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<p>Aerosol hygroscopicity is a critical parameter for predicting radiative forcing and climate sensitivity, particularly under sub-saturated regimes where it drives complex aerosol–water interactions. Here, we show that externally mixed aerosols exert a stronger influence on direct radiative forcing than is currently represented in models. Incorporating our findings into radiative forcing calculations indicates a stronger aerosol cooling effect, especially at suburban sites, highlighting the importance of representing regional differences in mixing state. The conventional bulk-chemistry approach, which assumes volume-based mixing with limited spatial variability, exhibits low predictive performance for aerosol hygroscopicity (R² ≈ 0.61) at urban and suburban sites. Using an interpretable machine learning framework trained on geographically diverse, region-specific datasets can capture this variability with higher accuracy (R² ≈ 0.97), identifying key chemical compositional and mixing-state drivers.</p><p></p>

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Regional aerosol hygroscopicity influences radiative forcing globally

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月7日

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  • Shravan Deshmukh,
  • Pau Ferrer-Cid,
  • Baseerat Romshoo,
  • Laurent Poulain,
  • Jose M. Barcelo-Ordinas,
  • Jorge Garcia-Vidal,
  • Aliki Christodoulou,
  • Spyros Bezantakos,
  • Cyrielle Denjean,
  • Barbara D’Anna,
  • Paola Formenti,
  • Subrata Mukherjee,
  • Gazala Habib,
  • Prashant Kumar,
  • Shan Huang,
  • Zhijun Wu,
  • Birgit Wehner,
  • Silvia Henning,
  • Mar Viana,
  • Markus D. Petters,
  • Ajit Ahlawat,
  • Mira Pöhlker

摘要

Aerosol hygroscopicity is a critical parameter for predicting radiative forcing and climate sensitivity, particularly under sub-saturated regimes where it drives complex aerosol–water interactions. Here, we show that externally mixed aerosols exert a stronger influence on direct radiative forcing than is currently represented in models. Incorporating our findings into radiative forcing calculations indicates a stronger aerosol cooling effect, especially at suburban sites, highlighting the importance of representing regional differences in mixing state. The conventional bulk-chemistry approach, which assumes volume-based mixing with limited spatial variability, exhibits low predictive performance for aerosol hygroscopicity (R² ≈ 0.61) at urban and suburban sites. Using an interpretable machine learning framework trained on geographically diverse, region-specific datasets can capture this variability with higher accuracy (R² ≈ 0.97), identifying key chemical compositional and mixing-state drivers.