Impact of Vertical Resolution on ENSO Teleconnection Simulation
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摘要: 模式分辨率对气候模式的模拟效果具有重要影响。然而，当前模式开发对于垂直分辨率的重视不够。以ENSO（厄尔尼诺-南方涛动）遥相关为例，利用CESM（Community Earth System Model）模式，探究不同模式垂直分辨率设置下模式模拟的ENSO对平流层、对流层影响的差异，评估模式垂直分辨率在气候模拟中的重要性。结果表明，提高垂直分辨率可以显著改进模式对ENSO遥相关的模拟能力。以ECMWF（European Centre for Medium-Range Weather Forecasts）第五代再分析数据集（ERA5）为参照，ENSO对纬向平均温度的影响在北半球中高纬地区冬季呈现出“负正负”的三极子模态。CESM默认的垂直分辨率设置（L66）不能模拟出这一模态，而提高模式垂直分辨率（L103）后则可以较好地模拟出这个模态。对于水平分布而言，L66模拟的ENSO在对流层的信号与再分析资料相比明显偏强，L103则可以显著改善。同时，L103对ENSO影响平流层的模拟效果也比L66有所改善。进一步分析发现，L103模拟的行星波从对流层向平流层的传播更强，更接近再分析资料。提高垂直分辨率可以改善模式对大气波活动以及平流层-对流层动力耦合的模拟，重视模式的研发。Abstract: Model resolution significantly influences the simulation effect of climate models. However, model vertical resolution has not been a primary focus in current model development. To evaluate the importance of model vertical resolution in climate modeling, this study took the El Niño-Southern Oscillation (ENSO) teleconnection as an example and explored the impact of different vertical resolution settings in the Community Earth System Model (CESM) on the simulation of ENSO teleconnections in the stratosphere and the troposphere. The results show that increasing vertical resolution can significantly boost the model's capability to simulate ENSO teleconnection. Based on the fifth generation reanalysis (ERA5) dataset from the European Centre for Medium-Range Weather Forecasts, the present study explored the effect of ENSO on zonal mean temperature and found a'negative-positive-negative'tripole mode in winter over the mid-to-high latitudes in the Northern Hemisphere. The CESM model with default vertical resolution (66 vertical levels, L66) failed in simulating this mode, while the model with higher vertical resolution (103 vertical levels, L103) performed better in capturing this mode. For the horizontal distribution of ENSO teleconnection, the tropospheric signal of ENSO simulated by L66 was stronger than that indicated by the reanalysis data, whereas the signal in L103 was significantly improved. The L103 model also simulated a more accurate ENSO teleconnection in the stratosphere compared to L66. Further analysis shows that the planetary waves simulated by L103 propagated more robustly from the troposphere to the stratosphere, which was more consistent with the ERA5 reanalysis data. Overall, improving vertical resolution can significantly improve the simulation of atmospheric wave activities and the stratospheretroposphere dynamic coupling, which should be prioritized in model development.
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图 2 冬季温度对ENSO响应的经向剖面
ERA5（a）、L66（b）和L103（c）模拟的纬向平均温度与Niño3.4指数进行线性拟合得到的回归系数的经向分布；敏感性试验：ENS6-FS6_L66（d）；敏感性试验：ENS6-FS6_L103（e）；ABS（ENS6-FS6_L103-ERA5）-ABS（ENS6-FS6_L66-ERA5）（f），即两组敏感性试验与ERA5再分析资料差距对比（填色，单位：K，打点区域通过了0.05显著性水平检验）。
图 3 冬季纬向风对ENSO响应的经向剖面
ERA5（a）、L66（b）和L103（c）模拟的纬向风与Niño3.4指数进行线性拟合得到的回归系数的经向分布；敏感性试验：ENS6-FS6_L66（d）；敏感性试验：ENS6-FS6_L103（e）；ABS（ENS6-FS6_L103-ERA5）-ABS（ENS6-FS6_L66-ERA5）（f），即两组敏感性试验与ERA5再分析资料差距对比（填色，单位：m·s-1，打点区域通过了0.05显著性水平检验）。
图 4 对流层（500 hPa）温度的ENSO信号的水平分布对比
ERA5（a）、L66（b）和L103（c）模拟的温度与Niño3.4指数进行线性拟合得到的回归系数的水平分布；敏感性试验：ENS6-FS6_L66（d）；敏感性试验：ENS6-FS6_L103（e）；ABS（L103-ERA5）-ABS（L66-ERA5）（f），即两组长期响应与ERA5再分析资料差距对比（填色，单位：K，打点区域通过了0.05显著性水平检验）；（g）关键区ENSO信号强度对比（单位：K，area1：红框区域；area2：绿框区域；area3：黑框区域）。
图 5 对流层顶附近（100 hPa）温度的ENSO信号的水平分布对比
ERA5（a）、L66（b）和L103（c）模拟的温度与Niño3.4指数进行线性拟合得到的回归系数的水平分布；敏感性试验：ENS6-FS6_L66（d）；敏感性试验：ENS6-FS6_L103（e）；ABS（L103-ERA5）-ABS（L66-ERA5）（f），即两组长期响应与ERA5再分析资料差距对比（填色，单位：K，打点区域通过了0.05显著性水平检验）；关键区ENSO信号强度对比（g）（单位：K，area1：绿框区域；area2：黑框区域）。
图 6 平流层（50 hPa）温度的ENSO信号的水平分布对比
ERA5（a）、L66（b）和L103(c)模拟的温度与Niño3.4指数进行线性拟合得到的回归系数的水平分布；敏感性试验：ENS6-FS6_L66（d）；敏感性试验：ENS6-FS6_L103（e）；ABS（L103-ERA5）-ABS（L66-ERA5）（f），即两组长期响应与ERA5再分析资料差距对比（填色，单位：K，打点区域通过了0.05显著性水平检验）；关键区ENSO信号强度对比（g）（单位：K，area1：绿框区域）。
图 7 对流层（500 hPa）纬向风的ENSO信号的水平分布对比
ERA5（a）、L66（b）和L103（c）模拟的纬向风与Niño3.4指数进行线性拟合得到的回归系数的水平分布；敏感性试验：ENS6-FS6_L66（d）；敏感性试验：ENS6-FS6_L103（e）；ABS（L103-ERA5）-ABS（L66-ERA5）（f），即两组长期响应与ERA5再分析资料差距对比（填色，单位：m·s-1，打点区域通过了0.05显著性水平检验）；关键区ENSO信号强度对比（g）（单位：m·s-1，area1：绿框区域；area2：黑框区域）。
图 8 对流层顶附近（100 hPa）纬向风的ENSO信号的水平分布对比
ERA5（a）、L66（b）和L103（c）模拟的纬向风与Niño3.4指数进行线性拟合得到的回归系数的水平分布；敏感性试验：ENS6-FS6_L66（d）；敏感性试验：ENS6-FS6_L103（e）；ABS（L103-ERA5）-ABS（L66-ERA5）（f），即两组长期响应与ERA5再分析资料差距对比（填色，单位：m·s-1，打点区域通过了0.05显著性水平检验）；关键区ENSO信号强度对比（g）（单位：m·s-1，area1：绿框区域）。
图 9 平流层（50 hPa）纬向风的ENSO信号的水平分布对比
ERA5（a）、L66（b）和L103（c）模拟的纬向风与Niño3.4指数进行线性拟合得到的回归系数的水平分布；敏感性试验：ENS6-FS6_L66（d）；敏感性试验：ENS6-FS6_L103（e）；ABS（L103-ERA5）-ABS（L66-ERA5）（f），即两组长期响应与ERA5再分析资料差距对比（填色，单位：m·s-1，打点区域通过了0.05显著性水平检验）。
图 10 ERA5再分析资料和历史回溯模拟数据L66、L103中的冬季ENSO信号在1~3波的分量分布（填色，单位：K）
不同数据中ENSO信号的1波分量（上）、2波分量（中）、3波分量（下）。
图 11 ERA5再分析资料（a）与模式模拟（b、c）的冬季EP通量（箭矢，单位：m2·s-2）及其散度（填色，单位：m·s-1·d-1；正值表示辐散，负值表示辐合）与Niño3.4指数线性拟合的纬向分布
表 1 试验设置
模式 垂直层数 时间范围/年 集合成员（模拟数量） Run_L66 66 1951—2010 1 Run_L103 103 1951—2010 1 ENS6L66 66 2001 6 FS6L66 66 1991—2001 6 ENS6L103 103 2001 6 FS6L103 103 1991—2001 6 
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