Cloud-resolving simulation of TOGA-COARE using parameterized large-scale dynamics
Variations in deep convective activity during the 4 month Tropical Ocean Global Atmosphere-Coupled Ocean Atmosphere Response Experiment (TOGA-COARE) field campaign are simulated using a cloud-resolving model (CRM). Convection in the model is coupled to large-scale vertical velocities that are parameterized using one of two different methods: the damped gravity wave (Damped-wave) method and the weak temperature gradient (WTG) method. The reference temperature profiles against which temperature anomalies are computed are taken either from observations or from a model integration with no large-scale vertical motion (but other forcings taken from observations); the parameterized large-scale vertical velocities are coupled to those temperature (or virtual temperature) anomalies. Sea surface temperature, radiative fluxes, and relaxation of the horizontal mean horizontal wind field are also imposed. Simulations with large-scale vertical velocity imposed from the observations are performed for reference. The primary finding is that the CRM with parameterized large-scale vertical motion can capture the intraseasonal variations in rainfall to some degree. Experiments in which one of several observation-derived forcings is set to its time-mean value suggest that those which influence direct forcings on the moist static energy budget—surface wind speed and sea surface temperature (which together influence surface evaporation) and radiative cooling—play the most important roles in controlling convection, particularly when the Damped-wave method is used. The parameterized large-scale vertical velocity has a vertical profile that is too bottom-heavy compared to observations when the Damped-wave method is used with vertically uniform Rayleigh damping on horizontal wind, but is too top-heavy when the WTG method is used.
- Wang_Sobel_Kuang_2013_JGR.pdf application/pdf 4.3 MB Download File
Also Published In
- Journal of Geophysical Research: Atmospheres