Technical note: Aerosol forcing decomposition methods and recommendations

Abstract. Aerosol effective radiative forcing (ERF), particularly its aerosol–cloud interaction component (ERFaci), is commonly diagnosed using decomposition methods such as online double-call, approximate partial radiative perturbation (APRP), radiative kernels, and related proxy diagnostics. These approaches differ in whether they rely on aerosol-free or aerosol-inclusive formulations and in whether they account for cloud masking of non-cloud adjustments and, where relevant, direct aerosol effects. Here we identify a systematic bias in ERFaci estimation arising from aerosol vertical structure. When aerosols reside above clouds, they attenuate incoming shortwave radiation before it reaches cloud top, thereby reducing the radiative impact of a given cloud perturbation. As a result, methods based on aerosol-free radiative sensitivities overestimate the shortwave component of ERFaci, with the bias amplified in colder climate states where aerosol-over-cloud configurations are more prevalent. Thus, an apparent weakening of shortwave ERFaci with warming may reflect not only a physical change in cloud response but also a reduction in aerosol-over-cloud bias as the background climate changes. In addition, neglect of cloud masking can misattribute direct aerosol effects and non-cloud adjustments. Because aerosol forcing is partitioned into direct and cloud-mediated components, this bias also affects the inferred aerosol–radiation interaction component. Simulations across multiple climate states show that diagnosed ERF partitions exhibit apparent state-dependence arising from both methodological biases and physical changes. These results highlight a broader limitation of decomposition frameworks that neglect aerosol vertical structure and cloud masking, and they underscore the need for cautious interpretation of ERF partitioning.