Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin
Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin
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DOI：
10.1007/s00376-019-9051-8
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发表时间：
2020-01-01
影响因子：
5.8
通讯作者：
Bett, Philip E.
中科院分区：
文献类型：
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作者：
Martin, Gill M.;Dunstone, Nick J.;Bett, Philip E.
结果表明，GloSea5业务季节预报系统对长江中下游地区6月平均降水量的预报具有显著的提前4个月的能力。这个地区六月的降雨大部分是由梅雨带贡献的。我们发现，类似的技能存在于月时间尺度上的东亚夏季风指数（EASMI）的预测，后者可以作为一个代理，预测区域降水。然而，似乎没有得到从使用预测的EASMI作为代理区域降雨量的月时间尺度相比，直接预测的降雨量。虽然6月平均降雨量的年际变化受天气和季节内变化的影响，这可能是固有的不可预测的季节预报时间尺度上，赤道太平洋海表温度的主要影响，从前一个冬天的6月平均降雨量的捕获模式，通过其对西北太平洋副热带高压的影响。在长达4个月的提前期内预测长江中下游流域6月平均降雨量的能力表明，有可能为应急规划人员提供有关夏季水资源可用性的早期信息。
We demonstrate that there is significant skill in the GloSea5 operational seasonal forecasting system for predicting June mean rainfall in the middle/lower Yangtze River basin up to four months in advance. Much of the rainfall in this region during June is contributed by the mei-yu rain band. We find that similar skill exists for predicting the East Asian summer monsoon index (EASMI) on monthly time scales, and that the latter could be used as a proxy to predict the regional rainfall. However, there appears to be little to be gained from using the predicted EASMI as a proxy for regional rainfall on monthly time scales compared with predicting the rainfall directly. Although interannual variability of the June mean rainfall is affected by synoptic and intraseasonal variations, which may be inherently unpredictable on the seasonal forecasting time scale, the major influence of equatorial Pacific sea surface temperatures from the preceding winter on the June mean rainfall is captured by the model through their influence on the western North Pacific subtropical high. The ability to predict the June mean rainfall in the middle and lower Yangtze River basin at a lead time of up to 4 months suggests the potential for providing early information to contingency planners on the availability of water during the summer season.