Multimodel ensemble (MME) reforecasts of rainfall at subseasonal time scales in the southwest tropical Pacific are constructed using six models (BoM, CMA, ECCC, ECMWF, Météo‐France, and UKMO) from the Subseasonal‐to‐Seasonal (S2S) database by member pooling. These reforecasts are verified at each grid point of the 110°E to 200°E; 30°S to 0° domain for the 1996–2013 DJF period. The evaluation is based on correlation and on the ROC skill score of the upper quintile of precipitation for both weekly targets and Weeks 3–4 outlook. Confirming previous results at the seasonal time scales, the MME reaches the highest skill and also improves the reliability of probabilistic forecasts. However, an equivalent ensemble size comparison between the MME and the individual models shows that the better performance of the MME compared to the best individual models is significantly related to the larger ensemble size of the MME. Forecast skill is then explained in light of potential sources of predictability by evaluating the performance of the models depending on the initial ENSO and MJO state. While the role of ENSO on predictability is quite consistent with its related rainfall anomalies, the role of the MJO is more ambiguous and strongly depends on the location: An initialization in active MJO conditions does not necessarily imply better forecasts. This influence of ENSO and the MJO on predictability does not change when switching from individual models to the MME.

中文翻译：

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西南热带太平洋次季节时间尺度降水的多模型预测

西南热带太平洋亚季节时标上的多模式集合（MME）降雨再预报是使用来自Subseasonal-to-Seasonal（S2S）数据库的六个模型（BoM，CMA，ECCC，ECMWF，Météo-France和UKMO）构建的。成员池。在110°E至200°E的每个网格点上对这些重新预测进行了验证；1996年至2013年DJF时期为30°S到0°范围。评估基于相关性以及每周目标和第3-4周展望的上半部降水量的ROC技能得分。MME在季节性时间尺度上确认了先前的结果，达到了最高的技能，并且还提高了概率预报的可靠性。然而，MME和单个模型之间的等效集合大小比较表明，与最佳单个模型相比，MME的更好性能与MME的更大集合大小显着相关。然后根据潜在的可预测性源，通过根据初始ENSO和MJO状态评估模型的性能来解释预测技能。尽管ENSO在可预测性上的作用与其相关的降雨异常非常吻合，但MJO的作用更加模棱两可，并且很大程度上取决于位置：在活跃MJO条件下的初始化并不一定意味着更好的预报。从单个模型切换到MME时，ENSO和MJO对可预测性的影响不会改变。然后根据潜在的可预测性来源，通过根据初始ENSO和MJO状态评估模型的性能来解释预测技巧。尽管ENSO在可预测性方面的作用与其相关的降雨异常非常吻合，但MJO的作用更加模棱两可，并且很大程度上取决于位置：在活跃MJO条件下的初始化并不一定意味着更好的预报。从单个模型切换到MME时，ENSO和MJO对可预测性的影响不会改变。然后根据潜在的可预测性源，通过根据初始ENSO和MJO状态评估模型的性能来解释预测技能。尽管ENSO在可预测性上的作用与其相关的降雨异常非常吻合，但MJO的作用更加模棱两可，并且很大程度上取决于位置：在活跃MJO条件下的初始化并不一定意味着更好的预报。从单个模型切换到MME时，ENSO和MJO对可预测性的影响不会改变。MJO的角色更加模棱两可，并且在很大程度上取决于位置：在活动MJO条件下的初始化并不一定意味着更好的预测。从单个模型切换到MME时，ENSO和MJO对可预测性的影响不会改变。MJO的角色更加模棱两可，并且在很大程度上取决于位置：在活动MJO条件下进行初始化并不一定意味着更好的预测。从单个模型切换到MME时，ENSO和MJO对可预测性的影响不会改变。