Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models.
Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models.
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每日和每月的温度和降水统计数据作为区域气候模型的性能指标。
DOI：
10.3354/cr00932
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发表时间：
2010
期刊：
影响因子：
1.1
通讯作者：
E. Sánchez
中科院分区：
文献类型：
--
作者：
E. Kjellström;F. Boberg;M. Castro;J. Christensen;G. Nikulin;E. Sánchez
我们评估了每日和每月的最高和最低温度和降水量的16个区域气候模式（RCMs）的边界条件强迫从1961年至1990年的再分析数据的合奏统计。使用了欧洲陆地地区的高分辨率网格化观测数据集。技能分数的计算基于模拟和观察到的经验概率密度函数的匹配。对不同变量、季节和地区的评价表明，在总体意义上，一些模型比其他模型好/差。它还表明，没有一个模型在所有变量、季节或地区都是最好/最差的。日降水量的偏差在概率分布的最湿部分最明显，与观测值相比，RCM倾向于高估降水量。我们还将技能分数作为权重，用于计算变量的加权整体均值。我们发现，加权系综平均值比相应的未加权系综平均值为大多数季节，地区和变量的观测结果略好。一些敏感性测试表明，权重是高度敏感的选择的技能得分指标和数据集的比较。
We evaluated daily and monthly statistics of maximum and minimum temperatures and precipitation in an ensemble of 16 regional climate models (RCMs) forced by boundary conditions from reanalysis data for 1961-1990. A high-resolution gridded observational data set for land areas in Europe was used. Skill scores were calculated based on the match of simulated and observed empirical probability density functions. The evaluation for different variables, seasons and regions showed that some models were better/worse than others in an overall sense. It also showed that no model that was best/worst in all variables, seasons or regions. Biases in daily precipitation were most pronounced in the wettest part of the probability distribution where the RCMs tended to overestimate precipitation compared to observations. We also applied the skill scores as weights used to calculate weighted ensemble means of the variables. We found that weighted ensemble means were slightly better in comparison to observations than corresponding unweighted ensemble means for most seasons, regions and variables. A number of sensitivity tests showed that the weights were highly sensitive to the choice of skill score metric and data sets involved in the comparison.