Investigating the Shipping Effect on Marine Clouds Using Satellite Observations and Vessel Density

Shipping activities emit aerosols that can modify the microphysical and optical properties of low-level marine clouds. In the framework of the ESA ACtIon4Cooling (Aerosol Cloud Interactions for Cooling) project, marine clouds influenced by ship-track emissions are investigated as natural analogues to assess the monitoring capabilities of various Solar Radiation Modification (SRM) approaches, including Marine Cloud Brightening (MCB).

In this study, we combine high-resolution satellite observations from SUOMI-NPP/VIIRS (Visible Infrared Imaging Radiometer Suite) and Sentinel-5p/TROPOMI (TROPOspheric Monitoring Instrument) with vessel density data from EMODNET (European Marine Observation and Data Network) to detect cloud anomalies in the shipping corridors and quantify the ship-relevant cloud perturbations. VIIRS-derived cloud variables include cloud top height, cloud top emissivity, effective radius, liquid water path, and optical depth, while TROPOMI provides similar cloud information in the Oxygen A-band. Additional TROPOMI L2 products such as the absorbing aerosol index, aerosol type, and tropospheric NO₂ columns can also provide suitable proxies for ship emissions. The detection of the ship-tracks can be further improved when actual AIS (Automatic Identification System) data are used instead of the monthly aggregated EMODNET vessel density maps.

Cargo and tanker ships dominate the upper range of ship lengths, often between 150 and 300 meters, with some exceeding 400 meters, while passenger ships also include very large vessels over 200 meters, corresponding to cruise liners. Since ship length serves as a proxy for vessel capacity and engine power, larger ships generally consume more fuel and emit greater amounts of aerosol precursors. As a result, cargo, tanker, and passenger ships are more important for atmospheric emissions and ship track formation, even though smaller vessels might be more numerous.

Perturbations of the cloud parameters due to ship emissions are detected using machine learning classifiers with Logistic Regression being the baseline and more advanced models like Random Forest Regressor and Gradient Boosting (XGBoost). To quantify the ship-relevant cloud perturbations, the detected perturbations are fed directly to the Radiative Transfer Model pyDOME, which returns the full radiance field together with TOA (top-of-the atmosphere) forcing, surface irradiance and heating‑rate profiles for every perturbation. In order to synthesize the observations-based results and to explore the large-scale implications of the perturbations in marine low-level clouds, simulations are conducted with the state-of-the-art atmospheric general circulation model ICON (the ICOsahedral Non-hydrostatic model).