CMIP6全球气候模式对中国10 m风速模拟能力的评估

李如清; 梁潇云; 陈静; 刘旭; 李鹤远

doi:10.16032/j.issn.1004-4965.2026.008

CMIP6全球气候模式对中国10 m风速模拟能力的评估

doi: 10.16032/j.issn.1004-4965.2026.008

1.
成都信息工程大学大气科学学院，四川成都 610225
2.
中国气象局地球系统数值预报中心，北京 100081
3.
南京信息工程大学大气科学学院，江苏南京 210044

基金项目:

国家自然科学基金 42341209

详细信息

通讯作者:
梁潇云，女，河北省人，研究员，主要从事气候数值模式和集合预测研发、气候变化研究。E-mail: lxy@cma.gov.cn

中图分类号: P467
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出版历程
- 收稿日期: 2024-12-24
- 修回日期: 2025-03-06
- 刊出日期: 2026-06-20

Evaluation of the 10m Wind Speed over China Based on CMIP6 GCM

1.
Chengdu University of Information Technology, Chengdu 610225, China
2.
China Meteorological Administration Earth System Modeling and Prediction Center (CEMC), Beijing 100081, China
3.
Nanjing University of Information Science and Technology, Nanjing 210044, China

摘要

摘要: 风速的准确模拟对风电和太阳能等新能源领域未来发展布局具有重要指导意义，但目前全球气候模式对风速的模拟能力有限。基于国家基准台站观测资料，评估了第六次国际耦合模式比较计划（Coupled Model Intercomparison Project Phase 6, CMIP6）的十个全球气候模式（Global Climate Model, GCM）对中国大陆10 m风速1961—2014年的时空分布特征的模拟能力。评估结果显示，在气候态特征上，十个CMIP6 GCM均能模拟出1961—2014年时期我国10 m风速春季最大、秋季最小的气候态季节变化特征，大多数模式存在全年系统性高估现象，尤其是BCC-CSM2-MR和FGOALS-f3-L模式。在年际变率上，十个CMIP6模式也均能再现我国风速年平均和季节平均的年际变率空间分布特征，但是量值较观测偏小。在年代际趋势上，十个模式中有九个可以模拟出中国区域平均的年均风速在1961—2014年期间的减弱趋势，但减弱趋势要较观测明显偏小1个量级，只有GFDL-ESM4模式对于变化趋势的模拟与观测相反是增强的。因此，深入剖析CMIP6模式对中国10 m风速模拟的优势与不足，对准确预估风资源变化、合理开发风能及制定能源战略具有重要现实意义。
- 10 m风速 /
- CMIP6 /
- 误差特征 /
- 全球气候模式
Abstract: Accurate simulation of wind speed is of significant guiding importance for the future development layout of new energy sectors such as wind and solar power. However, current global climate models exhibit limitations in their ability to simulate wind speeds. Based on CN05.1 high-resolution gridded observations from national benchmark stations, this study evaluates the capability of ten Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the spatiotemporal distribution characteristics of 10-meter wind speeds over mainland China during 1961—2014. Results show that all ten CMIP6 GCMs can capture the climatological seasonal variation of 10-meter wind speeds over China, with maximum speeds in spring and minimum speeds in autumn during the period from 1961 to 2014. However, most models exhibit a systematic overestimation throughout the year, particularly the BCC-CSM2-MR and FGOALS-f3-L models. These ten CMIP6 models are also capable of reproducing the spatial distribution characteristics of the interannual variability of annual and seasonal mean wind speeds over China, although the simulated values are generally smaller than the observed values. In simulating the decadal trends of 10-meter wind speeds from 1961 to 2014, only some of the ten CMIP6 GCMs could capture the weakening trend of annual and seasonal mean 10-meter wind speeds over most regions of China during this period. Moreover, the simulated weakening trends are significantly smaller than the observed trends, and over some regions, the models even fail to simulate the weakening trend, instead showing an increasing trend, which is contrary to the observations. The uncertainties in historical climate simulations also lead to considerable uncertainties in future climate change projections. Therefore, to obtain more reliable future scenario projections, it is essential to apply bias corrections to the model simulations and projections.
- 10-meter wind speeds /
- CMIP6 /
- bias distribution /
- GCM

HTML全文

图 1 1961—2014年中国大陆区域10 m风速的逐月平均变化（单位：m·s^-1）

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图 2 1961—2014年逐日平均的中国大陆区域10 m风速概率密度分布（单位：m·s^-1）

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图 3 1961—2014年年平均10 m风速分布及CMIP6模式与观测的偏差（单位：m·s^-1）

左边两列（a~k）为气候平均，右边两列(l~u)为CMIP6模式与观测的偏差。

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图 4 1961—2014年春季平均10 m风速分布及CMIP6模式与观测间的偏差（单位：m·s^-1）

左边两列（a~k）为气候平均，右边两列(l~u)为CMIP6模式与观测的偏差。

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图 5 1961—2014年秋季平均10 m风速分布及CMIP6模式与观测间的偏差（单位：m·s^-1）

左边两列（a~k）为气候平均，右边两列（l~u）为CMIP6模式与观测的偏差。

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图 6 CMIP6模式对中国大陆区域10 m风速模拟结果与观测之间的泰勒图

a.年平均；b. 春季；c. 夏季；d.秋季；e. 冬季。

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图 7 1961—2014年10 m风速年平均标准偏差（单位：m·s^-1）的空间分布

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图 8 1961—2014年期间中国大陆区域平均的10 m风速变化趋势(单位：m·s^-1·(10 a) ^-1)

左列：年平均，中间列：春季，右列：秋季。

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图 9 1961—2014年年平均10 m风速的线性趋势(单位：m·s^-1·(10 a) ^-1)

a. CN05.1资料，b~k. CMIP6模式模拟结果。

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图 10 1961—2014年春季平均10 m风速的线性趋势(单位：m·s^-1·(10 a) ^-1)

a. CN05.1资料，b~k. CMIP6模式模拟结果。

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图 11 1961—2014年秋季平均10 m风速的线性趋势(单位：m·s^-1·(10 a) ^-1)

a. CN05.1资料，b~k. CMIP6模式模拟结果。

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附图 1 1961—2014年10 m风速春季平均标准偏差（单位：m·s^-1）的空间分布

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附图 2 1961—2014年10 m风速秋季平均标准偏差（单位：m·s^-1）的空间分布

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表 1 研究中使用的十个GCM的信息

模式名称	国家	空间分辨率
ACCESS-CM2	澳大利亚	1.25°×1.875°
BCC-CSM2-MR	中国	1.125°×1.125°
CMCC-CM2-SR5	意大利	0.94°×1.25°
EC-Earth3-Veg-LR	欧洲中心	1.125°×1.125°
FGOALS-f3-L	中国	1.25°×0.94°
GFDL-ESM4	美国	1.25°×1.0°
INM-CM5-0	俄罗斯	1.50°×2.0°
IPSL-CM6A-LR	法国	2.5°×1.259°
MIROC6	日本	1.406°×1.406°
MPI-ESM1-2-LR	德国	1.865°×1.875°

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