With enhanced computational capacity, the treatment of subgrid processes in global Earth System Models (ESMs) has grown increasingly complex. Despite these enhancements, critical biases remain in the modeling of fundamental processes that govern both the mean climate and the development of extreme weather phenomena of high societal impact.

The importance of the parameterization of momentum flux within the boundary layer (PBL) for modeled tropical cyclones (TCs) is well established for models at various spatial resolutions. However, few studies have specifically explored the modulation of TC structure by the PBL parameterization in a coarser-resolution ESM. In this study, we evaluate the role of the PBL scheme on modeled TC structure in the Community Atmosphere Model version 6 (CAM6), which is the atmospheric component of the Community Earth System Model version 2 (CESM2). CAM6 employs the Cloud Layers Unified by Binormals (CLUBBX) scheme.

We perform a sensitivity analysis, the Morris one-at-a-time (MOAT) method, to evaluate the influence of various tunable CLUBBX input parameters on process-based metrics that characterize TC structure in an idealized framework. We find that certain tunable CLUBBX parameters controlling vertical turbulent mixing in the PBL modulate key TC metrics like jet height, inflow angle, and surface heat flux. We further demonstrate that targeted perturbations to these influential parameters can reduce established ESM biases in modeled TC structure.

We then repeat the MOAT sensitivity analysis on global ESM simulations to evaluate how these CLUBBX input parameters impact process-based climate metrics on regional and global scales. We find that CLUBBX parameters that influence TC structure also influence various regionally and globally-averaged climate metrics, including thermodynamic profiles, cloud-radiative forcing, and surface wind stress, at both short (3 days) and long (10 years) timescales. We explore physical mechanisms for these demonstrated parameter sensitivities and discuss practical implications of targeted model tunings for long-term climate simulations.