Applying the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) to seasonal precipitation forecasts across East Africa

East Africa is particularly vulnerable to precipitation variability, as rainfed agriculture and pastoralism provides a livelihood to much of the population. Skillful seasonal forecasts can help small scale farmers and disaster management organizations make climate smart decisions and improve food security and water management infrastructure.

Recently, the Greater Horn of Africa Climate Outlook Forum (GHACOF; https://www.icpac.net/seasonal-forecast/) and several African nations’ government weather services began implementing dynamic climate models into their operational seasonal climate forecasts. However, Africa’s complex topography, large mountain ranges, and a widespread rift system pose a significant challenge for models. Scientists attribute these issues to insufficient parameterization of convective and boundary-layer processes between different topographical regions.

Striving to improve prediction skill and overall model capabilities, a research team analyzed the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0). Forecasters can rely on this model to supplement summer seasonal precipitation forecasts throughout East Africa.

“Our analysis results show that the model has positive skill, or makes significant contributions to seasonal forecasts, across the majority of Ethiopia, Kenya, Uganda, and Tanzania,” said Temesgen Gebremariam working at the Institute of Geophysics Space Science and Astronomy, Addis Ababa University, and lead author of the study published in Advances of Atmospheric Sciences. “Additionally, seasonal precipitation products from NUIST-CFS1.0 are still useful and relatively accurate throughout the rest of East Africa. Our study lies a foundation for future climatology work in Africa.”

Researchers also identified the model’s limitations, indicating plenty of opportunity for future improvements. This includes enhancing its products so that even smallholder farmers may be benefited from better seasonal forecasts.

“Climatologists and meteorologists may use this analysis as a base for future seasonal forecast improvement studies in the region.” Said Prof. Jingjia Luo, a scientist who develops climate system models at Nanjing University of Information Science and Technology, also the corresponding author of this study. “Primarily, our study suggests that model scientists may want to implement statistical post-processing and machine learning techniques into models to explore their potential for local-scale applications.”

According to the team, it’s a major step toward developing a downscaling forecast system focused on Africa. East African smallholder farmers will likely see the greatest benefits from higher resolution modeling.