高谦,江志红,李肇新. 2017. 多模式动力降尺度对中国中东部地区极端气温指数的模拟评估[J]. 气象学报, 75(6):917-933, doi:10.11676/qxxb2017.067
多模式动力降尺度对中国中东部地区极端气温指数的模拟评估
Simulation and evaluation of multi-model dynamical downscaling of temperature extreme indices over the Middle and East China
投稿时间:2017-02-27  修订日期:2017-06-15
DOI:10.11676/qxxb2017.067
中文关键词:  动力降尺度  LMDZ4  极端气温
英文关键词:Dynamical downscaling  LMDZ4  Extreme temperature
基金项目:国家重点研发计划项目(2016YFA0600402、2017YFA0603804)、国家自然科学基金项目(41230528)。
作者单位E-mail
高谦 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际联合实验室/气象灾害预报预警与评估协同创 新中心, 南京, 210044
嘉祥县气象局, 嘉祥, 272400 
 
江志红 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际联合实验室/气象灾害预报预警与评估协同创 新中心, 南京, 210044 zhjiang@nuist.edu.cn 
李肇新 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际联合实验室/气象灾害预报预警与评估协同创 新中心, 南京, 210044
法国科研中心动力气象实验室, 巴黎, 法国 
 
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中文摘要:
      利用LMDZ4变网格大气环流模式分别嵌套于BCC-csm1.1-m、CNRM-CM5、FGOALS-g2、IPSL-CM5A-MR和MPI-ESM-MR等5个全球模式,进行中国中东部地区1961-2005年动力降尺度模拟试验,对比分析降尺度前后各模式对中国中东部极端气温指数的模拟能力。结果表明,相较全球模式,LMDZ4模式较好地刻画了青藏高原、四川盆地等复杂地形的变化,能更好地表现出中国中东部地区极端气温的空间分布。但降尺度改善效果具有明显的区域性差异,对于最高气温、最低气温和霜冻日数,降尺度之后主要在东北、西北、青藏高原以及西南地区改善明显,与观测场的空间相关系数提高至0.95以上,均方根误差低于0.5℃(0.5 d),且降尺度后模式对最低气温和最高气温空间相关系数的改善程度随地形升高而增大;对于热浪指数,降尺度后在东北、华南以及西南地区热浪分布大值区改善效果明显,但模式间的一致性不高。降尺度在一定程度上模拟出与观测一致的最高、最低气温的线性趋势空间分布,在东北、华北、青藏高原和西南地区最低气温和霜冻日数趋势误差较全球模式小。降尺度模式集合(RMME)对极端气温气候平均场和线性趋势均有较高的模拟能力。多模式动力降尺度能够提高全球模式对中国区域极端气温的模拟能力,为提高未来预估能力提供了基础。
英文摘要:
      The variable-resolution atmospheric general circulation model LMDZ4 that is nested into five global climate models (GCMs) (BCC-csm1.1-m, CNRM-CM5, FGOALS-g2, IPSL-CM5A-MR and IPSL-CM5A-MR) is used to conduct an ensemble dynamical downscaling simulation for China during 1961-2005. The performances of the above GCMs and the downscaling results for extreme temperature in China are evaluated comprehensively. Compared with GCMs, LMDZ4 shows its superiority by better depicting the terrain characteristics at regional scale like the Tibetan Plateau and Sichuan Basin and spatial distribution of extreme temperature in China. However, the improvement of dynamical downscaling shows significant regional differences. For mean minimum temperature, mean maximum temperature and frost days, the simulation by dynamical downscaling is mainly improved in Northeast China, Northwest China, the Tibetan Plateau and Southwest China. The correlation coefficients are increased to 0.95, the normalized root mean square errors are decreased to below 0.5℃ (0.5 d), and the improvements of the correlation coefficient for mean minimum temperature and mean maximum temperature both increase with terrain height. The improvement of heat wave duration index in Northeast China, North China and Southwest China is significant, but there are large differences between various models. Furthermore, compared with GCMs, the downscaling model is able to reproduce, to a certain extent, the spatial distributions of the trends of mean maximum temperature and mean minimum temperature in China, and reduce the trend errors of mean minimum temperature and frost days in Northeast China, North China, the Tibetan Plateau and Southwest China. The downscaling model ensemble also performs well in reproducing the observed spatial patterns of climate state and trends of temperature extremes in China. Dynamical downscaling can improve the simulation capability of GCMs for extreme temperature, which can be applied to the projection of future extreme temperature.
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