Low-level marine clouds are a key feature of the atmosphere, determining incoming solar radiation to the ocean surface. In subtropical regions, low-level clouds often begin their life as a stratocumulus cloud deck associated with high cloud cover and transition to open-cell or cumulus clouds with reduced cloud cover. The stratocumulus to cumulus transition (SCT) is strongly controlled by large scale circulation, but the transition rate is also tightly coupled to microphysical controls on precipitation formation that ultimately determine boundary layer thermodynamics. Accurately representing the micro-to-macro physics driving this key cloud type has challenged coarse resolution models and likely contributes to large SST biases in coupled simulations. A key challenge for improving simulated SCT is a lack of diagnostics that
inform micro-to-macro model development, especially for coarse resolution Earth system models that are tasked with simulating climate and require simplified microphysics. As part of the NSF NCAR DO Initiative Integrating Field Observations and Research Models (INFORM), we
aim to amplify the utility of existing airborne observations in model assessment and development. In this talk, we will present the design of a diagnostic framework for macro-, micro-, and process-level assessments of the SCT cloud regime. The SCT diagnostic framework utilizes a regime-based analysis that composites aircraft observations and model data into stratocumulus and cumulus cloud types. The compositing method was developed based on airborne radar observations combined with environmental conditions from reanalysis. We will present composite-based comparisons for a series of CAM6 and CAM7 simulations that explore the optimum nudged configuration for assessing the representation of physical processes while constraining the meteorology sufficiently for meaningful comparison. We will also present results of utilizing this SCT framework as part of ongoing model development targeting physics relevant to the SCT.