Seasonal rainfall predictability over the Lake Kariba catchment area
Seasonal rainfall predictability over the Lake Kariba catchment area
复制标题
卡里巴湖流域的季节性降雨量预测
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
D. Lötter
中科院分区：
文献类型：
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作者：
S. Muchuru;W. Landman;D. Dewitt;D. Lötter
南部非洲的卡里巴湖集水区是所有主要河流流域中气候变化最大的地区之一，整个集水区和整个时间的条件都极为不同。降雨量明显的季节性和年际波动是集水区的一个重要方面。为了确定卡里巴湖集水区的季节性降雨总量的可预测性，本研究使用了南部非洲海洋-大气环流耦合模式（CGCM）的低层大气环流（850 hPa位势高度场），统计上缩小到集水区的网格季节性降雨总量。这种降尺度配置被用来追溯预测3个月的降雨季节的9月，10月，11月至2月，3月，4月，超过14年的独立测试期，从1994年开始。追溯预报的提前时间长达5个月，并对气候记录的第25和第75百分位值的极端降雨阈值进行概率预报性能评估。追溯预报的验证表明，在流域的降雨是可预测的，在延长的前置时间，但可预测性主要是南半球仲夏降雨。这个季节也与可以从季节性预测中得出的最高潜在经济价值有关。最近的极端降雨季节（2010/11年），位于验证期之外的预测案例研究作为证据的统计降尺度系统的业务能力。
The Lake Kariba catchment area in southern Africa has one of the most variable climates of any major river basin, with an extreme range of conditions across the catchment and through time. Marked seasonal and interannual fluctuations in rainfall are a significant aspect of the catchment. To determine the predictability of seasonal rainfall totals over the Lake Kariba catchment area, this study used the low-level atmospheric circulation (850 hPa geopotential height fields) of a coupled ocean-atmosphere general circulation model (CGCM) over southern Africa, statistically downscaled to gridded seasonal rainfall totals over the catchment. This downscaling configuration was used to retroactively forecast the 3-month rainfall seasons of September-October-November through February-March-April, over a 14-year independent test period extending from 1994. Retroactive forecasts are produced for lead times of up to 5 months and probabilistic forecast performances evaluated for extreme rainfall thresholds of the 25 th and 75 th percentile values of the climatological record. The verification of the retroactive forecasts shows that rainfall over the catchment is predictable at extended lead-times, but that predictability is primarily found for austral mid-summer rainfall. This season is also associated with the highest potential economic value that can be derived from seasonal forecasts. A forecast case study of a recent extreme rainfall season (2010/11) that lies outside of the verification period is presented as evidence of the statistical downscaling system’s operational capability.