Prediction and predictability of tropical intraseasonal convection: seasonal dependence and the Maritime Continent prediction barrier
Prediction and predictability of tropical intraseasonal convection: seasonal dependence and the Maritime Continent prediction barrier
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DOI：
10.1007/s00382-018-4492-9
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发表时间：
2018-10
期刊：
影响因子：
4.6
通讯作者：
Shuguang Wang;A. Sobel;M. Tippett;F. Vitart
中科院分区：
文献类型：
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作者：
Shuguang Wang;A. Sobel;M. Tippett;F. Vitart
利用基于实时OLR的MJO（ROMI）指数对WMO亚季节到季节（S2 S）预报数据库中热带季节内对流的预报和可预报性进行了评估。S2 S模式中的ROMI预报技巧，以预报和观测之间的二元相关系数超过0.6的最大提前时间来衡量，在北方冬季的范围从~ 15到~ 36天，这比基于MJO RMM指数的MJO环流预报技巧高5-10天。ROMI预测技能在夏季比冬季系统地低5-10天。可预测性措施显示出类似的季节性对比，在两个赛季。这些结果表明，季节内对流在夏季比在冬季固有的可预测性差。假设完美的振幅或完美的相位预测的相关技能的进一步评估表明，相位偏差是技能退化的主要贡献者在较长的预测提前期。几乎所有的S2 S模式都有较少的技能，在目标日期中，MJO对流是集中在海洋大陆（MC）在北方冬季，和相位偏差有助于这个MC预测障碍。这个问题在北方夏季不太普遍。许多S2 S模型在较长的预测提前期显著低估了ROMI振幅。使用排序概率技能得分（RPSS）进一步评估预测ROMI幅度的S2 S模型技能的概率评估。不同型号的RPSS差异很大，从没有技能到超过30天，这部分是由于型号配置，部分是由于振幅偏差。考虑到幅度的系统低估，RPSS得到改善。
Prediction and predictability of tropical intraseasonal convection in the WMO subseasonal to seasonal (S2S) forecast database is assessed using the real-time OLR based MJO (ROMI) index. ROMI prediction skill in the S2S models, as measured by the maximum lead time at which the bivariate correlation coefficient between forecasts and observations exceeds 0.6, ranges from ~ 15 to ~ 36 days in boreal winter, which is 5–10 days higher than the MJO circulation prediction skill based on the MJO RMM index. ROMI prediction skill is systematically lower by 5–10 days in summer than in winter. Predictability measures show similar seasonal contrast in the two seasons. These results indicate that intraseasonal convection is inherently less predictable in summer than in winter. Further evaluation of correlation skill assuming either perfect amplitude or perfect phase forecasts indicates that phase bias is the main contributor to skill degradation at longer forecast lead times. Nearly all the S2S models have lesser skill for target dates in which the MJO convection is centered over the Maritime Continent (MC) in boreal winter, and phase bias contributes to this MC prediction barrier. This issue is less prevalent in boreal summer. Many S2S models significantly underestimate ROMI amplitudes at longer forecast leads. Probabilistic evaluation of the S2S model skills in forecasting ROMI amplitude is further assessed using the ranked probability skill score (RPSS). RPSS varies significantly across models, from no skill to more than 30 days, which is partly due to model configuration and partly due to amplitude bias. Accounting for the systematic underestimates of the amplitude improves RPSS.