Diriba Korecha

Abstract

Ethiopia composes diversified topographic structures; undulated plateaus and mountains, raged valleys and plains. The highlands and ever-green portions of the county are fringed by the Sahara and Arabian deserts as well as East African arid climates. In contrast, climate of the major parts of the country is influenced majorly by tropical features while partly interacted with inter- hemispheric weather systems. Ethiopia‘s climate is prone to both extended rainfall deficits and excesses. In extreme cases, these may lead to droughts, economic hardship and humanitarian disasters. Droughts are the most natural catastrophes that impose impended social and economic crisis in the history of Ethiopia that have been manifested in tampering agriculture and food security, livestock development, hydro-electricity production, transport, water resource management, health and public safety. Numerous evidences have been documented that when any one of these sectors become affected, the effect can spread quickly and a whole country may suffer. Skillful prediction of seasonal rainfall would therefore bring sound change in disaster risk reduction and prevention and economic benefit to the country that depends on rain-fed agriculture. It would enable timely actions to be taken by the government and the public in order to avert or minimize potential hunger, poverty and famine resulting from drought.

Since the issuance of the seasonal climate prediction has begun in Ethiopia, the National Meteorological Agency has gone through continuous improvement in order to enhance the skill of predicting seasonal rainfall anomalies for various occasions. However, there are a lot of constraints in quantifying the seasonal rainfall trends and homogenizing their spatial and temporal patterns. Although seasonal climatic features are complex in nature, this thesis has focused mainly on the characterization of the predictability of rainy seasons in Ethiopia. Four manuscripts are included. The first manuscript provides an overview of NMA‘s operational seasonal rainfall prediction skills in various rainfall regimes of Ethiopia. The second manuscript provides spatially coherent homogeneous rainfall regimes as the main platform for developing region-specific climate prediction model. The third one deals with the construction of multivariate statistical seasonal rainfall based on ENSO indices for the main rainy season in major portions of Ethiopia. The fourth manuscript provides an overview of drought episodes in all parts of Ethiopia during the recent decades.

In the forecast verification manuscript (Paper I), we evaluated the skill of the National Meteorological Agency of Ethiopia‘s operational seasonal rainfall forecast for the February–May (FMAM) and June–September (JJAS) rainy seasons for the period 1999–2011. Our analysis showed that the forecasting system was biased toward the near-normal category. The ranked probability skill scores (RPSS) which computes the relative skill of the probabilistic forecast over that of the climatology is positive for all 16 forecasts series, indicating that the forecast has better skill as compared to chance. The results further suggested that the forecasting system has problems in capturing below normal rainfall events. This under-forecasting of dry events is of great practical importance. In contrast, the forecast showed slightly higher skills for above normal than below normal rainfall categories during both seasons and hence indicated that there is a greater reluctance to assign higher terciles for below normal than for above normal rainfall as a forecast for dry conditions would be considered more serious and may lead to initiation of drought preventive actions.

In the homogeneous rainfall classification (Paper II), we analysed a spatial and temporal rainfall patterns of Ethiopia based on 162 quality-controlled point stations and 717 grid-points generated from satellite rainfall estimate- merged with meteorological stations. Analysis of various clusters on the monthly rainfall data indicated the presence of distinct spatial rainfall patterns 5

over Ethiopia. Principal Component Analysis (PCA) was broadly categorized Ethiopia in three major rainfall regions that vividly identified the dominance of large rainfall dissimilarities and strong seasonality, which separate June- September (Kiremt) rain-benefiting from February-May (Belg) and October- January (Bega) rainfall regimes. The application of Cluster Analysis (CA), on the other hand identified twelve distinct rainfall regions for the country. The characteristic of each homogeneous rainfall region is the reflection of the typical seasonal cycle that prevails in Ethiopia. The identification of specific rainfall regions add values in the local seasonal climate forecasting, monitoring of climate variability and change on regional and national scales. In this study, the mountainous chains that bisect northwestern from the northeastern regions were well replicated in our spatial delineations. The formation of the dry corridors of the northern Rift Valley and southeastern lowlands are among the most interesting clearly depicted regional features, where understanding of the meteorological mechanisms may provide a benefit to realize the impact of rainfall variation on social and economic activities of the region.

In paper III, we examined the predictive potential for June–September rainy seasonal in Ethiopia using multivariate statistical approaches. The skill of a dynamical approach to predicting the El Niño–Southern Oscillation (ENSO), which impacts Ethiopian rainfall, was assessed. The study attempts to identify global and more regional processes affecting the large-scale summer climate patterns that govern rainfall anomalies. Multivariate statistical techniques are applied to diagnose and predict seasonal rainfall patterns using historical monthly mean global sea surface temperatures and other physically relevant predictor data. We showed that Ethiopia‘s June–September rainy season is governed primarily by ENSO, and secondarily reinforced by more local climate indicators near Africa and the Atlantic and Indian Oceans, which revealed in this case that 67% (85%) of dry (wet) events are associated to El 6

Niño (La Niña) episodes. It is therefore scientifically judicious that rainfall anomaly patterns can be predicted with some skill within a short lead time of the summer season, based on emerging ENSO developments. We further identified that the ENSO predictability barrier in the Northern Hemisphere spring poses a major challenge to providing seasonal rainfall forecasts two or more months in advance.

In the drought analysis (Paper IV), meteorological observations were used to construct monthly time series for 14 homogeneous rainfall zones, covering all of Ethiopia during 1971–2010/2011. The Standardized Precipitation Index (SPI) was then calculated for each zone on time scales of 3, 4, 6, 9, 12, 24 and 48 months. The results indicate that 2009 was one of the driest years in Ethiopia since 1971, and that there has been a cluster of dry spring (locally known as Belg) seasons in most of the country during the last 10–15 years. Linear regression analysis confirmed a decline in precipitation in southern Ethiopia, both in the spring and in the summer (locally known as Kiremt). The trend analysis did not give us reason to draw any conclusions for central and northern Ethiopia, but the clustering of dry spring seasons during the last 10– 15 years was apparent also in this part of the country.