Characteristics of GRAPES Dynamical Core in Long Term Integration

Hu Jianglin; Shen Xueshun; Zhang Hongliang; Yang Xuesheng; Wang Panxing

Vol.18, NO.3, 2007

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2007 > 18(3): 276-284

Hu Jianglin, Shen Xueshun, Zhang Hongliang, et al. Characteristics of GRAPES dynamical core in long term integration. J Appl Meteor Sci, 2007, 18(3): 276-284.

Citation:

Hu Jianglin, Shen Xueshun, Zhang Hongliang, et al. Characteristics of GRAPES dynamical core in long term integration. J Appl Meteor Sci, 2007, 18(3): 276-284.

Hu Jianglin, Shen Xueshun, Zhang Hongliang, et al. Characteristics of GRAPES dynamical core in long term integration. J Appl Meteor Sci, 2007, 18(3): 276-284.

Citation:

Hu Jianglin, Shen Xueshun, Zhang Hongliang, et al. Characteristics of GRAPES dynamical core in long term integration. J Appl Meteor Sci, 2007, 18(3): 276-284.

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Characteristics of GRAPES Dynamical Core in Long Term Integration

1.
Department of Atmospheric Sciences, Nanjing University of Information & Technology, Nanjing 210044
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

Received Date: 2006-03-08
Rev Recd Date: 2007-01-12
Publish Date: 2007-06-30

Abstract

Abstract

In order to evaluate the characteristics of the dynamical core of GRAPES (Global/Regional Assimilation and PrEdiction System) and to verify whether the GRAPES dynamical core can be taken as a framework of AGCM, the long time integration has been carried out with GRAPES dynamical core followed by the benchmark test similar to the technique introduced by Held and Suarez. The GRAPES has a finite-difference dynamical core with fully compressible, non-hydrostatic framework and semi-implicit and semi-lagrangian (SISL) time integration scheme using latitude and longitude horizontal coordinate as well as following terrain height vertical coordinate. The computational design uses two simple physical processes to represent physics processes in the model during the time integration. The first process is to relax the model variant, potential temperature, to a prescribed potential temperature field which is a function of latitude and height during the model time integration. Following Held and Suarez, the second one is the momentum drag in the lower troposphere to consume the kinetic energy in the model atmosphere. The GRAPES dynamical core runs with 31 levels in vertical direction and 3 horizontal resolutions:1.25°, 2.5°and 5.0°latitude or longitude. The benchmark tests integrate for 1460 days under each horizontal resolution and the last 1000 days data are analyzed by discarding the previous 460 day results. The statistic results show that the GRAPES dynamical core can reproduce basic features of atmospheric circulation, including reasonably realistic zonal mean temperature and its eddy variance as well as zonal-mean wind and its eddy variance. A single westerly jet is generated with maximum strength of roughly 28 m/s near 45°latitude in the tropopause. And eddy temperature variance shows two middle latitude maxima, one in the low troposphere and the other above tropopause. The GRAPES dynamical core also shows stability for the energy and momentum as well as vertical wind speed during the long term integration although SISL scheme does not observe the conservations. The GRAPES dynamical core has convergence features along with increasing resolution, although 5.0° resolution in latitude and longitude may be too coarse to represent the circulation details. Finally, the sensitivity to non-hydrostatic is discussed. The results indicate that it is feasible using the GRAPES frame as a dynamical core for AGCM and climate investigation. The numerical experiments provide clues and evidences for GRAPES dynamical core improvement also.
- benchmark tests;
- AGCM;
- dynamical core;
- numerical experiments

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References(20)

Figures and Tables

图 1 平衡位温θ_eq (a) 和温度 (b) 随纬度和高度的分布 (单位:K)

Fig. 1 The distributions of equilibrium potential temperature (a) and temperature (b) with latitude and air pressure (unit:K)

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图 2 1.25°×1.25°31层GRAPES动力框架长时间 (1000 d, 下同) 积分的纬向平均温度 (单位:K)(a), 纬向风速 (单位:m/s)(b) 和纬向平均的垂直速度 (单位:m/s)(c)

Fig. 2 The averaged fields with 1000 d simulations produced by GRAPES dynamical core using the resolution of 1.25°×1.25°grid points and 31 levels (a) zonal mean temperature (unit:K), (b) mean zonal wind (unit:m/s), (c) zonal vertical speed (unit:m/s)

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图 3 1.25°×1.25°31层GRAPES动力框架长时间积分 (a) 纬向平均温度方差 (单位:K²), (b) 纬向风速方差 (单位:m²·s^-2)

Fig. 3 The eddy variance with 1000 d simulations produced by GRAPES dynamical core using the resolution of 1.25°×1.25°grid points and 31 levels (a) temperature (unit:K²), (b) zonal wind (unit:m²·s^-2)

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图 4 1.25°×1.25°31层GRAPES模式动力框架长时间积分的物理量随时间变化 (a) 总能量, (b) 纬向风动量, (c) 单位质量的垂直速度动能 (全球平均)

Fig. 4 The variables produced by the 1.25°×1.25°grid points and 31 levels GRAPES dynamical core with 1000 d simulation (a) general energy, (b) the zonal wind moment, (c) kinetic energy produced by vertical movement per unit mass (averaged by global)

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图 5 不同水平分辨率的GRAPES动力框架长时间积分纬向平均温度场 (单位:K)(a)2.5°×2.5°, (b)5.0°×5.0°

Fig. 5 The zonal mean temperature averaged with 1000 d simulations produced by GRAPES dynamical core with varied horizontal resolutions (unit:K)(a)2.5°×2.5°, (b)5.0°×5.0°

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图 6 不同水平分辨率的GRAPES动力框架长时间积分纬向平均风速 (单位:m·s^-1)(a)2.5°×2.5°, (b)5.0°×5.0°

Fig. 6 Same as in Fig. 5 but for zonal wind (unit:m·s^-1)(a)2.5°×2.5°, (b)5.0°×5.0°

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图 7 GRAPES动力框架长时间积分的纬向平均温度 (单位:K)(a) 和纬向平均风速 (单位:m/s)(b) 的对比 (非静力-静力)

Fig. 7 The difference between nonhydrostatic and hydrostatic for zonal mean temperature (unit:K)(a) and wind (unit:m/s)(b) with 1000 d simulations produced by GRAPES dynamical core

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