Skill of dynamical and GHACOF consensus seasonal forecasts of East African rainfall

被引:48
作者
Walker, Dean P. [1 ]
Birch, Cathryn E. [1 ]
Marsham, John H. [1 ,2 ]
Scaife, Adam A. [3 ,4 ]
Graham, Richard J. [3 ]
Segele, Zewdu T. [5 ]
机构
[1] Univ Leeds, Sch Earth & Environm, Leeds, W Yorkshire, England
[2] Natl Ctr Atmospher Sci, Leeds, W Yorkshire, England
[3] Met Off, Hadley Ctr, Exeter, Devon, England
[4] Univ Exeter, Coll Engn Math & Phys Sci, Exeter, Devon, England
[5] IGAD Climate Predict & Applicat Ctr, Nairobi, Kenya
基金
英国科研创新办公室; 英国自然环境研究理事会;
关键词
Seasonal climate forecasts; Consensus outlooks; East Africa; Precipitation; Probabilistic verification; INDIAN-OCEAN DIPOLE; MADDEN-JULIAN OSCILLATION; SHORT RAINS; MULTIMODEL ENSEMBLE; GREATER HORN; INTERANNUAL VARIABILITY; CIRCULATION MECHANISMS; ANNUAL CYCLE; PREDICTABILITY; PREDICTION;
D O I
10.1007/s00382-019-04835-9
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
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.
引用
收藏
页码:4911 / 4935
页数:25
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