Research Highlights

The Aerosol-Cloud-Drizzle Convection Chamber (ACDC2) collaboration has developed a comprehensive concept and modeling hierarchy for a convection-cloud chamber facility designed to investigate the chain of events from aerosol activation to cloud droplet growth and drizzle formation within turbulent clouds. The proposed 9-meter-tall chamber enables steady-state turbulence and microphysical conditions, facilitating continuous direct observation of cloud and aerosol properties.

This paired study presents a comprehensive investigation of convective boundary layer (CBL) dynamics by integrating four years of high-resolution Doppler lidar observations from five Atmospheric Radiation Measurement (ARM) User Facility Southern Great Plains (SGP) sites with advanced thermodynamics-guided machine learning.The observational analysis (Fig. 1) first quantified significant sub-grid scale heterogeneity—despite relatively flat terrain, daily maximum mixing layer height (MLH) varied by up to 1-km (∼30% of the mean) within a 100-km domain. The 4-year weekly composite diurnal–seasonal MLHs (Fig. 1c–f) revealed a pronounced east–west contrast that reverses seasonally, driven by land-surface gradients. Rigorous statistical analysis further demonstrated that MLH is positively correlated with surface-sensible heat flux (SHF) and negatively correlated with lower tropospheric stability (LTS). However, these traditional correlations could not fully explain the observed site-to-site differences.Building upon these findings, the team developed a novel thermodynamics-guided machine learning framework to overcome the limitations of conventional statistics. By incorporating physics-informed energy-balance constraints and the full diurnal cycle as input features, AutoML (TPOT + AutoKeras) was used to identify the optimal model architecture and parameters. The resulting models achieved high-predictive accuracy (R² = 0.84 at the Central Facility; R² = 0.79–0.81 when transferred to nearby sites). SHAP (SHapley Additive exPlanations) interpretability analysis (Fig. 2) then revealed that LTS remains the dominant predictor year-round, with modest seasonal shifts in feature importance (<10%) and notably higher model uncertainty in summer (JJA), reflecting greater surface-atmospheric interference.

Understanding the effects of gentle topography on turbulent flows above forests is key to studying planetary boundary layer (PBL) dynamics and forest-atmosphere exchanges. It also has direct implications on how we interpret tower measurements and eddy-covariance fluxes. Large-eddy simulations (LES) using real topography generate overwhelming complexity in the wind fields, making it difficult to formulate general conclusions about physical processes. Idealized simulations typically use topography that are not representative of the large plateaus and narrow valleys encountered in the Amazon forest. We developed a new framework to study flow over simplified topography using LES, which is designed to capture the differences between hills and valleys. We find that while hills tend to generate elevated shear layers emanating from the hilltop that largely enhance turbulence kinetic energy (TKE) and mixing in the lower portion of the PBL, valleys produce regions of very low TKE at the top of the forest, reducing forest-atmosphere exchanges.