Thursday, 19 April 2018: 9:30 AM
Heritage Ballroom (Sawgrass Marriott)
Mesoscale organized convection is generally lacking or misrepresented in large-scale convective parameterization of contemporary weather and climate models. Which affects the upscale convection processes and renders some strong precipitation large-scale phenomenas (e.g., Madden-Jullian Oscillation) missing in subseasonal to decadal forecasts. Super-parameterization attempts to resolve this issue by embedding a 2D cloud-resolving model in each column of the GCMs. Studies have found that this multiscale modeling framework enhances the mesoscale organization processes and is able to produce realistic large-scale con- vectively coupled waves. We developed an algorithm to detect and cluster the 3-hourly 2D resolved MCSs in the cloud-permitting Super-Parameterized Community Atmosphere Model (SPCAM). We then applied composite analysis to compare the large-scale cumulus heat source and moisture sink of SPCAM with the simultaneous response of CAM. This allows us to see if the two models exibit large-scale organization properties of MCSs. The result shows SPCAM with reasonable growth to decay of a deep convection dominant mode which is characteristic to MCSs. Cold pool stength and low level wind shear in SPCAM also shown to be supporting the active MCSs. Whereas CAM reacts with an invariant stratiform dominant mode lacking the phase transition of organization. With the better representation of MCS organization in SPCAM, we proposed an approach to incorporate the mesoscale organization process with stochastic parameterization in CAM.
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