基于全球模式对中国极端气温指数模拟的评估

doi:10.11821/xb200803001

地理学报 ›› 2008, Vol. 63 ›› Issue (3): 227-236.doi: 10.11821/xb200803001

• 气候变化 • 下一篇

基于全球模式对中国极端气温指数模拟的评估

王冀¹, 江志红¹, 宋洁^1,2, 丁裕国¹

1. 南京信息工程大学, 江苏省气象灾害重点实验室, 大气科学学院, 南京210044;
2. 北伊利诺伊大学, 美国

收稿日期:2007-07-24 修回日期:2007-10-09 出版日期:2008-03-25 发布日期:2008-03-25
作者简介:王冀(1972-), 男, 博士研究生, 主要从事全球气候变化研究,E-mail: wangji_zl@163.com
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

WANG Ji¹, JIANG Zhihong¹, SONG Jie^1,2, DING Yuguo¹

1. Nanjing University of Information Science & Technology, Jiangsu Key Laboratory of Meteorological Disaster, Nanjing 210044, China;
2. Department of Geography, Northern Illinois University, DeKalb, Illinois 60115, USA

Received:2007-07-24 Revised:2007-10-09 Online:2008-03-25 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

摘要/Abstract

摘要：

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。

关键词: 全球海气耦合模式, 未来极端气候预估, 中国区域, 极端气温指数

Abstract:

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.

Key words: global ocean-atmosphere coupling model, future extreme climate assessment, extreme temperature index in China

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.

WANG Ji, JIANG Zhihong, SONG Jie, DING Yuguo. Evaluating the Simulation of the GCMS on the Extreme Temperatur e Indices in China[J]. Acta Geographica Sinica, 2008, 63(3): 227-236.

参考文献

[1] IPCC. Climate Change 2001. Houghton J T et al. (eds.) Cambridge: Cambridge University Press, 2001.

[2] Xu Yinlong. Analyses on scenario simulations of the 21st century climate change in China. Journal of Nanjing Institute of Meteorology, 2005, 28(3): 324-329.
[许吟隆. 中国21 世纪气候变化的情景模拟分析. 南京气象学院学报, 2005, 28 (3): 324-329.]

[3] Chen Qiying, Yu Yongqiang, Guo Yufu. Climatic change in East Asia induced by greenhouse effect. Climatic and Environmental Research, 1996, 1(2): 114-123.
[陈起英, 俞永强, 郭裕福. 温室效应引起的东亚区域气候变化. 气候与环境研究, 1996, 1(2): 114-123.]

[4] Jiang Dabang, Wang Huijun, Lang Xianmei. East Asian climate change trend under global warming background. Chinese Journal of Geophysics, 2004, 47(4): 591-596.
[姜大膀, 王会军, 郎咸梅. 全球变暖背景下东亚气候变化的最新情景预测. 地球物理学报, 2004, 47(4): 591-596.]

[5] Jiang Dabang, Wang Huijun, Lang Xianmei. Multimodel ensemble prediction for climate change trend of China under SRES A2 Scenario. Chinese Journal of Geophysics, 2004, 47(5): 777-784.
[姜大膀, 王会军, 郎咸梅. SRES A2 情景下中国气候未来变化的多模式集合预测结果. 地球物理学报, 2004, 47(5): 777-784.]

[6] Gao Xuejie, Zhao Zongci, Ding Yihui et al. Climate change due to greenhouse effects in China as simulated by a regional climate model (Part II). Acta Meteorologica Sinica, 2003, 61(1): 30-38.
[高学杰, 赵宗慈, 丁一汇等. 温室效应引起的中国区域气候变化的数值模拟(II). 气象学报, 2003, 61(1): 30-38.]

[7] Gao X J, Zhao Z C, Ding Y H et al. Climate change due to greenhouse effects in China as simulated by a regional climate model. Advances in Atmospheric Sciences, 2001, 18(6): 1225-1230.

[8] Gao Xuejie, Zhao Zongci, Ding Yihui et al. Climate change due to greenhouse effects in China as simulated by a regional climate model. Part II. Acta Meteorologica Sinica, 2003, 61(1): 21-28.
[高学杰, 赵宗慈, 丁一汇等. 温室效应引起的中国区域气候变化的数值模拟(II) . 气象学报, 2003, 61(1): 21-28.]

[9] Xu Ying, Ding Yihui, Zhao Zongci. Detection and evaluation of effect of human activities on climatic change in East Asia in recent 30 years. Journal of Applied Meteorological Science, 2002, 13(5): 514-525.
[徐影, 丁一汇, 赵宗慈. 近 30 年人类活动对东亚地区气候变化影响的检测与评估. 应用气象学报, 2002, 13(5): 514-525.]

[10] Sun Ying. Introduction to analyses of climate model simulations for the IPCC AR4. Advances in Climate Change Research, 2005, (4): 162-163.
[孙颖. 用于IPCC 第四次评估报告的气候模式比较研究简介. 气候变化研究进展, 2005, (4): 162-163.]

[11] Zhou Tianjun, Zhao Zongci. Attribution of the climate warming in China for the 20th century. Advances in Climate Change Research, 2006, 2(1): 28-31.
[周天军, 赵宗慈. 20 世纪中国气候变暖的归因分析. 气候变化研究进展, 2006, 2(1): 29-31.]

[12] Zhou Tianjun, Yu Rucong. Twentieth century surface air temperature over China and the globe simulated by coupled climate models. Journal of Climate, 2006, 19(22): 5843-5858.

[13] Frich P, Alexander L V, Della-Marta P et al. Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res., 2002, 19: 193-212.

[14] Salasy M'elia D, Chauvin F, D'equ'e M et al. Description and validation of the CNRM-CM3 global coupled model. Climate Dyn., 2006, 26: 199-214.

[15] Delworth T L, Broccoli A J, Rosati A et al. GFDLs CM2 global coupled climate models (Part 1): Formulation and simulation characteristics. J. Climate, 2006, 19(5): 643-674.

[16] Gnanadesikan A et al. GFDL's CM2 global coupled climate models. Part II: The baseline ocean simulation. Journal of Climate, 2006, 19: 675-697.

[17] Diansky N A, Volodin E M. Simulation of presentday climate with a coupled atmosphere-ocean general circulation model. Izv. Atmos. Oceanic Phys., 2002, 38: 732-747.

[18] Marti O et al. The new IPSL climate system model: IPSLCM4, Tech. Rep., Institut Pierre Simon Laplace des Sciences del' Environnement Global, IPSL, Case 101, 4 place Jussieu, Paris, France, 2005.

[19] Hasumi H, Emori S. K-1 coupled model (MIROC) description. K-1 Tech. Rep. 1, Center for Climate System Research, University of Tokyo, 2004, 34.

[20] Thomas C. Peterson and Christopher Folland Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1-143.

[21] Frich P. REWARD: A Nordic Collaborative Project. Annex of "Meeting of the Joint CCl/CLIVAR Task Group on Climate Indices, Bracknell, UK, 2-4 September 1998. Folland C K, Horton E B, Scholefield P R (eds.). World Climate Data and Monitoring Programme, WCDMP-No. 37, WMO-TD No.930. WMO, 1999.

[22] Alexander L V, Zhang X, Peterson T C et al. Global observed changes in daily climate extremes of temperature and precipitation. Geophys. Res., 2006, 111: 1-22.

基于全球模式对中国极端气温指数模拟的评估

Evaluating the Simulation of the GCMS on the Extreme Temperatur e Indices in China

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

Metrics

本文评价

[1]	杜军, 路红亚, 建军. 1961-2010 年西藏极端气温事件的时空变化[J]. 地理学报, 2013, 68(9): 1269-1280.
[2]	王英杰, 余卓渊, 严虹, 张燕. 中国区域发展电子地图集设计[J]. 地理学报, 2001, 56(7s): 64-72.
[3]	叶冰, 李平. 一部全面论述中国区域问题的力作——《1997中国区域发展报告》[J]. 地理学报, 1998, 53(3): 285-287.