Quantifying Global Black Carbon Aging Responses to Emission Reductions Using Machine Learning-based Climate Model

Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon (BC) aging to emission changes remains unclear. Using the Community Atmosphere Model version 6 (CAM6) with machine-learning-integrated four-mode version of the Modal Aerosol Module (MAM4-ML), we quantify global BC aging responses to emission reductions for 2011–2018 and 2050/2100 under carbon neutrality. During 2011–2018, global trends in BC aging degree (mass ratio of coatings to BC, RBC) exhibited marked regional disparities, with a significant increase in China (5.4% yr⁻¹) contrasting with minimal changes in the USA, Europe, and India. The divergence arose from opposing trends in secondary organic aerosol (SOA) and sulfate coatings, driven by regional changes in the emission ratios of corresponding coating precursors to BC (volatile organic compounds-VOCs/BC and SO₂/BC). Projections under carbon neutrality reveal that RBC will increase globally by 47%/118% in 2050/2100, with strong convergent increases across major source regions. The RBC increase, primarily driven by enhanced SOA coatings due to sharper BC reductions relative to VOCs, will enhance global BC mass absorption cross-section (MAC) by 11%/17% in 2050/2100. Consequently, while global BC burden will reduce sharply (-60%/-76%), the enhanced MAC partially offsets BC direct radiative effect (DRE) decline magnitude, moderating global BC DRE decreases to 88%/92% of the BC burden reductions in 2050/2100. This study highlights the globally enhanced BC aging and light absorption capacity under carbon neutrality, thereby partly offsetting the impact of BC direct emission reductions on future changes in BC radiative effects globally.