Accurately quantifying the radiative properties of halogenated compounds is essential for climate change research, given their potent warming potential. While radiative transfer models facilitate explicit calculation of radiative efficiencies for individual gases, notable discrepancies persist among different studies. In this study, we apply an improved radiative transfer model to calculate the radiative efficiencies with stratospheric temperature adjustment for 15 key halogenated compounds, thereby revising marked overestimates identified in earlier applications of the original model. The results show that chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), and hydrofluorocarbons (HFCs) contribute 64%, 16%, and 13%, respectively, to the 2023 radiative forcing (0.352 W/m²) from the 15 species. The radiative forcing of chlorinated compounds is in steady decline due to emission restrictions, whereas that of HFCs is highly scenario-dependent but projected to be 1.3 times the present level by 2100 under a policy-inclusive scenario. Furthermore, we assess the distinct impacts of long-lived and short-lived greenhouse gas emissions on global surface temperature using various emissions metrics. Temperature response simulations demonstrate that the conventional global warming potential (GWP-100) underestimates the initial warming from short-lived gas emissions by at least 50% while overestimating the post-mitigation warming by nearly a factor of four. Comparatively, emerging metrics contribute to reducing ambiguity in future warming estimates, owing to their closer alignment with model simulations. This study provides an updated physical basis for evaluating the radiative properties of halogenated compounds and supports more comprehensive climate policy assessments.

The subject of “atmospheric radiation” includes not only fundamental theories on atmospheric gaseous absorption and the scattering and radiative transfer of particles (molecules, cloud, and aerosols), but also their applications in weather, climate, and atmospheric remote sensing, and is an essential part of the atmospheric sciences. This review includes two parts (Part I and Part II); following the first part on gaseous absorption and particle scattering, this part (Part II) reports the progress that has been made in radiative transfer theories, models, and their common applications, focusing particularly on the contributions from Chinese researchers. The recent achievements on radiative transfer models and methods developed for weather and climate studies and for atmospheric remote sensing are firstly reviewed. Then, the associated applications, such as surface radiation estimation, satellite remote sensing algorithms, radiative parameterization for climate models, and radiative-forcing related climate change studies are summarized, which further reveals the importance of radiative transfer theories and models.

Atmospheric radiation is a major branch of atmospheric physics that encompasses the fundamental theories of atmospheric absorption, particle scattering (aerosols and clouds), and radiative transfer. Specifically, the simulations of atmospheric gaseous absorption and scattering properties of particles are the essential components of atmospheric radiative transfer models. Atmospheric radiation has important applications in weather, climate, data assimilation, remote sensing, and atmospheric detection studies. In Part I, a comprehensive review of the progress in the field of gas absorption and particle scattering research over the past 30 years with a particular emphasis on the contributions from Chinese scientists is presented. The review of gas absorption includes the construction of absorption databases, the impact of different atmospheric absorption algorithms on radiative calculations, and their applications in weather and climate models and remote sensing. The review on particle scattering starts with the theoretical and computational methods and subsequently explores the optical modeling of aerosols and clouds in remote sensing and atmospheric models. Additionally, the paper discusses potential future research directions in this field.