Monday, 9 July 2018
Regency A/B/C (Hyatt Regency Vancouver)
Depicting a realistic relationship between subtropical marine boundary layer (MBL) cloudiness and inversion strength is essential to understanding how that cloudiness may change in the future, which will affect global energy balance. Here, I present a study in which a Single Column Model (SCM) is used to simulate atmospheres over a variety of environmental conditions along a transect between Los Angeles and Hawaii. The SCM is composed of the JPL Eddy-Diffusivity/Mass-Flux (EDMF) mixing formulation, a fractional cloudiness scheme, and the RRTM-G radiation model. Thermodynamic states are provided by the MERRA2 reanalysis and simulated SCM cloudiness is compared to co-located pixel-level observations from the polar-orbiting CloudSat Cloud Profiling Radar, which accurately retrieves vertically-resolved cloud fraction. This framework presents a unique opportunity to ensure that the cloud parameterization is able to simulate a continuum of atmospheres between shallow cumulus and stratiform clouds under real-world conditions, as opposed to a handful of large eddy simulations based on idealized thermodynamic profiles from field campaigns.
By systematically varying EDMF parameters that are important to shallow convection and stratiform cloudiness, I will show that the implementation of both interactive Mass-Flux entrainment rates and accurate Eddy-Diffusivity mixing lengths are essential for capturing observed increases in MBL cloudiness with increased inversion strength, measured here as lower tropospheric stability (LTS). While simulations made with fixed entrainment rates are able to provide an accurate mean cloud fraction, SCM performance suffers when composited against stability regime, as measured by LTS. The Eddy-Diffusivity mixing length is also shown to play an important role, as too much small-scale turbulence mixes warm air from above the inversion/stratocumulus boundary, reducing the inversion strength and MBL cloud fraction. Joint probability density distributions of MBL cloud fraction, height, and LTS show that the EDMF is able to improve upon its reanalysis input when compared to cloud height and frequency from observations.
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