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CMIP6模式对北冰洋海洋热含量的模拟能力评估
引用本文:谢龙,白学志,龙上敏.CMIP6模式对北冰洋海洋热含量的模拟能力评估[J].海洋学报,2021,43(7):35-51.
作者姓名:谢龙  白学志  龙上敏
作者单位:河海大学 海洋学院,江苏 南京 210098
基金项目:国家重点研发计划(2017YFA0604602);江苏省研究生科研与实践创新计划项目(B200203134);国家自然科学基金面上项目(41676019)
摘    要:本文利用PHC、ECCO2、SODA、GECCO3和CMIP6资料,分析了北冰洋热含量的水平分布特征、季节变化和长期变化趋势等,评估了CMIP6模式对北冰洋海洋热含量的模拟能力。研究发现,北冰洋海洋热含量表现出明显的季节变化:热含量在4月份最低,9月份最高;在历史情形下(1850?2014年),相较观测和再分析资料,CMIP6多模式集合平均(MME)的上层500 m热含量在格陵兰海偏暖,在挪威海、巴伦支海和欧亚海盆偏冷,MME的全水深热含量在北冰洋几乎所有区域均偏暖,在格陵兰海偏差最大;CMIP6模式对北冰洋温度剖面模拟偏差较大,MME平均温度在1 000 m以深均高于观测和再分析资料。在未来情形下(2015?2100年),MME表现出明显的北冰洋增暖情形,但绝大多数中国模式没有表现出明显的增暖情形。中国模式中,BCC-CSM2-MR和BCC-ESM1对北冰洋年平均热含量的模拟较差,CIESM对热含量季节和年代际变化模拟较差,FIO-ESM-2-0对北冰洋上层500 m年平均热含量及热含量季节和年代际变化的模拟都比较好。

关 键 词:北冰洋热含量    空间分布    季节变化    CMIP6模式    模式评估
收稿时间:2020-12-31

Assessment of the ability of CMIP6 models to simulate the heat content of the Arctic Ocean
Institution:College of Oceanography, Hohai University, Nanjing 210098, China
Abstract:The PHC, ECCO2, SODA, GECCO3 and CMIP6 data were used to analyze the horizontal distribution characteristics, seasonal variation and long-term trend of the Arctic Ocean heat content, and analyze the simulation ability of the CMIP6 models in this paper. The results show that the heat content of the Arctic Ocean shows obvious seasonal change, with the lowest in April and the highest in September. Under historical circumstances (1850?2014), compared with the observation and reanalysis data, the heat content of the upper 500 m of the CMIP6 models ensemble average (MME) is warmer in the Greenland Sea, colder in the Norwegian sea, Barents Sea and Eurasian Basin, while the whole water column heat content of MME is warmer in almost all regions of the Arctic Ocean, with the largest deviation in the Greenland Sea. CMIP6 models have a large deviation in the simulation of Arctic Ocean temperature profile, and the average temperature of MME is higher than the observation and reanalysis data at the depth of more than 1 000 m. In the future case (2015?2100), the simulation of ocean heat content of MME shows obvious Arctic Ocean warming, but most of the Chinese models show no obvious warming situation. BCC-CSM2-MR and BCC-ESM1 are poor in simulating the annual mean heat content of the Arctic Ocean, CIESM is poor in simulating the seasonal and interdecadal variations of ocean heat content, while FIO-ESM-2-0 is good in simulating the annual heat content of the upper 500 m, the seasonal and interdecadal variations of heat content of the Arctic Ocean.
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