Evaluation of regional climate model simulations versus gridded observed and regional reanalysis products using a combined weighting scheme
Evaluation of regional climate model simulations versus gridded observed and regional reanalysis products using a combined weighting scheme
复制标题
使用组合加权方案评估区域气候模型模拟与网格观测和区域再分析产品
作者：
H. Eum;P. Gachon;R. Laprise;T. Ouarda
本文提出了一种组合加权方案，该方案包含五个属性，反映了区域气候模式（RCMs）对观测和区域再分析产品模拟的气候数据的准确性，即短期（日）、中期（年）和长期（年）时间尺度，以及空间格局和极值。加拿大魁北克省和安大略省的南部地区被用作研究区域。在NCEP和ERA40全球再分析产品的驱动下，利用加拿大两个不同版本的RCM （CRCM4.1.1和CRCM4.2.3）在1979 - 2001年的23年间进行了三个系列的模拟。以北美地区的一系列区域再分析数据（即NARR）作为比较和验证的参考，以及网格化的降水和温度的历史观测日数据，这两个系列都事先在CRCM 45 km网格分辨率上插值。计算月加权因子，然后将其合并为四个季节，以反映气候资料精度的季节变化。此外，本研究还生成了不同权重因子和集合大小的加权平均参考（WARs）作为新的参考气候数据集。模拟结果表明，NARR总体上优于CRCM模拟降水值，但CRCM4.1.1在冬季提供了最高的权重因子。对于最低和最高温度，CRCM4.1.1和NARR产品分别提供了最高的权重因子。NARR提供了更精确的短期和中期气候数据，而CRCM的两个版本提供了更精确的长期数据、空间格局和极端事件。研究还证实，在CRCM运行中用作边界条件的全球再分析数据（即NCEP与ERA40）对CRCM模拟降水和温度值的准确性具有不可忽略的影响。此外，本研究还表明，所提出的加权因子能很好地反映所有五个属性，加权平均参考的性能优于最佳单一模型。本研究还发现，战争性能的提高是由于rcm的可靠性（准确性），而不是由于集合大小。
This study presents a combined weighting scheme which contains five attributes that reflect accuracy of climate data, i.e. short-term (daily), mid-term (annual), and long-term (decadal) timescales, as well as spatial pattern, and extreme values, as simulated from Regional Climate Models (RCMs) with respect to observed and regional reanalysis products. Southern areas of Quebec and Ontario provinces in Canada are used for the study area. Three series of simulation from two different versions of the Canadian RCM (CRCM4.1.1, and CRCM4.2.3) are employed over 23 years from 1979 to 2001, driven by both NCEP and ERA40 global reanalysis products. One series of regional reanalysis dataset (i.e. NARR) over North America is also used as reference for comparison and validation purpose, as well as gridded historical observed daily data of precipitation and temperatures, both series have been beforehand interpolated on the CRCM 45-km grid resolution. Monthly weighting factors are calculated and then combined into four seasons to reflect seasonal variability of climate data accuracy. In addition, this study generates weight averaged references (WARs) with different weighting factors and ensemble size as new reference climate data set. The simulation results indicate that the NARR is in general superior to the CRCM simulated precipitation values, but the CRCM4.1.1 provides the highest weighting factors during the winter season. For minimum and maximum temperature, both the CRCM4.1.1 and the NARR products provide the highest weighting factors, respectively. The NARR provides more accurate short- and mid-term climate data, but the two versions of the CRCM provide more precise long-term data, spatial pattern and extreme events. Or study confirms also that the global reanalysis data (i.e. NCEP vs. ERA40) used as boundary conditions in the CRCM runs has non-negligible effects on the accuracy of CRCM simulated precipitation and temperature values. In addition, this study demonstrates that the proposed weighting factors reflect well all five attributes and the performances of weighted averaged references are better than that of the best single model. This study also found that the improvement of WARs’ performance is due to the reliability (accuracy) of RCMs rather than the ensemble size.