Articles | Volume 26, issue 10
https://doi.org/10.5194/nhess-26-4663-2026
https://doi.org/10.5194/nhess-26-4663-2026
Research article
 | 
01 Oct 2026
Research article |  | 01 Oct 2026

Anthropogenic aerosol forcing of European windstorms in CMIP6 climate models

Stephen Cusack

Data sets

CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP piControl T. Lovato and D. Peano https://doi.org/10.22033/ESGF/CMIP6.3874

CMCC CMCC-CM2-SR5 model output prepared for CMIP6 DAMIP hist-aer T. Lovato and D. Peano https://doi.org/10.22033/ESGF/CMIP6.17947

CCCma CanESM5 model output prepared for CMIP6 CMIP historical N. C. Swart, J. N. S. Cole, V. V. Kharin, et al. https://doi.org/10.22033/ESGF/CMIP6.3610

CCCma CanESM5 model output prepared for CMIP6 DAMIP hist-aer N. C. Swart, J. N. S. Cole, V. V. Kharin, et al. https://doi.org/10.22033/ESGF/CMIP6.3597

MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP piControl J. Ridley, M. Menary, T. Kuhlbrodt, et al. https://doi.org/10.22033/ESGF/CMIP6.6294

MOHC HadGEM3-GC31-LL model output prepared for CMIP6 DAMIP hist-aer G. Jones https://doi.org/10.22033/ESGF/CMIP6.6052

MIROC MIROC6 model output prepared for CMIP6 CMIP piControl H. Tatebe and M. Watanabe https://doi.org/10.22033/ESGF/CMIP6.5711

MIROC MIROC6 model output prepared for CMIP6 DAMIP hist-aer H. Shiogama https://doi.org/10.22033/ESGF/CMIP6.5579

MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP piControl K.-H. Wieners, M. Giorgetta, J. Jungclaus, et al. https://doi.org/10.22033/ESGF/CMIP6.6675

MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP hist-aer W. M\"{u}ller, T. Ilyina, H. Li, et al. https://doi.org/10.22033/ESGF/CMIP6.15024

MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP piControl S. Yukimoto, T. Koshiro, H. Kawai, et al. https://doi.org/10.22033/ESGF/CMIP6.6900

MRI MRI-ESM2.0 model output prepared for CMIP6 DAMIP hist-aer S. Yukimoto, T. Koshiro, H. Kawai, et al. https://doi.org/10.22033/ESGF/CMIP6.6821

Model code and software

dplR: Dendrochronology Program Library in R. R package version 1.7.4 A. Bunn, M. Korpela, F. Biondi, et al. https://CRAN.R-project.org/package=dplR

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Short summary
European windstorm damages varied by a factor three over recent multidecadal periods, and a better understanding of these changes could improve how this risk is managed. Here, we explored the impacts of anthropogenic aerosols (AA) using results from climate model experiments, and found AA boosted European wind losses by an average of 45 % in the late 20th century, though varying from zero to 100 % between the six models. Validation data suggest the signal may be at the higher end of this range.
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