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.

  • Article
  • Published:

Increase in MJO predictability under global warming

Nature Climate Change volume 14, pages 68–74 (2024)Cite this article

Subjects

Abstract

The Madden–Julian Oscillation (MJO) is a dominant source of subseasonal atmospheric variability in the tropics and significantly impacts global weather and climate predictability. Changes in its activity and predictability due to human-induced global climate change have profound implications for future global weather prediction. Here we investigate changes in MJO predictability in reanalysis and climate model data and find that MJO predictability has increased over the past century. This increase can be attributed to anthropogenic warming and continues during the twenty-first century in projections. The increased predictability is accompanied by stronger MJO amplitude, more regular oscillation patterns and organized eastward propagation under global warming. Our results suggest that greenhouse warming will increase the predictability of the MJO, with far-reaching consequences for global weather prediction.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Time series of the MJO predictability during the past century based on the ensemble forecast method.
Fig. 2: Time series of the MJO predictability during the past century based on the WPE method.
Fig. 3: A comparison of the MJO predictability change between the historical runs and the control run.
Fig. 4: A comparison of the MJO predictability change between the ssp585 runs and the control run.
Fig. 5: Time series of the weighted occurrence frequency Pwi of MJO patterns in ssp585 runs.

Similar content being viewed by others

Data availability

CERA-20C reanalysis is provided by ECMWF on a data portal (https://apps.ecmwf.int/datasets/data/cera20c/). CESM2 and CESM2–WACCM control run, historical simulations and future projections are accessible through the Coupled Model Intercomparison Project (Phase 6) search interface (https://esgf-node.llnl.gov/search/cmip6/). CSF-20C hindcast ensemble data62 are available via https://catalogue.ceda.ac.uk/uuid/6e1c3df49f644a0f812818080bed5e45 under the Open Government License v.3.0. The MJO RMMI time series we computed in this study has been posted for public access63. Source data are provided with this paper.

Code availability

The Python code used for the WPE computation is available on Zenodo at https://zenodo.org/records/10031057 (ref. 64).

References

  1. Vitart, F., Robertson, A. W. & Anderson, D. L. Subseasonal to seasonal prediction project: bridging the gap between weather and climate. Bull. World Meteorol. Organ. 61, 23 (2012).

    Google Scholar 

  2. Vitart, F. Evolution of ECMWF sub-seasonal forecast skill scores. Q. J. R. Meteorol. Soc. 140, 1889–1899 (2014).

    Article  Google Scholar 

  3. Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).

    Article  Google Scholar 

  4. Trenberth, K. E. et al. Global warming and changes in drought. Nat. Clim. Change 4, 17–22 (2014).

    Article  Google Scholar 

  5. Allamano, P., Claps, P. & Laio, F. Global warming increases flood risk in mountainous areas. Geophys. Res. Lett. 36 L24404 (2009).

  6. Min, S.-K., Zhang, X., Zwiers, F. W. & Hegerl, G. C. Human contribution to more-intense precipitation extremes. Nature 470, 378–381 (2011).

    Article  CAS  Google Scholar 

  7. Pall, P. et al. Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470, 382–385 (2011).

    Article  CAS  Google Scholar 

  8. Stott, P. A., Stone, D. A. & Allen, M. R. Human contribution to the European heatwave of 2003. Nature 432, 610–614 (2004).

    Article  CAS  Google Scholar 

  9. Dosio, A., Mentaschi, L., Fischer, E. M. & Wyser, K. Extreme heat waves under 1.5 °C and 2 °C global warming. Environ. Res. Lett. 13, 054006 (2018).

    Article  Google Scholar 

  10. Madden, R. A. & Julian, P. R. Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci. 28, 702–708 (1971).

    Article  Google Scholar 

  11. Madden, R. A. & Julian, P. R. Description of global-scale circulation cells in the tropics with a 40–50 day period. J. Atmos. Sci. 29, 1109–1123 (1972).

    Article  Google Scholar 

  12. Waliser, D., Lau, K., Stern, W. & Jones, C. Potential predictability of the Madden–Julian oscillation. Bull. Am. Meteorol. Soc. 84, 33–50 (2003).

    Article  Google Scholar 

  13. Kim, H., Vitart, F. & Waliser, D. E. in MJO Prediction: Current Status and Future Challenges 289–299 (World Scientific, 2020).

  14. Zhang, C. Madden–Julian oscillation: bridging weather and climate. Bull. Am. Meteorol. Soc. 94, 1849–1870 (2013).

    Article  Google Scholar 

  15. McPhaden, M. J. Genesis and evolution of the 1997–98 El Niño. Science 283, 950–954 (1999).

    Article  CAS  Google Scholar 

  16. Marshall, A. G., Hendon, H. H., Son, S.-W. & Lim, Y. Impact of the quasi-biennial oscillation on predictability of the Madden–Julian oscillation. Clim. Dyn. 49, 1365–1377 (2017).

    Article  Google Scholar 

  17. Wang, S., Tippett, M. K., Sobel, A. H., Martin, Z. K. & Vitart, F. Impact of the QBO on prediction and predictability of the MJO convection. J.Geophys. Res. Atmos. 124, 11766–11782 (2019).

    Article  Google Scholar 

  18. Vitart, F. & Brown, A. S2s forecasting: towards seamless prediction. WMO Bull. 68, 70–74 (2019).

    Google Scholar 

  19. Bauer, P., Thorpe, A. & Brunet, G. The quiet revolution of numerical weather prediction. Nature 525, 47–55 (2015).

    Article  CAS  Google Scholar 

  20. Jones, C. & Carvalho, L. M. Changes in the activity of the Madden–Julian oscillation during 1958–2004. J. Clim. 19, 6353–6370 (2006).

    Article  Google Scholar 

  21. Subramanian, A. et al. The MJO and global warming: a study in CCSM4. Clim. Dyn. 42, 2019–2031 (2014).

    Article  Google Scholar 

  22. Maloney, E. D., Adames, Á. F. & Bui, H. X. Madden–Julian oscillation changes under anthropogenic warming. Nat. Clim. Change 9, 26–33 (2019).

    Article  Google Scholar 

  23. Roxy, M. et al. Twofold expansion of the Indo-Pacific warm pool warps the MJO life cycle. Nature 575, 647–651 (2019).

    Article  CAS  Google Scholar 

  24. Wheeler, M. C. & Hendon, H. H. An all-season real-time multivariate MJO index: development of an index for monitoring and prediction. Mon. Weather Rev. 132, 1917–1932 (2004).

    Article  Google Scholar 

  25. Kim, H.-M., Webster, P. J., Toma, V. E. & Kim, D. Predictability and prediction skill of the MJO in two operational forecasting systems. J. Clim. 27, 5364–5378 (2014).

    Article  Google Scholar 

  26. Neena, J., Lee, J. Y., Waliser, D., Wang, B. & Jiang, X. Predictability of the Madden–Julian oscillation in the intraseasonal variability hindcast experiment (ISVHE). J. Clim. 27, 4531–4543 (2014).

    Article  Google Scholar 

  27. Liu, X. et al. MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center. Clim. Dyn. 48, 3283–3307 (2017).

    Article  Google Scholar 

  28. Zhu, J., Kumar, A. & Wang, W. Dependence of MJO predictability on convective parameterizations. J. Clim. 33, 4739–4750 (2020).

    Article  Google Scholar 

  29. Zhu, J. & Kumar, A. Role of sea surface salinity feedback in MJO predictability: a study with CFSV2. J. Clim. 32, 5745–5759 (2019).

    Article  Google Scholar 

  30. Rashid, H. A., Hendon, H. H., Wheeler, M. C. & Alves, O. Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system. Clim. Dyn. 36, 649–661 (2011).

    Article  Google Scholar 

  31. Wang, S., Sobel, A. H., Tippett, M. K. & Vitart, F. Prediction and predictability of tropical intraseasonal convection: seasonal dependence and the maritime continent prediction barrier. Clim. Dyn. 52, 6015–6031 (2019).

    Article  Google Scholar 

  32. Kim, H., Richter, J. H. & Martin, Z. Insignificant QBO–MJO prediction skill relationship in the subx and s2s subseasonal reforecasts. J. Geophys. Res. Atmos. 124, 12655–12666 (2019).

    Article  Google Scholar 

  33. Miyakawa, T. et al. Madden–Julian oscillation prediction skill of a new-generation global model demonstrated using a supercomputer. Nat. Commun. 5, 3769 (2014).

    Article  CAS  Google Scholar 

  34. Yoo, C., Park, S., Kim, D., Yoon, J.-H. & Kim, H.-M. Boreal winter MJO teleconnection in the Community Atmosphere Model Version 5 with the unified convection parameterization. J. Clim. 28, 8135–8150 (2015).

    Article  Google Scholar 

  35. Laloyaux, P. et al. CERA-20C: a coupled reanalysis of the twentieth century. J. Adv. Model. Earth Syst. 10, 1172–1195 (2018).

    Article  Google Scholar 

  36. Fadlallah, B., Chen, B., Keil, A. & Príncipe, J. Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information. Phys. Rev. E 87, 022911 (2013).

    Article  Google Scholar 

  37. Bandt, C. & Pompe, B. Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88, 174102 (2002).

    Article  Google Scholar 

  38. Garland, J., James, R. & Bradley, E. Model-free quantification of time-series predictability. Phys. Rev. E 90, 052910 (2014).

    Article  Google Scholar 

  39. Xia, J., Shang, P., Wang, J. & Shi, W. Permutation and weighted-permutation entropy analysis for the complexity of nonlinear time series. Commun. Nonlinear Sci. Numer. Simul. 31, 60–68 (2016).

    Article  Google Scholar 

  40. Yin, Y. & Shang, P. Weighted permutation entropy based on different symbolic approaches for financial time series. Physica A 443, 137–148 (2016).

    Article  Google Scholar 

  41. Pennekamp, F. et al. The intrinsic predictability of ecological time series and its potential to guide forecasting. Ecol. Monogr. 89, e01359 (2019).

    Article  Google Scholar 

  42. Scarpino, S. V. & Petri, G. On the predictability of infectious disease outbreaks. Nat. Commun. 10, 898 (2019).

    Article  Google Scholar 

  43. Garland, J. et al. Anomaly detection in paleoclimate records using permutation entropy. Entropy 20, 931 (2018).

    Article  CAS  Google Scholar 

  44. Amigó, J. M., Zambrano, S. & Sanjuán, M. A. Combinatorial detection of determinism in noisy time series. Europhys. Lett. 83, 60005 (2008).

    Article  Google Scholar 

  45. Riedl, M., Müller, A. & Wessel, N. Practical considerations of permutation entropy: a tutorial review. Eur. Phys. J. Spec. Top. 222, 249–262 (2013).

    Article  Google Scholar 

  46. Chowdary, J. et al. Interdecadal variations in ENSO teleconnection to the Indo-Western Pacific for 1870–2007. J. Clim. 25, 1722–1744 (2012).

    Article  Google Scholar 

  47. Danabasoglu, G. et al. The Community Earth System Model Version 2 (CESM2). J. Adv. Model. Earth Syst. 12, e2019MS001916 (2020).

    Article  Google Scholar 

  48. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  49. Latif, M. Dynamics of interdecadal variability in coupled ocean–atmosphere models. J. Clim. 11, 602–624 (1998).

    Article  Google Scholar 

  50. Kiladis, G. N. et al. A comparison of OLR and circulation-based indices for tracking the MJO. Mon. Weather Rev. 142, 1697–1715 (2014).

    Article  Google Scholar 

  51. Le, P. V., Guilloteau, C., Mamalakis, A. & Foufoula-Georgiou, E. Underestimated MJO variability in CMIP6 models. Geophys. Res. Lett. 48, e2020GL092244 (2021).

    Article  Google Scholar 

  52. Cui, J. & Li, T. Changes of MJO propagation characteristics under global warming. Clim. Dyn. 53, 5311–5327 (2019).

    Article  Google Scholar 

  53. Dasgupta, P., Metya, A., Naidu, C., Singh, M. & Roxy, M. Exploring the long-term changes in the Madden–Julian oscillation using machine learning. Sci. Rep. 10, 18567 (2020).

    Article  CAS  Google Scholar 

  54. Liu, P. MJO evolution and predictability disclosed by the RMM variant with balanced MJO variance in convection and zonal winds. Clim. Dyn. 52, 2529–2543 (2019).

    Article  Google Scholar 

  55. Arnold, N. P., Branson, M., Kuang, Z., Randall, D. A. & Tziperman, E. MJO intensification with warming in the superparameterized CESM. J. Clim. 28, 2706–2724 (2015).

    Article  Google Scholar 

  56. Wolding, B. O., Maloney, E. D., Henderson, S. & Branson, M. Climate change and the Madden–Julian oscillation: a vertically resolved weak temperature gradient analysis. J. Adv. Model. Earth Syst. 9, 307–331 (2017).

    Article  Google Scholar 

  57. Chang, C.-W. J., Tseng, W.-L., Hsu, H.-H., Keenlyside, N. & Tsuang, B.-J. The Madden–Julian oscillation in a warmer world. Geophys. Res. Lett. 42, 6034–6042 (2015).

    Article  Google Scholar 

  58. Adames, Á. F., Kim, D., Sobel, A. H., Del Genio, A. & Wu, J. Characterization of moist processes associated with changes in the propagation of the MJO with increasing CO2. J. Adv. Model. Earth Syst. 9, 2946–2967 (2017).

    Article  Google Scholar 

  59. Zhou, W., Yang, D., Xie, S.-P. & Ma, J. Amplified Madden–Julian oscillation impacts in the Pacific–North America region. Nat. Clim. Change 10, 654–660 (2020).

    Article  CAS  Google Scholar 

  60. Weisheimer, A. et al. Seasonal forecasts of the twentieth century. Bull. Am. Meteorol. Soc. 101, 1413–1426 (2020).

    Article  Google Scholar 

  61. Vitart, F. Monthly forecasting at ECMWF. Mon. Weather Rev. 132, 2761–2779 (2004).

    Article  Google Scholar 

  62. Weisheimer, A. & O’Reilly, C. Initialised Seasonal Forecast of the 20th Century (Centre for Environmental Data Analysis, 2020).

  63. Du, D. et al. Increase in MJO predictability under global warming. Figshare https://doi.org/10.6084/m9.figshare.24082053 (2023).

  64. Du, D. et al. Danni-Du/WPE-computation: code for “Increase in MJO Predictability Under Global Warming”. Zenodo https://doi.org/10.5281/zenodo.10031056 (2023).

Download references

Acknowledgements

D.D., W.H. and A.C.S. disclose support from NASA (21-OSST21-0026). D.D. and A.C.S. disclose support from the ONR MISOBOB research initiative (N00014-17-S-B001) and NOAA (NA18OAR4310405). E.B. discloses support from NSF (AGS 2001670). W.H. also acknowledges the travel support of the Johannes Geiss Fellowship from the International Space Science Institute, Bern Switzerland. We also acknowledge high-performance computing support and CESM2 data access from Cheyenne provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation. All analyses were done with the Casper data analysis and visualization cluster.

Author information

Authors and Affiliations

  1. Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA

    Danni Du, Aneesh C. Subramanian, Weiqing Han & Jeffrey B. Weiss

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

    William E. Chapman

  3. Department of Computer Science, University of Colorado, Boulder, CO, USA

    Elizabeth Bradley

  4. Santa Fe Institute, Santa Fe, NM, USA

    Elizabeth Bradley

Authors
  1. Danni Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aneesh C. Subramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weiqing Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William E. Chapman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jeffrey B. Weiss
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Elizabeth Bradley
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.D. conducted most of the analyses and drafted the manuscript. All other authors contributed to the discussions on shaping this work, as well as the writing and editing of the manuscript. A.C.S., W.H. and J.B.W. offered suggestions on conceiving and designing the analyses. W.E.C. computed the RMMI in ECMWF seasonal forecasts and in CERA-20C. E.B. suggested using the WPE method and provided mathematical consultants.

Corresponding author

Correspondence to Danni Du.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Daehyun Kim, Kai-Chih Tseng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 An example MJO diagram.

It shows the MJO trajectory for 1997 January (blue), February (red) and March (black) in CERA-20C (ensemble member m0).

Source data

Extended Data Fig. 2 Time series of the MJO predictability in the model outputs represented by WPE.

CESM2 control run (pre-1850; black), CESM2/CESM2-WACCM historical simulation (1850-2015; blue for ensemble mean), and CESM2/CESM2-WACCM ssp585 future projection (2015-2100; red for ensemble mean) are shown. The WPE time series of different ensemble members are shown as the thin colored lines. Note that only the WPE values for the last 100 years in the CESM2 control run are plotted. Though these values are plotted as pre-1850 on the time axis, they are actually the results of a free model run initialized from conditions of Year 1850, and do not correspond to any calendar years.

Source data

Extended Data Fig. 3 The budget analysis of WPE.

It visualizes how individual permutations contribute to the WPE changing rate of RMM1, RMM2, the MJO amplitude and the MJO propagation in ssp585 runs. The unit of the y-axis values is year−1.

Source data

Supplementary information

Supplementary Information

Supplementary Sections 1–4 with Figs. 1–19.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, D., Subramanian, A.C., Han, W. et al. Increase in MJO predictability under global warming. Nat. Clim. Chang. 14, 68–74 (2024). https://doi.org/10.1038/s41558-023-01885-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-023-01885-0

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