistically different from a zero trend. This would possibly\n605 strengthen the claim that “EEW is a robust signal in the CMIP6 models”.\n607 standard deviation of 36 models to represent the inter-model spread in Figure 1a. The\n608 results show that although the inter-model spread of EEW is larger than zonal SST\n609 gradient, the EEW in the MME of CMIP6 models is positive, with error bars mostly in\n610 positive values, with inter-model agreement exceeding 77%. In contrast, the zonal SST\n611 gradient in the MME of CMIP6 models is close to zero, with error bars covering\n612 positive and negative values. It confirms our conclusion that the EEW is a more robust\n613 signal than zonal SST gradient in the CMIP6 models. We have clarified this description\n614 in Lines 103-105 of the revised manuscript.\n615 • Lines 160-162: There is upwelling due to Ekman in the central equatorial Atlantic and\n616 upwelling due to equatorial waves in the eastern equatorial Atlantic. See Wang et al.\n617 (2017), doi: https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2016JC012537.\n619 the Atlantic includes upwelling due to Ekman transport and equatorial Kelvin waves.\n623 • Figure 1: What is shown by the black dots in b and c? This is not explained in the\n624 figure caption.\n626 the trend is statistically significant at the 95% level of confidence based on Student’s\n627 t-test. We have added the related description in the figure caption of Figure 1 in the\n628 revised manuscript.\n630 1. Geng, T. et al. Increased occurrences of consecutive La Niña events under global\n631 warming. Nature 619, 774–781 (2023).\n632 2. Carton, J.A., Chepurin, G.A. & Chen, L. SODA3: a new ocean climate reanalysis.\n634 3. Giese, B. S. & Ray, S. El Niño variability in simple ocean data assimilation (SODA),\n635 1871–2008. J. Geophys. Res. 116, C02024 (2011).\n636 4. Planton, Y. Y. et al. Evaluating Climate Models with the CLIVAR 2020 ENSO\n638 5. McKenna, S., Santoso, A., Sen Gupta, A. & Taschetto, A. S. Understanding biases\n639 in Indian Ocean seasonal SST in CMIP6 models. J. Geophys. Res. Oceans 129,\n640 e2023JC020330 (2024).\n641 6. Jiang, F., Seager, R. & Cane, M.A. A climate change signal in the tropical Pacific\n642 emerges from decadal variability. Nat. Commun. 15, 8291 (2024).\n643 7. Chen, HH. et al. Combined oceanic and atmospheric forcing of the 2013/14 marine\n644 heatwave in the northeast Pacific. npj. Clim. Atmos. Sci. 6, 3 (2023).\n645 8. Behringer, D.W. & Xue, Y. Evaluation of the global ocean data assimilation system\n646 at NCEP: The Pacific Ocean. Eighth Symposium on Integrated Observing and\n649 15 (2004)\n650 9. Xie, S. P. et al. Global warming pattern formation: Sea surface temperature and\n651 rainfall. J. Clim. 23, 966–986 (2010).\n652 10. Booth, B. et al. Aerosols implicated as a prime driver of twentieth-century North\n654 11. Sippel, S. et al. Early-twentieth-century cold bias in ocean surface temperature\n655 observations. Nature 635, 618–624 (2024).\n656 12. Zhou, W., Xie, S. P. & Yang, D. Enhanced equatorial warming causes deep-tropical\n657 contraction and subtropical monsoon shift. Nat. Clim. Chang. 9, 834–839 (2019).\n658 13. Knutson, T. R. & Manabe, S. Time-mean response over the tropical Pacific to\n659 increasedCO2 in a coupled ocean–atmosphere model. J. Clim. 8, 2181–2199\n660 (1995).\n661 14. Meehl, G. A. & Washington, W. M. El Niño-like climate change in a model with\n662 increased atmospheric CO2 concentrations. Nature 382, 56–60 (1996).\n663 15. Clement, A. C., Seager, R., Cane, M. A. & Zebiak, S. E. An ocean dynamical\n664 thermostat. J. Clim. 9, 2190–2196 (1996).\n665 16. Seager, R. & Murtugudde, R. Ocean dynamics, thermocline adjustment and\n666 regulation of tropical SST. J. Clim. 10, 521–539 (1997).\n667 17. Liu, Z. The role of the ocean in the response of tropical climatology to global\n668 warming: The west–east SST contrast. J. Clim. 11, 864–875 (1998).\n669 18. Meehl, G. A. et al. Response of the NCAR Climate System Model to increased\n671 19. Cane, M. The evolution of El Niño, past and future. Earth Planet. Sci. Lett. 230,\n672 227–240 (2005).\n673 20. Yu, B. & Boer, G. J. The roles of radiation and dynamic processes in the El Niño-\n674 like response to global warming. Clim. Dyn. 19, 539–553 (2002)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "This article is highly scientifically relevant because it addresses a current concern: the impacts on planet Earth's climate due to climate change, with an emphasis on anthropogenic influence. A series of numerical experiments using anthropogenic influence as an external forcing are analyzed. I am satisfied with all the answers given by the authors to the questions I raised. I believe they understood the relevance of the criticisms and suggestions made. I believe the manuscript should be accepted for publication in its current format.", "reviewer_4": "This is my second review of „Emergence of the enhanced equatorial Atlantic warming as a fingerprint of global warming“ by Lu Dong & Co-authors. I find the manuscript to be substantially improved and almost all of my concerns and suggestions have been adequately addressed. In particular, the more detailed mixed layer heat budget showing that vertical advection trends dominate ocean dynamic driven SST is convincing. I include a couple of specific comments below. Once these are addressed, I recommend the manuscript for publication in Nature Communications. • Regarding my major concern 3 from the first review: I agree that SODA and the mentioned studies cover different time periods and have likely different trends for this reason. But it would be a meaningful way to validate the SODA reanalysis in case it reproduces the trends shown from observations. Even though this is a shorter time period. • Regarding my major concern 4 and lines 435-444: I appreciate that the authors examine the advection term in the mixed layer heat budget. They also mention mixing, but do not further investigate this term. What are trends in mixing? • Line 33: “remains being debated” instead of “keeps debated”. • Line 34: I recommend to replace “hotly-debated” with something “commonly examined”. • Line 42: “changes in” instead of “changes of”. • Lines 48-52: What about the Pacific Decadal Oscillation (PDO) which plays a key role in shaping trends surface winds and SST over last few decades? The PDO is also mentioned further below by the authors (line 141) as a contributor to internal variability, so it should be mentioned here as well. • Line 56: “remains being debated” instead of “keeps debated”. • Line 67: “… such a meridional SST gradient change in the tropical Atlantic …” instead of “… such meridional SST gradient change in tropical Atlantic”. • Line 84: “… EEW is already detectable in the Atlantic, …” instead of “… EEW has already been detectable over the Atlantic, …”. • Line 85: “equatorial upwelling” instead of “upwelling”. • Lines 91-92: This sentence should be revised. Selecting a certain time period does not ensure the accuracy of the observations. This time period is selected because observations are more accurate since 1951. • Line 104: “values” instead of “value”. • Figure 1a: Why were the error bars for the individual models not included? This way the figures says that the MMW EEW trend is not statistically significant from zero. But it would be helpful to visualize which models do show a significant trend. • Line 133: “when averaged” instead of “averaged”. • Line 175: “in the Indian Ocean” instead of “of Indian Ocean”. • Line 191: “term” instead of “item”. • Lines 200-202: It seems redundant to say that the weakening of equatorial upwelling is strong along the equator. • Line 260: “the SST pattern” instead of “SST pattern”. • Line 274: “decreases” instead of “decrease”. • Line 274 and 280: “the SST warming pattern” instead of “patterned SST warming”. • Line 286: Why 37 CMIP6 models? Earlier it was mentioned that 36 were used. And only 35 for the mixed layer head budget analysis. In Line 347 it is mentioned that 38 CMIP6 models were analyzed. This is a little confusing. • Line 311: “the Atlantic” instead of “Atlantic”."}, "author_response": "Response to Reviewer # 4\nWe thank the reviewer for the detailed suggestions to improve the manuscript. The\ncomments are all valuable and helpful for revising our paper. Based on these comments,\nwe provide a point-by-point response. The text in black is the original review comments,\nand the text in blue is the authors’ response.\nThis is my second review of “Emergence of the enhanced equatorial Atlantic warming\nas a fingerprint of global warming” by Lu Dong & Co-authors. I find the manuscript to\nbe substantially improved and almost all of my concerns and suggestions have been\nadequately addressed. In particular, the more detailed mixed layer heat budget showing\nthat vertical advection trends dominate ocean dynamic driven SST is convincing. I\ninclude a couple of specific comments below. Once these are addressed, I recommend\nthe manuscript for publication in Nature Communications.\n• Regarding my major concern 3 from the first review: I agree that SODA and the\nmentioned studies cover different time periods and have likely different trends for this\nreason. But it would be a meaningful way to validate the SODA reanalysis in case it\nreproduces the trends shown from observations. Even though this is a shorter time\nperiod.\nResponse: Thanks for your comment. Following your suggestion, we have analyzed\nthe changes of the equatorial upwelling in the Atlantic based on SODA during 2008-\n2018, which is the time period for the observations provided in the mentioned studies.\nAs shown in Fig. R1, the equatorial upwelling of the Atlantic exhibits a pronounced\nstrengthening based on SODA during 2008-2018. It is worth noting that this\nstrengthening of the upwelling occurs slightly north of the equator. This is consistent\nwith the findings of Tuchen et al. (2020), who concluded that the subtropical cells (STC)\nintensification primarily occurs in the Northern Hemisphere based on observations\nduring 2008-2018. Therefore, the SODA reanalysis could reproduce the results of the\nmentioned studies based on observations, further confirming the reliability of our\nresults based on SODA\nFig. R1 Strengthened oceanic upwelling north of the equator in the Atlantic based\non SODA reanalysis during 2008-2018. (a) Vertical mean (0-150m) ocean vertical\nvelocity trend (shading, in m day-1 per decade) over 2008-2018 and climatological\nvertical velocity (black contours, contour interval 2 m day-1; solid > 0 m day-1, dashed\n<0 m day-1; thick 0 m day-1) based on SODA. Stippling indicates the regions where the\ntrend is statistically significant at the 95% level of confidence based on Student’s t-test.\n(b) The zonal mean of vertical velocity trend (red line, in m day-1 per decade) and\nclimatological vertical velocity (black line, in m day-1) in (a). Blue shading indicates\n3°S-3°N.\n• Regarding my major concern 4 and lines 435-444: I appreciate that the authors\nexamine the advection term in the mixed layer heat budget. They also mention mixing,\nbut do not further investigate this term. What are trends in mixing?\nResponse: Thanks for your question. Indeed, the ocean dynamic effects (𝐷 ) include\n𝑜\nthree-dimensional advection (ADV) and mixing. In our research, we mainly focus on\nthe ADV from horizontal and vertical temperature advection, but don’t discuss the\ninfluence of turbulent mixing. Because monthly data cannot resolve sub-monthly and\nsub-grid processes such as turbulent mixing and mesoscale eddies in the mixed layer\nheat budget analyses1. The results reveal the dominant role of the equatorial upwelling\nchange in the total oceanic dynamic effects (Fig. 4a), indicating the minor role of\nmixing in oceanic dynamic effects on such a long-term period during 1951-2020. We\nhave clarified this explanation as “Since the sub-monthly and sub-grid processes such\nas turbulent mixing and mesoscale oceanic eddies cannot be well resolved by monthly\nheat budget analysis, we mainly focus on the ADV from horizontal and vertical\ntemperature advection in this study. More importantly, the results clearly suggest the\ndominant role of the equatorial upwelling change in the total oceanic dynamic effects\n(Fig. 4a), indicating the minor role of mixing in oceanic dynamic effects on the EEW\ntrend during 1951-2020” in Lines 440-446 of the revised manuscript.\n• Line 33: “remains being debated” instead of “keeps debated”.\nResponse: Done\n• Line 34: I recommend to replace “hotly-debated” with something “commonly\nexamined”.\nResponse: Done\n• Line 42: “changes in” instead of “changes of”.\nResponse: Done\n• Lines 48-52: What about the Pacific Decadal Oscillation (PDO) which plays a key\nrole in shaping trends surface winds and SST over last few decades? The PDO is also\nmentioned further below by the authors (line 141) as a contributor to internal variability,\nso it should be mentioned here as well.\nResponse: Thanks for your suggestion. As you have mentioned, the Pacific Decadal\nOscillation (PDO) is also a dominant mode of variability in the Pacific Ocean. We have\nadded the related description in Lines 51-52 in the revised manuscript.\n• Line 56: “remains being debated” instead of “keeps debated”.\nResponse: Done\n• Line 67: “… such a meridional SST gradient change in the tropical Atlantic …”\ninstead of “… such meridional SST gradient change in tropical Atlantic”.\nResponse: Done\n• Line 84: “… EEW is already detectable in the Atlantic, …” instead of “… EEW has\nalready been detectable over the Atlantic, …”.\nResponse: Done\n• Line 85: “equatorial upwelling” instead of “upwelling”.\nResponse: Done\n• Lines 91-92: This sentence should be revised. Selecting a certain time period does not\nensure the accuracy of the observations. This time period is selected because\nobservations are more accurate since 1951.\nResponse: Thanks for your suggestion. We have revised the description as “We select\nthis time period for analysis because the observations are more accurate after 1951” in\nLines 91-92 of the revised manuscript.\n• Line 104: “values” instead of “value”.\nResponse: Done\n• Figure 1a: Why were the error bars for the individual models not included? This way\nthe figures says that the MMW EEW trend is not statistically significant from zero. But\nit would be helpful to visualize which models do show a significant trend.\nResponse: Thanks for your suggestion. We have re-drawn the markers in Figure 1a to\nvisualize the statistically significance of each model based on the trends of the EEW\nand SST zonal gradient of the tropical Pacific. The markers with edges in red, blue, or\ngray denote statistically significant trends in EEW, the zonal gradient, or neither,\nrespectively. Note that there are no models showing significant trends in both EEW and\nSST zonal gradient of the tropical Pacific. The results show that the EEW trend in the\nMME of CMIP6 models is statistically significant, with 17 models simulating a\nstatistically significant EEW trend. In contrast, there are only 3 models simulating a\nstatistically significant zonal gradient trend. It confirms our conclusion that the EEW is\na more robust signal than zonal SST gradient in the CMIP6 models. We have added this\ndescription in Lines 105-107 of the revised manuscript.\n• Line 133: “when averaged” instead of “averaged”.\nResponse: Done\n• Line 175: “in the Indian Ocean” instead of “of Indian Ocean”.\nResponse: Done\n• Line 191: “term” instead of “item”.\nResponse: Done\n• Lines 200-202: It seems redundant to say that the weakening of equatorial upwelling\nis strong along the equator.\nResponse: Thanks for your suggestion. We have revised the description as “In the\nhistorical period, the weakening of equatorial upwelling is evident in the upper Atlantic\nOcean (0-150m) based on CMIP6 models, with the most pronounced weakening\noccurring in the region (3°S-3°N) where the climatological upwelling is the strongest”\nin Lines 203-206 of the revised manuscript.\n• Line 260: “the SST pattern” instead of “SST pattern”.\nResponse: Done\n• Line 274: “decreases” instead of “decrease”.\nResponse: Done\n• Line 274 and 280: “the SST warming pattern” instead of “patterned SST warming”.\nResponse: Done\n• Line 286: Why 37 CMIP6 models? Earlier it was mentioned that 36 were used. And\nonly 35 for the mixed layer head budget analysis. In Line 347 it is mentioned that 38\nCMIP6 models were analyzed. This is a little confusing.\nResponse: Thanks for your question. We have used a total of 38 CMIP6 models for\nanalysis throughout this study. The variation in the number of models used across\ndifferent figures is due to the different availability of the required variables among all\nthe models, which has been listed clearly in Supplementary Data Table 1. To ensure the\nreliability of our results, we use as many models as possible for each variable available\nat the time we conducted this analysis. To avoid confusion, we have added the related\nexplanation about the variation in the number of models used across figures in Lines\n355-357 of the revised manuscript.\n• Line 311: “the Atlantic” instead of “Atlantic”.\nResponse: Done\nReference:\n1. Bian, C., Jing, Z., Wang, H. et al. Oceanic mesoscale eddies as crucial drivers of\nglobal marine heatwaves. Nat. Commun. 14, 2970 (2023)."}]

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