Abstract: 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.
Abstract: The inter-annual and spatial variability of different rainfall variables is analysed over Equatorial East Africa (Kenya and northeastern Tanzania). At the 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 inter-annual variability of the onset and cessation of the rains is next considered, at the 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 inter-annual variability and a strong spatial coherence, has a prime role. Conversely, in the short rains, though the onset is again more decisive than the cessation, the different intra-seasonal descriptors of the rains are more strongly inter-related.
Publication by: Otieno et al.
Abstract: This study evaluated the skill of forecasting seasonal rainfall over the Greater Horn of Africa (GHA) using Ensemble Model Technique from a cluster of four General Circulation Climate Models (GCMs) from Global Producing Centres (GPCs). The spatial distribution of rainfall anomalies of the observed models output during extreme events showed that the ensemble model was able to simulate El-Niño (1997) and La-Niña (2000) years. The ensemble models did not show good skill in capturing the magnitude of the extreme events. The skill of the ensemble model was higher than that for the member models in terms of its ability to capture the rainfall peaks during the El-Niño Southern Oscillation (ENSO) phenomena. The analysis for the correlation coefficients showed higher values for the ensemble model output than for the individual models over the Equatorial region with the stations in the northern and southern sectors of the GHA comparatively giving low skill. The ensemble modeling technique significantly improved the skill of forecasting, including the sectors where individual models had low skill. In general, the skill of the models was relatively higher during the onset of the ENSO event and became low towards the decaying phase of the ENSO period. Generally, the study has shown that the ensemble seasonal forecasting significantly adds skill to the forecasts especially for October-December (OND) rainy seasons. From the study, ensemble seasonal forecasting significantly adds skill to the forecasts over the region. Blending dynamical ensemble forecasts with statistical forecast currently being produced during Regional Climate Outlook Forums (RCOFs) would add value to seasonal forecasts. This significantly reduces the impacts and damages associated with climate extremes over the region.
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Publication by: Mwangi et al.
Publication Date: 2013
Abstract: The humanitarian crisis caused by the recent droughts (2008–2009 and 2010–2011) in the East African region have illustrated that the ability to make accurate drought predictions with adequate lead time is essential. The use of dynamical model forecasts and 5 drought indices, such as Standardized Precipitation Index (SPI), promises to lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for both rain seasons when evaluated against measurements from the 10 available in-situ stations from East Africa. The October–December rain season has higher skill that the March–May season. ECMWF forecasts add value to the statistical forecasts produced during the Greater Horn of Africa Climate Outlook Forums (GHACOF) which is the present operational product. Complementing the raw precipitation forecasts with SPI provides additional information on the spatial extend and intensity of 15 the drought event.
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Publication Date: 2013
Abstract: This study evaluates the ability of 10 regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) in simulating the characteristics of rainfall patterns over eastern Africa. The seasonal climatology, annual rainfall cycles, and interannual variability of RCM output have been assessed over three homogeneous subregions against a number of observational datasets. The ability of the RCMs in simulating large-scale global climate forcing signals is further assessed by compositing the El Nino–Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) ~ events. It is found that most RCMs reasonably simulate the main features of the rainfall climatology over the three subregions and also reproduce the majority of the documented regional responses to ENSO and IOD forcings. At the same time the analysis shows significant biases in individual models depending on subregion and season; however, the ensemble mean has better agreement with observation than individual models. In general, the analysis herein demonstrates that the multimodel ensemble mean simulates eastern Africa rainfall adequately and can therefore be used for the assessment of future climate projections for the region
Publication Date: 2013
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.
Publication Date : 2013
Abstract: Extracting large scale water storage (WS) patterns is essential for understanding the hydrological cycle and improving the water resource management of Iran, a country that is facing challenges of limited water resources. The Gravity Recovery and Climate Experiment (GRACE) mission offers a unique possibility of monitoring total water storage (TWS) changes. An accurate estimation of terrestrial and surface WS changes from GRACE-TWS products, however, requires a proper signal separation procedure. To perform this separation, this study proposes a statistical approach that uses a priori spatial patterns of terrestrial and surface WS changes from a hydrological model and altimetry data. The patterns are then adjusted to GRACE-TWS products using a least squares adjustment (LSA) procedure, thereby making the best use of the available data. For the period of October 2002 to March 2011, monthly GRACE-TWS changes were derived over a broad region encompassing Iran. A priori patterns were derived by decomposing the following auxiliary data into statistically independent components: (i) terrestrial WS change outputs of the Global Land Data Assimilation System (GLDAS); (ii) steric-corrected surface WS changes of the Caspian Sea; (iii) that of the Persian and Oman Gulfs; (iv) WS changes of the Aral Sea; and (v) that of small lakes of the selected region. Finally, the patterns of (i) to (v) were adjusted to GRACE-TWS maps so that their contributions were estimated and GRACE-TWS signals separated. After separation, our results indicated that the annual amplitude of WS changes over the Caspian Sea was 152 mm, 101 mm over both the Persian and Oman Gulfs, and 71 mm for the Aral Sea. Since January 2005, terrestrial WS in most parts of Iran, specifically over the center and northwestern parts, exhibited a mass decrease with an average linear rate of ∼ 15 mm/yr. The estimated linear trends of groundwater storage for the drought period of 2005 to March 2011, corresponding to the six main basins of Iran: Khazar, Persian and Oman Gulfs, Urmia, Markazi, Hamoon, and Srakhs were -6.7, -6.1, -11.2, -9.1, -3.1, and -4.2 mm/yr, respectively. The estimated results after separation agree fairly well with 256 in-situ piezometric observations. Keywords: GRACE-TWS, Signal separation, Independent components, Terrestrial and surface water storage, Groundwater, Iran
Publication Date : 2007
Abstract: This study was designed to understand the nature of the relationship between the Indian Ocean Dipole (IOD) index and seasonal rainfall in East Africa using statistical approaches. The statistical methods used in this study include correlation, regression and composite analyses. Results from the analyses suggested that the pattern in Sea Surface Temperature (SST) anomalies manifested during the IOD events have strong signals on the regional climate system during the October to December rainfall season. The study has demonstrated that some of the extreme rainfall conditions over East Africa were associated with positive and negative IOD phases. The linkages were strong during El Niño/La Niña years. Such information will help to improve monitoring, prediction and early warning of extreme rainfall events over east Africa to reduce the vulnerability of the society of the region to negative impacts of extreme rainfall events that are common in the region.
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Publication Date: 2013
Abstract: Recent special reports on climate extremes have shown evidences of changes in the patterns of climate extremes at global, regional and local scales. Understanding the characteristics of climate extremes at regional and local levels is critical not only for the development of preparedness and early warning systems, but is also fundamental in the development of any adaptation strategies. There is still very limited knowledge regarding the past, present and future patterns of climate extremes in the Greater Horn of Africa (GHA). This study, which was supported by the World Bank Global Facility for Disaster Reduction and Recovery (WB-GFDRR) and implemented by the World Meteorological Organization, was organized in terms of three workshops with three main objectives; (1) analysis of daily rainfall and temperature extremes for ten countries in the GHA region using observed in situ data running from 1971 to 2006, (2) assessing whether the United Kingdom Met-office and Hadley centre Providing REgional Climates for Impact Studies (UK-PRECIS) modelling system can provide realistic representation of the past and present climate extremes as observed by available in situ data, and (3) studying the future regional climate extremes under different scenarios to further assess the expected changes in climate extremes. This paper, therefore, uses the outputs of these workshops and also includes post-workshop analyses to assess the changes of climate extremes within the GHA. The results showed a significant decrease in total precipitation in wet days greater than 1 mm and increasing warm extremes, particularly at night, while cold extremes are decreasing. Considering a combination of geophysical models and satellite gravimetry observations from the Gravity Recovery and Climate Experiment (GRACE) mission in the frame of GRACE daily Kalman-smoothing models, for the years 2002 to 2010, we explored a decline in total water storage variations over the GHA.