Improved Seasonal Prediction of Rainfall over East Africa for Application in Agriculture: Statistical Downscaling of CFSv2 and GFDL-FLOR

Abstract

Statistically downscaled forecasts of October–December (OND) rainfall are evaluated over East Africa from two general circulation model (GCM) seasonal prediction systems. The method uses canonical correlation analysis to relate variability in predicted large-scale rainfall (characterizing, e.g., predicted ENSO and Indian Ocean dipole variability) to observed local variability over Kenya and Tanzania. Evaluation is performed for the period 1982–2011 and for the real-time forecast for OND 2015, a season when a strong El Niño was active. The seasonal forecast systems used are the National Centers for Environmental Prediction Climate Forecast System, version 2 (CFSv2), and the Geophysical Fluid Dynamics Laboratory Forecast-Oriented Low Ocean Resolution (GFDL-FLOR) version of CM2.5. The Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) rainfall dataset—a blend of in situ station observations and satellite estimates—was used at 5 km × 5 km resolution over Kenya and Tanzania as benchmark data for the downscaling. Results for the case-study forecast for OND 2015 show that downscaled output from both models adds realistic spatial detail relative to the coarser raw model output—albeit with some overestimation of rainfall that may have been derived from the downscaling procedure introducing a wet response to El Niño more typical of historical cases. Assessment of the downscaled forecasts over the 1982–2011 period shows positive long-term skill better than that documented in previous studies of unprocessed GCM forecasts for the region. Climate forecast downscaling is thus a key undertaking worldwide in the generation of more reliable products for sector specific application including agricultural planning and decision-making.

Direct and indirect seasonal rainfall forecasts for East Africa using global dynamical models

“…The CPT package has been widely used for seasonal forecasting: see, for example, Landman et al (2012), Manzano and Ines (2015), Kipkogei et al (2017), and references therein. As precipitation data distributions are often not Gaussian and linear methods assume Gaussian data, CPT includes an option to apply a transformation to the Y field such that the temporal distributions are Gaussian prior to the CCA analysis-and this option was used in our CCA operations.…”

Understanding the Role of User Needs and Perceptions Related to Sub-Seasonal and Seasonal Forecasts on Farmers' Decisions in Kenya: A Systematic Review

“…Kenya, like many other Sub-Saharan African countries, is highly vulnerable to extreme weather and climate risks associated with poor infrastructure and low adaptive capacity. Climate change impacts will alter agricultural production and increase pressure on communities' livelihoods (Brown and Hansen, 2008;Kipkogei et al, 2017). For example, droughts and floods are more frequent in Kenya and the region in the last decade (Bunyasi, 2012;Huho and Kosonei, 2013;Mugalavai and Kipkorir, 2013;Yvonne et al, 2016).…”

Skill of Seasonal Rainfall and Temperature Forecasts for East Africa

“…1a and 4a), so the country scale may reduce skill where it is high only in parts of the countries. Downscaling may also help improve skill in this region (Diro et al 2012;Kipkogei et al 2017).…”