Observational and Numerical Case Study of the Aerosol-Cloud-Precipitation Interaction in Warm Boundary Layer Clouds over the Eastern North Atlantic

Previous studies have revealed the significant influence of weather regimes on the observed correlation of Aerosol-Cloud Interaction (ACI). For instance, ACI within marine boundary layer clouds has been noted to be enhanced during cold-air outbreaks. The Eastern North Atlantic (ENA) region, situated in the transition between subtropical and midlatitude dynamic regimes, experiences diverse meteorological conditions even in summertime without winter storms. These conditions encompass relatively consistent trade wind patterns, mid-latitude cyclones accompanied by fronts, and the Azores high-pressure system.

The long-term cloud observations conducted by the U.S. DOE Atmospheric Radiation Measurement (ARM) program in the ENA region offer an ideal resource for investigating interactions involving clouds, aerosols, and precipitation across different synoptic scenarios. Therefore, we conducted an analysis using ARM ENA ground-based observations and geostationary cloud retrievals. This analysis focused on the period from June to August, covering 2016 to 2021. Within this timeframe, we identified over 120 days that provided a comprehensive dataset of ARM cloud, precipitation, Planetary Boundary Layer (PBL), and surface Cloud Condensation Nuclei (CCN) measurements. These days represented typical warm boundary layer cloud conditions in the ENA region.

From these warm boundary layer cloud days, we further refined our selection to include cases aligned with the typical synoptic regimes associated with marine boundary layer clouds over the ENA region. These chosen cases also serve as the basis for our Large-Eddy Simulation (LES) experiments. Our presentation aims to provide an overview of both observational and numerical modeling results derived from these selected cases, shedding light on aerosol-cloud-precipitation interactions under varying synoptic conditions in the ENA region.

Acknowledgment: This work is supported by the DOE Office of Science Early Career Research Program and the ASR Program. This work was performed under the auspices of the U.S. DOE by LLNL under contract DE-AC52-07NA27344. LLNL-ABS-848721