Quantifying Global Black Carbon Aging Responses to Emission Reductions Using a 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 with a machine-learning-integrated four-mode version of the Modal Aerosol Module, we quantify global BC aging responses to emission reductions for 2011–2018 and for 2050 and 2100 under carbon neutrality. During 2011–2018, global trends in BC aging degree (mass ratio of coatings to BC, R_BC) exhibited marked regional disparities, with a significant increase in China (5.4% yr^-1), which contrasts with minimal changes in the USA, Europe, and India. The divergence is attributed to 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 R_BC will increase globally by 47% (118%) in 2050 (2100), with strong convergent increases expected across major source regions. The R_BC increase, primarily driven by enhanced SOA coatings due to sharper BC reductions relative to VOCs, will enhance the global BC mass absorption cross-section (MAC) by 11% (17%) in 2050 (2100). Consequently, although the global BC burden will decline sharply by 60% (76%), the enhanced MAC partially offsets the magnitude of the decline in the BC direct radiative effect, resulting in the moderation of 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.