CMIP5耦合模式对欧亚大陆冬季雪水当量的模拟及预估

杨笑宇; 林朝晖; 王雨曦; 陈红; 俞越

doi:10.3878/j.issn.1006-9585.2016.16104

CMIP5耦合模式对欧亚大陆冬季雪水当量的模拟及预估

doi: 10.3878/j.issn.1006-9585.2016.16104

杨笑宇^{1, 2,},
林朝晖^{1, 3, ,},
王雨曦^{1, 2},
陈红¹,
俞越^{1, 2}

1.
中国科学院大气物理研究所国际气候与环境科学中心, 北京 100029
2.
中国科学院大学, 北京 100049
3.
南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京 210044

基金项目:

国家重点研发项目 2016YFC0402702

国家自然科学基金项目 41575095

国家自然科学基金项目 41575080

中国科学院国际合作一带一路专项 134111KYSB20160010

详细信息

作者简介:
杨笑宇, 男, 1988年出生, 博士研究生, 主要从事陆面水文循环研究。E-mail: yangxiaoyu@mail.iap.ac.cn

通讯作者:
林朝晖, E-mail: lzh@mail.iap.ac.cn

中图分类号: P456
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出版历程
- 收稿日期: 2016-05-18
- 网络出版日期: 2016-12-16
- 刊出日期: 2017-05-20

Simulation and Projection of Snow Water Equivalent over the Eurasian Continent by CMIP5 Coupled Models

Xiaoyu YANG^{1, 2
,},
Zhaohui LIN^{1, 3
, ,},
Yuxi WANG^{1, 2},
Hong CHEN¹,
Yue YU^{1, 2}

1.
International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
2.
University of Chinese Academy of Sciences, Beijing 100049
3.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044

Funds:

National key Research and Development Program of China 2016YFC0402702

Natural Science Foundation of China 41575095

Natural Science Foundation of China 41575080

Chinese Academy of Science "The the Belt and Road Initiatives" Program: Climate Change Research and Observation Project 134111KYSB20160010

摘要

摘要: 基于美国冰雪资料中心（NSIDC）提供的卫星遥感雪水当量资料，评估了26个CMIP5（Coupled ModelInter-comparison Project）耦合模式对1981~2005年欧亚大陆冬季雪水当量的模拟能力，在此基础上应用多模式集合平均结果，预估了21世纪欧亚大陆雪水当量的变化情况。结果表明，CMIP5耦合模式对欧亚大陆冬季雪水当量空间分布具有一定的模拟能力，能够再现出欧亚大陆冬季雪水当量由南向北递增、青藏高原积雪多于同纬度其他地区的特征；就雪水当量的幅值而言，几乎所有模式均显著低估了西伯利亚中部雪水当量的大值中心，对中国东北地区雪水当量的模拟也显著偏低，但模式对乌拉尔山以西的东欧平原、我国北方及蒙古地区冬季雪水当量的模拟却比卫星遥感资料显著偏大，此外模式对堪察加半岛及以北的西伯利亚东北部地区的雪水当量也明显偏大。对于青藏高原地区，虽然部分模式可以模拟出青藏高原东部的雪水当量大值区，但大多数模式对青藏高原西部雪水当量的模拟却明显偏大，存在虚假的大值中心。对遥感反演资料的EOF（Empirical Orthogonal Function）分解表明，对于EOF第一个模态所对应欧亚大陆全区一致的年代际变化特征，仅有少数模式具有一定的模拟能力，大多数模式以及多模式集合的结果均未能予以反映；对应于欧亚大陆雪水当量年际变化的EOF第二模态而言，仅有少数模式（如俄罗斯的INMCM4）具有一定的再现能力，绝大多数模式对该模态及其时间演变的特征没有模拟能力。比较CMIP5多模式的集合预估结果与1981~2005年基准时段的雪水当量，可以发现在RCP4.5排放情景下，西伯利亚中东部地区的雪水当量相对于基准时段显著增加，区域平均的增加量在21世纪前、中、后期分别为4.1mm、5.4 mm和6.8 mm，且随时间增加得更显著；对90°E以西的欧洲大陆和青藏高原地区，其雪水当量则相对减少，减少的幅度和显著性也随时间而增大。就雪水当量的相对变化而言，在欧亚大陆东北部存在雪水当量相对变化的大值区，在21世纪后期相对变化显著区大都在5%~10%；但在青藏高原、斯堪的纳维亚半岛进和东欧平原，并没有发现雪水当量相对变化的髙值区，这是由于这些区域冬季雪水当量的幅值较大的缘故。RCP8.5情景下欧亚大陆雪水当量的变化特征与RCP4.5相类似，只是变化的幅度更大。
- CMIP5模式 /
- 雪水当量 /
- 模式评估 /
- 气候预估
Abstract: Based on the remote sensing data from National Snow and ICE Data Center (NSIDC), the performance of CMIP5 (Coupled Model Inter-comparison Project) models in reproducing the winter snow water equivalent (SWE) in the Eurasian continent during 1981-2005 was evaluated first, and the multi-model ensemble (MME) technique was then applied to project the SWE changes over Eurasian continent in the 21st century under the conditions of two different representative concentration pathways (RCP4.5 and RCP8.5) using eight good CMIP models out of total 26 models. The results show that the models were able to reproduce the spatial pattern of winter mean SWE in the Eurasia, i.e. the 25-year average of SWE increased from south to north and SWE in the Tibetan Plateau was much higher than those in other regions of the same latitude. However, some errors still existed in the models. For example, almost all models underestimated the maximum SWE in central Siberia, and SWE in northeastern China was also underestimated. It was found that SWE to the west of Ural Mountains and over northern part of China and Mongolia was overestimated when compared with observation. Meanwhile, only a subset of the models could produce the maximum SWE on the eastern Tibetan Plateau, and the spurious maximum SWE could be found on the western Tibetan Plateau in most CMIP5 models. The spatial and temporal characteristics of winter SWE from CMIP5 model simulations and observations were further analyzed using the Empirical Orthogonal Function (EOF) analysis, and the results suggested that only a small number of CMIP5 models could reproduce main features of the first eigenvector that reflects the decadal variation of SWE over the whole Eurasia. The second mode reflects the annual variation of SWE over the Eurasia, and only a few models (e.g., INMCM4) could reproduce the spatial and temporal characteristics of the second mode to some extent. With respect to the reference period 1981-2005, projection of SWE by the MME under the RCP4.5 shows that SWE in the northeastern Eurasia continent would increase significantly with an increase of 4.1 mm for the 25-year averaged winter SWE in the early stage of the 21st century, followed by 5.4-mm and 6.8-mm increases in the middle and late 21st century, respectively. In contrast, there would exist a decrease of SWE in continental Europe to the west of 90°E and over the Tibetan Plateau and the decrease would become more severe with time. In terms of percentage change of SWE, the region with large magnitudes was found in the northeastern Eurasian continent, where the increase of SWE could be around 5%-10%. However, no maximum centers were found in the Tibetan Plateau, Scandinavian Peninsula and East European Plain possibly because of the large values of winter SWE in these regions. Projection of SWE changes by the MME under the high emission scenario RCP8.5 shows a similar pattern with results under the emission scenario RCP4.5, but with larger amplitudes of changes in snow water equivalence.
- CMIP5 models /
- Snow water equivalent /
- Model evaluation /
- Climate projection

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图 1 1981~2005年平均遥感反演（Obs）和26个CMIP5模式模拟的欧亚大陆冬季平均雪水当量空间分布（MME表示26个模式的集合平均结果）

Figure 1. Winter mean SWE (Snow Water Equivalent) from remote sensing data (Obs) and the 26 CMIP5 (Coupled Model Inter-comparison Project) models during 1981–2005 over the Eurasian continent (MME indicates multi-model ensemble)

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图 2 1981~2005年26个CMIP5模式模拟与遥感观测欧亚大陆冬季平均雪水当量偏差百分率的空间分布

Figure 2. Percentage bias of winter mean SWE between the 26 CMIP5 models and remote sensing data during 1981–2005 over the Eurasian continent

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图 3 1981~2005年遥感观测和CMIP5模式模拟的欧亚大陆冬季雪水当量EOF第一模态空间型，左上角为其解释方差

Figure 3. The first EOF mode of winter SWE from remote sensing data and the 26 CMIP5 models during 1981−2005 over the Eurasian continent. The variances explained by first EOF principal component are shown at the top left of each panel

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图 4 1981~2005年遥感观测和CMIP5模式模拟的欧亚大陆冬季雪水当量EOF第一模态时间序列，左上角为其解释方差

Figure 4. The time series of first EOF mode of winter SWE from remote sensing data and CMIP5 models during 1981−2005 over the Eurasian continent. The variances explained by first EOF principal component are shown at the top left of each panel

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图 5 1981~2005年遥感观测和CMIP5模式模拟的欧亚大陆冬季雪水当量EOF第二模态空间型，左上角为其解释方差

Figure 5. The second EOF mode of winter SWE from remote sensing data and the 26 CMIP5 models during 1981–2005 over the Eurasian continent. The variances explained by the second EOF principal component are shown at the top left of each panel

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图 6 1981~2005年遥感观测和CMIP5模式模拟的欧亚大陆冬季雪水当量EOF第二模态时间序列，左上角为其解释方差

Figure 6. The time series of second EOF mode of winter SWE from remote sensing data and the 26 CMIP5 models during 1981–2005 over the Eurasian continent. The variances explained by the second EOF principal component are shown at the top left of each panel

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图 7 不同样本的多模式集合对1981~2005年欧亚大陆冬季平均雪水当量模拟的相对偏差：（a）26个CMIP5模式的集合，记为MME(26)；（b）6个优选CMIP5模式的集合平均，记为MME(6)

Figure 7. The percentage biases of winter mean SWE during 1981−2005 over the Eurasian continent: (a) Between all-model ensemble result [MME(26)] and remote sensing data; (b) between the ensemble of six good models [MME(6)] and remote sensing data

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图 8 相对于1981~2005年基准期，RCP4.5（左列）和RCP8.5（右列）两种排放情景下，多模式集合预估的21世纪不同时期冬季雪水当量的变化：（a、b）21世纪早期（2016~2040年）；（c、d）21世纪中期（2046~2070年）；（e、f）21世纪后期（2076~2100年）

Figure 8. Changes in winter mean SWE over the Eurasian continent as projected by MME(6): (a, b) Early 21st century (2016–2040); (c, d) middle 21st century (2046–2070); (e, f) late 21st century (2076–2100). The left panels are for RCP4.5 scenario, and right panels are for RCP8.5 scenario, respectively. The reference period used in this study is 1981–2005

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图 9 相对于1981~2005年基准期，RCP4.5（左列）和RCP8.5两种排放情景下，6个优选CMIP5模式集合预估的21世纪不同时期冬季雪水当量的相对变化：（a、b）21世纪早期（2016~2040年）；（c、d）21世纪中期（2046~2070年）；（e、f）21世纪后期（2076~2100年）

Figure 9. Percentage changes of winter mean SWE over the Eurasian continent as projected by MME(6): (a, b) Early 21st century (2016–2040); (c, d) middle 21st century (2046–2070); (e, f) late 21st century (2076–2100). Left panels are for RCP4.5 scenario, and right panels are for RCP8.5 scenario. The reference period used in this study is 1981–2005

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表 1 26个CMIP5气候模式基本信息介绍

Table 1. Description of the 26 CMIP5 climate models used in this study

模式名称	空间分辨率（纬度×经度）	研发国家
ACCESS1.0	1.3°×1.9°	澳大利亚
ACCESS1.3	1.3°×1.9°	澳大利亚
BCC	2.8°×2.8°	中国
BCC-m	1.1°×1.1°	中国
CanESM2	2.8°×2.8°	加拿大
CCSM4	0.94°×1.3°	美国
CESM1-BGC	0.94°×1.3°	美国
CSIRO-Mk3.6.0	1.9°×1.9°	澳大利亚
FGOALS-g2	3.0°×2.8°	中国
FIO-ESM	2.8°×2.8°	中国
GFDL-CM3	2.0°×2.5°	美国
GFDL-ESM2G	2.0°×2.5°	美国
GFDL-ESM2M	2.0°×2.5°	美国
GISS-E2-H	2.0°×2.5°	美国
GISS-E2-R	2.0°×2.5°	美国
GISS-E2-H-CC	2.0°×2.5°	美国
GISS-E2-R-CC	2.0°×2.5°	美国
HadGEM2-AO	1.3°×1.9°	韩国
INMCM4	1.5°×2.0°	俄罗斯
MIROC5	1.4°×1.4°	日本
MIROC-ESM	2.8°×2.8°	日本
MIROC-ESM-CHEM	2.8°×2.8°	日本
MPI-ESM-LR	1.9°×1.9°	德国
MPI-ESM-MR	1.9°×1.9°	德国
MRI-CGCM3	1.1°×1.1°	日本
NorESM1-ME	1.9°×2.5°	挪威

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表 2 CMIP5模式模拟与遥感反演的1981~2005年欧亚大陆冬季雪水当量间的偏差百分率、空间相关系数、空间标准差之比以及综合性能评估S指数

Table 2. Percentage biases and pattern correlation coefficients of winter mean SWE during 1981–2005 over the Eurasian continent between the 26 CMIP5 models and remote sensing data, and the ratio of the spatial standard deviations of the 26 CMIP5 models against that of remote sensing data, and the skill scores (S) of the CMIP models

模式名称	偏差百分率	空间相关系数	空间标准差之比	综合性能评估指数
ACCESS1.0	–30.76%	0.62	0.66	0.37
ACCESS1.3	–33.43%	0.61	0.72	0.38
BCC	–7.75%	0.60	0.77	0.39
BCC-m	1.04%	0.64	1.00	0.45
CanESM2	–18.73%	0.66	0.67	0.41
CCSM4	–2.28%	0.65	1.00	0.46
CESM1-BGC	–2.67%	0.66	1.02	0.47
CSIRO-Mk3.6.0	–35.18%	0.62	0.59	0.33
FGOALS-g2	37.3%	0.64	0.96	0.45
FIO-ESM	45.09%	0.56	0.72	0.33
GFDL-CM3	4.32%	0.26	1.34	0.15
GFDL-ESM2G	–17.19%	0.40	1.21	0.23
GFDL-ESM2M	–18.19%	0.42	1.09	0.25
GISS-E2-H	39.08%	0.25	1.89	0.10
GISS-E2-R	16.74%	0.39	1.19	0.23
GISS-E2-H-CC	33.08%	0.26	1.72	0.12
GISS-E2-R-CC	17.93%	0.38	1.22	0.22
HadGEM2-AO	–31.88%	0.64	0.66	0.39
INMCM4	–4.83%	0.66	0.82	0.46
MIROC5	–6.48%	0.63	0.77	0.41
MIROC-ESM	13.48%	0.58	0.89	0.39
MIROC-ESM-CHEM	12.63%	0.59	0.86	0.39
MPI-ESM-LR	–27.76%	0.71	0.74	0.49
MPI-ESM-MR	–30.34%	0.69	0.73	0.47
MRI-CGCM3	8.08%	0.54	1.12	0.35
NorESM1-ME	–4.72%	0.63	1.07	0.44
MME(26)	–4.39%	0.60	0.95	0.41

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表 3 26个CMIP5模式与遥感资料1981~2005年欧亚大陆冬季平均雪水当量EOF前两个模态的空间和时间相关系数，ACC1代表第一模态空间相关系数；ACC2代表第二模态空间相关系数；TCC1代表第一模态时间序列的相关系数；TCC2代表第二模态时间序列的相关系数

Table 3. Spatial and temporal correlation coefficients for first two EOF principal components of winter mean SWE during 1981-2005 over the Eurasian continent between CMIP5 models and remote sensing data. ACC1 and ACC2 indicate the spatial correlation coefficients for the first and second EOF principal components, respectively. TCC1 and TCC2 labels the temporal correlation coefficients for the first and second principal components, respectively

模式名称	EOF1		EOF2
模式名称	ACC1	TCC1	ACC2	TCC2
ACCESS1.0	0.07	–0.48**	0.20	–0.29
ACCESS1.3	0.32	0.12	0.15	–0.14
BCC	0.05	0.03	0.18	–0.04
BCC-m	0.05	0.08	0.05	–0.07
CanESM2	0.24	–0.08	0.10	0.05
CCSM4	0.06	–0.03	0.21	–0.05
CESM1-BGC	0.13	0.41^**	0.21	–0.05
CSIRO-Mk3.6.0	0.35	0.41^**	0.07	–0.19
FGOALS-g2	0.29	0.08	-0.10	–0.05
FIO-ESM	0.01	–0.29	0.05	0.32
GFDL-CM3	0.22	0.48^**	0.02	–0.03
GFDL-ESM2G	0.15	0.00	0.08	0.23
GFDL-ESM2M	0.20	–0.41^**	0.21	–0.22
GISS-E2-H	0.00	–0.75^**	0.10	–0.08
GISS-E2-R	0.01	0.38^*	0.15	0.15
GISS-E2-H-CC	-0.04	–0.72^**	0.19	0.17
GISS-E2-R-CC	0.03	–0.64^**	0.14	0.00
HadGEM2-AO	0.19	-0.09	0.22	–0.10
INMCM4	0.22	0.02	0.26	0.10
MIROC5	0.02	-0.19	–0.06	0.21
MIROC-ESM	0.10	0.15	0.14	–0.22
MIROC-ESM-CHEM	0.07	–0.27	0.06	–0.10
MPI-ESM-LR	0.07	–0.14	0.10	0.07
MPI-ESM-MR	0.08	0.05	0.13	0.17
MRI-CGCM3	0.32	–0.09	0.15	0.01
NorESM1-ME	0.08	–0.14	–0.06	0.03
MME(26)	0.03	0.56^**	0.01	0.15
^、^*分别表示通过了90%、95%信度检验。

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表 4 用于预估的6个优选模式基本信息

Table 4. Descriptions of the six selected climate models used for projection

模式名称	模式全名	分辨率（纬度×经度）	研发国家
BCC-m	Beijing Climate Center Climate System Model version 1.1 running on a Moderate Resolution	1.1°×1.1°	中国
CCSM4	Community Climate System Model version 4	0.94°×1.3°	美国
CESM-BGC	Community Earth System Model, version1 Biogeochemistry	0.94°×1.3°	美国
INMCM4	Institute for Numerical Mathematics Climate Model version 4	1.5°×2.0°	俄罗斯
MIROC5	Model for Interdisciplinary Research on Climate 5	1.4°×1.4°	日本
NorESM1-ME	Norwegian Earth System Model 1 running on Medium Resolution with capability to be fully Emission riven	1.9°×2.5°	挪威

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