There is debate about slowing of the Atlantic Meridional Overturning Circulation (AMOC), a key component of the global climate system. Some focus is on the sea surface temperature (SST) slightly cooling in parts of the subpolar North Atlantic despite widespread ocean warming. Atlantic SST is influenced by the AMOC, especially on decadal timescales and beyond. The local cooling could thus reflect AMOC slowing and diminishing heat transport, consistent with climate model responses to rising atmospheric greenhouse gas concentrations. Here we show from Atlantic SST the prevalence of natural AMOC variability since 1900. This is consistent with historical climate model simulations for 1900–2014 predicting on average AMOC slowing of about 1 Sv at 30° N after 1980, which is within the range of internal multidecadal variability derived from the models’ preindustrial control runs. These results highlight the importance of systematic and sustained in-situ monitoring systems that can detect and attribute with high confidence an anthropogenic AMOC signal.
Global surface warming (global warming hereafter) since the beginning of the twentieth century is unequivocal, and humans are the main cause through the emission of vast amounts of greenhouse gases (GHGs), especially carbon dioxide (CO2)1,2,3. The oceans have stored more than 90% of the heat trapped in the climate system caused by the accumulation of GHGs in the atmosphere, thereby contributing to sea-level rise and leading to more frequent and longer lasting marine heat waves4. Moreover, the oceans have taken up about one third of the total anthropogenic CO2 emissions since the start of industrialization, causing ocean acidification5. Both ocean warming and acidification already have adverse consequences for marine ecosystems6. Some of the global warming impacts, however, unfold slowly in the ocean due to its large thermal and dynamical inertia. Examples are sea-level rise and the response of the Atlantic Meridional Overturning Circulation (AMOC), a three-dimensional system of currents in the Atlantic Ocean with global climatic relevance7,8,9,10.
Climate models predict substantial AMOC slowing if atmospheric GHG concentrations continue to rise unabatedly1,11,12,13,14. Substantial AMOC slowing would drive major climatic impacts such as shifting rainfall patterns on land15, accelerating regional sea-level rise16,17 and reducing oceanic CO2 uptake. However, it is still unclear as to whether sustained AMOC slowing is underway18,19,20,21,22. Direct ocean-circulation observation in the North Atlantic (NA) is limited9,23,24,25,26,27. Inferences drawn about the AMOC’s history from proxy data28 or indices derived from other variables, which may provide information about the circulation’s variability (for example, sea surface temperature (SST)21,29,30, salinity31 or Labrador Sea convection32), are subject to large uncertainties.
Pronounced decadal and longer timescale (hereafter, long-term) natural variability32 complicates the detection of anthropogenic signals over the NA sector. Further, the drivers of long-term climate variability are not well understood (for example, the causes of the Atlantic Multidecadal Oscillation/Variability (AMO/V), the leading mode of long-term NA-SST variability33,34,35). Internal36,37 as well as external38,39,40 mechanisms have been proposed to contribute to the AMO/V, and the mechanisms also differ greatly among climate models39,41,42,43,44. Observations suggest that the SST contrast between the NA and South Atlantic, termed interhemispheric dipole45, could be linked to the AMOC, which in turn is forced by the low-frequency portion of the North Atlantic Oscillation (NAO; Fig. 1d; Latif et al.19), the leading mode of the Northern Hemisphere’s atmospheric winter-circulation variability46. This link is supported by a number of climate models (for example, Delworth et al.47). However, the relationships between AMO/V, AMOC and NAO are obscured by variable external forcing48,49,50. Remote forcing, for example, from the Southern Ocean (Martin et al.51), may also impact the AMOC.
Ocean-circulation changes most prominently influence the SST on a sub-basin scale. The SST cooling in parts of the subpolar NA, which is referred to as the North Atlantic warming hole (NAWH; Extended Data Fig. 1a), may be an example and the consequence of GHG-induced AMOC slowing and diminishing related northward ocean heat transport52,53,54. Other causes of the NAWH have been discussed too, for example, increased ocean heat transport out of the subpolar NA into higher latitudes55. Moreover, the deep mixed layers in the NAWH (Extended Data Fig. 1b), indicating strong vertical heat exchange, would delay externally forced surface warming. Anthropogenic aerosols reducing net surface solar radiation could also counteract the surface warming in the extratropical NA39.
The 1900–2019 SST averaged over the NAWH (small boxes in Figs. 2b and 3b) and obtained from two observational datasets (Methods) exhibits strong long-term variability (Fig. 1a). Two other indices (Fig. 1b,c), averaging the SST over larger areas of the NA (Methods), exhibit similar long-term variability. All three indices indicate a major multidecadal cooling trend during 1930–1970. The later part of the cooling trend has been discussed in Hodson et al.56. They conclude that internal as well as external factors could have contributed to the cooling in the 1960s and early 1970s.
The SST in the NAWH (Fig. 1a) underwent a fast and strong warming in the 1990s and featured pronounced multiannual-to-decadal variability after 2000 without any obvious trend. The atmospheric CO2 forcing (Fig. 1d) and net top-of-the-atmosphere effective radiative forcing (ERF; Methods and Figs. 2a and 3a), however, strongly increased from the 1970s onward. We note that the strong year-to-year variability in the SST of the NAWH is mostly due to atmospheric heat-flux forcing linked to the winter NAO57. This short-term variability is not addressed here, as the focus of this study is on the long-term variability.
Spatial structure of long-term SST change
Empirical orthogonal function (EOF) analysis is applied to the observed annual SST anomalies (blue boxes in Figs. 2b and 3b; Methods), which is a variance-maximizing multivariate statistical technique58. The two leading modes, EOF1 and EOF2, are dominated by long-term variability and are of interest here. EOF1 accounts for 52.2% of the total Atlantic SST variability. The time evolution (principal component) of EOF1, PC1 (Fig. 2a), is governed by an accelerating upward trend with substantial superimposed less-energetic interannual-to-multidecadal variability. PC1 follows the long-term evolution of the ERF well, with a correlation coefficient of 0.83 when the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5; Methods) scenario is considered during 2015–2019. We suggest that EOF1 describes the externally forced Atlantic SST variability, in particular the effects of global warming. The prominent, short-lived, downward spikes in the ERF during the second half of the twentieth century, however, are not well captured by PC1 and have been attributed to explosive volcanic eruptions59.
The pattern of EOF1, shown as the local regression coefficients upon PC1 (Fig. 2b), exhibits positive anomalies over the Atlantic basin, with the exception of the NAWH. PC1 accounts for up to 80% of the SST variability in the tropical Atlantic but hardly any in the NAWH. In most other regions of the Atlantic, the explained variances amount to at least 50%. EOF1 explains a relatively large fraction of the SST variability over most of the global ocean. We note the warming hole in the subpolar Southern Ocean (Fig. 2b and Extended Data Fig. 1a); there, stronger westerly surface winds (associated with stratospheric ozone loss60,61) or Antarctic meltwater62 may have offset GHG-forced warming. Moreover, in climate models, the internal long-term variability is particularly strong in both the Southern Ocean and NA63,64,65,66,67, hindering detection of externally forced signals.
The second most energetic mode (EOF2; Fig. 3) accounts for 12.1% of the total Atlantic SST variability. The corresponding time series, PC2 (Fig. 3a), has no obvious relationship with the ERF and exhibits pronounced decadal-to-multidecadal variability but no trend. EOF2 is picking up the signal of the Atlantic interhemispheric dipole, which has been linked to long-term AMOC variability in climate models29,30,44,68,69,70. Centres with opposite sign are observed in the subpolar NA and in the South Atlantic (Fig. 3b), where EOF2 typically accounts for up to 60% and 20% of the local SST variability, respectively. There is a highly significant correlation amounting to 0.7 between PC2 and an Atlantic interhemispheric dipole index as defined by Latif et al.19. EOF2 explains considerably less variance in the tropical Atlantic where EOF1 explains the most. The salient feature of PC2 (Fig. 3a) is the marked decline during 1930–1970. We note that the multidecadal decline in PC2 was preceded by a multidecadal decline in the winter NAO index (Extended Data Fig. 2). However, the largest significant correlation, which is negative, is observed with no lag (Extended Data Fig. 3). We hypothesize that EOF2 describes the internal, AMOC-related, SST variability in the Atlantic.
Historical climate model simulations
The observed NA-SST variability is within the range of the historical simulations with state-of-the-art climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6; Methods and Fig. 1a–c), employing estimates of external forcing from 1900 to 2014 (ref. 71). A measure of the externally forced variability is given by the ensemble mean, that is, by averaging over all simulations. The ensemble-mean SST variability is much weaker than the observed variability in the NAWH (Fig. 1a) and subpolar NA (Fig. 1b), suggesting the prevalence of internal SST variability in these two regions. The observed SST variability is also underestimated by the ensemble mean (but to a lesser extent) when averaging over the whole NA (Fig. 1c), supporting a major role of external forcing in driving tropical and subtropical Atlantic SST. This is consistent with EOF1, which is closely linked to the ERF (Fig. 2a), explaining the most variance in the tropics and subtropics. The ensemble-mean NA SSTs lead the observed SSTs by about a decade in all three index regions (Fig. 1a–c). We note that historical external forcing is subject to considerable uncertainties. Aerosol–cloud interaction is the largest source of uncertainty in estimating historical anthropogenic radiative forcing, and representing this interaction in climate models poses a major challenge72,73. Finally, in the regions of the three SST indices, the 40-year linear SST trend during 1975–2014 lies within the trend distribution derived from the preindustrial control integrations of the models (Extended Data Fig. 4).
An EOF analysis is applied jointly to the ensemble-mean Atlantic SST and Atlantic meridional overturning streamfunction (MOC). The SST pattern (Fig. 4a) of the leading mode, EOF1mod, accounting for 53.6% of the joint variance is similar to that of EOF1 (Fig. 2b), with major warming over most of the Atlantic and a warming hole in the subpolar NA south of Greenland. Variances in SST explained by EOF1mod are generally large, exhibiting a maximum in the subtropical NA with values in localized regions exceeding 90%, and a minimum south of Greenland with values of less than 10% (Fig. 4a). The MOC pattern of EOF1mod exhibits negative loadings north of the Equator, indicating weaker overturning, that are centred near 40° N in the depth range of 1,000–2,000 m (Fig. 4b). Positive loadings are observed south of the Equator. With regard to MOC, the explained variances are largest in the centre of the negative loadings, where they amount to about 80% (Fig. 4b). Regions of large explained variances indicate a high model consistency.
In climate models, MOC anomalies linked to changes in North Atlantic Deep Water formation first appear in the subpolar NA and then propagate southward74. Fully developed AMOC slowing is characterized by basin-wide negative streamfunction anomalies in the models13. EOF1mod is thus consistent with the initial stage of AMOC slowing. Because EOF1mod is the leading mode of the externally forced variability in SST and MOC in the models, PC1mod (Fig. 4c) is expected to be significantly correlated with the ERF. The correlation coefficient between the two time series amounts to 0.79. PC1mod is governed by multidecadal variability until about 1980, and it exhibits a sustained upward trend thereafter. Since 1980, there is a reduction in the AMOC transport at 30° N that amounts to about 1 Sv (106 m3 s−1), which is assumed to be within the range of natural variability. This is supported by the distribution of non-overlapping 40-year MOC trends calculated from the preindustrial control integrations of the CMIP6 models (Fig. 4d), providing estimates of the internal MOC variability. The ensemble-mean reduction in ocean heat transport at 50° N is about 0.03 PW. We compare the AMOC index at 26.5° N reconstructed from EOF1mod with the RAPID data75 (Extended Data Fig. 1f). The 2004–2014 trend from RAPID amounts to −4.1 Sv and that from the reconstruction to −0.5 Sv. When assuming that the CMIP6-ensemble mean realistically represents the externally forced MOC variability, the trend from RAPID must contain a strong contribution from long-term internal variability. This is consistent with Weijer et al.11, who analysed another subset of the CMIP6 models, reporting that linear trends over the entire 1850–2014 historical period are generally neutral.
Observed SSTs and a large ensemble of historical simulations with state-of-the-art climate models suggest the prevalence of internal AMOC variability since the beginning of the twentieth century. Observations and individual model runs show comparable SST variability in the NAWH region. However, the models’ ensemble-mean signal is much smaller, indicative of the prevalence of internal variability. Further, most of the SST cooling in the subpolar NA, which has been attributed to anthropogenic AMOC slowing21, occurred during 1930–1970, when the radiative forcing did not exhibit a major upward trend. We conclude that the anthropogenic signal in the AMOC cannot be reliably estimated from observed SST. A linear and direct relationship between radiative forcing and AMOC may not exist. Further, the relevant physical processes could be shared across EOF modes, or a mode could represent more than one process.
A relatively stable AMOC and associated northward heat transport during the past decades is also supported by ocean syntheses combining ocean general circulation models and data76,77, hindcasts with ocean general circulation models forced by observed atmospheric boundary conditions78 and instrumental measurements of key AMOC components9,22,79,80,81. Neither of these datasets suggest major AMOC slowing since 1980, and neither of the AMOC indices from Rahmstorf et al.20 or Caesar et al.21 show an overall AMOC decline since 1980.
An important remaining issue is the question of how well the externally forced part of the Atlantic SSTs can be determined. Standard EOF analysis has been used above to identify the forced component. We additionally used two other methods. First, we applied principal oscillation pattern (POP) analysis (Methods) to the observed SSTs, again only over the Atlantic Ocean. The results of the POP analysis (Extended Data Fig. 5) are similar to those of the EOF analysis (Fig. 2), in that the time series of the leading POP mode (POP1) is highly correlated with the ERF (r = 0.85; Extended Data Fig. 5a) and POP1 (Extended Data Fig. 5b) is similar to EOF1 (Fig. 2b). Second, we derived the externally forced component from the historical simulations with the CMIP6 models by applying signal-to-noise ratio (S/N) maximizing EOF analysis (Methods) to the modelled SSTs over the global ocean (Extended Data Fig. 6). As expected, the time series of the leading mode (S/N EOF1) is highly correlated with the ERF (r = 0.9, Extended Data Fig. 6a). The associated regression pattern (Extended Data Fig. 6b) is similar to that linked to both EOF1 (Fig. 2b) and POP1 (Extended Data Fig. 5b), indicating little sensitivity to the choice of the statistical method in determining the externally forced SST variability.
We subtracted the variability associated with S/N-EOF1 from the observed SSTs to estimate the internal SST variability, and standard EOF analysis was applied to the residuals over the Atlantic. The second most energetic EOF mode (EOF2res; Extended Data Fig. 7), accounting for 19.2% of the variability in the residuals, is multidecadal (Extended Data Fig. 7a). EOF2res is the interhemispheric dipole, with explained variances up to 40% in the NAWH and in the centre of the southern pole (Extended Data Fig. 7b). EOF2res is similar to EOF2 (Fig. 3), which is reassuring and supports the notion that EOF2 actually reflects internal SST variability.
The leading EOF of the residuals over the Atlantic (EOF1res; Extended Data Fig. 8) accounts for 27.1% of the variance. Its time series (PC1res) is characterized by a marked centennial change with a minimum in the 1940s (Extended Data Fig. 8a). Over the Atlantic, the regression coefficients upon PC1res are largest north of the Equator (Extended Data Fig. 8b) and reminiscent of the “horseshoe” pattern linked to the positive phase of the NAO. PC1res and the annual-mean NAO index exhibit a statistically significant correlation of 0.4.
A number of factors, internal and external, can influence the AMOC. Menary et al.38 report that the multi-model mean AMOC strengthened by approximately 10% from 1850 to 1985 in historical simulations with CMIP5 and CMIP6 models, which has been attributed to aerosol forcing. This is consistent with our study in that the AMOC slowing largely takes place after 1980. Analysis of single-forcing experiments by Menary et al.38 reveal that the forced response of the AMOC is a balance of opposing contributions from aerosols, increasing AMOC, and GHGs, decreasing AMOC. Besides surface heat flux, surface freshwater input from Greenland ice melt is thought to be an important factor causing anthropogenic AMOC slowing. Meltwater forcing is not considered in the historical simulations with the CMIP6 models, which may cause underestimation of AMOC slowing. A high-resolution ocean general circulation model study finds that the present meltwater forcing from the west Greenland Ice Sheet is not sufficiently large to drive significant reductions in North Atlantic Deep Water formation and thus AMOC strength82. Climate model sensitivity to external forcing, however, is a long-standing issue, also with regard to the AMOC’s sensitivity to freshwater forcing83. In summary, our results reinforce the need for systematic and sustained in-situ AMOC observation systems to detect with high confidence externally forced AMOC slowing9,84,75.
Observed SST during 1900–2019 with 2° × 2° resolution is used here and obtained from Extended Reconstructed Sea Surface Temperature Version 5 (ERSSTv.5; ref. 85). SSTs from Kaplan Extended SST v.2 during 1900–2019 with 5° × 5° resolution are also analysed86. We use the station-based NAO index from 1900 to 2019 retrieved from https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-station-based. This NAO index is based on the difference in normalized Northern Hemisphere winter (December–March) sea-level pressure between Lisbon, Portugal and Stykkisholmur/Reykjavik, Iceland. The annual-mean NAO index is also used.
Mixed-layer depth climatology with 2° × 2° resolution is from de Boyer Montégut et al.87 and based on a density threshold of 0.03 kg m−3.
Annual satellite sea-level data for 1993–2019 are from Copernicus (https://cds.climate.copernicus.eu). In the EOF analysis, the Sea Level Anomaly product was used, which is defined relative to the mean of 1993–2012 (ref. 88).
Net ERF (expressed in W m−2) is the globally averaged net downward radiative flux at the top of the atmosphere after allowing for atmospheric temperature, water vapour and clouds to adjust but with surface temperature or a portion of surface conditions unchanged1. The ERF data are from Smith et al.89, where they include 14 components: CO2, CH4, N2O, other well-mixed GHGs (halogenated compounds), tropospheric O3, stratospheric O3, stratospheric H2O, aviation contrails and contrail cirrus, aerosol–radiation interactions, aerosol–cloud interactions, black carbon on snow, land-use change, volcanoes and solar.
In this study, four combinations of the SSPs (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5)90 are used. In Fig. 1d, the combination of the historical ERF due to the CO2 concentration (1900–2014) and SSP5-8.5 (2015–2019) is shown. The other SSPs are shown in Fig. 2a.
An ensemble of 35 historical simulations for 1900–2014 from CMIP6 is used in the calculation of SST indices and S/N-maximizing EOF. The models are: ACCESS-CM2, ACCESS-Earth System Model (ESM) v.1.5, Canadian ESM v.5, CESM2, CESM2-WACCM, CESM2-WACCM-FV2, CIESM, CMCC-CM2-HR4, E3SM-1-0, E3SM-1-1, E3SM-1-1-ECA, FGOALS-f3-L, FGOALS-g3, INM-CM4-8, INM-CM5-0, MIROC6, MPI-ESM-1-2-HAM, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorCPM1, NorESM2-LM, NorESM2-MM, SAM0-UNICON, BCC-CSM2-MR, BCC-ESM1, CAMS-CSM1-0, CAS-ESM2-0, CMCC-CM2-SR5, FIO-ESM2-0, GISS-E2-1G, GISS-E2-1H, IPSL-CM6A-LR and MCM-UA-1-0. Only one ensemble member from each model is used. SST and MOC fields from the first 24 models are used to calculate the combined EOF in Fig. 4. SST and MOC fields are linearly interpolated to 1° × 1° resolution.
EOF analysis58 is a multivariate statistical method. The EOFs are the eigenvectors of the covariance matrix, and they are sorted in descending order by the explained variance. Time series, termed principal components (PCs), are obtained by projecting the original data onto the EOFs. In the joint EOF analysis of the ensemble-mean Atlantic SST and MOC, each variable was normalized by its own field sum of standard deviations, making each field have identical total variance.
POP analysis is a multivariate statistical method and is defined as the normal modes of a linear dynamical representation of the data in terms of a first‐order autoregressive-vector process with residual noise forcing91,92,93. For practical purposes, the original process is usually reduced into the subspace of leading EOFs. Here we use the first 10 EOFs accounting for 90.8% of the total variance.
We show global maps of local linear regression coefficients of SST upon the principal components (PCs) that have been normalized by their respective standard deviation. An F test is used to assess the significance of the regression coefficients.
S/N-maximizing EOF analysis refers to a method of identifying the fingerprints of external forcing in an ensemble of forced climate model experiments. The method allows us to distinguish between the response to prescribed external forcing common to all ensemble members and internal variability, which is temporally uncorrelated between ensemble members. We follow the formulation of Venzke et al.94. The leading EOF has the largest S/N, and the corresponding PC represents the time evolution of the most dominant forced response.
Significance of correlations
The Student’s t-test and Monte Carlo simulation based on nonparametric random phase are applied to assess the statistical significance of the correlation coefficients95. All correlation coefficients mentioned in the main text are statistically significant at the 95% confidence level.
All the datasets used in this study are publicly available: ERSSTv.5 data at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html; Kaplan Extended SST v.2 data at https://www.psl.noaa.gov/data/gridded/data.kaplan_sst.html; the station-based NAO index at https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-station-based; mixed-layer depth climatology data at http://www.ifremer.fr/cerweb/deboyer/mld/home.php; satellite sea-level data at https://cds.climate.copernicus.eu; ERF data at https://doi.org/10.5281/zenodo.4624765; and all the CMIP6 model data at https://esgf-node.llnl.gov/projects/cmip6/.
The figures were generated using MATLAB and m_map (https://www.eoas.ubc.ca/~rich/map.html), where the basemap data (GSHHG, https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html) are used under the GNU Lesser General Public license. All the codes used in the data processing and visualization are available via Figshare at https://doi.org/10.6084/m9.figshare.19318004. Other code is available upon request to the corresponding author J.S.
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J.S. was supported by a PhD scholarship funded jointly by the China Scholarship Council (CSC, no. 201606330057). M.L. was supported by the Roadmap Project funded by the German Ministry of Education and Research (BMBF) (01LP2002C). M.L. and M.V. were supported by the project RACE, funded by the German Ministry of Education and Research (BMBF) (03F0824C). We acknowledge the World Climate Research Program’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model outputs. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This study has been conducted using EU Copernicus Marine Service information.
Open access funding provided by GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel.
The authors declare no competing interests.
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Extended Data Fig. 1 a) Map of the linear trends 1900–2019 of observed sea surface temperature (SST, °C/120 years). b) Mean mixed layer depth (MLD, m) from observations and averaged over 1961–2008. The criterion for defining MLD is based on a fixed threshold of 0.03 kg/m3. c) Leading EOF mode of sea-level variability (EOF1SL, m), calculated over the period 1993–2019. EOF1SL accounts for 36.3% of the total sea-level variance. d) The corresponding principal component time series (PC1SL) of EOF1. e) As c), but with the global average removed (dynamic sea level linked to EOF1SL). The globally averaged trend in annual-mean sea level during 1993–2019 amounts to 2.86 mm/year. f) Time series of EOF1SL-related dynamic sea level (m, blue curve) averaged over the box southeast of Greenland (40°W-17°W, 53°N-61°N), which is shown in e). Superimposed are the overturning (Sv) data 2004-2018 from the RAPID array at 26.5°N (orange curve) and the AMOC index 1993-2014 at 26.5 °N (green curve) that has been reconstructed from the joint (SST, MOC) EOF1mod calculated from the historical simulations with the CMIP6 models (Fig. 4).
Extended Data Fig. 2 PC2 (thin blue curve) of EOF2 and the winter-NAO index (December to February, station-based, thin orange curve). Dashed thick curves indicate the low-pass (LW) filtered indices, PC2 (blue lines), winter-NAO index (orange lines).
Extended Data Fig. 3 Cross-correlation function as a function of the time lag coefficient between PC2 and winter-NAO index. Filled dots indicate significance at the 95% confidence level.
Extended Data Fig. 4 Probability distribution of non-overlapping 40-year SST trends in the regions of the three SST indices (with an interval of 0.5 °C/40 years), derived from the control integrations of the CMIP6 models. Vertical black solid line indicates the 40-year trend from ERSST (1975–2014), vertical black dashed line indicates Kaplan SST (1975–2014), vertical black dashed-dotted line indicates ensemble mean of the historical CMIP6 models (1975–2014).
Extended Data Fig. 5 Leading (most energetic) Principal Oscillation Pattern mode (POP1), accounting for 44.3% of the total Atlantic SST variability. a) Standardized POP coefficient time series of POP1 (PC1, blue) and the net effective radiative forcing (ERF; W/m2, orange) with different scenarios after 2014 (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). PC1 and ERF with SSP5-8.5 are correlated at 0.85, which is significant at the 95% confidence level. b) POP1 pattern shown by the local regression coefficients (°C) of the observed SSTs (ERSST_v5) upon PC1. The contours denote the explained variance, the contour interval is 0.2, and the dots indicate significance at the 95% level.
Extended Data Fig. 6 Leading signal-to-noise ratio maximizing Empirical Orthogonal Function (S/N-EOF1) calculated from the historical simulations (1850 to 2014) with the CMIP6 models. a) The principal component time series (S/N-PC1, blue) of S/N-EOF1 and the standardized historical ERF (orange, with SSP5-8.5 pathway at the end). The correlation coefficient between the PC1 and ERF is 0.9, which is significant at 95% confidence level. b) Map of local regression coefficients (°C) of the observed SSTs (ERSST_v5) upon S/N-PC1. The contours denote the explained variance, the contour interval is 0.2, and the dots indicate significance at the 95% level.
Extended Data Fig. 7 Second most energetic EOF mode (EOF2res) after removing the leading signal-to-noise ratio maximizing Empirical Orthogonal Function (S/N-EOF1, Extended Data Fig. 6) calculated from the historical simulations with the CMIP6 models. a) The corresponding principal component time series (PC2res). b) Map of local regression coefficients (°C) of the observed SSTs (ERSST_v5) upon PC2res. The contours denote the explained variance, the contour interval is 0.2, and the dots indicate significance at the 95% level.
Extended Data Fig. 8 Leading EOF mode (EOF1res) after removing the leading signal-to-noise ratio maximizing Empirical Orthogonal Function (S/N-EOF1 (Extended Data Fig. 6) calculated from the historical simulations with the CMIP6 models. a) The corresponding principal component time series (PC1res). b) Map of local regression coefficients (°C) of the observed SSTs (ERSST_v5) upon PC1res. The contours denote the explained variance, the contour interval is 0.2, and the dots indicate significance at the 95% level.
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Latif, M., Sun, J., Visbeck, M. et al. Natural variability has dominated Atlantic Meridional Overturning Circulation since 1900. Nat. Clim. Chang. 12, 455–460 (2022). https://doi.org/10.1038/s41558-022-01342-4
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