10 research outputs found

    Atmospheric and oceanic conditions associated with early and late onset for Eastern Africa short rains

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    Timing of the rainy season is essential for a number of climate sensitive sectors over Eastern Africa. This is particularly true for the agricultural sector, where most activities depend on both the spatial and temporal distribution of rainfall throughout the season. Using a combination of observational and reanalysis datasets, the present study investigates the atmospheric and oceanic conditions associated with early and late onset for Eastern Africa short rains season (October–December). Our results indicate enhanced rainfall in October and November during years with early onset and rainfall deficit in years with late onset for the same months. Early onset years are found to be associated with warmer sea surface temperatures (SSTs) in the western Indian Ocean, and an enhanced moisture flux and anomalous low-level flow into Eastern Africa from as early as the first dekad of September. The late onset years are characterized by cooler SSTs in the western Indian Ocean, anomalous westerly moisture flux and zonal flow limiting moisture supply to the region. The variability in onset date is separated into the interannual and decadal components, and the links with SSTs and low-level circulation over the Indian Ocean basin are examined separately for both timescales. Significant correlations are found between the interannual variability of the onset and the Indian Ocean dipole mode index. On decadal timescales the onset is shown to be partly driven by the variability of the SSTs over the Indian Ocean. Understanding the influence of these potentially predictable SST and moisture patterns on onset variability has huge potential to improve forecasts of the East African short rains. Improved prediction of the variability of the rainy season onset has huge implications for improving key strategic decisions and preparedness action in many sectors, including agriculture

    Advances, gaps and way forward in provision of climate services over the Greater Horn of Africa

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    The Greater Horn of Africa is prone to extreme climatic conditions, thus, making climate services increasingly important in supporting decision-making processes across a range of climate sensitive sectors. This study aims to provide a comprehensive review of the recent advances, gaps and challenges in the provision of climate services over the region, for each of the components of the Global Framework for Climate Services. The study explores various milestones that have been achieved toward climate service delivery. The achievements include improvement of station network coverage, and enhancing the capacity of member states to utilize various tools in data analysis and generate routine climate products. The advancement in science, and availability of High-Performance Computing has made it possible for forecast information to be provided from nowcasting to seasonal timescales. Moreover, operationalizing of the objective forecasting method for monthly and seasonal forecasts has made it possible to translate tercile forecasts for applications models. Additionally, innovative approaches to user engagement through co-production, communication channels, user-friendly interfaces, and dissemination of climate information have also been developed. Despite the significant progress that has been made in the provision of climate services, there are still many challenges and gaps that need to be overcome in order to ensure that these services are effectively meeting the needs of users. The research of the science underpinning climate variability, capacity building and stakeholder engagement, as well as improved data management and quality control processes are some of the gaps that exist over the region. Additionally, communication and dissemination of climate information, including timely warnings and risk communication, require improvement to reach diverse user groups effectively. Addressing these challenges will require strengthened partnerships, increased investment in capacity building, enhanced collaboration between the climate information producers and stakeholders, and the development of user-friendly climate products. Bridging these gaps will foster greater resilience to climate-related hazards and disasters in the Greater Horn of Africa and support sustainable development in the region

    Customization and Validation of a Regional Climate Model Using Satellite Data Over East Africa

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    This study focused on the customization of the fourth generation International Center for Theoretical Physics Regional Climate Model version 4.4 and its ability to reproduce the mean climate and most dominant modes of variability over East Africa. The simulations were performed at a spatial resolution of 25 km for the period 1998–2013. The model was driven by ERA-Interim reanalysis. The customization focus was on cumulus and microphysics schemes during the Short Rains for the year 2000. The best physics combinations were then utilized for the validation studies. The East Africa region and Lake Victoria Basin region are adapted to carry out empirical orthogonal function analysis, during the Short and Long Rains. Tropical Rainfall Measuring Mission data was utilized in the validation of the model. The first mode of variability from the model and observational data during the Short Rains was associated with the warming of the Pacific Ocean and the sea surface temperature gradients over the Indian Ocean. During the Long rains, the inter-annual rainfall variability over the Lake Victoria region was associated with the sea surface temperature anomaly over the Indian Ocean and for the East Africa region the associations were weak. The drivers during the Long Rains over East Africa region were then further investigated by splitting the season to the March–April and May periods. The March–April period was positively correlated to the West Pacific and Indian Ocean dipole index, while May was associated with the Quasi-Biennial Oscillation. In conclusion, although the model can reproduce the dominant modes of variability as in the observational data sets during the Short Rains, skill was lower during the Long Rains
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