Research Highlights

Aerosols influence how clouds form, persist, and reflect sunlight, but their interactions remain one of the largest uncertainties in earth system modeling. Researchers used a regional testbed of the Energy Exascale Earth System Model (E3SM) with regionally refined meshes (RRMs) to explore how kilometer-scale resolution changes the simulation of aerosols and clouds across diverse regions—from the Central United States to the Southern Ocean. Simulations were evaluated against observational data from Atmospheric Radiation Measurement (ARM) User Facility, satellite, and other field campaign and surface measurements. The field campaigns (HI-SCALE, ACE-ENA, CSET, and SOCRATES) supply in situ aerosol and cloud microphysical properties, while satellite and surface observations provide additional constraints on cloud cover, cloud condensate amount, and precipitation. Convection-permitting RRM improves heavy-rain representation but worsens light-drizzle biases in marine regimes; cloud cover and liquid water path (LWP) agree better with geostationary satellite retrievals, while some surface comparisons favor the coarse-resolution model. For aerosols, kilometer-scale simulations exhibit higher ultrafine aerosol number concentration due to stronger new particle formation (NPF) while reducing accumulation-mode aerosol numbers through more efficient precipitation scavenging over oceans. Increasing resolution also enhances deposition and coagulation in some continental boundary layers. These shifts cut cloud condensation nuclei (CCN) and drive large reductions in cloud-droplet number (Nd), with broader implications for albedo and lifetime effects. Notably, several ACI process relationships improve: the CCN–Nd correlation moderates toward observations, and LWP–Nd behavior is better captured, indicating gains in the realism of ACI coupling even as absolute biases persist. These results reveal how model resolution modifies the processes linking aerosols to clouds and highlight where physical representations must be refined.

Collaborative capabilities were designed to enable unique measurements of aerosol optical properties, water uptake, cloud formation potential, and chemical composition to understand how sources, aging and mixing affect energy within earth systems. Three aerosol regimes were probed in depth during a summer campaign in Houston, Texas: urban, particle growth, and dust.

The ARM Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) applies a multi-sensor approach to classify thermodynamic cloud phase by integrating lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. Cloud Hydrometeors are classified into seven phase categories including: liquid, drizzle, liquid + drizzle (liq_driz), rain, ice, snow, and mixed-phase. In this study, we evaluated a machine learning (ML) method for thermodynamic cloud phase classification, trained on three years of THERMOCLDPHASE VAP observations.