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Development and prospects of the regional MiKlip decadal prediction system over Europe: predictive skill, added value of regionalization, and ensemble size dependency

机译:区域Miklip Decadal预测系统的开发和前景在欧洲:预测技巧,区域化的附加值,以及集合尺寸依赖性

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The current state of development and the prospects of the regional MiKlip decadal prediction system for Europe are analysed. The MiKlip regional system consists of two 10-member hindcast ensembles computed with the global coupled model MPI-ESM-LR downscaled for the European region with COSMO-CLM to a horizontal resolution of 0.22° (~25 km). Prediction skills are computed for temperature, precipitation, and wind speed using E-OBS and an ERA-Interim-driven COSMO-CLM simulation as verification datasets. Focus is given to the eight European PRUDENCE regions and to lead years?1–5 after initialization. Evidence of the general potential for regional decadal predictability for all three variables is provided. For example, the initialized hindcasts outperform the uninitialized historical runs for some key regions in Europe, particularly in southern Europe. However, forecast skill is not detected in all cases, but it depends on the variable, the region, and the hindcast generation. A comparison of the downscaled hindcasts with the global MPI-ESM-LR runs reveals that the MiKlip prediction system may distinctly benefit from regionalization, in particular for parts of southern Europe and for Scandinavia. The forecast accuracy of the MiKlip ensemble is systematically enhanced when the ensemble size is increased stepwise, and 10?members is found to be suitable for decadal predictions. This result is valid for all variables and European regions in both the global and regional MiKlip ensemble. The present results are encouraging for the development of a regional decadal prediction system.
机译:分析了目前的发展状态和欧洲区域Miklip Decadal预测系统的前景。 Miklip区域系统由两个成员的Hindcast合奏组成,使用欧洲地区的全球耦合型号MPI-ESM-LR计算,其中COSMO-CLM为0.22°(〜25公里)的水平分辨率。使用E-OBS的温度,降水和风速计算预测技能,并将ERA中间驱动的COSMO-CLM模拟作为验证数据集。初始化后,焦点归于八个欧洲谨慎地区并达到1-5。提供了所有三个变量的区域截止可预测性的一般潜力的证据。例如,初始化的Hindcasts优于欧洲一些关键地区的未初始化的历史运行,特别是在欧洲南部。但是,在所有情况下都未检测到预测技能,但这取决于变量,区域和Hindcast生成。与全球MPI-ESM-LR运行的较低的HindCasts的比较揭示了Miklip预测系统可能明显从区域化中受益,特别是对于南欧和斯堪的纳维亚的部分地区。在逐步增加的集合尺寸增加时,Miklip集合的预测精度得到系统地增强,并发现成员适用于二等预测。此结果对于全球和区域Miklip集合中的所有变量和欧洲地区有效。目前的结果令人抱歉地发展区域截止地区预测系统。

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