This study evaluates the potential use of the ECMWF System-4 seasonal forecasts (S4) for impact analysis over East Africa. For use, these forecasts should have skill and small biases. We used the 15-member ensemble of 7-month forecasts initiated every month, and tested forecast skill of precipitation (tp), near-surface air temperature (tas) and surface downwelling shortwave radiation (rsds). We validated the 30-year (1981-2010) hindcast version of S4 against the WFDEI reanalysis (WATCH Forcing Data ERA-Interim) and to independent relevant observational data sets. Probabilistic skill is assessed using anomaly correlation, ranked probability skill score (RPSS) and the relative operating curve skill score (ROCSS) at both grid cell and over six distinct homogeneous rainfall regions for the three growing seasons of East Africa (i.e. MAM, JJA and OND).S4 exhibits a wet bias in OND, a dry bias in MAM and a mix of both in JJA. Temperature biases are similar in all seasons, constant with lead-time and correlate with elevation. Biases in rsds correlate with cloud/rain patterns. Bias correction clears biases but does not affect probabilistic skills.Predictability of the three variables varies with season, location and lead-time. The choice of validating dataset plays little role in the regional patterns and magnitudes of probabilistic skill scores. The OND tp forecasts show skill over a larger area up to 3 months lead-time compared to MAM and JJA. Upper-and lower-tercile tp forecasts are 20-80% better than climatology. Temperature forecasts are skillful for at least 3 months lead-time and they are 40-100% better than climatology. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher in all lead months against the alternative datasets. The forecast system captures El-Niño Southern Oscillation (ENSO)-related anomalous years with region-dependent skill.

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The Turkana Low-level Jet (LLJ) is an intrinsic part of the African climate system. It is the principle conduit for water vapour transport to the African interior from the Indian Ocean, and droughts in East Africa tend to occur when the jet is strong. The only direct observations of the Turkana Jet come from manual tracking of pilot balloons in the 1980s. Now, modern reanalysis datasets disagree with one another over the strength of jet winds and underestimate the strength of the jet by 25-75% compared to the pilot balloon data. This article gives an overview of a month-long field campaign based in northwest Kenya - the Radiosonde Investigation For the Turkana Jet (RIFTJet) - which measured the Turkana Jet for the first time in forty years using modern technologies. Radiosonde data reveal a persistent low-level jet, which formed on every night of the campaign, with an average low-level maximum wind speed of 16.8 m.s-1 at 0300LT. One of the latest reanalysis datasets (ERA5) underestimates low-level wind speeds by an average of 24% (4.1 m.s-1) at 0300LT, and by 33% (3.6 m.s-1) at 1500LT. The measurements confirm the role of the Turkana LLJ in water vapor transport: mean water vapour transport at Marsabit is 172 kg.m.s-1. The dataset provides new opportunities to understand regional dynamics, and to evaluate models in one of the most data sparse regions in the world.

Climate variability is an important driver for regionally anomalous production levels of especially rainfed crops, with implication for food security of subsistence farmers and economic performance for market oriented agriculturalists. In large parts of the tropics, modern seasonal ensemble forecast systems have useful levels of skill, that open up the possibility to develop climate services that assist agriculturalist and others in the food chain (farm suppliers, commodity traders, aid organisations) to anticipate on expected anomalous conditions. In this thesis we explore the forecast skill at various steps in the modelling chain for seasonal maize yield anomalies in East Africa. First, we analyse the skill of ECMWF System-4 (S4) climate forecasts for primary meteorological variables against gridded observations and find both potential and real skill for rainfall and temperature in typical cropping seasons in eastern Africa. However, forecast skill is a function of geographical region, season, climate variable (i.e. higher skill in temperature, rainfall, downwelling shortwave radiation in that order) and forecast lead-time, as such skill assessment should not be generalized over a large geographical area. Next we analyse correlations between reported production and anomalous weather conditions, using a range of climate indicators relevant for arable farming, such as growing and killing degree days, and rainfall amount, evenness, random independent events (unevenness), and timing during consequent maize growth phases in two case study regions. In this case significant levels of correlation and skill are revealed that open up the potential for statistical forecasting by use of climate forecast derived variables. Sensitivity of yields to climate indicators depend on geographical location, for example, higher sensitivity to rainfall is found in northern Ethiopia while in a location in equatorial-western Kenya, there is higher sensitivity to temperature indicators. At the next level of complexity we explore the use of full process based crop models forced by seasonal climate forecasts to forecast anomalous water-limited maize yield in the region, and find again potentially useful levels of skill with at least two months lead before planting, in most agricultural regions. But this again depends on regions, for example yield forecasts in Tanzania, in the season starting October did not have skill. Finally, we try to attribute skill levels to physiographic characteristics (soils, maize cultivars, geographical region etc.) and address some issues of scale of aggregation for two case study regions in Kenya and Ethiopia. The results showed that skill assessment at national boundaries and high resolution crop simulation units may inform both maize production related policy decisions at regional or national levels, and also support maize production decisions at specific cropping locations such as farm management decisions made by farmers. We conclude with a synthesis discussing further on found skill levels in relation to p...