气候变化研究进展 ›› 2016, Vol. 12 ›› Issue (4): 268-275.doi: 10.12006/j.issn.1673-1719.2015.188

• 气候系统变化 • 上一篇    下一篇

ACCMIP全球模式对中国地面PM2.5浓度模拟能力评估

吴 婕1,2,徐 影2,周波涛2   

  1. 1 中国气象科学研究院,北京100081;
    2 中国气象局国家气候中心,北京100081
  • 收稿日期:2015-11-10 修回日期:2015-12-24 出版日期:2016-07-30 发布日期:2016-07-30
  • 通讯作者: 徐影 E-mail:xuying@cma.gov.cn
  • 基金资助:

    国家科技支撑计划;公益性行业(气象)专项

The Evaluation of Surface PM2.5 Concentration over China Based on ACCMIP Models

Wu Jie1, 2, Xu Ying2, Zhou Botao2   

  1. 1 Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 10081, China; 
    2 National Climate Center, China Meteorological Administration, Beijing 100081, China
  • Received:2015-11-10 Revised:2015-12-24 Online:2016-07-30 Published:2016-07-30

摘要:

本文利用MODIS和MISR卫星反演的地面PM2.5浓度和来自大气化学和气候模式比较计划(ACCMIP)的4个耦合了大气化学模块的气候模式(GFDL-AM3、NCAR-CAM3.5、GISS-E2-R和MIROC-CHEM)模拟的PM2.5浓度数据,评估分析了4个全球模式对中国地区地面PM2.5浓度时空变化特征的模拟能力。结果表明:4个模式集合模拟的PM2.5浓度在中国东部模拟效果较好。对比单个模式,GFDL-AM3模式对中国PM2.5浓度的空间分布型模拟效果最好。模式结果之间的一致性差异显著的地区主要出现在新疆中部和内蒙古西部地区。从整个中国地区的区域平均的时间序列来看,4个模式集合平均结果与观测结果相差不大,基本能够反映出东北、华中、华东沿海、新疆西部地区的PM2.5浓度的变化趋势。

关键词: ACCMIP, PM2.5浓度, 模拟能力评估

Abstract:

The performance of 4 ACCMIP models (GFDL-AM3, NCAR-CAM3.5, GISS-E2-R and MIROC-CHEM) were evaluated in simulating surface PM2.5 concentration by using observation data, which obtained from MODIS and MISR satellite data. Specifically, the performance of reproducing the spatial distribution and time evolution were evaluated in this study. As shown from the results, ACCMIP models can reproduce the spatial pattern of surface PM2.5 concentration well over eastern China. The performance of GFDL-AM3 in simulating surface PM2.5 spatial pattern is the best, among the four models. The greater inconsistency was found in central Xinjiang and western Inner Mongolia. There is a good agreement in time series between the observational data and the model data. Multimodel ensemble mean can simulate the trends of surface PM2.5 concentration in Northeast China, Central China, coastal region of East China and western Xinjiang.

Key words: ACCMIP, PM2.5 concentration, the performance of evaluation

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