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Intensified Atlantic multidecadal variability in a warming climate

Nature Climate Change volume 15, pages 293–300 (2025)Cite this article

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Abstract

The Atlantic multidecadal variability (AMV) is a basin-scale mode of sea surface temperature (SST) variability in the North Atlantic, exerting a global impact, including contribution to the multidecadal Sahel drought and subsequent recovery and the post-1998 global warming hiatus. How greenhouse warming affects AMV remains unclear. Here, using models with multicentury-long outputs of future climate, we find an intensified AMV under greenhouse warming. Surface warming and freshwater input from sea-ice melt increase surface buoyancy, leading to a slowdown of the Atlantic meridional overturning circulation (AMOC). Reduced vertical mixing associated with suppressed oceanic deep convection results in a thinned mixed layer and its variability, favouring stronger AMV SST variability. Further, a weakened AMOC and the associated northward heat transport prolong the lifespan of the AMV, providing a long time for the AMV to grow. Thus, multidecadal global surface fluctuations and the associated climate extremes are likely to be more intense.

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Fig. 1: Projected increase in AMV variability under global warming.
Fig. 2: Shallowed climatological mixed layer conducive to increase in the variability of AMV.
Fig. 3: Arctic sea-ice loss weakens the AMOC and reduces MLD in a warming climate.
Fig. 4: Reduced MLD variability further intensifies AMV.
Fig. 5: Prolonged advection time of meridional OHT favours intensified AMV.

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Data availability

Data related to the paper can be downloaded from the following: HadISST v.1.1, https://www.metoffice.gov.uk/hadobs/hadisst/; ERSST v.5, https://www.ncei.noaa.gov/products/extended-reconstructed-sst; and CMIP6 database, https://aims2.llnl.gov/search/cmip6/.

Code availability

The code for climate model data processing and analyses is available via Zenodo at https://doi.org/10.5281/zenodo.14286522 (ref. 45).

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Acknowledgements

S.L. is supported by the National Natural Science Foundation of China (NSFC) projects (42376198 and 92058203), the Science and Technology Innovation Project of Laoshan Laboratory (LSKJ202202602) and the Youth Talent Programme of Ocean University of China. T.G. is supported by Taishan Scholars Program. B.G. is supported by the National Key Research and Development Program of China (2023YFF0805103). Y.Y. is supported by the NSFC project (42322601) and the National Key Research and Development Program of China (2023YFF0805100). We acknowledge the WCRP Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output.

Author information

Authors and Affiliations

  1. Key Laboratory of Physical Oceanography/Frontier Science Centre for Deep Ocean Multispheres and Earth system, Ocean University of China, Qingdao, China

    Shujun Li, Lixin Wu, Wenju Cai, Bolan Gan & Zhao Jing

  2. Institute of Marine Development, Ocean University of China, Qingdao, China

    Shujun Li

  3. Laoshan Laboratory, Qingdao, China

    Shujun Li, Lixin Wu, Tao Geng, Wenju Cai, Bolan Gan & Zhao Jing

  4. Academy of Future Ocean, Ocean University of China, Qingdao, China

    Yiting Wang

  5. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, China

    Wenju Cai

  6. State Key Laboratory of Marine Environmental Science & College of Ocean and Earth Sciences, Xiamen University, Xiamen, China

    Wenju Cai

  7. Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Yun Yang

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Contributions

S.L. and L.W. conceived the study and wrote the initial manuscript in discussion with W.C. and T.G. S.L. and Y.W. performed model analysis and generated final figures. S.L., L.W., W.C., T.G., B.G., Z.J. and Y.Y. contributed to interpreting results, discussion of the associated dynamics and improvement of this paper.

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Correspondence to Shujun Li.

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Extended data

Extended Data Fig. 1 Characteristics of AMV in observations and CMIP6 historical simulations.

a, Observed SST pattern associated with AMV (unit: °C), which is derived from regressing SST anomalies onto the observed AMV index during the period of 1870-2014. Shown is the mean result of two reanalysis datasets (that is HadISST v1.1 and ERSST.v5). b, Same as a, but for the multimodel ensemble mean result of the CMIP6 historical outputs. c, Time evolution of the AMV indices in observations (black line for HadISST v1.1 and grey line for ERSST.v5) and CMIP6 multimodels (colored lines). The AMV index is biquadratically detrended and filtered by 21-yr running mean. Models reasonably simulate the observed AMV.

Extended Data Fig. 2 Life cycle of the AMV and associated fields in CMIP6 models under PiControl scenario.

The regression patterns of a, SST (shading; unit: °C) and net surface heat flux (Qnet, contours; unit: W/m2; positive in red and negative in blue; positive downward into the ocean), b, mixed layer depth (MLD; unit: m), c, Atlantic meridional overturning stream function (AMOC; unit: Sv) and d, boreal winter sea ice concentration (SIC; unit: %) anomalies onto the normalized AMV index at different lags in CMIP6 PiControl simulations. Shown are the multimodel ensemble mean results. The AMV is predominantly sustained by the AMOC related oceanic heat transport, while the Qnet plays a damping role.

Extended Data Fig. 3 Spectral analysis of unfiltered AMV index in PiControl and under SSP585 (or RCP85) scenario in CMIP6 (or CMIP5) models.

The power spectrum in PiControl experiments is signified by blue solid line, and the power spectrum under a SSP585 or RCP85 scenario is signified by red solid line. The 90% confidence level is signified by dashed line based on a red noise null hypothesis. The upper (lower) panel show the 7 models from CMIP6 (CMIP5) dataset. The AMV in most models shows increased multidecadal variability and a prolonged periodicity under global warming.

Extended Data Fig. 4 The climatological mean of the mixed layer depth (MLD), net surface heat flux (Qnet) and boreal wintertime sea ice concentration (SIC).

a, The climatological pattern of the annual mean MLD under the PiControl, SSP126 and SSP585 scenario. Unit is m. Shown is the multimodel ensemble mean result. b, Same as a, but for the Qnet. Unit is W/m2. Positive values denote heat downward into the ocean. c, Same as a, but for the boreal wintertime (DJF) Arctic sea ice concentration. Unit is percentage (%). Projected shallowed MLD in the subpolar North Atlantic is key to increased AMV under global warming, despite damping from the Qnet, and melt of Arctic sea ice.

Extended Data Fig. 5 Grid-point standard deviation in the MLD decadal variability in PiControl and SSP585 scenarios.

a, Decadal variability of MLD at each grid point is derived by removing the biquardratic trend and smoothed by 21-year running mean. Unit is m. Shown is the MMEM result. b, Same as a, but for the SSP585 scenario. Models simulate decreased MLD decadal variability in a warming climate.

Extended Data Fig. 6 Atlantic meridional ocean heat transport (OHT) on average under PiControl, SSP126 and SSP585 scenario.

a, The OHT under PiControl scenario. Unit is PW (1 PW = 1015 W). b, Same as a, but for the SSP126 scenario. c, Same as a, but for the SSP585 scenario. The Atlantic OHT is northward in both hemispheres and across the equator due to the AMOC, with a maximum OHT value located around 20°N. The OHT decreases in response to global warming.

Extended Data Fig. 7 An accumulative effect of the prolonged advection for the intensified AMV.

a, Inter-model relationship between the standard deviation changes in Atlantic OHT variability (unit: W) at 46°N and change in the AMV. Both are scaled by the climate sensitivity in respective model. Correlation coefficient and P value are also indicated based on a two-tailed Student’s t-test. b, Inter-model relationship between changes in the standard deviation of the time integral of the Atlantic OHT (unit: W) at 46°N and changes in the AMV. Both are scaled by the climate sensitivity in respective model. Correlation coefficient and P value are also indicated based on a two-tailed Student’s t-test. A greater increase in variability of the time integral of Atlantic OHT favors an increase in variability of the AMV.

Extended Data Fig. 8 Example of OHT decadal variability and the integral of OHT in each phase.

We take the MRI-ESM2-0 model as an example. To consider the effect of meridional advection time length in each AMV cycle, we integrate the OHT over the time of a positive or of a negative phase, respectively. The approach yields a new index of the integral of each phase.

Extended Data Fig. 9 Oceanic heat budget analysis based on the CMIP6 PiControl simulations for the upper 500 m ocean in the subpolar North Atlantic.

Terms shown are the seven models averaged regression coefficients of eight controlling factors onto the temperature tendency terms (\(\frac{\partial T{\prime} }{\partial t}\)) to estimate individual contributions based on Eq. (1). \(\bar{U}\frac{\partial }{\partial x}{T}^{{\prime} }\) and \({u}^{{\prime} }\frac{\partial }{\partial x}\bar{T}\) denote the temperature anomalies carried by the mean and anomalous zonal advection; \(\bar{V}\frac{\partial }{\partial y}{T}^{{\prime} }\) and \({v}^{{\prime} }\frac{\partial }{\partial y}\bar{T}\) denote the temperature anomalies carried by the mean and anomalous meridional advection; \(\bar{W}\frac{\partial }{\partial z}{T}^{{\prime} }\) and \({w}^{{\prime} }\frac{\partial }{\partial z}\bar{T}\) denote the temperature anomalies carried by the mean and anomalous vertical advection; \(\frac{{Q}^{{\prime} }}{{\rho }_{0}{C}_{P}H}\) is atmospheric heating/cooling from net heat flux (positive downward), in which \({\rho }_{0}\) is the density of seawater taken as 1025 kg m−3, \({C}_{P}\) the heat capacity of seawater taken as 4200 J kg−1 K−1, and H the ocean upper layer depth taken as 500 m; and Res is residual which includes mixing and diffusive terms. Error bar of each term shows one standard deviation value of 7 models in PiControl experiment. Temperature tendency is largely balanced between heat input from mean meridional heat advection and damping by air-sea net heat flux.

Extended Data Fig. 10 Changes in the AMV-related Q’ under global warming.

We show the changes in the regression patterns of Qnet anomalies onto the normalized AMV index between PiControl and SSP585, with an area-averaged increase of 0.43 W/m2. Shown is the MMEM result. AMV-related Q’ is projected to increase under global warming.

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Supplementary Tables 1 and 2 and Figs. 1–4.

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Li, S., Wu, L., Wang, Y. et al. Intensified Atlantic multidecadal variability in a warming climate. Nat. Clim. Chang. 15, 293–300 (2025). https://doi.org/10.1038/s41558-025-02252-x

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