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Evaluation of Global Energy Cycle for CMA-GFS Based on Scale Analysis
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摘要: 全球模式能量循环和能量转换规律可准确反映模式动力和物理过程相互作用的物理机制, 是诊断大气环流特征的重要方法。基于混合时空域能量循环框架, 采用尺度分析方法, 利用2022年中国气象局全球数值预报系统(CMA Global Forecast System, CMA-GFS)全球预报产品及欧洲中期天气预报中心第5代再分析资料(ECMWF reanalysis version 5, ERA5), 考察CMA-GFS不同尺度下的能量蓄能及转换特征, 以此诊断模式的误差来源。结果表明:CMA-GFS可有效预报大气能量循环基本特征, 但其对斜压性的高估导致平均环流有效位能偏强, 且具有随预报时效逐渐增长的趋势。定常和瞬变涡动能量分别受行星尺度和天气及以下尺度分量主导。涡动有效位能误差由模式斜压性决定, 其中CMA-GFS的定常涡动有效位能偏高而瞬变涡动有效位能偏低。定常和瞬变涡动动能均存在系统性低估, 负误差主要集中在副热带急流和极夜急流中心附近, 偏强的正压输送使更多能量向平均环流转换, 涡动能量偏弱。CMA-GFS的4种涡动能量在冬季预报偏低, 而在夏季偏高或略偏低, 严重削弱了季节变化影响。Abstract: The first step in improving a model is to identify deficiencies in the model forecast. With the continuous advancement of numerical prediction technology, the precise assessment and analysis of model prediction errors, particularly the traceable technology of systematic errors, has gradually become a pivotal issue in model evaluation. Atmospheric energy circulation, as a fundamental principle of atmospheric motion, accurately represents the dynamic and physical interaction mechanisms. With a deeper understanding of the atmospheric energy cycle process, its applications have also expanded continuously. Particularly in recent decades, it has been used to assess the performance of numerical models and reanalysis datasets, serving as an essential metric for understanding model forecast capability and identifying systematic errors. Encompassed within the mixed space-time domain energy cycle are the mean circulation, stationary (deviation from the zonal mean), and transient (deviation from the temporal mean) eddies, and their interconversions of the available potential energy and kinetic energy, with each component holding physical significance. Based on the mixed space-time domain energy cycle framework and scale analysis methods, the energy cycle error characteristics and sources in CMA-GFS at planetary scales (zonal wavenumber 1-3) and synoptic and below (greater than zonal wavenumber 3) scales are examined using CMA-GFS global forecast product and ERA5 global reanalysis data in 2022. Results show that CMA-GFS can effectively replicate characteristics of the atmospheric energy cycle. However, its overestimation of baroclinity results in a stronger available potential energy of the mean circulation, which shows an increasing trend with forecast lead time. The stationary and transient eddy energy are dominated by planetary scales and synoptic and below scales, respectively. Errors in the available potential energy of the stationary eddy component and transient eddy component are determined by thermal conditions. CMA-GFS shows higher stationary eddy available potential energy and less transient eddy available potential energy. Systematic underestimations are observed in kinetics of stationary eddy component and transient eddy component, with predominantly negative errors concentrated near centers of subtropical jets and the polar night jet. This is primarily due to stronger barotropic transports, which transfer more energy from eddies to the mean circulation. As the baroclinity gradually increased, the transient eddy also increased after 120 h lead time. CMA-GFS underestimates four eddy energies in the boreal winter and overestimates or slightly underestimates them in the boreal summer, leading to a significant weakening of their seasonal variation.
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图 1 2022年7月ERA5和CMA-GFS混合时空域能量循环全球积分值随预报时效变化
Fig. 1 Variation of globally integrated values of mixed space-time domain energy cycle for ERA5 and CMA-GFS with lead time in Jul 2022
图 2 2022年7月CMA-GFS纬向平均温度误差分布
Fig. 2 Distribution of zonal mean temperature error of CMA-GFS in Jul 2022
图 3 2022年7月ERA5和CMA-GFS不同尺度定常涡动有效位能的纬度-气压分布
Fig. 3 Latitude-pressure distribution of stationary eddy component of available potential energy of different scale components for ERA5 and CMA-GFS in Jul 2022
图 4 2022年7月ERA5和CMA-GFS中静力稳定度、定常温度涡动和瞬变温度涡动的全球积分值随预报时效变化
Fig. 4 Variation of globally integrated values of static stability, stationary temperature eddies and transient temperature eddies for ERA5 and CMA-GFS in Jul 2022
图 5 2022年7月ERA5和CMA-GFS不同尺度瞬变涡动有效位能的纬度-气压分布
Fig. 5 Latitude-pressure distribution of transient eddy component of available potential energy of different scale components for ERA5 and CMA-GFS in Jul 2022
图 6 2022年7月ERA5和CMA-GFS不同尺度定常涡动动能的纬度-气压分布
Fig. 6 Latitude-pressure distribution of the stationary eddy component of kinetic energy of different scale components for ERA5 and CMA-GFS in Jul 2022
图 7 2022年7月ERA5和CMA-GFS不同尺度分量瞬变涡动动能的纬度-气压分布
Fig. 7 Latitude-pressure distribution of transient eddy component of kinetic energy of different scale components for ERA5 and CMA-GFS in Jul 2022
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