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FGOALS-g3 Model Datasets for CMIP6 Flux-Anomaly-Forced Model Intercomparison Project
Advances in Atmospheric Sciences ( IF 5.8 ) Pub Date : 2020-08-13 , DOI: 10.1007/s00376-020-2045-8
Yaqi Wang , Zipeng Yu , Pengfei Lin , Hailong Liu , Jiangbo Jin , Lijuan Li , Yanli Tang , Li Dong , Kangjun Chen , Yiwen Li , Qian Yang , Mengrong Ding , Yao Meng , Bowen Zhao , Jilin Wei , Jinfeng Ma , Zhikuo Sun

The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) is an endorsed Model Intercomparison Project in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The goal of FAFMIP is to investigate the spread in the atmosphere-ocean general circulation model projections of ocean climate change forced by increased CO2, including the uncertainties in the simulations of ocean heat uptake, global mean sea level rise due to ocean thermal expansion and dynamic sea level change due to ocean circulation and density changes. The FAFMIP experiments have already been conducted with the Flexible Global Ocean-Atmosphere-Land System Model, gridpoint version 3.0 (FGOALS-g3). The model datasets have been submitted to the Earth System Grid Federation (ESGF) node. Here, the details of the experiments, the output variables and some baseline results are presented. Compared with the preliminary results of other models, the evolutions of global mean variables can be reproduced well by FGOALS-g3. The simulations of spatial patterns are also consistent with those of other models in most regions except the North Atlantic and the Southern Ocean, indicating large uncertainties in the regional sea level projections of these two regions.

中文翻译:

CMIP6 通量异常强制模型比对项目的 FGOALS-g3 模型数据集

通量异常强制模型比对项目 (FAFMIP) 是耦合模型比对项目 (CMIP6) 第 6 阶段中已获认可的模型比对项目。FAFMIP 的目标是调查 CO2 增加迫使海洋气候变化在大气-海洋环流模型预测中的扩散,包括海洋热吸收模拟的不确定性、由于海洋热膨胀和动态变化引起的全球平均海平面上升。海洋环流和密度变化引起的海平面变化。FAFMIP 实验已经使用灵活的全球海洋-大气-陆地系统模型,网格点版本 3.0 (FGOALS-g3) 进行。模型数据集已提交给地球系统网格联盟 (ESGF) 节点。在这里,实验的细节,给出了输出变量和一些基线结果。与其他模型的初步结果相比,FGOALS-g3 可以很好地再现全局平均变量的演变。除北大西洋和南大洋外,大部分地区的空间格局模拟也与其他模式的模拟一致,表明这两个地区的区域海平面预测存在很大的不确定性。
更新日期:2020-08-13
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