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Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer

机译:北方夏季海洋大陆降水的多模型集合季节性预测

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摘要

The Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during boreal summer (from May to September) was analyzed using the 12-year reforecasts data from five Sub-seasonal to Seasonal (S2S) models, including the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), the National Centers for Environmental Prediction (NCEP), and the Met Office (UKMO). The result shows that, compared with the individual models, our newly derived median multi-model ensemble (MME) can significantly improve the prediction skill of sub-seasonal precipitation in the MC. Both the Temporal Correlation Coefficient (TCC) skill and the Pattern Correlation Coefficient (PCC) skill reached 0.6 in lead week 1, dropped the following week, did not exceed 0.2 in lead week 3, and then lost their significance. The results show higher prediction skill near the Equator than in the north at 10° N. It is difficult to make effective predictions with the models beyond three weeks. The prediction ability of the median MME improves significantly as the total number of model members increases. The prediction performance of the median MME depends not only on the diversity of models but also on the number of model members. Moreover, the prediction skill is particularly sensitive to the intensity and phase of Boreal Summer Intraseasonal Oscillation 1 (BSISO1) with the highest skills appearing at initial phases 1 and 5.
机译:海洋大陆(MC)是一个关键的地区,具有独特的地理条件和显着的季风活动,在全球气候变化中起着至关重要的作用。在这项研究中,使用来自五个季节性到季节性(S2S)模型的12年的Reforecast数据,在包括中国气象学管理(CMA)的情况下,分析了在北方夏季(5月至9月)期间对MC的降水量的每周预测,欧洲中距离(ECMWF),环境和气候变化加拿大(ECCC),环境预测中心(NCEP)和MET办公室(UKMO)。结果表明,与各个模型相比,我们的新派生中位数多模型集合(MME)可以显着提高MC中季季节降水的预测技巧。在11周1中达到0.6的时间相关系数(TCC)技能和模式相关系数(PCC)技能均达到0.6,在接下来的一周中掉落,在11周3中没有超过0.2,然后损失了重要性。结果表明赤道附近的更高的预测技巧比在10°N处的北方。难以与超过三周的模型进行有效的预测。随着模型成员的总数增加,MEDIM MME的预测能力显着提高。 MEDIA MME的预测性能不仅取决于模型的多样性,还取决于模型成员的数量。此外,预测技能对北夏季初始振荡1(BSISO1)的强度和相位特别敏感,具有在初始阶段1和5处出现的最高技能。

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