Skill of dynamical and GHACOF consensus seasonal forecasts of East African rainfall
Skill of dynamical and GHACOF consensus seasonal forecasts of East African rainfall
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DOI：
10.1007/s00382-019-04835-9
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发表时间：
2019-06
期刊：
影响因子：
4.6
通讯作者：
Dean P. Walker;C. Birch;J. Marsham;Adam A. Scaife;Richard J. Graham;Z. Segele
中科院分区：
文献类型：
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作者：
Dean P. Walker;C. Birch;J. Marsham;Adam A. Scaife;Richard J. Graham;Z. Segele
热带地区的许多用户认为降雨的季节性预报是优先的时间尺度。在东非，大非洲之角气候展望论坛（GHACOF）制作了该区域的主要业务季节预报，并在每个雨季之前发布。本研究评估和比较GHACOF的共识预测与动力模型预测从英国气象局GloSea 5季节性预测系统的两个雨季。GloSea在1个月前的短时降雨中表现出正的技能（r = 0.69）。相比之下，由于缺乏对驱动因素的可预测性，长时间降雨的技能较低。对于这两个季节，GHACOF的预测显示，技术水平普遍低于GloSea。几个系统误差内的GHACOF预测确定;最大的是倾向于高估接近正常降雨的可能性，超过70%（80%）的预测，这类最高的概率在短（长）雨。在一个更详细的评估GloSea，一个大的湿偏差，增加预测的前置时间，确定在短的降雨。这种偏差是由于发展中的冷SST偏差在东印度洋，驱动偏东风的偏差在赤道印度洋。这些偏差会影响平均状态水分的可用性，并可能采取行动，以减少动力学模型在预测年际变化的能力，这也可能是相关的耦合模型的预测较长的时间尺度。
Seasonal forecasts of rainfall are considered the priority timescale by many users in the tropics. In East Africa, the primary operational seasonal forecast for the region is produced by the Greater Horn of Africa Climate Outlook Forum (GHACOF), and issued ahead of each rainfall season. This study evaluates and compares the GHACOF consensus forecasts with dynamical model forecasts from the UK Met Office GloSea5 seasonal prediction system for the two rainy seasons. GloSea demonstrates positive skill (r = 0.69) for the short rains at 1 month lead. In contrast, skill is low for the long rains due to lack of predictability of driving factors. For both seasons GHACOF forecasts show generally lower levels of skill than GloSea. Several systematic errors within the GHACOF forecasts are identified; the largest being the tendency to over-estimate the likelihood of near normal rainfall, with over 70% (80%) of forecasts giving this category the highest probability in the short (long) rains. In a more detailed evaluation of GloSea, a large wet bias, increasing with forecast lead time, is identified in the short rains. This bias is attributed to a developing cold SST bias in the eastern Indian Ocean, driving an easterly wind bias across the equatorial Indian Ocean. These biases affect the mean state moisture availability, and could act to reduce the ability of the dynamical model in predicting interannual variability, which may also be relevant to predictions from coupled models on longer timescales.