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Decadal Prediction Skill of BCC-CSM1.1 with Different Initialization Strategies
Xin, Xiaoge; Wei, Min1,2,3; Li, Qingquan4; Zhou, Wei5; Luo, Yong2,3; Zhao, Zongci2,3
2019
Source PublicationJOURNAL OF THE METEOROLOGICAL SOCIETY OF JAPAN
ISSN0026-1165
Volume97Issue:3Pages:733
AbstractTwo sets of decadal prediction experiments were performed with Beijing Climate Center climate system model version 1.1 (BCC-CSM1.1) with different initialization strategies. One experiment is relaxing modeled ocean temperature to the Simple Ocean Data Assimilation (SODA) reanalysis data (SODAInit). In the other (EnOI_ Hadlnit) experiment, the modeled ocean temperature were relaxed toward the assimilated ocean data, which were generated by assimilating sea surface temperature (SST) of the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) data to the ocean model of BCC-CSM1.1 using Ensemble Optimum Interpolation (EnOI) method. Comparisons between EnOI_HadInit and SODAInit hindcasts show that EnOI_HadInit is more skillful in predicting SST over the North Pacific, the southern Indian Ocean, and the North Atlantic. Improved prediction skill is also found for surface air temperature (SAT) over South Europe, North Africa, and Greenland, which is associated with the skillful prediction of the Atlantic multi-decadal oscillation in EnOl_Hadlnit. EnOl_Hadlnit and SODAInit are both skillful in predicting East Asian SAT, which is related to their skillful predictions of the tropical western Pacific SST. The result indicates that assimilated data generated by the ocean model of BCC-CSM1.1 with EnOI assimilation provide better initial conditions than SODA reanalysis data for the decadal predictions of BCC-CSMI.1.
DepartmentLTO
Keyworddecadal prediction initialization Beijing Climate Center climate system model Ensemble Optimum Interpolation
DOI10.2151/jmsj.2019-043
Citation statistics
Document Type期刊论文
Identifierhttp://ir.scsio.ac.cn/handle/344004/17713
Collection热带海洋环境国家重点实验室(LTO)
Affiliation1.Natl Climate Ctr, Lab Climate Studies, China Meteorol Adm, Beijing, Peoples R China
2.China Meteorol Adm, Natl Meteorol Informat Ctr, Beijing, Peoples R China
3.Tsinghua Univ, Minist Educ, Key Lab Earth Syst Modeling, Dept Earth Syst Sci, Beijing, Peoples R China
4.Tsinghua Univ, JCGCS, Beijing, Peoples R China
5.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat OfMeteo, Nanjing, Jiangsu, Peoples R China
6.Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Xin, Xiaoge,Wei, Min,Li, Qingquan,et al. Decadal Prediction Skill of BCC-CSM1.1 with Different Initialization Strategies[J]. JOURNAL OF THE METEOROLOGICAL SOCIETY OF JAPAN,2019,97(3):733, 744.
APA Xin, Xiaoge,Wei, Min,Li, Qingquan,Zhou, Wei,Luo, Yong,&Zhao, Zongci.(2019).Decadal Prediction Skill of BCC-CSM1.1 with Different Initialization Strategies.JOURNAL OF THE METEOROLOGICAL SOCIETY OF JAPAN,97(3),733.
MLA Xin, Xiaoge,et al."Decadal Prediction Skill of BCC-CSM1.1 with Different Initialization Strategies".JOURNAL OF THE METEOROLOGICAL SOCIETY OF JAPAN 97.3(2019):733.
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