Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system “Mittelfristige Klimaprognose” (MiKlip). Among the tested methods, three tackle aspects of model‐consistent initialization using the ensemble Kalman filter, the filtered anomaly initialization, and the initialization method by partially coupled spin‐up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter corrects each ensemble member with the ensemble mean during model integration. And the bred vectors perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low‐resolution configuration (Preop‐LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop‐LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the ensemble Kalman filter and filtered anomaly initialization show the most distinct improvements over Preop‐LR for surface temperatures and upper ocean heat content, followed by the bred vectors, the ensemble dispersion filter, and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin‐wide long‐term mean and variability and with respect to the temporal evolution at the 26° N latitude.

中文翻译：

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年代际气候预测的初始化和集合生成：不同方法的比较

研究了五种初始化和集合生成方法对德国年代际预测系统​​“ Mittelfristige Klimaprognose”（MiKlip）的预测技能的影响。在测试的方法中，使用集成卡尔曼滤波器解决了模型一致的初始化，滤波异常初始化以及通过部分耦合旋转（MODINI）进行初始化的方法这三个方面。剩下的两种方法会更改集合的生成：集合散布滤波器在模型集成过程中使用集合均值校正每个集合成员。繁殖的媒介会以最快的生长方式干扰气候状态。在低分辨率配置（Preop-LR）中，将新方法与最新的MiKlip系统进行了比较，它使用滞后几天的气候状态进行集合生成，然后对海洋和大气层进行重新分析以进行初始化。结果表明，与Preop-LR相比，所测试的方法为预报技能提供了附加值，因为它们提高了东部和中部太平洋以及北大西洋不同地区的预报技能。在这方面，集合卡尔曼滤波器和滤波异常初始化显示出相对于Preop-LR在地表温度和海洋高热量方面有最明显的改进，其次是繁殖矢量，集合色散滤波器和MODINI。但是，就所考虑的所有指标而言，不存在一种优于其他方法的方法。特别是，所有方法都以不同方式影响大西洋子午线翻转环流，