Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall
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The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from El Niño to Yangtze River basin rainfall meant that the strong El Niño in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system. This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin, building on previous work demonstrating the retrospective skill of such a forecast. A simple forecasting methodology is presented, in which the forecast probabilities are derived from the historical relationship between hindcast and observations. Its performance for 2016 is discussed. The heavy rainfall in the May–June–July period was correctly forecast well in advance. August saw anomalously low rainfall, and the forecasts for the June–July–August period correctly showed closer to average levels. The forecasts contributed to the confidence of decision-makers across the Yangtze River basin. Trials of climate services such as this help to promote appropriate use of seasonal forecasts, and highlight areas for future improvements.
Key wordsseasonal forecasting flood forecasting Yangtze basin rainfall ENSO hydroelectricity
长江历史上一直遭受着洪涝灾害的影响. 近年来的严重灾害, 如1998年的大洪水, 造成了重大的人民生命财产损失. 洪水带来的径流在沿江的大坝的控制下, 同时是该地区重要的电力来源. 因此, 能够提前对这种灾害性事件进行有效预测, 有巨大的潜在价值. 最近季节预测能力的提高表明动力气候模式可以直接进行业务化的气候服务. 长江流域降水与厄尔尼诺联系密切, 因而2015/16年冬季强厄尔尼诺事件的发生为我们提供了一个检验动力预测系统应用到长江流域夏季降水预测的宝贵机会. 因此, 在前期回报工作呈现出一定预测技巧基础上, 本文对长江流域的实时季节预测进行了实例研究. 本文使用了一种简单的预测方法, 根据历史回报和观测的关系推算出预测事件发生的概率, 进而讨论了2016的预测结果. 结果表明, 2016年5月至7月的强降水预测准确. 8月份降水异常偏低, 而模式预测的6月至8月降水准确接近气候平均的结果. 这些成功的预测结果为长江流域防洪减灾并进行决策提供了信心. 此类气候服务的展开可以促进季节预测结果的应用推广, 并有助于未来气候预测服务领域的提升.
关键词季节预测 洪水预测 长江流域 ENSO 水力发电
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This work and its contributors (PB, AS, ND, DS, CH, NG) were supported by the UK–China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership China as part of the Newton Fund. CL and RL were supported by the National Natural Science Foundation of China (Grant No. 41320104007). HR was supported by the Project for Development of Key Techniques in Meteorological Operation Forecasting (Grant No. YBGJXM201705). The trial forecast service was first suggested by AS in 2015. The GPCP precipitation data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, via their website at https://doi.org/www.esrl.noaa.gov/psd. The Yangtze River basin shapefile used in the maps was obtained from https://doi.org/worldmap.harvard.edu/data/geonode:ch wtrshed_30mar11 and is based on the watersheds shown in the China Environmental Atlas (2000), © Chinese Academy of Sciences, Environmental Data Center."
- Hardiman, S., and Coauthors, 2017: The asymmetric response of Yangtze River basin summer rainfall to El Niño/La Niña. Environmental Research Letters, https://doi.org/10.1088/1748-9326/aaa172. (in press)Google Scholar
- Hunke, E. C., and W. H. Lipscomb, 2010: CICE: The Los Alamos Sea Ice model documentation and software user’s manual, version 4.1. Report LA-CC-06-012, Los Alamos National Laboratory. [Available online from http://oceans11.lanl.gov/trac/CICE]Google Scholar
- Jiao, M. Y., and Coauthors, 2013: Addressing the potential climate effects of China’s Three Gorges Project. WMO Bulletin, 62 (Special Issue), 49–53. [Available online from http://library.wmo.int/opac/index.php?lvl=bulletin display&id=2738]Google Scholar
- Kwon, H. H., C. Brown, K. Q. Xu, and U. Lall, 2009: Seasonal and annual maximum streamflow forecasting using climate information: Application to the Three Gorges Dam in the Yangtze River basin, China. Hydrological Sciences Journal, 54, 582–595, https://doi.org/10.1623/hysj.54.3.582. CrossRefGoogle Scholar
- Madec, G., 2008: NEMO ocean engine. Note du Pôle de modélisation, Institut Pierre-Simon Laplace (IPSL). France, No. 27. [Available online from http://www.nemo-ocean.eu/About-NEMO/Reference-manuals]Google Scholar
- Podlaha, A., S. Bowen, and C. Darbinyan, 2016: July 2016 Global Catastrophe Recap. Aon Benfield Impact Forecasting, [Available online from http://thoughtleadership.aonbenfield.com/sitepages/display.aspx?tl=601]Google Scholar
- Rae, J. G. L., H. T. Hewitt, A. B. Keen, J. K. Ridley, A. E. West, C. M. Harris, E. C. Hunke, and D. N. Walters, 2015: Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model. Geoscientific Model Development, 8, 2221–2230, https://doi.org/10.5194/gmd-8-2221-2015. CrossRefGoogle Scholar
- Wang, B., B. Q. Xiang, and J. Y. Lee, 2013: Subtropical high predictability establishes a promising way for monsoon and tropical storm predictions. Proceedings of the National Academy of Sciences of the United States of America, 110, 2718–2722, https://doi.org/10.1073/pnas.1214626110. CrossRefGoogle Scholar
- Wilks, D. S., 2011: Statistical forecasting. International Geophysics, 100, 215–300, https://doi.org/10.1016/b978-0-12-385022-5.00007-5. CrossRefGoogle Scholar
- WMO, 2017: WMO statement on the state of the global climate in 2016. WMO-No. 1189. Geneva: World Meteorological Organization. [Available online from http://library.wmo.int/opac/doc num.php?explnum id=3414]Google Scholar