Aerosol-cloud interactions (ACIs) remain one of the largest uncertainties in Earth’s climate system, partly because large-scale meteorology can independently influence both aerosols and clouds, complicating causal attribution. To leverage the rich ground-based measurements from Atmospheric Radiation Measurement (ARM) campaigns and improve aerosol effect attribution, we develop an approach to identify ship-emission-influenced observation cases through source tracing. The method detects local peaks in cloud condensation nuclei number concentrations (N<sub>CCN</sub>) and applies 24 h backward trajectories to determine whether the sampled air masses intersect ship emissions in the past day. Applying this framework to the ARM Eastern North Atlantic (ENA) observations in 2023 yields several dozen ship influenced cases, including a stratocumulus case in which two ship plumes contribute to a pronounced N<sub>CCN</sub> enhancement. An increase in cloud fraction and liquid water path, along with a 1 h delay in precipitation, is observed by the comprehensive ARM measurements at the time of the N<sub>CCN</sub> spike. As large-scale meteorological conditions remain steady, the cloud responses are more likely an aerosol-driven signal rather than meteorology-mediated covariability. Preliminary application to the Marine ARM GPCI Investigation of Clouds (MAGIC) campaign is also discussed. This framework provides a basis for building a multi-year, multi-site library of ship-emission-influenced cloud measurements, offering improved observational constraints for ACI research and model evaluation.