Abstract
Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River (MLYR) is of urgent demand for the local economic and societal development. This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0, previously SINTEX-F). The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands, initialized from 1 March. In addition, the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature (SST) anomalies, but the individual members show great discrepancies, indicating large uncertainty in the forecasts. Furthermore, the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982–2020, albeit with underestimated magnitudes. The Weather Forecast and Research (WRF) downscaling hindcast experiments with a finer resolution of 30 km, which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method, display improved predictions of the extreme summer precipitation anomalies to some extent. However, the performance of the downscaling predictions is highly dependent on the global model forecast skill, suggesting that further improvements on both the global and regional climate models are needed.
摘 要
长江中下游夏季降水的准确预测对当地社会的经济与发展有着重要意义。本研究基于南京信息工程大学全球气候预报系统(NUIST-CFS1.0,原SINTEX-F),评估模式每年3月1日起报的长江中下游地区夏季(6-8月)降水的预报性能。结果表明,该模式对长江中下游地区夏季降水的年际变化具有中等水平的预测技巧。此外,该模式九成员集合平均的结果可以真实再现长江中下游降水,及其对应的大尺度环流与热带海表温度(SST)异常之间的遥相关关系,但模式不同个体成员之间的差异很大,说明目前动力模式对大气内部动力过程预测存在较大的不确定性。尽管模拟结果低估了降雨强度,但是NUIST-CFS1.0预测出了1982-2020年间长江中下游夏季七个极端异常降水中的五个。随后采用 NUIST-CFS1.0预测的大尺度信息驱动区域模式WRF进行动力降尺度后报实验,得到具有更精细水平分辨率(30公里)的区域气候预测结果。结果显示区域气候模式在一定程度上改进了全球模式对长江中下游地区夏季极端降水异常的预测技巧。进一步分析发现,区域气候模式的预测技巧高度依赖于全球模式的预测技能,这表明若要进一步提高长江中下游夏季降水的预测水平,需要继续改进气候模式,尤其是全球气候模式。
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Acknowledgements
This work is supported by National Natural Science Foundation of China (Grant Nos. 42030605 and 42088101) and National Key R&D Program of China (Grant No. 2020YFA0608004). The model simulation is conducted in the High Performance Computing Center of Nanjing University of Information Science & Technology. The Hadley monthly mean SST data is downloaded from https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. CMAP precipitation data and NCEP-NCAR Reanalysis-II data provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, are downloaded from the website at https://psl.noaa.gov/.
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Article Highlights
• Seasonal prediction of summer precipitation in the middle and lower reaches of the Yangtze River (MLYR) at a four-month lead is assessed based on NUIST-CFS1.0.
• The model ensemble mean can successfully reproduce the teleconnection between the MLYR precipitation and tropical sea surface temperature anomalies.
• WRF downscaling can improve the prediction of extreme precipitation over the MLYR region, especially for their magnitudes.
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Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0
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Ying, W., Yan, H. & Luo, JJ. Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0. Adv. Atmos. Sci. 39, 1561–1578 (2022). https://doi.org/10.1007/s00376-022-1389-7
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DOI: https://doi.org/10.1007/s00376-022-1389-7