EC-Earth V2.2: description and validation of a new seamless earth system prediction model
EC-Earth V2.2: description and validation of a new seamless earth system prediction model
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DOI：
10.1007/s00382-011-1228-5
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发表时间：
2012-12-01
期刊：
影响因子：
4.6
通讯作者：
van der Wiel, K.
中科院分区：
文献类型：
--
作者：
Hazeleger, W.;Wang, X.;van der Wiel, K.
基于欧洲中期天气预报中心（ECMWF）的季节预报系统，提出了一个新的地球系统模型EC-Earth。将模式2.2版本（V2.2）的性能与观测数据、再分析数据和其他耦合的大气-海洋-海冰模式进行了比较。很好地模拟了大气、海洋和海冰的大尺度物理特征。与其他相似复杂性的耦合模式相比，该模式对对流层场和动力变量的模拟效果较好，而对地表温度和通量的模拟效果较差。除了南大洋地区和北半球部分温带地区外，地表温度太低。年际气候变率的主要模式得到了很好的体现。二氧化碳浓度增强的实验显示出众所周知的北极放大、陆海对比、对流层变暖和平流层变冷的响应。当前版本EC-Earth的全球气候敏感性略小于1 K/(W m(-2))。发现水文循环的加强和降水的强烈区域变化，影响季风特征。结果表明，基于可操作季节预测系统的耦合模型可用于气候研究，支持新兴的无缝预测策略。
EC-Earth, a new Earth system model based on the operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF), is presented. The performance of version 2.2 (V2.2) of the model is compared to observations, reanalysis data and other coupled atmosphere-ocean-sea ice models. The large-scale physical characteristics of the atmosphere, ocean and sea ice are well simulated. When compared to other coupled models with similar complexity, the model performs well in simulating tropospheric fields and dynamic variables, and performs less in simulating surface temperature and fluxes. The surface temperatures are too cold, with the exception of the Southern Ocean region and parts of the Northern Hemisphere extratropics. The main patterns of interannual climate variability are well represented. Experiments with enhanced CO2 concentrations show well-known responses of Arctic amplification, land-sea contrasts, tropospheric warming and stratospheric cooling. The global climate sensitivity of the current version of EC-Earth is slightly less than 1 K/(W m(-2)). An intensification of the hydrological cycle is found and strong regional changes in precipitation, affecting monsoon characteristics. The results show that a coupled model based on an operational seasonal prediction system can be used for climate studies, supporting emerging seamless prediction strategies.