Work

Microphysical evolution and column loading drive nonlinear regional contrast in black carbon top-of-atmosphere forcing

Year: 2026

Type: article

Abstract: . Black carbon (BC) aerosols remain among the most uncertain contributors to anthropogenic climate forcing, as their radiative impact depends sensitively on microphysical evolution and atmospheric loading. This study presents a physics-informed, machine learning (ML) approach to estimate clear-sky BC top-of-atmosphere direct radiative forcing (BCTOA) at high spatial-temporal resolution while retaining physical interpretability. The study derives necessary optical properties for radiative transfer modeling (RTM), by constraining them with multi-platform, multi-waveband observations and their associated uncertainties. The RTM outputs are then used to train the ML surrogates and applied over two contrasting urban agglomerates-Xuzhou, China, and Dhaka, Bangladesh. The ML framework closely reproduces physics-based regional climatological mean (–17.6 ± 2.2 W m-2 versus –17.4 ± 2.6 W m-2 over Xuzhou; –14.9 ± 1.1 W m-2 versus –15.0 ± 1.2 W m-2 for Dhaka), while achieving high predictive fidelity R2 > 0.95; RMSE ~1.5–1.8 W m-2 and strong cross-regional consistency (r > 0.9). Predictor decomposition reveals BCTOA is primarily modulated by BC aerosol optical depth (BCAOD), column number density, and mixing state, with their relative importance and influence varying non-linearly across cooling-to-warming regimes. Crucially, similar BC loading can yield contrasting absorption-scattering dynamics across region, which are not captured by simplfied forcing parameterization. Together, the physics-informed ML framework and the multi-domain evaluation provide an efficient and transferable tool for constraining BC radiative impacts across real-world heterogeneity. The analysis also offers new mechanistic insight into how regional properties reshape BC regional radiative forcing.

Source: Atmospheric chemistry and physics

DOI: 10.5194/acp-26-9149-2026