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Unraveling volcanic impacts in semi-arid climates: machine learning and CMIP6 insights from Krakatau and Santa Maria
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  • Published: 02 April 2026

Unraveling volcanic impacts in semi-arid climates: machine learning and CMIP6 insights from Krakatau and Santa Maria

  • Abdolazim Saman1,
  • Gholamreza Roshan  ORCID: orcid.org/0000-0003-4097-69401,
  • Stefan W. Grab2,
  • Muhammad Mubashar Dogar3 &
  • …
  • Tarig A. Ali  ORCID: orcid.org/0000-0001-5884-74644,5 

Scientific Reports , Article number:  (2026) Cite this article

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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

  • Climate sciences
  • Natural hazards

Abstract

Volcanic eruptions are among the strongest natural climate forcings, yet their regional impacts remain poorly resolved, particularly in semi-arid hotspots. Here, we investigate the climatic fingerprints of the Krakatau (1883) and Santa Maria (1902) eruptions on surface temperatures over Iran by integrating CMIP6 multi-model simulations with Random Forest machine learning. This dual framework enables the first systematic assessment of Iran-specific volcanic teleconnection dynamics. Both eruptions triggered prolonged and spatially extensive cooling episodes, characterized by biphasic responses: an intense initial cooling wave followed by a weaker secondary phase linked to aerosol dispersion and decay. Spatial patterns reveal that localized anomalies in northern and western Iran eventually expanded to cover nearly the entire country. Teleconnection analysis highlights the Pacific Warm Pool and Tropical North Atlantic as dominant modulators, with additional contributions from ENSO and the North Pacific, including an anomalous negative TNA phase that amplified Santa Maria’s impact. Our results demonstrate how volcanic cooling is regionally mediated through ocean–atmosphere teleconnections, and reveal systematic model–observation mismatches in intensity and persistence. By bridging physical climate modeling with machine learning, this study provides a novel framework for attributing volcanic impacts in teleconnection-sensitive, semi-arid regions, with implications for climate adaptation, agricultural planning, and regional resilience under future extreme events.

Data availability

The datasets used in this study are publicly available. Historical simulations from various CMIP6 models can be accessed through the Copernicus Climate Data Store (CDS) at https://cds.climate.copernicus.eu/datasets/projections-cmip6. Teleconnection indices, which are freely and openly accessible, were obtained from the NOAA Physical Sciences Laboratory (PSL) at https://psl.noaa.gov/data/climateindices/list/.

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Author information

Authors and Affiliations

  1. Department of Geography, Golestan University, Shahid Beheshti, Gorgan, Iran

    Abdolazim Saman & Gholamreza Roshan

  2. School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, South Africa

    Stefan W. Grab

  3. Research Center for Environmental Modeling and Application (CEMA), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Kanagawa, 236-0001, Japan

    Muhammad Mubashar Dogar

  4. Department of Civil Engineering, American University of Sharjah, P O Box 26666, Sharjah, United Arab Emirates

    Tarig A. Ali

  5. Energy, Water, and Sustainable Environment Research Center, American University of Sharjah, P O Box 26666, Sharjah, United Arab Emirates

    Tarig A. Ali

Authors
  1. Abdolazim Saman
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  2. Gholamreza Roshan
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  3. Stefan W. Grab
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  4. Muhammad Mubashar Dogar
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  5. Tarig A. Ali
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Contributions

G.R.R. and A.S. conceptualized and designed the study. G.R.R. led the data analysis, model selection, and interpretation of results. A.S. contributed significantly to the methodology, data processing, and manuscript drafting. S.W.G. and M.M.D. assisted with CMIP6 simulations, data quality checks, and statistical analyses. T.A.A. provided guidance on climate teleconnections, supervised the overall framework, and contributed to the discussion and conclusions. G.R.R. and A.S. prepared all figures and tables. G.R.R., A.S., S.W.G., M.M.D., and T.A.A. reviewed and revised the manuscript, and all authors approved the final version.

Corresponding authors

Correspondence to Gholamreza Roshan or Tarig A. Ali.

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Saman, A., Roshan, G., Grab, S.W. et al. Unraveling volcanic impacts in semi-arid climates: machine learning and CMIP6 insights from Krakatau and Santa Maria. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45543-9

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  • Received: 24 September 2025

  • Accepted: 19 March 2026

  • Published: 02 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-45543-9

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Keywords

  • Tropical volcanic eruptions
  • Krakatau
  • Santa Maria
  • Climate response
  • Random forest model
  • Iran
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