赵得明,符淙斌. 2010. 区域环境系统集成模式对1997/1998年夏季中国两个极端气候事件模拟能力分析[J]. 气象学报, 68(3):325-338, doi:10.11676/qxxb2010.033
区域环境系统集成模式对1997/1998年夏季中国两个极端气候事件模拟能力分析
The analysis of the ability of RIEMS2.0 to simulate the two extreme climate events in the summers of 1997/1998 in China.
投稿时间:2008-11-10  修订日期:2009-12-17
DOI:10.11676/qxxb2010.033
中文关键词:  RIEMS2.0, 模拟能力, 集合模拟, 极端气候事件, 区域气候模式
英文关键词:RIEMS2.0, Simulation ability, Ensemble simulation, Extreme climate events
基金项目:国家重点基础研究发展计划项目(2006CB400500)和国家自然科学基金资助项目(40975053)
作者单位
赵得明 中国科学院大气物理研究所东亚区域气候环境重点实验室北京100029 
符淙斌 中国科学院大气物理研究所东亚区域气候环境重点实验室北京100029 
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中文摘要:
      区域环境系统集成模式(RIEMS2.0, Regional Integrated Environment Modeling System Version 2.0)是由中国科学院大气物理研究所东亚区域气候环境重点实验室在RIEMS1.0基础上发展的区域气候模式。为了检验RIEMS2.0对短期气候的模拟能力,利用降水和气温(2 m)观测资料检验RIESM2.0不同物理过程和初始条件集合模拟1997/1998年夏季中国华北地区高温干旱和长江流域洪涝两个连续极端气候事件的能力(连续积分时间(1997年3月1日—1998年8月31日)共18个月),比较模拟和观测的1997/1998年夏季降水和气温。集合模拟结果表明RIEMS2.0能很好模拟1997/1998年夏季降水和气温及其两年差值分布;模拟和观测的日降水和平均气温结果有很好的相关性,但是降水模拟总体高估,干旱和江淮及江南区气温模拟偏高而半干旱和湿润区气温模拟偏低。在不同物理过程集合模拟中,虽然集合平均距平相关系数(ACC)和均方根误差(RMSE)并不是优于所有集合成员值,但集合模拟能减小模式的不确定性,在一定程度上提高模拟精度。不同显式水汽方案和积云参数化方案对降水、气温模拟效果表现出很好的一致性,湿润区一致性最好。因此,RIEMS2.0模拟能揭示1997/1998年两个连续极端气候事件夏季降水和气温空间分布,反映不同子区域降水和气温分布特征,各集合成员的模拟结果存在差异的同时也保持了很好的稳定性,选择合适的物理过程可以提高模式对区域气候的模拟能力。
英文摘要:
      Regional Integrated Environment Modeling System Version 2.0(RIEMS2.0 ) is a regional climate model that is developed starting from RIEMS1.0 by the Key Laboratory of Regional Climate Environment Research for Temperate East Asia, the Institute of Atmospheric Physics, Chinese Academy of Sciences, China. In order to test RIEMS2.0’s ability to simulate short term climate, we perform ensemble simulations with different physics process schemes, as well as ensemble simulations with two cumulus convective parameterization schemes (Grell and Kain-Fritsch) under different initial conditions. The model is used to perform ensemble simulations on the two continuous extreme climate events, which are the severe drought with high temperature in the northern China in the summer (June, July and August) of 1997 and severe flood in the Yangtze River valley in the summer of 1998 (simulation period from 1 March 1997 to 31 August 1998). The simulated results in the summer of 1997/1998 are compared with the observed data, as well as the precipitation data from the Global Precipitation Climatology Center (GPCC) and surface (at an elevation of 2 m) air temperature data from the Climate Research Unit (CRU). It is found that RIEMS2.0 ensemble simulations can reproduce spatial distributions of the precipitation and surface air temperature, and of the differences between those for the summer of 1997 and for 1998. The simulated results are well correlated with the observed data with the correlation coefficients for surface air temperature greater than those for the precipitation. However, except for the summer of 1998 in the humid sub regions, the precipitation is overestimated as a whole, which is on the large side in the semi arid sub regions and on the small side in the humid sub regions. Meanwhile, surface air temperature is overestimated in the arid, Jianghuai and Jiangnan sub regions, and underestimated in the semi arid and humid sub regions. For the ensemble simulations with different physical process schemes, there are less biases for surface air temperature in the semi arid and humid sub regions, and greater bias in the arid, Jianghuai and Jiangnan sub regions. For either precipitation or surface air temperature, the ACC (anomaly correlation coefficients)/RMSE (root mean squared error) from ensembles are greater/less than the averaged one over those from individual ensemble members. Though ACCs and RMSEs from the ensembles aren’t better than those from every individual ensemble member, ensembles are able to decrease the model’s uncertainty and improve the simulation precision in a certain degree. There is nice consistency in the simulations on both the precipitation and surface air temperature between different explicit moisture schemes and cumulus convective parameterization schemes used, especially in the humid sub regions. Furthermore, comparisons of the simulations of the precipitation and surface air temperature between the different physical processes employed are able to disclose the importance of choosing suitable physical processes, which is very helpful to further model development and application. The simulations can be improved via selecting suitable schemes based on the regional characteristics. As a matter of fact, by doing so, RIEMS2.0 reproduced the spatial distribution of the precipitation and surface air temperature for these two continuous extreme climate events in the summers of 1997/1998, and disclosed the sub regional characteristics. Though there exists some difference among the simulated results of ensemble members, the model shows good stability. The model’s performance on the precipitation and surface air temperature simulation can be improved with suitable physics process schemes selected.
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