蒙古高原气温与降水变化特征及CMIP5气候模式评估

doi:10.18402/resci.2016.05.15

摘要/Abstract

摘要：

蒙古高原受自然与人为因素影响,生态环境面临严峻压力,研究气候变化特征及气候模式评估对该地区气候及生态环境等相关研究具有重要的参考意义。本研究以整个蒙古高原为研究区,研究蒙古高原气温与降水要素的变化趋势,对比研究中国内蒙古自治区与蒙古国地区气候变化特征,并评价了全球耦合模式比较计划第五阶段（CMIP5）模式输出的气温与降水序列在研究区的表现,利用一种改进的秩评分方法对CMIP5模式的模拟能力进行了综合评价。结果表明,蒙古高原过去几十年整体呈增温减湿的趋势,最低气温升温幅度明显大于最高气温升温幅度,中国内蒙古地区变暖趋势强于蒙古国地区;CMIP5模式模拟的平均气温普遍偏低,且低估了该区域的增温趋势,而模拟的降水量普遍偏高。不同要素间,CMIP5模式对平均气温的模拟能力最强,最高与最低气温次之,降水相对较差。模式评价结果对评价指标有较大的依赖性,因此,评估气候模式的区域效果时,建议使用多指标进行综合评价。

关键词: 气候模式, GCM, 综合评价, 变化趋势, 概率密度分布, 蒙古高原

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.

Key words: climate model, GCM, overall evaluation, trend, probability density function, Mongolian Plateau

刘兆飞, 王蕊, 姚治君. 蒙古高原气温与降水变化特征及CMIP5气候模式评估[J]. 资源科学, 2016, 38(5): 956-969.

LIU Zhaofei,WANG Rui,YAO Zhijun. Air temperature and precipitation over the Mongolian Plateau and assessment of CMIP 5 climate models[J]. Resources Science, 2016, 38(5): 956-969.

图/表 9

图1

表1

CMIP5气候模式"

序号	模式	分辨率		研究机构	国家
序号	模式	纬向	经向	研究机构	国家
1	BCC-CSM1.1	2.79	2.81	Beijing Climate Center	中国
1	BCC-CSM1.1	2.79	2.81	Beijing Climate Center	中国
2	BNU-ESM	2.79	2.81	Beijing Normal University	中国
3	FIO-ESM	2.79	2.81	The First Institute of Oceanography,SOA	中国
4	FGOALS-g2	2.79	2.81	Institute of Atmospheric Physics,Chinese Academy of Sciences	中国
5	INMCM4.0	1.50	2.00	Russian Academy of Sciences,Institute of Numerical Mathematics	俄罗斯
6	CSIRO-Mk3.6.0	1.87	1.88	Commonwealth Scientific and Industrial Research Organization/Queensland Climate Change Centre of Excellence	澳大利亚澳大利亚澳大利亚
7	ACCESS1.0	1.25	1.88	Commonwealth Scientific and Industrial Research Organization/Bureau of Meteorology
8	ACCESS1.3	1.25	1.88
9	IPSL-CM5A-LR	1.89	3.75	Institut Pierre Simon Laplace	法国法国法国法国
10	IPSL-CM5A-MR	2.54	2.50
11	IPSL-CM5B-LR	1.89	3.75
12	CNRM-CM5	1.40	1.41	Centre National de RecherchesMétéorologiques,Centre Européen de Recherche et de Formation AvancéeenCalculScientifique
13	MPI-ESM-LR	1.87	1.88	Max Planck Institute for Meteorology	德国德国
14	MPI-ESM-MR	1.87	1.88	Max Planck Institute for Meteorology	德国德国
15	EC-EARTH	1.12	1.13	EC-EARTH consortium published at Irish Centre for High-End Computing	荷兰/爱尔兰
16	CMCC-CMS	3.71	3.75	Centro Euro-Mediterraneo sui CambiamentiClimatici	意大利
17	CanESM2	2.79	2.81	Canadian Centre for Climate Modelling and Analysis	加拿大
18	MIROC-ESM	2.79	2.81	Atmosphere and Ocean Research Institute （The University of Tokyo）,National Institute for Environmental Studies,and Japan Agency for Marine-Earth Science and Technology	日本日本日本
19	MIROC5	1.40	1.41
20	MRI-CGCM3	1.12	1.13	Meteorological Research Institute
21	NorESM1-M	1.89	2.50	Bjerknes Centre for Climate Research,Norwegian Meteorological Institute	挪威挪威
22	NorESM1-ME	1.89	2.50		挪威挪威
23	HadGEM2-A	1.25	1.88	Met Office Hadley Centre	英国英国英国
24	HadGEM2-CC	1.25	1.88
25	HadGEM2-ES	1.25	1.88
26	CCSM4	0.94	1.25	National Center for Atmospheric Research	美国
27	CESM1（BGC）	0.94	1.25		美国
28	CESM1（CAM5）	0.94	1.25		美国
29	CESM1（WACCM）	1.88	2.50		美国
30	GISS-E2-H	2.00	2.50	NASA/GISS （Goddard Institute for Space Studies）	美国
31	GISS-E2-R	2.00	2.50	NASA/GISS （Goddard Institute for Space Studies）	美国
32	GFDL-CM3	2.00	2.50	Geophysical Fluid Dynamics Laboratory	美国
33	GFDL-ESM2G	2.02	2.00		美国
34	GFDL-ESM2M	2.02	2.50		美国

表1

图2

表2

图 3

图4

图5

图6

表3

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变化幅度	蒙古国	中国境内地区	蒙古高原
最高气温/（℃/10a）	0.45▲	0.45▲	0.46▲
最低气温/（℃/10a）	0.36▲	0.64▲	0.55▲
平均气温/（℃/10a）	0.40▲	0.54▲	0.49▲
降水量/（mm/10a）	-0.55▽	-0.35▽	-0.45▽
潜在蒸散发量/（mm/10a）	3.38▲	0.38△	1.33▲

模式	各评价指标综合RS评分				总评分
模式	平均气温	最高气温	最低气温	降水量	总评分
BCC-CSM1.1	0.68	0.62	0.83	0.51	0.66
BNU-ESM	0.70	0.63	0.84	0.19	0.59
FIO-ESM	0.72	0.62	0.73	0.40	0.62
FGOALS-g2	0.57	0.47	0.75	0.63	0.60
INMCM4.0	0.41	0.40	0.40	0.56	0.44
CSIRO-Mk3.6.0	0.65	0.50	0.73	0.57	0.61
ACCESS1.0	0.72	0.66	0.80	0.72	0.73
ACCESS1.3	0.76	0.62	0.75	0.73	0.72
IPSL-CM5A-LR	0.35	0.53	0.22	0.65	0.44
IPSL-CM5A-MR	0.70	0.68	0.45	0.54	0.59
IPSL-CM5B-LR	0.70	0.54	0.41	0.65	0.58
CNRM-CM5	0.48	0.59	0.69	0.70	0.61
MPI-ESM-LR	0.73	0.70	0.74	0.78	0.74
MPI-ESM-MR	0.71	0.67	0.73	0.69	0.70
EC-EARTH	0.70	0.68	0.78	0.78	0.73
CMCC-CMS	0.79	0.80	0.80	0.75	0.79
CanESM2	0.74	0.66	0.77	0.49	0.67
MIROC-ESM	0.55	0.45	0.67	0.44	0.53
MIROC5	0.79	0.70	0.87	0.67	0.76
MRI-CGCM3	0.66	0.61	0.75	0.82	0.71
NorESM1-M	0.85	0.80	0.86	0.69	0.80
NorESM1-ME	0.59	-	-	0.67	0.63
HadGEM2-A	0.68	0.60	0.77	0.73	0.70
HadGEM2-CC	0.47	0.42	0.67	0.66	0.55
HadGEM2-ES	0.62	0.57	0.75	0.69	0.66
CCSM4	0.69	0.61	0.81	0.71	0.70
CESM1（BGC）	0.75	0.70	0.78	0.68	0.73
CESM1（CAM5）	0.68	0.70	0.66	0.70	0.68
CESM1（WACCM）	0.71	0.77	0.68	0.66	0.70
GISS-E2-H	0.36	0.37	0.49	0.40	0.40
GISS-E2-R	0.44	0.46	0.56	0.36	0.46
GFDL-CM3	0.70	0.68	0.76	0.70	0.71
GFDL-ESM2G	0.63	0.60	0.66	0.76	0.66
GFDL-ESM2M	0.73	0.70	0.71	0.75	0.72