Wavelet Analysis of Variability, Teleconnectivity, and Predictability of the September–November East African Rainfall

Abstract

By applying wavelet analysis and wavelet principal component analysis (WPCA) to individual wavelet-scale power and scale-averaged wavelet power, homogeneous zones of rainfall variability and predictability were objectively identified for September–November (SON) rainfall in East Africa (EA). Teleconnections between the SON rainfall and the Indian Ocean and South Atlantic Ocean sea surface temperatures (SST) were also established for the period 1950–97. Excluding the Great Rift Valley, located along the western boundaries of Tanzania and Uganda, and Mount Kilimanjaro in northeastern Tanzania, EA was found to exhibit a single leading mode of spatial and temporal variability. WPCA revealed that EA suffered a consistent decrease in the SON rainfall from 1962 to 1997, resulting in 12 droughts between 1965 and 1997. Using SST predictors identified in the April–June season from the Indian and South Atlantic Oceans, the prediction skill achieved for the SON (one-season lead time) season by the nonlinear model known as artificial neural network calibrated by a genetic algorithm (ANN-GA) was high [Pearson correlation ρ ranged between 0.65 and 0.9, Hansen–Kuipers (HK) scores ranged between 0.2 and 0.8, and root-mean-square errors (rmse) ranged between 0.4 and 0.75 of the standardized precipitation], but that achieved by the linear canonical correlation analysis model was relatively modest (ρ between 0.25 and 0.55, HK score between −0.05 and 0.3, and rmse between 0.4 and 1.2 of the standardized precipitation).

Droughts and floods over the upper catchment of the Blue Nile and their connections to the timing of El Niño and La Niña events

“…Several studies attempted to use oceanic and atmospheric variables as predictors in seasonal hydrologic forecasting over East Africa (Mutai et al, 1998;Hastenrath et al, 2004;Philippon et al, 2002;Yeshanew and Jury, 2007;Mwale and Gan, 2005;Funk, 2011, 2010), however none of these studies focused on the June-September rainfall in Ethiopia.…”

Seasonal-to-Interannual Variability of Ethiopia/Horn of Africa Monsoon. Part I: Associations of Wavelet-Filtered Large-Scale Atmospheric Circulation and Global Sea Surface Temperature

“…Bowden and Semazzi (2007), for example, noted that previous studies of intraseasonal variability focus on case studies of wet or dry years linked with the El Niñ o-Southern Oscillation (ENSO) phenomenon. Furthermore, this research also was geographically limited to the equatorial East Africasouthern Horn of Africa region (e.g., Mutai and Ward 2000;Mwale and Gan 2005) and primarily investigated variability of the ''short'' October-December rainy season (e.g., Bowden and Semazzi 2007). Although October-November brings the second seasonal rains for southern and southeastern Ethiopia and adjoining Somalia, it is a dry period for much of the northern two-thirds of Ethiopia and adjacent Eritrea and Djibouti.…”

“…According to Mwale and Gan (2005), such irregularity in flood and drought events and the associated large time variability constitutes nonstationarity. Nonstationarity hampers use of the Fourier transform, the most common tool for power frequency spectrum analysis, which assumes time series homogeneity and stationarity (e.g., Weng and Lau 1994;Baliunas et al 1997;Torrence and Compo 1998).…”

“…Unlike the Fourier transform, the wavelet transform localizes a signal in both the frequency and time domains and is well suited to analysis of multiscale, nonstationary time series that result from nonlinear interactions between several physical processes occurring on a range of temporal and spatial scales (Lau and Weng 1995;Webster and Hoyos 2004). The wavelet transform uses generalized base functions (wavelets) that can be stretched and translated with a flexible resolution (e.g., Weng and Lau 1994;Torrence and Compo 1998) and is increasingly employed to decompose geophysical time series into time-frequency space to detect periodicities and trends (e.g., Wang and Wang 1996;Baliunas et al 1997;Chapa et al 1998, Barrett and Leslie 2009) and to develop statistical prediction models (e.g., Webster and Hoyos 2004;Mwale and Gan 2005).…”

Variability and teleconnectivity of northeast monsoon rainfall over India

“…Kripalani and Pankaj Kumar (2004) found a direct influence of the Indian Ocean Dipole (IOD) mode on the interannual and decadal NEM rainfall variability over south India. Mwale and Gan (2005) conducted a study over Africa for the September-November season, where the dominant scale in ENSO band explains 80% of the total variance of rainfall. But no such study has been done over the Indian region for the NEM rainfall, using the SAP.…”