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Abstract
As the horizontal resolution of general circulation model (GCM) is increased, the sub-grid scale vertical transport has to be decreased appropriately. In the present study, a resolution-dependent (so-called scale-adaptive or scale-aware) deep convection was formulated by controlling the cumulus base mass flux. Using a three-dimensional cloud resolving model simulation, we estimated the appropriate ratios of the sub-grid scale vertical transport to the total vertical transport of moist static energy for different horizontal resolutions, whose values are about 0.8 for 100 km resolution and about 0.6 for 50 km resolution. Those values were used as a guideline to decrease to the ratio of convective precipitation to the total precipitation in a high-resolution GCM. The cumulus base mass flux is reduced by multiplying a reduction coefficient, which is 0.2 for the 100 km resolution and 0.09 for the 50 km resolution in the present GCM. The GCM with the scale-adaptive deep convection produces the climatological mean precipitation similar to that of the original GCM, whereas it simulates the heavy precipitation frequency and the Madden and Julian Oscillation much better than those of the original GCM.
Climate modeling: Resolution dependent convective parameterization
As the spatial resolution of global climate model is increased, a question has been raised for how much of the convective cloud processes could be resolved and/or parameterized. Prof. In-Sik Kang’s group at Seoul National University has presented a simple way of resolution-dependent convective parameterization. Using a cloud resolving model simulation, they estimated appropriate ratios of the sub-grid scale convective vertical transport to the total vertical transport of moist static energy for different horizontal resolutions, and then controlled the cloud base mass flux for the GCM to produce the appropriate ratio. The GCM with the scale-dependent convective parameterization improves the simulation of heavy precipitation frequency and the Madden-Julian Oscillation.
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Details
1 Chonnam National University, Department of Oceanography, Gwangju, Korea (GRID:grid.14005.30) (ISNI:0000 0001 0356 9399); University of Washington, Department of Atmospheric Sciences, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657); Seoul National University, School of Earth and Environmental Sciences, Seoul, Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905)
2 Seoul National University, School of Earth and Environmental Sciences, Seoul, Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); King Abdulaziz University, Center of Excellence of Climate Change Research, Jeddah, Saudi Arabia (GRID:grid.412125.1) (ISNI:0000 0001 0619 1117)