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The Tropical Subseasonal Variability Simulated in the NASA GISS General Circulation Model

Cited 141 time in Web of Science Cited 146 time in Scopus

Kim, Daehyun; Sobel, Adam H.; Del Genio, Anthony D.; Chen, Yonghua; Camargo, Suzana J.; Yao, Mao-Sung; Kelley, Maxwell; Nazarenko, Larissa

Issue Date
JOURNAL OF CLIMATE, Vol.25 No.13, pp.4641-4659
The tropical subseasonal variability simulated by the Goddard Institute for Space Studies general circulation model, Model E2, is examined. Several versions of Model E2 were developed with changes to the convective parameterization in order to improve the simulation of the Madden-Julian oscillation (MJO). When the convective scheme is modified to have a greater fractional entrainment rate, Model E2 is able to simulate MJO-like disturbances with proper spatial and temporal scales. Increasing the rate of rain reevaporation has additional positive impacts on the simulated MJO. The improvement in MJO simulation comes at the cost of increased biases in the mean state, consistent in structure and amplitude with those found in other GCMs when tuned to have a stronger MJO. By reinitializing a relatively poor-MJO version with restart files from a relatively better-MJO version, a series of 30-day integrations is constructed to examine the impacts of the parameterization changes on the organization of tropical convection. The poor-MJO version with smaller entrainment rate has a tendency to allow convection to be activated over a broader area and to reduce the contrast between dry and wet regimes so that tropical convection becomes less organized. Besides the MJO, the number of tropical-cyclone-like vortices simulated by the model is also affected by changes in the convection scheme. The model simulates a smaller number of such storms globally with a larger entrainment rate, while the number increases significantly with a greater rain reevaporation rate.
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  • College of Natural Sciences
  • Department of Earth and Environmental Sciences
Research Area Climate Change, Earth & Environmental Data, Severe Weather, 기후과학, 위험기상, 지구환경 데이터과학


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