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### 蒙古高原气温与降水变化特征及CMIP5气候模式评估

1. 中国科学院地理科学与资源研究所资源利用与环境修复重点实验室 北京 100101
• 收稿日期:2015-12-10 修回日期:2016-04-21 出版日期:2016-05-25 发布日期:2016-05-23
• 作者简介:

作者简介:刘兆飞,男,河南郑州人,博士,副研究员,主要从事气候变化及其对水文水资源的影响研究工作。E-mail:zfliu@igsnrr.ac.cn

• 基金资助:
科技部国家国际科技合作专项项目（2013DFA91700）;科技部基础工作专项项目（2012FY111400）

### Air temperature and precipitation over the Mongolian Plateau and assessment of CMIP 5 climate models

LIU Zhaofei(), WANG Rui, YAO Zhijun()

1. Key Lab for Resources Use & Environmental Remediation,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
• Received:2015-12-10 Revised:2016-04-21 Online:2016-05-25 Published:2016-05-23

Abstract:

Trends in air temperature and precipitation for the Mongolian Plateau were detected and changes between the Inner Mongolian region and Mongolian region were analyzed. Based on multi-evaluation indexes,Coupled Model Intercomparison Project Phase 5 （CMIP5）climate models were assessed using observed station data for air temperature and precipitation over the Plateau. The overall performance of CMIP5 models for all six variables was evaluated by a revised rank score evaluation method. We found that NorESM1-M were the relative best models for overall performance over the region and had better ability modeling each variable. Although some models were not good enough for overall performance,they showed better ability when modeling some individual variables. CMIP5 modes tended to underestimate mean values and trends in mean air temperature and overestimate precipitation. For different variables,CMIP5 models showed the best model performance for mean air temperature over the region. The models also showed better model performance for maximum air temperature and minimum air temperature. Some models showed ability in modeling precipitation amount over the region. The assessment results were too dependent on evaluation indexes and when different evaluation indexes were used,completely opposite results were often obtained. In other words,selection of evaluation indexes was very important to assessment. For example,as for mean characteristic indexes,ACCESS1.3,CanESM2 and MIROC-ESM models showed the best model performance for mean air temperature over the region,but ACCESS1.3 and MIROC-ESM showed the worst model performance using the probability density function index,and CanESM2 showed the worst performance when using the spatial correlation coefficient index. Therefore,in the assessment of climate models at a regional scale,multi-evaluation indexes representing different characteristic are suggested for overall evaluations.