Prediction of summer rainfall over the source region of the Blue Nile by using teleconnections based on sea surface temperatures

Alhamshry, Asmaa; Fenta, Ayele Almaw; Yasuda, Hiroshi; Shimizu, Katsuyuki; Kawai, Tadashi

doi:10.1007/s00704-019-02796-x

Cited by 14 publications

(22 citation statements)

References 69 publications

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“…These common problems cause inhomogeneity in the climate data series, causing an abrupt change in the average values and in series trend [46]. Additionally, gridded rainfall data have been widely used for different hydro-climatological analyses and climate variability studies [10,[47][48][49][50][51].…”

Section: Rainfall Datamentioning

confidence: 99%

“…Different studies have shown that SST can provide crucial predictive information regarding hydrological variability in different regions of the globe [10][11][12]. For instance, most previous studies [1,2,4,[7][8][9][10]13,14] have focused only on addressing the linkage between summer rains (June-September) that contribute to about 65-95% of the total annual rainfall in Ethiopia [15] and global SSTs. Generally, the majority of previous studies relied on SST as a main predictor of Ethiopian seasonal rainfall.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal Rainfall Variability in Ethiopia and Its Long-Term Link to Global Sea Surface Temperatures

Alhamshry

Fenta

Yasuda

et al. 2019

Water

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Investigating the influence of sea surface temperatures (SSTs) on seasonal rainfall is a crucial factor for managing Ethiopian water resources. For this purpose, SST and rainfall data were used to study a wide range of inhomogeneous areas in Ethiopia with uneven distribution of rainfall for both summer and spring seasons. Firstly, a preliminary subdivision of rainfall grid points into zones was applied depending on spatial homogeneity and seasonality of rainfall. This introduced new clusters, including nine zones for summer rainfall peak (July/August) and five zones for spring rainfall peak (April/May). Afterward, the time series for each zone was derived by calculating the rainfall averaged over grid points within the zone. Secondly, the oceanic regions that significantly correlated with the Ethiopian rainfall were identified through cross-correlations between rainfalls averaged over every homogeneous zone and the monthly averaged SST. For summer rainfall as a main rainy season, the results indicated that the Gulf of Guinea and southern Pacific Ocean had a significant influence on rainfall zones at a lag time of 5-6 and 6-7 months. Besides, for summer rainfall zones 8 and 9 at lag time 5-6 months, the common SST regions of the southern Pacific Ocean showed the opposite sense of positive and negative correlations. Thus, the difference in SSTs between the two regions was more strongly correlated (r ≥ 0.46) with summer rainfall in both zones than others. For the spring season, the results indicated that SST of the northern Atlantic Ocean had a strong influence on spring rainfall zones (3 and 5) at a lag time 6-7 months, as indicated by a significant correlation (r ≥ −0.40). Therefore, this study suggests that SSTs of southern Pacific and northern Atlantic oceans can be used as effective inputs for prediction models of Ethiopian summer and spring rainfalls, respectively.

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Section: Rainfall Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seasonal Rainfall Variability in Ethiopia and Its Long-Term Link to Global Sea Surface Temperatures

Alhamshry

Fenta

Yasuda

et al. 2019

Water

Self Cite

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“…The atmospheric-ocean interactions can help to find mechanisms that affect the distribution and magnitude of precipitation in a region [5]. Several researchers have found a relationship between large-scale circulation drivers and temporary climate variations [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Among all, the El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) are distinguished as the most essential phenomena due to its considerable impacts on hydro-climatic systems.…”

Section: Introductionmentioning

confidence: 99%

Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices

Dehghani

Salehi

Mosavi

et al. 2020

IJGI

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Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Also, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.

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“…MAM and AMJ) where correlations with local SST are weak, farm planning can rely on predictions based on the remote SST, namely Niño3.4-SST. Although the correlations peak at 0.45, the teleconnection is still strong enough to provide information to farmers on the likelihood of the indices during MAM and AMJ seasons (Opoku-Ankomah and Cordery 1994;Alhamshry et al 2019).…”

Section: Performance Of the Dynamical And Statistical Modelmentioning

confidence: 98%

Rainfall and dry spell occurrence in Ghana: trends and seasonal predictions with a dynamical and a statistical model

Gbangou

Ludwig

Slobbe

et al. 2020

Theor Appl Climatol

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Improved information on the distribution of seasonal rainfall is important for crop production in Ghana. The predictability of key agro-meteorological indices, namely, seasonal rainfall, maximum dry spell length (MDSL) and dry spell frequency (DSF) was investigated across Ghana (with an interest on the coastal savannah agro-ecological zone). These three variables are relevant for local agricultural water management. A dynamical model (i.e. European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 seasonal forecasts) and a statistical model (i.e. response to sea surface temperatures (SSTs)) were used and analysed using correlation and other discrimination skill metrics. ECMWF-System 4 was bias-corrected and verified with 14 local stations' observations. Results show that differences in variability and skills of the agro-meteorological indices are small between agro-ecological zones as compared to the differences between stations. The dynamic model System 4 explains up to 31% of the variability of the MDSL and seasonal rainfall indices. Coastal savannah exhibits the highest level of discrimination skills. However, these skills are generally higher for the below and above normal MDSL and seasonal rainfall categories at lead time 0. Similarity in skills for the agro-meteorological indices over the same zones and stations is found both for the dynamical and statistical models. Although System 4 performs slightly better than the statistical model, especially, for dry spell length and seasonal rainfall. For dry spell frequency and longer lead time dry spell length, the statistical model tends to perform better. These results suggest that the agro-meteorological indices derived from System 4′ updated versions, corrected with local observations, together with the response to SST information, can potentially support decision-making of local smallholder farmers in Ghana.

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Prediction of summer rainfall over the source region of the Blue Nile by using teleconnections based on sea surface temperatures

Cited by 14 publications

References 69 publications

Seasonal Rainfall Variability in Ethiopia and Its Long-Term Link to Global Sea Surface Temperatures

Seasonal Rainfall Variability in Ethiopia and Its Long-Term Link to Global Sea Surface Temperatures

Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices

Rainfall and dry spell occurrence in Ghana: trends and seasonal predictions with a dynamical and a statistical model

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