Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Atlantic and Pacific tropics connected by mutually interactive decadal-timescale processes

Nature Geoscience volume 14, pages 36–42 (2021)Cite this article

Subjects

Abstract

Decadal climate prediction presumes there are decadal-timescale processes and mechanisms that, if initialized properly in models, potentially provide predictive skill more than one or two years into the future. Candidate mechanisms involve Pacific decadal variability and Atlantic multidecadal variability, elements of which involve slow fluctuations of tropical Pacific and Atlantic sea surface temperatures (SSTs) from positive anomalies (positive phase) to negative anomalies (negative phase). Here we use model experiments to show that there tends to be a weak opposite-sign SST response in the tropical Pacific when observed SSTs are specified in the Atlantic, while there is a weak same-sign SST response in the tropical Atlantic when observed SSTs are specified in the tropical Pacific. Net surface heat flux in the Atlantic and ocean dynamics in the Pacific play contrasting roles in the ocean response to specified SSTs in the respective basins. We propose that processes in the Pacific and Atlantic are sequentially interactive through the atmospheric Walker circulation along with contributions from midlatitude teleconnections for the Atlantic response to the Pacific.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Mechanisms of Atlantic–Pacific connections.
Fig. 2: Atlantic–Pacific two-way leads/lags.
Fig. 3: Decadal Atlantic–Pacific interactions.
Fig. 4: Positive PDV and AMV trending positive to negative.
Fig. 5: Positive AMV and PDV trending positive to negative.

Data availability

HadISST data are available from https://www.metoffice.gov.uk/hadobs/hadisst/. The ERA-I data are available from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim. ERSSTv4 data are available from https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v4.

Code availability

Previous and current CESM versions are freely available at www.cesm.ucar.edu:/models/cesm2/. The CESM solutions/datasets used in this study are also freely available from the NCAR Digital Asset Services Hub (DASH) at data.ucar.edu or from the links provided from the CESM website at www.cesm.ucar.edu.

References

  1. McGregor, S. et al. Recent Walker circulation strengthening and Pacific cooling amplified by Atlantic warming. Nat. Clim. Change https://doi.org/10.1038/NCLIMATE2330 (2014).

  2. McGregor, S. et al. Model tropical Atlantic biases underpin diminished Pacific decadal variability. Nat. Clim. Change https://doi.org/10.1038/s41558-018-0163-4 (2018).

  3. Ruprich-Robert, Y. et al. Assessing the climate impacts of the observed Atlantic multidecadal variability using the GFDL CM2.1 and NCAR CESM1 global coupled models. J. Clim. 30, 2785–2810 (2017).

    Article  Google Scholar 

  4. Levine, A. F. Z., McPhaden, M. J. & Frierson, D. M. W. The impact of the AMV on multidecadal ENSO variability. Geophys. Res. Lett. 44, 3877–3886 (2017).

    Article  Google Scholar 

  5. Geng, X., Zhang, W. & Jin, F.-F. Modulation of the relationship between ENSO and its combination mode by the Atlantic multidecdal oscillation. J. Clim. 33, 4679–4695 (2020).

    Article  Google Scholar 

  6. Yang, Y.-M., An, S.-I., Wang, B. & Park, J. H. A global-scale multidecadal variability driven by Atlantic multidecadal oscillation. Natl Sci. Rev. 7, 1190–1197 (2020).

    Article  Google Scholar 

  7. Kumar, A., Bhaskar, J. & Wang, H. Attribution of SST variability in global oceans and the role of ENSO. Clim. Dyn. 43, 209–220 (2014).

    Article  Google Scholar 

  8. Taschetto, A. S., Rodrigues, R. R., Meehl, G. A., McGregor, S. & England, M. H. How sensitive are the Pacific–North Atlantic teleconnections to the position and intensity of El Niño-related warming. Clim. Dyn. https://doi.org/10.1007/s00382-015-2679-x (2015).

  9. Meehl, G. A., Hu, A., Santer, B. D. & Xie, S.-P. Contribution of the Interdecadal Pacific Oscillation to twentieth-century global surface temperature trends. Nat. Clim. Change https://doi.org/10.1038/nclimate3107 (2016).

  10. Li, X., Xie, S.-P., Gille, S. T. & Yoo, C. Atlantic-induced pan-tropical climate change over the past three decades. Nat. Clim. Change https://doi.org/10.1038/NCLIMATE2840 (2015).

  11. Cai, W. et al. Pantropical climate interactions. Science 363, eaav4236 (2019).

    Article  Google Scholar 

  12. Elsbury, D. et al. The atmospheric response to positive IPV, positive AMV, and their combination in boreal winter. J. Clim. 32, 4193–4213 (2019).

    Article  Google Scholar 

  13. Nigam, S., Sengupta, A. & Ruiz-Barradas, A. Atlantic–Pacific links in observed multidecadal SST variability: is the Atlantic multidecadal oscillation’s phase reversal orchestrated by the Pacific decadal oscillation? J. Clim. 33, 5479–5505 (2020).

    Article  Google Scholar 

  14. Meehl, G. A., Chung, C. T. Y., Arblaster, J. M., Holland, M. M. & Bitz, C. M. Tropical decadal variability and the rate of Arctic sea ice retreat. Geophys. Res. Lett. https://doi.org/10.1029/2018GL079989 (2018).

  15. Meehl, G. A., Arblaster, J. M., Bitz, C., Chung, C. T. Y. & Teng, H. Antarctic sea ice expansion between 2000–2014 driven by tropical Pacific decadal climate variability. Nat. Geosci. https://doi.org/10.1038/NGEO2751 (2016).

  16. Meehl, G. A. et al. Sustained ocean changes contributed to sudden Antarctic sea ice retreat in late 2016. Nat. Commun. 10, 14 (2019).

    Article  Google Scholar 

  17. Booth, B. B. B., Dunstone, N. J., Halloran, P. R., Andrews, T. & Bellouin, N. Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature 484, 228–232 (2012).

    Article  Google Scholar 

  18. Smith, D. M. et al. Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown. Nat. Clim. Change https://doi.org/10.1038/NCLIMATE3058 (2016).

  19. Watanabe, M. & Tatebe, H. Reconciling roles of sulphate aerosol forcing and internal variability in Atlantic multidecadal climate changes. Clim. Dyn. 53, 4651–4665 (2019).

    Article  Google Scholar 

  20. Cassou, C. et al. Decadal climate variability and predictability. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/BAMS-D-16-0286.1 (2018).

  21. Kay, J. E. et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).

    Article  Google Scholar 

  22. Ting, M., Kushnir, Y., Seager, R. & Li, C. Forced and internal twentieth-century SST trends in the North Atlantic. J. Clim. 22, 1469–1481 (2009).

    Article  Google Scholar 

  23. Smith, T. M., Reynolds, R. W., Peterson, T. C. & Lawrimore, J. Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J. Clim. 21, 2283–2296 (2008).

    Article  Google Scholar 

  24. Kosaka, Y. & Xie, S.-P. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501, 403–407 (2013).

    Article  Google Scholar 

  25. Schneider, D. P. & Deser, C. Tropically driven and externally forced patterns of Antarctic sea ice change: reconciling observed and modeled trends. Clim. Dyn. https://doi.org/10.1007/s00382-017-3893-5 (2017).

  26. Trenberth, K. E. & Shea, D. R. Atlantic hurricanes and natural variability in 2005. Geophys. Res. Lett. https://doi.org/10.1029/2006GL026894 (2006).

Download references

Acknowledgements

Portions of this study were supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the US Department of Energy’s Office of Biological & Environmental Research (BER) via National Science Foundation IA 1947282. This work also was supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation (NSF) under Cooperative Agreement No. 1852977. F.C. was supported by the NSF under the Collaborative Research EaSM2 Grant OCE-1243015 and EaSM3 Grant OCE-1419569 and by the NOAA Climate Program Office under the Climate Variability and Predictability Program Grant NA13OAR4310138. J.M.A., M.H.E. and S.M. were supported by the Australian Research Council through grant CE110001028, while S.M. was also supported by the Australian Research Council through grant FT160100162. Computing resources (https://doi.org/10.5065/D6RX99HX) were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies.

Author information

Authors and Affiliations

  1. National Center for Atmospheric Research, Boulder, CO, USA

    Gerald A. Meehl, Aixue Hu, Frederic Castruccio, Susan C. Bates, Gokhan Danabasoglu, Julie M. Arblaster & Nan Rosenbloom

  2. Climate Change Research Centre (CCRC), University of New South Wales, Sydney, New South Wales, Australia

    Matthew H. England

  3. ARC Centre of Excellence for Climate Extremes, and School of Earth, Atmosphere and Environment, Monash University, Clayton, Victoria, Australia

    Shayne McGregor & Julie M. Arblaster

  4. Scripps Institution of Oceanography, La Jolla, CA, USA

    Shang-Ping Xie

Authors
  1. Gerald A. Meehl
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aixue Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frederic Castruccio
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthew H. England
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Susan C. Bates
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gokhan Danabasoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shayne McGregor
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Julie M. Arblaster
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shang-Ping Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Nan Rosenbloom
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

G.A.M. directed this work with contributions from all authors. G.A.M., A.H. and F.C. performed the analyses. All of the authors discussed the results and contributed to writing the manuscript.

Corresponding author

Correspondence to Gerald A. Meehl.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editor: James Super. Nature Geoscience thanks Mingfang Ting, Yu Kosaka and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–10 and Discussion

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meehl, G.A., Hu, A., Castruccio, F. et al. Atlantic and Pacific tropics connected by mutually interactive decadal-timescale processes. Nat. Geosci. 14, 36–42 (2021). https://doi.org/10.1038/s41561-020-00669-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-020-00669-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing