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We present a new seasonal forecasting model for the June-September rains in Ethiopia. It has previously been found that the total June-September rainfall over the whole country is difficult to predict using statistical methods. A detailed study of all available data shows the rainfall seasonality varies greatly from one region to another, which would explain why the total June-September rainfall over all regions is a difficult property to forecast. In addition, the correlation between rainfall and the southern oscillation index varies spatially, with a strong teleconnection present only in some regions. This study accounts for the spatial variability in rainfall by grouping the rain gauge stations into four geographical clusters based on seasonality and cross-correlation of rainfall anomalies. Linear regression equations are then developed separately for each cluster. The variables we use for the regressions are sea-surface temperature anomalies in the preceding March, April and May of the tropical western Indian Ocean, the tropical eastern Indian Ocean, and Niño3.4. Formal skill testing of the equations shows that the new forecasting scheme is more effective in central western Ethiopia than either climatology or persistence -the methods currently used by the Ethiopian National Meteorological Services Agency.

African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30 year , temporally consistent rainfall data set for Africa known as TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series) using archived Meteosat thermal infrared imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10 day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation data sets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit and Global Precipitation Climatology Centre gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by À0.37 mm d À1 (21%) compared to other data sets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time.

Rainfall information is essential for many applications in developing countries, and yet, continually updated information at fine temporal and spatial scales is lacking. In Africa, rainfall monitoring is particularly important given the close relationship between climate and livelihoods. To address this information gap, this paper describes two versions (v2.0 and v3.0) of the TAMSAT daily rainfall dataset based on high-resolution thermal-infrared observations, available from 1983 to the present. The datasets are based on the disaggregation of 10-day (v2.0) and 5-day (v3.0) total TAMSAT rainfall estimates to a daily time-step using daily cold cloud duration. This approach provides temporally consistent historic and near-real time daily rainfall information for all of Africa. The estimates have been evaluated using ground-based observations from five countries with contrasting rainfall climates (Mozambique, Niger, Nigeria, Uganda, and Zambia) and compared to other satellite-based rainfall estimates. The results indicate that both versions of the TAMSAT daily estimates reliably detects rainy days, but have less skill in capturing rainfall amount—results that are comparable to the other datasets.

Numerous factors are associated with poverty and underdevelopment in Africa, including climate variability. Rainfall, and climate more generally, are implicated directly in the United Nations “Millennium Development Goals” to eradicate extreme poverty and hunger, and reduce child mortality and incidence of diseases such as malaria by the target date of 2015. But, Africa is not currently on target to meet these goals. We pose a number of questions from a climate science perspective aimed at understanding this background: Is there a common origin to factors that currently constrain climate science? Why is it that in a continent where human activity is so closely linked to interannual rainfall variability has climate science received little of the benefit that saw commercialization driving meteorology in the developed world? What might be suggested as an effective way for the continent to approach future climate variability and change? We make the case that a route to addressing the challenges of climate change in Africa rests with the improved management of climate variability. We start by discussing the constraints on climate science and how they might be overcome. We explain why the optimal management of activities directly influenced by interannual climate variability (which include the development of scientific capacity) has the potential to serve as a forerunner to engagement in the wider issue of climate change. We show this both from the perspective of the climate system and the institutions that engage with climate issues. We end with a thought experiment that tests the benefits of linking climate variability and climate change in the setting of smallholder farmers in Limpopo Province, South Africa.