| Southeast Pacific low-cloud simulation in the NCEP GFS : role of vertical mixing and shallow convection - :6964 | National Weather Service (NWS)
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Southeast Pacific low-cloud simulation in the NCEP GFS : role of vertical mixing and shallow convection
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    Two simple modifications are made in the physics of the NCEP Global Forecast System (GFS) model to alleviate an important systematic error: the lack of stratocumulus in the southeast Pacific region. A preliminary step consists of estimating the location of a lowlevel inversion at each grid point. The modifications are based on (a) elimination of background vertical diffusion above the low-level inversion and (b) incorporation of a tunable parameter based on the cloud-top entrainment instability (CTEI) criterion, according to which shallow convection does not destroy the inversion when it is strong. A control simulation and three additional experiments are performed to examine the individual effect of each modification and the combined effect of the two modifications on the generation of the stratocumulus clouds. It is found that both modifications result in enhanced cloudiness in the Southeast Pacific (SEP) region, although cloudiness is still low in reference to the ISCCP climatology. If the two modifications are applied together, however, the total cloudiness produced in the southeast Pacific has realistic values. This nonlinearity arises as both modifications reinforce each other in reducing the leakage of moisture across the inversion. Less mixing traps more moisture at low levels below the inversion and results in more condensation. In generating clouds, the large-scale condensation process was the main contributor. The convective parameterization also provided a positive, but smaller contribution. Although the amount of total cloudiness obtained with both modifications has the correct magnitude, the relative contributions of low, middle, and high layers may differ from the observed. Our experiments demonstrate that it is possible to simulate realistic marine boundary clouds in large-scale models with relatively simple and physically based changes in the model parameterizations

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