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Evaluating Low-Cloud Simulation from an Upgraded Multiscale Modeling Framework...

Xu, K., and A. Cheng (2013), Evaluating Low-Cloud Simulation from an Upgraded Multiscale Modeling Framework Model. Part II: Seasonal Variations over the Eastern Pacific, J. Climate, 26, 5741-5760, doi:10.1175/JCLI-D-12-00276.1.

The eastern Pacific is a climatologically important region. Conventional coupled atmosphere–ocean general circulation models produce positive sea surface temperature biases of 2–5 K in this region because of insufficient stratocumulus clouds. In this study, a global multiscale modeling framework (MMF), which replaces traditional cloud parameterizations with a 2D cloud-resolving model (CRM) in each atmospheric column, is used to examine the seasonal variations of this Pacific region. The CRM component contains an advanced third-order turbulence closure, helping it to better simulate boundary layer turbulence and lowlevel clouds. Compared to available satellite observations of cloud amount, liquid water path, cloud radiative effects, and precipitation, this MMF produces realistic seasonal variations of the eastern Pacific region, although there are some disagreements in the exact location of maximum cloudiness centers in the Peruvian region and the intensity of ITCZ precipitation. Analyses of profile- and subcloud-based decoupling measures reveal very small amplitudes of seasonal variations in the decoupling strength in the subtropics except for those regions off the subtropical coasts where the decoupling measures suggest that the boundary layers should be well coupled in all four seasons. In the Peruvian and Californian regions, the seasonal variations of low clouds are related to those in the boundary layer height and the strength of inversion. Factors that influence the boundary layer and the inversion, such as solar incident radiation, subcloud-layer turbulent mixing, and large-scale subsidence, can collectively explain the seasonal variations of low clouds rather than the deepening–warming mechanism of Bretherton and Wyant cited in earlier studies.

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Modeling Analysis and Prediction Program (MAP)