How does large-scale nudging in a regional climate model contribute to improving the simulation of weather regimes and seasonal extremes over North America?
How does large-scale nudging in a regional climate model contribute to improving the simulation of weather regimes and seasonal extremes over North America?
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DOI：
10.1007/s00382-015-2623-0
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发表时间：
2016-02
期刊：
影响因子：
4.6
通讯作者：
P. Lucas‐Picher;J. Cattiaux;Alexandre Bougie;R. Laprise
中科院分区：
文献类型：
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作者：
P. Lucas‐Picher;J. Cattiaux;Alexandre Bougie;R. Laprise
为了确定区域气候模式(RCM)在多大程度上保护其驱动场的大尺度大气环流，我们研究了两个区域气候模式模拟再现北美地区天气状况的能力。每个RCM模拟都是由ERA-中期重新分析在其侧边界驱动的，但其中一个在域内部使用了额外的大尺度轻推(LSN)。在冬季和夏季确定了描述北美大尺度大气动力变化的四种天气状况。分析表明，对于两个季节，两次RCM模拟的四种天气形势的平均出现频率和持续时间与ERA-临时模式相当。然而，对于经典的横向边界驱动的模拟来说，在日常和季节性基础上对特定区域进行错误的每日归因的频率非常高，尤其是在夏季。这些错误的归因在很大程度上通过LSN进行了纠正。对每种天气类型采用合成方法，发现了与大尺度大气环流有关的相当大的2m气温和降水异常。这些异常在冬季比夏季更大。模拟的验证表明，2米气温偏差取决于天气状况，特别是在夏季。相反，不同地区的降水偏差差异很大，尤其是在冬季。总体而言，结果表明，经典的RCM可以很好地模拟天气形势的平均统计，但LSN对于再现与驾驶场相匹配的每日天气形势和季节性异常是必要的。
To determine the extent to which regional climate models (RCMs) preserve the large-scale atmospheric circulation of their driving fields, we investigate the ability of two RCM simulations to reproduce weather regimes over North America. Each RCM simulation is driven at its lateral boundaries by the ERA-Interim reanalysis, but one of them uses additional large-scale nudging (LSN) in the domain interior. Four weather regimes describing the variability of the large-scale atmospheric dynamics over North America are identified in winter and in summer. The analysis shows that for both seasons, the mean frequency of occurrence and persistence of the four weather regimes for the two RCM simulations are comparable to those of ERA-Interim. However, the frequency of false daily attributions of a specific regime on day-to-day and seasonal bases is significantly high, especially in summer, for the classic lateral-boundary driven simulation. Those false attributions are largely corrected with LSN. Using composite means for each weather regimes, substantial 2-m air temperature and precipitation anomalies associated to the large-scale atmospheric circulation are found. These anomalies are larger in winter than in summer. The validation of the simulations reveals that the 2-m air temperature bias is dependent on the weather regimes, especially in summer. Conversely, the precipitation bias varies significantly from one regime to another, especially in winter. Overall, the results suggest that a classic RCM simulates the mean statistics of the weather regimes well, but that LSN is necessary to reproduce daily weather regimes and seasonal anomalies that match with the driving field.