基于全球模式对中国极端气温指数模拟的评估
收稿日期: 2007-07-24
修回日期: 2007-10-09
网络出版日期: 2008-03-25
基金资助
国家自然基金项目(40675043); 江苏气象灾害重点实验室项目(KLME050209)
Evaluating the Simulation of the GCMS on the Extreme Temperatur e Indices in China
Received date: 2007-07-24
Revised date: 2007-10-09
Online published: 2008-03-25
Supported by
National Natural Science Foundation of China, No.40675043; Key Project for Jiangsu Key Laboratory of Meteorological Disaster, No.KLME050209
对IPCC 所提供的7 个全球海气耦合模式输出信息(年霜冻日数、生物生长季、温度 年较差、暖夜指数、热浪指数), 利用同期(1961-2000 年) 中国地区极端气温观测资料检测并 评价模式的预估效能。结果表明, 这些模式对中国地区的极端气温都具有一定的模拟能力, 但同时各个模式的模拟场都有各自的系统误差; 综合评价, 在7 个模式中GFDL-CM2.0 和 MIROC3.2 (hires) 两个模式对中国区域极端气温的模拟效果均为最佳。模拟所得的最优指数 为霜冻日数, 其后依次为: 暖夜指数、热浪指数、气温年较差和生物生长季; 而就空间分布 结构来看, 除暖夜指数的模拟效果较差之外, 其余指数均能较好地模拟出其空间分布特征。
王冀, 江志红, 宋洁, 丁裕国 . 基于全球模式对中国极端气温指数模拟的评估[J]. 地理学报, 2008 , 63(3) : 227 -236 . DOI: 10.11821/xb200803001
Based on the same term observations of extreme temperature data during 1961-2000 in China, we have evaluated seven model's output product including frost days (FD), growing season length (GSL), extreme temperature range (ETR), warm nights (TN90), and heat wave duration index (HDWI) supplied by the IPCC-AR4. The results show that all the models have the capability of modeling temperature characteristics in spatial and temporal variations and there are systematic errors in each model. This result indicates that the models' simulation accuracies for the five temperature indices are in the order from the best to the worst: FD, TN90, HWDI, ETR and GSL. In terms of the spatial distribution, the bad modeling effect is TN90, the characteristic distributions of other extreme temperature indices can be modeled. Generally, GFDL-CM2.0 and MIROC3.2 (hires) can best model the extreme temperature indices in China.
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