| BCC S2S模式对亚洲夏季风准双周振荡预报评估 |
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| 引用本文： | 贺铮,徐邦琪,高迎侠.BCC S2S模式对亚洲夏季风准双周振荡预报评估[J].应用气象学报,2018,29(4):436-448. |
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| 作者姓名： | 贺铮 徐邦琪 高迎侠 |
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| 作者单位： | 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 南京 210044 |
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| 基金项目： | 国家重点基础研究发展计划（2015CB453200） |
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| 摘 要： | 利用1994-2013年ERA-Interim及NCEP/NCAR再分析数据，对国家气候中心（BCC）次季节到季节尺度模式（S2S）1994-2013年的回报试验数据进行亚洲季风区准双周振荡（QBWO）预报能力评估，并诊断模式预报误差来源。结果表明:BCC S2S模式对QBWO的预报能力随着预报提前时间的增长而降低，9 d后预报技巧明显减弱，其周期、传播特征和强度出现误差；在提前9 d预报中，印度洋地区QBWO对流-环流系统结构松散，信号偏弱，对流向东传播，这与印度洋平均态的预报误差有关，夏季对流平均态低层水汽场在西太平洋和阿拉伯海较强，而东印度洋、孟加拉湾一带偏弱；西北太平洋地区QBWO具有向西北传播的特征，但强度偏弱，可能原因是预报低估了QBWO对流西北侧低层涡度的超前信号，经涡度方程诊断发现，地转涡度平流正贡献微弱，相对涡度平流在对流西北侧引发负涡度，从而减弱了对流西北侧由低层正涡度引发的有利条件。
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| 关 键 词： | BCC S2S模式 准双周振荡 预报技巧 模式误差诊断 |
| 收稿时间： | 2018/3/9 0:00:00 |
| 修稿时间： | 2018/5/17 0:00:00 |
Evaluation of Quasi-biweekly Oscillation Prediction in the Asian Summer Monsoon Regions by BCC S2S Model |
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| He Zheng,Hsu Pang and Gao Yingxia.Evaluation of Quasi-biweekly Oscillation Prediction in the Asian Summer Monsoon Regions by BCC S2S Model[J].Quarterly Journal of Applied Meteorology,2018,29(4):436-448. |
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| Authors: | He Zheng Hsu Pang and Gao Yingxia |
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| Institution: | Key Laboratory of Meteorological Disaster of Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044 |
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| Abstract: | The quasi-biweekly oscillation (QBWO) plays an important role in global weather and climate change. It''s a very important source of sub-seasonal to seasonal (S2S) predictability. Using the sub-seasonal to seasonal forecast model of Beijing Climate Center (BCC S2S), the boreal summer QBWO is simulated, the forecast skill is discussed, and the model bias is analyzed. QBWO can be obtained from the third and fourth modes of multivariate empirical orthogonal function (MV-EOF) analysis on daily anomalies of outgoing longwave radiation (OLR) and zonal wind at 850 hPa (U850) in the Asian monsoon region. According to reanalysis data, QBWO shows a northeast-southwest-tilted convection-circulation structure, propagating north/northwestward from the equatorial western Pacific and Indian Ocean. The forecast skill of BCC S2S on QBWO decreases as the forecast lead time increases, and biases become very significant in the period, propagation characteristics and strength when the lead time comes to 9 days. BCC S2S reveals a higher forecast skill of QBWO structure and propagation over the western North Pacific, while it significantly underestimated convection signal of QBWO over the tropical Indian Ocean. The convection-circulation wave structure of QBWO in 9-day lead time prediction over the Indian Ocean is loose and appears over the Arabian Sea (instead of over the tropical eastern Indian Ocean and Bay of Bengal where the reanalyzed QBWO is active). It suggests that the unrealistic Indian Ocean QBWO is related to biases of model mean state. The simulated low-level moisture and convection during boreal summer are enhanced over the western Pacific and the Arabian Sea. However, the model underestimates the abundant moisture and vigorous convection over the eastern Indian Ocean and Bay of Bengal. BCC S2S captures the structure and propagation of QBWO over the western North Pacific, but slightly underestimates the strength of QBWO wave train. This underestimation of QBWO convection might be attributable to the relatively weaker vorticity to the northwest of QBWO convection. By diagnosing the vorticity equation, it''s found that although the model well simulates positive contributions of geostrophic vorticity advection and convergence effects to the northwest of convection, these contributions are still underestimated. Moreover, the simulated relative vorticity advection shows an opposite effect to reanalysis data in the 9-day lead time prediction, weakening the favorable environment of QBWO development associated with positive vorticity to the northwestern part of convection. |
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| Keywords: | BCC S2S model quasi-biweekly oscillation prediction skill model bias diagnosis |
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