A multivariate empirical‐orthogonal‐function‐based measure of climate model performance
A multivariate empirical‐orthogonal‐function‐based measure of climate model performance
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DOI：
10.1029/2004jd004584
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发表时间：
2004-08
影响因子：
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通讯作者：
Q. Mu;C. Jackson;P. Stoffa
中科院分区：
文献类型：
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作者：
Q. Mu;C. Jackson;P. Stoffa
[1]基于经验正交函数(EOFs)的使用，开发了一种多个场的气候模型预测与观测之间的平均距离的度量。EOFS的应用提供了一种使用自然变率中的空间相关性信息的方法，以更平衡地查看跨多个领域、季节和区域的模型预测变化的重要性。对国家大气研究中心社区气候模式3.10版(CCM3.10)中控制初始云下沉质量通量(ALFA)的参数--张和麦克法兰[1995]对流方案中的一个重要参数--基于EOF的量度和按格点方差和空间方差进行归一化的量度进行了比较。所有测量都提出了一致的观点，即从其缺省值增加阿尔法可以显著改善降水、到达地面的短波辐射和地表潜热通量，但代价是降低对总云量、近地表气温、大气顶部净短波辐射和相对湿度的预测。然而，每一个变化的相对重要性，以及模型性能变化的平均视图，都受到每个模型性能衡量标准如何处理内部变异性很小或没有变化的地区的细节的显著影响。总体而言，基于EOF的衡量标准侧重于模型观测差异较大的地区，不包括内部变异性较小的地区。指标词：3309气象与大气动力学：气候学(1620)；3314气象与大气动力学：对流过程；3337气象与大气动力学：数值模拟与数据同化；3394气象与大气动力学：仪器与技术；关键词：气候预测、技能得分、数值模拟
[1] A measure of the average distance between climate model predictions of multiple fields and observations has been developed that is based on the use of empirical orthogonal functions (EOFs). The application of EOFs provides a means to use information about spatial correlations in natural variability to provide a more balanced view of the significance of changes in model predictions across multiple fields, seasons, and regions. A comparison is made between the EOF-based measure and measures that are normalized by grid point variance and spatial variance for changes in the National Center for Atmospheric Research Community Climate Model, Version 3.10 (CCM3.10), parameter controlling initial cloud downdraft mass flux (ALFA), an important parameter within the Zhang and McFarlane [1995] convection scheme. All measures present consistent views that increasing ALFA from its default value creates significant improvements in precipitation, shortwave radiation reaching the surface, and surface latent heat fluxes at the expense of degrading predictions of total cloud cover, near-surface air temperature, net shortwave radiation at the top of the atmosphere, and relative humidity. However, the relative importance of each of these changes, and therefore the average view of the change in model performance, is significantly impacted by the details of how each measure of model performance handles regions with little or no internal variability. In general, the EOF-based measure emphasizes regions where modeledobservational differences are large, excluding those regions where internal variability is small. INDEX TERMS: 3309 Meteorology and Atmospheric Dynamics: Climatology (1620); 3314 Meteorology and Atmospheric Dynamics: Convective processes; 3337 Meteorology and Atmospheric Dynamics: Numerical modeling and data assimilation; 3394 Meteorology and Atmospheric Dynamics: Instruments and techniques; KEYWORDS: climate prediction, skill scores, numerical modeling