Spatial coherence and potential predictability assessment of intraseasonal statistics of wet and dry spells over Equatorial Eastern Africa

Abstract

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.

Climate and climatic variability of rainfall over eastern Africa

“…Several papers have examined such characteristics as mean daily rainfall intensity, frequency of dry spells, and number or frequency of rain days and compared them with seasonal characteristics such as onset, cessation, and total seasonal rainfall amount [ Moron et al ., ; Camberlin et al ., ; Gitau et al ., ]. Results have been very consistent.…”

Performance of the WRF model to simulate the seasonal and interannual variability of hydrometeorological variables in East Africa: a case study for the Tana River basin in Kenya

“…Precipitation is considered to be the crucial hydrometeorological variable in East Africa and is characterized by large spatio-temporal variability (Endris et al 2013;Gitau et al 2013). Zhang (2007) used the Weather Research and Forecasting (WRF) model (Skamarock et al 2008) at 60 km horizontal resolution to investigate the hydrological cycle in East Africa, with emphasis on model result sensitivity to different radiation schemes, for the period 1994 to 1998.…”

Trends of intraseasonal descriptors of wet and dry spells over equatorial eastern Africa

“…Figure shows the network of the 36 stations used. The southern part of Tanzania (south of 7°S) exhibits both rainfall regimes and interannual rainfall variations that are quite dissimilar to those of the other parts of East Africa (Indeje et al, ; Camberlin and Philippon, ; Gitau et al ., , ) and was thus excluded from this study.…”

“…Figure 1 shows the network of the 36 stations used. The southern part of Tanzania (south of 7 ∘ S) exhibits both rainfall regimes and interannual rainfall variations that are quite dissimilar to those of the other parts of East Africa (Indeje et al, 2000;Camberlin and Philippon, 2002;Gitau et al, 2013Gitau et al, , 2015 and was thus excluded from this study. Earlier studies have suggested and shown that the early and the late parts of the long rainfall season over eastern Africa exhibit slight distinct regional patterns and teleconnections (Ininda, 1995;Mutai and Ward, 2000;Camberlin and Philippon, 2002;Moron et al, 2013).…”

“…A threshold of 1.0 mm was used to delineate wet days from dry days (Gitau et al, 2013(Gitau et al, , 2015. Lower thresholds were not considered due to possible measurement errors associated with light rainfall that may readily evaporate given the high evapo-transpiration rate at the study region (Ogallo and Chillambo, 1982;Douguedroit, 1987;Lazaro et al, 2001;Frei et al, 2003;Gitau et al, 2013Gitau et al, , 2015.…”