Abstract/Summary

Skilful seasonal forecasts can provide useful information for decision makers, particularly in regions heavily dependent on agriculture, such as East Africa. We analyse prediction skill for seasonal East African rainfall and temperature one to four months ahead from two seasonal forecasting systems: the US National Centers for Environmental Prediction (NCEP) Coupled Forecast System Model Version 2 (CFSv2) and the UK Met Office (UKMO) Global Seasonal Forecast System Version 5 (GloSea5). We focus on skill for low or high temperature and rainfall, below the 25th or above the 75th percentile respectively, as these events can have damaging effects in this region. We find skill one month ahead for both low and high rainfall from CFSv2 for December-January-February in Tanzania, and from GloSea5 for September-October-November in Kenya. Both models have higher skill for temperature than for rainfall across Ethiopia, Kenya and Tanzania, two months ahead in some cases. Performance for rainfall and temperature change in the two models during certain El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) phases, the impacts of which vary by country, season and sometimes by model. While most changes in performance are within the range of uncertainty due to the relatively small sample size in each phase, they are significant in some cases. For example, La Niña lowers performance for Kenya September-October-November rainfall in CFSv2 but does not affect skill in GloSea5.