CMIP5 Climate Multi-model Ensemble Optimization Based on Spatial-Temporal Distribution

doi:10.13209/j.0479-8023.2020.057

Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2020, Vol. 56 ›› Issue (5): 805-814.DOI: 10.13209/j.0479-8023.2020.057

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CMIP5 Climate Multi-model Ensemble Optimization Based on Spatial-Temporal Distribution

ZUO Zhengkang^1,2, ZHANG Feizhou², ZHANG Ling^1,3, SUN Yiyuan², ZHANG Ruihua2, YU Tian⁴, LU Jianzhong^1,†

1. State Key laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079 2. Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871 3. Department of Atmospheric Science, School of Environmental Studies, China University of Geoscience, Wuhan 430078 4. Information Engineering College, Capital Normal University, Beijing 100048

Received:2019-09-08 Revised:2019-12-20 Online:2020-09-20 Published:2020-09-20
Contact: LU Jianzhong, E-mail: lujzhong(at)whu.edu.cn

基于时空分布式的CMIP5气候多模式集合优化

左正康^1,2, 张飞舟², 张玲^1,3, 孙逸渊², 张瑞华², 于田⁴, 陆建忠^1,†

1. 武汉大学测绘遥感信息工程国家重点实验室, 武汉 430079 2. 北京大学遥感与地理信息系统研究所, 北京 100871 3. 中国地质大学(武汉)环境学院大气科学系, 武汉 430078 4. 首都师范大学信息工程学院, 北京 100048

通讯作者: 陆建忠, E-mail: lujzhong(at)whu.edu.cn
基金资助:
国家重点研发计划项目(2018YFC1506506)、武汉市应用基础前沿专项(2019020701011502)、湖北省自然科学基金(2019CFB736)、江西省重点研发计划(20201BBG71002)和测绘遥感信息工程国家重点实验室专项科研经费资助

Abstract

Abstract:

The multi-mode ensemble based on spatiotemporal distribution is constructed to reduce the uncertainty of a single-model and the non-uniform distribution of the traditional model ensembles. The improved genetic algorithm is employed to optimize the multi-model ensemble of CMIP5 global climate data from temporal and spatial scales, and Taylor diagram is used to evaluate its simulation performance. The experimental results show that the multi-mode ensemble based on spatiotemporal distribution is superior to the traditional equal weight multimode ensemble scheme.

Key words: multi-mode ensemble, optimization, genetic algorithm, spatial-temporal distribution, CMIP5

摘要：

为了降低单一模式的不确定性和传统模式集合在时间和空间上分布的不均匀性, 建立时空分布式的CMIP5气候多模式集合。利用改进的遗传算法, 从时间和空间尺度优化CMIP5全球气候数据多模式集合, 并采用Taylor图评估其模拟性能。实验结果表明, 基于时空分布式的多模式集合优于单一模式和等权集合。

关键词: 多模式集合, 优化, 遗传算法, 时空分布, CMIP5

ZUO Zhengkang, ZHANG Feizhou, ZHANG Ling, SUN Yiyuan, ZHANG Ruihua, YU Tian, LU Jianzhong. CMIP5 Climate Multi-model Ensemble Optimization Based on Spatial-Temporal Distribution[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2020, 56(5): 805-814.

左正康, 张飞舟, 张玲, 孙逸渊, 张瑞华, 于田, 陆建忠. 基于时空分布式的CMIP5气候多模式集合优化[J]. 北京大学学报自然科学版, 2020, 56(5): 805-814.

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CMIP5 Climate Multi-model Ensemble Optimization Based on Spatial-Temporal Distribution

基于时空分布式的CMIP5气候多模式集合优化

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