RACORO: Evaluation of the SCAM5 Shallow Cumulus Parameterization

Monday, May 12, 2014 - 07:00
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The FAst-physics System TEstbed and Research (FASTER) project has constructed three cases over the Atmospheric Radiation Measurement (ARM) Climate Research Facility's Southern Great Plain site during the RACORO Campaign [Routine ARM Aerial Facility (AAF) Clouds with Low Optical Water Depth (CLOWD) Optical Radiative Observations] to facilitate the research of model representation of continental boundary-layer clouds (see the Vogelmann et al. poster). The cumulus case is used to evaluate the parameterization of shallow cumulus convection in single-column Community Atmosphere Model version 5 (SCAM5). Multiple observationally-constrained large-scale forcings are used to drive the SCAM5 simulations. The model's shallow cumulus convection scheme tends to significantly under-produce clouds during the time when shallow cumulus activity prevails in the observations. Large-eddy simulations that are driven by the same large-scale forcings (see the Endo et al. poster), along with flight measurements, are used to guide the investigation of the underlying causes for the SCAM5 model biases. It is found that the weaker simulated boundary layer turbulence is directly responsible for the weaker cumulus activity that leads to insufficient cumulus cloud production. It is also found that the fractional entrainment and detrainment rates in the model's cumulus cloud layer are one order of magnitude smaller than the estimates from the LES simulations and flight measurements. However, the smaller fractional entrainment/detrainment rates are unlikely directly responsible for the SCAM5's weaker bulk cumulus activity. The shallower model boundary layer, as a result of the weaker boundary layer turbulence, along with insufficient convective ventilation leads to excessive stratiform cloud production both when cumulus is still active and after cumulus activity subsides, especially during nighttime. This is a clear example that underrepresentation of one cloud type can lead to an enhanced misrepresentation of other cloud types. The results also manifest the challenge of having a modularized treatment of intimately related physical processes for cloud representation, since biases in one module can propagate into and become amplified in other modules.