Characterizing the skill of CFSv2-based seasonal drought prediction at multiple spatiotemporal scales over China

Yang Lang, Beijing Normal University, Beijing, China, Lifeng Luo, Michigan State University, Department of Geography, Environment, and Spatial Sciences, East Lansing, MI, United States, Maura Casey, Michigan State University, East Lansing, MI, United States and Qingyun Duan, Hohai University, Nanjing, China
Abstract:
A climate model’s predictive skill for seasonal temperature and precipitation generally varies with multiple factors, such as location, lead-time, season, and temporal and spatial scales. To fully understand the potential and limitation of the NCEP Climate Forecast System version 2 (CFSv2) in predicting seasonal drought in China, this study investigated how the seasonal drought predictive skill varies with such multiple factors in China. Six-month standardized precipitation index (SPI6) is used as the primary drought indicator to measure the medium-term meteorological drought. The predictive skill was then assessed by the correlation coefficient between observation-based SPI6 and CFSv2 forecast-based SPI6 at multiple spatial scales as well as multiple lead times during the period 1982-2008. Through this analysis, we will better characterize the capability of CFSv2 in seasonal climate forecast, which can help us to better utilize forecast information from such a system in drought prediction and water resource management.
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