CMIP5模式对EU、WP遥相关型的模拟评估和预估

卢国阳; 任保华; 马鹏里; 郑建秋

doi:10.3878/j.issn.1006-9895.1701.16219

CMIP5模式对EU、WP遥相关型的模拟评估和预估

doi: 10.3878/j.issn.1006-9895.1701.16219

卢国阳^{1, 2,},
任保华^1, ,,
马鹏里²,
郑建秋¹

1.
中国科学技术大学地球和空间科学学院, 合肥 230026
2.
甘肃省气象局西北区域气候中心, 兰州 730000

基金项目:

国家自然科学基金项目 41675066

详细信息

作者简介:
卢国阳, 男, 1991年出生, 硕士, 助理工程师, 主要从事气候预测研究。E-mail:lugy@mail.ustc.edu.cn

通讯作者:
任保华, E-mail:ren@ustc.edu.cn

中图分类号: P467
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出版历程
- 收稿日期: 2016-08-26
- 网络出版日期: 2017-03-08
- 刊出日期: 2017-07-15

Evaluation and Estimation of Eurasian and West Pacific Teleconnection Pattern in CMIP5

Guoyang LU^{1, 2
,},
Baohua REN^{1
, ,},
Pengli MA²,
Jianqiu ZHENG¹

1.
School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026
2.
Lanzhou Regional Climate Center, Gansu Province Meteorology Bureau, Lanzhou 730020

Funds:

National Natural Science Foundation of China 41675066

摘要

摘要: 基于国际第5次耦合模式比较计划（CMIP5）历史试验输出资料和情景模拟试验结果，评估了14个耦合模式对北半球冬季影响东亚冬季气候的遥相关型——欧亚型（EU）和西太平洋型（WP）的模拟能力以及其对局地气温、降水影响的模拟效果，并预估未来EU和WP变化。结果表明：（1）模式对EU、WP信号的整体年际变率有一定模拟技巧，对空间模态特征的模拟能较好再现遥相关的异常中心，但也存在一定的位置偏差。（2）模式和多模式集合能再现EU与东亚以及西北太平洋地区表面气温的负相关性，但对我国华北以及黄淮流域降水负相关性模拟能力较差，且低估EU与东亚地区气温、降水的关系。（3）各模式对WP与东亚—西太平洋区相关性的南负北正分布均有较好模拟能力，空间相关系数为0.5~0.9；多数模式能再现WP与降水在鄂霍次克海的正相关性，但对于我国大陆至西太平洋的负相关性模拟能力较弱，且各模式对WP和东亚地区表面气温关系的模拟优于其与降水的关系。（4）对EU、WP遥相关整体模拟能力S评分可知，CSIRO-Mk3.6.0对EU整体评估能力最强，CNRM-CM5对WP综合评估能力最好；而HadCM3整体评分较低。（5）RCP4.5情景下，EU和WP在未来略趋于负位相发展；EU与东亚气温相关范围向东南移动，与降水相关不显著；WP与气温相关范围高纬西撤、低纬东移，与降水相关显著增强。
- CMIP5模式 /
- 遥相关 /
- 区域气候 /
- 模式评估
Abstract: The historical and future relationships between Eurasian (EU) and West Pacific (WP) teleconnection patterns, and regional winter temperature and precipitation in East Asia are evaluated by using 14 general circulation models from the Coupled Model Intercomparison Project phase 5. The main conclusions are as follows:(1) Most of the models in CMIP5 have a good ability in simulating the interannual variabilities and spatial patterns of EU and WP, but there still exist certain deviations in the simulated positions of EU and WP. (2) Some of these models could reproduce the negative correlativity between EU and East Asia and Northwest Pacific surface air temperature, but their skill in simulating the relationship between EU and precipitation in North China and Huang-Huai valley was poor. Meanwhile, all of models underestimated the relationship between EU and temperature and precipitation in East Asia. (3) Each individual model and multi-model ensemble mean both showed a high capability in simulating the relationship between WP and temperature in East Asia-Western Pacific region. The positive correlation between WP and precipitation in Northeast Asia and the Okhotsk Sea could be well reproduced; however, their capability for the simulation of the negative correlation from mainland China to western Pacific was poor. It is found that all the models could better simulate the relationship between WP and temperature compared to the simulation of relationship between WP and precipitation. (4) In terms of simulation ability score, CSIRO-Mk3.6.0 on the EU simulation is the strongest and CNRM-CM5 on WP is the best. HadCM3 has the worst model skill scores. (5) Under the RCP4.5 scenario, EU and WP in the future tend to be slightly more in negative phase. Significantly correlated area between EU and temperature in East Asia would shift southeastward based on CSIRO-Mk3.6.0 simulation, while the correlation between EU and precipitation would be insignificant. The correlated area between WP and temperature would shift westward in the high latitudes and eastward in the low latitudes, and the correlation between WP and precipitation would become stronger according to CNRM-CM5 simulation.
- CMIP5 /
- Teleconnection pattern /
- Regional climate /
- Model evaluation

HTML全文

图 1 观测和15个模式资料的北半球欧亚地区冬季500 hPa位势高度距平场EOF分解所得EU型分布

Figure 1. Spatial patterns of EU based on EOF analysis of the H500 (500-hPa geopotential height) anomalies from observations (lower right corner) and simulations of 15 GCMs

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图 2 Intermodel-EOF分解EU型（a, b）前两个空间模态以及（c, d）PC（模态对应的时间系数）序列

Figure 2. (a) The first and (b) second Intermodel-EOF modes of EU pattern simulated by 14 GCMs; (c, d) corresponding two PCs (Principal Components)

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图 3 观测和15个模式资料的冬季500 hPa位势高度距平场EOF分解所得WP型分布

Figure 3. Spatial patterns of WP based on EOF analysis of the H500 anomalies from observations (lower right corner) and simulations of 15 GCMs

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图 4 同图 2，但为Intermodel-EOF分解WP型

Figure 4. Same as Fig. 2, but for Intermodel-EOF modes of WP pattern

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图 5 观测和模式资料中EU模态对应的PC序列与冬季东亚地区地表气温相关系数分布（等值线间隔为0.2，粗实线为零线，虚线为负值；浅、深黄（蓝）阴影区为通过95%、99%信度检验）

Figure 5. Observed and GCMs simulated correlation coefficients between the PC that corresponds to EU mode and regional winter surface temperature over East Asia-the West Pacific. (Contour interval is 0.2. The thick solid lines indicate the zero contour and the dashed lines indicate negative values. The light and dark yellow (blue) shadings are for values that exceed the 95% and 99% confidence levels, respectively)

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图 6 同图 5，但为PC序列与冬季东亚地区降水量相关系数分布

Figure 6. Same as Fig. 5, but for correlations between the PC and regional winter precipitation

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图 9 模式模拟（a）EU、（b）WP指数与局地表面气温（SAT，红圈）、降水量（Precip，蓝三角）相关性和观测资料的泰勒图（简言之，离REF点的距离越近，说明该模式模拟能力越佳）

Figure 9. Taylor diagrams of the relationships of (a) EU and (b) WP indexes with regional surface air temperature (SAT, red circles) and precipitation (Precip, blue triangles)

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图 7 同图 5，但为WP指数与冬季东亚地区地表气温相关系数分布

Figure 7. Same as Fig. 5, but for correlations between the WP index and regional winter surface temperature

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图 8 同图 5，但为WP指数与冬季东亚地区降水量相关系数分布

Figure 8. Same as Fig. 5, but for correlations between the WP index and regional winter precipitation

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图 10 各模式对EU、WP遥相关整体模拟能力评分

Figure 10. The skill scores of each model for overall simulation capability of EU and WP

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图 11 RCP4.5情景下未来标准化EU、WP指数的时间变化（蓝色实线为9年滑动平均，红色虚线为线性趋势线）

Figure 11. The future changes in normalized EU and WP indexes under the RCP4.5 scenario (the blue solid line shows the 9-year running mean, the red dashed line indicates the linear trend)

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图 12 RCP4.5情景下CSIRO-Mk3.6.0模式中EU指数与冬季东亚地区（a）气温和（b）降水量的相关分布图（等值线间隔为0.1，粗实线为零线，实线为正，虚线为负，浅、深黄（蓝）阴影表示通过95%、99%信度检验）

Figure 12. CSIRO-Mk3.6.0 simulated correlation coefficients between EU index and (a) regional winter air temperature, (b) winter precipitation over East Asia-the West Pacific (contour interval is 0.1. The thick solid lines indicate the zero contour, the solid lines and dashed lines indicate positive and negative values, respectively. The light and dark yellow (blue) shadings are for values that exceed the 95% and 99% confidence levels, respectively)

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图 13 同图 12，但为CNRM-CM5模式中WP指数与冬季东亚（a）气温与（b）降水量相关

Figure 13. Same as Fig. 12, but for the CNRM-CM5 simulated correlation between WP index and regional winter air temperature and precipitation

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表 1 CMIP5中14个模式来源以及分辨率简介

Table 1. CMIP5 models used in this study, including their acronyms, host institutions, and spatial resolution.

模式	所属机构	分辨率（纬度×经度）
BCC-CSM1.1	北京气候中心—中国气象局（中国）	2.81°×2.81°
CanESM2	加拿大气候模拟与分析中心（加拿大）	2.81°×2.79°
CCSM4	美国国家大气研究中心（美国）	1.25°×0.95°
CNRM-CM5	法国气象局气象研究中心（法国）	1.41°×1.40°
CSIRO-Mk3.6.0	澳大利亚联邦科学与工业研究和昆士兰州气候变化研究中心（澳大利亚）	1.875°×1.85°
FGOALS-s2	中国科学院大气物理研究所（中国）	2.81°×1.41°
GFDL-CM3	美国地球物理流体动力学实验室（美国）	2.5°×2.0°
GISS-E2-R	美国航空和航天局（美国）	2.5°×2.0°
HadCM3	哈德来气候预测和研究中心（英国）	3.75°×2.5°
INM-CM4	俄罗斯数值数学研究院（俄罗斯）	2.0°×1.5°
IPSL-CM5A-LR	皮埃尔—西蒙·拉普拉斯研究所（法国）	3.75°×1.89°
MIROC5	日本气候系统研究中心（日本）	1.41°×1.40°
MRI-CGCM3	日本气象研究所（日本）	1.13°×1.12°
NorESM1-M	挪威气候中心（挪威）	2.50°×1.89°

下载: 导出CSV

表 2 CMIP5各模式遥相关指数与NCEP资料指数的相关系数及均方差

Table 2. Correlation coefficients of EU and WP indexes between CMIP5 simulations and NCEP observations, and the mean square deviations

模式		EU指数		WP指数
模式		相关系数	均方差	相关系数	均方差
1	BCC-CSM1.1	0.13	0.71	-0.06	0.81
2	CanESM2	-0.25	0.67	-0.04	0.75
3	CCSM4	0.03	0.73	-0.09	0.71
4	CNRM-CM5	-0.05	0.78	-0.05	0.83
5	CSIRO-Mk3.6.0	0.02	0.72	0.16	0.68
6	FGOALS-s2	-0.16	0.71	-0.03	0.68
7	GFDL-CM3	-0.15	0.74	0.03	0.72
8	GISS-E2-R	-0.06	0.86	-0.06	0.71
9	HadCM3	-0.05	0.80	0.15	0.78
10	INM-CM4	0.12	0.77	0.03	0.88
11	IPSL-CM5A-LR	-0.09	0.72	-0.03	0.75
12	MIROC5	0.03	0.74	0.14	0.76
13	MRI-CGCM3	0.11	0.66	0.05	0.72
14	NorESM1-M	-0.30	0.74	-0.10	0.85
15	MME	-0.19	0.68	-0.02	0.69

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表 3 CMIP5各模式与NCEP再分析资料遥相关型的空间相关系数

Table 3. Spatial correlation coefficients of EU and WP modes between CMIP5 models and NCEP data

模式		EU型(再分析资料EOF2)		WP型(再分析资料EOF1)
模式		模态	相关系数	模态	相关系数
1	BCC-CSM1.1	EOF4	0.57	EOF1	0.83
2	CanESM2	EOF3	0.70	EOF1	0.79
3	CCSM4	EOF3	0.69	EOF1	0.81
4	CNRM-CM5	EOF1	0.62	EOF1	0.88
5	CSIRO-Mk3.6.0	EOF3	0.81	EOF1	0.83
6	FGOALS-s2	EOF2	0.50	EOF1	0.79
7	GFDL-CM3	EOF3	0.54	EOF1	0.73
8	GISS-E2-R	EOF1	0.74	EOF1	0.66
9	HadCM3	EOF2	0.79	EOF1	0.59
10	INM-CM4	EOF2	0.81	EOF1	0.91
11	IPSL-CM5A-LR	EOF4	0.64	EOF1	0.86
12	MIROC5	EOF2	0.47	EOF2	0.83
13	MRI-CGCM3	EOF3	0.78	EOF3	0.82
14	NorESM1-M	EOF2	0.67	EOF1	0.65
15	MME	EOF2	0.75	EOF1	0.86

下载: 导出CSV

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