Sub-Seasonal Predictability of the Northeast China Cold Vortex in BCC and ECMWF S2S Model Forecasts for 2006–2021

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作者
Yiqiu YU [1 ,2 ,3 ]
Jie WU [4 ,5 ]
Yihe FANG [1 ,2 ,3 ]
Chunyu ZHAO [1 ,2 ,3 ]
Zongjian KE [4 ]
Yitong LIN [1 ,2 ,3 ]
机构
[1] Liaoning Provincial Climate Centre, Liaoning Provincial Meteorological Administration
[2] Key Opening Laboratory for Northeast China Cold Vortex Research,China Meteorological Administration
[3] Panjin National Climate Observatory
[4] China Meteorological Administration Key Laboratory for Climate Prediction Studies,National Climate Centre, China Meteorological Administration
[5] Jilin Provincial Climate Centre, Jilin Provincial Meteorological
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摘要
As an important atmospheric circulation system in the mid–high latitudes of East Asia, the Northeast China cold vortex(NCCV) substantially influences weather and climate in this region. So far, systematic assessment on the performance of numerical prediction of the NCCVs has not been carried out. Based on the Beijing Climate Centre(BCC) and the ECMWF model hindcast and forecast data that participated in the Sub-seasonal to Seasonal(S2S) Prediction Project, this study systematically examines the performance of both models in simulating and forecasting the NCCVs at the sub-seasonal timescale. The results demonstrate that the two models can effectively capture the seasonal variations in the intensity, active days, and spatial distribution of NCCVs; however, the duration of NCCVs is shorter and the intensity is weaker in the models than in the observations. Diagnostic analysis shows that the differences in the intensity and location of the East Asian subtropical westerly jet and the wave train pattern from North Atlantic to East Asia may be responsible for the deficient simulation of NCCV events in the S2S models. Nonetheless, in the deterministic forecasts, BCC and ECMWF provide skillful prediction on the anomalous numbers of NCCV days and intensity at a lead time of 4–5(5–6) pentads, and the skill limit of the ensemble mean is 1–2 pentads longer than that of individual members. In the probabilistic forecasts of daily NCCV activities, BCC and ECMWF exhibit a forecasting skill of approximately 7 and 11 days, respectively; both models show seasonal dependency in the simulation performance and forecast skills of NCCV events, with better performance in winter than in summer.The results from this study provide helpful references for further improvement of the S2S prediction of NCCVs.
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页码:453 / 468
页数:16
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