期刊
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
卷 7, 期 4, 页码 1643-1660出版社
AMER GEOPHYSICAL UNION
DOI: 10.1002/2015MS000488
关键词
cloud-resolving models; superparameterization; large eddy simulation
资金
- NOAA MAPP [GC13-560]
- U.S. Department of Energy [DE-SC0012152, DE-SC0012451, DE-SC0012548]
- U.S. Department of Energy (DOE) [DE-SC0012152, DE-SC0012451, DE-SC0012548] Funding Source: U.S. Department of Energy (DOE)
Large eddy simulations and cloud-resolving models (CRMs) are routinely used to simulate boundary layer and deep convective cloud processes, aid in the development of moist physical parameterization for global models, study cloud-climate feedbacks and cloud-aerosol interaction, and as the heart of superparameterized climate models. These models are computationally demanding, placing practical constraints on their use in these applications, especially for long, climate-relevant simulations. In many situations, the horizontal-mean atmospheric structure evolves slowly compared to the turnover time of the most energetic turbulent eddies. We develop a simple scheme to reduce this time scale separation to accelerate the evolution of the mean state. Using this approach we are able to accelerate the model evolution by a factor of 2-16 or more in idealized stratocumulus, shallow and deep cumulus convection without substantial loss of accuracy in simulating mean cloud statistics and their sensitivity to climate change perturbations. As a culminating test, we apply this technique to accelerate the embedded CRMs in the Superparameterized Community Atmosphere Model by a factor of 2, thereby showing that the method is robust and stable to realistic perturbations across spatial and temporal scales typical in a GCM.
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