Technical note: Aerosol forcing decomposition methods and recommendations

Abstract. Aerosol effective radiative forcing (ERF), particularly its aerosol–cloud interaction component (ERF_aci), 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 ERF_aci 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 ERF_aci, with the bias amplified in colder climate states where aerosol-over-cloud configurations are more prevalent. Thus, an apparent weakening of shortwave ERF_aci 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.