A climate change signal in the tropical Pacific emerges from decadal variability

A climate change signal in the tropical Pacific emerges from decadal variability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A climate change signal in the tropical Pacific emerges from decadal variability Feng Jiang, Richard Seager, Mark Cane This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4656683/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Sep, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Recent debates have centered around whether the La Niña-like sea surface temperature (SST) trend pattern in the tropical Pacific in the past several decades is a response to anthropogenic forcings or internal variability, particularly the Interdecadal Pacific Oscillation (IPO). This study identifies an emerging SST warming pattern in the tropical Pacific featuring a narrow equatorial cooling band, in stark contrast to the meridionally broad SST trend pattern shaped by the IPO. The emerging SST trend pattern is associated with changes in subsurface temperature structure and sea level height that are distinct from those related to the recurrent IPO. The differences are primarily driven by their different surface wind stress patterns. The emerging wind stress pattern also drives distinctive ocean dynamical processes, fostering the unique eastern Pacific cooling. Our findings set a path to distinguish the often-tangled tropical Pacific climate change signals from internal variability through the underlying dynamics of each. MAIN TEXT Earth and environmental sciences/Climate sciences/Climate change Earth and environmental sciences/Ocean sciences/Physical oceanography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The fact that under greenhouse gas-driven global warming some parts of Earth do not warm, or even cool, presents a fascinating problem. The surface tropical eastern Pacific stands out as one such region that resists warming 1 , associated with cooling and shoaling of the tropical Pacific Ocean thermocline over past decades 2 , 3 . Evaluated up to now, whether over recent decades or back a century or more, the tropical Pacific exhibited a La Niña-like trend pattern with pronounced warming in the west and lack of warming in the cold tongue region 1 – 6 . In contrast, state-of-the-art climate models respond to anthropogenic greenhouse gas (GHG) forcing by generally simulating enhanced warming in the eastern Pacific and an El Niño-like warming pattern 7 , 8 . Numerous studies have sought to reconcile this discrepancy and two primary arguments have been proposed. One suggests that the climate models exhibit systematic biases in simulating the tropical Pacific SST response to external forcing 2 , 3 , 9 , 10 , and that correcting the cold tongue bias would align the models' simulations with observations, which are argued to be the forced response 2 . The other suggests that the internal variability significantly contributes to the recent observed trend, and the observed trend would fall within the range of climate model simulations once internal variabilities were sufficiently sampled in models 11 , 12 . Although the evidence is not definitive, the Coupled Model Intercomparison Projects (CMIP) models and Large Ensembles rarely even come close to the observed long-term trends, which casts doubts on the ability of climate models to correctly respond to rising GHGs with the proper dynamic and thermodynamic balances 9 . Alternatively, we may consider using observations to estimate the forced climate response. However, observational analyses, which inherently rely on a single realization, come with their own limitations. Beyond the inescapable observational error/uncertainty 13 , 14 , a major challenge arises from the fact that the tropical Pacific is dynamically active on decadal to multi-decadal timescales, so that the background internally-generated long-term variability can sometimes overwhelm the climate change signal 15 , 16 . For example, the phase transition from positive to negative phases in the Interdecadal Pacific Oscillation (IPO) — the dominant climate mode in the Pacific ocean on decadal to multi-decadal time scales and closely related to the Pacific Decadal Oscillation 17 – 20 — is argued to have contributed to the observed La Niña-like SST trend pattern over the past four decades 15 , 21 , 22 . In contrast, some other studies highlight that the long-term change in the tropical Pacific in response to external forcing exhibits distinctive patterns unlike those on decadal timescales. For instance, while SST anomalies associated with the IPO in the tropical Pacific typically feature a meridionally broad pattern 20 , the long-term lack of warming is found to be narrowly confined to the cold tongue 2 – 4 , 6 , 13 . Seager et al. 17 further elaborate that some individual model simulations, containing both internal variability and the model’s forced response, come close to the observations but nonetheless fail to represent several fundamental elements of the observed long-term trends, including the distinctive narrowness of the lack of warming and the underlying subsurface ocean dynamical processes. These findings suggest that the tropical Pacific’s responses to anthropogenic forcing might present unique characteristics that set them apart from those typically associated with internal decadal variabilities. In this study, we seek to answer whether there is a detectable climate change signal in the tropical Pacific that can be clearly separated from internal variability and is robust across different datasets. This is crucial for setting a benchmark against which to assess the ability of climate models to reproduce the observed forced response in the presence of strong internal variability. In subsequent sections, we will demonstrate a distinctive climate change signal across various atmosphere-ocean fields in the tropical Pacific that is emerging from the internally-generated decadal variability and investigate its underlying dynamics. Results Recurrent and emerging SST trend patterns. The SST trend pattern after the satellite data become available (1980–2022) is often used as the observational reference for assessing climate model simulations of the forced response (Fig. 1 a). This pattern is characterized by meridionally broad negative anomalies in the tropical Pacific, particularly south of the equator, and positive anomalies in the northwestern and southwestern Pacific. As noted by previous studies 21 , 22 , and shown in Figure S1 a, the pattern is akin to that of the typical internal IPO variability on decadal to multi-decadal scale 17 , 19 . However, should we posit that the decadal variability (i.e., the phase transition of IPO from positive to negative phases) dominates this trend pattern, it would follow that this pattern is not unique in historical periods, given that the IPO has experienced several phase transitions in the observed record. Thus, we assess whether this short-term trend is a manifestation of decadal variability by calculating pattern correlations for tropical Pacific (30 \(^\circ\) S-30 \(^\circ\) N, 120 \(^\circ\) E-270 \(^\circ\) W) SST trends across equivalent time spans of 43 years but ending in different years to the most recent one (e.g., 1979 to 2021, 1978 to 2020 and so on all the way back to 1870 to 1912; Fig. 1 c). The pattern correlations range from around − 0.8 to 1, demonstrating an oscillatory behavior matching the IPO's phase transitions over the last century (Fig. S1 b). Analyses for the whole Pacific (60 \(^\circ\) S-60 \(^\circ\) N, 120 \(^\circ\) E-270 \(^\circ\) W) or the equatorial Pacific (10 \(^\circ\) S-10 \(^\circ\) N, 120 \(^\circ\) E-270 \(^\circ\) W) yield similar conclusion (not shown). In comparison, a different SST trend pattern beginning in the mid-1950s is shown in Fig. 1 b. The exact start year 1958 is chosen to approximate the time when the tropical Pacific warm pool starts to warm up (insets of Fig. 1 b), as well as facilitating the analysis of subsurface ocean processes based on the Ocean ReAnalysis System-5 (ORAs5) data which become available in that year 2 , 3 , 23 . The lack of warming in the tropical Pacific is still evident in this long-term trend pattern, however, it is markedly confined meridionally to the equatorial region (Fig. 1 b), in contrast with the much broader meridional distribution that is more indicative of the IPO (Fig. 1 a). Also, the warm anomalies in the northwestern and southwestern Pacific are less pronounced. This long-term trend pattern shows an emerging feature as evidenced in the pattern correlations of the latest trend with historical trends for equivalent 65-year spans. These show a rapid decline when the end year of the trend is adjusted backward and lingering around zero in earlier periods. We further identify periods with the strongest positive (P1:1870–1912 and P3:1942–1984) and strongest negative (P2: 1914–1956) pattern correlations with the most recent short-term trend, and the strongest negative pattern correlation with the most recent long-term trend (P4: 1870–1934) (Fig. S1 c-f). The SST trend patterns in P1, P2 and P3 in the tropical Pacific broadly match that in the most recent 43-year trend, showing an IPO-like spatial distribution (Fig. S1 c-e). Notwithstanding, the southward-displaced center of the cooling in the eastern Pacific (Fig. 1 a) is less apparent in these historical periods, implying that the feature is potentially indicative of a climate change signal rather than a result of internal variability 24 , 25 . In contrast, the trend in P4 (Fig. S1 f) appears to be a weak reflection of the IPO pattern rather than a negative analogue of the latest 65-year trend pattern, again confirming the distinctiveness of the emerging SST trend pattern recently. The recurring feature of the short-term trend and the emerging feature of the long-term trend based on HadISST were consistently identified across different SST datasets including ERSSTv5, Kaplan, COBE (Figs. 1 c,d and S2). The emerging feature is apparent when examining the histogram and probability distributions of pattern correlations for short-term and long-term trends in the historical records (Fig. 2 a,b). While the overall distribution of the short-term trends closely resembles a symmetrical distribution around zero over time, and there are earlier periods that had trends with the opposite spatial pattern to the most recent one, the long-term trend has increasingly skewed towards positive values in recent decades. There are no past ant-analogs of the long-term trend. These trends were further categorized into two subsets, an earlier period with presumably weak climate change signal and a later period subject to pronounced anthropogenic forcing, based on whether they end before or after 1975. This cutoff year was selected to roughly split the periods evenly, and qualitative conclusions remain the same with slight changes in the cutoff year. The probability distributions for earlier and later periods elucidate that while the short-term trends in recent decades cannot be separated from those in earlier periods, the long-term trends in recent decades are notably separated from the background intrinsic decadal variability in earlier period. We further quantified the IPO’s contribution to the SST trend (Fig. 2 c) by comparing the trend in the tropical Pacific zonal SST gradient and the IPO’s contribution, which is calculated using the trend in the IPO index in different time span and the IPO-related zonal SST gradient based on Figure S1 . Consistent with Fig. 2 a,b, the internal variability of the IPO predominantly shapes both the short-term and long-term trends during the earlier period when the climate change signal is relatively weak. In the recent period, however, the imprints of climate change are distinctly reflected in both short-term and long-term trends (Fig. 2 c). However, as noted above, the spatial pattern of the most recent short-term trend is overwhelmed by the IPO (Fig. 1 ) with the IPO accounting for over half of the observed zonal SST gradient trend, while the impact of the IPO on the most recent long-term trend is minor. Moreover, when examining short-term trends with a negligible IPO impact, such as that from 1969–2012, the short-term trend pattern closely resembles the emerging SST pattern detected in the extended period (Figs. 1 c and 2 d). Distinct ocean dynamics for emerging climate change signal and decadal variability Our analysis has established that there is a distinctive SST trend pattern detected in an extended period emerging from the short-term SST trend pattern primarily related to the IPO. A closed Bjerknes feedback loop necessitates corresponding changes in the surface wind stress and the subsurface thermocline depth, in accordance with SST variations 26 , 27 . In Fig. 3 , we show that there exists a recurring trend pattern of thermocline depth with a dipole-like structure (Fig. 3 a,e). In contrast, the emerging trend pattern is characterized with an overall shoaling throughout the tropical Pacific (Fig. 3 b,e). Also, the recurring surface wind stress pattern features a broad strengthening of the zonal wind stress across the tropical Pacific (Fig. 3 c,e), while the emerging wind stress trend pattern displays a dipole structure with strengthened wind stress in the central equatorial Pacific and weakened wind stress in the eastern equatorial Pacific (Fig. 3 d,e). We also examined changes in the surface wind stress and the subsurface thermocline depth that are directly linked to the IPO (Fig. S3). These IPO-related patterns with dipole-like thermocline change and same-signed wind stress change in the tropical Pacific bear a close resemblance to the short-term trends, which again substantiate the argument that the short-term trends over the span of approximately an IPO cycle predominantly reflect the recurrent influence of the IPO manifested in various ocean-atmosphere fields in the tropical Pacific. In the tropical Pacific, the thermocline depth and SSH are closely linked to each other and exhibit similar interannual fluctuations associated with El Niño-Southern Oscillation 27 , 28 . On decadal timescales, the dipole-like pattern characterized with much stronger sea level rise in the tropical western Pacific compared to the east, and thermocline deepening in the west and shoaling in the east, are also present in the short-term trends (Fig. 3 a,c,f). In contrast to the shorter-term trend, the emerging long-term trend exhibits an overall, but small, sea level fall in the central-to-eastern equatorial Pacific region, accompanied by sea level rise in the off-equatorial regions (Fig. 3 d). The long-term thermocline depth trend is also different from the shorter-term one in that it has shoaling or little change across the basin, even though the shoaling is greater in the east. The thermocline depth-SSH-wind stress changes associated with the short-term trend oscillate back and forth together (Fig. 3 e) but those associated with the long-term trend are collectively emerging over time for the long-term trend (Fig. 3 f). These observed SSH and thermocline trends were previously demonstrated to be some combination of wind-driven, thermal expansion and the influence of changes in the Earth’s gravity field due to loss of land ice 29 – 31 . While there is general consensus on zonal dipole-like SSH change related to internal variability in both observations 32 and CMIP models 33 , the anthropogenic contribution to SSH change remains elusive 31 , 33 , 34 . Our subsequent analyses will focus on examining SSH. A 1.5-layer reduced-gravity model is used here to investigate the dynamic linkages between the surface and subsurface components (see details in Methods; note that the same dynamics can be formulated as describing a single baroclinic vertical mode and hence have more general applicability). The reduced-gravity model can simulate the wind stress driven changes in SSH and thermocline depth 35 , 36 ; here we solve analytically an equilibrium version of the reduced gravity system (see Methods, Eq. (6)). As shown in Fig. 4 a,b, both the short-term IPO-related trend and the long-term emerging trend can be quite realistically simulated by prescribing their corresponding surface wind stress trend patterns. The pattern correlations between observations and model for the observed and wind stress-driven trend patterns reach as high as 0.87 for the short-term trend and 0.72 for the long-term in the tropical Pacific, suggesting the dominant role of the wind-driven redistribution of the heat content in the tropical Pacific upper ocean by the surface wind stress for both decadal variability and the emerging climate change signal. According to Eq. (6), variations in SSH at each longitude are determined by the impact of surface wind stress and its horizontal gradients zonally integrated from the eastern boundary to that longitude. The spatial distribution of the wind stress effects (B in Eq. (6); Fig. 4 c,d) underscores that it is the wind stress and its horizonal gradients in the central tropical Pacific that are most important in redistributing the heat content and driving the SSH changes in the tropical Pacific, for both climate change and decadal variability. The different wind stress patterns also drive different ocean circulation changes. Figure 5 presents the zonally averaged ocean current trends over the central-to-eastern Pacific associated with the recurrent short-term and emerging long-term trends. We also display the IPO-related ocean currents, which again exhibit a strong consistency with the short-term trends. The most striking difference between the decadal variability-related and climate change-related trends is the opposite-signed surface zonal currents in the equatorial and north off-equatorial regions (Fig. 5 a; compare Fig. 5 d,e). There is a significant strengthening of the surface westward zonal currents, except in the central equatorial Pacific, for the decadal variability (contours in Fig. 6 a). In contrast, the emerging pattern shows a weakening of westward surface currents in the central-to-eastern Pacific (Fig. 6 b). The changes in surface zonal currents in the tropical Pacific are predominantly governed by its geostrophic component (Eqs. (7–8); shadings in Fig. 6 a,b), which follows the spatial pattern of the SSH that has been established to be connected to the surface wind stress trend patterns (Fig. 4 ). Wind stress impacts can also be detected in the Ekman pumping change (Eq. (11)). While the decadal upwelling pattern is approximately symmetrical around the equator, the meridional center of the emerging upwelling pattern is displaced towards near 5 degrees south. The overall strengthening of zonal wind stress across the equatorial Pacific linked to decadal variability fosters pronounced upwelling from the western to eastern equatorial Pacific (Fig. 6 c). In comparison, the emerging dipole-like wind stress pattern contributes to enhanced upwelling in the central equatorial Pacific and weakened upwelling to the east, while stronger trade winds south of the equator contribute to increased local upwelling (Fig. 6 d), thereby accounting for the different meridional locations of upwelling change. The meridional currents averaged in the mixed layer, indicative of the strength of the shallow overturning circulation, are quite similar for the short-term and long-term trends, showing a consistent strengthening of the poleward transport in both hemispheres despite different magnitudes. These changes in wind-driven ocean currents, in turn, account for the IPO-related and emerging climate change related temperature change in the equatorial Pacific via different ocean dynamical processes (Fig. 6 ). The IPO-related cooling in the equatorial eastern Pacific (Fig. 1 a) is primarily driven by the cooling effect of the zonal advection ( \(UcTa\) ) (Fig. 6 a) resulting from strengthened zonal current (Fig. 5 d). The meridional advection ( \(VaTc\) ) related to enhanced poleward transport and the change due to thermocline shoaling ( \(WcTa\) ) also contribute to the IPO-related cooling in the equatorial region. In contrast, the emerging cooling signal is relatively muted, primarily because the zonal advective warming effect due to weakened zonal current largely offsets the cooling effect related to the thermocline feedback due to the thermocline shoaling (Fig. 6 b). The mean meridional advection ( \(VcTa\) ) also contributes to the emerging cooling trend in the central-to-eastern Pacific (Fig. 6 b) via the strengthened meridional temperature gradient (Fig. 1 b). Such effect is not observed for the IPO-related variability (Fig. 6 a), where broader eastern Pacific cooling leads to insignificant changes in the meridional temperature gradient. Although the vertical upwelling changes are evident for the equatorial region, the contributions of the Ekman pumping term to the wider equatorial Pacific (5°S-5°N) temperature change, either related to the IPO or the emerging climate change, are rather minor due to the immediate opposite-signed effect off the equator for both variabilities. Discussion In this study, we identify an emerging climate change signal in the tropical Pacific across different observational datasets, which exhibits distinctive ocean-atmosphere dynamics that differ from those typically associated with IPO-related decadal variability. The emerging SST trend pattern features a narrow band of cooling in the eastern equatorial Pacific, linked to thermocline shoaling/SSH decreases in the central-to-eastern Pacific and dipole-like changes in zonal surface wind stress. In contrast, the recurrent IPO-driven SST trend pattern is characterized by a meridionally broader cooling in the eastern Pacific, corresponding to zonal dipole-like thermocline/SSH changes and an overall strengthening of tropical Pacific zonal wind stress. The different changes in wind stress pattern lead to distinct ocean circulation changes. These oceanic responses to the surface wind stress account for their surface cooling in the eastern Pacific, with the thermocline shoaling playing a dominant role in the emerging cooling and enhanced zonal advective cooling mainly driving the IPO-related cooling. While basic geophysical fluid dynamics underpin our argument that the observed oceanic changes can be interpreted as adjustments to variations in surface wind stress, further investigations including targeted ocean model experiments are required to comprehensively assess the relative contributions of local versus remote wind effects 37 , as well as to understand the initial wind response to GHGs. The climatological settings of the tropical Pacific may inherently predispose it to different initial SST response in the warm pool and cold tongue region, and a corresponding trade wind response 2 , 38 . Due to the increased atmospheric static stability in response to GHG forcings 39 , 40 related to stronger temperature change in the upper troposphere compared to the surface (Fig. S4), this initial response to rising GHGs might not be amplified as efficiently via Bjerknes feedback as those observed for the internal modes on interannual to decadal timescales. Additionally, climate variations outside of the tropical Pacific have been argued to influence the tropical Pacific trade winds through teleconnections 24 , 25 , 41 – 44 . Further, it has been argued that pronounced decadal-to-multidecadal SST changes in the Atlantic Ocean are also dominated by the response to the same external forcing that the tropical Pacific encounters 45 , suggesting an alternative explanation for the co-occurrence of these long-term variabilities across different regions, and the potential for an inter-basin interaction in the pattern of SST response to rising GHGs. More work is needed to disentangle causal relationships among the long-term changes in different basins 46 , 47 . It is also critical to acknowledge that while we aim to distinguish between the recurrent IPO-related decadal variability and the climate change signal, these two may have become coupled together. We have emphasized the differences between the ocean-atmosphere dynamics of each, however, they do share much in common: shoaling of the thermocline in the east, enhanced upwelling somewhere in the central-to-eastern equatorial Pacific and an enhanced zonal SST gradient across the equatorial Pacific. It seems reasonable to postulate that if the response to radiative forcing is the emerging pattern seen here, then it will initiate coupled ocean-atmosphere feedbacks that favor a negative IPO state that also has an enhanced SST gradient. This might explain why the most recent IPO swing has been extreme and robust (as our analysis shows in Figs. 1 and 2 ). If so, this suggests that in nature forcing is projecting onto natural modes of variability, while it is not clear whether climate models can reproduce that kind of physical behavior. This would require a new perspective on how internal variability interacts with the climate change signal in future studies. Materials and Methods Datasets The SST data used here are the Hadley Centre data HadISST version 1.1 with horizontal resolutions of 1° \(\times\) 1° 48 , the National Oceanic and Atmospheric Administration ERSSTv5 data with horizontal resolutions of 2° \(\times\) 2° 49 , the Centennial in Situ Observation Based Estimates of SST (COBE) from the Japanese Meteorological Agency with horizontal resolutions of 1° \(\times\) 1° 50 , and Kaplan Extended SST version 2 with horizontal resolutions of 5° \(\times\) 5° 51 . HadISST, ERSSTv5, and Kaplan were used from 1870 to 2022 and COBE from 1890 to 2022. We utilized subsurface temperature, surface wind stress, SSH, zonal and meridional currents from ORAs5 with horizontal resolutions of 0.25° \(\times\) 0.25° and 75 vertical levels, the latest ocean reanalysis products provided by the European Centre for Medium-Range Weather Forecasts 23 . The vertical velocity for ORAs5 was derived from zonal and meridional currents based on mass continuity 52 . We also used subsurface temperature and surface wind stress from the Simple Ocean Data Assimilation (SODA), version 2.2.4 with horizontal resolutions of 0.25° × 0.25° and 40 vertical layers during 1871–1979 53 , in conjunction with the version 3.3.2 with horizontal resolutions of 0.25° × 0.25° and 50 vertical layers during 1980–2018 54 . The air temperature data was obtained from the National Centers for the Environmental Prediction–National Center for the Atmospheric Research (NCEP–NCAR) reanalysis 1 with horizontal resolutions of 2.5°×2.5° and 17 vertical layers 55 . All datasets were interpolated onto a horizontal grid of 1° \(\times\) 1° to enable comparison among datasets. Statistical methods and definitions of indices Anomalies for all variables were calculated as departures from the monthly climatology unless specified otherwise. Statistical significance tests were performed based on the two-tailed Student’s t-test with n–2 degrees of freedom, where n is the sample size. The thermocline depth was identified as the depth of the 20 \(℃\) isotherm. The qualitative conclusion remains similar based on the thermocline depth defined by the maximum vertical temperature gradient. The zonal SST gradient in the tropical Pacific was defined as the temperature difference between the western Pacific (5 \(^\circ\) S–5 \(^\circ\) N, 140 \(^\circ\) E–170 \(^\circ\) E, indicated by the left box in Fig. 1 b) and the eastern Pacific (5 \(^\circ\) S–5 \(^\circ\) N, 190 \(^\circ\) W–270 \(^\circ\) W, indicated by the right box in Fig. 1 b). The IPO index was calculated based on the difference between the SST anomalies averaged over the central equatorial Pacific (10°S–10°N, 170°E–90°W ) and the average of the SST anomalies in the northwest (25°N–45°N, 140°E–145°W) and southwest Pacific (50°S–15°S, 150°E–160°W) following Henley et al. 17 . Then a 13-year low-pass filter based on Fast Fourier Transform was applied to extract the decadal-scale component of IPO variability. To assess the IPO's contribution to the short-term trend of variable \(x\) , we calculate the IPO-related trend by regressing the detrended \(x\) against the IPO index and then multiplying this by the IPO index's linear trend from 1980 to 2022. Reduced gravity system linking SSH and thermocline depth to surface wind stress A 1.5-layer reduced-gravity system is considered here following the formulation of Veronis 36 to establish the relationship between the change in the surface wind stress and change in the SSH and thermocline depth over the tropical Pacific. We made several modifications to Veronis' framework by including the meridional wind stress component \({\tau }^{y}\) (previously set to zero), the zonally-varying zonal wind stress \({\tau }^{x}\) (previously assumed to be zonally-uniform), and a damping term (previously not considered) and using an equatorial \(\beta\) -plane ( \(f=\beta y\) , in which \(\beta =\) 2.3 \(\text{*}\) 10 −11 m − 1 s − 1 ). We also adopt a linear system with a specified spatially-uniform climatological upper layer thickness in the tropical Pacific ( \(\stackrel{-}{h}=\) 150 m) 56 , Taking \(\varDelta \rho = 2.7\) kg/m 3 as the density contrast between upper and bottom layers yields a first baroclinic mode gravity wave speed of c ~ 2.0 m/s, where c 2 = \({g}^{{\prime }}\stackrel{-}{h}\) , \({g}^{{\prime }}=g\frac{\varDelta \rho }{{\rho }_{0} },\) and \({\rho }_{0}= 1025\) kg/m 3 is the reference density. The governing equations on an equatorial \(\beta\) -plane are: \(-fV=-{g}^{{\prime }}\stackrel{-}{h}\frac{\partial h}{\partial x}+\frac{{\tau }^{x}}{{\rho }_{0} }\) Eq. (1) \(fU= -{g}^{{\prime }}\stackrel{-}{h}\frac{\partial h}{\partial y}+\frac{{\tau }^{y}}{{\rho }_{0} }\) Eq. (2) \(\frac{\partial U}{\partial x}+\frac{\partial V}{\partial y}=-rh\) Eq. (3) in which \(h\) is the upper layer thickness between SSH ( \({h}_{1};\text{m})\) and thermocline depth ( \({h}_{2};\text{m}),\) \(r=\) 1/5.5 year − 1 is the damping coefficient, \(U={\int }_{{h}_{1}}^{{h}_{2}}udz\) ( \(u\) the zonal current; m/s), and \(V={\int }_{{h}_{1}}^{{h}_{2}}vdz\) ( \(v\) the meridional current; m/s). Cross-differentiating Eqs. (1–2) and using Eq. (3), we obtain the linkage between layer thickness change and wind stress change: \(\frac{\partial h}{\partial x}- \frac{\beta {y}^{2}r}{{g}^{{\prime }}\stackrel{-}{h}}h=\frac{y}{{{\rho }_{0}g}^{{\prime }}\stackrel{-}{h}}(\frac{{\tau }^{x}}{y}+\frac{\partial {\tau }^{y}}{\partial x}-\frac{\partial {\tau }^{x}}{\partial y})\) Eq. (4) Let \(A=-\frac{\beta {y}^{2}r}{{g}^{{\prime }}\stackrel{-}{h}}h\) and Eq. (4 \(B=\frac{y}{{{\rho }_{0}g}^{{\prime }}\stackrel{-}{h}}(\frac{{\tau }^{x}}{y}+\frac{\partial {\tau }^{y}}{\partial x}-\frac{\partial {\tau }^{x}}{\partial y}),\) ) can be solved as: \(h={e}^{A({x}_{e}-x)}{h}_{e}+{\int }_{{x}_{e}}^{x}B{e}^{A({x}^{{\prime }}-x)}dx{\prime }\) Eq. (5) in which \({x}_{e}\) indicates the eastern boundary, \({h}_{e}\) the layer thickness at the eastern boundary. The change in the SSH can then be directly linked to the change in the surface wind stress if the change of SSH near the eastern boundary is neglected (which is approximately justified on the basis of the changes in Fig. 3 ): \({h}_{1}=\frac{\varDelta \rho }{\rho +\varDelta \rho }{\int }_{{x}_{e}}^{x}B{e}^{A({x}^{{\prime }}-x)}dx{\prime }\) Eq. (6) Estimation of geostrophic zonal current and Ekman pumping The geostrophic component of the surface current can be determined by considering the balance between the Coriolis force and the pressure gradient force. In spherical coordinates, the geostrophic zonal current \({(u}_{g})\) outside of the equatorial region is expressed as: \({u}_{g}=-\frac{g}{f}\frac{\partial h}{\partial y}\) Eq. (7) At the equator where \(f=0\) , an estimate of the equatorial semi-geostrophic zonal current ( \({u}_{sg})\) is derived by calculating the second derivative of the SSH on an equatorial \(\beta\) -plane, which are suggested to be in good agreement with measured velocities 57 , 58 : \({u}_{sg}=-\frac{g}{\beta }\frac{{\partial }^{2}h}{\partial {y}^{2}}\) Eq. (8) Following the approach of Cane and Zebiak 59 , the Ekman transport ( \({U}_{E}, {V}_{E}\) ) in the tropical region is formulated by incorporating a frictional component as: \({U}_{E}=({r}_{s}{\tau }^{x}+f{\tau }^{y})/{\rho }_{0}({f}^{2}+{{r}_{s}}^{2})\) Eq. (9) \({V}_{E}=({r}_{s}{\tau }^{y}-f{\tau }^{x})/{\rho }_{0}({f}^{2}+{{r}_{s}}^{2})\) Eq. (10) where \({r}_{s}\) indicates the surface layer friction coefficient (1/2 day − 1 ). The Ekman transport away from the equator is consistent with classical Ekman theory. At the equator where \(f=0\) , the friction allows an Ekman transport in the direction of the wind stress. Ekman pumping velocity ( \({w}_{E}\) ) is thus derived from the divergence of the Ekman transport: \({w}_{E}=\frac{\partial {U}_{E}}{\partial x}+\frac{\partial {V}_{E}}{\partial y}\) Eq. (11) Mixed layer heat budget analysis for the long-term SST Change The heat budget for the mixed layer temperature 60 can be expressed as \(\frac{\partial T}{\partial t}=\underset{UaTc}{\underset{⏟}{-{u}_{a}{\frac{\partial T}{\partial x}}_{ c}}}\underset{UcTa}{\underset{⏟}{-{u}_{c}{\frac{\partial T}{\partial x}}_{ a}}}\underset{UaTa}{\underset{⏟}{-{u}_{a}{\frac{\partial T}{\partial x}}_{ a}}}\underset{VaTc}{\underset{⏟}{-{v}_{a}{\frac{\partial T}{\partial y}}_{ c}}}\underset{VcTa}{\underset{⏟}{-{v}_{c}{\frac{\partial T}{\partial y}}_{ a}}}\underset{VaTa}{\underset{⏟}{-{v}_{a}{\frac{\partial T}{\partial y}}_{ a}}}\underset{WaTc}{\underset{⏟}{-{w}_{a}{\frac{\partial T}{\partial z}}_{ c}}}\underset{WcTa}{\underset{⏟}{-{w}_{c}{\frac{\partial T}{\partial z}}_{ a}}}\underset{WaTa}{ \underset{⏟}{-{w}_{a}{\frac{\partial T}{\partial z}}_{ a}}}+R.\) Eq. (12) in which \(a\) denotes anomaly and \(c\) denotes climatology. The heat budget terms include changes in the mean current ( \(UaTc\) , \(VaTc\) , \(WaTc\) ), changes in the mean temperature gradient ( \(UcTa\) , \(VcTa\) , \(WcTa\) ), and their nonlinear interaction ( \(UaTa\) , \(VaTa\) , \(WaTa\) ). The zonal advection and meridional advection terms were averaged over a uniform mixed layer depth of 50 m. The vertical velocity was calculated at the bottom of the mixed layer, and the vertical advection between the 50–100 m and the upper 50 m layers was calculated only in the presence of upwelling. The residual term ( \(R\) ) for the mixed layer indicates the surface heat flux and subgrid/submonthly processes. To evaluate the heat budget related to the long-term emerging temperature changes, we identified two sub-periods during 1958–2022: the first 20 years (1958–1977) as a reference period, and the most recent 20 years (2003–2022) as the period of climate change. We then calculated the averages of each heat budget term in the quasi-equilibrium period P1 and climate change period P2 based on Eq. (12), and estimated the contributions of each term to the observed temperature changes by calculating their differences: \({\stackrel{-}{\frac{\partial T}{\partial t}}}_{P2}\approx {\stackrel{-}{\frac{\partial T}{\partial t}}}_{P2}-{\stackrel{-}{\frac{\partial T}{\partial t}}}_{P1}={\overline{UaTc}}_{P2}-{\overline{UaTc}}_{P1}+ \dots +{\overline{WaTa}}_{P2}-{\overline{WaTa}}_{P1}+ {\stackrel{-}{R}}_{P2}-{\stackrel{-}{R}}_{P1}\) Eq. (13) To reflect the contributions of these terms to the temperature change over one decade, we normalized their units to °C/month per decade by dividing by a factor of 6.5. In addition, to analyze the IPO's impact on the temperature change on decadal timescales, the detrended heat budget terms were regressed against the IPO index. The regression coefficients were then scaled by the linear trend in the IPO index from 1980 to 2022. Declarations Acknowledgments Funding: NSF award OCE-2219829 (FJ, RS, MAC) NSF award AGS-2217618 (RS) DESC0023333 (RS) Author contributions: Conceptualization: FJ, RS, MAC Methodology: FJ, RS, MAC Investigation: FJ Visualization: FJ Supervision: RS Writing—original draft: FJ Writing—review & editing: RS, MAC Competing interests: All other authors declare they have no competing interests. Data and materials availability: The datasets used to reproduce the results of this paper are located at https://metoffice.gov.uk/hadobs/hadisst/data/download.html (HadISST data), https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html (ERSSTv5), https://psl.noaa.gov/data/gridded/data.kaplan_sst.html (Kaplan data) https://psl.noaa.gov/data/gridded/data.cobe.html (COBE data), https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-oras5?tab=overview (ORAs5 data), https://iridl.ldeo.columbia.edu/SOURCES/.CARTON-GIESE/.SODA/.v2p2p4/.temp/ (SODA2.2.4 data), https://www2.atmos.umd.edu/~ocean/index_files/soda3.3.2_mn_download.htm (SODA3.3.2 data), and https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html (NCEP–NCAR data). References Cane MA et al (1997) Twentieth-Century Sea Surface Temperature Trends. Science 275:957–960 Seager R et al (2019) Strengthening tropical Pacific zonal sea surface temperature gradient consistent with rising greenhouse gases. Nat Clim Chang 9:517–522 Seager R, Henderson N, Cane M (2022) Persistent Discrepancies between Observed and Modeled Trends in the Tropical Pacific Ocean. J Clim 35:4571–4584 Zhang W, Li J, Zhao X (2010) Sea surface temperature cooling mode in the Pacific cold tongue. J Geophys Res 115:2010JC006501 Karnauskas KB, Seager R, Kaplan A, Kushnir Y, Cane MA (2009) Observed Strengthening of the Zonal Sea Surface Temperature Gradient across the Equatorial Pacific Ocean*. J Clim 22:4316–4321 Solomon A, Newman M (2012) Reconciling disparate twentieth-century Indo-Pacific ocean temperature trends in the instrumental record. Nat Clim Change 2:691–699 Knutson TR, Manabe S (1995) Time-Mean Response over the Tropical Pacific to Increased C0 2 in a Coupled Ocean-Atmosphere Model. J Clim 8:2181–2199 Meehl GA, Washington WM (1996) El Niño-like climate change in a model with increased atmospheric CO2 concentrations. Nature 382:56–60 Wills RCJ, Dong Y, Proistosecu C, Armour KC, Battisti DS (2022) Systematic Climate Model Biases in the Large-Scale Patterns of Recent Sea‐Surface Temperature and Sea‐Level Pressure Change. Geophysical Research Letters 49, eGL100011 (2022) Luo J-J, Wang G, Dommenget D (2018) May common model biases reduce CMIP5’s ability to simulate the recent Pacific La Niña-like cooling? Clim Dyn 50:1335–1351 Watanabe M, Dufresne J-L, Kosaka Y, Mauritsen T, Tatebe H (2021) Enhanced warming constrained by past trends in equatorial Pacific sea surface temperature gradient. Nat Clim Chang 11:33–37 Olonscheck D, Rugenstein M, Marotzke J (2020) Broad Consistency Between Observed and Simulated Trends in Sea Surface Temperature Patterns. Geophysical Research Letters 47, e2019GL086773 Coats S, Karnauskas KB (2017) Are Simulated and Observed Twentieth Century Tropical Pacific Sea Surface Temperature Trends Significant Relative to Internal Variability? Geophys Res Lett 44:9928–9937 Deser C, Phillips AS, Alexander MA (2010) Twentieth century tropical sea surface temperature trends revisited. Geophys Res Lett 37:2010GL043321 Dong L, Zhou T (2014) The formation of the recent cooling in the eastern tropical Pacific Ocean and the associated climate impacts: A competition of global warming, IPO, and AMO. JGR Atmos 119 Hansen J et al (1997) Forcings and chaos in interannual to decadal climate change. J Geophys Res 102:25679–25720 Henley BJ et al (2017) Spatial and temporal agreement in climate model simulations of the Interdecadal Pacific Oscillation. Environ Res Lett 12:044011 Mantua NJ, Hare SR, Zhang Y, Wallace JM, Francis RC (1997) A Pacific Interdecadal Climate Oscillation with Impacts on Salmon Production. Bull Amer Meteor Soc 78:1069–1079 Power S, Casey T, Folland C, Colman A, Mehta V (1999) Inter-decadal modulation of the impact of ENSO on Australia. Clim Dyn 15:319–324 Zhang Y, Wallace JM, Battisti DS (1997) ENSO-like Interdecadal Variability: 1900–93. J Clim 10:1004–1020 Wang B, Liu J, Kim H-J, Webster PJ, Yim S-Y (2012) Recent change of the global monsoon precipitation (1979–2008). Clim Dyn 39:1123–1135 Kosaka Y, Xie S-P (2013) Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501:403–407 Zuo H, Balmaseda MA, Tietsche S, Mogensen K, Mayer M (2019) The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment. Ocean Sci 15:779–808 Kang SM, Ceppi P, Yu Y, Kang I-S (2023) Recent global climate feedback controlled by Southern Ocean cooling. Nat Geosci 16:775–780 Kang SM et al (2023) Global impacts of recent Southern Ocean cooling. Proc. Natl. Acad. Sci. U.S.A. 120, e2300881120 Bjerknes J (1969) Atmospheric teleconnections from the equatorial Pacific. Mon Wea Rev 97:163–172 Wyrtki K (1975) El Niño—The Dynamic Response of the Equatorial Pacific Ocean to Atmospheric Forcing. J Phys Oceanogr 5:572–584 Rebert JP, Donguy JR, Eldin G, Wyrtki K (1985) Relations between sea level, thermocline depth, heat content, and dynamic height in the tropical Pacific Ocean. J Geophys Res 90:11719–11725 Bamber JL, Westaway RM, Marzeion B, Wouters B (2018) The land ice contribution to sea level during the satellite era. Environ Res Lett 13:063008 Wigley TML, Raper SCB (1987) Thermal expansion of sea water associated with global warming. Nature 330:127–131 Timmermann A, McGregor S, Jin F-F (2010) Wind Effects on Past and Future Regional Sea Level Trends in the Southern Indo-Pacific*. J Clim 23:4429–4437 Merrifield MA (2011) A Shift in Western Tropical Pacific Sea Level Trends during the 1990s. J Clim 24:4126–4138 Marcos M, Amores A (2014) Quantifying anthropogenic and natural contributions to thermosteric sea level rise: Marcos and Amores: Anthropogenic ocean warming. Geophys Res Lett 41:2502–2507 McGregor S, Gupta AS, England MH (2012) Constraining Wind Stress Products with Sea Surface Height Observations and Implications for Pacific Ocean Sea Level Trend Attribution*. J Clim 25:8164–8176 Sarachik ES, Cane MA (2010) The El Niño-Southern Oscillation Phenomenon. Cambridge University Press Veronis G (1973) Model of world ocean circulation: I. Wind-driven, two-layer. J Mar Res 31 Emile-Geay J, Cane MA (2009) Pacific Decadal Variability in the View of Linear Equatorial Wave Theory*. J Phys Oceanogr 39:203–219 Clement AC, Seager R, Cane MA, Zebiak SE (1996) An Ocean Dynamical Thermostat. J Clim 9:2190–2196 Manabe S, Wetherald RT (1975) The Effects of Doubling the CO 2 Concentration on the climate of a General Circulation Model. J Atmos Sci 32:3–15 Fu Q, Manabe S, Johanson CM (2011) On the warming in the tropical upper troposphere: Models versus observations. Geophys Res Lett 38:2011GL048101 Meehl GA et al (2021) Atlantic and Pacific tropics connected by mutually interactive decadal-timescale processes. Nat Geosci 14:36–42 McGregor S et al (2014) Recent Walker circulation strengthening and Pacific cooling amplified by Atlantic warming. Nat Clim Change 4:888–892 Luo J-J, Sasaki W, Masumoto Y (2012) Indian Ocean warming modulates Pacific climate change. Proc. Natl. Acad. Sci. U.S.A. 109, 18701–18706 Dong Y, Armour KC, Battisti DS, Blanchard-Wrigglesworth E (2022) Two-Way Teleconnections between the Southern Ocean and the Tropical Pacific via a Dynamic Feedback. J Clim 35:6267–6282 He C et al (2023) Tropical Atlantic multidecadal variability is dominated by external forcing. Nature 622:521–527 Deser C, Phillips AS (2023) Spurious Indo-Pacific Connections to Internal Atlantic Multidecadal Variability Introduced by the Global Temperature Residual Method. Geophys Res Lett 50, e2022GL100574 Fenske T, Clement A (2022) No Internal Connections Detected Between Low Frequency Climate Modes in North Atlantic and North Pacific Basins. Geophysical Research Letters 49, e2022GL097957 Rayner NA (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407 Huang B et al (2017) Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. J Clim 30:8179–8205 Ishii M, Shouji A, Sugimoto S, Matsumoto T (2005) Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection. Intl J Climatology 25:865–879 Kaplan A et al (1998) Analyses of global sea surface temperature 1856–1991. J Geophys Res 103:18567–18589 Vidard A, Bouttier P-A, Vigilant F (2015) NEMOTAM: tangent and adjoint models for the ocean modelling platform NEMO. Geosci Model Dev 8:1245–1257 Carton JA, Giese BS (2008) A Reanalysis of Ocean Climate Using Simple Ocean Data Assimilation (SODA). Mon Weather Rev 136:2999–3017 Carton JA, Chepurin GA, Chen L (2018) SODA3: A New Ocean Climate Reanalysis. J Clim 31:6967–6983 Kalnay E et al (1996) The NCEP/NCAR 40-Year Reanalysis Project. Bull Amer Meteor Soc 77:437–471 Zebiak SE (1985) Tropical Atmosphere–Ocean Interaction and the El Niño/Southern Oscillation Phenomenon. Ph.D. thesis, Massachusetts Institute of Technology, 260 pp Moore DWH, Philander SGH (1978) Modeling of the tropical oceanic circulation. The Sea. Wiley-Interscience, pp 319–361 Picaut J, Hayes SP, McPhaden MJ (1989) Use of the geostrophic approximation to estimate time-varying zonal currents at the equator. J Geophys Res 94:3228–3236 Zebiak SE, Cane MA (1987) A Model El Niño–Southern Oscillation. Mon Wea Rev 115:2262–2278 Jin F, An S, Timmermann A, Zhao J (2003) Strong El Niño events and nonlinear dynamical heating. Geophys Res Lett 30 Additional Declarations There is NO Competing Interest. Supplementary Files SummplementaryFileContrast0628.docx Cite Share Download PDF Status: Published Journal Publication published 27 Sep, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4656683","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":323121534,"identity":"d5f84c77-744a-4b55-924a-0720170fe828","order_by":0,"name":"Feng Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBAC9oaDDUDKRobhAJhvwcAgwQgSYcaphecAWEsaD1SLBDFawNRhZC1gBh4tjIcbPxf8Os/Dd7z5AMOPConEtbObGz8wVFgnNuDSwnCwWXpm320eyTPHEhh7zkgkbrtzsFmC4Uw6Ti32DAcbpHl7bvMY3MgxYGZsA2q5kdggwdh2GK8tv3l7zvEY3H8D19L8g/EfXi1t0jw/DgBt4YFraQMGGn4t1rwNyUC/pCUcBPrFGOiXNouEY+nGOLVIHH98m+ePnRzf8cMHH/yosJHddrv98Y0PNdayuLQwSBxgYGBsg7APwEUTcCkHAX6QYX/wqRgFo2AUjIIRDwC+PmSmoES2GgAAAABJRU5ErkJggg==","orcid":"","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Jiang","suffix":""},{"id":323121535,"identity":"fbc923f6-20ef-40f2-89f2-9a989d3f667f","order_by":1,"name":"Richard Seager","email":"","orcid":"https://orcid.org/0000-0003-4772-9707","institution":"Lamont Doherty Earth Observatory of Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Seager","suffix":""},{"id":323121536,"identity":"15d173ca-fbb9-4e01-9290-006f89840a66","order_by":2,"name":"Mark Cane","email":"","orcid":"https://orcid.org/0000-0001-5408-2388","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Cane","suffix":""}],"badges":[],"createdAt":"2024-06-28 20:40:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4656683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4656683/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-024-52731-6","type":"published","date":"2024-09-27T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59903404,"identity":"868c762b-4815-4a16-ad5a-565d7828f60a","added_by":"auto","created_at":"2024-07-09 06:18:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96026,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRecurrent and emerging SST trend patterns in the tropical Pacific.\u003c/strong\u003e The SST trend (°C per decade) based on HadISST during \u003cstrong\u003e(a)\u003c/strong\u003e1980-2022 and \u003cstrong\u003e(b) \u003c/strong\u003e1958-2022. The left insets of \u003cstrong\u003e(b) \u003c/strong\u003eshow the timeseries of raw (black dashed lines) and 15-year running mean (red solid lines) annual-mean SST anomalies in the cold tongue (top inset; 5°S–5°N, 190°W–270°W) and the warm pool (bottom inset; 5°S–5°N, 140°E–170°W). The SST anomalies were calculated relative to the climatology of the first 50 years (1870-1919). Dots in \u003cstrong\u003e(a-b)\u003c/strong\u003e indicate the trend exceeding the 95% confidence level. \u003cstrong\u003e(c)\u003c/strong\u003e Pattern correlations of 43-year SST trends in historical period with the trend during 1980-2022 in the tropical Pacific region (30°S-30°N, 120°E-270°W) based on HadISST (black solid line), ERSSTv5 (green dashed line), Kaplan (red dotted line) and COBE (blue dashed line). \u003cstrong\u003e(d)\u003c/strong\u003e Similar to \u003cstrong\u003e(c)\u003c/strong\u003e but for 65-year SST trends with the trend during 1958-2022. Arrows labeled P1, P2 and P3 indicate the periods with strongest positive and negative correlations in \u003cstrong\u003e(c)\u003c/strong\u003e and P4 with strongest negative correlation in \u003cstrong\u003e(d)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4656683/v1/c871fdce901c71294b86779a.png"},{"id":59903407,"identity":"0e6c3c74-2585-40cd-aa53-c7f7cdbb9316","added_by":"auto","created_at":"2024-07-09 06:18:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantification of internally-generated and emerging SST trends.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e Histograms of pattern correlations of 43-year SST trend patterns over the tropical Pacific (30°S-30°N, 120°E-270°W) in historical period with that during 1980-2022. The Gaussian distributions are inferred for 63 samples in earlier (blue line) and 48 samples in later (red line) periods, separately, based on the averages and variances of the pattern correlations for the trend patterns. \u003cstrong\u003e(b)\u003c/strong\u003e Similar to \u003cstrong\u003e(a)\u003c/strong\u003e but for the 65-year SST trend. The Gaussian distributions are inferred based on 41 samples for the earlier period and 48 long-term for the later period. \u003cstrong\u003e(c) \u003c/strong\u003eQuantification of the IPO’s contribution to the trend of zonal SST gradient in the equatorial Pacific (see Methods). The red solid line indicates the 43-year trend, and the red dashed line indicates the IPO’s contribution. Similarly, the blue solid line and dashed line indicate the 65-year trend and the IPO’s contribution, respectively. \u003cstrong\u003e(d) \u003c/strong\u003eThe 43-year SST trend (°C per decade) in 1969-2012 with near-zero IPO contribution. Dots in \u003cstrong\u003e(d) \u003c/strong\u003eindicate the trend exceeding the 95% confidence level.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4656683/v1/9437c7a1e2345779711a01c2.png"},{"id":59903405,"identity":"a7c2ac53-f372-4b18-a1c7-1cc3a7507b93","added_by":"auto","created_at":"2024-07-09 06:18:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":189967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRecurrent and emerging signals in thermocline depth, surface wind stress and SSH.\u003c/strong\u003e The thermocline depth trend (m/decade) based on ORAs5 subsurface temperature during \u003cstrong\u003e(a)\u003c/strong\u003e 1980-2022 and \u003cstrong\u003e(b)\u003c/strong\u003e 1958-2022. The surface wind stress (vectors; N/m2) and SSH (contours; m/decade) trend during \u003cstrong\u003e(c)\u003c/strong\u003e 1980-2022 and \u003cstrong\u003e(d)\u003c/strong\u003e 1958-2022. Dots in \u003cstrong\u003e(a-b)\u003c/strong\u003e and \u003cstrong\u003e(c-d)\u003c/strong\u003e indicate the trend in thermocline depth and SSH exceeding the 95% confidence level, respectively. \u003cstrong\u003e(e)\u003c/strong\u003e Pattern correlations of 43-year trend patterns in historical period for the thermocline depth (red lines), SSH (blue lines) and zonal wind stress (green lines) in the tropical Pacific region (30°S-30°N, 120°E-270°W) based on SODA (solid lines) and ORAs5 (dashed lines) with the corresponding trend during 1980-2022 based on ORAs5. \u003cstrong\u003e(f)\u003c/strong\u003e Similar to \u003cstrong\u003e(e)\u003c/strong\u003e but for 65-year trend with the trend during 1958-2022.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4656683/v1/f3e8e4c292d615d1a7627eb7.png"},{"id":59904270,"identity":"dceacc35-00a0-4834-8b0d-d29c3445b894","added_by":"auto","created_at":"2024-07-09 06:26:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":303080,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4656683/v1/1d9c363ad47f1a8979254d43.png"},{"id":59903408,"identity":"006d6758-2986-461f-9df0-b67729a6c438","added_by":"auto","created_at":"2024-07-09 06:18:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":131951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOcean dynamics for differences in ocean current trend patterns. \u003c/strong\u003eZonal mean trend of \u003cstrong\u003e(a)\u003c/strong\u003e surface zonal current\u003cstrong\u003e \u003c/strong\u003e(m/s per decade), \u003cstrong\u003e(b) \u003c/strong\u003eupper 50-m averaged meridional current (m/s per decade) and \u003cstrong\u003e(c)\u003c/strong\u003e vertical current at the 50-m depth (m/day per decade) over the tropical central-to-eastern Pacific (150°E-90°W) during 1980-2022 (blue lines) and 1958-2022 (red lines). The IPO-related trend in surface zonal current\u003cstrong\u003e \u003c/strong\u003e(m/s per decade), upper 50-m averaged meridional current (m/s per decade) and vertical current at the 50-m depth (m/day per decade) are also shown in grey lines for comparison (see details in Methods for IPO-related trend). \u003cstrong\u003e(d) \u003c/strong\u003eThe IPO-related trend in surface zonal current (shadings; m/s per decade) and its geostrophic component (contours; m/s per decade, at 0.01 m/s intervals with the zero line omitted) calculated based on Eqs. (7-8) using ORAs5 data. \u003cstrong\u003e(e) \u003c/strong\u003eThe surface zonal current trend (shadings; m/s per decade) and its geostrophic component during\u003cstrong\u003e \u003c/strong\u003e1958-2022 (contours; m/s per decade, at 0.01 m/s per decade intervals with the zero line omitted). Dots in \u003cstrong\u003e(d-e)\u003c/strong\u003e indicate the observed trend exceeding the 95% confidence level. \u003cstrong\u003e(f-g)\u003c/strong\u003e Similar to \u003cstrong\u003e(d-e)\u003c/strong\u003e but for the Ekman pumping calculated based on Eqs. (9-11). Dots in \u003cstrong\u003e(f-g)\u003c/strong\u003eindicate the values exceeding the 95% confidence level.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4656683/v1/64bfacade2861a2bb66851d3.png"},{"id":59903410,"identity":"2c4f83e4-60a9-40b1-8460-2a0f3552f06e","added_by":"auto","created_at":"2024-07-09 06:18:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":24796,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOcean dynamics for the eastern Pacific cooling linked to the IPO and climate change.\u003c/strong\u003e Heat budget terms averaged over the eastern equatorial Pacific (5°S–5°N, 190°W–270°W; left box in Fig. 1b)including ocean current change (\u003cem\u003eUcTa, VcTa, WcTa\u003c/em\u003e), temperature gradient change (\u003cem\u003eUcTa, VcTa, WcTa\u003c/em\u003e), nonlinear terms (\u003cem\u003eUaTa, VaTa, WaTa\u003c/em\u003e) and their sum (SUM) related to (a) the IPO and (b) the emerging SST trend (℃/month per decade; see Methods for detailed explanation). Dotted bars indicate heat budget terms exceeding 90% significance tests.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4656683/v1/f741833e405cfb9f056d8d57.png"},{"id":65485016,"identity":"55e1d3fe-ece5-411a-99d4-1525d87fcbe2","added_by":"auto","created_at":"2024-09-28 07:10:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1419313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4656683/v1/f010b93e-a113-47a4-8158-7eb8080a3a0a.pdf"},{"id":59904633,"identity":"07918f04-aaf7-476e-b141-6835ab83ea22","added_by":"auto","created_at":"2024-07-09 06:34:23","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6343177,"visible":true,"origin":"","legend":"","description":"","filename":"SummplementaryFileContrast0628.docx","url":"https://assets-eu.researchsquare.com/files/rs-4656683/v1/1b9839165e12107f09057f0e.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"A climate change signal in the tropical Pacific emerges from decadal variability","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe fact that under greenhouse gas-driven global warming some parts of Earth do not warm, or even cool, presents a fascinating problem. The surface tropical eastern Pacific stands out as one such region that resists warming \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, associated with cooling and shoaling of the tropical Pacific Ocean thermocline over past decades \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Evaluated up to now, whether over recent decades or back a century or more, the tropical Pacific exhibited a La Ni\u0026ntilde;a-like trend pattern with pronounced warming in the west and lack of warming in the cold tongue region \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In contrast, state-of-the-art climate models respond to anthropogenic greenhouse gas (GHG) forcing by generally simulating enhanced warming in the eastern Pacific and an El Ni\u0026ntilde;o-like warming pattern \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Numerous studies have sought to reconcile this discrepancy and two primary arguments have been proposed. One suggests that the climate models exhibit systematic biases in simulating the tropical Pacific SST response to external forcing \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and that correcting the cold tongue bias would align the models' simulations with observations, which are argued to be the forced response \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The other suggests that the internal variability significantly contributes to the recent observed trend, and the observed trend would fall within the range of climate model simulations once internal variabilities were sufficiently sampled in models \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough the evidence is not definitive, the Coupled Model Intercomparison Projects (CMIP) models and Large Ensembles rarely even come close to the observed long-term trends, which casts doubts on the ability of climate models to correctly respond to rising GHGs with the proper dynamic and thermodynamic balances \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Alternatively, we may consider using observations to estimate the forced climate response. However, observational analyses, which inherently rely on a single realization, come with their own limitations. Beyond the inescapable observational error/uncertainty \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, a major challenge arises from the fact that the tropical Pacific is dynamically active on decadal to multi-decadal timescales, so that the background internally-generated long-term variability can sometimes overwhelm the climate change signal \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. For example, the phase transition from positive to negative phases in the Interdecadal Pacific Oscillation (IPO) \u0026mdash; the dominant climate mode in the Pacific ocean on decadal to multi-decadal time scales and closely related to the Pacific Decadal Oscillation \u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e \u0026mdash; is argued to have contributed to the observed La Ni\u0026ntilde;a-like SST trend pattern over the past four decades \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In contrast, some other studies highlight that the long-term change in the tropical Pacific in response to external forcing exhibits distinctive patterns unlike those on decadal timescales. For instance, while SST anomalies associated with the IPO in the tropical Pacific typically feature a meridionally broad pattern \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, the long-term lack of warming is found to be narrowly confined to the cold tongue \u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Seager et al. \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e further elaborate that some individual model simulations, containing both internal variability and the model\u0026rsquo;s forced response, come close to the observations but nonetheless fail to represent several fundamental elements of the observed long-term trends, including the distinctive narrowness of the lack of warming and the underlying subsurface ocean dynamical processes. These findings suggest that the tropical Pacific\u0026rsquo;s responses to anthropogenic forcing might present unique characteristics that set them apart from those typically associated with internal decadal variabilities.\u003c/p\u003e \u003cp\u003eIn this study, we seek to answer whether there is a detectable climate change signal in the tropical Pacific that can be clearly separated from internal variability and is robust across different datasets. This is crucial for setting a benchmark against which to assess the ability of climate models to reproduce the observed forced response in the presence of strong internal variability. In subsequent sections, we will demonstrate a distinctive climate change signal across various atmosphere-ocean fields in the tropical Pacific that is emerging from the internally-generated decadal variability and investigate its underlying dynamics.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eRecurrent and emerging SST trend patterns.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe SST trend pattern after the satellite data become available (1980\u0026ndash;2022) is often used as the observational reference for assessing climate model simulations of the forced response (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). This pattern is characterized by meridionally broad negative anomalies in the tropical Pacific, particularly south of the equator, and positive anomalies in the northwestern and southwestern Pacific. As noted by previous studies \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea, the pattern is akin to that of the typical internal IPO variability on decadal to multi-decadal scale \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, should we posit that the decadal variability (i.e., the phase transition of IPO from positive to negative phases) dominates this trend pattern, it would follow that this pattern is not unique in historical periods, given that the IPO has experienced several phase transitions in the observed record. Thus, we assess whether this short-term trend is a manifestation of decadal variability by calculating pattern correlations for tropical Pacific (30\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eS-30\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eN, 120\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eE-270\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eW) SST trends across equivalent time spans of 43 years but ending in different years to the most recent one (e.g., 1979 to 2021, 1978 to 2020 and so on all the way back to 1870 to 1912; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). The pattern correlations range from around \u0026minus;\u0026thinsp;0.8 to 1, demonstrating an oscillatory behavior matching the IPO's phase transitions over the last century (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). Analyses for the whole Pacific (60\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eS-60\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eN, 120\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eE-270\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eW) or the equatorial Pacific (10\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eS-10\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eN, 120\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eE-270\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eW) yield similar conclusion (not shown).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn comparison, a different SST trend pattern beginning in the mid-1950s is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. The exact start year 1958 is chosen to approximate the time when the tropical Pacific warm pool starts to warm up (insets of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), as well as facilitating the analysis of subsurface ocean processes based on the Ocean ReAnalysis System-5 (ORAs5) data which become available in that year \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The lack of warming in the tropical Pacific is still evident in this long-term trend pattern, however, it is markedly confined meridionally to the equatorial region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), in contrast with the much broader meridional distribution that is more indicative of the IPO (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Also, the warm anomalies in the northwestern and southwestern Pacific are less pronounced. This long-term trend pattern shows an emerging feature as evidenced in the pattern correlations of the latest trend with historical trends for equivalent 65-year spans. These show a rapid decline when the end year of the trend is adjusted backward and lingering around zero in earlier periods.\u003c/p\u003e \u003cp\u003eWe further identify periods with the strongest positive (P1:1870\u0026ndash;1912 and P3:1942\u0026ndash;1984) and strongest negative (P2: 1914\u0026ndash;1956) pattern correlations with the most recent short-term trend, and the strongest negative pattern correlation with the most recent long-term trend (P4: 1870\u0026ndash;1934) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec-f). The SST trend patterns in P1, P2 and P3 in the tropical Pacific broadly match that in the most recent 43-year trend, showing an IPO-like spatial distribution (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec-e). Notwithstanding, the southward-displaced center of the cooling in the eastern Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) is less apparent in these historical periods, implying that the feature is potentially indicative of a climate change signal rather than a result of internal variability \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In contrast, the trend in P4 (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ef) appears to be a weak reflection of the IPO pattern rather than a negative analogue of the latest 65-year trend pattern, again confirming the distinctiveness of the emerging SST trend pattern recently. The recurring feature of the short-term trend and the emerging feature of the long-term trend based on HadISST were consistently identified across different SST datasets including ERSSTv5, Kaplan, COBE (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec,d and S2).\u003c/p\u003e \u003cp\u003eThe emerging feature is apparent when examining the histogram and probability distributions of pattern correlations for short-term and long-term trends in the historical records (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea,b). While the overall distribution of the short-term trends closely resembles a symmetrical distribution around zero over time, and there are earlier periods that had trends with the opposite spatial pattern to the most recent one, the long-term trend has increasingly skewed towards positive values in recent decades. There are no past ant-analogs of the long-term trend. These trends were further categorized into two subsets, an earlier period with presumably weak climate change signal and a later period subject to pronounced anthropogenic forcing, based on whether they end before or after 1975. This cutoff year was selected to roughly split the periods evenly, and qualitative conclusions remain the same with slight changes in the cutoff year. The probability distributions for earlier and later periods elucidate that while the short-term trends in recent decades cannot be separated from those in earlier periods, the long-term trends in recent decades are notably separated from the background intrinsic decadal variability in earlier period. We further quantified the IPO\u0026rsquo;s contribution to the SST trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) by comparing the trend in the tropical Pacific zonal SST gradient and the IPO\u0026rsquo;s contribution, which is calculated using the trend in the IPO index in different time span and the IPO-related zonal SST gradient based on Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Consistent with Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea,b, the internal variability of the IPO predominantly shapes both the short-term and long-term trends during the earlier period when the climate change signal is relatively weak. In the recent period, however, the imprints of climate change are distinctly reflected in both short-term and long-term trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). However, as noted above, the spatial pattern of the most recent short-term trend is overwhelmed by the IPO (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with the IPO accounting for over half of the observed zonal SST gradient trend, while the impact of the IPO on the most recent long-term trend is minor. Moreover, when examining short-term trends with a negligible IPO impact, such as that from 1969\u0026ndash;2012, the short-term trend pattern closely resembles the emerging SST pattern detected in the extended period (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDistinct ocean dynamics for emerging climate change signal and decadal variability\u003c/h2\u003e \u003cp\u003eOur analysis has established that there is a distinctive SST trend pattern detected in an extended period emerging from the short-term SST trend pattern primarily related to the IPO. A closed Bjerknes feedback loop necessitates corresponding changes in the surface wind stress and the subsurface thermocline depth, in accordance with SST variations \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we show that there exists a recurring trend pattern of thermocline depth with a dipole-like structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea,e). In contrast, the emerging trend pattern is characterized with an overall shoaling throughout the tropical Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb,e). Also, the recurring surface wind stress pattern features a broad strengthening of the zonal wind stress across the tropical Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec,e), while the emerging wind stress trend pattern displays a dipole structure with strengthened wind stress in the central equatorial Pacific and weakened wind stress in the eastern equatorial Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed,e). We also examined changes in the surface wind stress and the subsurface thermocline depth that are directly linked to the IPO (Fig. S3). These IPO-related patterns with dipole-like thermocline change and same-signed wind stress change in the tropical Pacific bear a close resemblance to the short-term trends, which again substantiate the argument that the short-term trends over the span of approximately an IPO cycle predominantly reflect the recurrent influence of the IPO manifested in various ocean-atmosphere fields in the tropical Pacific.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the tropical Pacific, the thermocline depth and SSH are closely linked to each other and exhibit similar interannual fluctuations associated with El Ni\u0026ntilde;o-Southern Oscillation \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. On decadal timescales, the dipole-like pattern characterized with much stronger sea level rise in the tropical western Pacific compared to the east, and thermocline deepening in the west and shoaling in the east, are also present in the short-term trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea,c,f). In contrast to the shorter-term trend, the emerging long-term trend exhibits an overall, but small, sea level fall in the central-to-eastern equatorial Pacific region, accompanied by sea level rise in the off-equatorial regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). The long-term thermocline depth trend is also different from the shorter-term one in that it has shoaling or little change across the basin, even though the shoaling is greater in the east. The thermocline depth-SSH-wind stress changes associated with the short-term trend oscillate back and forth together (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee) but those associated with the long-term trend are collectively emerging over time for the long-term trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003eThese observed SSH and thermocline trends were previously demonstrated to be some combination of wind-driven, thermal expansion and the influence of changes in the Earth\u0026rsquo;s gravity field due to loss of land ice \u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. While there is general consensus on zonal dipole-like SSH change related to internal variability in both observations \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and CMIP models \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, the anthropogenic contribution to SSH change remains elusive \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Our subsequent analyses will focus on examining SSH. A 1.5-layer reduced-gravity model is used here to investigate the dynamic linkages between the surface and subsurface components (see details in Methods; note that the same dynamics can be formulated as describing a single baroclinic vertical mode and hence have more general applicability). The reduced-gravity model can simulate the wind stress driven changes in SSH and thermocline depth \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e; here we solve analytically an equilibrium version of the reduced gravity system (see Methods, Eq.\u0026nbsp;(6)). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea,b, both the short-term IPO-related trend and the long-term emerging trend can be quite realistically simulated by prescribing their corresponding surface wind stress trend patterns. The pattern correlations between observations and model for the observed and wind stress-driven trend patterns reach as high as 0.87 for the short-term trend and 0.72 for the long-term in the tropical Pacific, suggesting the dominant role of the wind-driven redistribution of the heat content in the tropical Pacific upper ocean by the surface wind stress for both decadal variability and the emerging climate change signal. According to Eq.\u0026nbsp;(6), variations in SSH at each longitude are determined by the impact of surface wind stress and its horizontal gradients zonally integrated from the eastern boundary to that longitude. The spatial distribution of the wind stress effects (B in Eq.\u0026nbsp;(6); Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec,d) underscores that it is the wind stress and its horizonal gradients in the central tropical Pacific that are most important in redistributing the heat content and driving the SSH changes in the tropical Pacific, for both climate change and decadal variability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe different wind stress patterns also drive different ocean circulation changes. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the zonally averaged ocean current trends over the central-to-eastern Pacific associated with the recurrent short-term and emerging long-term trends. We also display the IPO-related ocean currents, which again exhibit a strong consistency with the short-term trends. The most striking difference between the decadal variability-related and climate change-related trends is the opposite-signed surface zonal currents in the equatorial and north off-equatorial regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea; compare Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed,e). There is a significant strengthening of the surface westward zonal currents, except in the central equatorial Pacific, for the decadal variability (contours in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In contrast, the emerging pattern shows a weakening of westward surface currents in the central-to-eastern Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The changes in surface zonal currents in the tropical Pacific are predominantly governed by its geostrophic component (Eqs.\u0026nbsp;(7\u0026ndash;8); shadings in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea,b), which follows the spatial pattern of the SSH that has been established to be connected to the surface wind stress trend patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Wind stress impacts can also be detected in the Ekman pumping change (Eq.\u0026nbsp;(11)). While the decadal upwelling pattern is approximately symmetrical around the equator, the meridional center of the emerging upwelling pattern is displaced towards near 5 degrees south. The overall strengthening of zonal wind stress across the equatorial Pacific linked to decadal variability fosters pronounced upwelling from the western to eastern equatorial Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). In comparison, the emerging dipole-like wind stress pattern contributes to enhanced upwelling in the central equatorial Pacific and weakened upwelling to the east, while stronger trade winds south of the equator contribute to increased local upwelling (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed), thereby accounting for the different meridional locations of upwelling change. The meridional currents averaged in the mixed layer, indicative of the strength of the shallow overturning circulation, are quite similar for the short-term and long-term trends, showing a consistent strengthening of the poleward transport in both hemispheres despite different magnitudes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese changes in wind-driven ocean currents, in turn, account for the IPO-related and emerging climate change related temperature change in the equatorial Pacific via different ocean dynamical processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The IPO-related cooling in the equatorial eastern Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) is primarily driven by the cooling effect of the zonal advection (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(UcTa\\)\u003c/span\u003e\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) resulting from strengthened zonal current (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). The meridional advection (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(VaTc\\)\u003c/span\u003e\u003c/span\u003e) related to enhanced poleward transport and the change due to thermocline shoaling (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(WcTa\\)\u003c/span\u003e\u003c/span\u003e) also contribute to the IPO-related cooling in the equatorial region. In contrast, the emerging cooling signal is relatively muted, primarily because the zonal advective warming effect due to weakened zonal current largely offsets the cooling effect related to the thermocline feedback due to the thermocline shoaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The mean meridional advection (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(VcTa\\)\u003c/span\u003e\u003c/span\u003e) also contributes to the emerging cooling trend in the central-to-eastern Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) via the strengthened meridional temperature gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Such effect is not observed for the IPO-related variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), where broader eastern Pacific cooling leads to insignificant changes in the meridional temperature gradient. Although the vertical upwelling changes are evident for the equatorial region, the contributions of the Ekman pumping term to the wider equatorial Pacific (5\u0026deg;S-5\u0026deg;N) temperature change, either related to the IPO or the emerging climate change, are rather minor due to the immediate opposite-signed effect off the equator for both variabilities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identify an emerging climate change signal in the tropical Pacific across different observational datasets, which exhibits distinctive ocean-atmosphere dynamics that differ from those typically associated with IPO-related decadal variability. The emerging SST trend pattern features a narrow band of cooling in the eastern equatorial Pacific, linked to thermocline shoaling/SSH decreases in the central-to-eastern Pacific and dipole-like changes in zonal surface wind stress. In contrast, the recurrent IPO-driven SST trend pattern is characterized by a meridionally broader cooling in the eastern Pacific, corresponding to zonal dipole-like thermocline/SSH changes and an overall strengthening of tropical Pacific zonal wind stress. The different changes in wind stress pattern lead to distinct ocean circulation changes. These oceanic responses to the surface wind stress account for their surface cooling in the eastern Pacific, with the thermocline shoaling playing a dominant role in the emerging cooling and enhanced zonal advective cooling mainly driving the IPO-related cooling.\u003c/p\u003e \u003cp\u003eWhile basic geophysical fluid dynamics underpin our argument that the observed oceanic changes can be interpreted as adjustments to variations in surface wind stress, further investigations including targeted ocean model experiments are required to comprehensively assess the relative contributions of local versus remote wind effects \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, as well as to understand the initial wind response to GHGs. The climatological settings of the tropical Pacific may inherently predispose it to different initial SST response in the warm pool and cold tongue region, and a corresponding trade wind response \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Due to the increased atmospheric static stability in response to GHG forcings \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e related to stronger temperature change in the upper troposphere compared to the surface (Fig. S4), this initial response to rising GHGs might not be amplified as efficiently via Bjerknes feedback as those observed for the internal modes on interannual to decadal timescales. Additionally, climate variations outside of the tropical Pacific have been argued to influence the tropical Pacific trade winds through teleconnections \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Further, it has been argued that pronounced decadal-to-multidecadal SST changes in the Atlantic Ocean are also dominated by the response to the same external forcing that the tropical Pacific encounters \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, suggesting an alternative explanation for the co-occurrence of these long-term variabilities across different regions, and the potential for an inter-basin interaction in the pattern of SST response to rising GHGs. More work is needed to disentangle causal relationships among the long-term changes in different basins \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is also critical to acknowledge that while we aim to distinguish between the recurrent IPO-related decadal variability and the climate change signal, these two may have become coupled together. We have emphasized the differences between the ocean-atmosphere dynamics of each, however, they do share much in common: shoaling of the thermocline in the east, enhanced upwelling somewhere in the central-to-eastern equatorial Pacific and an enhanced zonal SST gradient across the equatorial Pacific. It seems reasonable to postulate that if the response to radiative forcing is the emerging pattern seen here, then it will initiate coupled ocean-atmosphere feedbacks that favor a negative IPO state that also has an enhanced SST gradient. This might explain why the most recent IPO swing has been extreme and robust (as our analysis shows in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). If so, this suggests that in nature forcing is projecting onto natural modes of variability, while it is not clear whether climate models can reproduce that kind of physical behavior. This would require a new perspective on how internal variability interacts with the climate change signal in future studies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eDatasets\u003c/h2\u003e\n\u003cp\u003eThe SST data used here are the Hadley Centre data HadISST version 1.1 with horizontal resolutions of 1\u0026deg;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e1\u0026deg; \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, the National Oceanic and Atmospheric Administration ERSSTv5 data with horizontal resolutions of 2\u0026deg;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e2\u0026deg; \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, the Centennial in Situ Observation Based Estimates of SST (COBE) from the Japanese Meteorological Agency with horizontal resolutions of 1\u0026deg;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e1\u0026deg; \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and Kaplan Extended SST version 2 with horizontal resolutions of 5\u0026deg;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e5\u0026deg; \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. HadISST, ERSSTv5, and Kaplan were used from 1870 to 2022 and COBE from 1890 to 2022. We utilized subsurface temperature, surface wind stress, SSH, zonal and meridional currents from ORAs5 with horizontal resolutions of 0.25\u0026deg;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e0.25\u0026deg; and 75 vertical levels, the latest ocean reanalysis products provided by the European Centre for Medium-Range Weather Forecasts \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The vertical velocity for ORAs5 was derived from zonal and meridional currents based on mass continuity \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. We also used subsurface temperature and surface wind stress from the Simple Ocean Data Assimilation (SODA), version 2.2.4 with horizontal resolutions of 0.25\u0026deg; \u0026times; 0.25\u0026deg; and 40 vertical layers during 1871\u0026ndash;1979 \u003csup\u003e53\u003c/sup\u003e, in conjunction with the version 3.3.2 with horizontal resolutions of 0.25\u0026deg; \u0026times; 0.25\u0026deg; and 50 vertical layers during 1980\u0026ndash;2018 \u003csup\u003e54\u003c/sup\u003e. The air temperature data was obtained from the National Centers for the Environmental Prediction\u0026ndash;National Center for the Atmospheric Research (NCEP\u0026ndash;NCAR) reanalysis 1 with horizontal resolutions of 2.5\u0026deg;\u0026times;2.5\u0026deg; and 17 vertical layers \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. All datasets were interpolated onto a horizontal grid of 1\u0026deg;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e1\u0026deg; to enable comparison among datasets.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical methods and definitions of indices\u003c/h2\u003e\n\u003cp\u003eAnomalies for all variables were calculated as departures from the monthly climatology unless specified otherwise. Statistical significance tests were performed based on the two-tailed Student\u0026rsquo;s t-test with n\u0026ndash;2 degrees of freedom, where n is the sample size. The thermocline depth was identified as the depth of the 20 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(℃\\)\u003c/span\u003e\u003c/span\u003e isotherm. The qualitative conclusion remains similar based on the thermocline depth defined by the maximum vertical temperature gradient. The zonal SST gradient in the tropical Pacific was defined as the temperature difference between the western Pacific (5\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eS\u0026ndash;5\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eN, 140\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eE\u0026ndash;170\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eE, indicated by the left box in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb) and the eastern Pacific (5\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eS\u0026ndash;5\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eN, 190\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eW\u0026ndash;270\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003eW, indicated by the right box in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). The IPO index was calculated based on the difference between the SST anomalies averaged over the central equatorial Pacific (10\u0026deg;S\u0026ndash;10\u0026deg;N, 170\u0026deg;E\u0026ndash;90\u0026deg;W ) and the average of the SST anomalies in the northwest (25\u0026deg;N\u0026ndash;45\u0026deg;N, 140\u0026deg;E\u0026ndash;145\u0026deg;W) and southwest Pacific (50\u0026deg;S\u0026ndash;15\u0026deg;S, 150\u0026deg;E\u0026ndash;160\u0026deg;W) following Henley et al. \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Then a 13-year low-pass filter based on Fast Fourier Transform was applied to extract the decadal-scale component of IPO variability. To assess the IPO's contribution to the short-term trend of variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x\\)\u003c/span\u003e\u003c/span\u003e, we calculate the IPO-related trend by regressing the detrended \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x\\)\u003c/span\u003e\u003c/span\u003e against the IPO index and then multiplying this by the IPO index's linear trend from 1980 to 2022.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eReduced gravity system linking SSH and thermocline depth to surface wind stress\u003c/h2\u003e\n\u003cp\u003eA 1.5-layer reduced-gravity system is considered here following the formulation of Veronis \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e to establish the relationship between the change in the surface wind stress and change in the SSH and thermocline depth over the tropical Pacific. We made several modifications to Veronis' framework by including the meridional wind stress component \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau }^{y}\\)\u003c/span\u003e\u003c/span\u003e (previously set to zero), the zonally-varying zonal wind stress \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau }^{x}\\)\u003c/span\u003e\u003c/span\u003e (previously assumed to be zonally-uniform), and a damping term (previously not considered) and using an equatorial \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e-plane (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(f=\\beta y\\)\u003c/span\u003e\u003c/span\u003e, in which \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta =\\)\u003c/span\u003e\u003c/span\u003e 2.3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{*}\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;11\u003c/sup\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e). We also adopt a linear system with a specified spatially-uniform climatological upper layer thickness in the tropical Pacific (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{h}=\\)\u003c/span\u003e\u003c/span\u003e150 m) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, Taking \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta \\rho = 2.7\\)\u003c/span\u003e\u003c/span\u003e kg/m\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e as the density contrast between upper and bottom layers yields a first baroclinic mode gravity wave speed of c\u0026thinsp;~\u0026thinsp;2.0 m/s, where c\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({g}^{{\\prime }}\\stackrel{-}{h}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({g}^{{\\prime }}=g\\frac{\\varDelta \\rho }{{\\rho }_{0} },\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{0}= 1025\\)\u003c/span\u003e\u003c/span\u003e kg/m\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e is the reference density. The governing equations on an equatorial \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e-plane are:\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(-fV=-{g}^{{\\prime }}\\stackrel{-}{h}\\frac{\\partial h}{\\partial x}+\\frac{{\\tau }^{x}}{{\\rho }_{0} }\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(fU= -{g}^{{\\prime }}\\stackrel{-}{h}\\frac{\\partial h}{\\partial y}+\\frac{{\\tau }^{y}}{{\\rho }_{0} }\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\partial U}{\\partial x}+\\frac{\\partial V}{\\partial y}=-rh\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003ein which\u0026nbsp;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(h\\)\u003c/span\u003e\u003c/span\u003eis the upper layer thickness between SSH (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({h}_{1};\\text{m})\\)\u003c/span\u003e\u003c/span\u003e\u0026nbsp;and thermocline depth (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({h}_{2};\\text{m}),\\)\u003c/span\u003e\u003c/span\u003e\u0026nbsp;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(r=\\)\u003c/span\u003e\u003c/span\u003e\u0026nbsp;1/5.5 year\u003csup\u003e\u0026minus;\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u0026nbsp;is the damping coefficient,\u0026nbsp;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(U={\\int }_{{h}_{1}}^{{h}_{2}}udz\\)\u003c/span\u003e\u003c/span\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(u\\)\u003c/span\u003e\u003c/span\u003e\u0026nbsp;the zonal current; m/s), and\u0026nbsp;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(V={\\int }_{{h}_{1}}^{{h}_{2}}vdz\\)\u003c/span\u003e\u003c/span\u003e\u0026nbsp;(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(v\\)\u003c/span\u003e\u003c/span\u003e\u0026nbsp;the meridional current; m/s). Cross-differentiating Eqs.\u0026nbsp;(1\u0026ndash;2) and using Eq.\u0026nbsp;(3), we obtain the linkage between layer thickness change and wind stress change:\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\partial h}{\\partial x}- \\frac{\\beta {y}^{2}r}{{g}^{{\\prime }}\\stackrel{-}{h}}h=\\frac{y}{{{\\rho }_{0}g}^{{\\prime }}\\stackrel{-}{h}}(\\frac{{\\tau }^{x}}{y}+\\frac{\\partial {\\tau }^{y}}{\\partial x}-\\frac{\\partial {\\tau }^{x}}{\\partial y})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003eLet \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(A=-\\frac{\\beta {y}^{2}r}{{g}^{{\\prime }}\\stackrel{-}{h}}h\\)\u003c/span\u003e\u003c/span\u003e and Eq.\u0026nbsp;(4\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(B=\\frac{y}{{{\\rho }_{0}g}^{{\\prime }}\\stackrel{-}{h}}(\\frac{{\\tau }^{x}}{y}+\\frac{\\partial {\\tau }^{y}}{\\partial x}-\\frac{\\partial {\\tau }^{x}}{\\partial y}),\\)\u003c/span\u003e\u003c/span\u003e) can be solved as:\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabc\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(h={e}^{A({x}_{e}-x)}{h}_{e}+{\\int }_{{x}_{e}}^{x}B{e}^{A({x}^{{\\prime }}-x)}dx{\\prime }\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cp class=\"colspec\" align=\"left\"\u003ein which \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{e}\\)\u003c/span\u003e\u003c/span\u003e indicates the eastern boundary, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({h}_{e}\\)\u003c/span\u003e\u003c/span\u003ethe layer thickness at the eastern boundary. The change in the SSH can then be directly linked to the change in the surface wind stress if the change of SSH near the eastern boundary is neglected (which is approximately justified on the basis of the changes in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e):\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tabd\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({h}_{1}=\\frac{\\varDelta \\rho }{\\rho +\\varDelta \\rho }{\\int }_{{x}_{e}}^{x}B{e}^{A({x}^{{\\prime }}-x)}dx{\\prime }\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eEstimation of geostrophic zonal current and Ekman pumping\u003c/h2\u003e\n\u003cp\u003eThe geostrophic component of the surface current can be determined by considering the balance between the Coriolis force and the pressure gradient force. In spherical coordinates, the geostrophic zonal current \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({(u}_{g})\\)\u003c/span\u003e\u003c/span\u003eoutside of the equatorial region is expressed as:\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabe\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{g}=-\\frac{g}{f}\\frac{\\partial h}{\\partial y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003eAt the equator where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(f=0\\)\u003c/span\u003e\u003c/span\u003e, an estimate of the equatorial semi-geostrophic zonal current (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{sg})\\)\u003c/span\u003e\u003c/span\u003e is derived by calculating the second derivative of the SSH on an equatorial \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e-plane, which are suggested to be in good agreement with measured velocities \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e:\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabf\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35.0001px;\"\u003e\n\u003ctd style=\"height: 35.0001px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{sg}=-\\frac{g}{\\beta }\\frac{{\\partial }^{2}h}{\\partial {y}^{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.0001px;\" align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003eFollowing the approach of Cane and Zebiak \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, the Ekman transport (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({U}_{E}, {V}_{E}\\)\u003c/span\u003e\u003c/span\u003e) in the tropical region is formulated by incorporating a frictional component as:\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabg\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({U}_{E}=({r}_{s}{\\tau }^{x}+f{\\tau }^{y})/{\\rho }_{0}({f}^{2}+{{r}_{s}}^{2})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(9)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({V}_{E}=({r}_{s}{\\tau }^{y}-f{\\tau }^{x})/{\\rho }_{0}({f}^{2}+{{r}_{s}}^{2})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(10)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003ewhere\u0026nbsp;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{s}\\)\u003c/span\u003e\u003c/span\u003e\u0026nbsp;indicates the surface layer friction coefficient (1/2 day\u003csup\u003e\u003cstrong\u003e\u0026minus;\u003c/strong\u003e\u0026thinsp;1\u003c/sup\u003e). The Ekman transport away from the equator is consistent with classical Ekman theory. At the equator where\u0026nbsp;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(f=0\\)\u003c/span\u003e\u003c/span\u003e, the friction allows an Ekman transport in the direction of the wind stress. Ekman pumping velocity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{E}\\)\u003c/span\u003e\u003c/span\u003e) is thus derived from the divergence of the Ekman transport:\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabh\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{E}=\\frac{\\partial {U}_{E}}{\\partial x}+\\frac{\\partial {V}_{E}}{\\partial y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eMixed layer heat budget analysis for the long-term SST Change\u003c/h2\u003e\n\u003cp\u003eThe heat budget for the mixed layer temperature \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e can be expressed as\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabi\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\partial T}{\\partial t}=\\underset{UaTc}{\\underset{⏟}{-{u}_{a}{\\frac{\\partial T}{\\partial x}}_{ c}}}\\underset{UcTa}{\\underset{⏟}{-{u}_{c}{\\frac{\\partial T}{\\partial x}}_{ a}}}\\underset{UaTa}{\\underset{⏟}{-{u}_{a}{\\frac{\\partial T}{\\partial x}}_{ a}}}\\underset{VaTc}{\\underset{⏟}{-{v}_{a}{\\frac{\\partial T}{\\partial y}}_{ c}}}\\underset{VcTa}{\\underset{⏟}{-{v}_{c}{\\frac{\\partial T}{\\partial y}}_{ a}}}\\underset{VaTa}{\\underset{⏟}{-{v}_{a}{\\frac{\\partial T}{\\partial y}}_{ a}}}\\underset{WaTc}{\\underset{⏟}{-{w}_{a}{\\frac{\\partial T}{\\partial z}}_{ c}}}\\underset{WcTa}{\\underset{⏟}{-{w}_{c}{\\frac{\\partial T}{\\partial z}}_{ a}}}\\underset{WaTa}{ \\underset{⏟}{-{w}_{a}{\\frac{\\partial T}{\\partial z}}_{ a}}}+R.\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(12)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ein which \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(a\\)\u003c/span\u003e\u003c/span\u003e denotes anomaly and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(c\\)\u003c/span\u003e\u003c/span\u003e denotes climatology. The heat budget terms include changes in the mean current (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(UaTc\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(VaTc\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(WaTc\\)\u003c/span\u003e\u003c/span\u003e), changes in the mean temperature gradient (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(UcTa\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(VcTa\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(WcTa\\)\u003c/span\u003e\u003c/span\u003e), and their nonlinear interaction (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(UaTa\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(VaTa\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(WaTa\\)\u003c/span\u003e\u003c/span\u003e). The zonal advection and meridional advection terms were averaged over a uniform mixed layer depth of 50 m. The vertical velocity was calculated at the bottom of the mixed layer, and the vertical advection between the 50\u0026ndash;100 m and the upper 50 m layers was calculated only in the presence of upwelling. The residual term (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(R\\)\u003c/span\u003e\u003c/span\u003e) for the mixed layer indicates the surface heat flux and subgrid/submonthly processes.\u003c/p\u003e\n\u003cp\u003eTo evaluate the heat budget related to the long-term emerging temperature changes, we identified two sub-periods during 1958\u0026ndash;2022: the first 20 years (1958\u0026ndash;1977) as a reference period, and the most recent 20 years (2003\u0026ndash;2022) as the period of climate change. We then calculated the averages of each heat budget term in the quasi-equilibrium period P1 and climate change period P2 based on Eq.\u0026nbsp;(12), and estimated the contributions of each term to the observed temperature changes by calculating their differences:\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabj\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\stackrel{-}{\\frac{\\partial T}{\\partial t}}}_{P2}\\approx {\\stackrel{-}{\\frac{\\partial T}{\\partial t}}}_{P2}-{\\stackrel{-}{\\frac{\\partial T}{\\partial t}}}_{P1}={\\overline{UaTc}}_{P2}-{\\overline{UaTc}}_{P1}+ \\dots +{\\overline{WaTa}}_{P2}-{\\overline{WaTa}}_{P1}+ {\\stackrel{-}{R}}_{P2}-{\\stackrel{-}{R}}_{P1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEq.\u0026nbsp;(13)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo reflect the contributions of these terms to the temperature change over one decade, we normalized their units to \u0026deg;C/month per decade by dividing by a factor of 6.5. In addition, to analyze the IPO's impact on the temperature change on decadal timescales, the detrended heat budget terms were regressed against the IPO index. The regression coefficients were then scaled by the linear trend in the IPO index from 1980 to 2022.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;NSF award OCE-2219829 (FJ, RS, MAC)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;NSF award AGS-2217618 (RS)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;DESC0023333 (RS)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Conceptualization: FJ, RS, MAC\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Methodology: FJ, RS, MAC\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Investigation: FJ\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Visualization: FJ\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Supervision: RS\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Writing\u0026mdash;original draft: FJ\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Writing\u0026mdash;review \u0026amp; editing: RS, MAC\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e All other authors declare they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u003c/strong\u003e The datasets used to reproduce the results of this paper are located at https://metoffice.gov.uk/hadobs/hadisst/data/download.html (HadISST data), https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html (ERSSTv5), https://psl.noaa.gov/data/gridded/data.kaplan_sst.html (Kaplan data) https://psl.noaa.gov/data/gridded/data.cobe.html (COBE data), \u0026nbsp; \u0026nbsp;https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-oras5?tab=overview \u0026nbsp; \u0026nbsp; (ORAs5 data), https://iridl.ldeo.columbia.edu/SOURCES/.CARTON-GIESE/.SODA/.v2p2p4/.temp/ (SODA2.2.4 data), \u0026nbsp;https://www2.atmos.umd.edu/~ocean/index_files/soda3.3.2_mn_download.htm \u0026nbsp;(SODA3.3.2 data), and https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html\u003c/p\u003e\n\u003cp\u003e(NCEP\u0026ndash;NCAR data).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCane MA et al (1997) Twentieth-Century Sea Surface Temperature Trends. Science 275:957\u0026ndash;960\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeager R et al (2019) Strengthening tropical Pacific zonal sea surface temperature gradient consistent with rising greenhouse gases. Nat Clim Chang 9:517\u0026ndash;522\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeager R, Henderson N, Cane M (2022) Persistent Discrepancies between Observed and Modeled Trends in the Tropical Pacific Ocean. J Clim 35:4571\u0026ndash;4584\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Li J, Zhao X (2010) Sea surface temperature cooling mode in the Pacific cold tongue. J Geophys Res 115:2010JC006501\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarnauskas KB, Seager R, Kaplan A, Kushnir Y, Cane MA (2009) Observed Strengthening of the Zonal Sea Surface Temperature Gradient across the Equatorial Pacific Ocean*. J Clim 22:4316\u0026ndash;4321\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolomon A, Newman M (2012) Reconciling disparate twentieth-century Indo-Pacific ocean temperature trends in the instrumental record. Nat Clim Change 2:691\u0026ndash;699\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnutson TR, Manabe S (1995) Time-Mean Response over the Tropical Pacific to Increased C0 \u003csub\u003e2\u003c/sub\u003e in a Coupled Ocean-Atmosphere Model. J Clim 8:2181\u0026ndash;2199\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeehl GA, Washington WM (1996) El Ni\u0026ntilde;o-like climate change in a model with increased atmospheric CO2 concentrations. Nature 382:56\u0026ndash;60\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWills RCJ, Dong Y, Proistosecu C, Armour KC, Battisti DS (2022) Systematic Climate Model Biases in the Large-Scale Patterns of Recent Sea‐Surface Temperature and Sea‐Level Pressure Change. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e 49, eGL100011 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo J-J, Wang G, Dommenget D (2018) May common model biases reduce CMIP5\u0026rsquo;s ability to simulate the recent Pacific La Ni\u0026ntilde;a-like cooling? Clim Dyn 50:1335\u0026ndash;1351\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe M, Dufresne J-L, Kosaka Y, Mauritsen T, Tatebe H (2021) Enhanced warming constrained by past trends in equatorial Pacific sea surface temperature gradient. Nat Clim Chang 11:33\u0026ndash;37\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlonscheck D, Rugenstein M, Marotzke J (2020) Broad Consistency Between Observed and Simulated Trends in Sea Surface Temperature Patterns. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e 47, e2019GL086773\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoats S, Karnauskas KB (2017) Are Simulated and Observed Twentieth Century Tropical Pacific Sea Surface Temperature Trends Significant Relative to Internal Variability? Geophys Res Lett 44:9928\u0026ndash;9937\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeser C, Phillips AS, Alexander MA (2010) Twentieth century tropical sea surface temperature trends revisited. Geophys Res Lett 37:2010GL043321\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong L, Zhou T (2014) The formation of the recent cooling in the eastern tropical Pacific Ocean and the associated climate impacts: A competition of global warming, IPO, and AMO. JGR Atmos 119\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansen J et al (1997) Forcings and chaos in interannual to decadal climate change. J Geophys Res 102:25679\u0026ndash;25720\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenley BJ et al (2017) Spatial and temporal agreement in climate model simulations of the Interdecadal Pacific Oscillation. Environ Res Lett 12:044011\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMantua NJ, Hare SR, Zhang Y, Wallace JM, Francis RC (1997) A Pacific Interdecadal Climate Oscillation with Impacts on Salmon Production. Bull Amer Meteor Soc 78:1069\u0026ndash;1079\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePower S, Casey T, Folland C, Colman A, Mehta V (1999) Inter-decadal modulation of the impact of ENSO on Australia. Clim Dyn 15:319\u0026ndash;324\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Wallace JM, Battisti DS (1997) ENSO-like Interdecadal Variability: 1900\u0026ndash;93. J Clim 10:1004\u0026ndash;1020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang B, Liu J, Kim H-J, Webster PJ, Yim S-Y (2012) Recent change of the global monsoon precipitation (1979\u0026ndash;2008). Clim Dyn 39:1123\u0026ndash;1135\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosaka Y, Xie S-P (2013) Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501:403\u0026ndash;407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo H, Balmaseda MA, Tietsche S, Mogensen K, Mayer M (2019) The ECMWF operational ensemble reanalysis\u0026ndash;analysis system for ocean and sea ice: a description of the system and assessment. Ocean Sci 15:779\u0026ndash;808\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang SM, Ceppi P, Yu Y, Kang I-S (2023) Recent global climate feedback controlled by Southern Ocean cooling. Nat Geosci 16:775\u0026ndash;780\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang SM et al (2023) Global impacts of recent Southern Ocean cooling. \u003cem\u003eProc. Natl. Acad. Sci. U.S.A.\u003c/em\u003e 120, e2300881120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBjerknes J (1969) Atmospheric teleconnections from the equatorial Pacific. Mon Wea Rev 97:163\u0026ndash;172\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWyrtki K (1975) El Ni\u0026ntilde;o\u0026mdash;The Dynamic Response of the Equatorial Pacific Ocean to Atmospheric Forcing. J Phys Oceanogr 5:572\u0026ndash;584\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRebert JP, Donguy JR, Eldin G, Wyrtki K (1985) Relations between sea level, thermocline depth, heat content, and dynamic height in the tropical Pacific Ocean. J Geophys Res 90:11719\u0026ndash;11725\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBamber JL, Westaway RM, Marzeion B, Wouters B (2018) The land ice contribution to sea level during the satellite era. Environ Res Lett 13:063008\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWigley TML, Raper SCB (1987) Thermal expansion of sea water associated with global warming. Nature 330:127\u0026ndash;131\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimmermann A, McGregor S, Jin F-F (2010) Wind Effects on Past and Future Regional Sea Level Trends in the Southern Indo-Pacific*. J Clim 23:4429\u0026ndash;4437\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerrifield MA (2011) A Shift in Western Tropical Pacific Sea Level Trends during the 1990s. J Clim 24:4126\u0026ndash;4138\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarcos M, Amores A (2014) Quantifying anthropogenic and natural contributions to thermosteric sea level rise: Marcos and Amores: Anthropogenic ocean warming. Geophys Res Lett 41:2502\u0026ndash;2507\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGregor S, Gupta AS, England MH (2012) Constraining Wind Stress Products with Sea Surface Height Observations and Implications for Pacific Ocean Sea Level Trend Attribution*. J Clim 25:8164\u0026ndash;8176\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarachik ES, Cane MA (2010) The El Ni\u0026ntilde;o-Southern Oscillation Phenomenon. Cambridge University Press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeronis G (1973) Model of world ocean circulation: I. Wind-driven, two-layer. J Mar Res 31\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmile-Geay J, Cane MA (2009) Pacific Decadal Variability in the View of Linear Equatorial Wave Theory*. J Phys Oceanogr 39:203\u0026ndash;219\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClement AC, Seager R, Cane MA, Zebiak SE (1996) An Ocean Dynamical Thermostat. J Clim 9:2190\u0026ndash;2196\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManabe S, Wetherald RT (1975) The Effects of Doubling the CO \u003csub\u003e2\u003c/sub\u003e Concentration on the climate of a General Circulation Model. J Atmos Sci 32:3\u0026ndash;15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu Q, Manabe S, Johanson CM (2011) On the warming in the tropical upper troposphere: Models versus observations. Geophys Res Lett 38:2011GL048101\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeehl GA et al (2021) Atlantic and Pacific tropics connected by mutually interactive decadal-timescale processes. Nat Geosci 14:36\u0026ndash;42\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGregor S et al (2014) Recent Walker circulation strengthening and Pacific cooling amplified by Atlantic warming. Nat Clim Change 4:888\u0026ndash;892\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo J-J, Sasaki W, Masumoto Y (2012) Indian Ocean warming modulates Pacific climate change. \u003cem\u003eProc. Natl. Acad. Sci. U.S.A.\u003c/em\u003e 109, 18701\u0026ndash;18706\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong Y, Armour KC, Battisti DS, Blanchard-Wrigglesworth E (2022) Two-Way Teleconnections between the Southern Ocean and the Tropical Pacific via a Dynamic Feedback. J Clim 35:6267\u0026ndash;6282\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe C et al (2023) Tropical Atlantic multidecadal variability is dominated by external forcing. Nature 622:521\u0026ndash;527\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeser C, Phillips AS (2023) Spurious Indo-Pacific Connections to Internal Atlantic Multidecadal Variability Introduced by the Global Temperature Residual Method. Geophys Res Lett 50, e2022GL100574\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFenske T, Clement A (2022) No Internal Connections Detected Between Low Frequency Climate Modes in North Atlantic and North Pacific Basins. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e 49, e2022GL097957\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRayner NA (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang B et al (2017) Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. J Clim 30:8179\u0026ndash;8205\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshii M, Shouji A, Sugimoto S, Matsumoto T (2005) Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection. Intl J Climatology 25:865\u0026ndash;879\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaplan A et al (1998) Analyses of global sea surface temperature 1856\u0026ndash;1991. J Geophys Res 103:18567\u0026ndash;18589\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidard A, Bouttier P-A, Vigilant F (2015) NEMOTAM: tangent and adjoint models for the ocean modelling platform NEMO. Geosci Model Dev 8:1245\u0026ndash;1257\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarton JA, Giese BS (2008) A Reanalysis of Ocean Climate Using Simple Ocean Data Assimilation (SODA). Mon Weather Rev 136:2999\u0026ndash;3017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarton JA, Chepurin GA, Chen L (2018) SODA3: A New Ocean Climate Reanalysis. J Clim 31:6967\u0026ndash;6983\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalnay E et al (1996) The NCEP/NCAR 40-Year Reanalysis Project. Bull Amer Meteor Soc 77:437\u0026ndash;471\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZebiak SE (1985) \u003cem\u003eTropical Atmosphere\u0026ndash;Ocean Interaction and the El Ni\u0026ntilde;o/Southern Oscillation Phenomenon.\u003c/em\u003ePh.D. thesis, Massachusetts Institute of Technology, 260 pp\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore DWH, Philander SGH (1978) Modeling of the tropical oceanic circulation. The Sea. Wiley-Interscience, pp 319\u0026ndash;361\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicaut J, Hayes SP, McPhaden MJ (1989) Use of the geostrophic approximation to estimate time-varying zonal currents at the equator. J Geophys Res 94:3228\u0026ndash;3236\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZebiak SE, Cane MA (1987) A Model El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation. Mon Wea Rev 115:2262\u0026ndash;2278\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin F, An S, Timmermann A, Zhao J (2003) Strong El Ni\u0026ntilde;o events and nonlinear dynamical heating. Geophys Res Lett 30\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4656683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4656683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecent debates have centered around whether the La Ni\u0026ntilde;a-like sea surface temperature (SST) trend pattern in the tropical Pacific in the past several decades is a response to anthropogenic forcings or internal variability, particularly the Interdecadal Pacific Oscillation (IPO). This study identifies an emerging SST warming pattern in the tropical Pacific featuring a narrow equatorial cooling band, in stark contrast to the meridionally broad SST trend pattern shaped by the IPO. The emerging SST trend pattern is associated with changes in subsurface temperature structure and sea level height that are distinct from those related to the recurrent IPO. The differences are primarily driven by their different surface wind stress patterns. The emerging wind stress pattern also drives distinctive ocean dynamical processes, fostering the unique eastern Pacific cooling. Our findings set a path to distinguish the often-tangled tropical Pacific climate change signals from internal variability through the underlying dynamics of each.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMAIN TEXT\u003c/span\u003e \u003c/p\u003e","manuscriptTitle":"A climate change signal in the tropical Pacific emerges from decadal variability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-09 06:18:19","doi":"10.21203/rs.3.rs-4656683/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5a4143dc-ac70-4207-9cee-a8906b1b6cc5","owner":[],"postedDate":"July 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34173321,"name":"Earth and environmental sciences/Climate sciences/Climate change"},{"id":34173322,"name":"Earth and environmental sciences/Ocean sciences/Physical oceanography"}],"tags":[],"updatedAt":"2024-09-28T07:10:35+00:00","versionOfRecord":{"articleIdentity":"rs-4656683","link":"https://doi.org/10.1038/s41467-024-52731-6","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2024-09-27 04:00:00","publishedOnDateReadable":"September 27th, 2024"},"versionCreatedAt":"2024-07-09 06:18:19","video":"","vorDoi":"10.1038/s41467-024-52731-6","vorDoiUrl":"https://doi.org/10.1038/s41467-024-52731-6","workflowStages":[]},"version":"v1","identity":"rs-4656683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4656683","identity":"rs-4656683","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}