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The interannual and spatial variability of different rainfall variables is analysed over Equatorial East Africa (Kenya and northeastern Tanzania). At station level, three variables are considered: the total precipitation amount (P), the number of rain days (NRD) and the daily rainfall intensity (INT). Using a network of 34 stations, inter-station correlations (1958-1987) are computed for each of these variables. The spatial coherence of monthly or seasonal P and NRD is always much higher than that of rainfall intensity. However, large variations in spatial coherence are found in the course of the seasonal cycle. Coherence is highest at the peak of the Short Rains (October-December), and low during the Long Rains (March-May), except at its beginning. The interannual variability of the onset and cessation of the rains is next considered, at regional scale, and the study extended to 2001. In the Long Rains, the onset and cessation dates are independent of NRD and INT during the rainy season. Hence the Long Rains seasonal rainfall total depends on a combination of virtually unrelated factors, which may account for the difficulty in its prediction. However, the onset, which exhibits a large interannual variability and a strong spatial coherence, has a prime role. In the Short Rains conversely, though the onset is again more decisive than the cessation, the different intra-seasonal descriptors of the rains are more strongly interrelated.

The eastern Africa monsoons during the Northern Hemisphere spring (NHS) season are described based on composites derived from various rainfall anomaly scenarios.The years 1981/1984 were delineated as some of the recent wettest/driest years over this region during the NHS season. Wet/dry spells within these anomalous years were further selected using PCA T-mode analysis. Both the anomalous years and the wet/dry spells identified from these years were used to create wind composites that were used to describe the eastern Africa Monsoon circulation.Composite wind analyses showed the dominance of the westerlies/easterlies in the lower/upper troposphere and their migration from the Southern Hemisphere to the Northern Hemisphere. Analysis of the individual wettest/driest years showed that before the start of the equatorial eastern Africa (EEA) long-rains season, easterlies are dominant near the Equator and westerlies near 15°S in the lower troposphere, while at the end of the rainy season the westerlies were located to the north of the Equator, near 5°N, with easterlies on their lateral sides. However, during the EEA long-rains season westerly/easterly wind events occurred in alternation over the region. These westerly/easterly episodes were associated with wet/dry rainfall spells.Vertical sections of zonal wind component showed that the wind alignment during wet spells was similar to that generally observed in other monsoonal regions, with lower tropospheric westerlies overlain by upper tropospheric easterlies. But the reduced frequency of lower tropospheric westerlies is suggested to be due to the barrier effect of the north-south mountain chains which allow only the most intense westerlies to cross the mountains into the EEA region.

ABSTRACT:The aim of this study was to derive components of the intraseasonal rainfall variations from the daily rainfall in the Equatorial Eastern Africa region and assess their spatial coherence, a pointer to their potential predictability. Daily rainfall observations from 36 stations distributed over Equatorial Eastern Africa and extending from 1962 to 2000 were used. The March to May and October to December periods commonly referred to as the long and short rainfall seasons respectively were considered.Seasonal and intraseasonal statistics at the local (station) level were first defined. The stations were also grouped into near-homogeneous (sub-regional) zones based on daily rainfall. Similarly, seasonal and intraseasonal statistics were then derived at sub-regional level using three different approaches. Inter-station correlation coefficients of the intraseasonal statistics at local levels were finally computed and plotted as box-plots.For the two rainfall seasons, the two statistics showing the highest spatial coherence were the seasonal rainfall totals and the number of the wet days at sub-regional level. The local variance explained for these two variables, as an average over all the sub-regions, was more than 40%. At the bottom of the hierarchy were the mean rainfall intensity and frequency of dry spells of 5 days or more which showed the least coherence, with the local variance explained being less than 10% in each season. For each of the intraseasonal components of daily rainfall considered, the short rainfall season statistics were more coherent compared to the long rainfall season. Lag-correlations with key indices depicting sea-surface temperatures in the Pacific and Indian Oceans showed that the hierarchy between the rainfall statistics in the strength of the teleconnections reflected that of spatial coherence.

Despite earlier studies over various parts of the world including equatorial Eastern Africa (EEA) showing that intraseasonal statistics of wet and dry spells have spatially coherent signals and thus greater predictability potential, no attempts have been made to identify the predictors for these intraseasonal statistics. This study therefore attempts to identify the predictors (with a 1-month lead time) for some of the subregional intraseasonal statistics of wet and dry spells (SRISS) which showed the greatest predictability potential during the short rainfall season over EEA. Correlation analysis between the SRISS and seasonal rainfall totals on one hand and the predefined predictors on the other hand were initially computed and those that were significant at 95% confidence levels retained. To identify additional potential predictors, partial correlation analyses were undertaken between SRISS and large-scale oceanic and atmospheric fields while controlling the effects of the predefined predictors retained earlier. Cross-validated multivariate linear regression (MLR) models were finally developed and their residuals assessed for independence and for normal distribution. Four large-scale oceanic and atmospheric predictors with robust physical/dynamical linkages with SRISS were identified for the first time. The cross-validated MLR models for the SRISS of wet spells and seasonal rainfall totals mainly picked two of these predictors around the Bay of Bengal. The two predictors combined accounted for 39.5% of the magnitude of the SST changes between the July-August and October-November-December periods over the Western Pole of the Indian Ocean Dipole, subsequently impacting EEA rainfall. MLR models were defined yielding cross-validated correlations between observed and predicted values of seasonal totals and number of wet days ranging from 0.60 to 0.75, depending on the subregion. MLR models could not be developed over a few of the subregions suggesting that the local factors could have masked the global and regional signals encompassed in the additional potential predictors.