Diurnal variability of low clouds in the Southeast Pacific simulated by a...

Cheng, A., and K. Xu (2013), Diurnal variability of low clouds in the Southeast Pacific simulated by a multiscale modeling framework model, J. Geophys. Res., 118, 9191-9208, doi:10.1002/jgrd.50683.

This study analyzes the diurnal variations of austral-spring stratocumulus clouds in the Southeast Pacific and their physical mechanisms from a global multiscale modeling framework (MMF) simulation. This MMF contains an advanced third-order turbulence closure in its cloud-resolving model component, helping it to realistically simulate boundary layer turbulence and low-level clouds. The main finding is that the MMF simulation can reproduce the spatial pattern of the diurnal variations of low clouds within the region, with the day-night cloud fraction (CF) differences ranging from 0.10 at 30° off the shore to 0.40 near the shore. The diurnal phases and ranges of simulated liquid water path, CF, and surface cloud radiative effects agree well with available observations. The maximum CF occurs in the early morning and the minimum in the late afternoon over the open ocean. However, near the shore, the maximum/minimum CF anomalies are more variable. The spatial variability of the diurnal variations is attributed to the modulation of solar-forced variation by the orographically induced circulation. The solar radiation makes the lower cloud layer dissipated during the day, and clouds recover first there in the early evening, with the upper cloud layer changing relatively less in cloudiness. The southwestward propagating upsidence wave that is related to the orographical forcing modulates the CF anomalies near the shore. The orographically induced subsidence, however, extends too deeply into the boundary layer because of the model’s unrealistically smooth topography, and it dissipates rather than enhances the stratocumulus near the shore between the late night and the following noon.

PDF of Publication: 
Download from publisher's website.
Research Program: 
Modeling Analysis and Prediction Program (MAP)