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Middle East dust as an important external driver of the Indian Ocean Dipole
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  • Published: 29 January 2026

Middle East dust as an important external driver of the Indian Ocean Dipole

  • Guanyu Liu1,
  • Shang-Ping Xie  ORCID: orcid.org/0000-0002-3676-13252,
  • James E. Hansen3,
  • Xiaofan Wang4,
  • Yueming Dong1 &
  • …
  • Jing Li  ORCID: orcid.org/0000-0002-0540-04121,5,6 

Nature Communications , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Atmospheric science
  • Climate change

Abstract

The Indian Ocean Dipole (IOD) strongly influences Indo-Pacific climate extremes, yet its drivers remain incompletely understood. Using satellite observations, reanalysis, and climate model experiments, we identify Middle East dust emissions as a major external driver of IOD variability. Observational evidence shows that dust over the 1980-2020 period account for ~36% of interannual IOD variance, surpassing El Niño-Southern Oscillation as the main driver during boreal autumn. Climate simulations confirm that reduced dust enhances warming in the western Indian Ocean and inducing easterly winds that shoal the eastern thermocline. These anomalies trigger Bjerknes and wind–evaporation–sea surface temperature feedbacks, amplifying a positive IOD pattern, and vice versa. Our findings reveal a powerful dust–IOD teleconnection, highlighting the need to incorporate aerosol forcing into seasonal forecasts and climate projections.

Data availability

All datasets about the study’s key findings are fully described in the main text and the Methods section. The data generated in the study have been deposited in the https://doi.org/10.5281/zenodo.15683119.

Code availability

The codes are available in the Zenodo files (https://doi.org/10.5281/zenodo.15683119).

References

  1. McKenna, S., Santoso, A., Sen Gupta, A., Taschetto, A. S. & Cai, W. J. Indian Ocean Dipole in CMIP5 and CMIP6: characteristics, biases, and links to ENSO. Sci. Rep. 10, 11500 (2020).

  2. Schott, F. A., Xie, S.-P. & McCreary, J. P., Jr. Indian ocean circulation and climate variability. Rev. Geophys. 47, RG1002 (2009).

  3. Black, E., Slingo, J. & Sperber, K. R. An observational study of the relationship between excessively strong short rains in coastal East Africa and Indian Ocean SST. Monthly Weather Rev. 131, 74–94 (2003).

    Google Scholar 

  4. Cai, W. J. et al. Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming. Nature 510, 254 (2014).

    Google Scholar 

  5. Clark, C. O., Webster, P. J. & Cole, J. E. Interdecadal variability of the relationship between the Indian Ocean zonal mode and East African coastal rainfall anomalies. J. Clim. 16, 548–554 (2003).

    Google Scholar 

  6. Pan, X. et al. Compound drought and heat waves variation and association with SST modes across China. Sci. Total Environ.907, 167934 (2024).

  7. Yin, Y., Li, D., Sun, S., Wang, G. & Ke, Z. Global major weather and climate events in 2019 and the possible causes. Meteorol. Monthly 46, 538–546 (2020).

    Google Scholar 

  8. Zhou, Z.-Q., Xie, S.-P. & Zhang, R. Historic Yangtze flooding of 2020 tied to extreme Indian Ocean conditions. Proc. Natl. Acad. Sci. USA 118, e2022255118 (2021).

  9. Zheng, X. T., Lu, J. B. & Hui, C. Response of seasonal phase locking of Indian Ocean Dipole to global warming. Clim. Dyn. 57, 2737–2751 (2021).

    Google Scholar 

  10. Sang, Y. F., Singh, V. P. & Xu, K. Evolution of IOD-ENSO relationship at multiple time scales. Theor. Appl. Climatol. 136, 1303–1309 (2019).

    Google Scholar 

  11. Stuecker, M. F. et al. Revisiting ENSO/Indian Ocean Dipole phase relationships. Geophys. Res. Lett. 44, 2481–2492 (2017).

    Google Scholar 

  12. Banerjee, P. & Kumar, S. P. ENSO modulation of interannual variability of dust aerosols over the Northwest Indian Ocean*. J. Clim. 29, 1287–1303 (2016).

    Google Scholar 

  13. Jin, Q. J., Wei, J. F. & Yang, Z. L. Positive response of Indian summer rainfall to Middle East dust. Geophys. Res. Lett. 41, 4068–4074 (2014).

    Google Scholar 

  14. Zheng, Y. L. & Hoteit, I. Asymmetric impacts of Indian Ocean Dipole on summer climate over Arabian Peninsula. Geophys. Res. Lett. 52, e2025GL118195 (2025).

  15. Liu, G. Y., Li, J. & Ying, T. The shift of decadal trend in Middle East dust activities attributed to North Tropical Atlantic variability. Sci. Bull. 68, 1439–1446 (2023).

    Google Scholar 

  16. Xi, X. Revisiting the recent dust trends and climate drivers using horizontal visibility and present weather observations. J. Geophys. Res. Atmos. 126, e2021JD034687 (2021).

  17. Xia, W. W., Wang, Y. & Wang, B. Decreasing dust over the Middle East partly caused by irrigation expansion. Earths Future 10, e2021EF002252 (2022).

  18. Zhang, W. J. et al. Tropical Indo-Pacific compounding thermal conditions drive the 2019 Australian extreme drought. Geophys. Res. Lett. 48, e2020GL090323 (2021).

  19. Kim, D. et al. Asian and Trans-Pacific dust: a multimodel and multiremote sensing observation analysis. J. Geophys. Res.-Atmos. 124, 13534–13559 (2019).

    Google Scholar 

  20. Prospero, J. M., Delany, A. C., Delany, A. C. & Carlson, T. N. The Discovery of African dust transport to the western hemisphere and the Saharan Air Layer. Bull. Am. Meteorol. Soc. 102, E1239–E1260 (2021).

    Google Scholar 

  21. Yu, H. B. et al. Estimates of African dust deposition along the trans-atlantic transit using the decadelong record of aerosol measurements from CALIOP, MODIS, MISR, and IASI. J. Geophys. Res.Atmos. 124, 7975–7996 (2019).

    Google Scholar 

  22. Yu, Y., Kalashnikova, O. V., Garay, M. J. & Notaro, M. Climatology of Asian dust activation and transport potential based on MISR satellite observations and trajectory analysis. Atmos. Chem. Phys. 19, 363–378 (2019).

    Google Scholar 

  23. Kurniadi, A., Weller, E., Min, S. K. & Seong, M. G. Independent ENSO and IOD impacts on rainfall extremes over Indonesia. Int. J. Climatol. 41, 3640–3656 (2021).

    Google Scholar 

  24. Takaya, Y., Ishikawa, I., Kobayashi, C., Endo, H. & Ose, T. Enhanced Meiyu-Baiu rainfall in early summer 2020: aftermath of the 2019 super IOD event. Geophys. Res. Lett. 47, e2020GL090671 (2020).

  25. Shao, Y. P., Klose, M. & Wyrwoll, K. H. Recent global dust trend and connections to climate forcing. J. Geophys. Res.Atmos. 118, 11107–11118 (2013).

    Google Scholar 

  26. Yao, Z. X. et al. Assessment of the simulation of Indian Ocean Dipole in the CESM-Impacts of atmospheric physics and model resolution. J. Adv. Model. Earth Syst. 8, 1932–1952 (2016).

    Google Scholar 

  27. Freund, M. B. et al. Higher frequency of Central Pacific El Nino events in recent decades relative to past centuries. Nat. Geosci. 12, 450 (2019).

    Google Scholar 

  28. Lin, R. P., Zheng, F. & Dong, X. ENSO frequency asymmetry and the Pacific decadal oscillation in observations and 19 CMIP5 models. Adv. Atmos. Sci. 35, 495–506 (2018).

    Google Scholar 

  29. Chuang, C. C., Kelly, J. T., Boyle, J. S. & Xie, S. C. Sensitivity of aerosol indirect effects to cloud nucleation and autoconversion parameterizations in short-range weather forecasts during the May 2003 aerosol IOP. J. Adv. Model. Earth Syst. 4, M09001 (2012).

  30. Fletcher, C. G., Kravitz, B. & Badawy, B. Quantifying uncertainty from aerosol and atmospheric parameters and their impact on climate sensitivity. Atmos. Chem. Phys. 18, 17529–17543 (2018).

    Google Scholar 

  31. Christopher, S. A. & Jones, T. A. Dust radiative effects over global oceans. IEEE Geosci. Remote Sens. Lett. 5, 74–77 (2008).

    Google Scholar 

  32. Kok, J. F. et al. Mineral dust aerosol impacts on global climate and climate change. Nat. Rev. Earth Environ. 4, 71–86 (2023).

    Google Scholar 

  33. Azaneu, M., Matthews, A., and Baranowski, D. in EGU General Assembly 2022 EGU22-EGU4355 (Vienna, Austria, 2022).

  34. Carton, J. A., Grodsky, S. A. & Liu, H. Variability of the oceanic mixed layer, 1960-2004. J. Clim. 21, 1029–1047 (2008).

    Google Scholar 

  35. Prasad, T. G. & Bahulayan, N. Mixed layer depth and thermocline climatology of the Arabian Sea and western equatorial Indian Ocean. Indian J. Mar. Sci. 25, 189–194 (1996).

    Google Scholar 

  36. Sharma, R., Agarwal, N., Momin, I. M. & Agarwal, V. K. Mixed layer depth and its variability in the Eastern equatorial Indian Ocean as revealed by observations and model simulations. MarGe 33, 154–163 (2010).

    Google Scholar 

  37. Ramanathan, V. et al. Cloud-radiative forcing and climate - results from the Earth Radiation Budget Experiment. Science 243, 57–63 (1989).

    Google Scholar 

  38. Stephens, G. L. Cloud feedbacks in the climate system: a critical review. J. Clim. 18, 237–273 (2005).

    Google Scholar 

  39. Ceppi, P., Brient, F., Zelinka, M. D. & Hartmann, D. L. Cloud feedback mechanisms and their representation in global climate models. Wiley Interdisciplinary Rev. Clim. Change 8, e465 (2017).

  40. Xiao, Y., Tan, X. X. & Tang, Y. M. Definition of springtime easterly wind bursts in the Indian Ocean and their roles in triggering positive IOD events. Environ. Res. Commun. 6, 031005 (2024).

  41. Karnauskas, K. B. A simple coupled model of the wind-evaporation-SST feedback with a role for stability. J. Clim. 35, 2149–2160 (2022).

    Google Scholar 

  42. Mahajan, S., Saravanan, R. & Chang, P. Free and forced variability of the Tropical Atlantic Ocean: role of the wind-evaporation-sea surface temperature feedback. J. Clim. 23, 5958–5977 (2010).

    Google Scholar 

  43. Liu, L. et al. Indian Ocean variability in the CMIP5 multi-model ensemble: the zonal dipole mode. Clim. Dyn. 43, 1715–1730 (2014).

    Google Scholar 

  44. Liu, L., Yu, W. D. & Li, T. Dynamic and thermodynamic air-sea coupling associated with the Indian Ocean Dipole diagnosed from 23 WCRP CMIP3 models. J. Clim. 24, 4941–4958 (2011).

    Google Scholar 

  45. Lin, Y. J., Cesana, G., Proistosescu, C., Zelinka, M. D. & Armour, K. C. The relative importance of forced and unforced temperature patterns in driving the time variation of low-cloud feedback. J. Clim. 38, 513–529 (2025).

    Google Scholar 

  46. Wu, M. X., Su, H. & Neelin, J. D. Multi-objective observational constraint of tropical Atlantic and Pacific low-cloud variability narrows uncertainty in cloud feedback. Nat. Commun. 16, 218 (2025).

  47. Wyant, M. C., Bretherton, C. S., Rand, H. A. & Stevens, D. E. Numerical simulations and a conceptual model of the stratocumulus to trade cumulus transition. J. Atmos. Sci. 54, 168–192 (1997).

    Google Scholar 

  48. Gantt, B., He, J., Zhang, X., Zhang, Y. & Nenes, A. Incorporation of advanced aerosol activation treatments into CESM/CAM5: model evaluation and impacts on aerosol indirect effects. Atmos. Chem. Phys. 14, 7485–7497 (2014).

    Google Scholar 

  49. He, J. et al. Decadal simulation and comprehensive evaluation of CESM/CAM5.1 with advanced chemistry, aerosol microphysics, and aerosol-cloud interactions. J. Adv. Model. Earth Syst. 7, 110–141 (2015).

    Google Scholar 

  50. Kim, S. K. et al. Decreased Indian Ocean Dipole variability under prolonged greenhouse warming. Nat. Commun. 15, 2811 (2024).

  51. Park, H. J., An, S. I., Park, J. H., Yang, Y. M. & Kim, S. K. Sub-seasonal impact of El Niño-Southern Oscillation on development of the Indian Ocean Dipole. Commun. Earth Environ. 6, 374 (2025).

  52. Ng, B. & Cai, W. J. Present-day zonal wind influences projected Indian Ocean Dipole skewness. Geophys. Res. Lett. 43, 11392–11399 (2016).

    Google Scholar 

  53. Liu, J. et al. Historical footprints and future projections of global dust burden from bias-corrected CMIP6 models. Npj Clim. Atmos. Sci. 7, 1 (2024).

  54. Zhao, A., Ryder, C. L. & Wilcox, L. J. How well do the CMIP6 models simulate dust aerosols? Atmos. Chem. Phys. 22, 2095–2119 (2022).

    Google Scholar 

  55. Gelaro, R. et al. The modern-era retrospective analysis for research and applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).

    Google Scholar 

  56. Diner, D. J. et al. Multi-angle imaging SpectroRadiometer (MISR)—Instrument description and experiment overview. IEEE Trans. Geosci. Remote Sens. 36, 1072–1087 (1998).

    Google Scholar 

  57. Yu, H. B. et al. Interannual variability and trends of combustion aerosol and dust in major continental outflows revealed by MODIS retrievals and CAM5 simulations during 2003-2017. Atmos. Chem. Phys. 20, 139–161 (2020).

    Google Scholar 

  58. Nicolas, J. et al. Global surface hourly dataset. NOAA National Centers for Environmental Information (2001).

  59. Pu, B. & Ginoux, P. How reliable are CMIP5 models in simulating dust optical depth? Atmos. Chem. Phys. 18, 12491–12510 (2018).

    Google Scholar 

  60. Kalashnikova, O. V., Kahn, R., Sokolik, I. N. & Li, W. H. Ability of multiangle remote sensing observations to identify and distinguish mineral dust types: optical models and retrievals of optically thick plumes. J. Geophys. Res. Atmos. 110, D18S14 (2005).

  61. Klüser, L., Martynenko, D. & Holzer-Popp, T. Thermal infrared remote sensing of mineral dust over land and ocean: a spectral SVD based retrieval approach for IASI. Atmos. Meas. Tech. 4, 757–773 (2011).

    Google Scholar 

  62. Peyridieu, S. et al. Characterisation of dust aerosols in the infrared from IASI and comparison with PARASOL, MODIS, MISR, CALIOP, and AERONET observations. Atmos. Chem. Phys. 13, 6065–6082 (2013).

    Google Scholar 

  63. Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos. 108, 4407 (2003).

  64. Sun, S. W., Fang, Y., Zu, Y. C., Liu, L. & Li, K. P. Increased occurrences of early Indian Ocean Dipole under global warming. Sci. Adv. 8, eadd6025 (2022).

  65. Benedict, J. J., Pritchard, M. S. & Collins, W. D. Sensitivity of MJO propagation to a robust positive Indian Ocean dipole event in the superparameterized CAM. J. Adv. Model. Earth Syst. 7, 1901–1917 (2015).

    Google Scholar 

  66. Chen, G. W., Ling, J., Zhang, R. W., Xiao, Z. N. & Li, C. Y. The MJO from CMIP5 to CMIP6: perspectives from tracking MJO precipitation. Geophys. Res. Lett. 49 e2021GL095241 (2022).

  67. Karlsson, K. G. et al. CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023. Earth Syst. Sci. Data 15, 4901–4926 (2023).

  68. Global Modeling and Assimilation Office (GMAO) MERRA-2 tavgM_2d_rad_Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Radiation Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) (2015).

  69. Global Modeling and Assimilation Office (GMAO) MERRA-2 instM_3d_ana_Np: 3d,Monthly mean,Instantaneous,Pressure-Level,Analysis,Analyzed Meteorological Fields V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) (2015).

  70. Copernicus Climate Change Service, Climate Data Store, ORAS5 global ocean reanalysis monthly data from 1958 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2021).

  71. Holland, M. M. et al. New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model. Geosci. Model Dev. 17, 1585–1602 (2024).

    Google Scholar 

  72. Vrac, M., Thao, S. & Yiou, P. Changes in temperature-precipitation correlations over Europe: are climate models reliable? Clim. Dyn. 60, 2713–2733 (2023).

    Google Scholar 

  73. Maehara, T. & Murota, K. Simultaneous singular value decomposition. Linear Algebra Appl. 435, 106–116 (2011).

    Google Scholar 

  74. Vanloan, C. F. Generalizing singular value decomposition. Siam J. Numer. Anal. 13, 76–83 (1976).

    Google Scholar 

  75. Zarekarizi, M., Rana, A. & Moradkhani, H. Precipitation extremes and their relation to climatic indices in the Pacific Northwest USA. Clim. Dyn. 50, 4519–4537 (2018).

    Google Scholar 

  76. Zhou, H., Zhu, J. J., Xiao, H. & Wang, X. K. Singular value decomposition (SVD) based correlation analysis of climatic factors and extreme precipitation in Hunan Province, China, during 1960-2009. J. Water Clim. Change 12, 3602–3616 (2021).

    Google Scholar 

  77. Hurrell, J. W. et al. The community earth system model: a framework for collaborative research. Bull. Am. Meteorol. Soc. 94, 1339–1360 (2013).

    Google Scholar 

  78. Bitz, C. M. et al. Climate sensitivity of the community climate system model, version 4. J. Clim. 25, 3053–3070 (2012).

    Google Scholar 

  79. Zender, C. S., Bian, H. S. & Newman, D. Mineral dust entrainment and deposition (DEAD) model: description and 1990s dust climatology. J. Geophys. Res. Atmos. 108, AAC8-1 (2003).

  80. Neale, R. B. et al. Description of the NCAR Community Atmosphere Model (CAM 4.0). 207 (2024).

  81. DeFlorio, M. J. et al. Interannual modulation of subtropical Atlantic boreal summer dust variability by ENSO. Clim. Dyn. 46, 585–599 (2016).

    Google Scholar 

  82. Kok, J. F., Albani, S., Mahowald, N. M. & Ward, D. S. An improved dust emission model - Part 2: evaluation in the Community Earth System Model, with implications for the use of dust source functions. Atmos. Chem. Phys. 14, 13043–13061 (2014).

    Google Scholar 

  83. Wu, C. L. et al. A process-oriented evaluation of dust emission parameterizations in CESM: Simulation of a typical severe dust storm in East Asia. J. Adv. Model. Earth Syst. 8, 1432–1452 (2016).

    Google Scholar 

  84. Yeager, S. G. et al. Predicting near-term changes in the Earth system: a large ensemble of initialized decadal prediction simulations using the Community Earth System Model. Bull. Am. Meteorol. Soc. 99, 1867–1886 (2018).

    Google Scholar 

  85. Xie, X. N. et al. Modeling east asian dust and its radiative feedbacks in CAM4-BAM. J. Geophys. Res. Atmos. 123, 1079–1096 (2018).

    Google Scholar 

  86. Ackerley, D., Chadwick, R., Dommenget, D. & Petrelli, P. An ensemble of AMIP simulations with prescribed land surface temperatures. Geosci. Model Dev. 11, 3865–3881 (2018).

    Google Scholar 

  87. Hwang, Y. T., Xie, S. P., Deser, C. & Kang, S. M. Connecting tropical climate change with Southern Ocean heat uptake. Geophys. Res. Lett. 44, 9449–9457 (2017).

    Google Scholar 

  88. Kang, S. M. et al. Walker circulation response to extratropical radiative forcing. Sci. Adv. 6, eabd3021 (2020).

Download references

Acknowledgements

This study is funded by the National Natural Science Foundation of China (NSFC, Grants No.42425503, J.L.) and the Peking University – BHP Carbon and Climate Wei-Ming PhD Scholars Program (Program Number: WM202401, G.L.).

Author information

Authors and Affiliations

  1. Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

    Guanyu Liu, Yueming Dong & Jing Li

  2. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Shang-Ping Xie

  3. Climate Science, Awareness and Solutions, Columbia University Earth Institute, New York, NY, USA

    James E. Hansen

  4. College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

    Xiaofan Wang

  5. Institute of Carbon Neutrality, Peking University, Beijing, China

    Jing Li

  6. Center for Environment and Health, Peking University, Beijing, China

    Jing Li

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Contributions

G.L. and J.L. conceived the study. G.L., X.W., and Y.D. gathered the data. G.L., S.X., X.W., and Y.D. developed the methodology. S.X., J.E.H., and J.L. were responsible for the supervision. G.L., J.L. wrote the original draft. All authors reviewed and edited the article.

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Correspondence to Jing Li.

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Liu, G., Xie, SP., Hansen, J.E. et al. Middle East dust as an important external driver of the Indian Ocean Dipole. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68842-1

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  • Received: 20 September 2025

  • Accepted: 19 January 2026

  • Published: 29 January 2026

  • DOI: https://doi.org/10.1038/s41467-026-68842-1

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