Linkages between global sea surface temperatures and decadal rainfall variability over Eastern Africa region

Abstract

Linkages between dominant spatio-temporal decadal rainfall variability modes and the global sea surface temperature (SST) modes are investigated over East Africa region for the period 1950–2008. Singular value decomposition (SVD)andcanonicalcorrelationanalysis(CCA)techniquesareemployedtoexaminepotentiallinkagesandpredictabilityof decadal rainfall variability over the region. When the ten-year periodicity is filtered out from the observed monthly rainfall data, distinct decadal rainfall regimes are exhibited in the time series of mean seasonal rainfall anomalies. Spectral density analysisofrainfalltimeseriesshoweddominanceofaten-yearperiodicity,significantat95%confidencelevel.ThePrincipal Component Analysis (PCA) results yielded nine and seven homogeneous decadal rainfall zones for long rains; March–May (MAM), and the short rains: October–December (OND) seasons, respectively. The third season of June–August (JJA) which is mainly experienced in western and coastal sub-regions had eight homogenous zones delineated. Results show that the leading three SVD-coupled modes explain greater than 75% of the squared covariance between the two fields. The first SVD mode for Indian, Atlantic and Pacific Oceans contributed to 50, 43 and 38% of the total square covariance for MAM season, respectively. The same mode accounted for 65, 48 and 40% for OND rainfall season, respectively. For the JJA season, mode one contributed to about 61, 39 and 42% of the variance. The study showed that forcing of decadal rainfall over the region is associated with El Ni˜no mode that is prominent over the Pacific Ocean, while Indian Ocean dipole is the leading mode over the Indian Ocean basin. An inter-hemispheric dipole mode that is common during ENSO was a prominent feature in the Atlantic Ocean forcing regional decadal rainfall. The high variability of these modes highlighted the significant roles of all the global oceans in forcing decadal rainfall variability over the region. In addition, results from multiple linear regression model showed substantial variation of the model prediction skill of the decadal rainfall variability modes within various homogenous zones and for different seasons.