Regional aerosol hygroscopicity influences radiative forcing globally
Shravan Deshmukh (Leibniz-Institut für Troposphärenforschung)
Pau Ferrer-Cid (Universitat Politecnica de Catalunya)
Baseerat Romshoo (Leibniz-Institut für Troposphärenforschung)
Laurent Poulain (Leibniz-Institut für Troposphärenforschung)
Jose M. Barcelo-Ordinas (Universitat Politecnica de Catalunya)
Jorge Garcia-Vidal (Universitat Politecnica de Catalunya)
Aliki Christodoulou (The Cyprus Institute, Paul Scherrer Institut)
Spyros Bezantakos (The Cyprus Institute)
Cyrielle Denjean (CNRS-UPS)
Barbara D’Anna (Aix Marseille Université)
Paola Formenti (Université Paris-Est-Créteil)
Subrata Mukherjee (Indian Institute of Tropical Meteorology)
Gazala Habib (Indian Institute of Technology Delhi)
Prashant Kumar (University of Surrey)
Shan Huang (Jinan University)
Zhijun Wu (Peking University)
Birgit Wehner (Leibniz-Institut für Troposphärenforschung)
Silvia Henning (Leibniz-Institut für Troposphärenforschung)
Mar Viana (Institut de Diagnòstic Ambiental i Estudis de l'Aigua - CSIC, Spanish Ministry for Ecological Transition)
Markus D. Petters (University of California)
Ajit Ahlawat (TU Delft - Civil Engineering & Geosciences)
Mira Pöhlker (Leibniz-Institut für Troposphärenforschung, University of Leipzig)
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Abstract
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. (Figure presented.)
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