Published May 2019 in Modeling Earth Systems and Environment; this paper is OPEN ACCESS.
Authors: Eniola Olaniyan (Nigerian Meteorological Agency); Elijah A. Adefisan, (Federal University of Technology Akure and ACMAD); Ahmed A. Balogun, (Federal University of Technology, Akure); Kamoru A. Lawal, (Nigerian Meteorological Agency)
This study considered the implications of rainfall on the sustainability of the socio-economic activities in Nigeria. It assessed the skill of three sub-seasonal to seasonal (S2S) models, CMA, ECMWF, and UKMO, in predicting monsoon onset and its variability over Nigeria. The paper examined the global drivers modulating the variability and their teleconnections with rainfall onset anomaly. The results showed that each of the models used exhibits unique and different characteristics over each classified region in Nigeria. For instance, all three models are able to simulate the Northwards migration of the onset dates adequately with inherent biases. While the biases of both the CMA and the ECMWF models improve progressively towards the Sahel, the bias of the UKMO model over the Gulf of Guinea is considerably smaller (±10 days). In the case of the onset anomaly, results showed that despite the poor performance of the models over the Gulf of Guinea and the Sahel, there is a considerable improvement in the correlation skill of the models over the Savannah.
This paper results from SWIFT Work Package 6, S2S.